diff --git a/.gitignore b/.gitignore index 9e12afb775..ae18c58e08 100644 --- a/.gitignore +++ b/.gitignore @@ -46,6 +46,8 @@ nosetests.xml coverage.xml *,cover experiments/examples/output/ +experiments/IMC/output/ +experiments/IMC/yaml test.py # Translations @@ -67,6 +69,9 @@ target/ # Visual Studio Code /.vscode/ +# Qt Creator +CMakeLists.txt.user + # Virtual env .venv*/ .python-version diff --git a/cpprevolve/CMakeLists.txt b/cpprevolve/CMakeLists.txt index d39c8565d7..bcfc924d30 100644 --- a/cpprevolve/CMakeLists.txt +++ b/cpprevolve/CMakeLists.txt @@ -3,7 +3,8 @@ cmake_minimum_required (VERSION 3.7.0) # Project name project (Revolve) -set (CMAKE_CXX_STANDARD 11) +set (CMAKE_CXX_STANDARD 14) +find_package(Torch REQUIRED) # Include cmake subdirectories add_subdirectory(revolve/brains) diff --git a/cpprevolve/revolve/brains/CMakeLists.txt b/cpprevolve/revolve/brains/CMakeLists.txt index 07ad58b8ed..cd4a8b107b 100644 --- a/cpprevolve/revolve/brains/CMakeLists.txt +++ b/cpprevolve/revolve/brains/CMakeLists.txt @@ -1,13 +1,20 @@ -file(GLOB_RECURSE - CONTROLLER_SRCS - controller/*.cpp - controller/actuators/*.cpp - controller/sensors/*.cpp -) -file(GLOB_RECURSE - LEARNER_SRCS - learner/*.cpp +set (CMAKE_CXX_STANDARD 14) + +set(CONTROLLER_SRCS + controller/DifferentialCPG.cpp + controller/IMC/IMC.cpp + controller/IMC/FeedForwardNetwork.cpp + controller/IMC/InverseNetwork.cpp ) +set(LEARNER_SRCS + learner/Learner.cpp + learner/BayesianOptimizer.cpp + learner/HyperNEAT.cpp + learner/EA.cpp + learner/NIPES.cpp + learner/DifferentialEvo.cpp + learner/EA_misc/RandNum.cpp + learner/EA_misc/Novelty.cpp) # PKG-CONFIG find_package(PkgConfig REQUIRED) @@ -17,8 +24,12 @@ find_package(Boost REQUIRED COMPONENTS system) # Find Eigen3 - A lightweight C++ template library for vector and matrix math find_package(Eigen3 REQUIRED) - find_package(MultiNEAT REQUIRED) +find_package(libcmaes REQUIRED) + +# These dependencies are required for the AngleToTargetDetector Sensor +find_package(OpenCV REQUIRED) +#find_package(raspicam REQUIRED) #only on the raspberry side # Find NLOpt - Non Linear Optimization pkg_check_modules(NLOpt REQUIRED nlopt>=2.4) @@ -32,19 +43,30 @@ add_library(revolve-learners SHARED ${LEARNER_SRCS}) target_include_directories(revolve-controllers PUBLIC ${EIGEN3_INCLUDE_DIR} - PUBLIC ${Boost_INCLUDE_DIRS}) + PUBLIC ${Boost_INCLUDE_DIRS} + PUBLIC ${OpenCV_INCLUDE_DIRS}) target_include_directories(revolve-learners + PUBLIC ${EIGEN3_INCLUDE_DIR} PUBLIC ${Boost_INCLUDE_DIRS} PUBLIC ${LIMBO_DIR}/src PUBLIC ${NLOpt_INCLUDE_DIRS}) -target_include_directories(revolve-learners - PUBLIC ${NLOpt_LIBRARY_DIRS}) +target_compile_definitions(revolve-learners + PUBLIC USE_NLOPT=1 + PUBLIC CMAES) target_link_libraries(revolve-controllers + PUBLIC MultiNEAT::MultiNEAT + ${OpenCV_LIBS} + PUBLIC torch + PUBLIC libcmaes::cmaes + ) + +target_link_libraries(revolve-learners + revolve-controllers MultiNEAT::MultiNEAT) install(TARGETS revolve-controllers revolve-learners RUNTIME DESTINATION bin - LIBRARY DESTINATION lib) \ No newline at end of file + LIBRARY DESTINATION lib) diff --git a/cpprevolve/revolve/brains/controller/BrokenDifferentialCPG.cpp b/cpprevolve/revolve/brains/controller/BrokenDifferentialCPG.cpp new file mode 100644 index 0000000000..80fa89f158 --- /dev/null +++ b/cpprevolve/revolve/brains/controller/BrokenDifferentialCPG.cpp @@ -0,0 +1,477 @@ +/* + * Copyright (C) 2015-2018 Vrije Universiteit Amsterdam + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * + * Description: TODO: + * Author: Milan Jelisavcic & Maarten van Hooft + * Date: December 29, 2018 + * + */ + +#include "BrokenDifferentialCPG.h" + +// STL macros +#include +#include +#include +#include +#include +#include +#include +#include +#include + +// Project headers +#include "actuators/Actuator.h" + +#include "sensors/Sensor.h" + +// TODO: Resolve odd behaviour at the end of the validation procedure +// This behaviour is not present if you directly load a trained controller + +// Define namespaces +using namespace revolve; + +/** + * Constructor for BrokenDifferentialCPG class. + * + * @param _model + * @param robot_config + */ +BrokenDifferentialCPG::BrokenDifferentialCPG( + const BrokenDifferentialCPG::ControllerParams ¶ms, + const std::vector< std::shared_ptr< Actuator > > &actuators, + std::shared_ptr angle_to_target_sensor) + : Controller(ControllerType::DIFFERENTIAL_CPG) + , next_state(nullptr) + , n_motors(actuators.size()) + , output(new double[actuators.size()]) + , angle_to_target_sensor(std::move(angle_to_target_sensor)) +{ + // Controller parameters + this->reset_neuron_random = params.reset_neuron_random; + this->init_neuron_state = params.init_neuron_state; + this->range_lb = -params.range_ub; + this->range_ub = params.range_ub; + this->use_frame_of_reference = params.use_frame_of_reference; + this->signal_factor_all_ = params.signal_factor_all; + this->signal_factor_mid = params.signal_factor_mid; + this->signal_factor_left_right = params.signal_factor_left_right; + this->abs_output_bound = params.abs_output_bound; + + if (use_frame_of_reference and not this->angle_to_target_sensor) { + std::clog << "WARNING!: use_frame_of_reference is activated but no angle_to_target_sensor camera is configured. " + "Disabling the use of the frame of reference" << std::endl; + use_frame_of_reference = false; + } + + size_t j=0; + for (const std::shared_ptr &actuator: actuators) + { + // Pass coordinates + auto coord_x = actuator->coordinate_x(); + auto coord_y = actuator->coordinate_y(); + this->motor_coordinates[{coord_x, coord_y}] = j; + + // Set frame of reference + int frame_of_reference = 0; + // We are a left neuron + if (coord_y < 0) + { + frame_of_reference = -1; + } + // We are a right neuron + else if (coord_y > 0) + { + frame_of_reference = 1; + } + + // Save neurons: bias/gain/state. Make sure initial states are of different sign. + this->neurons[{coord_x, coord_y, 1}] = {0.f, 0.f, this->init_neuron_state, frame_of_reference}; //Neuron A + this->neurons[{coord_x, coord_y, -1}] = {0.f, 0.f, -this->init_neuron_state, frame_of_reference}; // Neuron B + j++; + } + + // Add connections between neighbouring neurons + int i = 0; + for (const std::shared_ptr &actuator: actuators) + { + // Get name and x,y-coordinates of all neurons. + double x = actuator->coordinate_x(); + double y = actuator->coordinate_y(); + + // Continue to next iteration in case there is already a connection between the 1 and -1 neuron. + // These checks feel a bit redundant. + // if A->B connection exists. + if (this->connections.count({x, y, 1, x, y, -1}) > 0) + { + continue; + } + // if B->A connection exists: + if (this->connections.count({x, y, -1, x, y, 1}) > 0) + { + continue; + } + + // Loop over all positions. We call it neighbours, but we still need to check if they are a neighbour. + for (const std::shared_ptr &neighbour: actuators) + { + // Get information of this neuron (that we call neighbour). + double near_x = neighbour->coordinate_x(); + double near_y = neighbour->coordinate_y(); + + // If there is a node that is a Moore neighbour, we set it to be a neighbour for their A-nodes. + // Thus the connections list only contains connections to the A-neighbourhood, and not the + // A->B and B->A for some node (which makes sense). + double dist_x = std::fabs(x - near_x); + double dist_y = std::fabs(y - near_y); + + // TODO: Verify for non-spiders + if (std::fabs(dist_x + dist_y - 2) < 0.01) + { + if(std::get<0>(this->connections[{x, y, 1, near_x, near_y, 1}]) != 1 or + std::get<0>(this->connections[{near_x, near_y, 1, x, y, 1}]) != 1) + { + this->connections[{x, y, 1, near_x, near_y, 1}] = std::make_tuple(1, i); + this->connections[{near_x, near_y, 1, x, y, 1}] = std::make_tuple(1, i); + i++; + } + } + } + } + + // Initialise array of neuron states for Update() method + this->next_state = new double[this->neurons.size()]; + this->n_weights = (int)(this->connections.size()/2) + this->n_motors; + + // Loading Brain + + // Save weights for brain + assert(params.weights.size() == this->n_weights); + this->sample.resize(this->n_weights); + for(size_t j = 0; j < this->n_weights; j++) + { + this->sample(j) = params.weights.at(j); + } + + // Set ODE matrix at initialization + this->set_ode_matrix(); + + std::cout << "Brain has been loaded." << std::endl; +} + +/** + * Destructor + */ +BrokenDifferentialCPG::~BrokenDifferentialCPG() +{ + delete[] this->next_state; + delete[] this->output; +} + +/** + * Callback function that defines the movement of the robot + * + * @param _motors + * @param _sensors + * @param _time + * @param _step + */ +void BrokenDifferentialCPG::update( + const std::vector< std::shared_ptr < Actuator > > &actuators, + const std::vector< std::shared_ptr < Sensor > > &sensors, + const double time, + const double step) +{ + // Send new signals to the motors + this->step(time, step); + + unsigned int p = 0; + for (const auto &actuator: actuators) + { + actuator->write(this->output + p, step); + p += actuator->n_outputs(); + } +} + +/** + * Make matrix of weights A as defined in dx/dt = Ax. + * Element (i,j) specifies weight from neuron i to neuron j in the system of ODEs + */ +void BrokenDifferentialCPG::set_ode_matrix(){ + // Initiate new matrix + std::vector> matrix; + + // Fill with zeroes + for(size_t i =0; i neurons.size(); i++) + { + // Initialize row in matrix with zeros + std::vector< double > row; + for (size_t j = 0; j < this->neurons.size(); j++) + { + row.push_back(0); + } + matrix.push_back(row); + } + + // Process A<->B connections + int index = 0; + for(size_t i =0; i neurons.size(); i++) + { + // Get correct index + int c = 0; + if (i%2 == 0){ + c = i + 1; + } + else{ + c = i - 1; + } + + // Add a/b connection weight + index = (int)(i/2); + auto w = this->sample(index) * + (this->range_ub - this->range_lb) + this->range_lb; + matrix[i][c] = w; + matrix[c][i] = -w; + } + + // A<->A connections + index++; + int k = 0; + std::vector connections_seen; + + for (auto const &connection : this->connections) + { + // Get connection information + int x1, y1, z1, x2, y2, z2; + std::tie(x1, y1, z1, x2, y2, z2) = connection.first; + + // Find location of the two neurons in this->neurons list + int l1 = -1; + int l2 = -1; + int c = 0; + for(auto const &neuron : this->neurons) + { + int x, y, z; + std::tie(x, y, z) = neuron.first; + if (x == x1 and y == y1 and z == z1) + { + l1 = c; + } + else if (x == x2 and y == y2 and z == z2) + { + l2 = c; + } + // Update counter + c++; + } + + // Add connection to seen connections + if(l1 > l2) + { + int l1_old = l1; + l1 = l2; + l2 = l1_old; + } + std::string connection_string = std::to_string(l1) + "-" + std::to_string(l2); + + // if not in list, add to list + auto connections_list = std::find(connections_seen.begin(), connections_seen.end(), connection_string); + if(connections_list == connections_seen.end()) + { + connections_seen.push_back(connection_string); + } + // else continue to next iteration + else{ + continue; + } + + // Get weight + auto w = this->sample(index + k) * + (this->range_ub - this->range_lb) + this->range_lb; + + // Set connection in weight matrix + matrix[l1][l2] = w; + matrix[l2][l1] = -w; + k++; + } + + // Update matrix + this->ode_matrix = matrix; + + // Reset neuron state + this->reset_neuron_state(); +} + + +/** + * Set states back to original value (that is on the unit circle) + */ +void BrokenDifferentialCPG::reset_neuron_state(){ + int c = 0; + for(auto const &neuron : this->neurons) + { + // Get neuron properties + int x, y, z, frame_of_reference; + double bias ,gain ,state; + std::tie(x, y, z) = neuron.first; + std::tie(bias, gain, state, frame_of_reference) = neuron.second; + + if (z == -1) + { + // Neuron B + if (this->reset_neuron_random) + { + this->neurons[{x, y, z}] = {0.f, + 0.f, + ((double) rand() / (RAND_MAX))*2*this->init_neuron_state - this->init_neuron_state, + frame_of_reference}; + } + else + { + this->neurons[{x, y, z}] = {0.f, 0.f, -this->init_neuron_state, frame_of_reference}; + } + } + else + { + // Neuron A + if (this->reset_neuron_random) + { + this->neurons[{x, y, z}] = {0.f, + 0.f, + ((double) rand() / (RAND_MAX))*2*this->init_neuron_state - this->init_neuron_state, + frame_of_reference}; + } + else + { + this->neurons[{x, y, z}] = {0.f, 0.f, +this->init_neuron_state, frame_of_reference}; + } + } + c++; + } +} + +/** + * Step function that is called from within Update() + * + * @param _time + * @param _output + */ +void BrokenDifferentialCPG::step( + const double time, + const double dt) +{ + int neuron_count = 0; + for (const auto &neuron : this->neurons) + { + // Neuron.second accesses the second 3-tuple of a neuron, containing the bias/gain/state. + double recipient_bias, recipient_gain, recipient_state; + int frame_of_reference; + std::tie(recipient_bias, recipient_gain, recipient_state, frame_of_reference) = neuron.second; + + // Save for ODE + this->next_state[neuron_count] = recipient_state; + neuron_count++; + } + + // Copy values from next_state into x for ODEINT + state_type x(this->neurons.size()); + for (size_t i = 0; i < this->neurons.size(); i++) + { + x[i] = this->next_state[i]; + } + + // Perform one step + stepper.do_step( + [this](const state_type &x, state_type &dxdt, double t) + { + for(size_t i = 0; i < this->neurons.size(); i++) + { + dxdt[i] = 0; + for(size_t j = 0; j < this->neurons.size(); j++) + { + dxdt[i] += x[j]*this->ode_matrix[j][i]; + } + } + }, + x, + time, + dt); + + // Copy values into nextstate + for (size_t i = 0; i < this->neurons.size(); i++) + { + this->next_state[i] = x[i]; + } + + double angle_difference = 0.0; + double slow_down_factor = 1.0; + if (use_frame_of_reference) { + angle_difference = angle_to_target_sensor->detect_angle(); + const double frame_of_reference_slower_power = 7.0; + slow_down_factor = std::pow( + (180.0 - std::abs(angle_difference))/180.0, frame_of_reference_slower_power); + } + + // Loop over all neurons to actually update their states. Note that this is a new outer for loop + auto i = 0; auto j = 0; + for (auto &neuron : this->neurons) + { + // Get bias gain and state for this neuron. Note that we don't take the coordinates. + // However, they are implicit as their order did not change. + double bias, gain, state; + int frame_of_reference; + std::tie(bias, gain, state, frame_of_reference) = neuron.second; + double x, y, z; + std::tie(x, y, z) = neuron.first; + neuron.second = {bias, gain, this->next_state[i], frame_of_reference}; + j = this->motor_coordinates[{x,y}]; + // Should be one, as output should be based on +1 neurons, which are the A neurons + if (i % 2 == 1) + { + // TODO: Add Milan's function here as soon as things are working a bit + // f(a) = (w_ao*a - bias)*gain + + // Apply saturation formula + auto x_input = this->next_state[i]; + + double output_j = this->output_function(x_input); + + // Use frame of reference + if(use_frame_of_reference and frame_of_reference != 0) + { + if ((frame_of_reference == 1 and angle_difference < 0) or + (frame_of_reference == -1 and angle_difference > 0)) //TODO >= / <= ? + { + output_j *= slow_down_factor; + //std::cout << "Slow down " << x <<','<< y <<','<< z << " with factor " << slow_down_factor << std::endl; + //output_j = 0; + } + } + + this->output[j] = output_j; + } + i++; + } +} + +double BrokenDifferentialCPG::output_function(double input) const +{ + return this->signal_factor_all_ + * this->abs_output_bound + * ( + (2.0) / (1.0 + std::pow(2.718, -2.0 * input / this->abs_output_bound)) + - 1 + ); +} diff --git a/cpprevolve/revolve/brains/controller/BrokenDifferentialCPG.h b/cpprevolve/revolve/brains/controller/BrokenDifferentialCPG.h new file mode 100644 index 0000000000..799dd2573e --- /dev/null +++ b/cpprevolve/revolve/brains/controller/BrokenDifferentialCPG.h @@ -0,0 +1,147 @@ +// +// Created by matteo on 14/06/19. +// + +#pragma once + +#include "Controller.h" +#include "actuators/Actuator.h" +#include "sensors/Sensor.h" +#include "sensors/AngleToTargetDetector.h" +#include +#include +#include + +typedef std::vector< double > state_type; + +namespace revolve +{ +class BrokenDifferentialCPG + : public Controller +{ +public: + struct ControllerParams { + bool reset_neuron_random; + bool use_frame_of_reference; + double init_neuron_state; + double range_ub; + double signal_factor_all; + double signal_factor_mid; + double signal_factor_left_right; + double abs_output_bound; + std::vector< double > weights; + }; + + /// \brief Constructor + /// \param[in] _modelName Name of the robot + /// \param[in] _node The brain node + /// \param[in] _motors Reference to a motor list, it be reordered + /// \param[in] _sensors Reference to a sensor list, it might be reordered + BrokenDifferentialCPG( + const ControllerParams ¶ms, + const std::vector< std::shared_ptr < Actuator > > &_actuators, + std::shared_ptr angle_to_target_sensor = nullptr); + + /// \brief Destructor + virtual ~BrokenDifferentialCPG(); + + /// \brief The default update method for the controller + /// \param[in] _motors Motor list + /// \param[in] _sensors Sensor list + /// \param[in] _time Current world time + /// \param[in] _step Current time step + virtual void update( + const std::vector< std::shared_ptr < Actuator > > &actuators, + const std::vector< std::shared_ptr < Sensor > > &sensors, + const double _time, + const double _step) override; + +protected: + + void step( + const double time, + const double step); + + void set_ode_matrix(); + +private: + /// \brief Function that resets neuron state + void reset_neuron_state(); + + /// \brief function that transforms the value of the CPG A-neurons and returns the correct output for the actuators + double output_function(double input) const; + +public: + std::map< std::tuple< double, double >, size_t > motor_coordinates; + +protected: + /// \brief Register of motor IDs and their x,y-coordinates +// std::map< std::string, std::tuple< int, int > > positions; + + /// \brief Register of individual neurons in x,y,z-coordinates + /// \details x,y-coordinates define position of a robot's module and + // z-coordinate define A or B neuron (z=1 or -1 respectively). Stored + // values are a bias, gain, state and frame of reference of each neuron. + std::map< std::tuple< int, int, int >, std::tuple< double, double, double, int > > + neurons; + + /// \brief Register of connections between neighnouring neurons + /// \details Coordinate set of two neurons (x1, y1, z1) and (x2, y2, z2) + // define a connection. The second tuple contains 1: the connection value and + // 2: the weight index corresponding to this connection. + std::map< std::tuple< int, int, int, int, int, int >, std::tuple > + connections; + + /// \brief Runge-Kutta 45 stepper + boost::numeric::odeint::runge_kutta4< state_type > stepper; + + /// \brief Used for ODE-int + std::vector> ode_matrix; + +private: + /// \brief Used to determine the next state array + double *next_state; + + /// \brief Used to determine the output to the motors array + double *output; + + /// \brief Limbo optimizes in [0,1] + double range_lb; + + /// \brief Limbo optimizes in [0,1] + double range_ub; + + /// \brief Loaded sample + Eigen::VectorXd sample; + + /// \brief The number of weights to optimize + size_t n_weights; + + /// \brief Factor to multiply output signal with + double signal_factor_all_; + + /// \brief Factor to multiply output signal with + double signal_factor_mid; + + /// \brief Factor to multiply output signal with + double signal_factor_left_right; + + /// \brief When reset a neuron state,do it randomly: + bool reset_neuron_random; + + /// \brief Holds the number of motors in the robot + size_t n_motors; + + /// \brief Initial neuron state + double init_neuron_state; + + /// \brief Use frame of reference {-1,0,1} version or not + bool use_frame_of_reference; + + double abs_output_bound; + +// targeted locomotion stuff + std::shared_ptr angle_to_target_sensor; +}; + +} diff --git a/cpprevolve/revolve/brains/controller/Controller.h b/cpprevolve/revolve/brains/controller/Controller.h index 4be01b3020..d0d0f5bb62 100644 --- a/cpprevolve/revolve/brains/controller/Controller.h +++ b/cpprevolve/revolve/brains/controller/Controller.h @@ -12,12 +12,23 @@ namespace revolve { +class DifferentialCPG; class Controller { public: + enum ControllerType { + NONE = 0, + NEURAL_NETWORK, + SPLINES, + DIFFERENTIAL_CPG, + // add new controller types here + } const controller_type; + /// \brief Constructor - explicit Controller() {} + explicit Controller(ControllerType controller_type) + : controller_type(controller_type) + {} /// \brief Deconstructor virtual ~Controller() {} @@ -28,6 +39,8 @@ class Controller const double _time, const double _step ) = 0; + + virtual DifferentialCPG* into_DifferentialCPG() { return nullptr; } }; } diff --git a/cpprevolve/revolve/brains/controller/DifferentialCPG.cpp b/cpprevolve/revolve/brains/controller/DifferentialCPG.cpp index 46a21cc525..c109694c69 100644 --- a/cpprevolve/revolve/brains/controller/DifferentialCPG.cpp +++ b/cpprevolve/revolve/brains/controller/DifferentialCPG.cpp @@ -26,12 +26,9 @@ #include #include #include -#include -#include #include #include #include -#include #include #include @@ -52,20 +49,23 @@ using namespace revolve; * @param robot_config */ DifferentialCPG::DifferentialCPG( - const DifferentialCPG::ControllerParams params, - const std::vector> &actuators) - : next_state(nullptr) + const DifferentialCPG::ControllerParams ¶ms, + const std::vector> &actuators, + std::shared_ptr angle_to_target_sensor) + : Controller(ControllerType::DIFFERENTIAL_CPG) + , next_state(nullptr) , n_motors(actuators.size()) , output(new double[actuators.size()]) - , sample(actuators.size(), 0) + , angle_to_target_sensor(std::move(angle_to_target_sensor)) + , connection_weights(actuators.size(), 0) { this->init_params_and_connections(params, actuators); // Save weights for brain assert(params.weights.size() == n_weights); - sample.resize(n_weights, 0); + connection_weights.resize(n_weights, 0); for(size_t j = 0; j < n_weights; j++) { - sample.at(j) = params.weights.at(j); + connection_weights.at(j) = params.weights.at(j); } // Set ODE matrix at initialization @@ -82,67 +82,20 @@ DifferentialCPG::DifferentialCPG( * @param config_cppn_genome */ DifferentialCPG::DifferentialCPG( - DifferentialCPG::ControllerParams params, + const DifferentialCPG::ControllerParams ¶ms, const std::vector> &actuators, - const NEAT::Genome &gen) - : next_state(nullptr) + const NEAT::Genome &gen, + std::shared_ptr angle_to_target_sensor) + : Controller(ControllerType::DIFFERENTIAL_CPG) + , next_state(nullptr) , n_motors(actuators.size()) , output(new double[actuators.size()]) - , sample(actuators.size(), 0) + , angle_to_target_sensor(std::move(angle_to_target_sensor)) + , connection_weights(actuators.size(), 0) { this->init_params_and_connections(params, actuators); - // build the NN according to the genome - NEAT::NeuralNetwork net; - gen.BuildPhenotype(net); - - // get weights for each connection - // assuming that connections are distinct for each direction - sample.resize(n_weights, 0); - std::vector inputs(8); - - for(const std::pair< const std::tuple< int, int, int>, size_t > &motor: motor_coordinates) - { - size_t k = motor.second; - - // convert tuple to vector - std::tie(inputs[0], inputs[1], inputs[2]) = motor.first; - inputs[3] = 1; - std::tie(inputs[4], inputs[5], inputs[6]) = motor.first; - inputs[7] = -1; - - net.Input(inputs); - net.Activate(); - double weight = net.Output()[0]; -#ifdef DifferentialCPG_PRINT_INFO - std::cout << "Creating weight [" - << inputs[0] << ';' << inputs[1] << ';' << inputs[2] << ';' << inputs[3] << '-' - << inputs[4] << ';' << inputs[5] << ';' << inputs[6] << ';' << inputs[7] - << "] to sample[" << k << "]\t-> " << weight << std::endl; -#endif - sample.at(k) = weight; // order of weights corresponds to order of connections. - } - - for(const std::pair, int > &con : connections) - { - int k = con.second; - // convert tuple to vector - std::tie(inputs[0], inputs[1], inputs[2], inputs[3], inputs[4], inputs[5], inputs[6], inputs[7]) = con.first; - net.Input(inputs); - net.Activate(); - double weight = net.Output()[0]; -#ifdef DifferentialCPG_PRINT_INFO - std::cout << "Creating weight [" - << inputs[0] << ';' << inputs[1] << ';' << inputs[2] << ';' << inputs[3] << '-' - << inputs[4] << ';' << inputs[5] << ';' << inputs[6] << ';' << inputs[7] - << "] to sample[" << k << "]\t-> " << weight << std::endl; -#endif - sample.at(k) = weight; // order of weights corresponds to order of connections. - } - - // Set ODE matrix at initialization - set_ode_matrix(); - + this->load_genome_to_controller(gen); std::cout << "DifferentialCPG brain with CPPN configuration has been loaded." << std::endl; } @@ -154,10 +107,15 @@ void DifferentialCPG::init_params_and_connections(const ControllerParams ¶ms this->range_lb = -params.range_ub; this->range_ub = params.range_ub; this->use_frame_of_reference = params.use_frame_of_reference; - this->signal_factor_all_ = params.signal_factor_all; - this->signal_factor_mid = params.signal_factor_mid; - this->signal_factor_left_right = params.signal_factor_left_right; + this->output_signal_factor = params.output_signal_factor; this->abs_output_bound = params.abs_output_bound; + this->connection_weights = params.weights; + + if (use_frame_of_reference and not angle_to_target_sensor) { + std::clog << "WARNING!: use_frame_of_reference is activated but no angle_to_target_sensor camera is configured. " + "Disabling the use of the frame of reference" << std::endl; + use_frame_of_reference = false; + } size_t j=0; for (const std::shared_ptr &actuator: actuators) @@ -175,7 +133,7 @@ void DifferentialCPG::init_params_and_connections(const ControllerParams ¶ms { frame_of_reference = -1; } - // We are a right neuron + // We are a right neuron else if (coord_x > 0) { frame_of_reference = 1; @@ -237,7 +195,7 @@ void DifferentialCPG::init_params_and_connections(const ControllerParams ¶ms std::cout << "Creating connnection [" << x << ';' << y << ';' << z << ';' << 1 << '-' << near_x << ';' << near_y << ';' << near_z << ';' << 1 - << "] to sample[" << i << ']' << std::endl; + << "] to connection_weights[" << i << ']' << std::endl; #endif this->connections[{x, y, z, 1, near_x, near_y, near_z, 1}] = i; //this->connections[{near_x, near_y, near_z, 1, x, y, z, 1}] = i; @@ -263,6 +221,78 @@ DifferentialCPG::~DifferentialCPG() delete[] this->output; } +void DifferentialCPG::set_connection_weights(std::vector weights){ + this->connection_weights = weights; + this->set_ode_matrix(); +} + +void DifferentialCPG::load_genome_to_controller(const NEAT::Genome &genome) +{ + // build the NN according to the genome + NEAT::NeuralNetwork net; + genome.BuildPhenotype(net); + unsigned int net_depth =99999;// net.CalculateNetworkDepth(); + + // get weights for each connection + // assuming that connections are distinct for each direction + connection_weights.resize(n_weights, 0); + std::vector inputs(8); + + for(const std::pair< const std::tuple< int, int, int>, size_t > &motor: motor_coordinates) + { + size_t k = motor.second; + + // convert tuple to vector + std::tie(inputs[0], inputs[1], inputs[2]) = motor.first; + inputs[3] = 1; + std::tie(inputs[4], inputs[5], inputs[6]) = motor.first; + inputs[7] = -1; + inputs[8] = 1; + + net.Flush(); + net.Input(inputs); + for (int i=0; i " << weight << std::endl; +#endif + this->connection_weights.at(k) = weight; // order of weights corresponds to order of connections. + } + + + for(const std::pair, int > &con : connections) + { + int k = con.second; + // convert tuple to vector + std::tie(inputs[0], inputs[1], inputs[2], inputs[3], inputs[4], inputs[5], inputs[6], inputs[7]) = con.first; + inputs[8] = 1; + + net.Flush(); + net.Input(inputs); + for (int i=0; i " << weight << std::endl; +#endif + this->connection_weights.at(k) = weight; // order of weights corresponds to order of connections. + } + + // Set ODE matrix at initialization + this->set_ode_matrix(); +} + +std::vector DifferentialCPG::get_connection_weights(){ + return this->connection_weights; +} + /** * Callback function that defines the movement of the robot * @@ -306,7 +336,7 @@ void DifferentialCPG::set_ode_matrix() matrix.emplace_back(row); } - // Process A<->A connections + // Process A<->B connections int index = 0; for (const Neuron &neuron: neurons) { @@ -321,10 +351,10 @@ void DifferentialCPG::set_ode_matrix() std::cout << "Setting connection [" << x << ';' << y << ';' << z << ';' << 1 << '-' << x << ';' << y << ';' << z << ';' << -1 - << "] to sample[" << k << "]\t-> " << this->sample.at(k) << std::endl; + << "] to connection_weights[" << k << "]\t-> " << this->connection_weights.at(k) << std::endl; #endif - auto weight = this->sample.at(k) * - (this->range_ub - this->range_lb) + this->range_lb; + auto weight = this->connection_weights.at(k) * + (this->range_ub - this->range_lb) + this->range_lb; size_t i = index; size_t c = index + 1; matrix.at(i).at(c) = weight; @@ -332,7 +362,7 @@ void DifferentialCPG::set_ode_matrix() index+=2; } - // A<->B connections + // A<->A connections index++; int k = 0; std::vector connections_seen; @@ -384,7 +414,7 @@ void DifferentialCPG::set_ode_matrix() else // else continue to next iteration { // actually, we should never encounter this, every connection should appear only once - std::cerr << "Should not see the same connection appearing twice" << std::endl; + std::cerr << "Should not see the same connection appearing twice: " << connection_string << std::endl; throw std::runtime_error("Should not see the same connection appearing twice"); continue; } @@ -394,11 +424,11 @@ void DifferentialCPG::set_ode_matrix() std::cout << "Setting connection [" << x1 << ';' << y1 << ';' << z1 << ';' << w1 << '-' << x2 << ';' << y2 << ';' << z2 << ';' << w2 - << "] to sample[" << sample_index << "]\t-> " << this->sample.at(sample_index) << std::endl; + << "] to connection_weights[" << sample_index << "]\t-> " << this->connection_weights.at(sample_index) << std::endl; #endif // Get weight - const auto w = this->sample.at(sample_index) * + const auto w = this->connection_weights.at(sample_index) * (this->range_ub - this->range_lb) + this->range_lb; // Set connection in weight matrix @@ -480,11 +510,11 @@ void DifferentialCPG::step( neuron_count++; } - // Copy values from next_state into x for ODEINT - state_type x(this->neurons.size()); + // Copy values from next_state into x_state for ODEINT + state_type x_state(this->neurons.size()); for (size_t i = 0; i < this->neurons.size(); i++) { - x[i] = this->next_state[i]; + x_state[i] = this->next_state[i]; } // Perform one step @@ -500,14 +530,31 @@ void DifferentialCPG::step( } } }, - x, + x_state, time, dt); // Copy values into nextstate for (size_t i = 0; i < this->neurons.size(); i++) { - this->next_state[i] = x[i]; + this->next_state[i] = x_state[i]; + } + +// // Load the angle value from the sensor +// double angle_difference = this->angle_to_goal - move_angle; +// if (angle_difference > 180) +// angle_difference -= 360; +// else if (angle_difference < -180) +// angle_difference += 360; +// this->angle_diff = angle_difference; + double angle_difference = 0.0; + double slow_down_factor = 1.0; + if (use_frame_of_reference) { + angle_difference = angle_to_target_sensor->detect_angle(); + std::cout << "Angle detected " << angle_difference << std::endl; + const double frame_of_reference_slower_power = 7.0; + slow_down_factor = std::pow( + (180.0 - std::abs(angle_difference))/180.0, frame_of_reference_slower_power); } // Loop over all neurons to actually update their states. Note that this is a new outer for loop @@ -531,30 +578,30 @@ void DifferentialCPG::step( // Apply saturation formula auto x_input = this->next_state[i]; + double output_j = this->output_function(x_input); + // Use frame of reference - if(use_frame_of_reference) + if(use_frame_of_reference and frame_of_reference != 0) { - - if (std::abs(frame_of_reference) == 1) - { - this->output[j] = this->signal_factor_left_right*this->abs_output_bound*((2.0)/(1.0 + std::pow(2.718, -2.0 * x_input / this->abs_output_bound)) - 1); - } - else if (frame_of_reference == 0) + if ((frame_of_reference == 1 and angle_difference < 0) or + (frame_of_reference == -1 and angle_difference > 0)) //TODO >= / <= ? { - this->output[j] = this->signal_factor_mid*this->abs_output_bound*((2.0)/(1.0 + std::pow(2.718, -2.0 * x_input / this->abs_output_bound)) - 1); + output_j *= slow_down_factor; } - else - { - std::clog << "WARNING: frame_of_reference not in {-1,0,1}." << std::endl; - } - - } - // Don't use frame of reference - else - { - this->output[j] = this->signal_factor_all_*this->abs_output_bound*((2.0)/(1.0 + std::pow(2.718, -2.0 * x_input / this->abs_output_bound)) - 1); } + + this->output[j] = output_j; } i++; } } + +double DifferentialCPG::output_function(double input) const +{ + return this->output_signal_factor + * this->abs_output_bound + * ( + (2.0) / (1.0 + std::pow(2.718, -2.0 * input / this->abs_output_bound)) + - 1 + ); +} diff --git a/cpprevolve/revolve/brains/controller/DifferentialCPG.h b/cpprevolve/revolve/brains/controller/DifferentialCPG.h index f21c993b34..c539f25859 100644 --- a/cpprevolve/revolve/brains/controller/DifferentialCPG.h +++ b/cpprevolve/revolve/brains/controller/DifferentialCPG.h @@ -8,6 +8,7 @@ #include "Controller.h" #include "actuators/Actuator.h" #include "sensors/Sensor.h" +#include "sensors/AngleToTargetDetector.h" #include #include @@ -27,9 +28,7 @@ class DifferentialCPG bool use_frame_of_reference; double init_neuron_state; double range_ub; - double signal_factor_all; - double signal_factor_mid; - double signal_factor_left_right; + double output_signal_factor; double abs_output_bound; std::vector< double > weights; /// can be null, indicating that there is no map @@ -40,17 +39,19 @@ class DifferentialCPG /// \param[in] params Parameters for the controller /// \param[in] _actuators Reference to a actuator list DifferentialCPG( - DifferentialCPG::ControllerParams params, - const std::vector> &_actuators); + const DifferentialCPG::ControllerParams ¶ms, + const std::vector> &_actuators, + std::shared_ptr angle_to_target_sensor = nullptr); /// \brief Constructor for Controller with config CPPN /// \param[in] params Parameters for the controller /// \param[in] _actuators Reference to a actuator list /// \param[in] config_cppn_genome Reference to the genome for configuring the weights in CPG DifferentialCPG( - DifferentialCPG::ControllerParams params, + const DifferentialCPG::ControllerParams ¶ms, const std::vector> &_actuators, - const NEAT::Genome &config_cppn_genome); + const NEAT::Genome &config_cppn_genome, + std::shared_ptr angle_to_target_sensor = nullptr); /// \brief Destructor virtual ~DifferentialCPG(); @@ -63,14 +64,23 @@ class DifferentialCPG virtual void update( const std::vector> &actuators, const std::vector> &sensors, - const double _time, - const double _step) override; + double _time, + double _step) override; + + /// \brief Set the connection weights of the Controller and make sure the matrix is set appropriately + /// \param[in] The weights to be set + void set_connection_weights(std::vector weights); + + void load_genome_to_controller(const NEAT::Genome &genome); + + DifferentialCPG* into_DifferentialCPG() override { return this; }; + + /// \brief Return the weights of the connections + std::vector get_connection_weights(); protected: - void step( - const double time, - const double step); + void step(double time, double step); void init_params_and_connections(const ControllerParams ¶ms, const std::vector> &actuators); @@ -80,6 +90,9 @@ class DifferentialCPG /// \brief Function that resets neuron state void reset_neuron_state(); + /// \brief function that transforms the value of the CPG A-neurons and returns the correct output for the actuators + double output_function(double input) const; + public: std::map< std::tuple< int, int, int >, size_t > motor_coordinates; @@ -111,6 +124,9 @@ class DifferentialCPG /// \brief Used for ODE-int std::vector> ode_matrix; + /// \brief Angle sensor holder + std::shared_ptr<::revolve::AngleToTargetDetector> angle_to_target_sensor; + private: /// \brief Used to determine the next state array double *next_state; @@ -124,20 +140,14 @@ class DifferentialCPG /// \brief Limbo optimizes in [0,1] double range_ub; - /// \brief Loaded sample - std::vector sample; + /// \brief Loaded weights + std::vector connection_weights; /// \brief The number of weights to optimize size_t n_weights; /// \brief Factor to multiply output signal with - double signal_factor_all_; - - /// \brief Factor to multiply output signal with - double signal_factor_mid; - - /// \brief Factor to multiply output signal with - double signal_factor_left_right; + double output_signal_factor; /// \brief When reset a neuron state,do it randomly: bool reset_neuron_random; @@ -152,7 +162,8 @@ class DifferentialCPG bool use_frame_of_reference; double abs_output_bound; - }; + +}; } diff --git a/cpprevolve/revolve/brains/controller/IMC/CMakeLists.txt b/cpprevolve/revolve/brains/controller/IMC/CMakeLists.txt new file mode 100644 index 0000000000..4fda9addc5 --- /dev/null +++ b/cpprevolve/revolve/brains/controller/IMC/CMakeLists.txt @@ -0,0 +1,183 @@ +#cmake_minimum_required(VERSION 3.0 FATAL_ERROR) +#project(IMC) +# +#if (${CUDA}) +# set(Torch_DIR "/home/fuda/Projects/pytorch/libtorch_cuda/share/cmake/Torch") +#else () +# set(Torch_DIR "/home/fuda/Projects/pytorch/libtorch/share/cmake/Torch") +#endif() +# +## CMake flag to help local projects find the build dir +#set(CMAKE_LIBRARY_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}/build/lib") +#set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}/build/lib") +#set(CMAKE_RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}/build") +# +#include_directories(${CMAKE_SOURCE_DIR}/cpprevolve) +# +## Pass source dir to preprocessor +#add_definitions(-DSOURCE_DIR=${CMAKE_SOURCE_DIR}) +# +## Compiler options +## TODO This currently assumes GCC, add Windows support in due time +#add_definitions(-pedantic -Wno-long-long -Wall -Wextra -Wformat=2 +# -Wredundant-decls -Wwrite-strings -Wmissing-include-dirs +# -Wswitch-enum -Wuninitialized +# -Wswitch-default -Winit-self -Wfloat-equal -fPIC ) +# +#set (CMAKE_CXX_STANDARD 11) +# +## Debug Flags +#set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -O0 -ggdb3 -DDEBUG") +# +## Release flags +##set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3 -funroll-loops -finline-functions -fomit-frame-pointer -DNDEBUG") +# +# +# +## Finding dependencies +## _____________________________________________________________________________ +# +## PKG-CONFIG +#find_package(PkgConfig REQUIRED) +# +## Find Boost +#find_package(Boost REQUIRED COMPONENTS system) +#include_directories(${Boost_INCLUDE_DIRS}) +# +## Find Eigen3 - A lightweight C++ template library for vector and matrix math +#find_package(Eigen3 REQUIRED) +#include_directories(${EIGEN3_INCLUDE_DIR}) +# +## Find NLOpt - Non Linear Optimization +#pkg_check_modules(NLOpt REQUIRED nlopt>=2.4) +#include_directories(${NLOpt_INCLUDE_DIRS}) +#link_directories(${NLOpt_LIBRARY_DIRS}) +# +## Find Limbo - LIbrary for Model-Based Optimization +#set(LIMBO_DIR ${CMAKE_SOURCE_DIR}/thirdparty/limbo) +#set(LIMBO_DEFINES USE_NLOPT) +#include_directories(${LIMBO_DIR}/src) +# +## Find GSL - GNU Scientific Library +#find_package(GSL REQUIRED) +#include_directories(${GSL_INCLUDE_DIRS}) +# +## Find Yaml-cpp +#find_package(yaml-cpp REQUIRED) +#include_directories(${YAML_CPP_INCLUDE_DIR}) +# +## Find Gazebo +#set(LOCAL_GAZEBO_DIR "/home/fuda/gazebo/cmake") +#if (LOCAL_GAZEBO_DIR) +# find_package(gazebo 10 REQUIRED CONFIG +# PATHS "${LOCAL_GAZEBO_DIR}" +# NO_DEFAULT_PATH) +# message(WARNING "Using local Gazebo @ ${gazebo_DIR}") +#else() +# find_package(gazebo 10 REQUIRED) +#endif() +#include_directories(${GAZEBO_INCLUDE_DIRS}) +#link_directories(${GAZEBO_LIBRARY_DIRS}) +# +## Gazebo dependencies +## Find avcodec +#pkg_check_modules(libavcodec libavcodec) +#if (NOT libavcodec_FOUND) +# BUILD_WARNING ("libavcodec not found. Audio-video capabilities of gazebo are probably disabled.") +#else() +# include_directories(${libavcodec_INCLUDE_DIRS}) +# link_directories(${libavcodec_LIBRARY_DIRS}) +#endif () +# +## Find Protobuf +## TODO: This part is currently a mess, and it should be handeled better +#find_package(Protobuf REQUIRED) +# +## Find the Protobuf import directory for Gazebo. Found in this +## tutorial: http://gazebosim.org/tutorials?tut=custom_messages&cat=transport +#set(GAZEBO_PROTOBUF_DIR) +#foreach(ITR ${GAZEBO_INCLUDE_DIRS}) +# if(ITR MATCHES ".*gazebo-[0-9.]+$") +# set(GAZEBO_PROTO_PATH "${ITR}/gazebo/msgs/proto") +# set(GAZEBO_PROTO_INCLUDE "${ITR}/gazebo/msgs") +# endif() +#endforeach() +#include_directories( +# ${CMAKE_SOURCE_DIR} +# ${PROTOBUF_INCLUDE_DIRS} +# ${GAZEBO_PROTO_INCLUDE} +#) +# +## Add Gazebo C++ flags (this includes c++11) +#list(APPEND CMAKE_CXX_FLAGS "${GAZEBO_CXX_FLAGS}") +# +## Directory where the .proto files reside within revolve +#set(SPEC_DIR "msgs") +# +## All protobuf files we need, including the Gazebo ones +#file(GLOB_RECURSE REVOLVE_PROTOS ${SPEC_DIR}/*.proto) +# +## Do the protobuf generation by hand for more flexibility. The files are +## generated in a subdirectory such that it can potentially be added to the +## include path for work-in-progress projects I co-develop with this. +## Copied most of this code from +## http://stackoverflow.com/questions/29346488/protobuf-generate-cpp-not-generating-src-and-header-files +## Also see +## https://github.com/Kitware/CMake/blob/master/Modules/FindProtobuf.cmake +#set(PROTO_SRCS) +#set(PROTO_HDRS) +#set(PROTO_OUTPUT_BASE ${CMAKE_CURRENT_BINARY_DIR}/../) +# +# +## Include the directory where the protobuf files will be placed +#include_directories(${PROTO_OUTPUT_BASE}) +# +#file(MAKE_DIRECTORY ${PROTO_OUTPUT_BASE}/revolve/msgs) +#foreach(RV_PROTO ${REVOLVE_PROTOS}) +# get_filename_component(RV_ABS_PROTO ${RV_PROTO} ABSOLUTE) +# get_filename_component(RV_NAME_PROTO ${RV_PROTO} NAME_WE) +# get_filename_component(RV_DIR_PROTO ${RV_PROTO} DIRECTORY) +# +# list(APPEND PROTO_SRCS +# "${PROTO_OUTPUT_BASE}/revolve/msgs/${RV_NAME_PROTO}.pb.cc") +# list(APPEND PROTO_HDRS +# "${PROTO_OUTPUT_BASE}/revolve/msgs/${RV_NAME_PROTO}.pb.h") +# +# add_custom_command( +# OUTPUT "${PROTO_OUTPUT_BASE}/revolve/msgs/${RV_NAME_PROTO}.pb.cc" +# "${PROTO_OUTPUT_BASE}/revolve/msgs/${RV_NAME_PROTO}.pb.h" +# COMMAND ${PROTOBUF_PROTOC_EXECUTABLE} +# ARGS -I ${RV_DIR_PROTO} +# -I ${GAZEBO_PROTO_PATH} +# --cpp_out ${PROTO_OUTPUT_BASE}/revolve/msgs ${RV_ABS_PROTO} +# DEPENDS ${RV_ABS_PROTO} +# COMMENT "Running C++ protocol buffer compiler on ${RV_PROTO}" +# VERBATIM ) +#endforeach() +# +# +# +## Tell the compiler these files were generated +#set_source_files_properties( +# ${PROTO_SRCS} ${PROTO_HDRS} PROPERTIES GENERATED TRUE) +# +## Source subdirectories +## _____________________________________________________________________________ +# +## Plugin C++ files +#file(GLOB_RECURSE +# REVOLVE_GZ_SRC +# brains/*.cpp +# motors/*.cpp +# sensors/*.cpp +# util/*.cpp +# ) +# +# +# +# +#set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}") +# +#add_executable(Test_IMC Test_IMC.cpp FeedForwardNetwork.cpp FeedForwardNetwork.h InverseNetwork.cpp InverseNetwork.h) +#target_link_libraries(Test_IMC "${TORCH_LIBRARIES}") +#set_property(TARGET Test_IMC PROPERTY CXX_STANDARD 14) \ No newline at end of file diff --git a/cpprevolve/revolve/brains/controller/IMC/FeedForwardNetwork.cpp b/cpprevolve/revolve/brains/controller/IMC/FeedForwardNetwork.cpp new file mode 100644 index 0000000000..57cb23a344 --- /dev/null +++ b/cpprevolve/revolve/brains/controller/IMC/FeedForwardNetwork.cpp @@ -0,0 +1,48 @@ +/* +* Copyright (C) 2017 Vrije Universiteit Amsterdam +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* +* Author: Fuda van Diggelen +* +*/ +#include "torch/torch.h" +#include "FeedForwardNetwork.h" + + +///////////////////////////////////////////////// +FeedForwardNetworkImpl::FeedForwardNetworkImpl( + int actuatorsize) + : linear_In(actuatorsize * 3, actuatorsize * 3), + linear_H1(actuatorsize * 3, actuatorsize * 3), + linear_Out(actuatorsize * 3,actuatorsize* 2) +{ + // register_module() is needed if we want to use the parameters() method later on + register_module("linear_In", linear_In); + register_module("linear_H1", linear_H1); + register_module("linear_Out", linear_Out); + + bias_in = register_parameter("bias_in", torch::randn(actuatorsize * 3)); + bias_h1 = register_parameter("bias_h1", torch::randn(actuatorsize * 3)); + bias_out = register_parameter("bias_out", torch::randn(actuatorsize * 2)); +} + + +torch::Tensor FeedForwardNetworkImpl::forward(torch::Tensor x){ + x = torch::relu(linear_In->forward(x)+bias_in); + x = torch::relu(linear_H1->forward(x)+bias_h1); + x = torch::tanh(linear_Out->forward(x)+bias_out); + return x; +} + +FeedForwardNetworkImpl::~FeedForwardNetworkImpl() = default; + diff --git a/cpprevolve/revolve/brains/controller/IMC/FeedForwardNetwork.h b/cpprevolve/revolve/brains/controller/IMC/FeedForwardNetwork.h new file mode 100644 index 0000000000..e40c1e0ed4 --- /dev/null +++ b/cpprevolve/revolve/brains/controller/IMC/FeedForwardNetwork.h @@ -0,0 +1,47 @@ +/* +* Copyright (C) 2017 Vrije Universiteit Amsterdam +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* +* Description: Position based (servo) motor +* Author: Elte Hupkes +* +*/ + + +#include +#include "iostream" + +#include "torch/torch.h" + +class FeedForwardNetworkImpl : public torch::nn::Module { + /// \brief Constructor +public: explicit FeedForwardNetworkImpl( + int actuatorsize + ); + + /// \brief Destructor +public: ~FeedForwardNetworkImpl() override; + + /// \brief Forward function +public: torch::Tensor forward(torch::Tensor x); + +private: + /// \brief Layers + torch::nn::Linear linear_In, linear_H1, linear_Out; + + /// \brief Biases + torch::Tensor bias_in, bias_h1, bias_out; + +}; + +TORCH_MODULE(FeedForwardNetwork); \ No newline at end of file diff --git a/cpprevolve/revolve/brains/controller/IMC/IMC.cpp b/cpprevolve/revolve/brains/controller/IMC/IMC.cpp new file mode 100644 index 0000000000..402a9529ef --- /dev/null +++ b/cpprevolve/revolve/brains/controller/IMC/IMC.cpp @@ -0,0 +1,319 @@ +// +// Created by matteo on 14/06/19. +// +#include "IMC.h" +#include "../sensors/Sensor.h" +#include "../actuators/Actuator.h" +#include "../Controller.h" +#include "torch/torch.h" + +#include +#include +#include +#include "iostream" +#include + +const std::string project_root = "."; + + +using namespace revolve; + +IMC::IMC(std::unique_ptr<::revolve::Controller> wrapped_controller, + const std::vector> &_actuators, + const IMC::IMCParams ¶ms) + : Controller(wrapped_controller->controller_type) + , _wrapped_controller(std::move(wrapped_controller)) + , InverseNet(int(_actuators.size())) + , Inverse_Optim(nullptr) + , FeedForNet(int(_actuators.size())) + , FeedFor_Optim(nullptr) + +{ + this->Inverse_Optim = std::make_unique( + this->InverseNet->parameters(), + torch::optim::AdamOptions(params.learning_rate*2).betas({params.beta1, params.beta2}).weight_decay(params.weight_decay) + ); + this->FeedFor_Optim = std::make_unique( + this->FeedForNet->parameters(), + torch::optim::AdamOptions(params.learning_rate).betas({params.beta1, params.beta2}).weight_decay(params.weight_decay) + ); + + this->Reference_state_Prev = torch::ones(_actuators.size()*2, torch::kFloat)*0.5; + this->Predicted_state_Prev = this->Reference_state_Prev.requires_grad_(true); + this->Current_State_Prev = this->Predicted_state_Prev.requires_grad_(true); + this->Motor_Input_Prev = torch::ones(_actuators.size(), torch::kFloat)*0.5; + this->Motor_Input_Prev_fb = this->Motor_Input_Prev; + this->FeedForNet->to(torch::kDouble); + this->InverseNet->to(torch::kDouble); + + this->State_Memory = torch::ones({long(_actuators.size()*2), params.window_length}, torch::kFloat64)* 0.5; + this->Reference_Memory = torch::ones({long(_actuators.size()*2), params.window_length}, torch::kFloat64)* 0.4999/params.window_length; + + this->Save_Check = params.save_checkpoint; + this->model_name = params.model_name; + + if (params.restore_checkpoint & false) { + + this->Load_Progress(params.model_name); + bool test_best = false; + + if(test_best){ + this->Save_Check = false; + for (const auto& param : this->InverseNet->named_parameters()) { + param->requires_grad_(false); + } + for (const auto& param : this->FeedForNet->named_parameters()) { + param->requires_grad_(false); + } + for (int i = 0; i < int(_actuators.size()); ++i) { + std::ofstream ofs; + ofs.open(project_root + "/experiments/IMC/output" + this->model_name + "/act_info/A" + + std::to_string(i + 1) + ".log", std::ofstream::out | std::ofstream::trunc); + ofs.close(); + } + } + } + else {//clear log files for new + for (int i = 0; i < int(_actuators.size()); ++i) { + std::ofstream ofs; + ofs.open(project_root + "/experiments/IMC/output" + this->model_name + "/act_info/A" + + std::to_string(i + 1) + ".log", std::ofstream::out | std::ofstream::trunc); + ofs.close(); + } + } +// std::ofstream ofs; +// ofs.open(project_root+"/experiments/IMC/output"+this->model_name+"/IMC_time.txt", std::ofstream::out | std::ofstream::trunc); +// ofs.close(); + +} + + +torch::Tensor IMC::InverseModel( + const torch::Tensor& Current_State, + const torch::Tensor& Reference_State) +{ + torch::Tensor Network_Input_Inverse = torch::cat({Reference_State, Current_State}, 0).to(torch::kDouble); + this->InverseNet->zero_grad(); + return (this->InverseNet->forward(Network_Input_Inverse.detach())+1)/2; +} + +torch::Tensor IMC::FeedForModel( + const torch::Tensor& Current_State, + const torch::Tensor& Motor_Input) +{ + torch::Tensor Model_Input_FeedFor = torch::cat({Motor_Input, Current_State},0).to(torch::kDouble); + this->FeedForNet->zero_grad(); + return (this->FeedForNet->forward(Model_Input_FeedFor.detach())+1)/2; +} + +void IMC::Update_Weights( + const torch::Tensor& Current_State, + const torch::Tensor& Motor_Input) +{ + torch::nn::MSELoss MSE_FeedFor_loss; + torch::Tensor FeedFor_loss = MSE_FeedFor_loss(Current_State.clone().detach(),this->Predicted_state_Prev); + FeedFor_loss.backward(); + this->FeedFor_Optim->step(); + + this->State_Memory = torch::cat({this->State_Memory.narrow(1, 1, this->State_Memory.size(1) - 1).detach(), + Current_State.reshape({Motor_Input.size(0) * 2, 1})},1); + this->Reference_Memory = torch::cat({this->Reference_Memory.narrow(1,1,this->State_Memory.size(1)-1), + this->Reference_state_Prev.reshape({Motor_Input.size(0)*2,1})},1); + + torch::nn::MSELoss MSE_Inverse_loss; + torch::Tensor Inverse_loss = MSE_Inverse_loss(this->State_Memory,this->Reference_Memory.detach()); +// torch::Tensor Inverse_loss = MSE_Inverse_loss(Current_State.narrow(0,0,Motor_Input.size(0)), +// this->Reference_state_Prev.narrow(0,0,Motor_Input.size(0)));//.narrow(0,0,Motor_Input.size(0)).detach()) + + Inverse_loss.backward(); + this->Inverse_Optim->step(); + +// /// Logging results +// auto mi_a = this->Motor_Input_Prev.accessor(); +// auto fb_a = this->Motor_Input_Prev_fb.accessor(); +// auto ps_a = this->Predicted_state_Prev.accessor(); +// auto cs_a = this->Current_State_Prev.accessor(); +// auto rs_a = this->Reference_state_Prev.accessor(); +// +// +// for(int i=0 ; imodel_name+"/act_info/A"+std::to_string(i+1)+".log", std::ios::app); +// ofs<()<<","<()<<","<> &_actuators, + double dt) +{ + if(true){ + this->Current_State_Prev = current_state.requires_grad_(true); + this->Update_Weights(this->Current_State_Prev, this->Motor_Input_Prev); + + torch::Tensor feedback_error = (this->Predicted_state_Prev-current_state).detach(); + torch::Tensor motor_input = this->InverseModel(current_state, reference_state + feedback_error).to(torch::kFloat); + + torch::Tensor predicted_state = FeedForModel(current_state, motor_input).to(torch::kFloat); + torch::Tensor FeedFor_error = (reference_state-predicted_state).detach(); + + torch::Tensor feedback = feedback_error+FeedFor_error; + + motor_input = (motor_input-0.5)*dt+0.5 + + (feedback.narrow(0,0,int(_actuators.size()))*5.235988*1.0 + + feedback.narrow(0,int(_actuators.size()),int(_actuators.size()))*1.0)*dt; + + this->Reference_state_Prev = reference_state; + this->Predicted_state_Prev = predicted_state; + this->Current_State_Prev = current_state; + this->Motor_Input_Prev = motor_input.requires_grad_(true); + this->Motor_Input_Prev_fb = (feedback.narrow(0,0,int(_actuators.size()))*5.235988*2.0 + + feedback.narrow(0,int(_actuators.size()),int(_actuators.size()))*2.0)*dt+0.5; + + motor_input = motor_input.to(torch::kDouble); + unsigned int p = 0; + for (const auto &actuator: _actuators) + { + double *output = motor_input[p].data_ptr(); + actuator->write(output, dt); + p += 1; + } + } + else{ + torch::Tensor motor_input = reference_state.narrow(0,0,_actuators.size()); + +// /// Logging results +// auto mi_a = this->Motor_Input_Prev.accessor(); +// auto fb_a = this->Motor_Input_Prev_fb.accessor(); +// auto ps_a = this->Predicted_state_Prev.accessor(); +// auto cs_a = this->Current_State_Prev.accessor(); +// auto rs_a = this->Reference_state_Prev.accessor(); +// +// for(int i=0 ; imodel_name+"/act_info/A"+std::to_string(i+1)+".log", std::ios::app); +// ofs<Reference_state_Prev = reference_state; + this->Predicted_state_Prev = motor_input*0.0; + this->Current_State_Prev = current_state; + this->Motor_Input_Prev = motor_input; + this->Motor_Input_Prev_fb = motor_input*0.0; + + motor_input = motor_input.to(torch::kDouble); + unsigned int p = 0; + for (const auto &actuator: _actuators) + { + double *output = motor_input[p].data_ptr(); + actuator->write(output, dt); + p += 1; + } + } +} + +void IMC::Save_Progress(std::string model_name) +{ +// this->InverseNet->eval(); + std::cout<<"SAVE IMC NETWORK PROGRESS"<InverseNet, project_root+"/experiments/IMC/output"+model_name+"/"+model_name+"_Inverse_network.pt"); + torch::save(*this->Inverse_Optim, project_root+"/experiments/IMC/output"+model_name+"/"+model_name+"_Inverse_optimizer.pt"); + + torch::save(this->FeedForNet, project_root+"/experiments/IMC/output"+model_name+"/"+model_name+"_FeedForModel_network.pt"); + torch::save(*this->FeedFor_Optim, project_root+"/experiments/IMC/output"+model_name+"/"+model_name+"_FeedForModel_optimizer.pt"); + } + else { + this->Save_Check = false; + } +} + +void IMC::Load_Progress(std::string model_name) +{ + + std::ifstream net(project_root+"/experiments/IMC/output"+model_name+"/"+model_name+"_Inverse_network.pt"); + if(net) { + std::cout<<"load IMC"<InverseNet, project_root+"/experiments/IMC/output"+model_name+"/"+model_name+"_Inverse_network.pt"); + torch::load(*this->Inverse_Optim, project_root+"/experiments/IMC/output"+model_name+"/"+model_name+"_Inverse_optimizer.pt"); + + torch::load(this->FeedForNet, project_root+"/experiments/IMC/output"+model_name+"/"+model_name+"_FeedForModel_network.pt"); + torch::load(*this->FeedFor_Optim, project_root+"/experiments/IMC/output"+model_name+"/"+model_name+"_FeedForModel_optimizer.pt"); + } +} + +void IMC::update( + const std::vector > &actuators, + const std::vector > &sensors, + double time, double dt) +{ + size_t output_size = 0; + for (const auto &actuator: actuators) { + output_size += actuator->n_outputs(); + } + + std::vector actuator_buffer(output_size); + double *actuator_buffer_data_p = actuator_buffer.data(); + + size_t output_cursor = 0; + std::vector > fake_actuators; + for (const auto &actuator: actuators) { + const size_t size = actuator->n_outputs(); + fake_actuators.emplace_back(std::make_shared( + actuator_buffer_data_p+output_cursor, + actuator)); + output_cursor += size; + } + + this->_wrapped_controller->update(fake_actuators, sensors, time, dt); + + // ================= READ STATE ======================= + std::vector current_state_v; + current_state_v.reserve(actuators.size()*2); + for (const auto &actuator: actuators) { + current_state_v.emplace_back(actuator->Current_State(Actuator::StateType::POSITION)/2+.5); + } + for (const auto &actuator: actuators) { + current_state_v.emplace_back(actuator->Current_State(Actuator::StateType::VELOCITY)/2/5.235988+.5); + } + + // Current state of the servo motor + torch::Tensor current_state = torch::tensor(current_state_v); + + // State you want to go to (controller output) + torch::Tensor reference_state = torch::tensor(actuator_buffer)/2+.5; + + torch::Tensor reference_state_dot = ((reference_state-this->Reference_state_Prev.narrow(0,0,actuators.size()))/dt/5.235988+.5).clamp(0,1); + reference_state = torch::cat({reference_state,reference_state_dot},0); + + this->Step(current_state, reference_state, actuators, dt); + + if((std::fmod(time,60.0) == 0) & this->Save_Check){ + this->Save_Progress(this->model_name); + if(time == dt){ + for(const auto& pair : this->InverseNet->named_parameters().pairs()){ + this->InverseNet->named_parameters().find(pair.first)-> + set_requires_grad(pair.first.find("ut") == std::string::npos); + } + for(const auto& pair : this->InverseNet->named_parameters().pairs()){ + this->InverseNet->named_parameters().find(pair.first)-> + set_requires_grad(pair.first.find("ut") == std::string::npos); + } + } + } +} \ No newline at end of file diff --git a/cpprevolve/revolve/brains/controller/IMC/IMC.h b/cpprevolve/revolve/brains/controller/IMC/IMC.h new file mode 100644 index 0000000000..61aa720855 --- /dev/null +++ b/cpprevolve/revolve/brains/controller/IMC/IMC.h @@ -0,0 +1,133 @@ +// +// +// + + +#include "../sensors/Sensor.h" +#include "../actuators/Actuator.h" +#include "../Controller.h" +#include "torch/torch.h" +#include "FeedForwardNetwork.h" +#include "InverseNetwork.h" +#include "../DifferentialCPG.h" + + +namespace revolve { + +class IMC : public Controller +{ +public: + struct IMCParams { + double learning_rate = 5e-2; + double beta1 = 0.9; + double beta2 = 0.99; + double weight_decay = 0.001; + int window_length = 60; + bool restore_checkpoint = false; +<<<<<<< HEAD + bool save_checkpoint = false; +======= + bool save_checkpoint = true; +>>>>>>> 1b077afcba9df8d1c45154780b47411c13e9973c + std::string model_name; + }; + /// \brief Constructor + /// \param[in] _controller Reference + /// \param[in] _actuators Reference to a actuator list + /// \param[in] params Parameters for the IMC learning rate/beta_factor/restore_checkpoint + IMC( + std::unique_ptr<::revolve::Controller> wrapped_controller, + const std::vector> &_actuators, + const IMC::IMCParams ¶ms); + + /// \brief Inverse Model function + torch::Tensor InverseModel( + const torch::Tensor& Current_State, + const torch::Tensor& Reference_State); + + /// \brief FeedForward Model function + torch::Tensor FeedForModel( + const torch::Tensor& Current_State, + const torch::Tensor& Motor_Input); + + /// \brief Generic functions + void Update_Weights( + const torch::Tensor& Current_State, + const torch::Tensor& Motor_Input); + + void Step( + const torch::Tensor& current_state, + const torch::Tensor& reference_state, + const std::vector> &_actuators, + double dt); + + void update( + const std::vector> &actuators, + const std::vector> &sensors, + double time, + double step + ) override; + + void Save_Progress(std::string model_name); + void Load_Progress(std::string model_name); + + DifferentialCPG* into_DifferentialCPG() override { return this->_wrapped_controller->into_DifferentialCPG(); } + +private: + class FakeActuator: public Actuator { + double *const buffer; + + public: + explicit FakeActuator(double *const buffer, const std::shared_ptr &wrapped) + : Actuator(wrapped->n_outputs(), wrapped->coordinate_x(), wrapped->coordinate_y(), wrapped->coordinate_z()) + , buffer(buffer) + {}; + + double Current_State( StateType /*type*/ ) override { return 0.0; } + + void write(const double *output, double /*step*/) override + { + for (unsigned int i=0; ibuffer[i] = output[i]; + } + } + }; + + +protected: + std::unique_ptr<::revolve::Controller> _wrapped_controller; + + /// \brief Inverse Network + InverseNetwork InverseNet; + + /// \brief Loaded Inverse weights + std::unique_ptr Inverse_Optim; + + /// \brief Feed Forward Network + FeedForwardNetwork FeedForNet; + + /// \brief Loaded Feed Forward weights + std::unique_ptr FeedFor_Optim; + + /// \brief Previous reference state + torch::Tensor Reference_state_Prev; + + /// \brief Previous reference state + torch::Tensor Predicted_state_Prev; + + torch::Tensor Motor_Input_Prev; + torch::Tensor Motor_Input_Prev_fb; + torch::Tensor Current_State_Prev; + + torch::Tensor State_Memory; + torch::Tensor Reference_Memory; + bool Save_Check; + std::string model_name; + + std::string model_name; + +}; + +} + + diff --git a/cpprevolve/revolve/brains/controller/IMC/InverseNetwork.cpp b/cpprevolve/revolve/brains/controller/IMC/InverseNetwork.cpp new file mode 100644 index 0000000000..89cfae31be --- /dev/null +++ b/cpprevolve/revolve/brains/controller/IMC/InverseNetwork.cpp @@ -0,0 +1,49 @@ +/* +* Copyright (C) 2017 Vrije Universiteit Amsterdam +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* +* Author: Fuda van Diggelen +* +*/ + +#include "torch/torch.h" +#include "InverseNetwork.h" + + +///////////////////////////////////////////////// +InverseNetworkImpl::InverseNetworkImpl( + int actuatorsize) + : linear_In(actuatorsize * 4, actuatorsize * 4), + linear_H1(actuatorsize * 4, actuatorsize * 4), + linear_Out( actuatorsize* 4,actuatorsize) +{ + // register_module() is needed if we want to use the parameters() method later on + register_module("linear_In", linear_In); + register_module("linear_H1", linear_H1); + register_module("linear_Out", linear_Out); + + bias_in = register_parameter("bias_in", torch::randn(actuatorsize * 4)); + bias_h1 = register_parameter("bias_h1", torch::randn(actuatorsize * 4)); + bias_out = register_parameter("bias_out", torch::randn(actuatorsize)); +} + + +torch::Tensor InverseNetworkImpl::forward(torch::Tensor x){ + x = torch::relu(linear_In->forward(x)+bias_in); + x = torch::relu(linear_H1->forward(x)+bias_h1); + x = torch::tanh(linear_Out->forward(x)+bias_out); + return x; +} + +InverseNetworkImpl::~InverseNetworkImpl() = default; + diff --git a/cpprevolve/revolve/brains/controller/IMC/InverseNetwork.h b/cpprevolve/revolve/brains/controller/IMC/InverseNetwork.h new file mode 100644 index 0000000000..b63f793fb1 --- /dev/null +++ b/cpprevolve/revolve/brains/controller/IMC/InverseNetwork.h @@ -0,0 +1,47 @@ +/* +* Copyright (C) 2017 Vrije Universiteit Amsterdam +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* +* Description: Position based (servo) motor +* Author: Elte Hupkes +* +*/ + + +#include +#include "iostream" + +#include "torch/torch.h" + +class InverseNetworkImpl : public torch::nn::Module { + /// \brief Constructor +public: explicit InverseNetworkImpl( + int actuatorsize + ); + + /// \brief Destructor +public: ~InverseNetworkImpl() override; + + /// \brief Forward function +public: torch::Tensor forward(torch::Tensor x); + +private: + /// \brief Layers + torch::nn::Linear linear_In, linear_H1, linear_Out; + + /// \brief Biases + torch::Tensor bias_in, bias_h1, bias_out; + +}; + +TORCH_MODULE(InverseNetwork); \ No newline at end of file diff --git a/cpprevolve/revolve/brains/controller/IMC/Test_IMC.cpp b/cpprevolve/revolve/brains/controller/IMC/Test_IMC.cpp new file mode 100644 index 0000000000..6fabef9561 --- /dev/null +++ b/cpprevolve/revolve/brains/controller/IMC/Test_IMC.cpp @@ -0,0 +1,160 @@ +// +// Created by fuda on 16-03-20. +// + +// +// Created by fuda on 25-02-20. +// +#include "FeedForwardNetwork.h" +#include "InverseNetwork.h" +#include "torch/torch.h" + +#include + + +const std::string NetworkFor = "/home/fuda/Projects/revolve/experiments/examples/output/testFor.log"; +const std::string NetworkInv = "/home/fuda/Projects/revolve/experiments/examples/output/testInv.log"; +int actuatorsize = 2; +double phase =0.3; +double freq = 2*3.141592654/75; + + +FeedForwardNetwork FeedForNet(actuatorsize); +torch::optim::Adam FeedFor_Optim(FeedForNet.parameters(), + torch::optim::AdamOptions(3e-2).beta1(0.0).weight_decay(0.001)); + + +InverseNetwork InvNet(actuatorsize); +torch::optim::Adam Inv_Optim(InvNet.parameters(), + torch::optim::AdamOptions(3e-2).beta1(0.0).weight_decay(0.001)); + + + +torch::Tensor Reference_state_Prev = torch::ones((0,actuatorsize*2)).to(torch::kFloat64)*.5; +torch::Tensor Predicted_state_Prev =Reference_state_Prev.requires_grad_(true); + + +torch::Tensor InvModel(const torch::Tensor& Current_State, + const torch::Tensor& Reference_State, + const torch::Tensor& error +){ + torch::Tensor Network_Input_Inv = torch::cat({Reference_State, Current_State}, 0); + InvNet.zero_grad(); + return (InvNet.forward(Network_Input_Inv)+1)/2; +} + +void Update_Weights_InvModel(const torch::Tensor& Current_State, + const torch::Tensor& Motor_Input) +{ +///////////////////////////////////////////////////////////////// +////////////// Update the weight of the inverse ///////////////// +///////////////////////////////////////////////////////////////// + auto C_a = Current_State.accessor(); + auto R_a = Reference_state_Prev.accessor(); + auto MI_a =Motor_Input.accessor(); + torch::nn::MSELoss lossInv; + torch::Tensor Inv_loss = lossInv(Current_State, Reference_state_Prev.detach()); + Inv_loss.backward(); + Inv_Optim.step(); + + std::ofstream foutInv; // Create Object of ofstream + foutInv.open (NetworkInv , std::ios::app); + foutInv<()<<"\t"<> &_actuators +) +{ + torch::Tensor Network_Input_FeedFor = torch::cat({Motor_Input, Current_State},0); + + FeedForNet.zero_grad(); + Predicted_state_Prev = (FeedForNet.forward(Network_Input_FeedFor.detach())+1)/2; +} + +void Update_Weights_FeedForModel(const torch::Tensor& Current_State, + const torch::Tensor& Motor_Input) +{ + + torch::nn::MSELoss lossFor; + torch::Tensor FeedFor_loss = lossFor(Predicted_state_Prev, Current_State.clone().detach()); + FeedFor_loss.backward(); + FeedFor_Optim.step(); + + auto P_a = Predicted_state_Prev.accessor(); + auto C_a = Current_State.accessor(); + + std::ofstream fout; // Create Object of ofstream + fout.open (NetworkFor , std::ios::app); + fout<()<<"\n"; + fout.close(); // Closing the file + + +} + + + +int main(){ + std::ofstream ofs; + ofs.open(NetworkFor, std::ofstream::out | std::ofstream::trunc); + ofs.close(); + ofs.open(NetworkInv, std::ofstream::out | std::ofstream::trunc); + ofs.close(); + + + torch::Tensor current_state = Predicted_state_Prev+0.0; + auto options = torch::TensorOptions().dtype(torch::kFloat64); + + std::time_t result = std::time(nullptr); + std::cout << current_state << "\n" << Predicted_state_Prev << "\n" << std::asctime(std::localtime(&result)); + + FeedForNet.to(torch::kDouble); + InvNet.to(torch::kDouble); + + + for(double t=0; t < 5000;) { + double data_input[]={ (cos(t*freq+phase)+sin(t*freq*2.5-phase)+2)/4, (-cos(t*freq+phase)-sin(t*freq*2.5-phase)+2)/4, - + ((sin(t*freq+phase)*freq+cos(t*freq*2.5-phase)*freq*2.5)/4+.2)*2.5, ((sin(t*freq+phase)*freq-cos(t*freq*2.5-phase)*freq*2.5)/4+.2)*2.5}; + + + torch::Tensor reference_state = torch::from_blob(data_input,{2*actuatorsize}, options); +// std::cout<< current_state-(Predicted_state_Prev-Reference_state_Prev); + torch::Tensor fb = (Predicted_state_Prev-current_state).detach(); + + + torch::Tensor motor_input = InvModel(current_state, reference_state+fb, fb); + + + FeedForModel( current_state, motor_input); + + torch::Tensor fb2 = (reference_state-Predicted_state_Prev).detach(); + motor_input = motor_input+fb.reshape({2,actuatorsize}).sum(0) + + fb2.reshape({2,actuatorsize}).sum(0); +// + torch::Tensor statedot = (torch::cat({torch::clamp( + current_state.narrow(0,actuatorsize,actuatorsize)*2-1, -1, 1), + (torch::clamp(motor_input,0,1)*2-1)}, + 0))*0.2; +// torch::Tensor statedot({(-sin(t*freq+phase)*freq+1)/2, (-sin(t*freq+phase)*freq+1)/2}); +// torch::Tensor statedot = (current_state-reference_state); +// std::cout << current_state; + current_state = clamp(current_state+statedot, 0 , 1); + + Update_Weights_FeedForModel(current_state,motor_input); + Update_Weights_InvModel(current_state,motor_input); + + Reference_state_Prev = reference_state; + current_state = current_state.detach(); +// current_state.to(options); +// std::cout << "\n\nData_out: \n" << data_output[0] << " " << data_output[1] << "\ncurrent_state: \n"<< current_state; +// std::cout << current_state; + t = t+1; + + } + + result = std::time(nullptr); + std::cout << std::asctime(std::localtime(&result)); +}; \ No newline at end of file diff --git a/cpprevolve/revolve/brains/controller/IMC/Test_IMC2.cpp b/cpprevolve/revolve/brains/controller/IMC/Test_IMC2.cpp new file mode 100644 index 0000000000..47ad51baf6 --- /dev/null +++ b/cpprevolve/revolve/brains/controller/IMC/Test_IMC2.cpp @@ -0,0 +1,226 @@ +// +// Created by fuda on 16-03-20. +// + +// +// Created by fuda on 25-02-20. +// +//#pragma once +#include +#include + +#include +#include +#include +#include +#include + +#include + +//#include "../plugin/RobotController.h" + +#include "IMC.h" +#include "torch/torch.h" + +#include "revolve/brains/learner/EvaluationReporter.h" +#include "revolve/brains/controller/sensors/Sensor.h" +#include "revolve/brains/controller/actuators/Actuator.h" +#include "revolve/brains/learner/Learner.h" +#include "revolve/brains/learner/Evaluator.h" +#include "revolve/brains/learner/NoLearner.h" +#include "revolve/brains/learner/BayesianOptimizer.h" +#include "revolve/brains/learner/HyperNEAT.h" +#include "revolve/brains/controller/Controller.h" + + + +#include +#include +#include +#include +#include + +#include +#include +#include +#include "revolve/gazebo/brains/GazeboReporter.h" +#include "revolve/gazebo/brains/Brains.h" +//#include +#include "revolve/gazebo/Types.h" +#include + + +const std::string sdfPath("/home/fuda/Projects/revolve/experiments/examples/yaml/Single_link.yaml.sdf"); + +// load and check sdf file +sdf::SDFPtr sdfElement(new sdf::SDF()); + + + +std::unique_ptr<::revolve::gazebo::Evaluator> evaluator; +std::unique_ptr<::revolve::EvaluationReporter> reporter; +std::shared_ptr<::revolve::gazebo::GazeboReporter> gazebo_reporter; +/// \brief Networking node +::gazebo::transport::NodePtr node_; + + +/// \brief Motors in this model +//std::vector< std::shared_ptr< revolve::Actuator > > motors_; +std::vector motors_; + +/// \brief Sensors in this model +//std::vector< std::shared_ptr< revolve::Sensor > > sensors_; +std::vector sensors_; + +/// \brief Pointer to the model +::gazebo::physics::ModelPtr model_; + +/// \brief Learner for the brain controlling this model +std::unique_ptr<::revolve::Learner> learner; + + +//namespace revolve + +int main () +{ + sdf::init(sdfElement); + if (!sdf::readFile(sdfPath, sdfElement)) + { + std::cerr << sdfPath << " is not a valid SDF file!" << std::endl; + } + // start parsing model + const sdf::ElementPtr _sdf = sdfElement->Root(); + + if (not _sdf->HasElement("rv:brain")) { + std::cerr << "No robot brain detected, this is probably an error." + << std::endl; + + } + + auto brain_sdf = _sdf->GetElement("rv:brain"); + auto controller_type = brain_sdf->GetElement("rv:controller")->GetAttribute("type")->GetAsString(); + // auto IMC_params = brain_sdf->GetElement("rv:IMC")->GetElement("rv:params"); + auto learner_type = brain_sdf->GetElement("rv:learner")->GetAttribute("type")->GetAsString(); + std::cout << "Loading controller " << controller_type << " and learner " << learner_type << std::endl; + + + //TODO parameters from SDF + const double evaluation_rate = 15.0; + const unsigned int n_learning_evaluations = 50; + + evaluator = std::make_unique< revolve::gazebo::Evaluator >(evaluation_rate, true, model_); + + // aggregated reporter + std::unique_ptr aggregated_reporter(new revolve::AggregatedReporter(model_->GetName())); + // std::cout reporter + aggregated_reporter->create<::revolve::PrintReporter>(); + // gazebo network publisher reporter + gazebo_reporter.reset(new ::revolve::gazebo::GazeboReporter(aggregated_reporter->robot_id, node_)); + aggregated_reporter->append(gazebo_reporter); + + reporter = std::move(aggregated_reporter); + + // SELECT CONTROLLER ------------------------------------------------------ + std::unique_ptr<::revolve::Controller> controller; + + if ("ann" == controller_type) { + controller = std::make_unique(model_, brain_sdf, motors_, sensors_); + } else if ("spline" == controller_type) { + if (not motors_.empty()) { + controller = std::make_unique(model_, brain_sdf, motors_, sensors_); + } + } else if ("cpg" == controller_type) { + controller = std::make_unique(brain_sdf, motors_); + } else if ("cppn-cpg") { + controller = std::make_unique(brain_sdf, motors_); + } else { + throw std::runtime_error("Robot brain: Controller \"" + controller_type + "\" is not supported."); + } + std::cout << "initialized the controller" << std::endl; + // ================= INITIALIZE IMC ==================== + revolve::IMC::IMCParams imc_params = revolve::IMC::IMCParams(); + std::unique_ptr<::revolve::Controller> controller2; + std::unique_ptr<::revolve::Controller> imc; + imc = std::make_unique(std::move(controller), motors_, imc_params); + + controller2 = std::move(imc); + + + // SELECT LEARNER --------------------------------------------------------- + if ("offline" == learner_type) { + learner = std::make_unique>(std::move(controller)); + } else if ("rlpower" == learner_type) { + //TODO make RLPower generic + if ("spline" != controller_type) { + throw std::runtime_error("Robot brain: Learner RLPower not supported for \"" + controller_type + "\" controller."); + } + learner = std::make_unique>(std::move(controller)); + } else if ("bo" == learner_type) { + learner = std::make_unique( + std::move(controller), + evaluator.get(), + reporter.get(), + evaluation_rate, + n_learning_evaluations); + } else if ("hyperneat" == learner_type) { + NEAT::Parameters neat_params = NEAT::Parameters(); + + const sdf::ElementPtr learner_sdf = brain_sdf->GetElement("rv:learner")->GetElement("rv:params"); + + + #define WRITE_DOUBLE_PARAM(x) std::cout << #x << " is set to: " << learner_sdf->GetAttribute(#x)->GetAsString() << std::endl; neat_params.x = stod(learner_sdf->GetAttribute(#x)->GetAsString()); + #define CHECK_PARAM(x) {stod(std::to_string(neat_params.x))==stod(learner_sdf->GetAttribute(#x)->GetAsString()) ? std::cout << std::left <<#x << " is set to: Default" << std::endl : WRITE_DOUBLE_PARAM(x)} + CHECK_PARAM(PopulationSize) + CHECK_PARAM(WeightDiffCoeff) + CHECK_PARAM(CompatTreshold) + CHECK_PARAM(YoungAgeTreshold) + CHECK_PARAM(OldAgeTreshold) + CHECK_PARAM(MinSpecies) + CHECK_PARAM(MaxSpecies) + CHECK_PARAM(RouletteWheelSelection) + CHECK_PARAM(RecurrentProb) + CHECK_PARAM(OverallMutationRate) + CHECK_PARAM(ArchiveEnforcement) + CHECK_PARAM(MutateWeightsProb) + CHECK_PARAM(WeightMutationMaxPower) + CHECK_PARAM(WeightReplacementMaxPower) + CHECK_PARAM(MutateWeightsSevereProb) + CHECK_PARAM(WeightMutationRate) + CHECK_PARAM(WeightReplacementRate) + CHECK_PARAM(MaxWeight) + CHECK_PARAM(MutateAddNeuronProb) + CHECK_PARAM(MutateAddLinkProb) + CHECK_PARAM(MutateRemLinkProb) + CHECK_PARAM(MinActivationA) + CHECK_PARAM(MaxActivationA) + CHECK_PARAM(ActivationFunction_SignedSigmoid_Prob) + CHECK_PARAM(ActivationFunction_UnsignedSigmoid_Prob) + CHECK_PARAM(ActivationFunction_Tanh_Prob) + CHECK_PARAM(ActivationFunction_SignedStep_Prob) + CHECK_PARAM(CrossoverRate) + CHECK_PARAM(MultipointCrossoverRate) + CHECK_PARAM(SurvivalRate) + CHECK_PARAM(MutateNeuronTraitsProb) + CHECK_PARAM(MutateLinkTraitsProb) + #undef CHECK_PARAM + #undef WRITE_DOUBLE_PARAM + + neat_params.DynamicCompatibility = (learner_sdf->GetAttribute("DynamicCompatibility")->GetAsString() == "true"); + neat_params.NormalizeGenomeSize = (learner_sdf->GetAttribute("NormalizeGenomeSize")->GetAsString() == "true"); + neat_params.AllowLoops = (learner_sdf->GetAttribute("AllowLoops")->GetAsString() == "true"); + neat_params.AllowClones = (learner_sdf->GetAttribute("AllowClones")->GetAsString() == "true"); + + int seed = 0; + + learner = std::make_unique( + std::move(controller), + evaluator.get(), + reporter.get(), + neat_params, + seed, + evaluation_rate, + n_learning_evaluations); + } else { + throw std::runtime_error("Robot brain: Learner \"" + learner_type + "\" is not supported."); + } +} diff --git a/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeCache.txt b/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeCache.txt new file mode 100644 index 0000000000..5f4eb92ec2 --- /dev/null +++ b/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeCache.txt @@ -0,0 +1,416 @@ +# This is the CMakeCache file. +# For build in directory: /home/fuda/Projects/revolve/cpprevolve/revolve/gazebo/IMC/cmake-build-debug +# It was generated by CMake: /snap/clion/103/bin/cmake/linux/bin/cmake +# You can edit this file to change values found and used by cmake. +# If you do not want to change any of the values, simply exit the editor. +# If you do want to change a value, simply edit, save, and exit the editor. +# The syntax for the file is as follows: +# KEY:TYPE=VALUE +# KEY is the name of a variable in the cache. +# TYPE is a hint to GUIs for the type of VALUE, DO NOT EDIT TYPE!. +# VALUE is the current value for the KEY. + +######################## +# EXTERNAL cache entries +######################## + +//No help, variable specified on the command line. +BUILD_ONLY_BRAIN:UNINITIALIZED=0 + +//No help, variable specified on the command line. +BUILD_RASPBERRY:UNINITIALIZED=0 + +//Path to a library. +C10_LIBRARY:FILEPATH=/home/fuda/Projects/pytorch/libtorch/lib/libc10.so + +//Path to a program. +CMAKE_AR:FILEPATH=/usr/bin/ar + +//Choose the type of build, options are: None Debug Release RelWithDebInfo +// MinSizeRel ... +CMAKE_BUILD_TYPE:STRING=Release + +//Id string of the compiler for the CodeBlocks IDE. 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identification macro. Try to identify the platform and guess that + it is the native compiler. */ +#elif defined(__hpux) || defined(__hpua) +# define COMPILER_ID "HP" + +#else /* unknown compiler */ +# define COMPILER_ID "" +#endif + +/* Construct the string literal in pieces to prevent the source from + getting matched. Store it in a pointer rather than an array + because some compilers will just produce instructions to fill the + array rather than assigning a pointer to a static array. */ +char const* info_compiler = "INFO" ":" "compiler[" COMPILER_ID "]"; +#ifdef SIMULATE_ID +char const* info_simulate = "INFO" ":" "simulate[" SIMULATE_ID "]"; +#endif + +#ifdef __QNXNTO__ +char const* qnxnto = "INFO" ":" "qnxnto[]"; +#endif + +#if defined(__CRAYXE) || defined(__CRAYXC) +char const *info_cray = "INFO" ":" "compiler_wrapper[CrayPrgEnv]"; +#endif + +#define STRINGIFY_HELPER(X) #X +#define STRINGIFY(X) STRINGIFY_HELPER(X) + +/* Identify known platforms by name. */ +#if defined(__linux) || defined(__linux__) || defined(linux) +# define PLATFORM_ID "Linux" + +#elif defined(__CYGWIN__) +# define PLATFORM_ID "Cygwin" + +#elif defined(__MINGW32__) +# define PLATFORM_ID "MinGW" + +#elif defined(__APPLE__) +# define PLATFORM_ID "Darwin" + +#elif defined(_WIN32) || defined(__WIN32__) || defined(WIN32) +# define PLATFORM_ID "Windows" + +#elif defined(__FreeBSD__) || defined(__FreeBSD) +# define PLATFORM_ID "FreeBSD" + +#elif defined(__NetBSD__) || defined(__NetBSD) +# define PLATFORM_ID "NetBSD" + +#elif defined(__OpenBSD__) || defined(__OPENBSD) +# define PLATFORM_ID "OpenBSD" + +#elif defined(__sun) || defined(sun) +# define PLATFORM_ID "SunOS" + +#elif defined(_AIX) || defined(__AIX) || defined(__AIX__) || defined(__aix) || defined(__aix__) +# define PLATFORM_ID "AIX" + +#elif defined(__hpux) || defined(__hpux__) +# define PLATFORM_ID "HP-UX" + +#elif defined(__HAIKU__) +# define PLATFORM_ID "Haiku" + +#elif defined(__BeOS) || defined(__BEOS__) || defined(_BEOS) +# define PLATFORM_ID "BeOS" + +#elif defined(__QNX__) || defined(__QNXNTO__) +# define PLATFORM_ID "QNX" + +#elif defined(__tru64) || defined(_tru64) || defined(__TRU64__) +# define PLATFORM_ID "Tru64" + +#elif defined(__riscos) || defined(__riscos__) +# define PLATFORM_ID "RISCos" + +#elif defined(__sinix) || defined(__sinix__) || defined(__SINIX__) +# define PLATFORM_ID "SINIX" + +#elif defined(__UNIX_SV__) +# define PLATFORM_ID "UNIX_SV" + +#elif defined(__bsdos__) +# define PLATFORM_ID "BSDOS" + +#elif defined(_MPRAS) || defined(MPRAS) +# define PLATFORM_ID "MP-RAS" + +#elif defined(__osf) || defined(__osf__) +# define PLATFORM_ID "OSF1" + +#elif defined(_SCO_SV) || defined(SCO_SV) || defined(sco_sv) +# define PLATFORM_ID "SCO_SV" + +#elif defined(__ultrix) || defined(__ultrix__) || defined(_ULTRIX) +# define PLATFORM_ID "ULTRIX" + +#elif defined(__XENIX__) || defined(_XENIX) || defined(XENIX) +# define PLATFORM_ID "Xenix" + +#elif defined(__WATCOMC__) +# if defined(__LINUX__) +# define PLATFORM_ID "Linux" + +# elif defined(__DOS__) +# define PLATFORM_ID "DOS" + +# elif defined(__OS2__) +# define PLATFORM_ID "OS2" + +# elif defined(__WINDOWS__) +# define PLATFORM_ID "Windows3x" + +# else /* unknown platform */ +# define PLATFORM_ID +# endif + +#elif defined(__INTEGRITY) +# if defined(INT_178B) +# define PLATFORM_ID "Integrity178" + +# else /* regular Integrity */ +# define PLATFORM_ID "Integrity" +# endif + +#else /* unknown platform */ +# define PLATFORM_ID + +#endif + +/* For windows compilers MSVC and Intel we can determine + the architecture of the compiler being used. This is because + the compilers do not have flags that can change the architecture, + but rather depend on which compiler is being used +*/ +#if defined(_WIN32) && defined(_MSC_VER) +# if defined(_M_IA64) +# define ARCHITECTURE_ID "IA64" + +# elif defined(_M_X64) || defined(_M_AMD64) +# define ARCHITECTURE_ID "x64" + +# elif defined(_M_IX86) +# define ARCHITECTURE_ID "X86" + +# elif defined(_M_ARM64) +# define ARCHITECTURE_ID "ARM64" + +# elif defined(_M_ARM) +# if _M_ARM == 4 +# define ARCHITECTURE_ID "ARMV4I" +# elif _M_ARM == 5 +# define ARCHITECTURE_ID "ARMV5I" +# else +# define ARCHITECTURE_ID "ARMV" STRINGIFY(_M_ARM) +# endif + +# elif defined(_M_MIPS) +# define ARCHITECTURE_ID "MIPS" + +# elif defined(_M_SH) +# define ARCHITECTURE_ID "SHx" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__WATCOMC__) +# if defined(_M_I86) +# define ARCHITECTURE_ID "I86" + +# elif defined(_M_IX86) +# define ARCHITECTURE_ID "X86" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC) +# if defined(__ICCARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__ICCRX__) +# define ARCHITECTURE_ID "RX" + +# elif defined(__ICCRH850__) +# define ARCHITECTURE_ID "RH850" + +# elif defined(__ICCRL78__) +# define ARCHITECTURE_ID "RL78" + +# elif defined(__ICCRISCV__) +# define ARCHITECTURE_ID "RISCV" + +# elif defined(__ICCAVR__) +# define ARCHITECTURE_ID "AVR" + +# elif defined(__ICC430__) +# define ARCHITECTURE_ID "MSP430" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__ghs__) +# if defined(__PPC64__) +# define ARCHITECTURE_ID "PPC64" + +# elif defined(__ppc__) +# define ARCHITECTURE_ID "PPC" + +# elif defined(__ARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__x86_64__) +# define ARCHITECTURE_ID "x64" + +# elif defined(__i386__) +# define ARCHITECTURE_ID "X86" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif +#else +# define ARCHITECTURE_ID +#endif + +/* Convert integer to decimal digit literals. */ +#define DEC(n) \ + ('0' + (((n) / 10000000)%10)), \ + ('0' + (((n) / 1000000)%10)), \ + ('0' + (((n) / 100000)%10)), \ + ('0' + (((n) / 10000)%10)), \ + ('0' + (((n) / 1000)%10)), \ + ('0' + (((n) / 100)%10)), \ + ('0' + (((n) / 10)%10)), \ + ('0' + ((n) % 10)) + +/* Convert integer to hex digit literals. */ +#define HEX(n) \ + ('0' + ((n)>>28 & 0xF)), \ + ('0' + ((n)>>24 & 0xF)), \ + ('0' + ((n)>>20 & 0xF)), \ + ('0' + ((n)>>16 & 0xF)), \ + ('0' + ((n)>>12 & 0xF)), \ + ('0' + ((n)>>8 & 0xF)), \ + ('0' + ((n)>>4 & 0xF)), \ + ('0' + ((n) & 0xF)) + +/* Construct a string literal encoding the version number components. */ +#ifdef COMPILER_VERSION_MAJOR +char const info_version[] = { + 'I', 'N', 'F', 'O', ':', + 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','[', + COMPILER_VERSION_MAJOR, +# ifdef COMPILER_VERSION_MINOR + '.', COMPILER_VERSION_MINOR, +# ifdef COMPILER_VERSION_PATCH + '.', COMPILER_VERSION_PATCH, +# ifdef COMPILER_VERSION_TWEAK + '.', COMPILER_VERSION_TWEAK, +# endif +# endif +# endif + ']','\0'}; +#endif + +/* Construct a string literal encoding the internal version number. */ +#ifdef COMPILER_VERSION_INTERNAL +char const info_version_internal[] = { + 'I', 'N', 'F', 'O', ':', + 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','_', + 'i','n','t','e','r','n','a','l','[', + COMPILER_VERSION_INTERNAL,']','\0'}; +#endif + +/* Construct a string literal encoding the version number components. */ +#ifdef SIMULATE_VERSION_MAJOR +char const info_simulate_version[] = { + 'I', 'N', 'F', 'O', ':', + 's','i','m','u','l','a','t','e','_','v','e','r','s','i','o','n','[', + SIMULATE_VERSION_MAJOR, +# ifdef SIMULATE_VERSION_MINOR + '.', SIMULATE_VERSION_MINOR, +# ifdef SIMULATE_VERSION_PATCH + '.', SIMULATE_VERSION_PATCH, +# ifdef SIMULATE_VERSION_TWEAK + '.', SIMULATE_VERSION_TWEAK, +# endif +# endif +# endif + ']','\0'}; +#endif + +/* Construct the string literal in pieces to prevent the source from + getting matched. Store it in a pointer rather than an array + because some compilers will just produce instructions to fill the + array rather than assigning a pointer to a static array. */ +char const* info_platform = "INFO" ":" "platform[" PLATFORM_ID "]"; +char const* info_arch = "INFO" ":" "arch[" ARCHITECTURE_ID "]"; + + + + +#if !defined(__STDC__) +# if (defined(_MSC_VER) && !defined(__clang__)) \ + || (defined(__ibmxl__) || defined(__IBMC__)) +# define C_DIALECT "90" +# else +# define C_DIALECT +# endif +#elif __STDC_VERSION__ >= 201000L +# define C_DIALECT "11" +#elif __STDC_VERSION__ >= 199901L +# define C_DIALECT "99" +#else +# define C_DIALECT "90" +#endif +const char* info_language_dialect_default = + "INFO" ":" "dialect_default[" C_DIALECT "]"; + +/*--------------------------------------------------------------------------*/ + +#ifdef ID_VOID_MAIN +void main() {} +#else +# if defined(__CLASSIC_C__) +int main(argc, argv) int argc; char *argv[]; +# else +int main(int argc, char* argv[]) +# endif +{ + int require = 0; + require += info_compiler[argc]; + require += info_platform[argc]; + require += info_arch[argc]; +#ifdef COMPILER_VERSION_MAJOR + require += info_version[argc]; +#endif +#ifdef COMPILER_VERSION_INTERNAL + require += info_version_internal[argc]; +#endif +#ifdef SIMULATE_ID + require += info_simulate[argc]; +#endif +#ifdef SIMULATE_VERSION_MAJOR + require += info_simulate_version[argc]; +#endif +#if defined(__CRAYXE) || defined(__CRAYXC) + require += info_cray[argc]; +#endif + require += info_language_dialect_default[argc]; + (void)argv; + return require; +} +#endif diff --git a/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeFiles/3.15.3/CompilerIdC/a.out b/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeFiles/3.15.3/CompilerIdC/a.out new file mode 100755 index 0000000000..11b7df452a Binary files /dev/null and b/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeFiles/3.15.3/CompilerIdC/a.out differ diff --git a/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeFiles/3.15.3/CompilerIdCXX/CMakeCXXCompilerId.cpp b/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeFiles/3.15.3/CompilerIdCXX/CMakeCXXCompilerId.cpp new file mode 100644 index 0000000000..4761ea2b8f --- /dev/null +++ b/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeFiles/3.15.3/CompilerIdCXX/CMakeCXXCompilerId.cpp @@ -0,0 +1,644 @@ +/* This source file must have a .cpp extension so that all C++ compilers + recognize the extension without flags. Borland does not know .cxx for + example. */ +#ifndef __cplusplus +# error "A C compiler has been selected for C++." +#endif + + +/* Version number components: V=Version, R=Revision, P=Patch + Version date components: YYYY=Year, MM=Month, DD=Day */ + +#if defined(__COMO__) +# define COMPILER_ID "Comeau" + /* __COMO_VERSION__ = VRR */ +# define COMPILER_VERSION_MAJOR DEC(__COMO_VERSION__ / 100) +# define COMPILER_VERSION_MINOR DEC(__COMO_VERSION__ % 100) + +#elif defined(__INTEL_COMPILER) || defined(__ICC) +# define COMPILER_ID "Intel" +# if defined(_MSC_VER) +# define SIMULATE_ID "MSVC" +# endif +# if defined(__GNUC__) +# define SIMULATE_ID "GNU" +# endif + /* __INTEL_COMPILER = VRP */ +# define COMPILER_VERSION_MAJOR DEC(__INTEL_COMPILER/100) +# define COMPILER_VERSION_MINOR DEC(__INTEL_COMPILER/10 % 10) +# if defined(__INTEL_COMPILER_UPDATE) +# define COMPILER_VERSION_PATCH DEC(__INTEL_COMPILER_UPDATE) +# else +# define COMPILER_VERSION_PATCH DEC(__INTEL_COMPILER % 10) +# endif +# if defined(__INTEL_COMPILER_BUILD_DATE) + /* __INTEL_COMPILER_BUILD_DATE = YYYYMMDD */ +# define COMPILER_VERSION_TWEAK DEC(__INTEL_COMPILER_BUILD_DATE) +# endif +# if defined(_MSC_VER) + /* _MSC_VER = VVRR */ +# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100) +# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100) +# endif +# if defined(__GNUC__) +# define SIMULATE_VERSION_MAJOR DEC(__GNUC__) +# elif defined(__GNUG__) +# define SIMULATE_VERSION_MAJOR DEC(__GNUG__) +# endif +# if defined(__GNUC_MINOR__) +# define SIMULATE_VERSION_MINOR DEC(__GNUC_MINOR__) +# endif +# if defined(__GNUC_PATCHLEVEL__) +# define SIMULATE_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__) +# endif + +#elif defined(__PATHCC__) +# define COMPILER_ID "PathScale" +# define COMPILER_VERSION_MAJOR DEC(__PATHCC__) +# define COMPILER_VERSION_MINOR DEC(__PATHCC_MINOR__) +# if defined(__PATHCC_PATCHLEVEL__) +# define COMPILER_VERSION_PATCH DEC(__PATHCC_PATCHLEVEL__) +# endif + +#elif defined(__BORLANDC__) && defined(__CODEGEARC_VERSION__) +# define COMPILER_ID "Embarcadero" +# define COMPILER_VERSION_MAJOR HEX(__CODEGEARC_VERSION__>>24 & 0x00FF) +# define COMPILER_VERSION_MINOR HEX(__CODEGEARC_VERSION__>>16 & 0x00FF) +# define COMPILER_VERSION_PATCH DEC(__CODEGEARC_VERSION__ & 0xFFFF) + +#elif defined(__BORLANDC__) +# define COMPILER_ID "Borland" + /* __BORLANDC__ = 0xVRR */ +# define COMPILER_VERSION_MAJOR HEX(__BORLANDC__>>8) +# define COMPILER_VERSION_MINOR HEX(__BORLANDC__ & 0xFF) + +#elif defined(__WATCOMC__) && __WATCOMC__ < 1200 +# define COMPILER_ID "Watcom" + /* __WATCOMC__ = VVRR */ +# define COMPILER_VERSION_MAJOR DEC(__WATCOMC__ / 100) +# define COMPILER_VERSION_MINOR DEC((__WATCOMC__ / 10) % 10) +# if (__WATCOMC__ % 10) > 0 +# define COMPILER_VERSION_PATCH DEC(__WATCOMC__ % 10) +# endif + +#elif defined(__WATCOMC__) +# define COMPILER_ID "OpenWatcom" + /* __WATCOMC__ = VVRP + 1100 */ +# define COMPILER_VERSION_MAJOR DEC((__WATCOMC__ - 1100) / 100) +# define COMPILER_VERSION_MINOR DEC((__WATCOMC__ / 10) % 10) +# if (__WATCOMC__ % 10) > 0 +# define COMPILER_VERSION_PATCH DEC(__WATCOMC__ % 10) +# endif + +#elif defined(__SUNPRO_CC) +# define COMPILER_ID "SunPro" +# if __SUNPRO_CC >= 0x5100 + /* __SUNPRO_CC = 0xVRRP */ +# define COMPILER_VERSION_MAJOR HEX(__SUNPRO_CC>>12) +# define COMPILER_VERSION_MINOR HEX(__SUNPRO_CC>>4 & 0xFF) +# define COMPILER_VERSION_PATCH HEX(__SUNPRO_CC & 0xF) +# else + /* __SUNPRO_CC = 0xVRP */ +# define COMPILER_VERSION_MAJOR HEX(__SUNPRO_CC>>8) +# define COMPILER_VERSION_MINOR HEX(__SUNPRO_CC>>4 & 0xF) +# define COMPILER_VERSION_PATCH HEX(__SUNPRO_CC & 0xF) +# endif + +#elif defined(__HP_aCC) +# define COMPILER_ID "HP" + /* __HP_aCC = VVRRPP */ +# define COMPILER_VERSION_MAJOR DEC(__HP_aCC/10000) +# define COMPILER_VERSION_MINOR DEC(__HP_aCC/100 % 100) +# define COMPILER_VERSION_PATCH DEC(__HP_aCC % 100) + +#elif defined(__DECCXX) +# define COMPILER_ID "Compaq" + /* __DECCXX_VER = VVRRTPPPP */ +# define COMPILER_VERSION_MAJOR DEC(__DECCXX_VER/10000000) +# define COMPILER_VERSION_MINOR DEC(__DECCXX_VER/100000 % 100) +# define COMPILER_VERSION_PATCH DEC(__DECCXX_VER % 10000) + +#elif defined(__IBMCPP__) && defined(__COMPILER_VER__) +# define COMPILER_ID "zOS" + /* __IBMCPP__ = VRP */ +# define COMPILER_VERSION_MAJOR DEC(__IBMCPP__/100) +# define COMPILER_VERSION_MINOR DEC(__IBMCPP__/10 % 10) +# define COMPILER_VERSION_PATCH DEC(__IBMCPP__ % 10) + +#elif defined(__ibmxl__) && defined(__clang__) +# define COMPILER_ID "XLClang" +# define COMPILER_VERSION_MAJOR DEC(__ibmxl_version__) +# define COMPILER_VERSION_MINOR DEC(__ibmxl_release__) +# define COMPILER_VERSION_PATCH DEC(__ibmxl_modification__) +# define COMPILER_VERSION_TWEAK DEC(__ibmxl_ptf_fix_level__) + + +#elif defined(__IBMCPP__) && !defined(__COMPILER_VER__) && __IBMCPP__ >= 800 +# define COMPILER_ID "XL" + /* __IBMCPP__ = VRP */ +# define COMPILER_VERSION_MAJOR DEC(__IBMCPP__/100) +# define COMPILER_VERSION_MINOR DEC(__IBMCPP__/10 % 10) +# define COMPILER_VERSION_PATCH DEC(__IBMCPP__ % 10) + +#elif defined(__IBMCPP__) && !defined(__COMPILER_VER__) && __IBMCPP__ < 800 +# define COMPILER_ID "VisualAge" + /* __IBMCPP__ = VRP */ +# define COMPILER_VERSION_MAJOR DEC(__IBMCPP__/100) +# define COMPILER_VERSION_MINOR DEC(__IBMCPP__/10 % 10) +# define COMPILER_VERSION_PATCH DEC(__IBMCPP__ % 10) + +#elif defined(__PGI) +# define COMPILER_ID "PGI" +# define COMPILER_VERSION_MAJOR DEC(__PGIC__) +# define COMPILER_VERSION_MINOR DEC(__PGIC_MINOR__) +# if defined(__PGIC_PATCHLEVEL__) +# define COMPILER_VERSION_PATCH DEC(__PGIC_PATCHLEVEL__) +# endif + +#elif defined(_CRAYC) +# define COMPILER_ID "Cray" +# define COMPILER_VERSION_MAJOR DEC(_RELEASE_MAJOR) +# define COMPILER_VERSION_MINOR DEC(_RELEASE_MINOR) + +#elif defined(__TI_COMPILER_VERSION__) +# define COMPILER_ID "TI" + /* __TI_COMPILER_VERSION__ = VVVRRRPPP */ +# define COMPILER_VERSION_MAJOR DEC(__TI_COMPILER_VERSION__/1000000) +# define COMPILER_VERSION_MINOR DEC(__TI_COMPILER_VERSION__/1000 % 1000) +# define COMPILER_VERSION_PATCH DEC(__TI_COMPILER_VERSION__ % 1000) + +#elif defined(__FUJITSU) || defined(__FCC_VERSION) || defined(__fcc_version) +# define COMPILER_ID "Fujitsu" + +#elif defined(__ghs__) +# define COMPILER_ID "GHS" +/* __GHS_VERSION_NUMBER = VVVVRP */ +# ifdef __GHS_VERSION_NUMBER +# define COMPILER_VERSION_MAJOR DEC(__GHS_VERSION_NUMBER / 100) +# define COMPILER_VERSION_MINOR DEC(__GHS_VERSION_NUMBER / 10 % 10) +# define COMPILER_VERSION_PATCH DEC(__GHS_VERSION_NUMBER % 10) +# endif + +#elif defined(__SCO_VERSION__) +# define COMPILER_ID "SCO" + +#elif defined(__ARMCC_VERSION) && !defined(__clang__) +# define COMPILER_ID "ARMCC" +#if __ARMCC_VERSION >= 1000000 + /* __ARMCC_VERSION = VRRPPPP */ + # define COMPILER_VERSION_MAJOR DEC(__ARMCC_VERSION/1000000) + # define COMPILER_VERSION_MINOR DEC(__ARMCC_VERSION/10000 % 100) + # define COMPILER_VERSION_PATCH DEC(__ARMCC_VERSION % 10000) +#else + /* __ARMCC_VERSION = VRPPPP */ + # define COMPILER_VERSION_MAJOR DEC(__ARMCC_VERSION/100000) + # define COMPILER_VERSION_MINOR DEC(__ARMCC_VERSION/10000 % 10) + # define COMPILER_VERSION_PATCH DEC(__ARMCC_VERSION % 10000) +#endif + + +#elif defined(__clang__) && defined(__apple_build_version__) +# define COMPILER_ID "AppleClang" +# if defined(_MSC_VER) +# define SIMULATE_ID "MSVC" +# endif +# define COMPILER_VERSION_MAJOR DEC(__clang_major__) +# define COMPILER_VERSION_MINOR DEC(__clang_minor__) +# define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__) +# if defined(_MSC_VER) + /* _MSC_VER = VVRR */ +# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100) +# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100) +# endif +# define COMPILER_VERSION_TWEAK DEC(__apple_build_version__) + +#elif defined(__clang__) && defined(__ARMCOMPILER_VERSION) +# define COMPILER_ID "ARMClang" + # define COMPILER_VERSION_MAJOR DEC(__ARMCOMPILER_VERSION/1000000) + # define COMPILER_VERSION_MINOR DEC(__ARMCOMPILER_VERSION/10000 % 100) + # define COMPILER_VERSION_PATCH DEC(__ARMCOMPILER_VERSION % 10000) +# define COMPILER_VERSION_INTERNAL DEC(__ARMCOMPILER_VERSION) + +#elif defined(__clang__) +# define COMPILER_ID "Clang" +# if defined(_MSC_VER) +# define SIMULATE_ID "MSVC" +# endif +# define COMPILER_VERSION_MAJOR DEC(__clang_major__) +# define COMPILER_VERSION_MINOR DEC(__clang_minor__) +# define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__) +# if defined(_MSC_VER) + /* _MSC_VER = VVRR */ +# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100) +# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100) +# endif + +#elif defined(__GNUC__) || defined(__GNUG__) +# define COMPILER_ID "GNU" +# if defined(__GNUC__) +# define COMPILER_VERSION_MAJOR DEC(__GNUC__) +# else +# define COMPILER_VERSION_MAJOR DEC(__GNUG__) +# endif +# if defined(__GNUC_MINOR__) +# define COMPILER_VERSION_MINOR DEC(__GNUC_MINOR__) +# endif +# if defined(__GNUC_PATCHLEVEL__) +# define COMPILER_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__) +# endif + +#elif defined(_MSC_VER) +# define COMPILER_ID "MSVC" + /* _MSC_VER = VVRR */ +# define COMPILER_VERSION_MAJOR DEC(_MSC_VER / 100) +# define COMPILER_VERSION_MINOR DEC(_MSC_VER % 100) +# if defined(_MSC_FULL_VER) +# if _MSC_VER >= 1400 + /* _MSC_FULL_VER = VVRRPPPPP */ +# define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 100000) +# else + /* _MSC_FULL_VER = VVRRPPPP */ +# define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 10000) +# endif +# endif +# if defined(_MSC_BUILD) +# define COMPILER_VERSION_TWEAK DEC(_MSC_BUILD) +# endif + +#elif defined(__VISUALDSPVERSION__) || defined(__ADSPBLACKFIN__) || defined(__ADSPTS__) || defined(__ADSP21000__) +# define COMPILER_ID "ADSP" +#if defined(__VISUALDSPVERSION__) + /* __VISUALDSPVERSION__ = 0xVVRRPP00 */ +# define COMPILER_VERSION_MAJOR HEX(__VISUALDSPVERSION__>>24) +# define COMPILER_VERSION_MINOR HEX(__VISUALDSPVERSION__>>16 & 0xFF) +# define COMPILER_VERSION_PATCH HEX(__VISUALDSPVERSION__>>8 & 0xFF) +#endif + +#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC) +# define COMPILER_ID "IAR" +# if defined(__VER__) && defined(__ICCARM__) +# define COMPILER_VERSION_MAJOR DEC((__VER__) / 1000000) +# define COMPILER_VERSION_MINOR DEC(((__VER__) / 1000) % 1000) +# define COMPILER_VERSION_PATCH DEC((__VER__) % 1000) +# define COMPILER_VERSION_INTERNAL DEC(__IAR_SYSTEMS_ICC__) +# elif defined(__VER__) && (defined(__ICCAVR__) || defined(__ICCRX__) || defined(__ICCRH850__) || defined(__ICCRL78__) || defined(__ICC430__) || defined(__ICCRISCV__)) +# define COMPILER_VERSION_MAJOR DEC((__VER__) / 100) +# define COMPILER_VERSION_MINOR DEC((__VER__) - (((__VER__) / 100)*100)) +# define COMPILER_VERSION_PATCH DEC(__SUBVERSION__) +# define COMPILER_VERSION_INTERNAL DEC(__IAR_SYSTEMS_ICC__) +# endif + + +/* These compilers are either not known or too old to define an + identification macro. Try to identify the platform and guess that + it is the native compiler. */ +#elif defined(__hpux) || defined(__hpua) +# define COMPILER_ID "HP" + +#else /* unknown compiler */ +# define COMPILER_ID "" +#endif + +/* Construct the string literal in pieces to prevent the source from + getting matched. Store it in a pointer rather than an array + because some compilers will just produce instructions to fill the + array rather than assigning a pointer to a static array. */ +char const* info_compiler = "INFO" ":" "compiler[" COMPILER_ID "]"; +#ifdef SIMULATE_ID +char const* info_simulate = "INFO" ":" "simulate[" SIMULATE_ID "]"; +#endif + +#ifdef __QNXNTO__ +char const* qnxnto = "INFO" ":" "qnxnto[]"; +#endif + +#if defined(__CRAYXE) || defined(__CRAYXC) +char const *info_cray = "INFO" ":" "compiler_wrapper[CrayPrgEnv]"; +#endif + +#define STRINGIFY_HELPER(X) #X +#define STRINGIFY(X) STRINGIFY_HELPER(X) + +/* Identify known platforms by name. */ +#if defined(__linux) || defined(__linux__) || defined(linux) +# define PLATFORM_ID "Linux" + +#elif defined(__CYGWIN__) +# define PLATFORM_ID "Cygwin" + +#elif defined(__MINGW32__) +# define PLATFORM_ID "MinGW" + +#elif defined(__APPLE__) +# define PLATFORM_ID "Darwin" + +#elif defined(_WIN32) || defined(__WIN32__) || defined(WIN32) +# define PLATFORM_ID "Windows" + +#elif defined(__FreeBSD__) || defined(__FreeBSD) +# define PLATFORM_ID "FreeBSD" + +#elif defined(__NetBSD__) || defined(__NetBSD) +# define PLATFORM_ID "NetBSD" + +#elif defined(__OpenBSD__) || defined(__OPENBSD) +# define PLATFORM_ID "OpenBSD" + +#elif defined(__sun) || defined(sun) +# define PLATFORM_ID "SunOS" + +#elif defined(_AIX) || defined(__AIX) || defined(__AIX__) || defined(__aix) || defined(__aix__) +# define PLATFORM_ID "AIX" + +#elif defined(__hpux) || defined(__hpux__) +# define PLATFORM_ID "HP-UX" + +#elif defined(__HAIKU__) +# define PLATFORM_ID "Haiku" + +#elif defined(__BeOS) || defined(__BEOS__) || defined(_BEOS) +# define PLATFORM_ID "BeOS" + +#elif defined(__QNX__) || defined(__QNXNTO__) +# define PLATFORM_ID "QNX" + +#elif defined(__tru64) || defined(_tru64) || defined(__TRU64__) +# define PLATFORM_ID "Tru64" + +#elif defined(__riscos) || defined(__riscos__) +# define PLATFORM_ID "RISCos" + +#elif defined(__sinix) || defined(__sinix__) || defined(__SINIX__) +# define PLATFORM_ID "SINIX" + +#elif defined(__UNIX_SV__) +# define PLATFORM_ID "UNIX_SV" + +#elif defined(__bsdos__) +# define PLATFORM_ID "BSDOS" + +#elif defined(_MPRAS) || defined(MPRAS) +# define PLATFORM_ID "MP-RAS" + +#elif defined(__osf) || defined(__osf__) +# define PLATFORM_ID "OSF1" + +#elif defined(_SCO_SV) || defined(SCO_SV) || defined(sco_sv) +# define PLATFORM_ID "SCO_SV" + +#elif defined(__ultrix) || defined(__ultrix__) || defined(_ULTRIX) +# define PLATFORM_ID "ULTRIX" + +#elif defined(__XENIX__) || defined(_XENIX) || defined(XENIX) +# define PLATFORM_ID "Xenix" + +#elif defined(__WATCOMC__) +# if defined(__LINUX__) +# define PLATFORM_ID "Linux" + +# elif defined(__DOS__) +# define PLATFORM_ID "DOS" + +# elif defined(__OS2__) +# define PLATFORM_ID "OS2" + +# elif defined(__WINDOWS__) +# define PLATFORM_ID "Windows3x" + +# else /* unknown platform */ +# define PLATFORM_ID +# endif + +#elif defined(__INTEGRITY) +# if defined(INT_178B) +# define PLATFORM_ID "Integrity178" + +# else /* regular Integrity */ +# define PLATFORM_ID "Integrity" +# endif + +#else /* unknown platform */ +# define PLATFORM_ID + +#endif + +/* For windows compilers MSVC and Intel we can determine + the architecture of the compiler being used. This is because + the compilers do not have flags that can change the architecture, + but rather depend on which compiler is being used +*/ +#if defined(_WIN32) && defined(_MSC_VER) +# if defined(_M_IA64) +# define ARCHITECTURE_ID "IA64" + +# elif defined(_M_X64) || defined(_M_AMD64) +# define ARCHITECTURE_ID "x64" + +# elif defined(_M_IX86) +# define ARCHITECTURE_ID "X86" + +# elif defined(_M_ARM64) +# define ARCHITECTURE_ID "ARM64" + +# elif defined(_M_ARM) +# if _M_ARM == 4 +# define ARCHITECTURE_ID "ARMV4I" +# elif _M_ARM == 5 +# define ARCHITECTURE_ID "ARMV5I" +# else +# define ARCHITECTURE_ID "ARMV" STRINGIFY(_M_ARM) +# endif + +# elif defined(_M_MIPS) +# define ARCHITECTURE_ID "MIPS" + +# elif defined(_M_SH) +# define ARCHITECTURE_ID "SHx" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__WATCOMC__) +# if defined(_M_I86) +# define ARCHITECTURE_ID "I86" + +# elif defined(_M_IX86) +# define ARCHITECTURE_ID "X86" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC) +# if defined(__ICCARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__ICCRX__) +# define ARCHITECTURE_ID "RX" + +# elif defined(__ICCRH850__) +# define ARCHITECTURE_ID "RH850" + +# elif defined(__ICCRL78__) +# define ARCHITECTURE_ID "RL78" + +# elif defined(__ICCRISCV__) +# define ARCHITECTURE_ID "RISCV" + +# elif defined(__ICCAVR__) +# define ARCHITECTURE_ID "AVR" + +# elif defined(__ICC430__) +# define ARCHITECTURE_ID "MSP430" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__ghs__) +# if defined(__PPC64__) +# define ARCHITECTURE_ID "PPC64" + +# elif defined(__ppc__) +# define ARCHITECTURE_ID "PPC" + +# elif defined(__ARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__x86_64__) +# define ARCHITECTURE_ID "x64" + +# elif defined(__i386__) +# define ARCHITECTURE_ID "X86" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif +#else +# define ARCHITECTURE_ID +#endif + +/* Convert integer to decimal digit literals. */ +#define DEC(n) \ + ('0' + (((n) / 10000000)%10)), \ + ('0' + (((n) / 1000000)%10)), \ + ('0' + (((n) / 100000)%10)), \ + ('0' + (((n) / 10000)%10)), \ + ('0' + (((n) / 1000)%10)), \ + ('0' + (((n) / 100)%10)), \ + ('0' + (((n) / 10)%10)), \ + ('0' + ((n) % 10)) + +/* Convert integer to hex digit literals. */ +#define HEX(n) \ + ('0' + ((n)>>28 & 0xF)), \ + ('0' + ((n)>>24 & 0xF)), \ + ('0' + ((n)>>20 & 0xF)), \ + ('0' + ((n)>>16 & 0xF)), \ + ('0' + ((n)>>12 & 0xF)), \ + ('0' + ((n)>>8 & 0xF)), \ + ('0' + ((n)>>4 & 0xF)), \ + ('0' + ((n) & 0xF)) + +/* Construct a string literal encoding the version number components. */ +#ifdef COMPILER_VERSION_MAJOR +char const info_version[] = { + 'I', 'N', 'F', 'O', ':', + 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','[', + COMPILER_VERSION_MAJOR, +# ifdef COMPILER_VERSION_MINOR + '.', COMPILER_VERSION_MINOR, +# ifdef COMPILER_VERSION_PATCH + '.', COMPILER_VERSION_PATCH, +# ifdef COMPILER_VERSION_TWEAK + '.', COMPILER_VERSION_TWEAK, +# endif +# endif +# endif + ']','\0'}; +#endif + +/* Construct a string literal encoding the internal version number. */ +#ifdef COMPILER_VERSION_INTERNAL +char const info_version_internal[] = { + 'I', 'N', 'F', 'O', ':', + 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','_', + 'i','n','t','e','r','n','a','l','[', + COMPILER_VERSION_INTERNAL,']','\0'}; +#endif + +/* Construct a string literal encoding the version number components. */ +#ifdef SIMULATE_VERSION_MAJOR +char const info_simulate_version[] = { + 'I', 'N', 'F', 'O', ':', + 's','i','m','u','l','a','t','e','_','v','e','r','s','i','o','n','[', + SIMULATE_VERSION_MAJOR, +# ifdef SIMULATE_VERSION_MINOR + '.', SIMULATE_VERSION_MINOR, +# ifdef SIMULATE_VERSION_PATCH + '.', SIMULATE_VERSION_PATCH, +# ifdef SIMULATE_VERSION_TWEAK + '.', SIMULATE_VERSION_TWEAK, +# endif +# endif +# endif + ']','\0'}; +#endif + +/* Construct the string literal in pieces to prevent the source from + getting matched. Store it in a pointer rather than an array + because some compilers will just produce instructions to fill the + array rather than assigning a pointer to a static array. */ +char const* info_platform = "INFO" ":" "platform[" PLATFORM_ID "]"; +char const* info_arch = "INFO" ":" "arch[" ARCHITECTURE_ID "]"; + + + + +#if defined(_MSC_VER) && defined(_MSVC_LANG) +#define CXX_STD _MSVC_LANG +#else +#define CXX_STD __cplusplus +#endif + +const char* info_language_dialect_default = "INFO" ":" "dialect_default[" +#if CXX_STD > 201703L + "20" +#elif CXX_STD >= 201703L + "17" +#elif CXX_STD >= 201402L + "14" +#elif CXX_STD >= 201103L + "11" +#else + "98" +#endif +"]"; + +/*--------------------------------------------------------------------------*/ + +int main(int argc, char* argv[]) +{ + int require = 0; + require += info_compiler[argc]; + require += info_platform[argc]; +#ifdef COMPILER_VERSION_MAJOR + require += info_version[argc]; +#endif +#ifdef COMPILER_VERSION_INTERNAL + require += info_version_internal[argc]; +#endif +#ifdef SIMULATE_ID + require += info_simulate[argc]; +#endif +#ifdef SIMULATE_VERSION_MAJOR + require += info_simulate_version[argc]; +#endif +#if defined(__CRAYXE) || defined(__CRAYXC) + require += info_cray[argc]; +#endif + require += info_language_dialect_default[argc]; + (void)argv; + return require; +} diff --git a/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeFiles/3.15.3/CompilerIdCXX/a.out b/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeFiles/3.15.3/CompilerIdCXX/a.out new file mode 100755 index 0000000000..71c2ceda86 Binary files /dev/null and b/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeFiles/3.15.3/CompilerIdCXX/a.out differ diff --git a/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeFiles/clion-environment.txt b/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeFiles/clion-environment.txt new file mode 100644 index 0000000000..12efafd246 Binary files /dev/null and b/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeFiles/clion-environment.txt differ diff --git a/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeFiles/clion-log.txt b/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeFiles/clion-log.txt new file mode 100644 index 0000000000..1bd058cd55 --- /dev/null +++ b/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeFiles/clion-log.txt @@ -0,0 +1,16 @@ +/snap/clion/103/bin/cmake/linux/bin/cmake -DCMAKE_BUILD_TYPE=Debug -DBUILD_RASPBERRY=0 -DBUILD_ONLY_BRAIN=0 -DCMAKE_BUILD_TYPE=Release && make -G "CodeBlocks - Unix Makefiles" /home/fuda/Projects/revolve/cpprevolve/revolve/gazebo/IMC +-- Configuring done +CMake Error at CMakeLists.txt:16 (add_executable): + Cannot find source file: + + FeedForwardModel.cpp + + Tried extensions .c .C .c++ .cc .cpp .cxx .cu .m .M .mm .h .hh .h++ .hm + .hpp .hxx .in .txx + + +CMake Error at CMakeLists.txt:16 (add_executable): + No SOURCES given to target: Test_IMC + + +CMake Generate step failed. Build files cannot be regenerated correctly. diff --git a/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeFiles/cmake.check_cache b/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeFiles/cmake.check_cache new file mode 100644 index 0000000000..3dccd73172 --- /dev/null +++ b/cpprevolve/revolve/brains/controller/IMC/cmake-build-debug/CMakeFiles/cmake.check_cache @@ -0,0 +1 @@ +# This file is generated by cmake for dependency checking of the CMakeCache.txt file diff --git a/cpprevolve/revolve/brains/controller/actuators/Actuator.h b/cpprevolve/revolve/brains/controller/actuators/Actuator.h index 21510077cf..a961aa4888 100644 --- a/cpprevolve/revolve/brains/controller/actuators/Actuator.h +++ b/cpprevolve/revolve/brains/controller/actuators/Actuator.h @@ -24,6 +24,14 @@ class Actuator inline double coordinate_y() const { return std::get<1>(this->coordinates); } inline double coordinate_z() const { return std::get<2>(this->coordinates); } + enum StateType { + POSITION, + VELOCITY, + TORQUE + }; + + virtual double Current_State( StateType type ) = 0; + virtual void write(const double *output, double step) = 0; inline unsigned int n_outputs() const {return this->_n_outputs;} diff --git a/cpprevolve/revolve/brains/controller/sensors/AngleToTargetDetector.cpp b/cpprevolve/revolve/brains/controller/sensors/AngleToTargetDetector.cpp new file mode 100644 index 0000000000..cefac09940 --- /dev/null +++ b/cpprevolve/revolve/brains/controller/sensors/AngleToTargetDetector.cpp @@ -0,0 +1,355 @@ +// +// Created by matteo on 2/28/20. +// + +#include "AngleToTargetDetector.h" +#include +#include +#include +#include + +revolve::AngleToTargetDetector::AngleToTargetDetector(const unsigned int shrink_factor, const bool show_image) + : Sensor(1) + , show_image(show_image) + , shrink_factor(shrink_factor) +// , angle(std::atan(img.cols/img.rows) * 180 / M_PI) + , angle(NAN) +{} + +void revolve::AngleToTargetDetector::read(double *input) +{ + input[0] = detect_angle(); +} + +float revolve::AngleToTargetDetector::detect_angle() +{ + get_image(raw_image); + unsigned int image_cols = raw_image.cols/shrink_factor; + unsigned int image_rows = raw_image.rows/shrink_factor; + cv::resize(raw_image, image, cv::Size(image_cols, image_rows)); + + cv::medianBlur(image, image_blur, 5); + cv::cvtColor(image_blur, image_hsv, cv::COLOR_BGR2HSV); + + //green + const int gLowH1=35,gHighH1=40,gLowH2=41,gHighH2=59,gLowS1=140,gLowS2=69,gHighS=255,gLowV=104,gHighV=255; + //blue + const int bLowH=99,bHighH=121,bLowS=120,bHighS=255,bLowV=57,bHighV=211; + + //detecting Blue + cv::inRange(image_hsv, cv::Scalar(bLowH,bLowS, bLowV), cv::Scalar(bHighH,bHighS, bHighV) ,image_blue); + //detecting Green + cv::inRange(image_hsv, cv::Scalar(gLowH1,gLowS1, gLowV), cv::Scalar(gHighH1,gHighS, gHighV),image_green1); + cv::inRange(image_hsv, cv::Scalar(gLowH2,gLowS2, gLowV), cv::Scalar(gHighH2,gHighS, gHighV),image_green2); + cv::add(image_green1, image_green2, image_green); + + std::vector> contours_blue, contours_green;; //contours_red, contours_yellow; + + cv::findContours(image_blue, contours_blue, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_NONE); + cv::findContours(image_green, contours_green, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_NONE); + //cv::findContours(image_red, contours_red, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_NONE); + //cv::findContours(image_yellow, contours_yellow, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_NONE); + + std::vector rect_coord, rect_coord_blue, rect_coord_green; //rect_coord_red, rect_coord_yellow; + + // blue contours + for (const std::vector &contours_blue_line : contours_blue) { + double image_blue_area_buf = cv::contourArea(contours_blue_line); + + if (image_blue_area_buf > 5) { + cv::Rect bounding_rect = cv::boundingRect(contours_blue_line); + rect_coord_blue.emplace_back(bounding_rect); + } + } + + // green contours + for (const std::vector &contours_green_line : contours_green) { + double image_blue_area_buf = cv::contourArea(contours_green_line); + + if (image_blue_area_buf > 5) { + cv::Rect bounding_rect = cv::boundingRect(contours_green_line); + rect_coord_green.emplace_back(bounding_rect); + } + } + + //// red contours + //for (const std::vector &contours_red_line : contours_red) { + // double image_blue_area_buf = cv::contourArea(contours_red_line); + // + // if (image_blue_area_buf > 5) { + // cv::Rect bounding_rect = cv::boundingRect(contours_red_line); + // rect_coord_red.emplace_back(bounding_rect); + // } + //} + // + //// yellow contours + //for (const std::vector &contours_yellow_line : contours_yellow) { + // double image_blue_area_buf = cv::contourArea(contours_yellow_line); + // + // if (image_blue_area_buf > 5) { + // cv::Rect bounding_rect = cv::boundingRect(contours_yellow_line); + // rect_coord_yellow.emplace_back(bounding_rect); + // } + //} + + rect_coord.reserve( rect_coord_blue.size() + rect_coord_green.size() ); // preallocate memory + // + rect_coord_red.size() + rect_coord_yellow.size() + rect_coord.insert( rect_coord.end(), rect_coord_blue.begin(), rect_coord_blue.end() ); + rect_coord.insert( rect_coord.end(), rect_coord_green.begin(), rect_coord_green.end() ); + //rect_coord.insert( rect_coord.end(), rect_coord_red.begin(), rect_coord_red.end() ); + //rect_coord.insert( rect_coord.end(), rect_coord_yellow.begin(), rect_coord_yellow.end() ); + +// ----- MAGIC GONGJIN CODE HERE ---------------------------------------------- + unsigned int num = rect_coord.size(); + int distanceBox[num][num], distanceBoxSum[num], numBox[num], minDistanceBox[num], min2DistanceBox[num],rectBoxHeight = 0, rectBoxHeightMax = 0; + for (int i = 0; i < num; i++) //calculating the suitable(medium) value of height + { + if (rect_coord[i].height > rectBoxHeightMax) + { + rectBoxHeight = rectBoxHeightMax; // set this value as the height of box + rectBoxHeightMax = rect_coord[i].height; + } + else if (rect_coord[i].height > rectBoxHeight) + rectBoxHeight = rect_coord[i].height; + } + + for (int j = 0; j < num; j++) //calculating the value of minimum and the second minimum distance for each box + { + minDistanceBox[j] = 800; + min2DistanceBox[j] = 800; + for (int x = 0; x < num; x++) + { + if (j != x) + { + distanceBox[j][x] = std::min( + std::abs(rect_coord[j].tl().x - rect_coord[x].br().x), + std::abs(rect_coord[j].br().x - rect_coord[x].tl().x) + ); + + if (distanceBox[j][x] < minDistanceBox[j]) + { + min2DistanceBox[j] = minDistanceBox[j]; //the second minimum distance + minDistanceBox[j] = distanceBox[j][x]; //the minimun distance + } + else if (distanceBox[j][x] < min2DistanceBox[j]) + { + min2DistanceBox[j] = distanceBox[j][x]; + } + } + } + distanceBoxSum[j] = minDistanceBox[j] + min2DistanceBox[j]; + } + + for (int i =0; i < num; i++) //sequence from minimum distance to maximum distance + { + numBox[i] = 0; + for (int j=0; j < num; j++) + { + if (i != j) // get the Box[i] sequence + { + if (distanceBoxSum[i] > distanceBoxSum[j]) + numBox[i]+=1; //numBox[i] = numBox[i] +1, save the number + if (distanceBoxSum[i] == distanceBoxSum[j]) + { + if (minDistanceBox[i] >= minDistanceBox[j]) //always have the same distance between two points each other + numBox[i]+=1; // + } + } + } + } + //-------------difine the ROIs of robot------------ + int lastnum = num, robNum, minRectCoorX[num], minRectCoorY[num], maxRectCoorX[num], maxRectCoorY[num]; + for (robNum = 0; lastnum >= 2 && robNum < num; robNum++) + { + int minNumBox=100; + for (int k = 0; k 2) //when robot only have 2 boxes at least, just combine the two boxes + numBox[i] = 100; //make it not included in the rest + minRectCoorX[robNum] = rect_coord[i].tl().x; + minRectCoorY[robNum] = rect_coord[i].tl().y; + maxRectCoorX[robNum] = rect_coord[i].br().x; + maxRectCoorY[robNum] = rect_coord[i].br().y; + int bufnum = 0, jBox[50] = {0}; + for (int j = 0; j < num; j++) //calculating the coordination of rectangle incluing boxes belong to the distance area + { + //-------------the first threshold condition------------------- + if (j != i && numBox[j] != 100 && distanceBox[i][j] < 4.3 * rectBoxHeight) //3.4, 3.5, 4.5, 4.3 justify if the box belong to the same robot by distance of boxeswith the center box + { + jBox[bufnum] = j; + lastnum --; + bufnum ++; //the number of boxes that match the threshold of (distanceBox[i][j] < 3.4 * rectBoxHeight) + } + //----calculating the max distance between boxes after the first threshold condition, preparing for next-------- + if (j == num - 1 && bufnum >= 1) //bufnum >= 1 (it have two candidate at least) + { + int maxBoxDisOut[num], max_in_out[num][num],maxBoxDisOutNum[num]; + for (int buf = 0; buf < bufnum; buf++) //calculating the max distance between boxes in jBox[bufnum] + { + maxBoxDisOut[jBox[buf]] = 0; + int rectCoor_tl_br, rectCoor_br_tl; + if (bufnum == 1) // one other box and one center box + { + rectCoor_tl_br = std::abs(rect_coord[i].tl().x - rect_coord[jBox[0]].br().x); //calculating the inside or outside distance between the same boxes + rectCoor_br_tl = std::abs(rect_coord[i].br().x - rect_coord[jBox[0]].tl().x); //calculating the inside or outside distance between the same boxes + maxBoxDisOut[jBox[0]] = std::min(rectCoor_tl_br,rectCoor_br_tl); //max, min + } + else + { + for (int buff = 0; buff < bufnum; buff++) + { + rectCoor_tl_br = std::abs(rect_coord[jBox[buf]].tl().x - rect_coord[jBox[buff]].br().x); //calculating the inside or outside distance between the same boxes + rectCoor_br_tl = std::abs(rect_coord[jBox[buf]].br().x - rect_coord[jBox[buff]].tl().x); //calculating the inside or outside distance between the same boxes + max_in_out[jBox[buf]][jBox[buff]] = std::min(rectCoor_tl_br,rectCoor_br_tl); //max,min + if (max_in_out[jBox[buf]][jBox[buff]] > maxBoxDisOut[jBox[buf]]) + { + maxBoxDisOut[jBox[buf]] = max_in_out[jBox[buf]][jBox[buff]]; + maxBoxDisOutNum[buf] = jBox[buff]; + } + } + } + } + //bufnum >1 guarantte the robot have center box and two other box (bufnum=2) at least, or not go to compare center box and another one box + if (bufnum >= 2) + { + int delNum = 0; + for (int bufff = 0; bufff < bufnum; bufff++) //compare the max distance (robot size from left to right) of boxes in jBox[bufnum] + { + if (maxBoxDisOut[jBox[bufff]] < 6.2 * rectBoxHeight) //if > the length of robot, delete far one, get the near one as rectangle + { + minRectCoorX[robNum] = std::min(rect_coord[jBox[bufff]].tl().x, minRectCoorX[robNum]); + minRectCoorY[robNum] = std::min(rect_coord[jBox[bufff]].tl().y, minRectCoorY[robNum]); + maxRectCoorX[robNum] = std::max(rect_coord[jBox[bufff]].br().x, maxRectCoorX[robNum]); + maxRectCoorY[robNum] = std::max(rect_coord[jBox[bufff]].br().y, maxRectCoorY[robNum]); + numBox[jBox[bufff]] = 100; //set a constant not zero and more than all of the numBox + } + //TODO this else if is doing exactly the same code as above, remove it + else if (distanceBox[i][jBox[bufff]] < distanceBox[i][maxBoxDisOutNum[bufff]]) //always have two boxes match this condition at the same time, choice one of them + { + minRectCoorX[robNum] = std::min(rect_coord[jBox[bufff]].tl().x, minRectCoorX[robNum]); + minRectCoorY[robNum] = std::min(rect_coord[jBox[bufff]].tl().y, minRectCoorY[robNum]); + maxRectCoorX[robNum] = std::max(rect_coord[jBox[bufff]].br().x, maxRectCoorX[robNum]); + maxRectCoorY[robNum] = std::max(rect_coord[jBox[bufff]].br().y, maxRectCoorY[robNum]); + numBox[jBox[bufff]] = 100; //set a constant not zero and more than all of the numBox + } + else + { + minRectCoorX[robNum] = std::min(rect_coord[maxBoxDisOutNum[bufff]].tl().x, minRectCoorX[robNum]); + minRectCoorY[robNum] = std::min(rect_coord[maxBoxDisOutNum[bufff]].tl().y, minRectCoorY[robNum]); + maxRectCoorX[robNum] = std::max(rect_coord[maxBoxDisOutNum[bufff]].br().x, maxRectCoorX[robNum]); + maxRectCoorY[robNum] = std::max(rect_coord[maxBoxDisOutNum[bufff]].br().y, maxRectCoorY[robNum]); + numBox[maxBoxDisOutNum[bufff]] = 100; + delNum ++; + } + } + lastnum = lastnum + delNum; //plus for the cancelled more one + bufnum = bufnum - delNum; + } + else //compare center box and another one box, when bufnum = 1 + { + if (maxBoxDisOut[jBox[0]] < 6.2 * rectBoxHeight) //the length of robot 9.4 + { + minRectCoorX[robNum] = std::min(rect_coord[jBox[0]].tl().x, minRectCoorX[robNum]); + minRectCoorY[robNum] = std::min(rect_coord[jBox[0]].tl().y, minRectCoorY[robNum]); + maxRectCoorX[robNum] = std::max(rect_coord[jBox[0]].br().x, maxRectCoorX[robNum]); + maxRectCoorY[robNum] = std::max(rect_coord[jBox[0]].br().y, maxRectCoorY[robNum]); + numBox[jBox[0]] = 100; //set a constant not zero and more than all of the numBox + } + else //just one center to rest + { + robNum --; + } + } + } + } + } + } + } + + // calculate the angle + if (std::isnan(angle) and robNum == 0) + { + // init first angle + angle = atan(image.cols / static_cast(image.rows)) * 180.0 / M_PI; + } + else + { + for (int i = 0; i < robNum; i++) + { + const int robCenterCoorX = 2*(minRectCoorX[i] + maxRectCoorX[i]); + const int robCenterCoorY = 2*(minRectCoorY[i] + maxRectCoorY[i]); + char textRobCenterCoor[64], textDistance[64]; + + if (show_image) { + cv::rectangle(raw_image, cv::Point(shrink_factor*minRectCoorX[i],shrink_factor*minRectCoorY[i]), cv::Point(shrink_factor*maxRectCoorX[i],shrink_factor*maxRectCoorY[i]), cv::Scalar(0,255,0),1); + cv::circle(raw_image, cv::Point(robCenterCoorX,robCenterCoorY),3, cv::Scalar(0,255,0),4); + + std::snprintf(textRobCenterCoor, sizeof(textRobCenterCoor), "(%d,%d)", robCenterCoorX, robCenterCoorY); + cv::putText(raw_image, textRobCenterCoor, cv::Point(robCenterCoorX + 10, robCenterCoorY + 3), + cv::FONT_HERSHEY_DUPLEX, 0.4, cv::Scalar(0, 255, 0), 1); + } + + const int leftLine = raw_image.cols / 2; + const int rightLine = raw_image.cols / 2; + if (robCenterCoorX < leftLine) + { + double distance = robCenterCoorX - leftLine; + angle = std::atan(distance/robCenterCoorY) * 180.0 / M_PI; + if (show_image) { + std::snprintf(textDistance, sizeof(textDistance), "L:%f Angle: %f", distance, angle); + cv::putText(raw_image, textDistance, cv::Point(0.0 * raw_image.cols, 15), cv::FONT_HERSHEY_DUPLEX, 0.5, + cv::Scalar(0, 255, 0), 1); + } + } + + if (robCenterCoorX > rightLine) + { + double distance = robCenterCoorX - rightLine; + angle = std::atan(distance/robCenterCoorY) * 180.0 / M_PI; + if (show_image) { + std::snprintf(textDistance, sizeof(textDistance), "R:%f Angle: %f", distance, angle); + cv::putText(raw_image, textDistance, cv::Point(0.5 * raw_image.cols, 15), cv::FONT_HERSHEY_DUPLEX, 0.5, + cv::Scalar(0, 255, 0), 1); + } + } + + if (show_image) { + cv::line(raw_image, cv::Point(shrink_factor * minRectCoorX[i], shrink_factor * minRectCoorY[i]), + cv::Point(shrink_factor * maxRectCoorX[i], shrink_factor * maxRectCoorY[i]), cv::Scalar(0, 255, 0), 1); + cv::line(raw_image, cv::Point(shrink_factor * minRectCoorX[i], shrink_factor * maxRectCoorY[i]), + cv::Point(shrink_factor * maxRectCoorX[i], shrink_factor * minRectCoorY[i]), cv::Scalar(0, 255, 0), 1); + cv::line(raw_image, cv::Point(leftLine, 0), cv::Point(leftLine, raw_image.rows), cv::Scalar(0, 255, 0), 1); + cv::line(raw_image, cv::Point(rightLine, 0), cv::Point(rightLine, raw_image.rows), cv::Scalar(0, 255, 0), 1); + } + } + } + + + if (robNum == 0 and show_image) // no robots in the field of view + { + // show image if no robot is detected + char textDistance[64]; + float text_pos; + if (angle < 0) text_pos = 0.0; + else text_pos = 0.5; + std::snprintf(textDistance, sizeof(textDistance), "Angle: %f", angle); + std::snprintf(textDistance, sizeof(textDistance), "Angle: %f", angle); + cv::putText(raw_image, textDistance, cv::Point(text_pos * raw_image.cols, 15), cv::FONT_HERSHEY_DUPLEX, 0.5, + cv::Scalar(255, 0, 0), 1); + } + + assert(not std::isnan(angle)); + + if (show_image) { + cv::imshow("revolve-controller", raw_image); + cv::waitKey(5); + } + return angle; +} diff --git a/cpprevolve/revolve/brains/controller/sensors/AngleToTargetDetector.h b/cpprevolve/revolve/brains/controller/sensors/AngleToTargetDetector.h new file mode 100644 index 0000000000..4ac5016db9 --- /dev/null +++ b/cpprevolve/revolve/brains/controller/sensors/AngleToTargetDetector.h @@ -0,0 +1,35 @@ +// +// Created by matteo on 2/28/20. +// + +#ifndef REVOLVE_ANGLETOTARGETDETECTOR_H +#define REVOLVE_ANGLETOTARGETDETECTOR_H + +#include "Sensor.h" +#include + +namespace revolve { + +class AngleToTargetDetector : public Sensor { +public: + explicit AngleToTargetDetector(unsigned int shrink_factor = 4, bool show_image = false); + virtual ~AngleToTargetDetector() = default; + + void read(double *input) override; + virtual float detect_angle(); + +private: + virtual void get_image(cv::Mat &image) = 0; + +protected: + const bool show_image; + const unsigned int shrink_factor; + double angle; + cv::Mat raw_image, image; + cv::Mat image_blur, image_hsv, image_blue, image_green1, image_green2, image_green; +}; + +} + + +#endif //REVOLVE_ANGLETOTARGETDETECTOR_H diff --git a/cpprevolve/revolve/brains/controller/sensors/AngleToTargetSensor.h b/cpprevolve/revolve/brains/controller/sensors/AngleToTargetSensor.h new file mode 100644 index 0000000000..5059c6eac0 --- /dev/null +++ b/cpprevolve/revolve/brains/controller/sensors/AngleToTargetSensor.h @@ -0,0 +1,29 @@ +// +// Created by Matteo De Carlo on 25/02/2020. +// + +#ifndef REVOLVE_ANGLETOTARGETSENSOR_H +#define REVOLVE_ANGLETOTARGETSENSOR_H + +#include "Sensor.h" + +namespace revolve +{ + +class AngleToTargetSensor : public Sensor { +public: + explicit AngleToTargetSensor() + : Sensor(1) + {} + + virtual double angle_to_target() = 0; + + void read(double *input) override + { + *input = angle_to_target(); + } +}; + +} + +#endif //REVOLVE_ANGLETOTARGETSENSOR_H diff --git a/cpprevolve/revolve/brains/controller/sensors/Sensor.h b/cpprevolve/revolve/brains/controller/sensors/Sensor.h index 3f293da27f..f2678ac3e1 100644 --- a/cpprevolve/revolve/brains/controller/sensors/Sensor.h +++ b/cpprevolve/revolve/brains/controller/sensors/Sensor.h @@ -14,6 +14,8 @@ class Sensor : _n_inputs(n_inputs) {} + virtual ~Sensor() = default; + /// \brief Read the value of the sensor into the /// \param[in] _input: array. /// \brief[in,out] _input Input value to write on diff --git a/cpprevolve/revolve/brains/learner/BayesianOptimizer.cpp b/cpprevolve/revolve/brains/learner/BayesianOptimizer.cpp index a0615ea638..894727b04f 100644 --- a/cpprevolve/revolve/brains/learner/BayesianOptimizer.cpp +++ b/cpprevolve/revolve/brains/learner/BayesianOptimizer.cpp @@ -2,4 +2,439 @@ // Created by matteo on 14/06/19. // +#include +#include +#include +#include +#include +#include #include "BayesianOptimizer.h" +#include "BoDefinitions.h" +#include "../controller/DifferentialCPG.h" +#include "../controller/Controller.h" + +using namespace revolve; + +// Copied from the limbo tutorial the BO implementation is based on +using Mean_t = limbo::mean::Data; +using Init_t = limbo::init::FlexibleLHS; +using Kernel_t = limbo::kernel::MaternFiveHalves; +using GP_t = limbo::model::GP; + +const static Eigen::IOFormat CSVFormat(11, Eigen::DontAlignCols, ", ", ","); +BayesianOptimizer::BayesianOptimizer( + std::unique_ptr controller, + Evaluator *evaluator, + EvaluationReporter *reporter, + const double evaluation_time, + const unsigned int n_learning_evaluations, + const std::string& model_name) + : Learner(evaluator, reporter, evaluation_time, n_learning_evaluations) + , _controller(std::move(controller)) + , n_init_samples(50) + //, init_method("LHS") + , kernel_noise(0.001) + , kernel_optimize_noise("false") + , kernel_sigma_sq(1.0) + , kernel_l(0.2) + , kernel_squared_exp_ard_k(3) + , acqui_gpucb_delta(0.1) + , acqui_ucb_alpha(3.0) + , acqui_ei_jitter(0.5) + , acquisition_function("UCB") +{ + assert(this->_controller && "BayesianOptimizer: passed null controller"); + switch (this->_controller->controller_type) + { + case revolve::Controller::DIFFERENTIAL_CPG: + devectorize_controller = [this](Eigen::VectorXd weights) { + // Eigen::vector -> std::vector + std::vector std_weights(weights.size()); + for (size_t j = 0; j < weights.size(); j++) { + std_weights[j] = weights(j); + } + + auto *temp_controller = dynamic_cast<::revolve::DifferentialCPG*>(this->_controller->into_DifferentialCPG()); + temp_controller->set_connection_weights(std_weights); + }; + + vectorize_controller = [this]() { + auto *controller = dynamic_cast<::revolve::DifferentialCPG*>(this->_controller->into_DifferentialCPG()); + const std::vector &weights = controller->get_connection_weights(); + + // std::vector -> Eigen::Vector + Eigen::VectorXd eigen_weights(weights.size()); + for (size_t j = 0; j < weights.size(); j++) { + eigen_weights(j) = weights.at(j); + } + + return eigen_weights; + }; + break; + default: + std::cerr << "[BO] Controller not supported" << std::endl; + throw std::runtime_error("[BO] Controller not supported"); + } + + this->output_dir = "./experiments/IMC/output"+model_name; + + std::ifstream fin(this->output_dir+"/fitnesses.txt"); + std::ifstream gin(this->output_dir+"/genotype.log"); + if(fin){ // Continue Learning/test best + double fitness; + while (fin >> fitness){ + // Limbo requires fitness value to be of type Eigen::VectorXd + Eigen::VectorXd observation = Eigen::VectorXd(1); + observation(0) = fitness; + // Save fitness to std::vector. This fitness corresponds to the solution of the previous iteration + this->observations.push_back(observation); +// std::cout<vectorize_controller().size(); + // Initialize Eigen::VectorXd here. + Eigen::VectorXd init_sample(n_weights); + std::string genome; + while (std::getline(gin, genome)) + { + std::stringstream ss_weight(genome); + std::string weight; + int j =0; + while (std::getline(ss_weight, weight, ',')) + { + init_sample(j) = stod(weight); + j++; + } + // Save the initialized weights + this->samples.push_back(init_sample); + } + + this->evaluation_counter = this->observations.size()-1; + int best_index = 1; + for (int i=0; iobservations.size(); i++){ + if (this->best_fitnessobservations[i][0]){ + this->best_fitness = this->observations[i][0]; + this->best_sample = this->samples[i]; + best_index = i; + } + } + std::cout<<"[BO] Observations: "<observations.size()<<" | Samples: "<samples.size()<observations.clear(); + this->evaluation_counter = -1; + + auto sec_best = this->samples[best_index - 1]; + this->devectorize_controller(sec_best); + this->samples.clear(); + this->samples.push_back(this->best_sample); +// this->samples.push_back(sec_best); + std::cout<<"Retesting sample fitness: "<< this->best_fitness<output_dir+"/fitness_decom.txt", std::ofstream::out | std::ofstream::trunc); + files.close(); + } + } + else{ + std::cout<<"[BO] Create clean fitness/genotype files"<output_dir+"/fitnesses.txt", std::ofstream::out | std::ofstream::trunc); + files.open(this->output_dir+"/genotype.log", std::ofstream::out | std::ofstream::trunc); + files.open("../ctime.txt", std::ofstream::out | std::ofstream::trunc); + files.close(); + } + + this->output_dir = "./experiments/IMC/output"+model_name; + + std::ifstream fin(this->output_dir+"/fitnesses.txt"); + std::ifstream gin(this->output_dir+"/genotype.log"); + if(gin){ // Continue Learning + double fitness; + while (fin >> fitness){ + // Limbo requires fitness value to be of type Eigen::VectorXd + Eigen::VectorXd observation = Eigen::VectorXd(1); + observation(0) = fitness; + // Save fitness to std::vector. This fitness corresponds to the solution of the previous iteration + this->observations.push_back(observation); +// std::cout<vectorize_controller().size(); + // Initialize Eigen::VectorXd here. + Eigen::VectorXd init_sample(n_weights); + std::string genome; + while (std::getline(gin, genome)) + { + std::stringstream ss_weight(genome); + std::string weight; + int j =0; + while (std::getline(ss_weight, weight, ',')) + { + init_sample(j) = stod(weight); + j++; + } + // Save the initialized weights + this->samples.push_back(init_sample); + } + this->evaluation_counter = this->samples.size()-1; + int best_index = 1; + for (int i=0; iobservations.size(),500); i++){ + if (this->best_fitnessobservations[i][0]){ + this->best_fitness = this->observations[i][0]; + this->best_sample = this->samples[i]; + best_index = i; + } + } + std::cout<<"Observations: "<observations.size()<<" | Samples: "<samples.size()<observations.clear(); + this->evaluation_counter = -1; + + this->devectorize_controller(this->best_sample); + this->samples.clear(); + this->samples.push_back(this->best_sample); + } + } + else{ + std::ofstream files; + files.open(this->output_dir+"/fitnesses.txt", std::ofstream::out | std::ofstream::trunc); + files.open(this->output_dir+"/genotype.log", std::ofstream::out | std::ofstream::trunc); + files.open("../ctime.txt", std::ofstream::out | std::ofstream::trunc); + files.close(); + } + +} + +/** + * Struct that holds the parameters on which BO is called. This is required + * by limbo. + */ +struct BayesianOptimizer::params +{ + + struct bayes_opt_boptimizer : public limbo::defaults::bayes_opt_boptimizer + { + }; + + // depending on which internal optimizer we use, we need to import different parameters +#ifdef USE_NLOPT + struct opt_nloptnograd : public limbo::defaults::opt_nloptnograd { + }; +#elif defined(USE_LIBCMAES) + struct opt_cmaes : public lm::defaults::opt_cmaes { + }; +#endif + + struct kernel : public limbo::defaults::kernel + { + BO_PARAM(double, noise, 0.001); + + BO_PARAM(bool, optimize_noise, false); + }; + + struct bayes_opt_bobase : public limbo::defaults::bayes_opt_bobase + { + // set stats_enabled to prevent creating all the directories + BO_PARAM(bool, stats_enabled, false); + + BO_PARAM(bool, bounded, true); + }; + + // 1 Iteration as we will perform limbo step by steop + struct stop_maxiterations : public limbo::defaults::stop_maxiterations + { + BO_PARAM(int, iterations, 1); + }; + + struct kernel_exp : public limbo::defaults::kernel_exp + { + /// @ingroup kernel_defaults + BO_PARAM(double, sigma_sq, 0.1); + + BO_PARAM(double, l, 0.1); // the width of the kernel. Note that it assumes equally sized ranges over dimensions + }; + + struct kernel_squared_exp_ard : public limbo::defaults::kernel_squared_exp_ard + { + /// @ingroup kernel_defaults + BO_PARAM(int, k, 3); // k number of columns used to compute M + /// @ingroup kernel_defaults + BO_PARAM(double, sigma_sq, 0.1); //brochu2010tutorial p.9 without sigma_sq + }; + + struct kernel_maternfivehalves : public limbo::defaults::kernel_maternfivehalves + { + BO_DYN_PARAM(double, sigma_sq); //brochu2010tutorial p.9 without sigma_sq + BO_DYN_PARAM(double, l); //characteristic length scale + }; + + struct acqui_gpucb : public limbo::defaults::acqui_gpucb + { + //UCB(x) = \mu(x) + \kappa \sigma(x). + BO_PARAM(double, delta, + 0.1);//acqui_gpucb_delta_); // default delta = 0.1, delta in (0,1) convergence guaranteed + }; + + struct acqui_ei : public limbo::defaults::acqui_ei + { + BO_PARAM(double, jitter, 0.5); + }; + + // This is just a placeholder to be able to use limbo with revolve + struct init_lhs : public limbo::defaults::init_lhs + { + BO_PARAM(int, samples, 0); + }; + + struct acqui_ucb : public limbo::defaults::acqui_ucb + { + //constexpr double ra = acqui_ucb_alpha_; + //UCB(x) = \mu(x) + \alpha \sigma(x). high alpha have high exploration + //iterations is high, alpha can be low for high accuracy in enough iterations. + // In contrast, the lsow iterations should have high alpha for high + // searching in limited iterations, which guarantee to optimal. + // BO_PARAM(double, alpha, transform_double(acqui_ucb_alpha_)); // default alpha = 0.5 + BO_DYN_PARAM(double, alpha); // default alpha = 0.5 + + }; +}; + +BO_DECLARE_DYN_PARAM(double, BayesianOptimizer::params::acqui_ucb, alpha); +BO_DECLARE_DYN_PARAM(double, BayesianOptimizer::params::kernel_maternfivehalves, sigma_sq); +BO_DECLARE_DYN_PARAM(double, BayesianOptimizer::params::kernel_maternfivehalves, l); + + +void BayesianOptimizer::init_first_controller() +{ +// assert(n_init_samples == 1 and "INIT SAMPLES > 1 not supported"); + std::cout<<"Intialization BO algorithm"<vectorize_controller().size(); + + // Initialize Eigen::VectorXd here. + Eigen::VectorXd init_sample(n_weights); + + // Working variable + double my_range = 1.f / this->n_init_samples; + + // If we have n dimensions, create n such vectors that we will permute + std::vector> all_dimensions; + + // Fill vectors + for (size_t i = 0; i < n_weights; i++) { + std::vector one_dimension; + + // Prepare for vector permutation + for (size_t j = 0; j < this->n_init_samples; j++) { + one_dimension.push_back(j); + } + + // Vector permutation + std::random_shuffle(one_dimension.begin(), one_dimension.end()); + + // Save permuted vector + all_dimensions.push_back(one_dimension); + } + + // For all samples + for (size_t i = 0; i < this->n_init_samples; i++) { + + // For all dimensions + for (size_t j = 0; j < n_weights; j++) { + // Take a LHS + init_sample(j) = all_dimensions.at(j).at(i) * my_range + ((double) rand() / (RAND_MAX)) * my_range; + } + + // Save the initialized weights + this->samples.push_back(init_sample); + } + + if (!this->samples.empty()){ + this->devectorize_controller(this->samples[0]); + } +} + +void BayesianOptimizer::init_next_controller() +{ + std::cout<<"[BO] start update"<samples.size()>this->observations.size()){ + x = this->samples[this->observations.size()]; + std::cout<<"Initializing BO with LHS | "<observations.size()+1<<"/"<< this->samples.size()<acqui_ucb_alpha); + params::kernel_maternfivehalves::set_l(this->kernel_l); + params::kernel_maternfivehalves::set_sigma_sq(this->kernel_sigma_sq); + + // Specify bayesian optimizer. TODO: Make attribute and initialize at bo_init + limbo::bayes_opt::BOptimizer, + limbo::modelfun, + limbo::acquifun>> boptimizer; + + // Optimize. Pass evaluation function and observations . + boptimizer.optimize(BayesianOptimizer::evaluation_function(this->samples[0].size()), + this->samples, + this->observations); + + x = boptimizer.last_sample(); + this->samples.push_back(x); + } + + // load into controller + this->devectorize_controller(x); + std::cout << "[BO] end update" < this->best_fitness) + { + this->best_fitness = fitness; + this->best_sample = this->samples.back(); + } + + std::cout<<"[BO] Resulting fitness: "<best_fitness<observations.push_back(observation); + + // ->GetAttribute("output_directory")->GetAsString(); + // Write fitness to file + std::ofstream fitness_file; + fitness_file.open(this->output_dir+"/fitnesses.txt", std::ios::app); + fitness_file<< std::setprecision(std::numeric_limits::digits10 +1) + << fitness << std::endl; + fitness_file.close(); + + // Write genotype to file + std::ofstream genolog(this->output_dir+"/genotype.log", std::ios::app); + if (genolog.is_open()) + { + genolog << this->samples.back().format(CSVFormat) << std::endl; + genolog.close(); + } + genolog.close(); +} + +void BayesianOptimizer::load_best_controller() +{ + this->devectorize_controller(this->best_sample); +} diff --git a/cpprevolve/revolve/brains/learner/BayesianOptimizer.h b/cpprevolve/revolve/brains/learner/BayesianOptimizer.h index 15fcaa2621..69efbee204 100644 --- a/cpprevolve/revolve/brains/learner/BayesianOptimizer.h +++ b/cpprevolve/revolve/brains/learner/BayesianOptimizer.h @@ -2,13 +2,104 @@ // Created by matteo on 14/06/19. // -#ifndef REVOLVE_BAYESIANOPTIMIZER_H -#define REVOLVE_BAYESIANOPTIMIZER_H +#pragma once +#include +#include +#include "Learner.h" +#include "../controller/Controller.h" +#include "../controller/DifferentialCPG.h" -class BayesianOptimizer { +namespace revolve { +class BayesianOptimizer : public Learner +{ +public: + /// \brief Constructor + explicit BayesianOptimizer( + std::unique_ptr <::revolve::Controller> controller, + Evaluator *evaluator, + EvaluationReporter *reporter, + double evaluation_time, + unsigned int n_learning_evaluations, + const std::string& model_name); -}; + /// \brief Destructor + ~BayesianOptimizer() = default; + + void init_first_controller() override; + void init_next_controller() override; + void finalize_current_controller(double fitness) override; + void load_best_controller() override; + + Controller *controller() override + { return this->_controller.get(); } + +public: + + /// \brief parameters for optimization + struct params; + + /// \brief Dummy function for limbo + class evaluation_function { + public: + explicit evaluation_function(size_t dim_in) + : _dim_in(dim_in) + {} // Number of input dimension (samples.size()) + size_t dim_in() const + { return _dim_in; } // number of dimensions of the fitness + + static size_t dim_out() + { return 1; } + + Eigen::VectorXd operator()(const Eigen::VectorXd &/*x*/) const + { + Eigen::VectorXd res(1); + res(0) = 0; + return res; + }; + + private: + const size_t _dim_in; + }; + +protected: + std::unique_ptr<::revolve::Controller> _controller; + // BO Learner parameters + double kernel_noise; + bool kernel_optimize_noise; + double kernel_sigma_sq; + double kernel_l; + int kernel_squared_exp_ard_k; + double acqui_gpucb_delta; + double acqui_ucb_alpha; + double acqui_ei_jitter; -#endif //REVOLVE_BAYESIANOPTIMIZER_H + /// \brief Specifies the acquisition function used + std::string acquisition_function; + + /// \brief Number of initial samples + size_t n_init_samples; + + /// \brief All samples seen so far. + std::vector samples; + + /// \brief All fitnesses seen so far. Called observations in limbo context + std::vector< Eigen::VectorXd > observations; + + /// \brief function to turn the controller into a sample + std::function vectorize_controller; + + /// \brief function to turn a sample into a controller + std::function devectorize_controller; + + /// \brief Best fitness seen so far + double best_fitness = -std::numeric_limits::infinity(); + + /// \brief Sample corresponding to best fitness + Eigen::VectorXd best_sample; + + /// \brief root output directory + std::string output_dir; +}; +} diff --git a/cpprevolve/revolve/gazebo/brains/DifferentialCPG_BO.h b/cpprevolve/revolve/brains/learner/BoDefinitions.h similarity index 88% rename from cpprevolve/revolve/gazebo/brains/DifferentialCPG_BO.h rename to cpprevolve/revolve/brains/learner/BoDefinitions.h index c859415630..115b9be24c 100644 --- a/cpprevolve/revolve/gazebo/brains/DifferentialCPG_BO.h +++ b/cpprevolve/revolve/brains/learner/BoDefinitions.h @@ -2,8 +2,7 @@ // Created by maarten on 03/02/19. // -#ifndef REVOLVE_BOPTIMIZER_CPG_H -#define REVOLVE_BOPTIMIZER_CPG_H +#pragma once // Standard libraries #include @@ -25,16 +24,35 @@ namespace limbo { BO_PARAM(int, hp_period, -1); }; } + namespace init { + template + struct FlexibleLHS + { + template + void operator()(const StateFunction &seval, const AggregatorFunction &, Opt &opt) const + { + assert(Params::bayes_opt_bobase::bounded()); + + Eigen::MatrixXd H = tools::random_lhs(seval.dim_in(), Params::init_lhs::samples()); + + for (int i = 0; i < Params::init_lhs::samples(); i++) { + opt.eval_and_add(seval, H.row(i)); + } + } + }; + } BOOST_PARAMETER_TEMPLATE_KEYWORD(acquiopt) namespace bayes_opt { - using boptimizer_signature = boost::parameter::parameters, + using boptimizer_signature = boost::parameter::parameters< + boost::parameter::optional, boost::parameter::optional, boost::parameter::optional, boost::parameter::optional, boost::parameter::optional, - boost::parameter::optional>; + boost::parameter::optional + >; // clang-format off /** @@ -97,7 +115,7 @@ namespace limbo { } else { std::cout << "OBSERVATION SET IS EMPTY \n"; - _model = model_t(StateFunction::dim_in(), StateFunction::dim_out()); + _model = model_t(sfun.dim_in(), StateFunction::dim_out()); } acqui_optimizer_t acqui_optimizer; @@ -106,13 +124,13 @@ namespace limbo { while (!this->_stop(*this, afun)) { - gettimeofday(&timeStart,NULL); + gettimeofday(&timeStart, nullptr); acquisition_function_t acqui(_model, this->_current_iteration); auto acqui_optimization = [&](const Eigen::VectorXd& x, bool g) { return acqui(x, afun, g); }; - Eigen::VectorXd starting_point = tools::random_vector(StateFunction::dim_in(), Params::bayes_opt_bobase::bounded()); + Eigen::VectorXd starting_point = tools::random_vector(sfun.dim_in(), Params::bayes_opt_bobase::bounded()); // new samples are from the acquisition optimizer Eigen::VectorXd new_sample = acqui_optimizer(acqui_optimization, starting_point, Params::bayes_opt_bobase::bounded()); @@ -131,7 +149,7 @@ namespace limbo { this->_current_iteration++; this->_total_iterations++; - gettimeofday(&timeEnd,NULL); + gettimeofday(&timeEnd, nullptr); timeDiff = 1000000 * (timeEnd.tv_sec - timeStart.tv_sec) + timeEnd.tv_usec - timeStart.tv_usec; //tv_sec: value of second, tv_usec: value of microsecond @@ -191,5 +209,3 @@ namespace limbo { using BOptimizerHPOpt = BOptimizer>, acquifun<_default_hp::acqui_t>, A1, A2, A3, A4>; } } - -#endif //REVOLVE_BOPTIMIZER_CPG_H diff --git a/cpprevolve/revolve/brains/learner/DifferentialEvo.cpp b/cpprevolve/revolve/brains/learner/DifferentialEvo.cpp new file mode 100644 index 0000000000..9bb1834f8d --- /dev/null +++ b/cpprevolve/revolve/brains/learner/DifferentialEvo.cpp @@ -0,0 +1,269 @@ +// +// Created by fuda on 18-12-20. +// + +#include "DifferentialEvo.h" +#include +#include +#include +const static Eigen::IOFormat CSVFormat(11, Eigen::DontAlignCols, ", ", ","); + +namespace revolve { + DifferentialEvo::DifferentialEvo(std::unique_ptr controller, + Evaluator *evaluator, + EvaluationReporter *reporter, + const DifferentialEvo::DE_Parameters ¶ms, + int seed, + double evaluation_time, + unsigned int n_learning_evaluations, + const std::string &model_name) + : EA(std::move(controller), evaluator, reporter, + params.EA_params, seed, evaluation_time, + n_learning_evaluations, + model_name) { + std::cout << "[DiffEvo] constructor" << std::endl; + this->DE_Param = params; + this->F = DE_Param.F; + this->CR = DE_Param.CR; + this->type = DE_Param.type; + this->n_parents = DE_Param.n_parents; + this->elitism = DE_Param.elitism; + + std::vector de_types{"de", "ade", "revde", "dex3"}; + + assert ((0. <= this->F) && (this->F <= 2.) && "F must be in [0, 2]"); + assert ((0. < this->CR) && (this->CR <= 1.) && "CR must be in (0, 1]"); + assert ((this->elitism>=0.) && (this->elitism < 1.0) && "Elitism must be in [0, 1)"); + assert ((std::find(de_types.begin(), de_types.end(), this->type) != de_types.end()) + && "type must be one in {de, dex3, ade, revde}"); + + + int n_params = this->get_genome().size(); + + std::vector initial_samples = randomNum->randVectd(EA_Params.min_weight, EA_Params.max_weight, + n_params*EA_Params.population_size); + + std::vector genome(n_params); + + for (int u = 0; u < EA_Params.population_size; u++) { + for (int v = 0; v < n_params; v++) + genome[v] = initial_samples[v+u]; + + Individual::indPtr ind(new Individual()); + ind->setGenome(genome); + population.push_back(ind); + } + std::cout << "[DiffEvo] population initialized" << std::endl; + } + + DifferentialEvo::~DifferentialEvo()= default; + + void DifferentialEvo::epoch() { + /** NOVELTY **/ + if (DE_Param.novelty_ratio > 0.) { + if (Novelty::k_value >= population.size()) + Novelty::k_value = int(population.size() / 2); + else Novelty::k_value = DE_Param.novelty_k_value; + + std::vector pop_desc; + for (const auto &ind : population) + pop_desc.push_back(ind->descriptor()); + //compute novelty + for (const auto &ind : population) { + Eigen::VectorXd ind_desc = ind->descriptor(); + double ind_nov = Novelty::sparseness(Novelty::distances(ind_desc, archive, pop_desc)); + ind->setNovelty(ind_nov); + } + + //update archive + for (const auto &ind : population) { + Eigen::VectorXd ind_desc = ind->descriptor(); + double ind_nov = ind->get_ctrl_novelty(); + Novelty::update_archive(ind_desc, ind_nov, archive, randomNum); + } + } + + this->selection(); + } + + void DifferentialEvo::selection() { + this->population.insert(this->population.end(), pop_s.begin(), pop_s.end()); + + std::vector pop_ind(population.size()); + std::size_t n(0); + std::generate(std::begin(pop_ind), std::end(pop_ind), [&]{ return n++; }); + + std::sort( std::begin(pop_ind), + std::end(pop_ind), + [&](int i1, int i2) { + return this->population[i1]->getFitness() > this->population[i2]->getFitness(); } ); + + std::ofstream gen_fitness; + gen_fitness.open(this->output_dir+"/gen_best_fitness.txt", std::ios::app); + gen_fitness<< std::setprecision(std::numeric_limits::digits10 +1) + << this->population[pop_ind[0]]->getFitness() << std::endl; + gen_fitness.close(); + + std::ofstream gen_genome; + gen_genome.open(this->output_dir+"/gen_best_genome.txt", std::ios::app); + gen_genome<< std::setprecision(std::numeric_limits::digits10 +1) + << this->population[pop_ind[0]]->genome.format(CSVFormat) << std::endl; + gen_genome.close(); + + pop_s.clear(); + for (int j = 0; j < EA_Params.population_size; j++) { + pop_s.push_back(this->population[pop_ind[j]]); + } + } + + void DifferentialEvo::init_next_pop() { + + auto [new_samples, parent_ind] = recombination(); + + int n_param = population[0]->get_ctrl_genome().size(); + std::vector genome(n_param); + + population.clear(); + for (int i = 0; i < new_samples.cols(); i++) { + + for (int j = 0; j < n_param; j++) + genome[j] = new_samples(j, i); + + Individual::indPtr ind(new Individual()); + ind->setGenome(genome); + population.push_back(ind); + } + } + + std::tuple> > DifferentialEvo::recombination(){ + std::vector> parent_ind; + + std::vector pop_ind(pop_s.size()); + + Eigen::ArrayXXd genomes(this->get_genome().size(),this->pop_s.size()); + std::vector parent_genomes; + // Prepare for vector permutation + for (size_t j = 0; j < pop_s.size(); j++) { + pop_ind[j] = j; + genomes.col(j) = getIndividual(pop_ind[j])->genome; + } + + for (int i=0; igenome; + } + } + + if (this->type == "de"){ + Eigen::ArrayXXd y_1(this->get_genome().size(),pop_s.size()); + y_1 = (parent_genomes[0] + this->F * (parent_genomes[1] - parent_genomes[2])) + .cwiseMax(EA_Params.min_weight).cwiseMin(EA_Params.max_weight); + + if (this->CR < 1.) { + int shape[]= {static_cast(y_1.rows()), static_cast(y_1.cols())}; + Eigen::ArrayXXd p_1 = this->randomNum->Bernoulli(this->CR, shape).cast(); + + y_1 = p_1* y_1 + (1. - p_1) * parent_genomes[0]; + } + return {y_1, parent_ind}; + } + else if ((this->type == "revde")) { + Eigen::ArrayXXd y_1(this->get_genome().size(),pop_s.size()); + Eigen::ArrayXXd y_2(this->get_genome().size(),pop_s.size()); + Eigen::ArrayXXd y_3(this->get_genome().size(),pop_s.size()); + + y_1 = (parent_genomes[0] + this->F * (parent_genomes[1] - parent_genomes[2])) + .cwiseMax(EA_Params.min_weight).cwiseMin(EA_Params.max_weight); + y_2 = (parent_genomes[1] + this->F * (parent_genomes[2] - y_1)) + .cwiseMax(EA_Params.min_weight).cwiseMin(EA_Params.max_weight); + y_3 = (parent_genomes[2] + this->F * (y_1 - y_2)) + .cwiseMax(EA_Params.min_weight).cwiseMin(EA_Params.max_weight); + +// uniform crossover + if (this->CR < 1.) { + int shape[]= {static_cast(y_1.rows()), static_cast(y_1.cols())}; + Eigen::ArrayXXd p_1 = this->randomNum->Bernoulli(this->CR, shape).cast(); + Eigen::ArrayXXd p_2 = this->randomNum->Bernoulli(this->CR, shape).cast(); + Eigen::ArrayXXd p_3 = this->randomNum->Bernoulli(this->CR, shape).cast(); + y_1 = p_1 * y_1 + (1. - p_1) * parent_genomes[0]; + y_2 = p_2 * y_2 + (1. - p_2) * parent_genomes[1]; + y_3 = p_3 * y_3 + (1. - p_3) * parent_genomes[2]; + } + + Eigen::ArrayXXd population(this->get_genome().size(),pop_s.size()*3); + population << y_1 , y_2 , y_3; + return {population, parent_ind}; + } + else if (this->type == "ade") { + Eigen::ArrayXXd y_1(this->get_genome().size(),pop_s.size()); + Eigen::ArrayXXd y_2(this->get_genome().size(),pop_s.size()); + Eigen::ArrayXXd y_3(this->get_genome().size(),pop_s.size()); + + y_1 = (parent_genomes[0] + this->F * (parent_genomes[1] - parent_genomes[2])) + .cwiseMax(EA_Params.min_weight).cwiseMin(EA_Params.max_weight); + y_2 = (parent_genomes[1] + this->F * (parent_genomes[2] - parent_genomes[0])) + .cwiseMax(EA_Params.min_weight).cwiseMin(EA_Params.max_weight); + y_3 = (parent_genomes[2] + this->F * (parent_genomes[0] - parent_genomes[1])) + .cwiseMax(EA_Params.min_weight).cwiseMin(EA_Params.max_weight); + +// uniform crossover + if (this->CR < 1.) { + int shape[]= {static_cast(y_1.rows()), static_cast(y_1.cols())}; + Eigen::ArrayXXd p_1 = this->randomNum->Bernoulli(this->CR, shape).cast(); + Eigen::ArrayXXd p_2 = this->randomNum->Bernoulli(this->CR, shape).cast(); + Eigen::ArrayXXd p_3 = this->randomNum->Bernoulli(this->CR, shape).cast(); + y_1 = p_1 * y_1 + (1. - p_1) * parent_genomes[0]; + y_2 = p_2 * y_2 + (1. - p_2) * parent_genomes[1]; + y_3 = p_3 * y_3 + (1. - p_3) * parent_genomes[2]; + } + + Eigen::ArrayXXd population(this->get_genome().size(),pop_s.size()*3); + population << y_1 , y_2 , y_3; + return {population, parent_ind}; + } + if (this->type == "dex3") { + Eigen::ArrayXXd y_1(this->get_genome().size(),pop_s.size()); + Eigen::ArrayXXd y_2(this->get_genome().size(),pop_s.size()); + Eigen::ArrayXXd y_3(this->get_genome().size(),pop_s.size()); + + y_1 = (parent_genomes[0] + this->F * (parent_genomes[1] - parent_genomes[2])) + .cwiseMax(EA_Params.min_weight).cwiseMin(EA_Params.max_weight); + y_2 = (parent_genomes[0] + this->F * (parent_genomes[3] - parent_genomes[4])) + .cwiseMax(EA_Params.min_weight).cwiseMin(EA_Params.max_weight); + y_3 = (parent_genomes[0] + this->F * (parent_genomes[5] - parent_genomes[6])) + .cwiseMax(EA_Params.min_weight).cwiseMin(EA_Params.max_weight); + +// uniform crossover + if (this->CR < 1.) { + int shape[]= {static_cast(y_1.rows()), static_cast(y_1.cols())}; + Eigen::ArrayXXd p_1 = this->randomNum->Bernoulli(this->CR, shape).cast(); + Eigen::ArrayXXd p_2 = this->randomNum->Bernoulli(this->CR, shape).cast(); + Eigen::ArrayXXd p_3 = this->randomNum->Bernoulli(this->CR, shape).cast(); + y_1 = p_1 * y_1 + (1. - p_1) * parent_genomes[0]; + y_2 = p_2 * y_2 + (1. - p_2) * parent_genomes[1]; + y_3 = p_3 * y_3 + (1. - p_3) * parent_genomes[2]; + } + + Eigen::ArrayXXd population(this->get_genome().size(),pop_s.size()*3); + population << y_1 , y_2 , y_3; + return {population, parent_ind}; + } + else { + throw std::runtime_error("Wrong name of the differential mutation!"); + } + } + + bool DifferentialEvo::is_finish() { + int maxNbrEval = EA_Params.max_eval; + return _is_finish || numberEvaluation >= maxNbrEval; + } + + bool DifferentialEvo::finish_eval() { + return EA::finish_eval(); + } +} \ No newline at end of file diff --git a/cpprevolve/revolve/brains/learner/DifferentialEvo.h b/cpprevolve/revolve/brains/learner/DifferentialEvo.h new file mode 100644 index 0000000000..c136fbbaff --- /dev/null +++ b/cpprevolve/revolve/brains/learner/DifferentialEvo.h @@ -0,0 +1,75 @@ +// +// Created by fuda on 18-12-20. +// + +#pragma once +#include +//#include +#include "Learner.h" +#include "Evaluator.h" +#include "EA.h" + +#ifndef REVOLVE_DIFFERENTIALEVO_H +#define REVOLVE_DIFFERENTIALEVO_H + +namespace revolve { + class DifferentialEvo : public EA { + public: + struct DE_Parameters { + std::string type = "de"; + + double F = 1.0; + double CR = 0.5; + double elitism = 0.; + int n_parents = 3; + + int novelty_k_value = 15; + double novelty_ratio = 1.; +// double novelty_decrement = 0.05; +// double novelty_threshold = 0.9; +// double novelty_archive_probability = 0.4; + + EA::Parameters EA_params; + }; + + explicit DifferentialEvo(std::unique_ptr controller, + Evaluator *evaluator, + EvaluationReporter *reporter, + const DifferentialEvo::DE_Parameters ¶ms, + int seed, + double evaluation_time, + unsigned int n_learning_evaluations, + const std::string &model_name); + + Controller *controller() override { return _controller.get(); } + + ~DifferentialEvo(); + + void epoch() override; + + void init_next_pop() override; + + void selection() override; + + std::tuple>> recombination(); + + bool is_finish() override; + + bool finish_eval() override; + + const std::vector &get_archive() { return archive; } + + protected: + std::vector pop_s; + std::string type; + bool _is_finish = false; + std::vector archive; + DifferentialEvo::DE_Parameters DE_Param; + double F; + double CR; + int n_parents; + double elitism; + }; +}; + +#endif //REVOLVE_DIFFERENTIALEVO_H diff --git a/cpprevolve/revolve/brains/learner/EA.cpp b/cpprevolve/revolve/brains/learner/EA.cpp new file mode 100644 index 0000000000..1c95f75800 --- /dev/null +++ b/cpprevolve/revolve/brains/learner/EA.cpp @@ -0,0 +1,177 @@ +// +// Created by fuda on 11/23/20. +// + +#include "../controller/DifferentialCPG.h" +#include "EA.h" +#include +using namespace revolve; +const static Eigen::IOFormat CSVFormat(11, Eigen::DontAlignCols, ", ", ","); + +EA::EA(std::unique_ptr controller, + Evaluator *evaluator, + EvaluationReporter *reporter, + const EA::Parameters ¶ms, + int seed, + const double evaluation_time, + unsigned int n_learning_evaluations, + const std::string& model_name) + : Learner(evaluator, reporter, evaluation_time, n_learning_evaluations) + , _controller(std::move(controller)) +// , params(params) +// , population(nullptr) +{ + max_learning_evaluations = int(n_learning_evaluations); + EA_Params = params; + this->output_dir = "./experiments/IMC/output"+model_name; + + revolve::RandNum rn(seed); + this->set_randomNum(std::make_shared(rn)); + + assert(this->_controller && "EA: passed null controller"); + switch (this->_controller->controller_type) + { + case revolve::Controller::DIFFERENTIAL_CPG: + EA_Params.max_weight = 1.0; + EA_Params.min_weight = 0.0; + + load_genome = [this](Eigen::VectorXd weights) { + std::vector std_weights(weights.size()); + for (size_t j = 0; j < weights.size(); j++) { + std_weights[j] = weights(j); + } + + auto *temp_controller = dynamic_cast<::revolve::DifferentialCPG*>(this->_controller->into_DifferentialCPG()); + temp_controller->set_connection_weights(std_weights); + }; + + get_genome = [this]() { + auto *controller = dynamic_cast<::revolve::DifferentialCPG*>(this->_controller->into_DifferentialCPG()); + const std::vector &weights = controller->get_connection_weights(); + Eigen::VectorXd eigen_weights(weights.size()); + for (size_t j = 0; j < weights.size(); j++) { + eigen_weights(j) = weights.at(j); + } + + return eigen_weights; + }; + break; + default: + std::cerr << "Controller not supported" << std::endl; + throw std::runtime_error("Controller not supported"); + } +} + +void EA::init_first_controller() +{ + current_Ind = population.begin(); + this->set_current_Ind_Index(0); + this->set_generation(0); + + //TODO load genome in controller + this->load_genome((*current_Ind)->genome); + + std::cout<<"[EA] initialized first controller"<generation << "\t Pop size: " << this->population.size()<< + "\n###### Best ind:\t"<< this->best_Ind << "\t Fitness: " << this->best_fitness << + "\n###### Genome: \t"; + for (auto g : this->getIndividual(this->best_Ind)->get_ctrl_genome()) { + std::cout << g << ", "; + }; + std::cout<epoch(); + this->init_next_pop(); + this->incr_generation(); + this->set_current_Ind_Index(0); + current_Ind = population.begin(); + } + if(this->is_finish()){ + if(EA_Params.verbose) + { + std::cout << "---------------------" << std::endl; + std::cout << "Evolution is Finished" << std::endl; + std::cout << "---------------------" << std::endl; + } + exit(0); + } + const Eigen::VectorXd &genome = (*current_Ind)->genome; + this->load_genome(genome); + std::cout <<"[EA] end update" << std::endl; +} + +void EA::finalize_current_controller(double fitness) +{ + (*current_Ind)->setFitness(fitness); + + if(fitness>best_fitness) + { + this->best_fitness = fitness; + this->best_genome = (*current_Ind)->genome; + this->best_Ind = current_Ind_Index; + } + + // Write fitness to file + std::ofstream fitness_file; + fitness_file.open(this->output_dir+"/fitnesses.txt", std::ios::app); + fitness_file<< std::setprecision(std::numeric_limits::digits10 +1) + <output_dir+"/genotype.log", std::ios::app); + if (genolog.is_open()) + { + genolog << (*current_Ind)->genome.format(CSVFormat) <load_genome(this->best_genome); +} + + + + +/// ############## virtual part for different EA implementations ############## +EA::~EA() +{ + randomNum.reset(); +// parameters.reset(); + for(auto& ind : population) + ind.reset(); +} +void EA::setSettings(const RandNum::Ptr &rn) +{ +// parameters = param; + randomNum = rn; +} +void EA::epoch(){ + evaluation(); + selection(); +} +void EA::init_next_pop(){ + replacement(); + crossover(); + mutation(); +} +Individual::indPtr EA::getIndividual(size_t index) const +{ + return population[index]; +} diff --git a/cpprevolve/revolve/brains/learner/EA.h b/cpprevolve/revolve/brains/learner/EA.h new file mode 100644 index 0000000000..42e90894d8 --- /dev/null +++ b/cpprevolve/revolve/brains/learner/EA.h @@ -0,0 +1,190 @@ +// +// Created by fuda on 11/23/20. +// + +#pragma once +#ifndef REVOLVE_EA_H +#define REVOLVE_EA_H + +#include "Learner.h" +#include "Evaluator.h" +#include +#include +#include "EA_misc/RandNum.h" +#include "EA_misc/Novelty.h" + +//#include "multineat/Genome.h" +//#include + +namespace revolve +{ + class Individual + { + public: + Individual(){}; + typedef std::shared_ptr indPtr; + Eigen::VectorXd genome; + double fitness = -std::numeric_limits::infinity(); + double novelty; + + void setFitness(double value){fitness = value;}; + void setNovelty(double ind_nov){novelty = ind_nov;} + void setGenome(std::vector gen){ +// this->genome = Eigen::VectorXd::Map(gen.data(), gen.size(), 0); + Eigen::VectorXd V(gen.size()); + for (int i=0; i get_ctrl_genome(){ + std::vector vec(genome.data(), genome.data() + genome.size()); + return vec; + } + Eigen::VectorXd descriptor(){ +// Eigen::VectorXd desc(3); +// desc << 0.0, 0.0, 0.0; + return genome; + }; + protected: + }; + + + typedef std::chrono::high_resolution_clock hr_clock; + class EA : public Learner + { + public: + struct Parameters { + bool verbose = false; + int population_size = 10; + int max_eval = 300; + double max_weight = 1.0; + double min_weight = 0.0; + }; + + /// \brief Constructor + explicit EA( + std::unique_ptr controller, + Evaluator *evaluator, + EvaluationReporter *reporter, + const EA::Parameters ¶ms, + int seed, + double evaluation_time, + unsigned int n_learning_evaluations, + const std::string& model_name); + + /// \brief Destructor + ~EA(); + + Controller *controller() override + { return _controller.get(); } + + void init_first_controller() override; + void init_next_controller() override; + void finalize_current_controller(double fitness) override; + void load_best_controller() override; + + + protected: + EA::Parameters EA_Params; + std::vector population; + std::vector::iterator current_Ind; +// Eigen::VectorXd current_genome_evaluating; + double best_fitness = -std::numeric_limits::infinity(); + Eigen::VectorXd best_genome; + int max_learning_evaluations; + + /// \brief ptr to the current robot controller + std::unique_ptr _controller; + + /// \brief function to load a genome into a controller + std::function load_genome; + + /// \brief function to turn the controller into a sample + std::function get_genome; + + + /// ############## virtual part for different EA implementations ############## + public: + typedef std::unique_ptr Ptr; + typedef std::unique_ptr ConstPtr; + typedef EA::Ptr (Factory)(const RandNum::Ptr&); + + std::vector popNextIndNumbers; + /// This method initilizes setting for EA and random number generator seed + void setSettings(const RandNum::Ptr &rn); + /// This method sets the fitness value of an individual + virtual void setObjectives(size_t indIndex, const double &objectives) + { + current_Ind_Index = indIndex; + population[indIndex]->setFitness(objectives); + } + + /** + * @brief Epoch method is called at the end of each generation + */ + virtual void epoch(); + /** + * @brief Initialisation of the population for next generation. Called at the end of each generation after the epoch function. + */ + virtual void init_next_pop(); + /** + * @brief ending condition of the algorithm + * @return true if ending condition is meet + */ + virtual bool is_finish(){ +// int maxGen = max_learning_evaluations; + return generation >= max_learning_evaluations; + } + /** + * @brief ending condition of the evaluation. This condition is added with OR to the default condition the time limit. + * @return + */ + virtual bool finish_eval(){ + return false; + } + + void incr_generation(){generation++;} + //GETTERS & SETTERS + Individual::indPtr getIndividual(size_t index) const; + size_t getPopSize() const {return population.size();} + const std::vector &get_population() const {return population;} + const EA::Parameters &get_parameters() const {return EA_Params;} + const RandNum::Ptr get_randomNum() const {return randomNum;} + int get_generation() const {return generation;} + int get_numberEvaluation() const {return numberEvaluation;} + std::chrono::nanoseconds getEvalCompTime() const { + return std::chrono::duration_cast + (endEvalTime - startEvalTime); + } + + void set_randomNum(const RandNum::Ptr& rn){randomNum = rn;} + void set_generation(int gen){generation = gen;} + void set_current_Ind_Index(int index){current_Ind_Index = index;} + void set_startEvalTime(const hr_clock::time_point& t){startEvalTime = t;} + void set_endEvalTime(const hr_clock::time_point& t){endEvalTime = t;} + protected: + /// This method initilizes a population of genomes + virtual void evaluation(){} // This is now only used by NEAT but can also be done for the other genomes. However, by passing the update function to the EA different EA objects can contain different scenarios making the plugin more flexible. + virtual void selection(){} // selection operator + virtual void replacement(){} // replacement operator + virtual void mutation(){} // mutation operator + virtual void crossover(){} //crossover + virtual void end(){} // last call to the EA, when simulation stops + + ///set the environment type, evolution type... + + ///random number generator for EA + RandNum::Ptr randomNum; + int generation = 0; + int numberEvaluation = 0; + int current_Ind_Index = 0; + int best_Ind = 0; + hr_clock::time_point startEvalTime; + hr_clock::time_point endEvalTime; + std::string output_dir; + }; +}//revolve +#endif //REVOLVE_EA_H diff --git a/cpprevolve/revolve/brains/learner/EA_misc/Novelty.cpp b/cpprevolve/revolve/brains/learner/EA_misc/Novelty.cpp new file mode 100644 index 0000000000..f0e819e8f5 --- /dev/null +++ b/cpprevolve/revolve/brains/learner/EA_misc/Novelty.cpp @@ -0,0 +1,104 @@ +#include "Novelty.h" + +using namespace revolve; + +int Novelty::k_value = 15; +double Novelty::novelty_thr = 0.9; +double Novelty::archive_adding_prob = 0.4; + +double Novelty::sparseness(const std::vector &dist){ + + + double sum = 0; + if(dist.size() > k_value + 1){ + for(int i = 0; i < k_value; i++) + sum += dist[i]; + } + if(std::isnan(sum/static_cast(k_value))){ + std::cerr << "NaN found" << std::endl; + } + return sum/static_cast(k_value); +} + +std::vector Novelty::distances(const Eigen::VectorXd &ind_desc, + const std::vector &archive, + const std::vector &pop){ + + std::vector dist(archive.size() + pop.size()); + // Comparing with archive + tbb::parallel_for(tbb::blocked_range(0,archive.size()), + [&](tbb::blocked_range r){ + + for(size_t i = r.begin(); i != r.end(); i++){ + dist[i] = (archive[i] - ind_desc).norm(); + } + }); + + + // Comparing with population + tbb::parallel_for(tbb::blocked_range(0,pop.size()), + [&](tbb::blocked_range r){ + for(size_t i = r.begin(); i != r.end(); i++){ + if(pop[i] == ind_desc) + dist[i+archive.size()] = 1.; + else + dist[i+archive.size()] = (pop[i] - ind_desc).norm(); + } + }); + + std::sort(dist.begin(),dist.end()); // Sorting distances + + return dist; +} + + +std::vector Novelty::distances(const Eigen::VectorXd &ind_desc, + const std::vector &archive, + const std::vector &pop, + std::vector & sorted_pop_indexes){ + + std::vector dist(archive.size() + pop.size()); + + // Comparing with achive + tbb::parallel_for(tbb::blocked_range(0,archive.size()), + [&](tbb::blocked_range r){ + + for(size_t i = r.begin(); i != r.end(); i++){ + dist[i] = (archive[i] - ind_desc).norm(); + } + }); + + // Comparing with population + std::vector pop_dist(pop.size()); + sorted_pop_indexes.resize(pop.size()); + tbb::parallel_for(tbb::blocked_range(0,pop.size()), + [&](tbb::blocked_range r){ + for(size_t i = r.begin(); i != r.end(); i++){ + sorted_pop_indexes[i] = i; + if(pop[i] == ind_desc) + pop_dist[i] = 1.; + else + pop_dist[i] = (pop[i] - ind_desc).norm(); + dist[i+archive.size()] = pop_dist[i]; + } + }); + + //sort indexes of population from closest to farthest of ind_desc. + std::sort(sorted_pop_indexes.begin(),sorted_pop_indexes.end(),[&](size_t a, size_t b){ + return pop_dist[a] < pop_dist[b]; + }); + std::sort(dist.begin(),dist.end()); // Sorting distances + + return dist; +} + +void Novelty::update_archive(const Eigen::VectorXd &ind_desc, + double ind_nov, + std::vector &archive, + const RandNum::Ptr &rn){ + + if(ind_nov > novelty_thr || rn->randInt(0,1) < archive_adding_prob){ + archive.push_back(ind_desc); + } + +} diff --git a/cpprevolve/revolve/brains/learner/EA_misc/Novelty.h b/cpprevolve/revolve/brains/learner/EA_misc/Novelty.h new file mode 100644 index 0000000000..dbe714881a --- /dev/null +++ b/cpprevolve/revolve/brains/learner/EA_misc/Novelty.h @@ -0,0 +1,68 @@ +// +// Created by fuda on 12/7/20. +// + +#ifndef REVOLVE_NOVELTY_H +#define REVOLVE_NOVELTY_H + +#include "../EA.h" +#include +#include +#include "RandNum.h" + +namespace revolve { + + struct Novelty { + + /** + * @brief compute sparseness from a given list of distances and k_value a static parameter + * @param a sorted list of distances. + * @return sparseness + */ + static double sparseness(const std::vector &dist); + + /** + * @brief add ind to the archive if its novelty score is above a threshold or according a certain probability + * @param individual + * @param novelty score of the individual + * @param archive + * @param seed for the add archive probability + */ + static void update_archive(const Eigen::VectorXd& ind_desc, + double ind_nov, + std::vector &archive, + const RandNum::Ptr &rn); + + + /** + * @brief Compute distances of a descriptor to the archive and a population. And return the vector of distances sorted in increasing order. + * @param descriptor + * @param archive of descriptor + * @param population + * @return vector of distances + */ + static std::vector distances(const Eigen::VectorXd& desc, + const std::vector &archive, + const std::vector &pop); + + /** + * @brief Compute distances of a descriptor to the archive and a population. And return the vector of distances sorted in increasing order. + * @param descriptor + * @param archive of descriptor + * @param population + * @param output : sorted indexes of the population from the closest to the farthest of the desc + * @return vector of distances + */ + static std::vector distances(const Eigen::VectorXd& desc, + const std::vector &archive, + const std::vector &pop, + std::vector & sorted_pop_indexes); + + + static int k_value; + static double novelty_thr; + static double archive_adding_prob; + }; +} + +#endif //REVOLVE_NOVELTY_H diff --git a/cpprevolve/revolve/brains/learner/EA_misc/RandNum.cpp b/cpprevolve/revolve/brains/learner/EA_misc/RandNum.cpp new file mode 100644 index 0000000000..d8d6fb2f5c --- /dev/null +++ b/cpprevolve/revolve/brains/learner/EA_misc/RandNum.cpp @@ -0,0 +1,61 @@ +#include "RandNum.h" +#include +#include + +using namespace revolve; + +RandNum::RandNum(int seed) +{ + gen.seed(seed); + std::cout << "seed set to " << seed << std::endl; + m_seed = seed; +} + + +RandNum::~RandNum() += default; + +double RandNum::randDouble(double lower, double upper) { + std::uniform_real_distribution<> dist(lower,upper); + return dist(gen); +} + + +float RandNum::randFloat(float lower, float upper) { + std::uniform_real_distribution<> dist(lower,upper); + return dist(gen); +} + +int RandNum::randInt(int lower, int upper) { + std::uniform_int_distribution<> dist(lower,upper); + return dist(gen); +} + +std::vector RandNum::randVectd(double lower, double upper, int size){ + std::vector res; + for(int i = 0; i < size; i++) + res.push_back(randDouble(lower,upper)); + return res; +} + +Eigen::ArrayXXi RandNum::Bernoulli(double p, const int shape[2]) { + Eigen::ArrayXXd bernoulli = Eigen::MatrixXd::Random(shape[0], shape[1]).array(); + return (bernoulli.abs()(); +} + +double RandNum::normalDist(double mu, double sigma){ + std::normal_distribution<> nd(mu,sigma); + return nd(gen); +} + + +void RandNum::setSeed(int seed) { + gen.seed(seed); + m_seed = seed; + std::cout << "Seed set to " << seed << std::endl; +} + +int RandNum::getSeed() +{ + return m_seed; +} diff --git a/cpprevolve/revolve/brains/learner/EA_misc/RandNum.h b/cpprevolve/revolve/brains/learner/EA_misc/RandNum.h new file mode 100644 index 0000000000..60206471aa --- /dev/null +++ b/cpprevolve/revolve/brains/learner/EA_misc/RandNum.h @@ -0,0 +1,71 @@ +#pragma once +#ifndef RANDNUM_H +#define RANDNUM_H + +#include +#include +#include + +namespace revolve { + +class RandNum +{ +public: + typedef std::shared_ptr Ptr; + typedef std::shared_ptr ConstPtr; + + explicit RandNum(int seed); // instantiate the class and specify the initial seed. + ~RandNum(); + + /** + * @brief random number between a lower bound and a upper bound included + * @param lower bound (double) + * @param upper bound (double) + * @return a double + */ + double randDouble(double lower, double upper); // creates a random float between two specified values. + + /** + * @brief random number between a lower bound and a upper bound included + * @param lower bound (float) + * @param upper bound (float) + * @return a float + */ + float randFloat(float lower, float upper); // creates a random float between two specified values. + + /** + * @brief random number between a lower bound and a upper bound included + * @param lower bound (int) + * @param upper bound (int) + * @return an integer + */ + int randInt(int lower, int upper); // creates a random integer (range, offset) + + /** + * @brief Generate a random vector of double + * @param lower bound + * @param upper bound (included + * @param size of the vector + * @return + */ + std::vector randVectd(double lower, double upper, int size); + + /** + * @brief Generate a random number from a normal distribution + * @param mean of the distribution + * @param variance of the distribution + * @return + */ + double normalDist(double mu, double sigma); + + void setSeed(int seed); // sets the seed of the random number generator + int m_seed = 0; + int getSeed(); + std::mt19937 gen; + + Eigen::ArrayXXi Bernoulli(double p, const int *len); +}; + +}//EA_misc + +#endif //RANDNUM_H diff --git a/cpprevolve/revolve/brains/learner/EvaluationReporter.h b/cpprevolve/revolve/brains/learner/EvaluationReporter.h new file mode 100644 index 0000000000..908ea754f0 --- /dev/null +++ b/cpprevolve/revolve/brains/learner/EvaluationReporter.h @@ -0,0 +1,93 @@ +// +// Created by matteo on 12/5/19. +// + +#pragma once + +#include +#include +#include +#include + +namespace revolve { + +// -------------------------------------------------------- +/// Reporter Abstract Class +class EvaluationReporter +{ +public: + explicit EvaluationReporter(const std::string id) + : robot_id(std::move(id)) + {} + + virtual ~EvaluationReporter() = default; + + virtual void report( + unsigned int eval, + bool dead, + double fitness) = 0; + + const std::string robot_id; +}; + +// -------------------------------------------------------- +/// Simple Reporter that prints the reported data on stdout +class PrintReporter : public EvaluationReporter +{ +public: + explicit PrintReporter(const std::string id) + : EvaluationReporter(std::move(id)) + {} + + ~PrintReporter() override = default; + + void report(unsigned int eval, + bool dead, + double fitness) override + { + std::cout << "Evaluation Report: robot id("<< robot_id + << ") eval(" << eval + << ") dead(" << dead + << ") fitness(" << fitness + << ')' << std::endl; + } +}; + +// -------------------------------------------------------- +/// Aggregated Reporter +class AggregatedReporter : public EvaluationReporter +{ +public: + explicit AggregatedReporter(const std::string robot_id) + : EvaluationReporter(std::move(robot_id)) + {} + ~AggregatedReporter() override = default; + + void report(unsigned int eval, + bool dead, + double fitness) override + { + for (std::shared_ptr &reporter: reporters) + { + reporter->report(eval, dead, fitness); + } + } + + /// Create a new Reporter in place + template + void create(Args &&... args) + { + reporters.emplace_back(new ReporterType(robot_id, std::forward(args)...)); + } + + void append(std::shared_ptr reporter) + { + assert(reporter->robot_id == this->robot_id); + reporters.emplace_back(std::move(reporter)); + } + +private: + std::vector> reporters; +}; + +} diff --git a/cpprevolve/revolve/brains/learner/Evaluator.h b/cpprevolve/revolve/brains/learner/Evaluator.h new file mode 100644 index 0000000000..be7a3cedf5 --- /dev/null +++ b/cpprevolve/revolve/brains/learner/Evaluator.h @@ -0,0 +1,33 @@ +// +// Created by andi on 27-11-19. +// + +#ifndef REVOLVE_EVALUATOR_H +#define REVOLVE_EVALUATOR_H + +namespace revolve { +class Evaluator +{ + /// \brief Constructor +public: + Evaluator() = default; + + /// \brief Destructor + virtual ~Evaluator() = default; + + /// \brief Initialisation method + virtual void reset() = 0; + + /// \brief Retrieve the fitness + /// \return A fitness value according to a given formula + virtual double fitness() = 0; + + /// \brief Update the position + /// \param[in] _pose Current position of a robot + //virtual void update(const ignition::math::Pose3d &_pose, + // const double time, + // const double step) = 0; +}; +} + +#endif //REVOLVE_EVALUATOR_H \ No newline at end of file diff --git a/cpprevolve/revolve/brains/learner/HyperNEAT.cpp b/cpprevolve/revolve/brains/learner/HyperNEAT.cpp new file mode 100644 index 0000000000..4b2a25f74f --- /dev/null +++ b/cpprevolve/revolve/brains/learner/HyperNEAT.cpp @@ -0,0 +1,101 @@ +// +// Created by matteo on 8/21/19. +// + +#include "HyperNEAT.h" +#include "../controller/DifferentialCPG.h" + +using namespace revolve; + +HyperNEAT::HyperNEAT( + std::unique_ptr controller, + Evaluator *evaluator, + EvaluationReporter *reporter, + const NEAT::Parameters ¶ms, + const int seed, + const double evaluation_time, + unsigned int n_evaluations) + : Learner(evaluator, reporter, evaluation_time, n_evaluations) + , _controller(std::move(controller)) + , params(params) + , population(nullptr) +{ + NEAT::Genome start_genome(0, 3, 0, 1, //TODO these are also parameters + false, + NEAT::UNSIGNED_SIGMOID, + NEAT::UNSIGNED_SIGMOID, + 0, + this->params, + 0); + + population.reset(new NEAT::Population( + start_genome, + params, + true, + 1.0, + seed + )); + assert(this->_controller && "HyperNEAT: passed null controller"); + switch (this->_controller->controller_type) + { + case revolve::Controller::DIFFERENTIAL_CPG: + load_genome = [this](std::vector::iterator config_cppn_genome) + { + auto *temp_controller = dynamic_cast<::revolve::DifferentialCPG *>(this->_controller.get()->into_DifferentialCPG()); + temp_controller->load_genome_to_controller(*config_cppn_genome); + }; + break; + default: + std::cerr << "Controller not supported" << std::endl; + throw std::runtime_error("Controller not supported"); + } +} + +void HyperNEAT::init_first_controller() +{ + current_specie_evaluating = population->m_Species.begin(); + current_genome_evaluating = current_specie_evaluating->m_Individuals.begin(); + + //TODO load genome in controller + this->load_genome(current_genome_evaluating); +} + +void HyperNEAT::init_next_controller() +{ + // load next genome + current_genome_evaluating++; + + // Finished a species + if (current_genome_evaluating == current_specie_evaluating->m_Individuals.end()) + { + current_specie_evaluating++; + + // Finished all species -> Generate new generation + if (current_specie_evaluating == population->m_Species.end()) + { + population->Epoch(); + current_specie_evaluating = population->m_Species.begin(); + } + + current_genome_evaluating = current_specie_evaluating->m_Individuals.begin(); + } + //TODO load genome in controller + this->load_genome(current_genome_evaluating); +} + +void HyperNEAT::finalize_current_controller(double fitness) +{ + current_genome_evaluating->SetFitness(fitness); + if(fitness>best_fitness) + { + this->best_fitness = fitness; + this->best_genome = current_genome_evaluating; + } +} + +void HyperNEAT::load_best_controller() +{ + //TODO load best genome into controller +// this->load_genome(current_genome_evaluating); + this->load_genome(this->best_genome); +} diff --git a/cpprevolve/revolve/brains/learner/HyperNEAT.h b/cpprevolve/revolve/brains/learner/HyperNEAT.h new file mode 100644 index 0000000000..83f4033fcb --- /dev/null +++ b/cpprevolve/revolve/brains/learner/HyperNEAT.h @@ -0,0 +1,51 @@ +// +// Created by matteo on 8/21/19. +// + +#pragma once + +#include "Learner.h" +#include "Evaluator.h" +#include +#include + +namespace revolve { + +class HyperNEAT: public Learner +{ +public: + explicit HyperNEAT( + std::unique_ptr controller, + Evaluator *evaluator, + EvaluationReporter *reporter, + const NEAT::Parameters ¶ms, + int seed, + double evaluation_time, + unsigned int n_evaluations); + + ~HyperNEAT() override = default; + + Controller *controller() override + { return _controller.get(); } + + void init_first_controller() override; + void init_next_controller() override; + void finalize_current_controller(double fitness) override; + void load_best_controller() override; + +private: + std::unique_ptr _controller; + + const NEAT::Parameters params; + std::unique_ptr population; + std::vector::iterator current_specie_evaluating; + std::vector::iterator current_genome_evaluating; + double best_fitness = -std::numeric_limits::infinity(); + std::vector::iterator best_genome; + +protected: + /// \brief function to load a genome into a controller + std::function::iterator)> load_genome; +}; + +} diff --git a/cpprevolve/revolve/brains/learner/Learner.cpp b/cpprevolve/revolve/brains/learner/Learner.cpp new file mode 100644 index 0000000000..e60be1260c --- /dev/null +++ b/cpprevolve/revolve/brains/learner/Learner.cpp @@ -0,0 +1,46 @@ +// +// Created by matteo on 12/6/19. +// + +#include "Learner.h" + +using namespace revolve; + +void Learner::optimize(double time, double /*dt*/) +{ + if (time < end_controller_time) return; + + bool finished = evaluation_counter >= static_cast(n_evaluations); + if (finished) + { + evaluation_reporter->report(evaluation_counter, true, evaluator->fitness()); + } + else + { + std::cout << "Learner evaluation_counter: " << evaluation_counter + 1 << std::endl; + // first evaluation + if (evaluation_counter < 0) { + evaluation_counter = 0; + this->init_first_controller(); + } else { + // finalize previous run + evaluation_counter++; + double fitness = evaluator->fitness(); + finished = evaluation_counter >= static_cast(n_evaluations); + + evaluation_reporter->report(evaluation_counter, finished, fitness); + this->finalize_current_controller(fitness); + std::cout << "Fitness: " << fitness << std::endl; + + // load next genome + if (finished) { + this->load_best_controller(); + } else { + this->init_next_controller(); + } + } + } + + evaluator->reset(); + end_controller_time = time + evaluation_time; +} diff --git a/cpprevolve/revolve/brains/learner/Learner.h b/cpprevolve/revolve/brains/learner/Learner.h new file mode 100644 index 0000000000..fbd2e0c5e5 --- /dev/null +++ b/cpprevolve/revolve/brains/learner/Learner.h @@ -0,0 +1,56 @@ +// +// Created by andi on 25-11-19. +// + +#pragma once + +#include +#include "Evaluator.h" +#include "EvaluationReporter.h" +#include "../controller/Controller.h" + +namespace revolve { + +class Learner +{ +public: + /// \brief Constructor + explicit Learner( + Evaluator *const evaluator, + EvaluationReporter *const reporter, + const double evaluation_time, + const unsigned int n_evaluations) + : evaluation_time(evaluation_time) + , end_controller_time(-std::numeric_limits::infinity()) + , evaluation_counter(-1) + , n_evaluations(n_evaluations) + , evaluator(evaluator) + , evaluation_reporter(reporter) + {} + + /// \brief Deconstructor + virtual ~Learner() = default; + + /// \brief performes the optimization of the controller + virtual void optimize(double time, double dt); + virtual void init_first_controller() = 0; + virtual void init_next_controller() = 0; + virtual void finalize_current_controller(double fitness) = 0; + virtual void load_best_controller() = 0; + + virtual revolve::Controller *controller() = 0; + +protected: + const double evaluation_time; + double end_controller_time; + + /// \brief Learning iterations counter + long evaluation_counter; + /// \brief Max number of learning iterations + const unsigned int n_evaluations; + + revolve::Evaluator *evaluator; + revolve::EvaluationReporter *evaluation_reporter; +}; + +} diff --git a/cpprevolve/revolve/brains/learner/NIPES.cpp b/cpprevolve/revolve/brains/learner/NIPES.cpp new file mode 100644 index 0000000000..aa5935cc38 --- /dev/null +++ b/cpprevolve/revolve/brains/learner/NIPES.cpp @@ -0,0 +1,337 @@ +// +// Created by matteo on 8/21/19. +// + +#include "NIPES.h" + +#include +#include "../controller/DifferentialCPG.h" +#include "EA.h" + +namespace revolve { + +std::map IPOPCMAStrategy::scriterias = {{cma::CONT, "OK"}, + {cma::AUTOMAXITER, "The automatically set maximal number of iterations per run has been reached"}, + {cma::TOLHISTFUN, "[Success] The optimization has converged"}, + {cma::EQUALFUNVALS, "[Partial Success] The objective function values are the same over too many iterations, check the formulation of your objective function"}, + {cma::TOLX, "[Partial Success] All components of covariance matrix are very small (e.g. < 1e-12)"}, + {cma::TOLUPSIGMA, "[Error] Mismatch between step size increase and decrease of all eigenvalues in covariance matrix. Try to restart the optimization."}, + {cma::STAGNATION, "[Partial Success] Median of newest values is not smaller than the median of older values"}, + {cma::CONDITIONCOV, "[Error] The covariance matrix's condition numfber exceeds 1e14. Check out the formulation of your problem"}, + {cma::NOEFFECTAXIS, "[Partial Success] Mean remains constant along search axes"}, + {cma::NOEFFECTCOOR, "[Partial Success] Mean remains constant in coordinates"}, + {cma::MAXFEVALS, "The maximum number of function evaluations allowed for optimization has been reached"}, + {cma::MAXITER, "The maximum number of iterations specified for optimization has been reached"}, + {cma::FTARGET, "[Success] The objective function target value has been reached"}}; + +bool IPOPCMAStrategy::reach_ftarget() { + cma::CMAES_LOG_IF(cma::INFO, !_parameters.quiet()) << "Best fitness : " << best_fitnesses.back() << std::endl; + + if (_parameters.get_ftarget() != std::numeric_limits::infinity()) { + if (best_fitnesses.back() <= _parameters.get_ftarget()) { + std::stringstream sstr; + sstr << "stopping criteria fTarget => fvalue=" << best_fitnesses.back() << " / ftarget=" + << _parameters.get_ftarget(); + log_stopping_criterias.push_back(sstr.str()); + cma::CMAES_LOG_IF(cma::INFO, !_parameters.quiet()) << sstr.str() << std::endl; + return true; + } + } + return false; +} + +bool IPOPCMAStrategy::pop_desc_stagnation() { + std::vector descriptors; + for (const auto &ind: _pop) + descriptors.push_back(std::dynamic_pointer_cast(ind)->descriptor()); + + Eigen::VectorXd mean = Eigen::VectorXd::Zero(_pop[0]->genome.size()); + for (Eigen::VectorXd desc : descriptors) { + mean += desc; + } + mean = mean / static_cast(descriptors.size()); + + Eigen::VectorXd stddev = Eigen::VectorXd::Zero(_pop[0]->genome.size()); + for (Eigen::VectorXd desc : descriptors) + stddev += (desc - mean).cwiseProduct(desc - mean); + + bool stop = true; + for (int i = 0; i < stddev.rows(); i++) + stop = stop && sqrt(stddev(i / static_cast(descriptors.size() - 1))) <= pop_stag_thres; + + if (stop) { + std::stringstream sstr; + sstr << "Stopping : standard deviation of the descriptor population is smaller than " << pop_stag_thres + << " : " << stddev; + log_stopping_criterias.push_back(sstr.str()); + cma::CMAES_LOG_IF(cma::INFO, !_parameters.quiet()) << sstr.str() << std::endl; + } + return stop; +} + +bool IPOPCMAStrategy::pop_fit_stagnation() { + std::vector fvalues; + for (const auto &ind : _pop) + fvalues.push_back(ind->getFitness()); + + + double mean = 0.0; + for (double fv : fvalues) + mean += fv; + mean = mean / static_cast(fvalues.size()); + + double stddev = 0.0; + for (double fv : fvalues) + stddev += (fv - mean) * (fv - mean); + + stddev = sqrt(stddev / static_cast(fvalues.size() - 1)); + cma::CMAES_LOG_IF(cma::INFO, !_parameters.quiet()) << "pop standard deviation : " << stddev << std::endl; + + if (stddev <= pop_stag_thres) { + std::stringstream sstr; + sstr << "Stopping : standard deviation of the population is smaller than 0.05 : " << stddev; + log_stopping_criterias.push_back(sstr.str()); + cma::CMAES_LOG_IF(cma::INFO, !_parameters.quiet()) << sstr.str() << std::endl; + return true; + } else return false; +} + +bool IPOPCMAStrategy::best_sol_stagnation() { + if (best_fitnesses.size() < len_of_stag) + return false; + double mean = 0.0; + for (size_t i = best_fitnesses.size() - len_of_stag; i < best_fitnesses.size(); i++) { + mean += best_fitnesses[i]; + } + mean = mean / static_cast(len_of_stag); + double stddev = 0.0; + for (size_t i = best_fitnesses.size() - len_of_stag; i < best_fitnesses.size(); i++) { + stddev += (best_fitnesses[i] - mean) * (best_fitnesses[i] - mean); + } + stddev = sqrt(stddev / static_cast(len_of_stag - 1)); + + if (stddev <= 0.05) { + std::stringstream sstr; + + sstr << "Stopping : standard deviation of the last " << len_of_stag + << " best fitnesses is smaller than 0.05 : " << stddev; + log_stopping_criterias.push_back(sstr.str()); + cma::CMAES_LOG_IF(cma::INFO, !_parameters.quiet()) << sstr.str() << std::endl; + return true; + } else return false; +} + +void IPOPCMAStrategy::eval(const dMat &candidates, const dMat &phenocandidates) { + // custom eval. + _solutions.candidates().clear(); + for (Individual::indPtr &r : _pop) { + dVec x; + x = r->genome; + double fvalue = r->getFitness(); + _solutions.candidates().push_back(cma::Candidate(fvalue, x)); + } + update_fevals(candidates.cols()); +} + +void IPOPCMAStrategy::tell() { + ipop_cmaes_t::tell(); + std::vector best_sample; + best_fitnesses.push_back(best_fitness(best_sample)); + if (novelty_ratio > 0) + novelty_ratio -= novelty_decr; + if (best_fitnesses.back() < best_seen_solution.first || best_fitnesses.size() == 1) + best_seen_solution = std::make_pair(best_fitnesses.back(), best_sample); + inc_iter(); +} + +bool IPOPCMAStrategy::stop() { + reached_ft = reach_ftarget(); + bool ipop_stop = ipop_cmaes_t::stop(); + bool pop_stag = pop_desc_stagnation(); + bool fit_stag = pop_fit_stagnation(); + bool best_sol_stag = false; + if (len_of_stag > 0) + best_sol_stag = best_sol_stagnation(); + + if (ipop_stop) { + log_stopping_criterias.push_back(scriterias[_solutions.run_status()]); + } + return pop_stag || best_sol_stag || ipop_stop || fit_stag; +} + +void IPOPCMAStrategy::reset_search_state() { + if (elitist_restart) + _parameters.set_x0(best_seen_solution.second, best_seen_solution.second); + + ipop_cmaes_t::reset_search_state(); + novelty_ratio = start_novelty_ratio; + best_fitnesses.clear(); +} + +double IPOPCMAStrategy::best_fitness(std::vector &best_sample) { + double bf = -std::numeric_limits::infinity(); + for (const auto &ind : _pop) { + if (bf < ind->getFitness()) { + bf = ind->getFitness(); + best_sample = ind->get_ctrl_genome(); + } + } + + return bf; +} + +/// ################### ARE: top ################# +NIPES::NIPES(std::unique_ptr controller, + Evaluator *evaluator, + EvaluationReporter *reporter, + const NIPES::NIPES_Parameters ¶ms, + int seed, + double evaluation_time, + unsigned int n_learning_evaluations, + const std::string &model_name) + : EA(std::move(controller), evaluator, reporter, + params.EA_params, seed, evaluation_time, + n_learning_evaluations, + model_name) { + + int lenStag = Nipes_Param.stagnation_length; + double step_size = Nipes_Param.CMAES_step; +// double ftarget = Nipes_Param.ftarget; + bool elitist_restart = Nipes_Param.elitist_restart; + double novelty_ratio = Nipes_Param.novelty_ratio; + double novelty_decr = Nipes_Param.novelty_decrement; + float pop_stag_thres = Nipes_Param.population_stagnation_threshold; + + Novelty::k_value = Nipes_Param.novelty_k_value; + Novelty::novelty_thr = Nipes_Param.novelty_threshold; + Novelty::archive_adding_prob = Nipes_Param.novelty_archive_probability; + + int n_params = this->get_genome().size(); + + std::vector initial_point = randomNum->randVectd(EA_Params.min_weight, EA_Params.max_weight, n_params); + + double lb[n_params], ub[n_params]; + for (int i = 0; i < n_params; i++) { + lb[i] = EA_Params.min_weight; + ub[i] = EA_Params.max_weight; + } + + geno_pheno_t gp(lb, ub, n_params); + + cma::CMAParameters cmaParam(initial_point, step_size, EA_Params.population_size, + randomNum->getSeed(), gp); +// cmaParam.set_ftarget(ftarget); + cmaParam.set_quiet(!EA_Params.verbose); + + cmaStrategy.reset(new IPOPCMAStrategy([](const double *, const int &) -> double {}, cmaParam)); + cmaStrategy->set_elitist_restart(elitist_restart); + cmaStrategy->set_length_of_stagnation(lenStag); + cmaStrategy->set_novelty_ratio(novelty_ratio); + cmaStrategy->set_novelty_decr(novelty_decr); + cmaStrategy->set_pop_stag_thres(pop_stag_thres); + + dMat init_samples = cmaStrategy->ask(); + + std::vector genome(n_params); + + for (int u = 0; u < EA_Params.population_size; u++) { + + for (int v = 0; v < n_params; v++) + genome[v] = init_samples(v, u); + + Individual::indPtr ind(new Individual()); + ind->setGenome(genome); + population.push_back(ind); + } + std::cout << "[NIPES] population initialized" << std::endl; +} + +NIPES::~NIPES() { + cmaStrategy.reset(); +} + +void NIPES::epoch() { + bool withRestart = Nipes_Param.restart; + bool incrPop = Nipes_Param.incremental_population; + bool elitist_restart = Nipes_Param.elitist_restart; + + /** NOVELTY **/ + if (Nipes_Param.novelty_ratio > 0.) { + if (Novelty::k_value >= population.size()) + Novelty::k_value = population.size() / 2; + else Novelty::k_value = Nipes_Param.novelty_k_value; + + std::vector pop_desc; + for (const auto &ind : population) + pop_desc.push_back(ind->descriptor()); + //compute novelty + for (const auto &ind : population) { + Eigen::VectorXd ind_desc = ind->descriptor(); + double ind_nov = Novelty::sparseness(Novelty::distances(ind_desc, archive, pop_desc)); + ind->setNovelty(ind_nov); + } + + //update archive + for (const auto &ind : population) { + Eigen::VectorXd ind_desc = ind->descriptor(); + double ind_nov = ind->get_ctrl_novelty(); + Novelty::update_archive(ind_desc, ind_nov, archive, randomNum); + } + } + /**/ + + cmaStrategy->set_population(population); + cmaStrategy->eval(); + cmaStrategy->tell(); + bool stop = cmaStrategy->stop(); +// if(cmaStrategy->have_reached_ftarget()){ +// _is_finish = true; +//// return; +// } + + if (withRestart && stop) { + if (EA_Params.verbose) + std::cout << "Restart !" << std::endl; + + cmaStrategy->capture_best_solution(best_run); + + if (incrPop) + cmaStrategy->lambda_inc(); + + cmaStrategy->reset_search_state(); + if (!elitist_restart) { + cmaStrategy->get_parameters().set_x0(EA_Params.min_weight, EA_Params.max_weight); + } + } +} + +void NIPES::init_next_pop() { + int pop_size = cmaStrategy->get_parameters().lambda(); + + dMat new_samples = cmaStrategy->ask(); + + int n_param = population[0]->get_ctrl_genome().size(); + std::vector genome(n_param); + + population.clear(); + for (int i = 0; i < pop_size; i++) { + + for (int j = 0; j < n_param; j++) + genome[j] = new_samples(j, i); + + Individual::indPtr ind(new Individual()); + ind->setGenome(genome); + population.push_back(ind); + } +} + + +bool NIPES::is_finish() { + int maxNbrEval = EA_Params.max_eval; + return _is_finish || numberEvaluation >= maxNbrEval; +} + +bool NIPES::finish_eval() { + return EA::finish_eval(); +} +}; \ No newline at end of file diff --git a/cpprevolve/revolve/brains/learner/NIPES.h b/cpprevolve/revolve/brains/learner/NIPES.h new file mode 100644 index 0000000000..8ab3192004 --- /dev/null +++ b/cpprevolve/revolve/brains/learner/NIPES.h @@ -0,0 +1,180 @@ +// +// Created by matteo on 8/21/19. +// + +#pragma once +#ifndef NIPES_HPP +#define NIPES_HPP + +#include "Learner.h" +#include "Evaluator.h" +#include "EA.h" + +#include + +namespace cma = libcmaes; +using geno_pheno_t = cma::GenoPheno; +using cov_update_t = cma::CovarianceUpdate; +using ipop_cmaes_t = cma::IPOPCMAStrategy; +using eostrat_t = cma::ESOStrategy,cma::CMASolutions,cma::CMAStopCriteria>; + +namespace revolve { + + class IPOPCMAStrategy : public ipop_cmaes_t + { + private: +// cma::FitFunc emptyObj = [](const double*,const int&) -> double{}; + + + public: + + static std::map scriterias; + + typedef std::shared_ptr Ptr; + typedef std::shared_ptr ConstPtr; + + IPOPCMAStrategy(cma::FitFunc func,cma::CMAParameters ¶meters) + :ipop_cmaes_t(func, parameters) + { +// std::cout<<"[[IPOPCMA] Constructor"<& pop){_pop = pop;} + void set_elitist_restart(bool er){elitist_restart = er;} + void set_length_of_stagnation(int los){len_of_stag = los;} + void set_novelty_ratio(double nr){novelty_ratio = nr; start_novelty_ratio = nr;} + void set_novelty_decr(double nd){novelty_decr = nd;} + void set_pop_stag_thres(float pst){pop_stag_thres = pst;} + + bool have_reached_ftarget(){return reached_ft;} + + std::vector log_stopping_criterias; + + + private: + std::vector _pop; + bool elitist_restart = false; + std::vector best_fitnesses; + std::pair> best_seen_solution; + int len_of_stag; + float pop_stag_thres; + double novelty_ratio; + double start_novelty_ratio; + double novelty_decr ; + + + + double best_fitness(std::vector &); + + bool reached_ft = false; + + }; + + + + +class NIPES: public EA +{ +public: + struct NIPES_Parameters{ + int stagnation_length = 20; + bool elitist_restart = true; + double CMAES_step = 1.0; + + int novelty_k_value = 15; + double novelty_ratio = 1.; + double novelty_decrement = 0.05; + double novelty_threshold = 0.9; + double novelty_archive_probability = 0.4; + + float population_stagnation_threshold = 0.05; + + bool restart = true; + bool incremental_population = true; + + EA::Parameters EA_params; + }; + + explicit NIPES(std::unique_ptr controller, + Evaluator *evaluator, + EvaluationReporter *reporter, + const NIPES::NIPES_Parameters ¶ms, + int seed, + double evaluation_time, + unsigned int n_learning_evaluations, + const std::string& model_name); + + Controller *controller() override + { return _controller.get(); } + + ~NIPES(); + + + void epoch() override; + void init_next_pop() override; + + bool is_finish() override; + bool finish_eval() override; + + bool restarted(){return !cmaStrategy->log_stopping_criterias.empty();} + std::string pop_stopping_criterias(){ + std::string res = cmaStrategy->log_stopping_criterias.back(); + cmaStrategy->log_stopping_criterias.pop_back(); + return res; + } + const std::vector &get_archive(){return archive;} + +protected: + IPOPCMAStrategy::Ptr cmaStrategy; + cma::CMASolutions best_run; + bool _is_finish = false; + std::vector archive; + int reevaluated = 0; + NIPES::NIPES_Parameters Nipes_Param; +}; +} + + +#endif //NIPES_HPP \ No newline at end of file diff --git a/cpprevolve/revolve/brains/learner/NoLearner.h b/cpprevolve/revolve/brains/learner/NoLearner.h new file mode 100644 index 0000000000..019038e8d1 --- /dev/null +++ b/cpprevolve/revolve/brains/learner/NoLearner.h @@ -0,0 +1,41 @@ +// +// Created by matteo on 11/22/19. +// +#pragma once + +#include +#include "Learner.h" +#include "../controller/DifferentialCPG.h" + +namespace revolve { + +template +class NoLearner : public Learner +{ +public: + explicit NoLearner(std::unique_ptr controller) + : Learner(nullptr, nullptr, 0, 0) //TODO add report + , _controller(std::move(controller)) + {} + + // This is inspired from the GNU `std::make_unique` source code + template + explicit NoLearner(_Args &&... args) + : Learner(nullptr, nullptr, 0, 0) //TODO add report + , _controller(new ControllerType(std::forward<_Args>(args)...)) + {} + + void optimize(double /*time*/, double /*dt*/) override {} + void init_first_controller() override {} + void init_next_controller() override {} + void finalize_current_controller(double /*fitness*/) override {} + void load_best_controller() override {} + + Controller *controller() override + { return this->_controller.get(); } + +protected: + std::unique_ptr _controller; +}; + +} diff --git a/cpprevolve/revolve/gazebo/CMakeLists.txt b/cpprevolve/revolve/gazebo/CMakeLists.txt index e01a3970fa..37cbce5ad5 100644 --- a/cpprevolve/revolve/gazebo/CMakeLists.txt +++ b/cpprevolve/revolve/gazebo/CMakeLists.txt @@ -78,7 +78,7 @@ if (LOCAL_GAZEBO_DIR) NO_DEFAULT_PATH) message(WARNING "Using local Gazebo @ ${gazebo_DIR}") else() - find_package(gazebo 9 REQUIRED) + find_package(gazebo 10 REQUIRED) endif() include_directories(${GAZEBO_INCLUDE_DIRS}) link_directories(${GAZEBO_LIBRARY_DIRS}) @@ -201,6 +201,7 @@ add_library( target_link_libraries( revolve-gazebo revolve-controllers + revolve-learners ${GAZEBO_LIBRARIES} ${Boost_LIBRARIES} ${GSL_LIBRARIES} @@ -226,6 +227,19 @@ target_link_libraries( ${GAZEBO_LIBRARIES} ) +# Create Realtime World plugin +add_library( + RealtimeWorldControlPlugin SHARED + plugin/RealtimeWorldController.cpp + plugin/register_realtime_world_plugin.cpp +) +target_link_libraries( + RealtimeWorldControlPlugin + revolve-gazebo + revolve-proto + ${GAZEBO_LIBRARIES} +) + # Create Analyzer plugin add_library( AnalyzerPlugin SHARED diff --git a/cpprevolve/revolve/gazebo/brains/Brains.h b/cpprevolve/revolve/gazebo/brains/Brains.h index c2bec687a1..81b978f76a 100644 --- a/cpprevolve/revolve/gazebo/brains/Brains.h +++ b/cpprevolve/revolve/gazebo/brains/Brains.h @@ -25,7 +25,7 @@ #include #include #include -#include +#include #include #endif // REVOLVE_GAZEBO_BRAINS_BRAINS_H_ diff --git a/cpprevolve/revolve/gazebo/brains/DifferentialCPG.cpp b/cpprevolve/revolve/gazebo/brains/DifferentialCPG.cpp index 99b75e8233..5892c797f6 100644 --- a/cpprevolve/revolve/gazebo/brains/DifferentialCPG.cpp +++ b/cpprevolve/revolve/gazebo/brains/DifferentialCPG.cpp @@ -17,1175 +17,65 @@ * Author: Milan Jelisavcic & Maarten van Hooft * Date: December 29, 2018 * + * Cleaned up by andi on 06-10-19. + * */ -// STL macros -#include -#include -#include -#include -#include -#include -#include -#include - -// Other libraries -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -// Project headers -#include "../motors/Motor.h" - -#include "../sensors/Sensor.h" - #include "DifferentialCPG.h" -#include "DifferentialCPG_BO.h" - -// TODO: Resolve odd behaviour at the end of the validation procedure -// This behaviour is not present if you directly load a trained controller - -// Define namespaces -namespace gz = gazebo; using namespace revolve::gazebo; -// Copied from the limbo tutorial the BO implementation is based on -using Mean_t = limbo::mean::Data; -using Init_t = limbo::init::LHS; -using Kernel_t = limbo::kernel::MaternFiveHalves; -using GP_t = limbo::model::GP; - -/** - * Constructor for DifferentialCPG class. - * - * @param _model - * @param robot_config - */ -DifferentialCPG::DifferentialCPG( - const ::gazebo::physics::ModelPtr &_model, - const sdf::ElementPtr robot_config, - const std::vector< revolve::gazebo::MotorPtr > &_motors, - const std::vector< revolve::gazebo::SensorPtr > &_sensors) - : next_state(nullptr) - , input(new double[_sensors.size()]) - , output(new double[_motors.size()]) -{ - - this->learner = robot_config->GetElement("rv:brain")->GetElement("rv:learner"); - - // Check for brain - if (not robot_config->HasElement("rv:brain")) - { - throw std::runtime_error("DifferentialCPG brain did not receive brain"); - } - auto brain = robot_config->GetElement("rv:brain"); - - // Check for learner - if (not brain->HasElement("rv:learner")) - { - throw std::runtime_error("DifferentialCPG brain did not receive learner"); - } - auto learner = brain->GetElement("rv:learner"); - - // Check for controller - if (not brain->HasElement("rv:controller")) - { - throw std::runtime_error("DifferentialCPG brain did not receive controller"); - } - auto controller = brain->GetElement("rv:controller"); - - // Check for actuators - if (not brain->HasElement("rv:actuators")) - { - throw std::runtime_error("DifferentialCPG brain did not receive actuators"); - } - auto actuators = brain->GetElement("rv:actuators"); - - // Controller parameters - this->reset_neuron_state_bool = std::stoi(controller->GetAttribute("reset_neuron_state_bool")->GetAsString()); - this->reset_neuron_random = std::stoi(controller->GetAttribute("reset_neuron_random")->GetAsString()); - this->init_neuron_state = std::stod(controller->GetAttribute("init_neuron_state")->GetAsString()); - this->range_lb = -std::stod(controller->GetAttribute("range_ub")->GetAsString()); - this->range_ub = std::stod(controller->GetAttribute("range_ub")->GetAsString()); - this->use_frame_of_reference = std::stoi(controller->GetAttribute("use_frame_of_reference")->GetAsString()); - this->signal_factor_all_ = std::stod(controller->GetAttribute("signal_factor_all")->GetAsString()); - this->signal_factor_mid = std::stod(controller->GetAttribute("signal_factor_mid")->GetAsString()); - this->signal_factor_left_right = std::stod(controller->GetAttribute("signal_factor_left_right")->GetAsString()); - - // Limbo BO Learner parameters - this->kernel_noise_ = std::stod(learner->GetAttribute("kernel_noise")->GetAsString()); - this->kernel_optimize_noise_ = std::stoi(learner->GetAttribute("kernel_optimize_noise")->GetAsString()); - this->kernel_sigma_sq_ = std::stod(learner->GetAttribute("kernel_sigma_sq")->GetAsString()); - this->kernel_l_ = std::stod(learner->GetAttribute("kernel_l")->GetAsString()); - this->kernel_squared_exp_ard_k_ = std::stoi(learner->GetAttribute("kernel_squared_exp_ard_k")->GetAsString()); - this->acqui_gpucb_delta_ = std::stod(learner->GetAttribute("acqui_gpucb_delta")->GetAsString()); - this->acqui_ucb_alpha_ = std::stod(learner->GetAttribute("acqui_ucb_alpha")->GetAsString()); - this->acqui_ei_jitter_ = std::stod(learner->GetAttribute("acqui_ei_jitter")->GetAsString()); - - // Non-limbo BO learner para - this->n_init_samples = std::stoi(learner->GetAttribute("n_init_samples")->GetAsString()); - this->n_learning_iterations = std::stoi(learner->GetAttribute("n_learning_iterations")->GetAsString()); - this->n_cooldown_iterations = std::stoi(learner->GetAttribute("n_cooldown_iterations")->GetAsString()); - this->init_method = learner->GetAttribute("init_method")->GetAsString(); - - // Meta parameters - this->startup_time = std::stoi(controller->GetAttribute("startup_time")->GetAsString()); - this->reset_robot_position = std::stoi(controller->GetAttribute("reset_robot_position")->GetAsString()); - this->run_analytics = std::stoi(controller->GetAttribute("run_analytics")->GetAsString()); - this->load_brain = controller->GetAttribute("load_brain")->GetAsString(); - this->evaluation_rate = std::stoi(learner->GetAttribute("evaluation_rate")->GetAsString()); - this->abs_output_bound = std::stoi(learner->GetAttribute("abs_output_bound")->GetAsString()); - this->verbose = std::stoi(controller->GetAttribute("verbose")->GetAsString()); - - // Create transport node - this->node_.reset(new gz::transport::Node()); - this->node_->Init(); - - // Get Robot - this->robot = _model; - this->n_motors = _motors.size(); - auto name = _model->GetName(); - - if(this->verbose) - { - std::cout << robot_config->GetDescription() << std::endl; - } - auto motor = actuators->HasElement("rv:servomotor") - ? actuators->GetElement("rv:servomotor") - : sdf::ElementPtr(); - auto j = 0; - while(motor) - { - if (not motor->HasAttribute("coordinates")) - { - std::cerr << "Missing required motor coordinates" << std::endl; - throw std::runtime_error("Robot brain error"); - } - - // Split string and get coordinates - auto coordinate_string = motor->GetAttribute("coordinates")->GetAsString(); - std::vector coordinates; - boost::split(coordinates, coordinate_string, boost::is_any_of(";")); - - // Check if we have exactly 2 coordinates - if (not coordinates.size() == 2) - { - throw std::runtime_error("Coordinates are not exactly of length two "); - } - - // Check if the coordinates are integers - try - { - for(auto coord : coordinates) - { - std::stoi(coord); - } - } - catch(std::invalid_argument e1) - { - std::cout << "Invalid argument: Cannot cast coordinates to integers " << std::endl; - }; - - // Pass coordinates - auto coord_x = std::stoi(coordinates[0]); - auto coord_y = std::stoi(coordinates[1]); - if (this->verbose) - { - std::cout << "coord_x,coord_y = " << coord_x << "," << coord_y << std::endl; - } - auto motor_id = motor->GetAttribute("part_id")->GetAsString(); - this->positions[motor_id] = {coord_x, coord_y}; - this->motor_coordinates[{coord_x, coord_y}] = j; - - // Set frame of reference - int frame_of_reference = 0; - // We are a left neuron - if (coord_x < 0) - { - frame_of_reference = -1; - } - // We are a right neuron - else if (coord_x > 0) - { - frame_of_reference = 1; - } - - // Save neurons: bias/gain/state. Make sure initial states are of different sign. - this->neurons[{coord_x, coord_y, 1}] = {0.f, 0.f, this->init_neuron_state, frame_of_reference}; //Neuron A - this->neurons[{coord_x, coord_y, -1}] = {0.f, 0.f, -this->init_neuron_state, frame_of_reference}; // Neuron B - - // TODO: Add check for duplicate coordinates - motor = motor->GetNextElement("rv:servomotor"); - j++; - } - - // Add connections between neighbouring neurons - int i = 0; - for (const auto &position : this->positions) - { - // Get name and x,y-coordinates of all neurons. - auto name = position.first; - int x, y; std::tie(x, y) = position.second; - - // Continue to next iteration in case there is already a connection between the 1 and -1 neuron. - // These checks feel a bit redundant. - // if A->B connection exists. - if (this->connections.count({x, y, 1, x, y, -1})) - { - continue; - } - // if B->A connection exists: - if (this->connections.count({x, y, -1, x, y, 1})) - { - continue; - } - - // Loop over all positions. We call it neighbours, but we still need to check if they are a neighbour. - for (const auto &neighbour : this->positions) - { - // Get information of this neuron (that we call neighbour). - int near_x, near_y; std::tie(near_x, near_y) = neighbour.second; - - // If there is a node that is a Moore neighbour, we set it to be a neighbour for their A-nodes. - // Thus the connections list only contains connections to the A-neighbourhood, and not the - // A->B and B->A for some node (which makes sense). - int dist_x = std::abs(x - near_x); - int dist_y = std::abs(y - near_y); - - // TODO: Verify for non-spiders - if (dist_x + dist_y == 2) - { - if(std::get<0>(this->connections[{x, y, 1, near_x, near_y, 1}]) != 1 or - std::get<0>(this->connections[{near_x, near_y, 1, x, y, 1}]) != 1) - { - if(this->verbose) - { - std::cout << "New connection at index " << i << ": " << x << ", " << y << ", " << near_x << ", " << near_y << std::endl; - } - this->connections[{x, y, 1, near_x, near_y, 1}] = std::make_tuple(1, i); - this->connections[{near_x, near_y, 1, x, y, 1}] = std::make_tuple(1, i); - i++; - } - } - } - } - - // Create directory for output. - this->directory_name = controller->GetAttribute("output_directory")->GetAsString(); - if(this->directory_name.empty()) - { - this->directory_name = "output/cpg_bo/"; - this->directory_name += std::to_string(time(0)) + "/"; - } - - std::system(("mkdir -p " + this->directory_name).c_str()); - - // Initialise array of neuron states for Update() method - this->next_state = new double[this->neurons.size()]; - this->n_weights = (int)(this->connections.size()/2) + this->n_motors; - - // Check if we want to load a pre-trained brain - if(!this->load_brain.empty()) - { - // Get line - if(this->verbose) - { - std::cout << "I will load the following brain:" << std::endl; - } - std::ifstream brain_file(this->load_brain); - std::string line; - std::getline(brain_file, line); - - // Get weights in line - std::vector weights; - boost::split(weights, line, boost::is_any_of(",")); - - // Save weights for brain - Eigen::VectorXd loaded_brain(this->n_weights); - for(size_t j = 0; j < this->n_weights; j++) - { - loaded_brain(j) = std::stod(weights.at(j)); - if(this->verbose) - { - std::cout << loaded_brain(j) << ","; - } - } - if(this->verbose) - { - std::cout << std::endl; - } - - // Close brain - brain_file.close(); - - // Save these weights - this->samples.push_back(loaded_brain); - - // Set ODE matrix at initialization - this->set_ode_matrix(); - - // Go directly into cooldown phase: Note we do require that best_sample is filled. Check this - this->current_iteration = this->n_init_samples + this->n_learning_iterations; - - if(this->verbose) - { - std::cout << std::endl << "Brain has been loaded." << std::endl; - } - } - else - { - if (this->verbose) - { - std::cout << "Don't load existing brain" << std::endl; - } +DifferentialCPG::DifferentialCPG(const sdf::ElementPtr brain_sdf, + const std::vector &_motors) + : Brain() + , revolve::DifferentialCPG(load_params_from_sdf(brain_sdf), _motors) +{} - // Initialize BO - this->bo_init_sampling(); - } +DifferentialCPG::DifferentialCPG(const sdf::ElementPtr brain_sdf, + const std::vector &_motors, + const NEAT::Genome &genome) + : Brain() + , revolve::DifferentialCPG(load_params_from_sdf(brain_sdf), _motors, genome) +{} - // Initiate the cpp Evaluator - this->evaluator.reset(new Evaluator(this->evaluation_rate)); - this->evaluator->directory_name = this->directory_name; -} -/** - * Destructor - */ -DifferentialCPG::~DifferentialCPG() +void DifferentialCPG::Update(const std::vector &_motors, + const std::vector &_sensors, + const double _time, + const double _step) { - delete[] this->next_state; - delete[] this->input; - delete[] this->output; + this->::revolve::DifferentialCPG::update(_motors, _sensors, _time, _step); } -/** - * Dummy function for limbo - */ -struct DifferentialCPG::evaluation_function{ - // Number of input dimension (samples.size()) - BO_PARAM(size_t, dim_in, 18); - - // number of dimensions of the fitness - BO_PARAM(size_t, dim_out, 1); - - Eigen::VectorXd operator()(const Eigen::VectorXd &x) const { - return limbo::tools::make_vector(0); - }; -}; - -/** - * Performs the initial random sampling for BO - */ -void DifferentialCPG::bo_init_sampling(){ - if(this->verbose) - { - // We only want to optimize the weights for now. - std::cout << "Number of weights = connections/2 + n_motors are " - << this->connections.size()/2 - << " + " - << this->n_motors - << std::endl; - - // Information purposes - std::cout << std::endl << "Sample method: " << this->init_method << ". Initial " - "samples are: " << std::endl; - } - - // Random sampling - if(this->init_method == "RS") - { - for (size_t i = 0; i < this->n_init_samples; i++) - { - // Working variable to hold a random number for each weight to be optimized - Eigen::VectorXd init_sample(this->n_weights); - - // For all weights - for (size_t j = 0; j < this->n_weights; j++) - { - // Generate a random number in [0, 1]. Transform later - double f = ((double) rand() / (RAND_MAX)); - - // Append f to vector - init_sample(j) = f; - } - - // Save vector in samples. - this->samples.push_back(init_sample); - } - } - // Latin Hypercube Sampling - else if(this->init_method == "LHS") - { - // Working variable - double my_range = 1.f/this->n_init_samples; - - // If we have n dimensions, create n such vectors that we will permute - std::vector> all_dimensions; - - // Fill vectors - for (size_t i=0; i < this->n_weights; i++) - { - std::vector one_dimension; - - // Prepare for vector permutation - for (size_t j = 0; j < this->n_init_samples; j++) - { - one_dimension.push_back(j); - } - - // Vector permutation - std::random_shuffle(one_dimension.begin(), one_dimension.end() ); - - // Save permuted vector - all_dimensions.push_back(one_dimension); - } - - // For all samples - for (size_t i = 0; i < this->n_init_samples; i++) - { - // Initialize Eigen::VectorXd here. - Eigen::VectorXd init_sample(this->n_weights); - - // For all dimensions - for (size_t j = 0; j < this->n_weights; j++) - { - // Take a LHS - init_sample(j) = all_dimensions.at(j).at(i)*my_range + ((double) rand() / (RAND_MAX))*my_range; - } - - // Append sample to samples - this->samples.push_back(init_sample); - } - } - else - { - std::cout << "Please provide a choice of init_method in {LHS, RS}" << std::endl; - } - - // Print samples - if(this->verbose) - { - for(auto init_sample :this->samples) - { - for (int h = 0; h < init_sample.size(); h++) - { - std::cout << init_sample(h) << ", "; - } - std::cout << std::endl; - } - } -} - -/** - * Function that obtains the current fitness by calling the evaluator and stores it - */ -void DifferentialCPG::save_fitness(){ - // Get fitness - double fitness = this->evaluator->Fitness(); - - // Save sample if it is the best seen so far - if(fitness >this->best_fitness) - { - this->best_fitness = fitness; - this->best_sample = this->samples.back(); - } - - if (this->verbose) - { - std::cout << "Iteration number " << this->current_iteration << " has fitness " << - fitness << ". Best fitness: " << this->best_fitness << std::endl; - } - - // Limbo requires fitness value to be of type Eigen::VectorXd - Eigen::VectorXd observation = Eigen::VectorXd(1); - observation(0) = fitness; - - // Save fitness to std::vector. This fitness corresponds to the solution of the previous iteration - this->observations.push_back(observation); - - // Write fitness to file - std::ofstream fitness_file; - fitness_file.open(this->directory_name + "fitnesses.txt", std::ios::app); - fitness_file << fitness << std::endl; - fitness_file.close(); -} - - - -/** - * Struct that holds the parameters on which BO is called. This is required - * by limbo. - */ -struct DifferentialCPG::Params - { - - struct bayes_opt_boptimizer : public limbo::defaults::bayes_opt_boptimizer { - }; - - // depending on which internal optimizer we use, we need to import different parameters -#ifdef USE_NLOPT - struct opt_nloptnograd : public limbo::defaults::opt_nloptnograd { - }; -#elif defined(USE_LIBCMAES) - struct opt_cmaes : public lm::defaults::opt_cmaes { - }; -#else -#error(NO SOLVER IS DEFINED) -#endif - struct kernel : public limbo::defaults::kernel { - BO_PARAM(double, noise, 0.001); - BO_PARAM(bool, optimize_noise, false); - }; - - struct bayes_opt_bobase : public limbo::defaults::bayes_opt_bobase { - // set stats_enabled to prevent creating all the directories - BO_PARAM(bool, stats_enabled, false); - BO_PARAM(bool, bounded, true); - }; - - // 1 Iteration as we will perform limbo step by steop - struct stop_maxiterations : public limbo::defaults::stop_maxiterations { - BO_PARAM(int, iterations, 1); - }; - - struct kernel_exp : public limbo::defaults::kernel_exp { - /// @ingroup kernel_defaults - BO_PARAM(double, sigma_sq, 0.1); - BO_PARAM(double, l, 0.1); // the width of the kernel. Note that it assumes equally sized ranges over dimensions - }; - - struct kernel_squared_exp_ard : public limbo::defaults::kernel_squared_exp_ard { - /// @ingroup kernel_defaults - BO_PARAM(int, k, 3); // k number of columns used to compute M - /// @ingroup kernel_defaults - BO_PARAM(double, sigma_sq, 0.1); //brochu2010tutorial p.9 without sigma_sq - }; - - struct kernel_maternfivehalves : public limbo::defaults::kernel_maternfivehalves - { - BO_DYN_PARAM(double, sigma_sq); //brochu2010tutorial p.9 without sigma_sq - BO_DYN_PARAM(double, l); //characteristic length scale - }; - - struct acqui_gpucb : public limbo::defaults::acqui_gpucb { - //UCB(x) = \mu(x) + \kappa \sigma(x). - BO_PARAM(double, delta, 0.1 );//acqui_gpucb_delta_); // default delta = 0.1, delta in (0,1) convergence guaranteed - }; - - struct acqui_ei : public limbo::defaults::acqui_ei{ - BO_PARAM(double, jitter, 0.5); - }; - - // This is just a placeholder to be able to use limbo with revolve - struct init_lhs : public limbo::defaults::init_lhs{ - BO_PARAM(int, samples, 0); - }; - - struct acqui_ucb : public limbo::defaults::acqui_ucb { - //constexpr double ra = acqui_ucb_alpha_; - //UCB(x) = \mu(x) + \alpha \sigma(x). high alpha have high exploration - //iterations is high, alpha can be low for high accuracy in enough iterations. - // In contrast, the lsow iterations should have high alpha for high - // searching in limited iterations, which guarantee to optimal. - // BO_PARAM(double, alpha, transform_double(acqui_ucb_alpha_)); // default alpha = 0.5 - BO_DYN_PARAM(double, alpha); // default alpha = 0.5 - - }; -}; - -BO_DECLARE_DYN_PARAM(double, DifferentialCPG::Params::acqui_ucb, alpha); -BO_DECLARE_DYN_PARAM(double, DifferentialCPG::Params::kernel_maternfivehalves, sigma_sq); -BO_DECLARE_DYN_PARAM(double, DifferentialCPG::Params::kernel_maternfivehalves, l); - -/** - * Wrapper function that makes calls to limbo to solve the current BO - * iteration and returns the best sample - */ -void DifferentialCPG::bo_step(){ - Params::acqui_ucb::set_alpha(this->acqui_ucb_alpha_); - Params::kernel_maternfivehalves::set_l(this->kernel_l_); - Params::kernel_maternfivehalves::set_sigma_sq(this->kernel_sigma_sq_); - - // Save all parameters once - if (this->current_iteration == 0) - { - // Save parameters - this->save_parameters(); - } - Eigen::VectorXd x; - - // In case we are done with the initial random sampling. - if (this->current_iteration >= this->n_init_samples) - { - // std::cout << "Acquisition function: " << this->acquisition_function << std::endl; - if(true) - { - - // Specify bayesian optimizer. TODO: Make attribute and initialize at bo_init - limbo::bayes_opt::BOptimizer, - limbo::modelfun, - limbo::acquifun>> boptimizer; - - // Optimize. Pass dummy evaluation function and observations . - boptimizer.optimize(DifferentialCPG::evaluation_function(), - this->samples, - this->observations); - x = boptimizer.last_sample(); - - // Write parametesr to verify thread-stability after the run - std::ofstream dyn_parameters_file; - dyn_parameters_file.open(this->directory_name + "dynamic_parameters.txt", std::ios::app); - dyn_parameters_file << Params::acqui_ucb::alpha() << ","; - dyn_parameters_file << Params::kernel_maternfivehalves::sigma_sq() << ","; - dyn_parameters_file << Params::kernel_maternfivehalves::l() << std::endl; - dyn_parameters_file.close(); - - - } - // else if(this->acquisition_function == "GP_UCB") - // { - // // Specify bayesian optimizer. TODO: Make attribute and initialize at bo_init - // limbo::bayes_opt::BOptimizer, - // limbo::modelfun, - // limbo::acquifun>> boptimizer; - // - // // Optimize. Pass dummy evaluation function and observations . - // boptimizer.optimize(DifferentialCPG::evaluation_function(), - // this->samples, - // this->observations); - // x = boptimizer.last_sample(); - // } - // else if(this->acquisition_function == "EI") - // { - // // Specify bayesian optimizer. TODO: Make attribute and initialize at bo_init - // limbo::bayes_opt::BOptimizer, - // limbo::modelfun, - // limbo::acquifun>> boptimizer; - // - // // Optimize. Pass dummy evaluation function and observations . - // boptimizer.optimize(DifferentialCPG::evaluation_function(), - // this->samples, - // this->observations); - // x = boptimizer.last_sample(); - // } - else - { - std::cout << "Specify correct acquisition function: {EI, UCB, GP_UCB}" << std::endl; - } - - // Save this x_hat_star - this->samples.push_back(x); - } -} - -/** - * Callback function that defines the movement of the robot - * - * @param _motors - * @param _sensors - * @param _time - * @param _step - */ -void DifferentialCPG::Update( - const std::vector< revolve::gazebo::MotorPtr > &_motors, - const std::vector< revolve::gazebo::SensorPtr > &_sensors, - const double _time, - const double _step) +revolve::DifferentialCPG::ControllerParams DifferentialCPG::load_params_from_sdf(sdf::ElementPtr brain_sdf) { - // Prevent two threads from accessing the same resource at the same time - boost::mutex::scoped_lock lock(this->networkMutex_); - - // Read sensor data and feed the neural network - unsigned int p = 0; - for (const auto &sensor : _sensors) - { - sensor->read(this->input + p); - p += sensor->n_inputs(); - } - - this->evaluator->Update(this->robot->WorldPose(), _time, _step); - - // Only start recording the fitness after the startup time each iteration - double elapsed_evaluation_time = _time - this->start_time; - if((std::fmod(elapsed_evaluation_time, (int)this->evaluation_rate) >= this->startup_time) & this->start_fitness_recording) - { - // Update position -// this->evaluator->Update(this->robot->WorldPose(), _time, _step); - this->start_fitness_recording = false; - } - // Evaluate policy on certain time limit, or if we just started - if ((elapsed_evaluation_time > this->evaluation_rate) or ((_time - _step) < 0.001)) - { - // Update position -// this->evaluator->Update(this->robot->WorldPose(), _time, _step); - this->start_fitness_recording = true; - - // Get and save fitness (but not at start) - if(not (_time - _step < 0.001 )) - { - this->save_fitness(); - } - - // Reset robot if opted to do - if(this->reset_robot_position) - { - //this->robot->Reset(); - this->robot->ResetPhysicsStates(); - auto start_pose = ::ignition::math::Pose3d(); - start_pose.Set(0.0, 0.0, 0.05, 0.0, 0.0, 0.0); - this->robot->SetWorldPose(start_pose); - this->robot->Update(); - } - - // Reset neuron state if opted to do - if(this->reset_neuron_state_bool) - { - this->reset_neuron_state(); - } - - // If we are still learning - if(this->current_iteration < this->n_init_samples + this->n_learning_iterations) - { - if(this->verbose) - { - if (this->current_iteration < this->n_init_samples) - { - std::cout << std::endl << "Evaluating initial random sample" << std::endl; - } - else - { - std::cout << std::endl << "I am learning " << std::endl; + // Get all params from the sdf + // TODO: Add exception handling + sdf::ElementPtr controller_sdf = brain_sdf->GetElement("rv:controller"); + revolve::DifferentialCPG::ControllerParams params; + params.reset_neuron_random = (controller_sdf->GetAttribute("reset_neuron_random")->GetAsString() == "true"); + params.use_frame_of_reference = (controller_sdf->GetAttribute("use_frame_of_reference")->GetAsString() == "true"); + params.init_neuron_state = stod(controller_sdf->GetAttribute("init_neuron_state")->GetAsString()); + params.range_ub = stod(controller_sdf->GetAttribute("range_ub")->GetAsString()); + params.output_signal_factor = stod(controller_sdf->GetAttribute("signal_factor_all")->GetAsString()); + params.abs_output_bound = stod(controller_sdf->GetAttribute("abs_output_bound")->GetAsString()); + + // Get the weights from the sdf: + // If loading with CPPN, the weights attribute does not exist + if (controller_sdf->HasAttribute("weights")) { + std::string sdf_weights = controller_sdf->GetAttribute("weights")->GetAsString(); + std::string delimiter = ";"; + + size_t pos = 0; + std::string token; + while ((pos = sdf_weights.find(delimiter)) != std::string::npos) { + token = sdf_weights.substr(0, pos); + params.weights.push_back(stod(token)); + sdf_weights.erase(0, pos + delimiter.length()); } - } - // Get new sample (weights) and add sample - this->bo_step(); - - // Set new weights - this->set_ode_matrix(); - - // Update position -// this->evaluator->Update(this->robot->WorldPose(), _time, _step); - } - // If we are finished learning but are cooling down - reset once - else if((this->current_iteration >= (this->n_init_samples + - this->n_learning_iterations)) - and (this->current_iteration < (this->n_init_samples + - this->n_learning_iterations + - this->n_cooldown_iterations - 1))) - { - if(this->verbose) - { - std::cout << std::endl << "I am cooling down " << std::endl; - } - - // Update robot position -// this->evaluator->Update(this->robot->WorldPose(), _time, _step); - - // Use best sample in next iteration - this->samples.push_back(this->best_sample); - - // Set ODE matrix - this->set_ode_matrix(); + // push the last element that does not end with the delimiter + params.weights.push_back(stod(sdf_weights)); } - // Else we don't want to update anything, but construct plots from this run once. - else - { -// // Create plots -// if(this->run_analytics) -// { -// // Construct plots -// this->get_analytics(); -// } - - // Exit - if(this->verbose) - { - std::cout << std::endl << "I am finished " << std::endl; - } - std::exit(0); - } - - // Evaluation policy here - this->start_time = _time; - this->evaluator->Reset(); - this->current_iteration += 1; - } - - // Send new signals to the motors - this->step(_time, this->output); - p = 0; - for (const auto &motor: _motors) - { - motor->write(this->output + p, _step); - p += motor->n_outputs(); - } -} - -/** - * Make matrix of weights A as defined in dx/dt = Ax. - * Element (i,j) specifies weight from neuron i to neuron j in the system of ODEs - */ -void DifferentialCPG::set_ode_matrix(){ - // Initiate new matrix - std::vector> matrix; - - // Fill with zeroes - for(size_t i =0; i neurons.size(); i++) - { - // Initialize row in matrix with zeros - std::vector< double > row; - for (size_t j = 0; j < this->neurons.size(); j++) - { - row.push_back(0); - } - matrix.push_back(row); - } - - // Process A<->B connections - int index = 0; - for(size_t i =0; i neurons.size(); i++) - { - // Get correct index - int c = 0; - if (i%2 == 0){ - c = i + 1; - } - else{ - c = i - 1; - } - - // Add a/b connection weight - index = (int)(i/2); - auto w = this->samples.at(this->current_iteration)(index) * - (this->range_ub - this->range_lb) + this->range_lb; - matrix[i][c] = w; - matrix[c][i] = -w; - } - - // A<->A connections - index++; - int k = 0; - std::vector connections_seen; - - for (auto const &connection : this->connections) - { - // Get connection information - int x1, y1, z1, x2, y2, z2; - std::tie(x1, y1, z1, x2, y2, z2) = connection.first; - - // Find location of the two neurons in this->neurons list - int l1, l2; - int c = 0; - for(auto const &neuron : this->neurons) - { - int x, y, z; - std::tie(x, y, z) = neuron.first; - if (x == x1 and y == y1 and z == z1) - { - l1 = c; - } - else if (x == x2 and y == y2 and z == z2) - { - l2 = c; - } - // Update counter - c++; - } - - // Add connection to seen connections - if(l1 > l2) - { - int l1_old = l1; - l1 = l2; - l2 = l1_old; - } - std::string connection_string = std::to_string(l1) + "-" + std::to_string(l2); - - // if not in list, add to list - auto connections_list = std::find(connections_seen.begin(), connections_seen.end(), connection_string); - if(connections_list == connections_seen.end()) - { - connections_seen.push_back(connection_string); - } - // else continue to next iteration - else{ - continue; - } - - // Get weight - auto w = this->samples.at(this->current_iteration)(index + k) * - (this->range_ub - this->range_lb) + this->range_lb; - - // Set connection in weight matrix - matrix[l1][l2] = w; - matrix[l2][l1] = -w; - k++; - } - - // Update matrix - this->ode_matrix = matrix; - - // Reset neuron state - this->reset_neuron_state(); - - // Save this sample to file - std::ofstream samples_file; - samples_file.open(this->directory_name + "samples.txt", std::ios::app); - auto sample = this->samples.at(this->current_iteration); - for(size_t j = 0; j < this->n_weights; j++) - { - samples_file << sample(j) << ", "; - } - samples_file << std::endl; - samples_file.close(); -} - - -/** - * Set states back to original value (that is on the unit circle) - */ -void DifferentialCPG::reset_neuron_state(){ - int c = 0; - for(auto const &neuron : this->neurons) - { - // Get neuron properties - int x, y, z, frame_of_reference; - double bias ,gain ,state; - std::tie(x, y, z) = neuron.first; - std::tie(bias, gain, state, frame_of_reference) = neuron.second; - - if (z == -1) - { - // Neuron B - if (this->reset_neuron_random) - { - this->neurons[{x, y, z}] = {0.f, - 0.f, - ((double) rand() / (RAND_MAX))*2*this->init_neuron_state - this->init_neuron_state, - frame_of_reference}; - } - else - { - this->neurons[{x, y, z}] = {0.f, 0.f, -this->init_neuron_state, frame_of_reference}; - } - } - else - { - // Neuron A - if (this->reset_neuron_random) - { - this->neurons[{x, y, z}] = {0.f, - 0.f, - ((double) rand() / (RAND_MAX))*2*this->init_neuron_state - this->init_neuron_state, - frame_of_reference}; - } - else - { - this->neurons[{x, y, z}] = {0.f, 0.f, +this->init_neuron_state, frame_of_reference}; - } - } - c++; - } -} - -/** - * Step function that is called from within Update() - * - * @param _time - * @param _output - */ -void DifferentialCPG::step( - const double _time, - double *_output) -{ - int neuron_count = 0; - for (const auto &neuron : this->neurons) - { - // Neuron.second accesses the second 3-tuple of a neuron, containing the bias/gain/state. - double recipient_bias, recipient_gain, recipient_state; - int frame_of_reference; - std::tie(recipient_bias, recipient_gain, recipient_state, frame_of_reference) = neuron.second; - - // Save for ODE - this->next_state[neuron_count] = recipient_state; - neuron_count++; - } - - // Copy values from next_state into x for ODEINT - state_type x(this->neurons.size()); - for (size_t i = 0; i < this->neurons.size(); i++) - { - x[i] = this->next_state[i]; - } - - // Stepper. The result is saved in x. Begin time t, time step dt - double dt = (_time - this->previous_time); - this->previous_time = _time; - - // Perform one step - stepper.do_step( - [this](const state_type &x, state_type &dxdt, double t) - { - for(size_t i = 0; i < this->neurons.size(); i++) - { - dxdt[i] = 0; - for(size_t j = 0; j < this->neurons.size(); j++) - { - dxdt[i] += x[j]*this->ode_matrix[j][i]; - } - } - }, - x, - _time, - dt); - - // Copy values into nextstate - for (size_t i = 0; i < this->neurons.size(); i++) - { - this->next_state[i] = x[i]; - } - - // Loop over all neurons to actually update their states. Note that this is a new outer for loop - auto i = 0; auto j = 0; - for (auto &neuron : this->neurons) - { - // Get bias gain and state for this neuron. Note that we don't take the coordinates. - // However, they are implicit as their order did not change. - double bias, gain, state; - int frame_of_reference; - std::tie(bias, gain, state, frame_of_reference) = neuron.second; - double x, y, z; - std::tie(x, y, z) = neuron.first; - neuron.second = {bias, gain, this->next_state[i], frame_of_reference}; - j = this->motor_coordinates[{x,y}]; - // Should be one, as output should be based on +1 neurons, which are the A neurons - if (i % 2 == 1) - { - // TODO: Add Milan's function here as soon as things are working a bit - // f(a) = (w_ao*a - bias)*gain - - // Apply saturation formula - auto x = this->next_state[i]; - - // Use frame of reference - if(use_frame_of_reference) - { - - if (std::abs(frame_of_reference) == 1) - { - this->output[j] = this->signal_factor_left_right*this->abs_output_bound*((2.0)/(1.0 + std::pow(2.718, -2.0*x/this->abs_output_bound)) -1); - } - else if (frame_of_reference == 0) - { - this->output[j] = this->signal_factor_mid*this->abs_output_bound*((2.0)/(1.0 + std::pow(2.718, -2.0*x/this->abs_output_bound)) -1); - } - else - { - std::cout << "WARNING: frame_of_reference not in {-1,0,1}." << std::endl; - } - - } - // Don't use frame of reference - else{ - this->output[j] = this->signal_factor_all_*this->abs_output_bound*((2.0)/(1.0 + std::pow(2.718, -2.0*x/this->abs_output_bound)) -1); - } - } - i++; - } - - // Comment to save disk space -// // Write state to file -// std::ofstream state_file; -// state_file.open(this->directory_name + "states.txt", std::ios::app); -// for(size_t i = 0; i < this->neurons.size(); i++) -// { -// state_file << this->next_state[i] << ","; -// } -// state_file << std::endl; -// state_file.close(); -// -// // Write signal to file -// std::ofstream signal_file; -// signal_file.open(this->directory_name + "signal.txt", std::ios::app); -// for(size_t i = 0; i < this->n_motors; i++) -// { -// signal_file << this->output[i] << ","; -// } -// signal_file << std::endl; -// signal_file.close(); -} - - -/** - * Save the parameters used in this run to a file. - */ -void DifferentialCPG::save_parameters(){ - // Write parameters to file - std::ofstream parameters_file; - parameters_file.open(this->directory_name + "parameters.txt"); - - // Various parameters - parameters_file << "Dimensions: " << this->n_weights << std::endl; - parameters_file << "n_init_samples: " << this->n_init_samples << std::endl; - parameters_file << "n_learning_iterations: " << this->n_learning_iterations << std::endl; - parameters_file << "n_cooldown_iterations: " << this->n_cooldown_iterations << std::endl; - parameters_file << "evaluation_rate: " << this->evaluation_rate << std::endl; - parameters_file << "abs_output_bound: " << this->abs_output_bound << std::endl; - parameters_file << "signal_factor_all: " << this->signal_factor_all_ << std::endl; - parameters_file << "range_lb: " << this->range_lb << std::endl; - parameters_file << "range_ub: " << this->range_ub << std::endl; - parameters_file << "run_analytics: " << this->run_analytics << std::endl; - parameters_file << "load_brain: " << this->load_brain << std::endl; - parameters_file << "reset_robot_position: " << this->reset_robot_position << std::endl; - parameters_file << "reset_neuron_state_bool: " << this->reset_neuron_state_bool << std::endl; - parameters_file << "reset_neuron_random: " << this->reset_neuron_random << std::endl; - parameters_file << "initial state value: " << this->init_neuron_state << std::endl; - - // BO hyper-parameters - parameters_file << std::endl << "Initialization method used: " << this->init_method << std::endl; - parameters_file << "Acqui. function used: " << this->acquisition_function << std::endl; - parameters_file << "EI jitter: " <directory_name - + " " - + std::to_string((int)this->n_init_samples) - + " " - + std::to_string((int)this->n_cooldown_iterations); - // Execute python command - std::system(std::string("python3 " + plot_command).c_str()); + return params; } diff --git a/cpprevolve/revolve/gazebo/brains/DifferentialCPG.h b/cpprevolve/revolve/gazebo/brains/DifferentialCPG.h index 2595f83621..d85e978984 100644 --- a/cpprevolve/revolve/gazebo/brains/DifferentialCPG.h +++ b/cpprevolve/revolve/gazebo/brains/DifferentialCPG.h @@ -1,275 +1,52 @@ -/* - * Copyright (C) 2015-2018 Vrije Universiteit Amsterdam - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * - * Description: TODO: - * Author: Milan Jelisavcic - * Date: December 29, 2018 - * - */ +// +// Created by andi on 20-09-19. +// -#ifndef REVOLVE_DIFFERENTIALCPG_H_ -#define REVOLVE_DIFFERENTIALCPG_H_ +#pragma once -// Standard libraries -#include -#include - -// External libraries -#include -#include - -// Project headers -#include "Evaluator.h" +#include +#include #include "Brain.h" -/// These numbers are quite arbitrary. It used to be in:13 out:8 for the -/// Arduino, but I upped them both to 20 to accommodate other scenarios. -/// Should really be enforced in the Python code, this implementation should -/// not be the limit. -#define MAX_INPUT_NEURONS 20 -#define MAX_OUTPUT_NEURONS 20 - -/// Arbitrary value -#define MAX_HIDDEN_NEURONS 30 - -/// Convenience -#define MAX_NON_INPUT_NEURONS (MAX_HIDDEN_NEURONS + MAX_OUTPUT_NEURONS) - -/// (bias, tau, gain) or (phase offset, period, gain) -#define MAX_NEURON_PARAMS 3 - -typedef std::vector< double > state_type; - namespace revolve { - namespace gazebo { - class DifferentialCPG - : public Brain + namespace gazebo { - /// \brief Constructor - /// \param[in] _modelName Name of the robot - /// \param[in] _node The brain node - /// \param[in] _motors Reference to a motor list, it be reordered - /// \param[in] _sensors Reference to a sensor list, it might be reordered - public: - DifferentialCPG( - const ::gazebo::physics::ModelPtr &_model, - const sdf::ElementPtr robot_config, - const std::vector< MotorPtr > &_motors, - const std::vector< SensorPtr > &_sensors); - - public: void set_ode_matrix(); - - /// \brief Destructor - public: virtual ~DifferentialCPG(); - - /// \brief The default update method for the controller - /// \param[in] _motors Motor list - /// \param[in] _sensors Sensor list - /// \param[in] _time Current world time - /// \param[in] _step Current time step - public: - virtual void Update( - const std::vector< MotorPtr > &_motors, - const std::vector< SensorPtr > &_sensors, - const double _time, - const double _step); - - protected: - void step( - const double _time, - double *_output); - - /// \brief Register of motor IDs and their x,y-coordinates - protected: std::map< std::string, std::tuple< int, int > > positions; - - public: std::map< std::tuple< int, int>, int> motor_coordinates; - - - /// \brief Register of individual neurons in x,y,z-coordinates - /// \details x,y-coordinates define position of a robot's module and - // z-coordinate define A or B neuron (z=1 or -1 respectively). Stored - // values are a bias, gain, state and frame of reference of each neuron. - protected: - std::map< std::tuple< int, int, int >, std::tuple< double, double, double, int > > - neurons; - - /// \brief Register of connections between neighnouring neurons - /// \details Coordinate set of two neurons (x1, y1, z1) and (x2, y2, z2) - // define a connection. The second tuple contains 1: the connection value and - // 2: the weight index corresponding to this connection. - protected: - std::map< std::tuple< int, int, int, int, int, int >, std::tuple > - connections; - - /// \brief Runge-Kutta 45 stepper - protected: boost::numeric::odeint::runge_kutta4< state_type > stepper; - - /// \brief Pointer to access parameters - private: sdf::ElementPtr learner; - - /// \brief Used to determine the next state array - private: double *next_state; - - /// \brief Used for ODE-int - protected: std::vector> ode_matrix; - protected: state_type x; - - /// \brief One input state for each input neuron - private: double *input; - - /// \brief Used to determine the output to the motors array - private: double *output; - - /// \brief Location where to save output - private: std::string directory_name; - - /// \brief Name of the robot - private: ::gazebo::physics::ModelPtr robot; - - /// \brief Init BO loop - public: void bo_init_sampling(); - - /// \brief Main BO loop - public: void bo_step(); - - /// \brief evaluation rate - private: double evaluation_rate; - - /// \brief Get fitness - private: void save_fitness(); - - /// \brief Pointer to the fitness evaluator - protected: EvaluatorPtr evaluator; - - /// \brief Holder for BO parameters - public: struct Params; - - /// \brief Save parameters - private: void save_parameters(); - - /// \brief Best fitness seen so far - private: double best_fitness = -10.0; - - /// \brief Sample corresponding to best fitness - private: Eigen::VectorXd best_sample; - - /// \brief Starting time - private: double start_time; - - /// \brief BO attributes - private: size_t current_iteration = 0; - - /// \brief Max number of iterations learning is allowed - private: size_t n_learning_iterations; - - /// \brief Number of initial samples - private: size_t n_init_samples; - - /// \brief Cool down period - private: size_t n_cooldown_iterations; - - /// \brief Limbo optimizes in [0,1] - private: double range_lb; - - /// \brief Limbo optimizes in [0,1] - private: double range_ub; - - /// \brief How to take initial random samples - private: std::string init_method; - - /// \brief All fitnesses seen so far. Called observations in limbo context - private: std::vector< Eigen::VectorXd > observations; - - /// \brief All samples seen so far. - private: std::vector< Eigen::VectorXd > samples; - - /// \brief The number of weights to optimize - private: size_t n_weights; - - /// \brief Dummy evaluation funtion to reduce changes to be made on the limbo package - public: struct evaluation_function; - - /// \brief Reset the robot to starting position each iteration. - private: bool reset_robot_position; - - /// \brief Reset neuron state at each iteration (also during validation) - private: bool reset_neuron_state_bool; - - /// \brief Factor to multiply output signal with - private: double signal_factor_all_; - - /// \brief Factor to multiply output signal with - private: double signal_factor_mid; - - /// \brief Factor to multiply output signal with - private: double signal_factor_left_right; - - /// \brief Function that resets neuron state - private: void reset_neuron_state(); - - /// \brief When reset a neuron state,do it randomly: - private: bool reset_neuron_random; - - /// \brief Boolean to enable/disable constructing plots - private: bool run_analytics; - - /// \brief Automatically generate plots - public: void get_analytics(); - - /// \brief Show output (1) or not (0) - public: int verbose; - - /// \brief Time to skip for fitness evaluation during training - public: int startup_time; - - /// \brief Helper for startup time - private: bool start_fitness_recording = true; - - /// \brief absolute bound on motor signal value - public: double abs_output_bound; - - /// \brief Holds the number of motors in the robot - private: size_t n_motors; - - /// \brief Helper for numerical integrator - private: double previous_time = 0; - - /// \brief Initial neuron state - private: double init_neuron_state; - - /// \brief Holder for loading a brain - private: std::string load_brain = ""; - - /// \brief Specifies the acquisition function used - public: std::string acquisition_function; - - /// \brief Use frame of reference {-1,0,1} version or not - private: bool use_frame_of_reference; - - // BO Learner parameters - private: double kernel_noise_; - private: bool kernel_optimize_noise_; - public: double kernel_sigma_sq_; - public: double kernel_l_; - private: int kernel_squared_exp_ard_k_; - private: double acqui_gpucb_delta_ ; - public: double acqui_ucb_alpha_; - private: double acqui_ei_jitter_; - }; - } + /// \brief connection between gazebo and revolve CPG + /// \details gets the sdf - model data and passes them to revolve + class DifferentialCPG: public Brain, public revolve::DifferentialCPG + { + public: + /// \brief Constructor + /// \param[in] brain_sdf ElementPtr containing the "brain" - tag of the model sdf + /// \param[in] _motors vector list of motors + /// \details Extracts controller parameters + /// from brain_sdf and calls revolve::DifferentialCPG's contructor. + explicit DifferentialCPG(const sdf::ElementPtr brain_sdf, + const std::vector< MotorPtr > &_motors); + + /// \brief updates the motor signals + /// \param[in] _motors vector list of motors + /// \param[in] _sensors vector list of sensors + /// \param[in] _time double + /// \param[in] _step double + void Update(const std::vector &_motors, + const std::vector &_sensors, + const double _time, + const double _step) override; + + protected: + explicit DifferentialCPG(const sdf::ElementPtr brain_sdf, + const std::vector &_motors, + const NEAT::Genome &genome); + + /// \brief extracts CPG controller parameters from brain_sdf + /// \param[in] brain_sdf ElementPtr containing the "brain" - tag of the model sdf + /// \return parameters of the CPG controller + /// \details get the strings of the controller parameters and convert them to the + /// appropriate datatype. Store them in a revolve::DifferentialCPG::ControllerParams + /// struct and return them. + static revolve::DifferentialCPG::ControllerParams load_params_from_sdf(sdf::ElementPtr brain_sdf); + }; + } } - -#endif //REVOLVE_DIFFERENTIALCPG_H_ diff --git a/cpprevolve/revolve/gazebo/brains/DifferentialCPGClean.cpp b/cpprevolve/revolve/gazebo/brains/DifferentialCPGClean.cpp deleted file mode 100644 index ebedfd3b6f..0000000000 --- a/cpprevolve/revolve/gazebo/brains/DifferentialCPGClean.cpp +++ /dev/null @@ -1,63 +0,0 @@ -// -// Created by andi on 06-10-19. -// - -#include "DifferentialCPGClean.h" - -using namespace revolve::gazebo; - -DifferentialCPGClean::DifferentialCPGClean(const sdf::ElementPtr brain_sdf, - const std::vector &_motors) - : Brain() - , revolve::DifferentialCPG(load_params_from_sdf(brain_sdf), _motors) -{} - -DifferentialCPGClean::DifferentialCPGClean(const sdf::ElementPtr brain_sdf, - const std::vector &_motors, - const NEAT::Genome &genome) - : Brain() - , revolve::DifferentialCPG(load_params_from_sdf(brain_sdf), _motors, genome) -{} - - -void DifferentialCPGClean::Update(const std::vector &_motors, - const std::vector &_sensors, - const double _time, - const double _step) -{ - this->::revolve::DifferentialCPG::update(_motors, _sensors, _time, _step); -} - -revolve::DifferentialCPG::ControllerParams DifferentialCPGClean::load_params_from_sdf(sdf::ElementPtr brain_sdf) { - // Get all params from the sdf - // TODO: Add exception handling - sdf::ElementPtr controller_sdf = brain_sdf->GetElement("rv:controller"); - revolve::DifferentialCPG::ControllerParams params; - params.reset_neuron_random = (controller_sdf->GetAttribute("reset_neuron_random")->GetAsString() == "true"); - params.use_frame_of_reference = (controller_sdf->GetAttribute("use_frame_of_reference")->GetAsString() == "true"); - params.init_neuron_state = stod(controller_sdf->GetAttribute("init_neuron_state")->GetAsString()); - params.range_ub = stod(controller_sdf->GetAttribute("range_ub")->GetAsString()); - params.signal_factor_all = stod(controller_sdf->GetAttribute("signal_factor_all")->GetAsString()); - params.signal_factor_mid = stod(controller_sdf->GetAttribute("signal_factor_mid")->GetAsString()); - params.signal_factor_left_right = stod(controller_sdf->GetAttribute("signal_factor_left_right")->GetAsString()); - params.abs_output_bound = stod(controller_sdf->GetAttribute("abs_output_bound")->GetAsString()); - - // Get the weights from the sdf: - // If loading with CPPN, the weights attribute does not exist - if (controller_sdf->HasAttribute("weights")) { - std::string sdf_weights = controller_sdf->GetAttribute("weights")->GetAsString(); - std::string delimiter = ";"; - - size_t pos = 0; - std::string token; - while ((pos = sdf_weights.find(delimiter)) != std::string::npos) { - token = sdf_weights.substr(0, pos); - params.weights.push_back(stod(token)); - sdf_weights.erase(0, pos + delimiter.length()); - } - // push the last element that does not end with the delimiter - params.weights.push_back(stod(sdf_weights)); - } - - return params; -} diff --git a/cpprevolve/revolve/gazebo/brains/DifferentialCPGClean.h b/cpprevolve/revolve/gazebo/brains/DifferentialCPGClean.h deleted file mode 100644 index 46ea487a54..0000000000 --- a/cpprevolve/revolve/gazebo/brains/DifferentialCPGClean.h +++ /dev/null @@ -1,55 +0,0 @@ -// -// Created by andi on 20-09-19. -// - -#ifndef REVOLVE_DIFFERENTIALCPGCLEAN_H -#define REVOLVE_DIFFERENTIALCPGCLEAN_H - -#include -#include -#include "Brain.h" - -namespace revolve -{ - namespace gazebo - { - /// \brief connection between gazebo and revolve CPG - /// \details gets the sdf - model data and passes them to revolve - class DifferentialCPGClean: public Brain, private revolve::DifferentialCPG - { - public: - /// \brief Constructor - /// \param[in] brain_sdf ElementPtr containing the "brain" - tag of the model sdf - /// \param[in] _motors vector list of motors - /// \details Extracts controller parameters - /// from brain_sdf and calls revolve::DifferentialCPG's contructor. - explicit DifferentialCPGClean(const sdf::ElementPtr brain_sdf, - const std::vector< MotorPtr > &_motors); - - /// \brief updates the motor signals - /// \param[in] _motors vector list of motors - /// \param[in] _sensors vector list of sensors - /// \param[in] _time double - /// \param[in] _step double - void Update(const std::vector &_motors, - const std::vector &_sensors, - const double _time, - const double _step) override; - - protected: - explicit DifferentialCPGClean(const sdf::ElementPtr brain_sdf, - const std::vector &_motors, - const NEAT::Genome &genome); - - /// \brief extracts CPG controller parameters from brain_sdf - /// \param[in] brain_sdf ElementPtr containing the "brain" - tag of the model sdf - /// \return parameters of the CPG controller - /// \details get the strings of the controller parameters and convert them to the - /// appropriate datatype. Store them in a revolve::DifferentialCPG::ControllerParams - /// struct and return them. - static revolve::DifferentialCPG::ControllerParams load_params_from_sdf(sdf::ElementPtr brain_sdf); - }; - } -} - -#endif //REVOLVE_DIFFERENTIALCPGCLEAN_H diff --git a/cpprevolve/revolve/gazebo/brains/DifferentialCPPNCPG.cpp b/cpprevolve/revolve/gazebo/brains/DifferentialCPPNCPG.cpp index eabd2013e3..8cf33fe469 100644 --- a/cpprevolve/revolve/gazebo/brains/DifferentialCPPNCPG.cpp +++ b/cpprevolve/revolve/gazebo/brains/DifferentialCPPNCPG.cpp @@ -6,7 +6,7 @@ #include "Brain.h" #include "DifferentialCPPNCPG.h" -#include "DifferentialCPGClean.h" +#include "DifferentialCPG.h" using namespace revolve::gazebo; @@ -26,7 +26,7 @@ bool string_replace(std::string& str, const std::string& from, const std::string DifferentialCPPNCPG::DifferentialCPPNCPG(const sdf::ElementPtr brain_sdf, const std::vector &motors) - : DifferentialCPGClean( + : DifferentialCPG( brain_sdf, motors, DifferentialCPPNCPG::load_cppn_genome_from_sdf(brain_sdf)) diff --git a/cpprevolve/revolve/gazebo/brains/DifferentialCPPNCPG.h b/cpprevolve/revolve/gazebo/brains/DifferentialCPPNCPG.h index 73b58f35e7..71efd803ff 100644 --- a/cpprevolve/revolve/gazebo/brains/DifferentialCPPNCPG.h +++ b/cpprevolve/revolve/gazebo/brains/DifferentialCPPNCPG.h @@ -6,7 +6,7 @@ #define REVOLVE_DIFFERENTIALCPPNCPG_H #include -#include "DifferentialCPGClean.h" +#include "DifferentialCPG.h" #include "Brain.h" @@ -15,7 +15,7 @@ namespace revolve { /// \brief connection between gazebo and revolve CPG with config CPPN /// \details gets the sdf - model data and passes them to revolve - class DifferentialCPPNCPG : public DifferentialCPGClean + class DifferentialCPPNCPG : public DifferentialCPG { public: /// \brief Constructor diff --git a/cpprevolve/revolve/gazebo/brains/Evaluator.cpp b/cpprevolve/revolve/gazebo/brains/Evaluator.cpp index e082cd1596..79c2004578 100644 --- a/cpprevolve/revolve/gazebo/brains/Evaluator.cpp +++ b/cpprevolve/revolve/gazebo/brains/Evaluator.cpp @@ -20,6 +20,10 @@ */ #include +#include +#include +#include + #include "Evaluator.h" @@ -35,66 +39,101 @@ double Evaluator::measure_distance( } ///////////////////////////////////////////////// -Evaluator::Evaluator(const double _evaluationRate, +Evaluator::Evaluator(const double evaluation_rate, + const bool reset_robot_position, + const ::gazebo::physics::ModelPtr &robot, const double step_saving_rate) - : last_step_time(-1) - , step_saving_rate(step_saving_rate) - , step_poses(0) + : ::revolve::Evaluator() + , evaluation_rate_(evaluation_rate) + , last_step_time(-1) + , step_saving_rate(step_saving_rate) + , step_poses(0) + , reset_robot_position(reset_robot_position) + , robot(robot) { - assert(_evaluationRate > 0 and "`_evaluationRate` should be greater than 0"); - this->evaluation_rate_ = _evaluationRate; - this->current_position_.Reset(); this->previous_position_.Reset(); this->start_position_.Reset(); - this->locomotion_type = "directed"; // {directed, gait} + this->locomotion_type = "gait"; // {turing_left,directed, gait} this->path_length = 0.0; + + std::string model_name = robot->GetName(); + this->output_dir = "./experiments/IMC/output"+model_name; + +// std::ofstream decom_file; +// decom_file.open(this->output_dir+"/fitness_decom.txt", std::ofstream::out | std::ofstream::trunc); +// decom_file.close(); + std::cout<<"Loading evaluator with task: "+this->locomotion_type<step_poses.clear(); //cleared to null - this->path_length = 0.0; - this->last_step_time = 0.0; - this->start_position_ = this->current_position_; + // Reset robot if opted to do + if (this->reset_robot_position) { + //this->robot->Reset(); + ::gazebo::physics::ModelPtr _robot = robot.lock(); + _robot->ResetPhysicsStates(); + auto start_pose = ::ignition::math::Pose3d(); + start_pose.Set(0.0, 0.0, 0.005, 0.0, 0.0, -.1); + for (const auto &joint_ : _robot->GetJoints()) { + std::string joint_name = joint_->GetScopedName(); + _robot->SetJointPosition(joint_name, 0.0); + joint_->SetPosition(0, 0.0); + } + _robot->SetWorldPose(start_pose); + for (const auto& joint_ : _robot->GetJoints()) { + std::string joint_name = joint_->GetScopedName(); + _robot->SetJointPosition(joint_name, 0.0); + joint_->SetPosition(0, 0.0); + + } + _robot->Update(); + this->current_position_ = start_pose; + } + + this->step_poses.clear(); //cleared to null + this->path_length = 0.0; + this->last_step_time = 0.0; + this->start_position_ = this->current_position_; } ///////////////////////////////////////////////// -double Evaluator::Fitness() +double Evaluator::fitness() { double fitness_value = 0.0; if(this->locomotion_type == "gait") { double dS; - dS = std::sqrt(std::pow(this->previous_position_.Pos().X() - + dS = std::sqrt(std::pow(this->start_position_.Pos().X() - this->current_position_.Pos().X(), 2) + - std::pow(this->previous_position_.Pos().Y() - + std::pow(this->start_position_.Pos().Y() - this->current_position_.Pos().Y(), 2)); fitness_value = dS / this->evaluation_rate_; + if(fitness_value > 5e-11){ + std::ofstream fitness_file; + fitness_file.open(this->output_dir + "/fitness_decom.txt", std::ios::app); + fitness_file << std::fixed + <start_position_.Pos().X() - this->current_position_.Pos().X(), 2)) + <<","<start_position_.Pos().Y() - this->current_position_.Pos().Y(), 2)) + <locomotion_type == "directed") { this->step_poses.push_back(this->current_position_); //step_poses: x y z roll pitch yaw - for (int i=1; i < this->step_poses.size(); i++) + for (size_t i=1; i < this->step_poses.size(); i++) { const auto &pose_i_1 = this->step_poses[i-1]; const auto &pose_i = this->step_poses[i]; this->path_length += Evaluator::measure_distance(pose_i_1, pose_i); - //save coordinations to coordinates.txt - std::ofstream coordinates; - coordinates.open(this->directory_name + "/coordinates.txt",std::ios::app); - - if(i == 1) - { - coordinates << std::fixed << start_position_.Pos().X() << " " << start_position_.Pos().Y() << std::endl; - } - coordinates << std::fixed << pose_i.Pos().X() << " " << pose_i.Pos().Y() << std::endl; } ////********** directed locomotion fitness function **********//// @@ -105,11 +144,6 @@ double Evaluator::Fitness() beta0 = 2 * M_PI - std::abs(beta0); } - //save direction to coordinates.txt: This is used to make Figure 8 - std::ofstream coordinates; - coordinates.open(this->directory_name + "/coordinates.txt",std::ios::app); - coordinates << std::fixed << beta0 << std::endl; - double beta1 = std::atan2( this->current_position_.Pos().Y() - this->start_position_.Pos().Y(), this->current_position_.Pos().X() - this->start_position_.Pos().X()); @@ -159,39 +193,90 @@ double Evaluator::Fitness() } //fitness_direction = dist_projection / (alpha + ksi) - penalty; - fitness_direction = std::abs(dist_projection) / path_length * - (dist_projection / (alpha + ksi) - penalty); +// fitness_direction = (dist_projection / (alpha + ksi) - penalty); + fitness_direction = dist_projection*std::abs(dist_projection) - dist_penalty*dist_penalty; fitness_value = fitness_direction; + + + double tot_dist = std::sqrt( + std::pow(dist_projection, 2.0) + std::pow(dist_penalty, 2.0)); + + // Write fitness to file + std::ofstream fitness_file; + fitness_file.open(this->output_dir + "/fitness_decom.txt", std::ios::app); + fitness_file << std::fixed + << fitness_value + <<","<locomotion_type == "turing_left") //anticlockwise + { + double orientations = 0.0; + double delta_orientations = 0.0; + double dS = 0.0; + for(int i = 1; i < this->step_poses.size(); i++) + { + const auto &pose_i_1 = this->step_poses[i-1]; + const auto &pose_i = this->step_poses[i]; + + dS = dS + Evaluator::measure_distance(pose_i_1, pose_i); + + double angle_i = pose_i.Rot().Yaw(); + double angle_i_1 = pose_i_1.Rot().Yaw(); + if(angle_i_1 > M_PI_2 and angle_i < - M_PI_2 ) // rotating left + { + delta_orientations = 2.0 * M_PI + angle_i - angle_i_1; + } + else if((angle_i_1 < - M_PI_2) and (angle_i > M_PI_2)) + { + delta_orientations = - (2.0 * M_PI - angle_i + angle_i_1); + } + else + { + delta_orientations = angle_i - angle_i_1; + } + orientations += delta_orientations; + + } + std::cout << "orientations: " << orientations << " dS: " << dS << std::endl; + double factor_orien_ds = 3.0; //TODO param + fitness_value = orientations - factor_orien_ds * dS; //dS in (0, 1.5) in 30s + } + + return fitness_value; } // update is always running in the loop -void Evaluator::Update(const ignition::math::Pose3d &_pose, - const double time, - const double step) +void Evaluator::simulation_update(const ignition::math::Pose3d &pose, + const double time, + const double step) { - // this->path_length += measure_distance(current_position_, _pose); + // this->path_length += measure_distance(current_position_, pose); this->previous_position_ = current_position_; - this->current_position_ = _pose; + this->current_position_ = pose; //If `last_step_time` is not initialized, do the initialization now if (this->last_step_time < 0) { this->last_step_time = time; // 0.005 - this->step_poses.push_back(_pose); + this->step_poses.push_back(pose); } //save the startPosition in the beginning of each iteration if (this->last_step_time < 0.001) // 0.001 < 0.005 { - this->step_poses.push_back(_pose); + this->step_poses.push_back(pose); this->last_step_time = time; } //update information each step if ((time - this->last_step_time) > this->evaluation_rate_ * this->step_saving_rate) { - this->step_poses.push_back(_pose); + this->step_poses.push_back(pose); this->last_step_time = time; }; } diff --git a/cpprevolve/revolve/gazebo/brains/Evaluator.h b/cpprevolve/revolve/gazebo/brains/Evaluator.h index 357634da57..4d22888f65 100644 --- a/cpprevolve/revolve/gazebo/brains/Evaluator.h +++ b/cpprevolve/revolve/gazebo/brains/Evaluator.h @@ -17,67 +17,70 @@ * */ -#ifndef REVOLVEBRAIN_BRAIN_EVALUATOR_H -#define REVOLVEBRAIN_BRAIN_EVALUATOR_H +#pragma once #include #include +#include -namespace revolve +namespace revolve { +namespace gazebo { +class Evaluator : public ::revolve::Evaluator { - namespace gazebo - { - class Evaluator - { - /// \brief Constructor - public: Evaluator(const double _evaluationRate, - const double step_saving_rate = 0.1); - - /// \brief Destructor - public: ~Evaluator(); - - /// \brief Initialisation method - public: void Reset(); - - /// \brief Retrieve the fitness - /// \return A fitness value according to a given formula - public: double Fitness(); - - public: double measure_distance( - const ignition::math::Pose3d &_pose1, - const ignition::math::Pose3d &_pose2); - - /// brief Specifies locomotion type - public: std::string locomotion_type; - - /// \brief Update the position - /// \param[in] _pose Current position of a robot - public: void Update(const ignition::math::Pose3d &_pose, - const double time, - const double step); - - /// \brief start position of a robot - protected: ignition::math::Pose3d start_position_; - - /// \brief Previous position of a robot - protected: ignition::math::Pose3d previous_position_; - - /// \brief Current position of a robot - protected: ignition::math::Pose3d current_position_; - - /// \brief - protected: double evaluation_rate_; - - protected: double path_length = 0.0; - - protected: double last_step_time; - protected: double step_saving_rate; - protected: std::vector step_poses; - // public: double current_dist_pro = 0.0; - public: std::string directory_name = ""; - }; - } -} +public: + /// \brief Constructor + Evaluator(double _evaluationRate, + bool reset_robot_position = true, + const ::gazebo::physics::ModelPtr &robot = nullptr, + double step_saving_rate = 0.1); + + /// \brief Destructor + ~Evaluator(); + + /// \brief Initialisation method + void reset() override; + + /// \brief Retrieve the fitness + /// \return A fitness value according to a given formula + double fitness() override; + + double measure_distance( + const ignition::math::Pose3d &_pose1, + const ignition::math::Pose3d &_pose2); + + /// brief Specifies locomotion type + std::string locomotion_type; + + /// \brief Update the position + /// \param[in] pose Current position of a robot + void simulation_update(const ignition::math::Pose3d &pose, + double time, + double step); + +protected: + /// \brief start position of a robot + ignition::math::Pose3d start_position_; -#endif // REVOLVEBRAIN_BRAIN_EVALUATOR_H + /// \brief Previous position of a robot + ignition::math::Pose3d previous_position_; + + /// \brief Current position of a robot + ignition::math::Pose3d current_position_; + + /// \brief + double evaluation_rate_; + + double path_length = 0.0; + + double last_step_time; + double step_saving_rate; + std::vector step_poses; + + const bool reset_robot_position; + const boost::weak_ptr<::gazebo::physics::Model> robot; + std::string output_dir; +}; + +} +} diff --git a/cpprevolve/revolve/gazebo/brains/GazeboReporter.cpp b/cpprevolve/revolve/gazebo/brains/GazeboReporter.cpp new file mode 100644 index 0000000000..fba278e9e5 --- /dev/null +++ b/cpprevolve/revolve/gazebo/brains/GazeboReporter.cpp @@ -0,0 +1,51 @@ +// +// Created by matteo on 12/5/19. +// + +#include "GazeboReporter.h" +#include +#include +#include + +using namespace revolve::gazebo; + + +GazeboReporter::GazeboReporter(const std::string id, ::gazebo::transport::NodePtr &node) + : EvaluationReporter(std::move(id)) + , last_eval(-1) +{ + robot_report_publisher = node->Advertise( + "~/revolve/robot_reports", 500); + message.set_id(robot_id); +} + +void GazeboReporter::report(const unsigned int eval, const bool dead, const double fitness) +{ + const std::lock_guard lock(message_mutex); + // construct protobuf message + message.set_eval(eval); + message.set_dead(dead); + message.set_fitness(fitness); + // behaviour is already collected in `simulation_update` + + // send msgs to Gazebo in python + this->robot_report_publisher->Publish(message); + + if (last_eval != eval) { + // Clear behaviour data + message.clear_behaviour(); + + last_eval = eval; + } +} + + +void GazeboReporter::simulation_update(const ignition::math::Pose3d &pose, + const ::gazebo::common::Time &time, + double /*step*/) +{ + const std::lock_guard lock(message_mutex); + ::revolve::msgs::BehaviourData *behaviour_data = message.add_behaviour(); + ::gazebo::msgs::Set(behaviour_data->mutable_pose(), pose); + ::gazebo::msgs::Set(behaviour_data->mutable_time(), time); +} diff --git a/cpprevolve/revolve/gazebo/brains/GazeboReporter.h b/cpprevolve/revolve/gazebo/brains/GazeboReporter.h new file mode 100644 index 0000000000..cfdb88898f --- /dev/null +++ b/cpprevolve/revolve/gazebo/brains/GazeboReporter.h @@ -0,0 +1,39 @@ +// +// Created by matteo on 12/5/19. +// + +#pragma once + +#include +#include +#include +#include +#include +#include + +namespace revolve { +namespace gazebo { + +class GazeboReporter : public EvaluationReporter +{ +public: + explicit GazeboReporter(std::string id, ::gazebo::transport::NodePtr &node); + ~GazeboReporter() override = default; + + /// \brief Sends proto message to python in gazebo + void report(unsigned int eval, bool dead, double fitness) override; + + void simulation_update(const ignition::math::Pose3d &pose, + const ::gazebo::common::Time &time, + double step); + +private: + ::gazebo::transport::PublisherPtr robot_report_publisher; + ::revolve::msgs::LearningRobotStates message; + + std::mutex message_mutex; + long last_eval; +}; + +} +} diff --git a/cpprevolve/revolve/gazebo/brains/NeuralNetwork.cpp b/cpprevolve/revolve/gazebo/brains/NeuralNetwork.cpp index 77eebbbba7..c30df9b21b 100644 --- a/cpprevolve/revolve/gazebo/brains/NeuralNetwork.cpp +++ b/cpprevolve/revolve/gazebo/brains/NeuralNetwork.cpp @@ -54,7 +54,8 @@ NeuralNetwork::NeuralNetwork( const sdf::ElementPtr &_settings, const std::vector< MotorPtr > &_motors, const std::vector< SensorPtr > &_sensors) - : flipState_(false) + : Controller(ControllerType::NEURAL_NETWORK) + , flipState_(false) , nInputs_(0) , nOutputs_(0) , nHidden_(0) @@ -395,7 +396,7 @@ void NeuralNetwork::Step(const double _time) } ///////////////////////////////////////////////// -void NeuralNetwork::Update( +void NeuralNetwork::update( const std::vector< MotorPtr > &_motors, const std::vector< SensorPtr > &_sensors, const double _time, diff --git a/cpprevolve/revolve/gazebo/brains/NeuralNetwork.h b/cpprevolve/revolve/gazebo/brains/NeuralNetwork.h index a1147f6663..cf98dcb526 100644 --- a/cpprevolve/revolve/gazebo/brains/NeuralNetwork.h +++ b/cpprevolve/revolve/gazebo/brains/NeuralNetwork.h @@ -19,14 +19,14 @@ * */ -#ifndef REVOLVE_GAZEBO_BRAIN_NEURALNETWORK_H_ -#define REVOLVE_GAZEBO_BRAIN_NEURALNETWORK_H_ +#pragma once #include #include #include #include +#include #include @@ -53,8 +53,7 @@ namespace revolve SUPG }; - class NeuralNetwork - : public Brain + class NeuralNetwork : public ::revolve::Controller { /// \brief Constructor /// \param[in] _modelName Name of the robot @@ -75,11 +74,11 @@ namespace revolve /// \param[in] _sensors Sensor list /// \param[in] _time Current world time /// \param[in] _step Current time step - public: virtual void Update( + public: void update( const std::vector< MotorPtr > &_motors, const std::vector< SensorPtr > &_sensors, - const double _time, - const double _step); + double _time, + double _step) override; /// \brief Steps the neural network protected: void Step(const double _time); @@ -153,5 +152,3 @@ namespace revolve }; } /* namespace gazebo */ } /* namespace revolve */ - -#endif /* REVOLVE_GAZEBO_BRAIN_NEURALNETWORK_H_ */ diff --git a/cpprevolve/revolve/gazebo/brains/RLPower.cpp b/cpprevolve/revolve/gazebo/brains/RLPower.cpp index a2b8e7f1dc..4cffc23d72 100644 --- a/cpprevolve/revolve/gazebo/brains/RLPower.cpp +++ b/cpprevolve/revolve/gazebo/brains/RLPower.cpp @@ -41,16 +41,13 @@ RLPower::RLPower( const ::gazebo::physics::ModelPtr &_model, const sdf::ElementPtr &_settings, const std::vector< MotorPtr > &_motors, - const std::vector< SensorPtr > &_sensors) - : generationCounter_(0) + const std::vector< SensorPtr > &/*_sensors*/) + : Controller(ControllerType::SPLINES) + , generationCounter_(0) , cycleStartTime_(-1) - , startTime_(-1) , evaluationRate_(30.0) // default + , startTime_(-1) { - // Create transport node - this->node_.reset(new gz::transport::Node()); - this->node_->Init(); - auto learner_settings = _settings->GetElement("rv:learner"); this->robot_ = _model; @@ -82,7 +79,7 @@ RLPower::RLPower( RLPower::~RLPower() = default; ///////////////////////////////////////////////// -void RLPower::Update( +void RLPower::update( const std::vector< MotorPtr > &_motors, const std::vector< SensorPtr > &/* _sensors */, double _time, @@ -103,7 +100,7 @@ void RLPower::Update( { this->UpdatePolicy(numMotors); this->startTime_ = _time; - this->evaluator_->Reset(); + this->evaluator_->reset(); } // generate outputs @@ -119,7 +116,7 @@ void RLPower::Update( } auto currPosition = this->robot_->WorldPose(); - this->evaluator_->Update(currPosition, _time, _step); +// this->evaluator_->update(currPosition, _time, _step); delete[] output; } @@ -476,7 +473,7 @@ const double RLPower::SIGMA = 0.98; double RLPower::Fitness() { - return this->evaluator_->Fitness(); + return this->evaluator_->fitness(); } void RLPower::Modify(ConstModifyPolicyPtr &/* _request */) diff --git a/cpprevolve/revolve/gazebo/brains/RLPower.h b/cpprevolve/revolve/gazebo/brains/RLPower.h index 2e49c0e9c7..c14e5bf195 100644 --- a/cpprevolve/revolve/gazebo/brains/RLPower.h +++ b/cpprevolve/revolve/gazebo/brains/RLPower.h @@ -30,6 +30,7 @@ #include #include +#include #include #include "Evaluator.h" @@ -39,8 +40,7 @@ namespace revolve { namespace gazebo { - class RLPower - : public Brain + class RLPower : public ::revolve::Controller { typedef const std::shared_ptr ConstModifyPolicyPtr; @@ -82,7 +82,7 @@ namespace revolve /// \param[in] _sensors: vector list of robot's sensors /// \param[in] _time: /// \param[in] _step: - public: void Update( + public: void update( const std::vector< MotorPtr > &_motors, const std::vector< SensorPtr > &_sensors, double _time, diff --git a/cpprevolve/revolve/gazebo/motors/PositionMotor.cpp b/cpprevolve/revolve/gazebo/motors/PositionMotor.cpp index 5ddbbb2899..744cf67d5b 100644 --- a/cpprevolve/revolve/gazebo/motors/PositionMotor.cpp +++ b/cpprevolve/revolve/gazebo/motors/PositionMotor.cpp @@ -18,7 +18,7 @@ #include #include - +#include #include "PositionMotor.h" namespace gz = gazebo; @@ -27,45 +27,45 @@ using namespace revolve::gazebo; ///////////////////////////////////////////////// PositionMotor::PositionMotor( - gz::physics::ModelPtr _model, - const std::string &_partId, - const std::string &_motorId, - const sdf::ElementPtr _motor, - const std::string &_coordinates) - : JointMotor(std::move(_model), _partId, _motorId, _motor, 1, _coordinates) - , positionTarget_(0) - , noise_(0) + gz::physics::ModelPtr _model, + const std::string &_partId, + const std::string &_motorId, + const sdf::ElementPtr _motor, + const std::string &_coordinates) + : JointMotor(std::move(_model), _partId, _motorId, _motor, 1, _coordinates) + , positionTarget_(0) + , noise_(0) { - // Retrieve upper / lower limit from joint set in parent constructor - // Truncate ranges to [-pi, pi] - this->upperLimit_ = std::fmin(M_PI, this->joint_->UpperLimit(0)); - this->lowerLimit_ = std::fmax(-M_PI, this->joint_->LowerLimit(0)); - this->fullRange_ = ((this->upperLimit_ - this->lowerLimit_ + 1e-12) >= - (2 * M_PI)); - - if (_motor->HasElement("rv:pid")) - { - auto pidElem = _motor->GetElement("rv:pid"); - this->pid_ = Motor::CreatePid(pidElem); - } - - auto noise = _motor->GetAttribute("noise"); - if (noise) - { - noise->Get(this->noise_); - } - - // I've asked this question at the Gazebo forums: - // http://answers.gazebosim.org/question/9071/joint-target-velocity-with-maximum-force/ - // Until it is answered I'm resorting to calling ODE functions directly - // to get this to work. This will result in some deprecation warnings. - // It has the added benefit of not requiring the world update - // connection though. - // updateConnection_ = gz::event::Events::ConnectWorldUpdateBegin(boost::bind( - // &PositionMotor::OnUpdate, this, _1)); - - auto maxEffort = joint_->GetEffortLimit(0); - joint_->SetParam("fmax", 0, maxEffort); + // Retrieve upper / lower limit from joint set in parent constructor + // Truncate ranges to [-pi, pi] + this->upperLimit_ = std::fmin(M_PI, this->joint_->UpperLimit(0)); + this->lowerLimit_ = std::fmax(-M_PI, this->joint_->LowerLimit(0)); + this->fullRange_ = ((this->upperLimit_ - this->lowerLimit_ + 1e-12) >= + (2 * M_PI)); + + if (_motor->HasElement("rv:pid")) + { + auto pidElem = _motor->GetElement("rv:pid"); + this->pid_ = Motor::CreatePid(pidElem); + } + + auto noise = _motor->GetAttribute("noise"); + if (noise) + { + noise->Get(this->noise_); + } + + // I've asked this question at the Gazebo forums: + // http://answers.gazebosim.org/question/9071/joint-target-velocity-with-maximum-force/ + // Until it is answered I'm resorting to calling ODE functions directly + // to get this to work. This will result in some deprecation warnings. + // It has the added benefit of not requiring the world update + // connection though. + // updateConnection_ = gz::event::Events::ConnectWorldUpdateBegin(boost::bind( + // &PositionMotor::OnUpdate, this, _1)); + + auto maxEffort = joint_->GetEffortLimit(0); + joint_->SetParam("fmax", 0, maxEffort); } ///////////////////////////////////////////////// @@ -76,60 +76,86 @@ PositionMotor::~PositionMotor() = default; // DoUpdate(info.simTime); // } +double PositionMotor::Current_State( Actuator::StateType type) +{ + if (type==0) + { + return this->joint_->Position(0); + } + else if (type == 1) + { + return this->joint_->GetVelocity(0); + } + else if (type == 2) + { + return this->joint_->GetForce(0); + } +} + ///////////////////////////////////////////////// void PositionMotor::write( - const double *outputs, - double /*step*/) + const double *outputs, + double /*step*/) { - // Just one network output, which is the first - auto output = outputs[0]; - - // Motor noise in range +/- noiseLevel * actualValue - output += ((2 * ignition::math::Rand::DblUniform() * this->noise_) - - this->noise_) * - output; - - // Truncate output to [0, 1] - // Note: Don't actually target the full joint range, this way a low update - // rate won't mess with the joint constraints as much leading to a more - // stable system. - output = std::fmin(std::fmax(1e-5, output), 0.99999); - this->positionTarget_ = this->lowerLimit_ + - (output * (this->upperLimit_ - this->lowerLimit_)); - - // Perform the actual motor update - this->DoUpdate(this->joint_->GetWorld()->SimTime()); + // Just one network output, which is the first + auto output = outputs[0]; + + // Motor noise in range +/- noiseLevel * actualValue + output += ((2 * ignition::math::Rand::DblUniform() * this->noise_) - + this->noise_) * + output; + + // Truncate output to [0, 1] + // Note: Don't actually target the full joint range, this way a low update + // rate won't mess with the joint constraints as much leading to a more + // stable system. + output = std::fmin(std::fmax(1e-5, output), 0.99999); +// this->positionTarget_ = this->lowerLimit_ + +// (output * (this->upperLimit_ - this->lowerLimit_)); + + this->positionTarget_ = output*2-1;//2*5.235988-5.235988; + // Perform the actual motor update + this->DoUpdate(this->joint_->GetWorld()->SimTime()); } ///////////////////////////////////////////////// void PositionMotor::DoUpdate(const ::gazebo::common::Time &_simTime) { - auto stepTime = _simTime - this->prevUpdateTime_; - if (stepTime <= 0) - { - // Only respond to positive step times - return; - } - - this->prevUpdateTime_ = _simTime; - auto position = this->joint_->Position(0); - - // TODO Make sure normalized angle lies within possible range - // I get the feeling we might be moving motors outside their - // allowed range. Also something to be aware of when setting - // the direction. - - if (this->fullRange_ and std::fabs(position - positionTarget_) > M_PI) - { - // Both the position and the position target will be in the range - // [-pi, pi]. For a full range of motion joint, using an angle +- 2 PI - // might result in a much shorter required movement. In this case we - // best correct the current position to something outside the range. - position += (position > 0 ? -2 * M_PI : 2 * M_PI); - } - - auto error = position - this->positionTarget_; - auto cmd = this->pid_.Update(error, stepTime); - - this->joint_->SetParam("vel", 0, cmd); + auto stepTime = _simTime - this->prevUpdateTime_; + if (stepTime <= 0) + { + // Only respond to positive step times + return; + } + + this->prevUpdateTime_ = _simTime; + auto position = this->joint_->Position(0); + + // TODO Make sure normalized angle lies within possible range + // I get the feeling we might be moving motors outside their + // allowed range. Also something to be aware of when setting + // the direction. + + if (this->fullRange_ and std::fabs(position - positionTarget_) > M_PI) + { + // Both the position and the position target will be in the range + // [-pi, pi]. For a full range of motion joint, using an angle +- 2 PI + // might result in a much shorter required movement. In this case we + // best correct the current position to something outside the range. + position += (position > 0 ? -2 * M_PI : 2 * M_PI); + } + const double mean = 0.0; + const double stddev = 0.05; + std::default_random_engine generator; + auto dist = std::bind(std::normal_distribution{mean, stddev}, + std::mt19937(std::random_device{}())); +// std::normal_distribution dist(mean, stddev); // + auto error = (position - this->positionTarget_); + auto cmd = this->pid_.Update(error, stepTime)/stepTime.Double(); +// auto cmd = this->positionTarget_;//##################################### + auto velLimit = joint_->GetVelocityLimit(0); + cmd = std::fmax(-velLimit,std::fmin(velLimit,cmd)); + + double pert = dist()*velLimit; + this->joint_->SetParam("vel", 0, cmd); } diff --git a/cpprevolve/revolve/gazebo/motors/PositionMotor.h b/cpprevolve/revolve/gazebo/motors/PositionMotor.h index 429b696404..4fdb0ab95a 100644 --- a/cpprevolve/revolve/gazebo/motors/PositionMotor.h +++ b/cpprevolve/revolve/gazebo/motors/PositionMotor.h @@ -55,6 +55,8 @@ namespace revolve const double *_outputs, double _step) override; + public: virtual double Current_State( Actuator::StateType type ) override ; + /// \brief World update event function // protected: void OnUpdate(const ::gazebo::common::UpdateInfo info); diff --git a/cpprevolve/revolve/gazebo/motors/VelocityMotor.cpp b/cpprevolve/revolve/gazebo/motors/VelocityMotor.cpp index d8be304c46..0d286925a3 100644 --- a/cpprevolve/revolve/gazebo/motors/VelocityMotor.cpp +++ b/cpprevolve/revolve/gazebo/motors/VelocityMotor.cpp @@ -67,6 +67,18 @@ VelocityMotor::~VelocityMotor() { } +double VelocityMotor::Current_State( Actuator::StateType type) { + if (type == 0) { + return this->joint_->Position(0); + } else if (type == 1) { + return this->joint_->GetVelocity(0); + } + else if (type == 2) + { + return this->joint_->GetForce(0); + } +} + void VelocityMotor::write( const double *outputs, double /*step*/) @@ -90,4 +102,12 @@ void VelocityMotor::DoUpdate(const ::gazebo::common::Time &/*simTime*/) // I'm caving for now and am setting ODE parameters directly. // See https://tinyurl.com/y7he7y8l this->joint_->SetParam("vel", 0, this->velocityTarget_); +// this->pid_.S + this->model_->GetJointController()->SetVelocityPID( + this->joint_->GetScopedName(),this->pid_); + + this->model_->GetJointController()->SetVelocityTarget( + this->joint_->GetScopedName(),this->velocityTarget_); + + } diff --git a/cpprevolve/revolve/gazebo/motors/VelocityMotor.h b/cpprevolve/revolve/gazebo/motors/VelocityMotor.h index 29868d5359..b2ccf3c54a 100644 --- a/cpprevolve/revolve/gazebo/motors/VelocityMotor.h +++ b/cpprevolve/revolve/gazebo/motors/VelocityMotor.h @@ -60,6 +60,8 @@ namespace revolve const double *outputs, double step); + public: virtual double Current_State( Actuator::StateType type ) override ; + /// \brief World update event function // protected: void OnUpdate(const ::gazebo::common::UpdateInfo info); diff --git a/cpprevolve/revolve/gazebo/msgs/robot_states_learning.proto b/cpprevolve/revolve/gazebo/msgs/robot_states_learning.proto new file mode 100644 index 0000000000..9c0f32b82d --- /dev/null +++ b/cpprevolve/revolve/gazebo/msgs/robot_states_learning.proto @@ -0,0 +1,19 @@ +syntax = "proto2"; +package revolve.msgs; +import "time.proto"; +import "pose.proto"; + +message BehaviourData { + required gazebo.msgs.Time time = 1; + optional gazebo.msgs.Pose pose = 2; + // optional gazebo.msgs.Balance balance = 3; + // optional gazebo.msgs.Touching touching = 4; +} + +message LearningRobotStates { + required string id = 1; + required uint32 eval = 2; + optional bool dead = 3; + required double fitness = 4; + repeated BehaviourData behaviour = 5; +} diff --git a/cpprevolve/revolve/gazebo/plugin/RealtimeWorldController.cpp b/cpprevolve/revolve/gazebo/plugin/RealtimeWorldController.cpp new file mode 100644 index 0000000000..1ab329aafa --- /dev/null +++ b/cpprevolve/revolve/gazebo/plugin/RealtimeWorldController.cpp @@ -0,0 +1,438 @@ +/* +* Copyright (C) 2017 Vrije Universiteit Amsterdam +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* +* Author: Elte Hupkes +* Date: June 6, 2015 +* +*/ + +#include + +#include "RealtimeWorldController.h" + +namespace gz = gazebo; + +using namespace revolve::gazebo; + +///////////////////////////////////////////////// +RealtimeWorldController::RealtimeWorldController() + : delete_robot_queue() + , robotStatesPubFreq_(5) + , lastRobotStatesUpdateTime_(0) + , robotStatesPub_(nullptr) +{ +} + +void unsubscribe(gz::transport::SubscriberPtr &subscription) +{ + if (subscription) + subscription->Unsubscribe(); +} + +void fini(gz::transport::PublisherPtr &publisher) +{ + if (publisher) + publisher->Fini(); +} + +RealtimeWorldController::~RealtimeWorldController() +{ + unsubscribe(this->requestSub_); + unsubscribe(this->responseSub_); + unsubscribe(this->modelSub_); + fini(this->requestPub_); + fini(this->responsePub_); + fini(this->robotStatesPub_); +} + +///////////////////////////////////////////////// +void RealtimeWorldController::Load( + gz::physics::WorldPtr world, + sdf::ElementPtr /*_sdf*/) +{ + gz::physics::PhysicsEnginePtr physicsEngine = world->Physics(); + assert(physicsEngine != nullptr); + + // Turn on threading + physicsEngine->SetParam("thread_position_correction", true); + physicsEngine->SetParam("island_threads", 8); + + + std::cout << "World plugin loaded." << std::endl; + + // Store the world + this->world_ = world; + + // Create transport node + this->node_.reset(new gz::transport::Node()); + this->node_->Init(); + + // Subscribe to insert request messages + this->requestSub_ = this->node_->Subscribe( + "~/request", + &RealtimeWorldController::HandleRequest, + this); + + // Publisher for `entity_delete` requests. + this->requestPub_ = this->node_->Advertise< gz::msgs::Request >( + "~/request"); + + // Publisher for inserted models + this->responseSub_ = this->node_->Subscribe( + "~/response", + &RealtimeWorldController::HandleResponse, + this); + + // Publisher for inserted models + this->responsePub_ = this->node_->Advertise< gz::msgs::Response >( + "~/response"); + + // Since models are added asynchronously, we need some way of detecting + // our model add. We do this using a model info subscriber. + this->modelSub_ = this->node_->Subscribe( + "~/model/info", + &RealtimeWorldController::OnModel, + this); + + // Bind to the world update event to perform some logic + this->onBeginUpdateConnection = gz::event::Events::ConnectWorldUpdateBegin( + [this] (const ::gazebo::common::UpdateInfo &_info) {this->OnBeginUpdate(_info);}); + + // Bind to the world update event to perform some logic + this->onEndUpdateConnection = gz::event::Events::ConnectWorldUpdateEnd( + [this] () {this->OnEndUpdate();}); + + // Robot pose publisher + this->robotStatesPub_ = this->node_->Advertise< revolve::msgs::RobotStates >( + "~/revolve/robot_states", 500); +} + +void RealtimeWorldController::Reset() +{ + this->lastRobotStatesUpdateTime_ = 0; //this->world_->SimTime().Double(); +} + +///////////////////////////////////////////////// +void RealtimeWorldController::OnBeginUpdate(const ::gazebo::common::UpdateInfo &_info) { + if (not this->robotStatesPubFreq_) { + return; + } + + auto secs = 1.0 / this->robotStatesPubFreq_; + auto time = _info.simTime.Double(); + if ((time - this->lastRobotStatesUpdateTime_) >= secs) { + // Send robot info update message, this only sends the + // main pose of the robot (which is all we need for now) + msgs::RobotStates msg; + gz::msgs::Set(msg.mutable_time(), _info.simTime); + + { + boost::recursive_mutex::scoped_lock lock_physics(*this->world_->Physics()->GetPhysicsUpdateMutex()); + for (const auto &model : this->world_->Models()) { + if (model->IsStatic()) { + // Ignore static models such as the ground and obstacles + continue; + } + + revolve::msgs::RobotState *stateMsg = msg.add_robot_state(); + const std::string scoped_name = model->GetScopedName(); + stateMsg->set_name(scoped_name); + stateMsg->set_id(model->GetId()); + + auto poseMsg = stateMsg->mutable_pose(); + auto relativePose = model->RelativePose(); + + gz::msgs::Set(poseMsg, relativePose); + + // Death sentence check + const std::string name = model->GetName(); + bool death_sentence = false; + double death_sentence_value = 0; + { + boost::mutex::scoped_lock lock_death(death_sentences_mutex_); + death_sentence = death_sentences_.count(name) > 0; + if (death_sentence) + death_sentence_value = death_sentences_[name]; + } + + if (death_sentence) { + if (death_sentence_value < 0) { + // Initialize death sentence + death_sentences_[name] = time - death_sentence_value; + stateMsg->set_dead(false); + } else { + bool alive = death_sentence_value > time; + stateMsg->set_dead(not alive); + + if (not alive) { + boost::mutex::scoped_lock lock(this->death_sentences_mutex_); + this->death_sentences_.erase(model->GetName()); + + this->models_to_remove.emplace_back(model); + } + } + } + } + } + + if (msg.robot_state_size() > 0) { + this->robotStatesPub_->Publish(msg); + this->lastRobotStatesUpdateTime_ = time; + } + } + + +// if (world_insert_remove_mutex.try_lock()) { + for (const auto &model: this->models_to_remove) { + std::cout << "Removing " << model->GetScopedName() << std::endl; +// this->world_->RemoveModel(model); +// gz::msgs::Request deleteReq; +// auto id = gz::physics::getUniqueId(); +// deleteReq.set_id(id); +// deleteReq.set_request("entity_delete"); +// deleteReq.set_data(model->GetScopedName()); +// this->requestPub_->Publish(deleteReq); + gz::transport::requestNoReply(this->world_->Name(), "entity_delete", model->GetScopedName()); + std::cout << "Removed " << model->GetScopedName() << std::endl; + + } + this->models_to_remove.clear(); +// this->world_insert_remove_mutex.unlock(); +// } +} + +void RealtimeWorldController::OnEndUpdate() +{ + { // check if there are robots to delete + std::tuple< ::gazebo::physics::ModelPtr, int> delete_robot; + { + boost::mutex::scoped_lock lock(this->deleteMutex_); + if (not this->delete_robot_queue.empty()) { + delete_robot = this->delete_robot_queue.front(); + this->delete_robot_queue.pop(); + } + } + auto model = std::get<0>(delete_robot); + auto request_id = std::get<1>(delete_robot); + if (model) + { + { +// boost::recursive_mutex::scoped_lock lock_physics(*this->world_->Physics()->GetPhysicsUpdateMutex()); + this->world_->RemoveModel(model); + } + + gz::msgs::Response resp; + resp.set_id(request_id); + resp.set_request("delete_robot"); + resp.set_response("success"); + this->responsePub_->Publish(resp); + } + } + + { // check if there are robots to insert + boost::mutex::scoped_lock lock(this->insertMutex_); + for (auto &iterator: this->insertMap_) + { + bool &insert_operation_pending = std::get<2>(iterator.second); + //std::cout << "trying to insert " << iterator.first << " - " << insert_operation_pending << std::endl; +// if (insert_operation_pending and this->world_insert_remove_mutex.try_lock()) + if (insert_operation_pending) + { + // Start insert operation! +// boost::recursive_mutex::scoped_lock lock_physics(*this->world_->Physics()->GetPhysicsUpdateMutex()); + const std::string &robotSDF = std::get<1>(iterator.second); + this->world_->InsertModelString(robotSDF); + insert_operation_pending = false; + break; + } + } + } +} + + +///////////////////////////////////////////////// +// Process insert and delete requests +void RealtimeWorldController::HandleRequest(ConstRequestPtr &request) +{ + if (request->request() == "delete_robot") + { + auto name = request->data(); + std::cout << "Processing request `" << request->id() + << "` to delete robot `" << name << "`" << std::endl; + +// auto model = this->world_->ModelByName(name); +// if (model) +// { +// // Tell the world to remove the model +// // Using `World::RemoveModel()` from here crashes the transport +// // library, the cause of which I've yet to figure out - it has +// // something to do with race conditions where the model is used by +// // the world while it is being updated. Fixing this completely +// // appears to be a rather involved process, instead, we'll use an +// // `entity_delete` request, which will make sure deleting the model +// // happens on the world thread. +// gz::msgs::Request deleteReq; +// auto id = gz::physics::getUniqueId(); +// deleteReq.set_id(id); +// deleteReq.set_request("entity_delete"); +// deleteReq.set_data(model->GetScopedName()); +// +// { +// boost::mutex::scoped_lock lock(this->deleteMutex_); +// this->delete_robot_queue.emplace(std::make_tuple(model, request->id())); +// } +// { +// boost::mutex::scoped_lock lock(this->death_sentences_mutex_); +// this->death_sentences_.erase(model->GetName()); +// } +// +// this->requestPub_->Publish(deleteReq); +// } +// else +// { + std::cerr << "Model `" << name << "` could not be found in the world." + << std::endl; + gz::msgs::Response resp; + resp.set_id(request->id()); + resp.set_request("delete_robot"); + resp.set_response("error"); + this->responsePub_->Publish(resp); +// } + } + else if (request->request() == "insert_sdf") + { + std::cout << "Processing insert model request ID `" << request->id() << "`." + << std::endl; + sdf::SDF robotSDF; + robotSDF.SetFromString(request->data()); + double lifespan_timeout = request->dbl_data(); + + // Get the model name, store in the expected map + auto name = robotSDF.Root()->GetElement("model")->GetAttribute("name") + ->GetAsString(); + + if (lifespan_timeout > 0) + { + boost::mutex::scoped_lock lock(death_sentences_mutex_); + // Initializes the death sentence negative because I don't dare to take the + // simulation time from this thread. + death_sentences_[name] = -lifespan_timeout; + } + + { + boost::mutex::scoped_lock lock(this->insertMutex_); + this->insertMap_[name] = std::make_tuple(request->id(), robotSDF.ToString(), true); + } + + //TODO insert here, it's better + //this->world_->InsertModelString(robotSDF.ToString()); + + // Don't leak memory + // https://bitbucket.org/osrf/sdformat/issues/104/memory-leak-in-element + robotSDF.Root()->Reset(); + } + else if (request->request() == "set_robot_state_update_frequency") + { + auto frequency = request->data(); + assert(frequency.find_first_not_of( "0123456789" ) == std::string::npos); + this->robotStatesPubFreq_ = (unsigned int)std::stoul(frequency); + std::cout << "Setting robot state update frequency to " + << this->robotStatesPubFreq_ << "." << std::endl; + + gz::msgs::Response resp; + resp.set_id(request->id()); + resp.set_request("set_robot_state_update_frequency"); + resp.set_response("success"); + + this->responsePub_->Publish(resp); + } +} + +///////////////////////////////////////////////// +void RealtimeWorldController::OnModel(ConstModelPtr &msg) +{ + auto name = msg->name(); + std::cout << "RealtimeWorldController::OnModel(" << name << ')' << std::endl; + + + int id; + bool insert_operation_pending; + { + boost::mutex::scoped_lock lock(this->insertMutex_); + if (this->insertMap_.count(name) <= 0) + { + // Insert was not requested here, ignore it + return; + } + const std::tuple &entry = this->insertMap_[name]; + id = std::get<0>(entry); + insert_operation_pending = std::get<2>(entry); + if (insert_operation_pending) + { + // Insert operation has not been done yet + // (but you should never be here, because we are in the "OnModel" function + return; + } + this->insertMap_.erase(name); + } + + // Respond with the inserted model + gz::msgs::Response resp; + resp.set_request("insert_sdf"); + resp.set_response("success"); + resp.set_id(id); + + msgs::ModelInserted inserted; + inserted.mutable_model()->CopyFrom(*msg); + gz::msgs::Set(inserted.mutable_time(), this->world_->SimTime()); + inserted.SerializeToString(resp.mutable_serialized_data()); + + this->responsePub_->Publish(resp); + +// this->world_insert_remove_mutex.unlock(); + + std::cout << "Model `" << name << "` inserted, world now contains " + << this->world_->ModelCount() << " models." << std::endl; +} + +///////////////////////////////////////////////// +void RealtimeWorldController::HandleResponse(ConstResponsePtr &response) +{ +// std::cout << "RealtimeWorldController::HandleResponse(" << response->request() << ')' << std::endl; + + if (response->request() not_eq "entity_delete") + { + return; + } + +// int id; +// { +// boost::mutex::scoped_lock lock(this->deleteMutex_); +// if (this->deleteMap_.count(response->id()) <= 0) +// { +// return; +// } +// +// id = this->deleteMap_[response->id()]; +// this->deleteMap_.erase(id); +// } + +// this->world_insert_remove_mutex.unlock(); + +// gz::msgs::Response resp; +// resp.set_id(id); +// resp.set_request("delete_robot"); +// resp.set_response("success"); +// this->responsePub_->Publish(resp); +} diff --git a/cpprevolve/revolve/gazebo/plugin/RealtimeWorldController.h b/cpprevolve/revolve/gazebo/plugin/RealtimeWorldController.h new file mode 100644 index 0000000000..3651b40729 --- /dev/null +++ b/cpprevolve/revolve/gazebo/plugin/RealtimeWorldController.h @@ -0,0 +1,133 @@ +/* +* Copyright (C) 2017 Vrije Universiteit Amsterdam +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* +* Description: TODO: +* Author: Elte Hupkes +* +*/ + +// +// Created by elte on 6-6-15. +// + +#pragma once + +#include +#include +#include + +#include + +#include +#include +#include +#include + +#include +#include + +namespace revolve { +namespace gazebo { + +class RealtimeWorldController: public ::gazebo::WorldPlugin +{ +public: + RealtimeWorldController(); + + virtual ~RealtimeWorldController(); + + virtual void Load( + ::gazebo::physics::WorldPtr _parent, + sdf::ElementPtr _sdf) override; + + virtual void Reset() override; + +protected: + // Listener for analysis requests + virtual void HandleRequest(ConstRequestPtr &request); + + // Listener for entity delete responses + virtual void HandleResponse(ConstResponsePtr &request); + + // Callback for model insertion + virtual void OnModel(ConstModelPtr &msg); + + // Method called + virtual void OnBeginUpdate(const ::gazebo::common::UpdateInfo &_info); + + virtual void OnEndUpdate(); + + // Maps model names to insert request IDs + // model_name -> request_id, SDF, insert_operation_pending + std::map > insertMap_; + + // Queue of `delete_robot` requests + std::queue> delete_robot_queue; + + // Stores the world + ::gazebo::physics::WorldPtr world_; + + // Transport node + ::gazebo::transport::NodePtr node_; + + // Mutex for the insertMap_ + boost::mutex insertMutex_; + + // Mutex for the deleteMap_ + boost::mutex deleteMutex_; + + // Request subscriber + ::gazebo::transport::SubscriberPtr requestSub_; + + // Request publisher + ::gazebo::transport::PublisherPtr requestPub_; + + // Response subscriber + ::gazebo::transport::SubscriberPtr responseSub_; + + // Response publisher + ::gazebo::transport::PublisherPtr responsePub_; + + // Subscriber for actual model insertion + ::gazebo::transport::SubscriberPtr modelSub_; + + // Publisher for periodic robot poses + ::gazebo::transport::PublisherPtr robotStatesPub_; + + // Frequency at which robot info is published + // Defaults to 0, which means no update at all + unsigned int robotStatesPubFreq_; + + // Pointer to the update event connection + ::gazebo::event::ConnectionPtr onBeginUpdateConnection; + ::gazebo::event::ConnectionPtr onEndUpdateConnection; + + // Last (simulation) time robot info was sent + double lastRobotStatesUpdateTime_; + + // Death sentence list. It collects all the end time for all robots that have + // a death sentence + // NEGATIVE DEATH SENTENCES mean total lifetime, death sentence not yet initialized. + std::map death_sentences_; + + // Mutex for the deleteMap_ + boost::mutex death_sentences_mutex_; + +// boost::mutex world_insert_remove_mutex; + + ::gazebo::physics::Model_V models_to_remove; +}; + +} // namespace gazebo +} // namespace revolve diff --git a/cpprevolve/revolve/gazebo/plugin/RobotController.cpp b/cpprevolve/revolve/gazebo/plugin/RobotController.cpp index 566efa4b92..30efed0b4c 100644 --- a/cpprevolve/revolve/gazebo/plugin/RobotController.cpp +++ b/cpprevolve/revolve/gazebo/plugin/RobotController.cpp @@ -17,13 +17,22 @@ * */ -#include - +#include +#include +#include #include #include #include #include +#include +#include +#include +#include +#include +#include +#include +#include #include "RobotController.h" @@ -199,44 +208,196 @@ SensorFactoryPtr RobotController::SensorFactory( ///////////////////////////////////////////////// void RobotController::LoadBrain(const sdf::ElementPtr _sdf) { - if (not _sdf->HasElement("rv:brain")) - { - std::cerr << "No robot brain detected, this is probably an error." - << std::endl; - return; - } + if (not _sdf->HasElement("rv:brain")) { + std::cerr << "No robot brain detected, this is probably an error." + << std::endl; + return; + } - auto brain_sdf = _sdf->GetElement("rv:brain"); - auto controller_type = brain_sdf->GetElement("rv:controller")->GetAttribute("type")->GetAsString(); - auto learner = brain_sdf->GetElement("rv:learner")->GetAttribute("type")->GetAsString(); - std::cout << "Loading controller " << controller_type << " and learner " << learner << std::endl; + auto brain_sdf = _sdf->GetElement("rv:brain"); + auto controller_type = brain_sdf->GetElement("rv:controller")->GetAttribute("type")->GetAsString(); +// auto IMC_params = brain_sdf->GetElement("rv:IMC")->GetElement("rv:params"); + auto learner_type = brain_sdf->GetElement("rv:learner")->GetAttribute("type")->GetAsString(); + std::cout << "Loading controller " << controller_type << " and learner " << learner_type << std::endl; - if ("offline" == learner and "ann" == controller_type) - { - brain_.reset(new NeuralNetwork(this->model_, brain_sdf, motors_, sensors_)); - } - else if ("rlpower" == learner and "spline" == controller_type) - { - if (not motors_.empty()) { - brain_.reset(new RLPower(this->model_, brain_sdf, motors_, sensors_)); + + //TODO parameters from SDF + const double evaluation_rate = 60.0; + const unsigned int n_learning_evaluations = 300; + + this->evaluator = std::make_unique<::revolve::gazebo::Evaluator>(evaluation_rate, true, this->model_); + + // aggregated reporter + std::unique_ptr aggregated_reporter(new AggregatedReporter(this->model_->GetName())); + + aggregated_reporter->create<::revolve::PrintReporter>(); + // gazebo network publisher reporter + this->gazebo_reporter.reset(new GazeboReporter(aggregated_reporter->robot_id, this->node_)); + aggregated_reporter->append(this->gazebo_reporter); + + this->reporter = std::move(aggregated_reporter); + + // SELECT CONTROLLER ------------------------------------------------------ + std::unique_ptr<::revolve::Controller> controller; + + if ("ann" == controller_type) { + controller = std::make_unique(this->model_, brain_sdf, motors_, sensors_); + } else if ("spline" == controller_type) { + if (not motors_.empty()) { + controller = std::make_unique(this->model_, brain_sdf, motors_, sensors_); + } + } else if ("cpg" == controller_type) { + controller = std::make_unique(brain_sdf, motors_); + } else if ("cppn-cpg" == controller_type) { + controller = std::make_unique(brain_sdf, motors_); + } else { + throw std::runtime_error("Robot brain: Controller \"" + controller_type + "\" is not supported."); + } + + sdf::ElementPtr IMC_sdf = brain_sdf->GetElement("rv:IMC"); + if( IMC_sdf->GetAttribute("active")->GetAsString() == "1"){ + std::cout << "Initializing IMC" << std::endl; + // ================= INITIALIZE IMC ==================== + IMC::IMCParams imc_params = IMC::IMCParams(); + imc_params.restore_checkpoint = (IMC_sdf->GetAttribute("restore_checkpoint")->GetAsString() == "1"); + imc_params.save_checkpoint = (IMC_sdf->GetAttribute("save_checkpoint")->GetAsString() == "1"); + imc_params.learning_rate = stod(IMC_sdf->GetAttribute("learning_rate")->GetAsString()); + imc_params.beta1 = stod(IMC_sdf->GetAttribute("beta1")->GetAsString()); + imc_params.beta2 = stod(IMC_sdf->GetAttribute("beta2")->GetAsString()); + imc_params.weight_decay = stod(IMC_sdf->GetAttribute("weight_decay")->GetAsString()); + imc_params.model_name = this->model_->GetName(); + + std::cout<<"IMC Parameters: lr:"<< imc_params.learning_rate<<", beta1:" << imc_params.beta1<<", beta2:" << imc_params.beta2<<", wd:" << imc_params.weight_decay << std::endl; + controller = std::make_unique(std::move(controller), motors_, imc_params); + std::cout<<"IMC has been Loaded"<>(std::move(controller)); + } else if ("rlpower" == learner_type) { + //TODO make RLPower generic + if ("spline" != controller_type) { + throw std::runtime_error("Robot brain: Learner RLPower not supported for \"" + controller_type + "\" controller."); + } + learner = std::make_unique>(std::move(controller)); + } else if ("bo" == learner_type) { + learner = std::make_unique( + std::move(controller), + this->evaluator.get(), + this->reporter.get(), + evaluation_rate, + n_learning_evaluations, + this->model_->GetName()); + } else if ("nipes" == learner_type) { + NIPES::NIPES_Parameters params = NIPES::NIPES_Parameters(); + + EA::Parameters EA_params = EA::Parameters(); + params.EA_params = EA_params; + params.EA_params.verbose = (brain_sdf->GetElement("rv:learner")->GetAttribute("verbose")->GetAsString() == "1"); + params.EA_params.population_size = stoi(brain_sdf->GetElement("rv:learner")->GetAttribute("population_size")->GetAsString()); + params.EA_params.max_eval = std::min(int(n_learning_evaluations), stoi(brain_sdf->GetElement("rv:learner")->GetAttribute("max_eval")->GetAsString())); + + auto dist = std::bind(std::uniform_int_distribution(), + std::mt19937(std::random_device{}())); + + learner = std::make_unique( + std::move(controller), + this->evaluator.get(), + this->reporter.get(), + params, + dist(), + evaluation_rate, + params.EA_params.max_eval, + this->model_->GetName()); + } else if ("de"==learner_type) { + DifferentialEvo::DE_Parameters params = DifferentialEvo::DE_Parameters(); + params.type = brain_sdf->GetElement("rv:learner")->GetAttribute("subtype")->GetAsString(); + params.CR = stod(brain_sdf->GetElement("rv:learner")->GetAttribute("CR")->GetAsString()); + params.F = stod(brain_sdf->GetElement("rv:learner")->GetAttribute("F")->GetAsString()); + params.n_parents = stoi(brain_sdf->GetElement("rv:learner")->GetAttribute("n_parents")->GetAsString());; + if (params.type == "dex3"){ + params.n_parents = 7; + } + + EA::Parameters EA_params = EA::Parameters(); + params.EA_params = EA_params; + params.EA_params.verbose = (brain_sdf->GetElement("rv:learner")->GetAttribute("verbose")->GetAsString() == "1"); + params.EA_params.population_size = stoi(brain_sdf->GetElement("rv:learner")->GetAttribute("population_size")->GetAsString()); + params.EA_params.max_eval = std::min(int(n_learning_evaluations), stoi(brain_sdf->GetElement("rv:learner")->GetAttribute("max_eval")->GetAsString())); + + auto dist = std::bind(std::uniform_int_distribution(), + std::mt19937(std::random_device{}())); + + learner = std::make_unique( + std::move(controller), + this->evaluator.get(), + this->reporter.get(), + params, + dist(), + evaluation_rate, + n_learning_evaluations, + this->model_->GetName()); +// } else if ("hyperneat" == learner_type) { +// NEAT::Parameters neat_params = NEAT::Parameters(); +// +// const sdf::ElementPtr learner_sdf = brain_sdf->GetElement("rv:learner")->GetElement("rv:params"); +// +//#define WRITE_DOUBLE_PARAM(x) std::cout << #x << " is set to: " << learner_sdf->GetAttribute(#x)->GetAsString() << std::endl; neat_params.x = stod(learner_sdf->GetAttribute(#x)->GetAsString()); +//#define CHECK_PARAM(x) {stod(std::to_string(neat_params.x))==stod(learner_sdf->GetAttribute(#x)->GetAsString()) ? std::cout << std::left <<#x << " is set to: Default" << std::endl : WRITE_DOUBLE_PARAM(x)} +// CHECK_PARAM(PopulationSize) +// CHECK_PARAM(WeightDiffCoeff) +// CHECK_PARAM(CompatTreshold) +// CHECK_PARAM(YoungAgeTreshold) +// CHECK_PARAM(OldAgeTreshold) +// CHECK_PARAM(MinSpecies) +// CHECK_PARAM(MaxSpecies) +// CHECK_PARAM(RouletteWheelSelection) +// CHECK_PARAM(RecurrentProb) +// CHECK_PARAM(OverallMutationRate) +// CHECK_PARAM(ArchiveEnforcement) +// CHECK_PARAM(MutateWeightsProb) +// CHECK_PARAM(WeightMutationMaxPower) +// CHECK_PARAM(WeightReplacementMaxPower) +// CHECK_PARAM(MutateWeightsSevereProb) +// CHECK_PARAM(WeightMutationRate) +// CHECK_PARAM(WeightReplacementRate) +// CHECK_PARAM(MaxWeight) +// CHECK_PARAM(MutateAddNeuronProb) +// CHECK_PARAM(MutateAddLinkProb) +// CHECK_PARAM(MutateRemLinkProb) +// CHECK_PARAM(MinActivationA) +// CHECK_PARAM(MaxActivationA) +// CHECK_PARAM(ActivationFunction_SignedSigmoid_Prob) +// CHECK_PARAM(ActivationFunction_UnsignedSigmoid_Prob) +// CHECK_PARAM(ActivationFunction_Tanh_Prob) +// CHECK_PARAM(ActivationFunction_SignedStep_Prob) +// CHECK_PARAM(CrossoverRate) +// CHECK_PARAM(MultipointCrossoverRate) +// CHECK_PARAM(SurvivalRate) +// CHECK_PARAM(MutateNeuronTraitsProb) +// CHECK_PARAM(MutateLinkTraitsProb) +//#undef CHECK_PARAM +//#undef WRITE_DOUBLE_PARAM +// +// neat_params.DynamicCompatibility = (learner_sdf->GetAttribute("DynamicCompatibility")->GetAsString() == "true"); +// neat_params.NormalizeGenomeSize = (learner_sdf->GetAttribute("NormalizeGenomeSize")->GetAsString() == "true"); +// neat_params.AllowLoops = (learner_sdf->GetAttribute("AllowLoops")->GetAsString() == "true"); +// neat_params.AllowClones = (learner_sdf->GetAttribute("AllowClones")->GetAsString() == "true"); +// +// int seed = 0; +// +// learner = std::make_unique( +// std::move(controller), +// this->evaluator.get(), +// this->reporter.get(), +// neat_params, +// seed, +// evaluation_rate, +// n_learning_evaluations); + } else { + throw std::runtime_error("Robot brain: Learner \"" + learner_type + "\" is not supported."); } - } - else if ("bo" == learner and "cpg" == controller_type) - { - brain_.reset(new DifferentialCPG(this->model_, _sdf, motors_, sensors_)); - } - else if ("offline" == learner and "cpg" == controller_type) - { - brain_.reset(new DifferentialCPGClean(brain_sdf, motors_)); - } - else if ("offline" == learner and "cppn-cpg" == controller_type) - { - brain_.reset(new DifferentialCPPNCPG(brain_sdf, motors_)); - } - else - { - throw std::runtime_error("Robot brain is not defined."); - } } ///////////////////////////////////////////////// @@ -254,7 +415,7 @@ void RobotController::CheckUpdate(const ::gazebo::common::UpdateInfo _info) { auto diff = _info.simTime - lastActuationTime_; - if (diff.Double() > actuationTime_) + if (diff.Double() >= actuationTime_) { this->DoUpdate(_info); lastActuationTime_ = _info.simTime; @@ -265,10 +426,28 @@ void RobotController::CheckUpdate(const ::gazebo::common::UpdateInfo _info) /// Default update function simply tells the brain to perform an update void RobotController::DoUpdate(const ::gazebo::common::UpdateInfo _info) { - auto currentTime = _info.simTime.Double() - initTime_; + const gz::common::Time current_time = _info.simTime - initTime_; + const double current_time_d = current_time.Double(); + const double delta_time = (_info.simTime - lastActuationTime_).Double(); + + //const ::ignition::math::Pose3d &relative_pose = model_->RelativePose(); + const ::ignition::math::Pose3d &world_pose = model_->WorldPose(); + + if (evaluator) { + evaluator->simulation_update(world_pose, current_time_d, delta_time); + } + + if (gazebo_reporter) { + gazebo_reporter->simulation_update(world_pose, current_time, delta_time); + } - if (brain_) - brain_->Update(motors_, sensors_, currentTime, actuationTime_); + if (learner) { + learner->optimize(current_time_d, delta_time); + revolve::Controller *controller = learner->controller(); + if (controller) { + controller->update(motors_, sensors_, current_time_d, delta_time); + } + } } ///////////////////////////////////////////////// diff --git a/cpprevolve/revolve/gazebo/plugin/RobotController.h b/cpprevolve/revolve/gazebo/plugin/RobotController.h index c72e36b3d2..48ec51d886 100644 --- a/cpprevolve/revolve/gazebo/plugin/RobotController.h +++ b/cpprevolve/revolve/gazebo/plugin/RobotController.h @@ -18,8 +18,7 @@ * */ -#ifndef REVOLVE_GAZEBO_PLUGIN_ROBOTCONTROLLER_H_ -#define REVOLVE_GAZEBO_PLUGIN_ROBOTCONTROLLER_H_ +#pragma once #include @@ -29,123 +28,133 @@ #include #include +#include #include "revolve/brains/controller/sensors/Sensor.h" #include "revolve/brains/controller/actuators/Actuator.h" +#include -namespace revolve +#include "revolve/brains/learner/NIPES.h" +#include "revolve/brains/learner/DifferentialEvo.h" + +namespace revolve { +namespace gazebo { + +class RobotController : public ::gazebo::ModelPlugin { - namespace gazebo - { - class RobotController - : public ::gazebo::ModelPlugin - { - /// \brief Constructor - public: RobotController(); +public: + /// \brief Constructor + RobotController(); - /// \brief Destructor - public: virtual ~RobotController(); + /// \brief Destructor + virtual ~RobotController(); - /// \brief Load method - public: void Load( - ::gazebo::physics::ModelPtr _parent, - sdf::ElementPtr _sdf) override; + /// \brief Load method + void Load( + ::gazebo::physics::ModelPtr _parent, + sdf::ElementPtr _sdf) override; - /// \return Factory class that creates motors for this model - public: virtual MotorFactoryPtr MotorFactory( - ::gazebo::physics::ModelPtr _model); + /// \return Factory class that creates motors for this model + virtual MotorFactoryPtr MotorFactory( + ::gazebo::physics::ModelPtr _model); - /// \return Factory class that creates motors for this robot - public: virtual SensorFactoryPtr SensorFactory( - ::gazebo::physics::ModelPtr _model); + /// \return Factory class that creates motors for this robot + virtual SensorFactoryPtr SensorFactory( + ::gazebo::physics::ModelPtr _model); - /// \brief Update event which, by default, is called periodically - /// according to the update rate specified in the robot plugin. - public: virtual void DoUpdate(const ::gazebo::common::UpdateInfo _info); + /// \brief Update event which, by default, is called periodically + /// according to the update rate specified in the robot plugin. + virtual void DoUpdate(const ::gazebo::common::UpdateInfo _info); - /// \brief Returns the battery level - /// \details Methods allows reading and writing the battery level in - /// the robot SDF. This is mostly useful for the `BatterySensor` to - /// obtain the battery state, and storing it in the SDF also means it - /// will be adequately backed up in an eventual snapshot. - public: double BatteryLevel(); + /// \brief Returns the battery level + /// \details Methods allows reading and writing the battery level in + /// the robot SDF. This is mostly useful for the `BatterySensor` to + /// obtain the battery state, and storing it in the SDF also means it + /// will be adequately backed up in an eventual snapshot. + double BatteryLevel(); - /// \brief Sets the battery level if possible - public: void SetBatteryLevel(double _level); + /// \brief Sets the battery level if possible + void SetBatteryLevel(double _level); - /// \brief Request listener for battery update - public: void UpdateBattery(ConstRequestPtr &_request); + /// \brief Request listener for battery update + void UpdateBattery(ConstRequestPtr &_request); - /// \brief Detects and loads motors in the plugin spec - protected: virtual void LoadActuators(const sdf::ElementPtr _sdf); + /// \brief Detects and loads motors in the plugin spec +protected: + virtual void LoadActuators(const sdf::ElementPtr _sdf); - /// \brief Detects and loads sensors in the plugin spec. - protected: virtual void LoadSensors(const sdf::ElementPtr _sdf); + /// \brief Detects and loads sensors in the plugin spec. + virtual void LoadSensors(const sdf::ElementPtr _sdf); - /// \brief Loads the brain from the `rv:brain` element. - /// \details By default this tries to construct a `StandardNeuralNetwork`. - protected: virtual void LoadBrain(const sdf::ElementPtr _sdf); + /// \brief Loads the brain from the `rv:brain` element. + /// \details By default this tries to construct a `StandardNeuralNetwork`. + virtual void LoadBrain(const sdf::ElementPtr _sdf); - /// \brief Loads / initializes the robot battery - protected: virtual void LoadBattery(const sdf::ElementPtr _sdf); + /// \brief Loads / initializes the robot battery + virtual void LoadBattery(const sdf::ElementPtr _sdf); - /// \brief Method called at the end of the default `Load` function. - /// \details This should be used to initialize robot actuation, i.e. - /// register some update event. By default, this grabs the - /// `update_rate` from the robot config pointer, and binds - protected: virtual void Startup( - ::gazebo::physics::ModelPtr _parent, - sdf::ElementPtr _sdf); + /// \brief Method called at the end of the default `Load` function. + /// \details This should be used to initialize robot actuation, i.e. + /// register some update event. By default, this grabs the + /// `update_rate` from the robot config pointer, and binds + virtual void Startup( + ::gazebo::physics::ModelPtr _parent, + sdf::ElementPtr _sdf); - /// \brief Default method bound to world update event, checks whether the - /// \brief actuation time has passed and updates if required. - protected: void CheckUpdate(const ::gazebo::common::UpdateInfo _info); + /// \brief Default method bound to world update event, checks whether the + /// \brief actuation time has passed and updates if required. + void CheckUpdate(const ::gazebo::common::UpdateInfo _info); - /// \brief Networking node - protected: ::gazebo::transport::NodePtr node_; +protected: + std::unique_ptr<::revolve::gazebo::Evaluator> evaluator; + std::unique_ptr<::revolve::EvaluationReporter> reporter; + std::shared_ptr<::revolve::gazebo::GazeboReporter> gazebo_reporter; - /// \brief Subscriber for battery update request - protected: ::gazebo::transport::SubscriberPtr batterySetSub_; + /// \brief Networking node + ::gazebo::transport::NodePtr node_; - /// \brief Responder for battery update request - protected: ::gazebo::transport::PublisherPtr batterySetPub_; + /// \brief Subscriber for battery update request + ::gazebo::transport::SubscriberPtr batterySetSub_; - /// \brief Holds an instance of the motor factory - protected: MotorFactoryPtr motorFactory_; + /// \brief Responder for battery update request + ::gazebo::transport::PublisherPtr batterySetPub_; - /// \brief Holds an instance of the sensor factory - protected: SensorFactoryPtr sensorFactory_; + /// \brief Holds an instance of the motor factory + MotorFactoryPtr motorFactory_; - /// \brief Brain controlling this model - protected: BrainPtr brain_; + /// \brief Holds an instance of the sensor factory + SensorFactoryPtr sensorFactory_; - /// \brief Actuation time, in seconds - protected: double actuationTime_; + /// \brief Learner for the brain controlling this model + std::unique_ptr<::revolve::Learner> learner; - /// \brief Time of initialisation - protected: double initTime_; + /// \brief Actuation time, in seconds + double actuationTime_; - /// \brief rv:battery element, if present - protected: sdf::ElementPtr batteryElem_; + /// \brief Time of initialisation + ::gazebo::common::Time initTime_; - /// \brief Time of the last actuation, in seconds and nanoseconds - protected: ::gazebo::common::Time lastActuationTime_; + /// \brief rv:battery element, if present + sdf::ElementPtr batteryElem_; - /// \brief Motors in this model - protected: std::vector< MotorPtr > motors_; + /// \brief Time of the last actuation, in seconds and nanoseconds + ::gazebo::common::Time lastActuationTime_; - /// \brief Sensors in this model - protected: std::vector< SensorPtr > sensors_; + /// \brief Motors in this model + std::vector motors_; - /// \brief Pointer to the model - protected: ::gazebo::physics::ModelPtr model_; + /// \brief Sensors in this model + std::vector sensors_; - /// \brief Pointer to the world - protected: ::gazebo::physics::WorldPtr world_; + /// \brief Pointer to the model + ::gazebo::physics::ModelPtr model_; - /// \brief Driver update event pointer - private: ::gazebo::event::ConnectionPtr updateConnection_; - }; - } /* namespace gazebo */ -} /* namespace revolve */ + /// \brief Pointer to the world + ::gazebo::physics::WorldPtr world_; -#endif /* REVOLVE_GAZEBO_PLUGIN_ROBOTCONTROLLER_H_ */ + /// \brief Driver update event pointer +private: + ::gazebo::event::ConnectionPtr updateConnection_; +}; + +} /* namespace gazebo */ +} /* namespace revolve */ diff --git a/cpprevolve/revolve/gazebo/plugin/WorldController.cpp b/cpprevolve/revolve/gazebo/plugin/WorldController.cpp index ea491681d7..cb584d0c8d 100644 --- a/cpprevolve/revolve/gazebo/plugin/WorldController.cpp +++ b/cpprevolve/revolve/gazebo/plugin/WorldController.cpp @@ -27,9 +27,20 @@ using namespace revolve::gazebo; ///////////////////////////////////////////////// WorldController::WorldController() - : delete_robot_queue() - , robotStatesPubFreq_(5) - , lastRobotStatesUpdateTime_(0) + : enable_parallelization(false) + , insertMap_() + , world_(nullptr) + , node_(nullptr) + , insertMutex_() + , requestSub_(nullptr) + , requestPub_(nullptr) + , responseSub_(nullptr) + , responsePub_(nullptr) + , modelSub_(nullptr) + , robotLearningStatesSub(nullptr) + , onBeginUpdateConnection(nullptr) + , onEndUpdateConnection(nullptr) + , models_to_remove() { } @@ -50,213 +61,95 @@ WorldController::~WorldController() unsubscribe(this->requestSub_); unsubscribe(this->responseSub_); unsubscribe(this->modelSub_); + unsubscribe(this->robotLearningStatesSub); fini(this->requestPub_); fini(this->responsePub_); - fini(this->robotStatesPub_); } ///////////////////////////////////////////////// void WorldController::Load( - gz::physics::WorldPtr world, - sdf::ElementPtr /*_sdf*/) + gz::physics::WorldPtr world, + sdf::ElementPtr /*_sdf*/) { gz::physics::PhysicsEnginePtr physicsEngine = world->Physics(); - assert(physicsEngine != nullptr); + assert(physicsEngine != nullptr && "Physics Engine is nullptr"); // Turn on threading - physicsEngine->SetParam("thread_position_correction", true); - physicsEngine->SetParam("island_threads", 8); - - - std::cout << "World plugin loaded." << std::endl; - - // Store the world - this->world_ = world; - - // Create transport node - this->node_.reset(new gz::transport::Node()); - this->node_->Init(); - - // Subscribe to insert request messages - this->requestSub_ = this->node_->Subscribe( - "~/request", - &WorldController::HandleRequest, - this); - - // Publisher for `entity_delete` requests. - this->requestPub_ = this->node_->Advertise< gz::msgs::Request >( - "~/request"); - - // Publisher for inserted models - this->responseSub_ = this->node_->Subscribe( - "~/response", - &WorldController::HandleResponse, - this); - - // Publisher for inserted models - this->responsePub_ = this->node_->Advertise< gz::msgs::Response >( - "~/response"); - - // Since models are added asynchronously, we need some way of detecting - // our model add. We do this using a model info subscriber. - this->modelSub_ = this->node_->Subscribe( - "~/model/info", - &WorldController::OnModel, - this); - - // Bind to the world update event to perform some logic - this->onBeginUpdateConnection = gz::event::Events::ConnectWorldUpdateBegin( - [this] (const ::gazebo::common::UpdateInfo &_info) {this->OnBeginUpdate(_info);}); - - // Bind to the world update event to perform some logic - this->onEndUpdateConnection = gz::event::Events::ConnectWorldUpdateEnd( - [this] () {this->OnEndUpdate();}); + if (this->enable_parallelization) { + physicsEngine->SetParam("thread_position_correction", true); + physicsEngine->SetParam("island_threads", 8); + } - // Robot pose publisher - this->robotStatesPub_ = this->node_->Advertise< revolve::msgs::RobotStates >( - "~/revolve/robot_states", 500); + // Store the world + this->world_ = world; + + // Create transport node + this->node_.reset(new gz::transport::Node()); + this->node_->Init(); + + // Subscribe to insert request messages + this->requestSub_ = this->node_->Subscribe( + "~/request", + &WorldController::HandleRequest, + this); + + // Publisher for `entity_delete` requests. + this->requestPub_ = this->node_->Advertise( + "~/request"); + + // Publisher for inserted models + this->responseSub_ = this->node_->Subscribe( + "~/response", + &WorldController::HandleResponse, + this); + + // Publisher for inserted models + this->responsePub_ = this->node_->Advertise( + "~/response"); + + // Since models are added asynchronously, we need some way of detecting + // our model add. We do this using a model info subscriber. + this->modelSub_ = this->node_->Subscribe( + "~/model/info", + &WorldController::OnModel, + this); + + // Bind to the world update event to perform some logic + this->onBeginUpdateConnection = gz::event::Events::ConnectWorldUpdateBegin( + [this](const ::gazebo::common::UpdateInfo &_info) { this->OnBeginUpdate(_info); }); + + // Bind to the world update event to perform some logic + this->onEndUpdateConnection = gz::event::Events::ConnectWorldUpdateEnd( + [this]() { this->OnEndUpdate(); }); + + // Robot reports subscription + this->robotLearningStatesSub = this->node_->Subscribe( + "~/revolve/robot_reports", + &WorldController::OnRobotReport, + this); + + std::cout << "World plugin loaded." << std::endl; } void WorldController::Reset() -{ - this->lastRobotStatesUpdateTime_ = 0; //this->world_->SimTime().Double(); -} +{} ///////////////////////////////////////////////// -void WorldController::OnBeginUpdate(const ::gazebo::common::UpdateInfo &_info) { - if (not this->robotStatesPubFreq_) { - return; - } - - auto secs = 1.0 / this->robotStatesPubFreq_; - auto time = _info.simTime.Double(); - if ((time - this->lastRobotStatesUpdateTime_) >= secs) { - // Send robot info update message, this only sends the - // main pose of the robot (which is all we need for now) - msgs::RobotStates msg; - gz::msgs::Set(msg.mutable_time(), _info.simTime); - - { - boost::recursive_mutex::scoped_lock lock_physics(*this->world_->Physics()->GetPhysicsUpdateMutex()); - for (const auto &model : this->world_->Models()) { - if (model->IsStatic()) { - // Ignore static models such as the ground and obstacles - continue; - } - - revolve::msgs::RobotState *stateMsg = msg.add_robot_state(); - const std::string scoped_name = model->GetScopedName(); - stateMsg->set_name(scoped_name); - stateMsg->set_id(model->GetId()); - - auto poseMsg = stateMsg->mutable_pose(); - auto relativePose = model->RelativePose(); - - gz::msgs::Set(poseMsg, relativePose); - - // Death sentence check - const std::string name = model->GetName(); - bool death_sentence = false; - double death_sentence_value = 0; - { - boost::mutex::scoped_lock lock_death(death_sentences_mutex_); - death_sentence = death_sentences_.count(name) > 0; - if (death_sentence) - death_sentence_value = death_sentences_[name]; - } - - if (death_sentence) { - if (death_sentence_value < 0) { - // Initialize death sentence - death_sentences_[name] = time - death_sentence_value; - stateMsg->set_dead(false); - } else { - bool alive = death_sentence_value > time; - stateMsg->set_dead(not alive); - - if (not alive) { - boost::mutex::scoped_lock lock(this->death_sentences_mutex_); - this->death_sentences_.erase(model->GetName()); - - this->models_to_remove.emplace_back(model); - } - } - } - } - } - - if (msg.robot_state_size() > 0) { - this->robotStatesPub_->Publish(msg); - this->lastRobotStatesUpdateTime_ = time; - } - } - +void WorldController::OnBeginUpdate(const ::gazebo::common::UpdateInfo &_info) +{ + boost::recursive_mutex::scoped_lock lock_physics(*this->world_->Physics()->GetPhysicsUpdateMutex()); -// if (world_insert_remove_mutex.try_lock()) { + if (model_remove_mutex.try_lock()) { for (const auto &model: this->models_to_remove) { - std::cout << "Removing " << model->GetScopedName() << std::endl; -// this->world_->RemoveModel(model); -// gz::msgs::Request deleteReq; -// auto id = gz::physics::getUniqueId(); -// deleteReq.set_id(id); -// deleteReq.set_request("entity_delete"); -// deleteReq.set_data(model->GetScopedName()); -// this->requestPub_->Publish(deleteReq); gz::transport::requestNoReply(this->world_->Name(), "entity_delete", model->GetScopedName()); - std::cout << "Removed " << model->GetScopedName() << std::endl; - } this->models_to_remove.clear(); -// this->world_insert_remove_mutex.unlock(); -// } + this->model_remove_mutex.unlock(); + } } void WorldController::OnEndUpdate() { - { // check if there are robots to delete - std::tuple< ::gazebo::physics::ModelPtr, int> delete_robot; - { - boost::mutex::scoped_lock lock(this->deleteMutex_); - if (not this->delete_robot_queue.empty()) { - delete_robot = this->delete_robot_queue.front(); - this->delete_robot_queue.pop(); - } - } - auto model = std::get<0>(delete_robot); - auto request_id = std::get<1>(delete_robot); - if (model) - { - { -// boost::recursive_mutex::scoped_lock lock_physics(*this->world_->Physics()->GetPhysicsUpdateMutex()); - this->world_->RemoveModel(model); - } - - gz::msgs::Response resp; - resp.set_id(request_id); - resp.set_request("delete_robot"); - resp.set_response("success"); - this->responsePub_->Publish(resp); - } - } - - { // check if there are robots to insert - boost::mutex::scoped_lock lock(this->insertMutex_); - for (auto &iterator: this->insertMap_) - { - bool &insert_operation_pending = std::get<2>(iterator.second); - //std::cout << "trying to insert " << iterator.first << " - " << insert_operation_pending << std::endl; -// if (insert_operation_pending and this->world_insert_remove_mutex.try_lock()) - if (insert_operation_pending) - { - // Start insert operation! -// boost::recursive_mutex::scoped_lock lock_physics(*this->world_->Physics()->GetPhysicsUpdateMutex()); - const std::string &robotSDF = std::get<1>(iterator.second); - this->world_->InsertModelString(robotSDF); - insert_operation_pending = false; - break; - } - } - } } @@ -264,98 +157,61 @@ void WorldController::OnEndUpdate() // Process insert and delete requests void WorldController::HandleRequest(ConstRequestPtr &request) { - if (request->request() == "delete_robot") - { - auto name = request->data(); - std::cout << "Processing request `" << request->id() - << "` to delete robot `" << name << "`" << std::endl; - -// auto model = this->world_->ModelByName(name); -// if (model) -// { -// // Tell the world to remove the model -// // Using `World::RemoveModel()` from here crashes the transport -// // library, the cause of which I've yet to figure out - it has -// // something to do with race conditions where the model is used by -// // the world while it is being updated. Fixing this completely -// // appears to be a rather involved process, instead, we'll use an -// // `entity_delete` request, which will make sure deleting the model -// // happens on the world thread. -// gz::msgs::Request deleteReq; -// auto id = gz::physics::getUniqueId(); -// deleteReq.set_id(id); -// deleteReq.set_request("entity_delete"); -// deleteReq.set_data(model->GetScopedName()); -// -// { -// boost::mutex::scoped_lock lock(this->deleteMutex_); -// this->delete_robot_queue.emplace(std::make_tuple(model, request->id())); -// } -// { -// boost::mutex::scoped_lock lock(this->death_sentences_mutex_); -// this->death_sentences_.erase(model->GetName()); -// } -// -// this->requestPub_->Publish(deleteReq); -// } -// else -// { - std::cerr << "Model `" << name << "` could not be found in the world." - << std::endl; - gz::msgs::Response resp; - resp.set_id(request->id()); - resp.set_request("delete_robot"); - resp.set_response("error"); - this->responsePub_->Publish(resp); -// } - } - else if (request->request() == "insert_sdf") - { - std::cout << "Processing insert model request ID `" << request->id() << "`." - << std::endl; - sdf::SDF robotSDF; - robotSDF.SetFromString(request->data()); - double lifespan_timeout = request->dbl_data(); - - // Get the model name, store in the expected map - auto name = robotSDF.Root()->GetElement("model")->GetAttribute("name") - ->GetAsString(); - - if (lifespan_timeout > 0) - { - boost::mutex::scoped_lock lock(death_sentences_mutex_); - // Initializes the death sentence negative because I don't dare to take the - // simulation time from this thread. - death_sentences_[name] = -lifespan_timeout; + const std::string &request_type = request->request(); + + if (request_type == "delete_robot") { + const std::string &name = request->data(); + std::cerr << "Removing Model `" << name << "` operation is not supported." << std::endl; + gz::msgs::Response resp; + resp.set_id(request->id()); + resp.set_request("delete_robot"); + resp.set_response("not_supported"); + this->responsePub_->Publish(resp); } - + else if (request_type == "insert_sdf") { - boost::mutex::scoped_lock lock(this->insertMutex_); - this->insertMap_[name] = std::make_tuple(request->id(), robotSDF.ToString(), true); - } + std::cout << "Processing insert model request ID `" << request->id() << "`." + << std::endl; + sdf::SDF robotSDF; + robotSDF.SetFromString(request->data()); - //TODO insert here, it's better - //this->world_->InsertModelString(robotSDF.ToString()); - - // Don't leak memory - // https://bitbucket.org/osrf/sdformat/issues/104/memory-leak-in-element - robotSDF.Root()->Reset(); - } - else if (request->request() == "set_robot_state_update_frequency") - { - auto frequency = request->data(); - assert(frequency.find_first_not_of( "0123456789" ) == std::string::npos); - this->robotStatesPubFreq_ = (unsigned int)std::stoul(frequency); - std::cout << "Setting robot state update frequency to " - << this->robotStatesPubFreq_ << "." << std::endl; + // Get the model name, store in the expected map + auto name = robotSDF.Root() + ->GetElement("model") + ->GetAttribute("name") + ->GetAsString(); - gz::msgs::Response resp; - resp.set_id(request->id()); - resp.set_request("set_robot_state_update_frequency"); - resp.set_response("success"); + { + boost::mutex::scoped_lock lock(this->insertMutex_); + this->insertMap_[name] = request->id(); + } - this->responsePub_->Publish(resp); - } + // insert here, it's better. Here you can insert when the world is paused + { + boost::recursive_mutex::scoped_lock lock_physics(*this->world_->Physics()->GetPhysicsUpdateMutex()); + this->world_->InsertModelString(robotSDF.ToString()); + } + + // Don't leak memory + // https://bitbucket.org/osrf/sdformat/issues/104/memory-leak-in-element + robotSDF.Root()->Reset(); + } + else if (request_type == "set_robot_state_update_frequency") + { + // Handle and fail this message, it could cause weird deadlocks if it's not responded properly + auto frequency = request->data(); + assert(frequency.find_first_not_of("0123456789") == std::string::npos); + unsigned int value = std::stoul(frequency); + std::cout << "Ignoring command to set robot state update frequency to " + << value << '.' << std::endl; + + gz::msgs::Response resp; + resp.set_id(request->id()); + resp.set_request("set_robot_state_update_frequency"); + resp.set_response("not_supported"); + + this->responsePub_->Publish(resp); + } } ///////////////////////////////////////////////// @@ -364,9 +220,7 @@ void WorldController::OnModel(ConstModelPtr &msg) auto name = msg->name(); std::cout << "WorldController::OnModel(" << name << ')' << std::endl; - int id; - bool insert_operation_pending; { boost::mutex::scoped_lock lock(this->insertMutex_); if (this->insertMap_.count(name) <= 0) @@ -374,15 +228,7 @@ void WorldController::OnModel(ConstModelPtr &msg) // Insert was not requested here, ignore it return; } - const std::tuple &entry = this->insertMap_[name]; - id = std::get<0>(entry); - insert_operation_pending = std::get<2>(entry); - if (insert_operation_pending) - { - // Insert operation has not been done yet - // (but you should never be here, because we are in the "OnModel" function - return; - } + id = this->insertMap_[name]; this->insertMap_.erase(name); } @@ -399,8 +245,6 @@ void WorldController::OnModel(ConstModelPtr &msg) this->responsePub_->Publish(resp); -// this->world_insert_remove_mutex.unlock(); - std::cout << "Model `" << name << "` inserted, world now contains " << this->world_->ModelCount() << " models." << std::endl; } @@ -408,30 +252,14 @@ void WorldController::OnModel(ConstModelPtr &msg) ///////////////////////////////////////////////// void WorldController::HandleResponse(ConstResponsePtr &response) { -// std::cout << "WorldController::HandleResponse(" << response->request() << ')' << std::endl; +} - if (response->request() not_eq "entity_delete") +void WorldController::OnRobotReport(const boost::shared_ptr &msg) +{ + if (msg->dead()) { - return; + boost::mutex::scoped_lock lock(this->model_remove_mutex); + gz::physics::ModelPtr model = world_->ModelByName(msg->id()); + this->models_to_remove.emplace_back(std::move(model)); } - -// int id; -// { -// boost::mutex::scoped_lock lock(this->deleteMutex_); -// if (this->deleteMap_.count(response->id()) <= 0) -// { -// return; -// } -// -// id = this->deleteMap_[response->id()]; -// this->deleteMap_.erase(id); -// } - -// this->world_insert_remove_mutex.unlock(); - -// gz::msgs::Response resp; -// resp.set_id(id); -// resp.set_request("delete_robot"); -// resp.set_response("success"); -// this->responsePub_->Publish(resp); } diff --git a/cpprevolve/revolve/gazebo/plugin/WorldController.h b/cpprevolve/revolve/gazebo/plugin/WorldController.h index 359badbd96..b4d10a3477 100644 --- a/cpprevolve/revolve/gazebo/plugin/WorldController.h +++ b/cpprevolve/revolve/gazebo/plugin/WorldController.h @@ -13,16 +13,11 @@ * limitations under the License. * * Description: TODO: -* Author: Elte Hupkes +* Author: Elte Hupkes and Matteo De Carlo * */ -// -// Created by elte on 6-6-15. -// - -#ifndef REVOLVE_WORLDCONTROLLER_H -#define REVOLVE_WORLDCONTROLLER_H +#pragma once #include #include @@ -37,22 +32,23 @@ #include #include +#include namespace revolve { namespace gazebo { -class WorldController: public ::gazebo::WorldPlugin +class WorldController : public ::gazebo::WorldPlugin { public: WorldController(); - virtual ~WorldController(); + ~WorldController() override; - virtual void Load( + void Load( ::gazebo::physics::WorldPtr _parent, sdf::ElementPtr _sdf) override; - virtual void Reset() override; + void Reset() override; protected: // Listener for analysis requests @@ -69,12 +65,14 @@ class WorldController: public ::gazebo::WorldPlugin virtual void OnEndUpdate(); + void OnRobotReport(const boost::shared_ptr &msg); + +protected: + const bool enable_parallelization; + // Maps model names to insert request IDs // model_name -> request_id, SDF, insert_operation_pending - std::map > insertMap_; - - // Queue of `delete_robot` requests - std::queue> delete_robot_queue; + std::map insertMap_; // Stores the world ::gazebo::physics::WorldPtr world_; @@ -85,9 +83,6 @@ class WorldController: public ::gazebo::WorldPlugin // Mutex for the insertMap_ boost::mutex insertMutex_; - // Mutex for the deleteMap_ - boost::mutex deleteMutex_; - // Request subscriber ::gazebo::transport::SubscriberPtr requestSub_; @@ -103,34 +98,16 @@ class WorldController: public ::gazebo::WorldPlugin // Subscriber for actual model insertion ::gazebo::transport::SubscriberPtr modelSub_; - // Publisher for periodic robot poses - ::gazebo::transport::PublisherPtr robotStatesPub_; - - // Frequency at which robot info is published - // Defaults to 0, which means no update at all - unsigned int robotStatesPubFreq_; + // Subscriber for periodic robot learning reports + ::gazebo::transport::SubscriberPtr robotLearningStatesSub; // Pointer to the update event connection ::gazebo::event::ConnectionPtr onBeginUpdateConnection; ::gazebo::event::ConnectionPtr onEndUpdateConnection; - // Last (simulation) time robot info was sent - double lastRobotStatesUpdateTime_; - - // Death sentence list. It collects all the end time for all robots that have - // a death sentence - // NEGATIVE DEATH SENTENCES mean total lifetime, death sentence not yet initialized. - std::map death_sentences_; - - // Mutex for the deleteMap_ - boost::mutex death_sentences_mutex_; - -// boost::mutex world_insert_remove_mutex; - + boost::mutex model_remove_mutex; ::gazebo::physics::Model_V models_to_remove; }; } // namespace gazebo } // namespace revolve - -#endif // REVOLVE_WORLDCONTROLLER_H diff --git a/cpprevolve/revolve/gazebo/plugin/register_realtime_world_plugin.cpp b/cpprevolve/revolve/gazebo/plugin/register_realtime_world_plugin.cpp new file mode 100644 index 0000000000..8d0be3ebd6 --- /dev/null +++ b/cpprevolve/revolve/gazebo/plugin/register_realtime_world_plugin.cpp @@ -0,0 +1,23 @@ +/* +* Copyright (C) 2018 Vrije Universiteit Amsterdam +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* +* Author: Matteo De Carlo +* +*/ + +#include +#include + +using namespace gazebo; +GZ_REGISTER_WORLD_PLUGIN(revolve::gazebo::RealtimeWorldController) diff --git a/cpprevolve/revolve/gazebo/util/YamlBodyParser.cpp b/cpprevolve/revolve/gazebo/util/YamlBodyParser.cpp index 2be14ae083..ec96bd756a 100644 --- a/cpprevolve/revolve/gazebo/util/YamlBodyParser.cpp +++ b/cpprevolve/revolve/gazebo/util/YamlBodyParser.cpp @@ -72,7 +72,7 @@ YamlBodyParser::YamlBodyParser(const std::string &_genome) } this->Init(yaml_genome); -}; +} /////////////////////////////////////////////////// void YamlBodyParser::ParseFile(const std::string &_file_path) diff --git a/cpprevolve/revolve/raspberry/AngleToTargetDetector.cpp b/cpprevolve/revolve/raspberry/AngleToTargetDetector.cpp new file mode 100644 index 0000000000..3576830bc4 --- /dev/null +++ b/cpprevolve/revolve/raspberry/AngleToTargetDetector.cpp @@ -0,0 +1,56 @@ +// +// Created by matteo on 2/28/20. +// + +#include "AngleToTargetDetector.h" +#include +#include + +using namespace revolve::raspberry; + +AngleToTargetDetector::AngleToTargetDetector(int camera_index, unsigned int shrink_factor, bool show_image) + : ::revolve::AngleToTargetDetector(shrink_factor, show_image) + , camera(nullptr) + , usb_camera(nullptr) +{ + if (camera_index == -1) { + camera = new raspicam::RaspiCam_Cv(); + if (not camera->open()) + throw std::runtime_error("Error opening the raspberry camera"); + } else { + usb_camera = new cv::VideoCapture(camera_index); + if (not usb_camera->isOpened()) + throw std::runtime_error("Error opening the usb camera at index " + std::to_string(camera_index)); + } +} + +AngleToTargetDetector::~AngleToTargetDetector() +{ + delete camera; + delete usb_camera; +} + +void AngleToTargetDetector::get_image(cv::Mat &raw_image) +{ + bool result = false; + + if (camera) { + result = camera->grab(); + camera->retrieve(raw_image); + } else if (usb_camera) { + result = usb_camera->read(raw_image); + } else { + throw std::runtime_error("Camera device not found"); + } + + if (not result or raw_image.empty()) { + throw std::runtime_error("Error! could not capture image from the camera"); + } +} + +float AngleToTargetDetector::detect_angle() +{ + float angle = ::revolve::AngleToTargetDetector::detect_angle(); + std::cout << "Detected angle: " << angle << std::endl; + return angle; +} diff --git a/cpprevolve/revolve/raspberry/AngleToTargetDetector.h b/cpprevolve/revolve/raspberry/AngleToTargetDetector.h new file mode 100644 index 0000000000..cac8d20835 --- /dev/null +++ b/cpprevolve/revolve/raspberry/AngleToTargetDetector.h @@ -0,0 +1,38 @@ +// +// Created by matteo on 2/28/20. +// + +#ifndef REVOLVE_RASPBERRY_ANGLETOTARGETDETECTOR_H +#define REVOLVE_RASPBERRY_ANGLETOTARGETDETECTOR_H + +#include +#include +#include "../brains/controller/sensors/AngleToTargetDetector.h" + +namespace revolve { +namespace raspberry { + +class AngleToTargetDetector : public ::revolve::AngleToTargetDetector { +public: + /** + * @param camera_index pass -1 to load the raspberry camera + */ + explicit AngleToTargetDetector(int camera_index, unsigned int shrink_factor = 4, bool show_image = false); + virtual ~AngleToTargetDetector(); + +public: + float detect_angle() override; + +private: + void get_image(cv::Mat &image) override; + +protected: + raspicam::RaspiCam_Cv *camera; + cv::VideoCapture *usb_camera; +}; + +} +} + + +#endif // REVOLVE_RASPBERRY_ANGLETOTARGETDETECTOR_H diff --git a/cpprevolve/revolve/raspberry/CMakeLists.txt b/cpprevolve/revolve/raspberry/CMakeLists.txt index 20101930cf..ecc2d7c2a3 100644 --- a/cpprevolve/revolve/raspberry/CMakeLists.txt +++ b/cpprevolve/revolve/raspberry/CMakeLists.txt @@ -2,6 +2,8 @@ message(WARNING "Building Raspberry code") # Find Yaml-cpp find_package(yaml-cpp REQUIRED) +find_package(raspicam REQUIRED) + include_directories(${YAML_CPP_INCLUDE_DIR}) file(GLOB_RECURSE @@ -15,9 +17,11 @@ target_link_libraries(revolve-raspberry PUBLIC revolve-controllers PUBLIC pigpio_if2 ${YAML_CPP_LIBRARIES} + ${raspicam_CV_LIBS} ) -include_directories(${PIGPIO_HEADER_DIR}) +target_include_directories(revolve-raspberry + PUBLIC ${PIGPIO_HEADER_DIR}) install(TARGETS revolve-raspberry RUNTIME DESTINATION bin diff --git a/cpprevolve/revolve/raspberry/PIGPIOConnection.h b/cpprevolve/revolve/raspberry/PIGPIOConnection.h index f0239d0272..474c0724ff 100644 --- a/cpprevolve/revolve/raspberry/PIGPIOConnection.h +++ b/cpprevolve/revolve/raspberry/PIGPIOConnection.h @@ -2,8 +2,8 @@ // Created by matteo on 14/06/19. // -#ifndef REVOLVE_PIGPIOCONNECTION_H -#define REVOLVE_PIGPIOCONNECTION_H +#ifndef REVOLVE_RASPBERRY_PIGPIOCONNECTION_H +#define REVOLVE_RASPBERRY_PIGPIOCONNECTION_H extern "C" { #include "pigpiod_if2.h" @@ -47,4 +47,4 @@ class PIGPIOConnection { }; -#endif //REVOLVE_PIGPIOCONNECTION_H +#endif //REVOLVE_RASPBERRY_PIGPIOCONNECTION_H diff --git a/cpprevolve/revolve/raspberry/RaspController.cpp b/cpprevolve/revolve/raspberry/RaspController.cpp index 1a424b2fc0..f98bb16097 100644 --- a/cpprevolve/revolve/raspberry/RaspController.cpp +++ b/cpprevolve/revolve/raspberry/RaspController.cpp @@ -3,31 +3,36 @@ // #include "RaspController.h" +#include +#include +#include #include "../brains/controller/DifferentialCPG.h" +#include "../brains/controller/BrokenDifferentialCPG.h" +#include "AngleToTargetDetector.h" #include -using namespace revolve; +using namespace revolve::raspberry; RaspController::RaspController( - std::vector > actuators, - std::vector > sensors, + std::vector > actuators, + std::vector > sensors, const YAML::Node &conf) : revolve_controller(nullptr) + , camera(nullptr) , actuators(std::move(actuators)) , sensors(std::move(sensors)) { + // camera can be nullptr + this->load_camera(conf["angle_to_target_detector"]); this->set_new_controller(conf); } RaspController::~RaspController() = default; -#include -#include - void RaspController::update() { - double step = this->timer.step(); - double time = this->timer.elapsed(); + const double step = this->timer.step_double(); + const double time = this->timer.elapsed_double(); if (step == 0) return; this->revolve_controller->update( @@ -36,33 +41,89 @@ void RaspController::update() time, step ); -// std::this_thread::sleep_for(std::chrono::milliseconds(125)); + + // negative update rate means run as fast as possible + if (this->update_rate <= std::chrono::milliseconds::zero()) return; + + const Timer::Seconds cpu_time_spent = this->timer.step_elapsed(); + const Timer::Seconds remaining_wait_time = this->update_rate - cpu_time_spent; + if (remaining_wait_time > Timer::Seconds::zero()) { + std::this_thread::sleep_for(remaining_wait_time); + } else { + std::clog << "CPU too slow, we missed the cycle deadline of " << (remaining_wait_time).count() / -1000 << " ms " << std::endl; + } +} + +void RaspController::load_camera(const YAML::Node &conf) +{ + if (not conf) { + std::cout << "Camera not found, the camera will be deactivated." << std::endl; + return; + } + + int camera_index; + std::string camera_type = conf["type"].as(); + if (camera_type == "raspberry-camera") { + camera_index = -1; + std::cout << "Loading Raspberry camera" << std::endl; + } else if (camera_type == "usb") { + camera_index = conf["index"].as(); + std::cout << "Loading usb camera at index " << camera_index << std::endl; + } else { + throw std::runtime_error("Camera type " + camera_type + "not recognized"); + } + + const unsigned int camera_shrink_factor = conf["shrink_factor"].as(4); + const bool show_camera = conf["show_camera"].as(false); + + std::cout << "Camera shrink_factor( " << camera_shrink_factor << " ) show_camera( " << (show_camera ? "true" : "false") << " )" << std::endl; + + camera.reset(new ::revolve::raspberry::AngleToTargetDetector(camera_index, camera_shrink_factor, show_camera)); } void RaspController::set_new_controller(const YAML::Node &conf) { + // Update rate in ms + int64_t _update_rate = conf["update_rate"].as(-1); + this->update_rate = std::chrono::duration_cast(std::chrono::milliseconds(_update_rate)); + std::string type = conf["type"].as(""); if (type.empty()) { throw std::runtime_error("Controller type not set"); + } else if (type == "broken-differential-cpg") { + BrokenDifferentialCPG::ControllerParams params; + params.reset_neuron_random = conf["reset_neuron_random"].as(false); + params.use_frame_of_reference = conf["use_frame_of_reference"].as(false); + params.init_neuron_state = conf["init_neuron_state"].as(0.707); + params.range_ub = conf["range_ub"].as(1.0); + params.signal_factor_all = conf["output_signal_factor"].as(1.0); + params.abs_output_bound = conf["abs_output_bound"].as(1.0); + + YAML::Node yaml_weights = conf["weights"]; + for(const YAML::Node &weight: yaml_weights) { + params.weights.emplace_back(weight.as()); + } + + revolve_controller = std::make_unique( + params, this->actuators, camera + ); } else if (type == "differential-cpg") { DifferentialCPG::ControllerParams params; - params.reset_neuron_random = false; - params.use_frame_of_reference = false; - params.init_neuron_state = 0.707; - params.range_ub = 1.0; - params.signal_factor_all = 1.0; - params.signal_factor_mid = 2.5; - params.signal_factor_left_right = 2.5; - params.abs_output_bound = 1.0; + params.reset_neuron_random = conf["reset_neuron_random"].as(false); + params.use_frame_of_reference = conf["use_frame_of_reference"].as(false); + params.init_neuron_state = conf["init_neuron_state"].as(0.707); + params.range_ub = conf["range_ub"].as(1.0); + params.output_signal_factor = conf["output_signal_factor"].as(1.0); + params.abs_output_bound = conf["abs_output_bound"].as(1.0); YAML::Node yaml_weights = conf["weights"]; for(const YAML::Node &weight: yaml_weights) { params.weights.emplace_back(weight.as()); } - revolve_controller.reset( - new DifferentialCPG(params,this->actuators) + revolve_controller = std::make_unique( + params, this->actuators, camera ); } else { throw std::runtime_error("Controller " + type + " not supported (yet)"); diff --git a/cpprevolve/revolve/raspberry/RaspController.h b/cpprevolve/revolve/raspberry/RaspController.h index 00ba47996e..c327b8abca 100644 --- a/cpprevolve/revolve/raspberry/RaspController.h +++ b/cpprevolve/revolve/raspberry/RaspController.h @@ -2,38 +2,43 @@ // Created by matteo on 14/06/19. // -#ifndef REVOLVE_RASPCONTROLLER_H -#define REVOLVE_RASPCONTROLLER_H +#ifndef REVOLVE_RASPBERRY_RASPCONTROLLER_H +#define REVOLVE_RASPBERRY_RASPCONTROLLER_H #include #include #include "../brains/controller/Controller.h" +#include "../brains/controller/sensors/AngleToTargetDetector.h" #include "Timer.h" namespace revolve { +namespace raspberry { -class RaspController -{ +class RaspController { public: explicit RaspController( - std::vector > actuators, - std::vector > sensors, + std::vector > actuators, + std::vector > sensors, const YAML::Node &conf); ~RaspController(); void update(); + void load_camera(const YAML::Node &conf); void set_new_controller(const YAML::Node &conf); private: - std::unique_ptr revolve_controller; + std::unique_ptr<::revolve::Controller> revolve_controller; + std::shared_ptr<::revolve::AngleToTargetDetector> camera; Timer timer; - std::vector< std::unique_ptr< revolve::Actuator > > actuators; - std::vector< std::unique_ptr< revolve::Sensor > > sensors; + std::vector > actuators; + std::vector > sensors; + + /// Update rate in seconds + Timer::Seconds update_rate; }; } - - -#endif //REVOLVE_RASPCONTROLLER_H +} +#endif //REVOLVE_RASPBERRY_RASPCONTROLLER_H diff --git a/cpprevolve/revolve/raspberry/Servo.cpp b/cpprevolve/revolve/raspberry/Servo.cpp index 2f221c0c23..23e4f9ef2c 100644 --- a/cpprevolve/revolve/raspberry/Servo.cpp +++ b/cpprevolve/revolve/raspberry/Servo.cpp @@ -5,7 +5,7 @@ #include "Servo.h" #include "PIGPIOConnection.h" -using namespace revolve; +using namespace revolve::raspberry; #define POSITION_OFF 0 #define POSITION_BEGIN 40 diff --git a/cpprevolve/revolve/raspberry/Servo.h b/cpprevolve/revolve/raspberry/Servo.h index 6c2ee1ef3c..33fdb6efa7 100644 --- a/cpprevolve/revolve/raspberry/Servo.h +++ b/cpprevolve/revolve/raspberry/Servo.h @@ -2,17 +2,17 @@ // Created by matteo on 14/06/19. // -#ifndef REVOLVE_SERVO_H -#define REVOLVE_SERVO_H +#ifndef REVOLVE_RASPBERRY_SERVO_H +#define REVOLVE_RASPBERRY_SERVO_H #include "PIGPIOConnection.h" #include "../brains/controller/actuators/Actuator.h" #include namespace revolve { +namespace raspberry { -class Servo: public revolve::Actuator -{ +class Servo : public ::revolve::Actuator { public: explicit Servo( double coordinate_x, @@ -20,12 +20,13 @@ class Servo: public revolve::Actuator double coordinate_z, PIGPIOConnection *connection, unsigned short pin, - unsigned int frequency=50, - int range=1000, - bool inverse=false + unsigned int frequency = 50, + int range = 1000, + bool inverse = false ); - ~Servo() { + ~Servo() + { this->off(); } @@ -36,7 +37,8 @@ class Servo: public revolve::Actuator */ void move_to_position(double position); - void center() { move_to_position(0); } + void center() + { move_to_position(0); } void off(); @@ -45,7 +47,8 @@ class Servo: public revolve::Actuator std::ostream &print(std::ostream &os) const { os << "Servo pin:\t" << this->pin << std::endl; - os << "\tcoordinates [" << this->coordinate_x() << ',' << this->coordinate_y() << ',' << this->coordinate_z() << ']' << std::endl; + os << "\tcoordinates [" << this->coordinate_x() << ',' << this->coordinate_y() << ',' + << this->coordinate_z() << ']' << std::endl; os << "\tfrequency:\t" << this->frequency << std::endl; os << "\trange: \t" << this->range << std::endl; os << "\tinverse: \t" << this->inverse; @@ -63,8 +66,9 @@ class Servo: public revolve::Actuator float maxPWM; }; +} } -std::ostream &operator<<(std::ostream &os, revolve::Servo const &s); +std::ostream &operator<<(std::ostream &os, revolve::raspberry::Servo const &s); -#endif //REVOLVE_SERVO_H +#endif //REVOLVE_RASPBERRY_SERVO_H diff --git a/cpprevolve/revolve/raspberry/Timer.h b/cpprevolve/revolve/raspberry/Timer.h index 16d0c17cfc..fec49c7fd3 100644 --- a/cpprevolve/revolve/raspberry/Timer.h +++ b/cpprevolve/revolve/raspberry/Timer.h @@ -2,38 +2,48 @@ // Created by matteo on 17/06/19. // -#ifndef REVOLVE_TIMER_H -#define REVOLVE_TIMER_H +#ifndef REVOLVE_RASPBERRY_TIMER_H +#define REVOLVE_RASPBERRY_TIMER_H + #include #include class Timer { public: + typedef std::chrono::high_resolution_clock Clock; + typedef std::chrono::duration > Seconds; + Timer() - : beg_(clock_::now()) + : beg_(Clock::now()) , last_step_(beg_) {} - void reset() { beg_ = clock_::now(); } - double step() { - std::chrono::time_point prev = last_step_; - last_step_ = clock_::now(); - return time_difference(prev, last_step_); + void reset() { beg_ = Clock::now(); } + + Seconds step() { + std::chrono::time_point prev = last_step_; + last_step_ = Clock::now(); + return last_step_ - prev; } - double elapsed() const { return time_difference(beg_, last_step_); } - double elapsed_now() const { return time_difference(beg_, clock_::now()); } + + Seconds step_elapsed() const { return Clock::now() - last_step_; } + Seconds elapsed() const { return last_step_ - beg_; } + Seconds elapsed_now() const { return Clock::now() - beg_; } + + double step_double() { return into_double(step()); } + double step_elapsed_double() const { return into_double(step_elapsed()); } + double elapsed_double() const { return into_double(elapsed()); } + double elapsed_now_double() const { return into_double(elapsed_now()); } private: - typedef std::chrono::high_resolution_clock clock_; - typedef std::chrono::duration > second_; - std::chrono::time_point beg_; - std::chrono::time_point last_step_; + std::chrono::time_point beg_; + std::chrono::time_point last_step_; - static double time_difference(const std::chrono::time_point start, const std::chrono::time_point end) + template + static double into_double(const T &duration) { - return std::chrono::duration_cast - (end - start).count(); + return std::chrono::duration_cast(duration).count(); } }; -#endif //REVOLVE_TIMER_H +#endif //REVOLVE_RASPBERRY_TIMER_H diff --git a/cpprevolve/revolve/raspberry/rasp_main.cpp b/cpprevolve/revolve/raspberry/rasp_main.cpp index 5f7681b4a3..c26ccaf6b2 100644 --- a/cpprevolve/revolve/raspberry/rasp_main.cpp +++ b/cpprevolve/revolve/raspberry/rasp_main.cpp @@ -8,7 +8,7 @@ #include #include -typedef std::unique_ptr Servo_p; +typedef std::shared_ptr Servo_p; std::vector read_conf(PIGPIOConnection &pigpio, const YAML::Node &yaml_servos); void reset(std::vector &servos); @@ -93,7 +93,7 @@ std::vector read_conf(PIGPIOConnection &pigpio, const YAML::Node &yaml_ auto frequency = yaml_servo["frequency"].as(50); auto range = yaml_servo["range"] .as(1000); auto inverse = yaml_servo["inverse"] .as(false); - servos.emplace_back(new revolve::Servo( + servos.emplace_back(new revolve::raspberry::Servo( x, y, z, @@ -112,14 +112,14 @@ std::vector read_conf(PIGPIOConnection &pigpio, const YAML::Node &yaml_ void control(std::vector &servos, const YAML::Node &controller_conf) { std::cout << "Staring controller" << std::endl; - std::vector> sensors; - std::vector> actuators; + std::vector> sensors; + std::vector> actuators; actuators.reserve(servos.size()); for (Servo_p &servo: servos) { actuators.emplace_back(std::move(servo)); } - revolve::RaspController controller( + revolve::raspberry::RaspController controller( std::move(actuators), std::move(sensors), controller_conf @@ -133,14 +133,14 @@ void control(std::vector &servos, const YAML::Node &controller_conf) void learner(std::vector &servos, const YAML::Node &controllers_conf) { std::cout << "Staring controller" << std::endl; - std::vector> sensors; - std::vector> actuators; + std::vector> sensors; + std::vector> actuators; actuators.reserve(servos.size()); for (Servo_p &servo: servos) { actuators.emplace_back(std::move(servo)); } - revolve::RaspController controller( + revolve::raspberry::RaspController controller( std::move(actuators), std::move(sensors), controllers_conf[0] @@ -152,7 +152,7 @@ void learner(std::vector &servos, const YAML::Node &controllers_conf) std::cout << "Loading controller[" << ++counter << "] fitness: " << controller_conf["fitness"].as(-1) << std::endl; controller.set_new_controller(controller_conf); timer.reset(); - while (timer.elapsed_now() < 30.0) { + while (timer.elapsed_now() < Timer::Seconds(30.0)) { controller.update(); } } diff --git a/cpprevolve/revolve/raspberry/robot_conf.yaml b/cpprevolve/revolve/raspberry/robot_conf.yaml index e139ac18e2..59b25a5402 100644 --- a/cpprevolve/revolve/raspberry/robot_conf.yaml +++ b/cpprevolve/revolve/raspberry/robot_conf.yaml @@ -1,7 +1,7 @@ robot_name: "spider9" robot_id: 1, robot_address: - ip: "192.168.1.25" + #ip: "192.168.1.25" #port: 8888 #"servo_pins": [17,18,27,22,23,10,9,25], @@ -34,31 +34,39 @@ servos: rgb_pins: [15,14,4] -#controller: + + +controller: # spider weights - #type: "differential-cpg" - #weights: [0.545275, 0.48118, 0.677335, 0.834078, 0.331732, 0.479091, 0.87384, 0.527239, 0.0148421, 0.131508, 0.711216, 0.672872, 0.648163, 0.204883, 0.788699, 0.38614, 0.483561, 0.0777244] + type: "differential-cpg" + weights: [0.545275, 0.48118, 0.677335, 0.834078, 0.331732, 0.479091, 0.87384, 0.527239, 0.0148421, 0.131508, 0.711216, 0.672872, 0.648163, 0.204883, 0.788699, 0.38614, 0.483561, 0.0777244] + angle_to_target_detector: + type: "raspberry-camera" +# type: "usb" +# index: 0 + shrink_factor: 4 + show_camera: False -controllers: - # baby+1 learning weights, crescendo - - type: "differential-cpg" - weights: [0.395516, 0.83811, 0.463123, 0.702373, 0.936804, 0.166412, 0.631632, 0.287821, 0.799552, 0.218267, 0.0623519, 0.799128] - fitness: 0.000503405 - - type: "differential-cpg" - weights: [0.871967, 0.441235, 0.684449, 0.58511, 0.899357, 0.497721, 0.342563, 0.164466, 0.676871, 0.475722, 0.405172, 0.370701] - fitness: 0.202879 - - type: "differential-cpg" - weights: [0.223283, 0.912134, 0.374909, 0.931379, 0.733706, 0.283053, 0.0923915, 0.0706944, 0.0915673, 0.53245, 0.962511, 0.512361] - fitness: 0.403299 - - type: "differential-cpg" - weights: [0.302763, 0.913937, 0.376248, 0.911346, 0.712692, 0.298535, 0.0272732, 0.0706944, 0.095008, 0.46318, 0.990895, 0.564726] - fitness: 0.600739 - - type: "differential-cpg" - weights: [0.319842, 0.848368, 0.415024, 0.946114, 0.665586, 0.26537, 0.0609814, 0.0757356, 0.100649, 0.461241, 0.972517, 0.485218] - fitness: 0.801518 - - type: "differential-cpg" - weights: [0.698179, 0.828084, 0.381213, 0.974549, 0.663302, 0.22419, 0.163161, 0.0362292, 0.0558966, 0.357249, 0.960544, 0.512658] - fitness: 1.00555 - - type: "differential-cpg" - weights: [0.65835, 0.723842, 0.431859, 0.919208, 0.721147, 0.160043, 0.138266, 0.0751595, 0.0335994, 0.395186, 0.88612, 0.601392] - fitness: 1.27441 +#controllers: +# # baby+1 learning weights, crescendo +# - type: "differential-cpg" +# weights: [0.395516, 0.83811, 0.463123, 0.702373, 0.936804, 0.166412, 0.631632, 0.287821, 0.799552, 0.218267, 0.0623519, 0.799128] +# fitness: 0.000503405 +# - type: "differential-cpg" +# weights: [0.871967, 0.441235, 0.684449, 0.58511, 0.899357, 0.497721, 0.342563, 0.164466, 0.676871, 0.475722, 0.405172, 0.370701] +# fitness: 0.202879 +# - type: "differential-cpg" +# weights: [0.223283, 0.912134, 0.374909, 0.931379, 0.733706, 0.283053, 0.0923915, 0.0706944, 0.0915673, 0.53245, 0.962511, 0.512361] +# fitness: 0.403299 +# - type: "differential-cpg" +# weights: [0.302763, 0.913937, 0.376248, 0.911346, 0.712692, 0.298535, 0.0272732, 0.0706944, 0.095008, 0.46318, 0.990895, 0.564726] +# fitness: 0.600739 +# - type: "differential-cpg" +# weights: [0.319842, 0.848368, 0.415024, 0.946114, 0.665586, 0.26537, 0.0609814, 0.0757356, 0.100649, 0.461241, 0.972517, 0.485218] +# fitness: 0.801518 +# - type: "differential-cpg" +# weights: [0.698179, 0.828084, 0.381213, 0.974549, 0.663302, 0.22419, 0.163161, 0.0362292, 0.0558966, 0.357249, 0.960544, 0.512658] +# fitness: 1.00555 +# - type: "differential-cpg" +# weights: [0.65835, 0.723842, 0.431859, 0.919208, 0.721147, 0.160043, 0.138266, 0.0751595, 0.0335994, 0.395186, 0.88612, 0.601392] +# fitness: 1.27441 diff --git a/experiments/IMC/IMC-experiment b/experiments/IMC/IMC-experiment new file mode 100644 index 0000000000..262829331c --- /dev/null +++ b/experiments/IMC/IMC-experiment @@ -0,0 +1,25 @@ +#!/bin/bash + +set -e +set -x +runs=5 +runs_start=0 +start_port=16000 +exp_name=IMC_spider +log_suffix='' +project_folder=experiments/IMC/ +manager="${project_folder}"manager.py + +for i in $(seq $runs) + do + run=$((i+runs_start)) + if ! [ -d "${project_folder}/output${exp_name}${run}" ]; then + mkdir "${project_folder}/output${exp_name}${run}" + fi + if ! [ -d "${project_folder}/output${exp_name}${run}/act_info" ]; then + mkdir "${project_folder}/output${exp_name}${run}/act_info" + fi + screen -d -m -S "${exp_name}_${run}" -L -Logfile "${project_folder}${exp_name}${log_suffix}_${run}.log" nice -n19 ./revolve.py --test-robot "${project_folder}/yaml/${exp_name}${run}.yaml" --simulator-cmd=/home/fuda/Projects/gazebo/build/gazebo/gzserver --experiment-name $exp_name --n-cores 1 --pose-update-frequency 5 --port-start $((${start_port} + ($run*10))) --run $run + done + +date +"%H:%M:%S" diff --git a/experiments/IMC/manager.py b/experiments/IMC/manager.py new file mode 100755 index 0000000000..5b2d8ba656 --- /dev/null +++ b/experiments/IMC/manager.py @@ -0,0 +1,108 @@ +import asyncio +import logging +import sys +import os + +from pygazebo.connection import DisconnectError +from pyrevolve import parser +from pyrevolve.custom_logging import logger +from pyrevolve.revolve_bot import RevolveBot +from pyrevolve.SDF.math import Vector3 +# from pyrevolve.tol.manage import World +from pyrevolve.tol.manage.single_robot_world import SingleRobotWorld as World +from pyrevolve.util.supervisor.supervisor_multi import DynamicSimSupervisor + + + + + +async def run(): + """ + The main coroutine, which is started below + """ + log = logger.create_logger('experiment', handlers=[ + logging.StreamHandler(sys.stdout), + ]) + + # Set debug level to DEBUG + log.setLevel(logging.DEBUG) + + # Parse command line / file input arguments + settings = parser.parse_args() + + + # Start Simulator + if settings.simulator_cmd != 'debug': + simulator_supervisor = DynamicSimSupervisor( + world_file=settings.world, + simulator_cmd=settings.simulator_cmd, + simulator_args=["--verbose"], + # simulator_args=[""] + plugins_dir_path=os.path.join('.', 'build', 'lib'), + models_dir_path=os.path.join('.', 'models'), + simulator_name='/home/fuda/Projects/gazebo/build/gazebo/gzserver' # /home/fuda/Projects/gazebo/build/gazebo/gzserver + ) + await simulator_supervisor.launch_simulator(port=settings.port_start) + await asyncio.sleep(0.1) + + + # Connect to the simulator and pause + connection = await World.create(settings, world_address=('127.0.0.1', settings.port_start)) + await asyncio.sleep(1) + await connection.pause(True) + await connection.reset(True) + + # initialization finished + + + # load robot file + robot = RevolveBot() + + # robot_file_path = "experiments/IMC/yaml/Single_link.yaml" #single link testing + # robot_file_path = "experiments/IMC/yaml/IMC_667710.yaml" #sven + # robot_file_path = "experiments/IMC/yaml/IMC_babyA4.yaml" + # robot_file_path = "experiments/IMC/yaml/IMC_babyB9.yaml" + # robot_file_path = "experiments/IMC/yaml/IMC_gecko5.yaml" #sven8 + robot_file_path = "experiments/IMC/yaml/IMC_snake1.yaml" #sven + # robot_file_path = "experiments/IMC/yaml/IMC_spider9.yaml" #spider9 + + + robot.load_file(robot_file_path, conf_type='yaml') + robot.update_substrate() + robot.save_file(f'{robot_file_path}.sdf', conf_type='sdf') + + + # insert robot into the simulator + robot_manager = await connection.insert_robot(robot, Vector3(0, 0, 0.05), life_timeout=None) + await connection.pause(False) + + + + # Start the main life loop + while True: + status = 'dead' if robot_manager.dead else 'alive' + best_fitness = None if robot_manager.best_evaluation is None else robot_manager.best_evaluation.fitness + log.info(f"status: {status} - Robot fitness: {best_fitness}") + await asyncio.sleep(5.0) + +def main(): + def handler(loop, context): + exc = context['exception'] + if isinstance(exc, DisconnectError) \ + or isinstance(exc, ConnectionResetError): + print("Got disconnect / connection reset - shutting down.") + sys.exit(0) + raise context['exception'] + + try: + loop = asyncio.get_event_loop() + loop.set_exception_handler(handler) + loop.run_until_complete(run()) + except KeyboardInterrupt: + print("Got CtrlC, shutting down.") + + +if __name__ == '__main__': + print("STARTING") + main() + print("FINISHED") diff --git a/experiments/IMC/yaml/Double_link.yaml b/experiments/IMC/yaml/Double_link.yaml new file mode 100644 index 0000000000..c086acedc6 --- /dev/null +++ b/experiments/IMC/yaml/Double_link.yaml @@ -0,0 +1,63 @@ +--- +id: Double_link +body: + id : Core + type : Core + params: + red: 0.04 + green: 0.26 + blue: 0.72 + children : + 0: + id : Leg00Joint + type : ActiveHinge + params: + red: 0.98 + green: 0.98 + blue: 0.98 + orientation: 0 + children : + 1: + id : Leg00 + type : FixedBrick + params: + red: 0.04 + green: 0.26 + blue: 0.72 + orientation : -90 + children: + 1: + id : Leg01Joint + type : ActiveHinge + params: + red: 0.98 + green: 0.98 + blue: 0.98 + children : + 1: + id : Leg01 + type : FixedBrick + params: + red: 0.04 + green: 0.26 + blue: 0.72 + orientation : 0 + +brain: + type: cpg + learner: + type : hyperneat + params: + PopulationSize: 350 + OldAgeTreshold: 20 + MaxSpecies: 15 + controller: + reset_neuron_random : false; + use_frame_of_reference : false; + init_neuron_state : 0.707; + range_ub : 1.0; + signal_factor_all : 1.0; + signal_factor_mid : 1.0; + signal_factor_left_right : 1.0; + abs_output_bound : 1.0; + weights: [ 0.86315, 0.26597, 0.941] diff --git a/experiments/IMC/yaml/IMCspider.yaml b/experiments/IMC/yaml/IMCspider.yaml new file mode 100644 index 0000000000..f7de62a0cb --- /dev/null +++ b/experiments/IMC/yaml/IMCspider.yaml @@ -0,0 +1,182 @@ +--- +id: IMC_spider3 +body: + id : Core + type : Core + params: + red: 0.04 + green: 0.26 + blue: 0.72 + children : + 0: + id : Leg00Joint + type : ActiveHinge + params: + red: 0.98 + green: 0.98 + blue: 0.98 + orientation : 90 + children : + 1: + id : Leg00 + type : FixedBrick + params: + red: 0.04 + green: 0.26 + blue: 0.72 + orientation : -90 + children : + 1: + id : Leg01Joint + type : ActiveHinge + params: + red: 0.98 + green: 0.98 + blue: 0.98 + children : + 1: + id : Leg01 + type : FixedBrick + params: + red: 0.04 + green: 0.26 + blue: 0.72 + orientation : 0 + 1: + id : Leg10Joint + type : ActiveHinge + params: + red: 0.98 + green: 0.98 + blue: 0.98 + orientation : 90 + children : + 1: + id : Leg10 + type : FixedBrick + params: + red: 0.04 + green: 0.26 + blue: 0.72 + orientation : -90 + children : + 1: + id : Leg11Joint + type : ActiveHinge + params: + red: 0.98 + green: 0.98 + blue: 0.98 + children : + 1: + id : Leg11 + type : FixedBrick + params: + red: 0.04 + green: 0.26 + blue: 0.72 + orientation : 0 + 2: + id : Leg20Joint + type : ActiveHinge + params: + red: 0.98 + green: 0.98 + blue: 0.98 + orientation : 90 + children : + 1: + id : Leg20 + type : FixedBrick + params: + red: 0.04 + green: 0.26 + blue: 0.72 + orientation : -90 + children : + 1: + id : Leg21Joint + type : ActiveHinge + params: + red: 0.98 + green: 0.98 + blue: 0.98 + children : + 1: + id : Leg21 + type : FixedBrick + params: + red: 0.04 + green: 0.26 + blue: 0.72 + orientation : 0 + 3: + id : Leg30Joint + type : ActiveHinge + params: + red: 0.98 + green: 0.98 + blue: 0.98 + orientation : 90 + children : + 1: + id : Leg30 + type : FixedBrick + params: + red: 0.04 + green: 0.26 + blue: 0.72 + orientation : -90 + children : + 1: + id : Leg31Joint + type : ActiveHinge + params: + red: 0.98 + green: 0.98 + blue: 0.98 + children : + 1: + id : Leg31 + type : FixedBrick + params: + red: 0.04 + green: 0.26 + blue: 0.72 + orientation : 0 +brain: + type: cpg + learner: + type : bo + n_init_samples: 50 #1450 + init_method: "LHS" + kernel_noise: 0.00000001 + kernel_optimize_noise: "false" + kernel_sigma_sq: 1.0 + kernel_l: 0.2 + kernel_squared_exp_ard_k: 4 + acqui_gpucb_delta: 0.5 + acqui_ucb_alpha: 3.0 + acqui_ei_jitter: 0 + acquisition_function: "UCB" + meta: + robot_size: 18 + run_analytics: "true" + n_learning_iterations: 1500 + n_cooldown_iterations: 1 + reset_robot_position: "false" + evaluation_rate: 60 + output_directory: "/home/fuda/Projects/revolve/experiments/IMC/output" + verbose: 0 + startup_time: 0 + controller: + reset_neuron_random : false; + use_frame_of_reference : false; + init_neuron_state : 0.707; + range_ub : 1.0; + signal_factor_all : 1.0; + signal_factor_mid : 1.0; + signal_factor_left_right : 1.0; + abs_output_bound : 1.0; + weights: [0.482167, 0.560357, 0.753772, 0.221536, 0.44513, 0.667353, 0.580933, 0.246228, 0.111797, + 0.110425, 0.667353, 0.519204, 0.11134, 0.667353, 0.70439, 0.000228624, 0.444673, 0.287837] diff --git a/experiments/IMC/yaml/Single_link.yaml b/experiments/IMC/yaml/Single_link.yaml new file mode 100644 index 0000000000..2347de1527 --- /dev/null +++ b/experiments/IMC/yaml/Single_link.yaml @@ -0,0 +1,47 @@ +--- +id: Single_link +body: + id : Core + type : Core + params: + red: 0.04 + green: 0.26 + blue: 0.72 + children : + 0: + id : Leg00Joint + type : ActiveHinge + params: + red: 0.98 + green: 0.98 + blue: 0.98 + orientation: 0 + children : + 1: + id : Leg00 + type : FixedBrick + params: + red: 0.04 + green: 0.26 + blue: 0.72 + orientation : -90 + + +brain: + type: cpg + learner: + type : bo +# params: +# PopulationSize: 350 +# OldAgeTreshold: 20 +# MaxSpecies: 15 + controller: + reset_neuron_random : false; + use_frame_of_reference : false; + init_neuron_state : 0.707; + range_ub : 1.0; + signal_factor_all : 1.0; + signal_factor_mid : 1.0; + signal_factor_left_right : 1.0; + abs_output_bound : 1.0; + weights: [ 1.482167] diff --git a/experiments/IMC/yaml/model.config b/experiments/IMC/yaml/model.config new file mode 100644 index 0000000000..5a29819ddf --- /dev/null +++ b/experiments/IMC/yaml/model.config @@ -0,0 +1,16 @@ + + + + IMCspider + 1.0 + IMCspider.yaml.sdf + + + Fuda + f.van.diggelen@student.vu.nl + + + + this is a robot + + diff --git a/experiments/examples/yaml/spider_cpg_bo.yaml b/experiments/examples/yaml/spider_cpg_bo.yaml new file mode 100644 index 0000000000..969bda227d --- /dev/null +++ b/experiments/examples/yaml/spider_cpg_bo.yaml @@ -0,0 +1,161 @@ +--- +id: example_spider +body: + id : Core + type : Core + params: + red: 0.04 + green: 0.26 + blue: 0.72 + children : + 0: + id : Leg00Joint + type : ActiveHinge + params: + red: 0.98 + green: 0.98 + blue: 0.98 + orientation : 90 + children : + 1: + id : Leg00 + type : FixedBrick + params: + red: 0.04 + green: 0.26 + blue: 0.72 + orientation : -90 + children : + 1: + id : Leg01Joint + type : ActiveHinge + params: + red: 0.98 + green: 0.98 + blue: 0.98 + children : + 1: + id : Leg01 + type : FixedBrick + params: + red: 0.04 + green: 0.26 + blue: 0.72 + orientation : 0 + 1: + id : Leg10Joint + type : ActiveHinge + params: + red: 0.98 + green: 0.98 + blue: 0.98 + orientation : 90 + children : + 1: + id : Leg10 + type : FixedBrick + params: + red: 0.04 + green: 0.26 + blue: 0.72 + orientation : -90 + children : + 1: + id : Leg11Joint + type : ActiveHinge + params: + red: 0.98 + green: 0.98 + blue: 0.98 + children : + 1: + id : Leg11 + type : FixedBrick + params: + red: 0.04 + green: 0.26 + blue: 0.72 + orientation : 0 + 2: + id : Leg20Joint + type : ActiveHinge + params: + red: 0.98 + green: 0.98 + blue: 0.98 + orientation : 90 + children : + 1: + id : Leg20 + type : FixedBrick + params: + red: 0.04 + green: 0.26 + blue: 0.72 + orientation : -90 + children : + 1: + id : Leg21Joint + type : ActiveHinge + params: + red: 0.98 + green: 0.98 + blue: 0.98 + children : + 1: + id : Leg21 + type : FixedBrick + params: + red: 0.04 + green: 0.26 + blue: 0.72 + orientation : 0 + 3: + id : Leg30Joint + type : ActiveHinge + params: + red: 0.98 + green: 0.98 + blue: 0.98 + orientation : 90 + children : + 1: + id : Leg30 + type : FixedBrick + params: + red: 0.04 + green: 0.26 + blue: 0.72 + orientation : -90 + children : + 1: + id : Leg31Joint + type : ActiveHinge + params: + red: 0.98 + green: 0.98 + blue: 0.98 + children : + 1: + id : Leg31 + type : FixedBrick + params: + red: 0.04 + green: 0.26 + blue: 0.72 + orientation : 0 +brain: + type: cpg + learner: + type : bo + controller: + reset_neuron_random : false; + use_frame_of_reference : false; + init_neuron_state : 0.707; + range_ub : 1.0; + signal_factor_all : 4.0; + signal_factor_mid : 2.5; + signal_factor_left_right : 2.5; + abs_output_bound : 1.0; + weights: [0.482167, 0.560357, 0.753772, 0.221536, 0.44513, 0.667353, 0.580933, 0.246228, 0.111797, + 0.110425, 0.667353, 0.519204, 0.11134, 0.667353, 0.70439, 0.000228624, 0.444673, 0.287837] diff --git a/models/rg_robot/meshes/ActiveCardanHinge_Servo_Holder.dae b/models/rg_robot/meshes/ActiveCardanHinge_Servo_Holder.dae index bb5147bdfd..67b9b00e02 100644 --- a/models/rg_robot/meshes/ActiveCardanHinge_Servo_Holder.dae +++ b/models/rg_robot/meshes/ActiveCardanHinge_Servo_Holder.dae @@ -1,63 +1,110 @@ - - - - - VCGLab - VCGLib | MeshLab - - Y_UP - do sep. 17 13:41:27 2015 - do sep. 17 13:41:27 2015 - - - - - - - - - -0.00462891 0.013625 0.00102316 -0.00468326 0.015125 0.00121587 -0.00477896 0.013625 0.00149116 -0.00487462 0.013625 0.00171763 -0.0049836 0.013625 0.001938 -0.0050839 0.015125 0.00211571 -0.00538637 0.013625 0.00255479 -0.00536183 0.015125 0.00252336 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+ + + + + + + + + + + + + + + + + + + diff --git a/pyrevolve/SDF/joint.py b/pyrevolve/SDF/joint.py index 220ebec0f5..d8a834349e 100644 --- a/pyrevolve/SDF/joint.py +++ b/pyrevolve/SDF/joint.py @@ -44,6 +44,7 @@ def to_robot_config_sdf(self): servomotor = xml.etree.ElementTree.Element('rv:servomotor', { 'type': 'position', + # 'type': 'velocity', 'id': "{}__rotate".format(self._id), 'part_id': self._id, 'part_name': self._name, @@ -57,9 +58,9 @@ def to_robot_config_sdf(self): servomotor.attrib['coordinates'] = ';'.join(str(i) for i in self._coordinates) pid = xml.etree.ElementTree.SubElement(servomotor, 'rv:pid') - SDF.sub_element_text(pid, 'rv:p', 0.9) + SDF.sub_element_text(pid, 'rv:p', 1) SDF.sub_element_text(pid, 'rv:i', 0.0) - SDF.sub_element_text(pid, 'rv:d', 0.0) + SDF.sub_element_text(pid, 'rv:d', 0.00) SDF.sub_element_text(pid, 'rv:i_max', 0.0) SDF.sub_element_text(pid, 'rv:i_min', 0.0) # SDF.sub_element_text(pid, 'rv:cmd_max', 0.0) @@ -77,10 +78,10 @@ def __init__(self, axis: SDF.math.Vector3): limit = xml.etree.ElementTree.SubElement(self, 'limit') # TODO calibrate this (load from configuration?) - SDF.sub_element_text(limit, 'lower', -7.853982e-01) - SDF.sub_element_text(limit, 'upper', 7.853982e-01) - SDF.sub_element_text(limit, 'effort', 1.765800e-01) - SDF.sub_element_text(limit, 'velocity', 5.235988e+00) + SDF.sub_element_text(limit, 'lower', -1) #-7.853982e-01 + SDF.sub_element_text(limit, 'upper', 1) + SDF.sub_element_text(limit, 'effort', 9.4*9.8e-02) + SDF.sub_element_text(limit, 'velocity', 5.235988e-00) def set_xyz(self, xyz: SDF.math.Vector3): self.xyz.text = '{:e} {:e} {:e}'.format(xyz[0], xyz[1], xyz[2]) diff --git a/pyrevolve/SDF/link.py b/pyrevolve/SDF/link.py index e1f9a3e78d..82f6c08c69 100644 --- a/pyrevolve/SDF/link.py +++ b/pyrevolve/SDF/link.py @@ -3,7 +3,7 @@ from pyrevolve import SDF from pyrevolve.SDF.inertial import transform_inertia_tensor -from ..custom_logging.logger import logger +from pyrevolve.custom_logging.logger import logger class Link(SDF.Posable): diff --git a/pyrevolve/SDF/pose.py b/pyrevolve/SDF/pose.py index 46c4d40beb..5433f8cbfe 100644 --- a/pyrevolve/SDF/pose.py +++ b/pyrevolve/SDF/pose.py @@ -3,7 +3,7 @@ from pyrevolve import SDF import pyrevolve.SDF.math -from ..custom_logging.logger import logger +from pyrevolve.custom_logging.logger import logger class Pose(xml.etree.ElementTree.Element): diff --git a/pyrevolve/SDF/revolve_bot_sdf_builder.py b/pyrevolve/SDF/revolve_bot_sdf_builder.py index 8167c19993..08daa9ada6 100644 --- a/pyrevolve/SDF/revolve_bot_sdf_builder.py +++ b/pyrevolve/SDF/revolve_bot_sdf_builder.py @@ -291,6 +291,14 @@ def _sdf_brain_plugin_conf( else: robot_brain_sdf.append(robot_controller) + try: + robot_IMC = robot_brain.IMC.controller_sdf() + except: + print("IMC in-active!") + xml.etree.ElementTree.SubElement(robot_brain_sdf, 'rv:IMC', {'active': 'false'}) + else: + robot_brain_sdf.append(robot_IMC) + # sensors sensors_elem = xml.etree.ElementTree.SubElement(robot_brain_sdf, 'rv:sensors') for sensor in sensors: diff --git a/pyrevolve/angle/manage/world.py b/pyrevolve/angle/manage/world.py index 31c8fd9a9b..1dadc0414f 100644 --- a/pyrevolve/angle/manage/world.py +++ b/pyrevolve/angle/manage/world.py @@ -38,6 +38,7 @@ def __init__( world_address=None, output_directory=None, state_update_frequency=None, + listen_to_contacts=False, restore=None, _private=None ): @@ -87,6 +88,8 @@ def __init__( self.do_restore = None + self._listen_to_contacts = listen_to_contacts + # Sorry Matteo if False: #output_directory: if not restore: @@ -165,19 +168,21 @@ def poses_header(): async def create( cls, world_address=("127.0.0.1", 11345), - pose_update_frequency=10 + pose_update_frequency=10, + listen_to_contacts=False, ): """ Coroutine to instantiate a Revolve.Angle WorldManager :param pose_update_frequency: :param world_address: - :param analyzer_address: + :param listen_to_contacts: :return: """ self = cls( _private=cls._PRIVATE, world_address=world_address, - state_update_frequency=pose_update_frequency + state_update_frequency=pose_update_frequency, + listen_to_contacts=listen_to_contacts, ) await self._init(builder=None, generator=None) return self @@ -199,7 +204,7 @@ async def _init(self): if self.manager is not None: return - await (super(WorldManager, self)._init()) + await (super()._init()) # Subscribe to pose updates self.pose_subscriber = await self.manager.subscribe( @@ -208,11 +213,12 @@ async def _init(self): self._update_states ) - self.contact_subscriber = await self.manager.subscribe( - '/gazebo/default/physics/contacts', - 'gazebo.msgs.Contacts', - self._update_contacts - ) + if self._listen_to_contacts: + self.contact_subscriber = await self.manager.subscribe( + '/gazebo/default/physics/contacts', + 'gazebo.msgs.Contacts', + self._update_contacts + ) # Awaiting this immediately will lock the program update_state_future = self.set_state_update_frequency( @@ -230,7 +236,8 @@ async def _init(self): # Wait for connections await self.pose_subscriber.wait_for_connection() - await self.contact_subscriber.wait_for_connection() + if self._listen_to_contacts: + await self.contact_subscriber.wait_for_connection() await update_state_future if self.do_restore: diff --git a/pyrevolve/data_analisys/visualize_robot.py b/pyrevolve/data_analisys/visualize_robot.py index 09a0b7879d..9dfe211f32 100644 --- a/pyrevolve/data_analisys/visualize_robot.py +++ b/pyrevolve/data_analisys/visualize_robot.py @@ -7,9 +7,8 @@ from pyrevolve.custom_logging import logger from pyrevolve.revolve_bot import RevolveBot from pyrevolve.SDF.math import Vector3 -from pyrevolve.tol.manage import World +from pyrevolve.tol.manage.single_robot_world import SingleRobotWorld from pyrevolve.util.supervisor.supervisor_multi import DynamicSimSupervisor -from pyrevolve.evolution import fitness async def test_robot_run(robot_file_path: str): @@ -37,8 +36,10 @@ async def test_robot_run(robot_file_path: str): await asyncio.sleep(0.1) # Connect to the simulator and pause - connection = await World.create(settings, world_address=('127.0.0.1', settings.port_start)) + connection = await SingleRobotWorld.create(settings, world_address=('127.0.0.1', settings.port_start)) await asyncio.sleep(1) + await connection.pause(True) + await connection.reset(True) # init finished @@ -47,16 +48,13 @@ async def test_robot_run(robot_file_path: str): robot.update_substrate() robot.save_file(f'{robot_file_path}.sdf', conf_type='sdf') - await connection.pause(False) robot_manager = await connection.insert_robot(robot, Vector3(0, 0, 0.25), life_timeout=None) - await asyncio.sleep(1.0) + await connection.pause(False) # Start the main life loop while True: # Print robot fitness every second status = 'dead' if robot_manager.dead else 'alive' - print(f"Robot fitness ({status}) is \n" - f" OLD: {fitness.online_old_revolve(robot_manager)}\n" - f" DISPLAC: {fitness.displacement(robot_manager, robot)}\n" - f" DIS_VEL: {fitness.displacement_velocity(robot_manager, robot)}") + best_fitness = None if robot_manager.best_evaluation is None else robot_manager.best_evaluation.fitness + log.info(f"status: {status} - fitness: {best_fitness}") await asyncio.sleep(1.0) diff --git a/pyrevolve/examples/analyze_body.py b/pyrevolve/examples/analyze_body.py deleted file mode 100644 index 636ceabf22..0000000000 --- a/pyrevolve/examples/analyze_body.py +++ /dev/null @@ -1,68 +0,0 @@ -""" -Generates a bot using the code in `generated_sdf`, -and sends it to the body analyzer to have it analyzed. - -If the analysis is accepted, it outputs the bot, otherwise -it generates a new one. Writes the final bot's contents to -stdout, statistics are written to stderr. -""" -from __future__ import absolute_import -from __future__ import print_function - -import sys -import random - -from pyrevolve.sdfbuilder.math import Vector3 -from .generated_sdf import generate_robot, builder, robot_to_sdf -from ..gazebo import get_analysis_robot, BodyAnalyzer -from ..custom_logging.logger import logger - -import asyncio - - -if len(sys.argv) > 1: - seed = int(sys.argv[1]) -else: - seed = random.randint(0, 10000) - -random.seed(seed) -logger.info("Seed: {}".format(seed)) - - -async def analysis_func(): - analyzer = await (BodyAnalyzer.create(address=("127.0.0.1", 11346))) - - # Try a maximum of 100 times - for _ in range(100): - # Generate a new robot - robot = generate_robot() - - sdf = get_analysis_robot(robot, builder) - - # Find out its intersections and bounding box - intersections, bbox = await ( - analyzer.analyze_robot(robot, builder=builder)) - - if intersections: - logger.info("Invalid model - intersections detected.", file=sys.stderr) - else: - logger.info("No model intersections detected!", file=sys.stderr) - if bbox: - # Translate the model in z direction so it stands directly on - # the ground - logger.info("Model bounding box: ({}, {}, {}), ({}, {}, {})".format( - bbox.min.x, - bbox.min.y, - bbox.min.z, - bbox.max.x, - bbox.max.y, - bbox.max.z - ), file=sys.stderr) - model = sdf.elements[0] - model.translate(Vector3(0, 0, -bbox.min.z)) - - logger.info(str(robot_to_sdf(robot, "test_bot", "controllerplugin.so"))) - break - -loop = asyncio.get_event_loop() -loop.run_until_complete(analysis_func()) diff --git a/pyrevolve/examples/from_yaml.py b/pyrevolve/examples/from_yaml.py deleted file mode 100644 index 67851e8234..0000000000 --- a/pyrevolve/examples/from_yaml.py +++ /dev/null @@ -1,59 +0,0 @@ -from __future__ import absolute_import -from __future__ import print_function - -from pyrevolve.build.sdf import RobotBuilder -from pyrevolve.build.sdf import BodyBuilder -from pyrevolve.build.sdf import NeuralNetBuilder -from pyrevolve.convert import yaml_to_proto -from pyrevolve.sdfbuilder import SDF -from pyrevolve.sdfbuilder.math import Vector3 - -from .generated_sdf import body_spec, brain_spec - -from ..custom_logging.logger import logger - -bot_yaml = ''' ---- -body: - id : Core - type : Core - children: - 1: - id: Hinge - type: Hinge - params: - length: 0.5 - red: 1.0 - green: 0.0 - blue: 0.0 - 4: - id: Wheel - type: Wheel - params: - red: 0.0 - green: 1.0 - blue: 0.0 - 5: - id: Wheel2 - type: Wheel - params: - red: 0.0 - green: 1.0 - blue: 0.0 -brain: - params: - Wheel-out-0: - type: Oscillator - period: 3 - Wheel2-out-0: - type: Oscillator - period: 3 -''' - -bot = yaml_to_proto(body_spec, brain_spec, bot_yaml) -builder = RobotBuilder(BodyBuilder(body_spec), NeuralNetBuilder(brain_spec)) -model = builder.sdf_robot(bot, "libRobotControlPlugin.so") -model.translate(Vector3(0, 0, 0.5)) -sdf = SDF() -sdf.add_element(model) -logger.info(str(sdf)) diff --git a/pyrevolve/examples/generated_sdf.py b/pyrevolve/examples/generated_sdf.py deleted file mode 100644 index f00d3f4993..0000000000 --- a/pyrevolve/examples/generated_sdf.py +++ /dev/null @@ -1,393 +0,0 @@ -""" -Demonstrates creating a simple SDF bot from a spec and a YAML file. -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import math - -from pyrevolve.sdfbuilder.sensor import Sensor as SdfSensor -from pyrevolve.sdfbuilder.math import Vector3 -from pyrevolve.sdfbuilder import SDF, Limit -from pyrevolve.sdfbuilder.structure import Box as BoxGeom - -# Module imports -from pyrevolve.generate import BodyGenerator -from pyrevolve.generate import NeuralNetworkGenerator - -from pyrevolve.spec import BodyImplementation -from pyrevolve.spec import default_neural_net -from pyrevolve.spec import PartSpec -from pyrevolve.spec import ParamSpec -from pyrevolve.spec import Robot - -from pyrevolve.build.sdf.body import Box -from pyrevolve.build.sdf.body import Cylinder -from pyrevolve.build.sdf.body import ComponentJoint as Joint - -from pyrevolve.build.sdf import RobotBuilder -from pyrevolve.build.sdf import BodyBuilder -from pyrevolve.build.sdf import NeuralNetBuilder -from pyrevolve.build.sdf import VelocityMotor -from pyrevolve.build.sdf import PID - -from ..custom_logging.logger import logger - -# Some configuration -# This is the number of times per second we will call our -# robot's brain update (in the default controller). We'll -# also use it as -UPDATE_RATE = 5 - - -# A utility function to generate color properties -channel_func = lambda channel: ParamSpec(channel, min_value=0, max_value=1, default=0.5) -color_params = [ - channel_func("red"), channel_func("green"), channel_func("blue") -] - - -class ColorMixin(object): - """ - Mixin class for "color" parts. Needs to be mixed - in with a body part, or it won't work. - """ - - def apply_color(self): - """ - Applies the "red", "green" and "blue" arguments - to all links in this body part. - """ - self.make_color( - self.part_params["red"], - self.part_params["green"], - self.part_params["blue"]) - - -# Below, we define some body parts -# The first is a simple box that serves as a root component -# We have the box include an IMU Sensor that registers the -# component's acceleration -class Core(Box, ColorMixin): - X = 0.5 - Y = 0.8 - Z = 0.5 - MASS = 1.0 - - def _initialize(self, **kwargs): - """ - We override the default box's initialize method to - include the color of the box. - """ - # Don't forget to call super when a parent class actually - # does something (Box, in this case). - super(Core, self)._initialize(**kwargs) - - # Now we will add the IMU sensor. First, we create the - # sensor as we would like to have it in SDF... - imu = SdfSensor("imu_sensor", "imu", update_rate=UPDATE_RATE) - - # .. we then add this to a specific component using `add_sensor`. - # The second argument to this function allows us to override - # the name of the sensor handler that will be loaded. It defaults - # to the sensor type, so specifying "imu" here has the same - # result as leaving it empty. - self.component.add_sensor(imu, "imu") - - # Apply generated color - self.apply_color() - - -# Next, we illustrate a more complex body part, read -# the class details. -class PassiveHinge(Box, ColorMixin): - """ - A passive joint (i.e. it can move but isn't actuated) with - two attached blocks. One of these blocks is of fixed size, - the other has an x-size determined by a parameter. - """ - X = 0.5 - Y = 0.3 - Z = 0.3 - MASS = 0.1 - - # Offset of the joint from the box edges - JOINT_OFFSET = 0.1 - - def _initialize(self, **kwargs): - """ - Initialize method to generate the hinge. Inheriting from - "box" makes sure there is a box of the given size in - `self.link`. - """ - super(PassiveHinge, self)._initialize(**kwargs) - - # Create the variable size block. `create_link` is the recommended - # way of doing this, because it sets some non-default link properties - # (such as self_collide) which you generally need. - length = kwargs["length"] - self.var_block = self.create_component( - BoxGeom(length, self.y, self.z, 0.1), "var-block") - - # We move the block in the x-direction so that it - # just about overlaps with the other block (to - # make it seem like a somewhat realistic joint) - self.var_block.translate( - Vector3(0.5 * (length + self.x) - self.JOINT_OFFSET, 0, 0)) - - # Now create a revolute joint at this same position. The - # joint axis is in the y-direction. - axis = Vector3(0, 1, 0) - passive_joint = Joint( - joint_type="revolute", - parent=self.component, - child=self.var_block, - axis=axis) - - # Set some movement limits on the joint - passive_joint.axis.limit = Limit( - lower=math.radians(-45), - upper=math.radians(45), - effort=1.0) - - # Set the joint position - in the child frame! - passive_joint.set_position( - Vector3(-0.5 * length + self.JOINT_OFFSET, 0, 0)) - - # Don't forget to add the joint and the link - self.add_joint(passive_joint) - - # Apply the generated color - self.apply_color() - - def get_slot(self, slot): - """ - This method should return the SDF link corresponding to a - certain slot. - """ - # Throw a clear error if the slot doesn't exist - self.check_slot(slot) - - # Slot 0 is the fixed box, slot 1 is the variable sized block - return self.component if slot == 0 else self.var_block - - def get_slot_position(self, slot): - """ - Returns the attachment position of each of the slots. - """ - # Again, prevent errors - self.check_slot(slot) - - # The constructor of `BodyPart` stores the initialization's kwargs - # parameters in `self.part_params`. - length = self.part_params["length"] - - # The center of the base box lies at (0, 0), move 1/2 x to the - # left to get that slot, or move 1/2 x to the right, plus the - # variable length minus the offset to get the other. - return Vector3(-0.5 * self.x, 0, 0) if slot == 0 \ - else Vector3(0.5 * self.x + length - self.JOINT_OFFSET, 0, 0) - - def get_slot_normal(self, slot): - """ - Return the slot normal vectors; in this case they are equal - to the position vectors except for their length. - - Actually, it is not strictly required for normal and tangent - vectors to be normalized, but it is good practice to do so. - """ - return self.get_slot_position(slot).normalized() - - def get_slot_tangent(self, slot): - """ - The tangent vectors determine the "zero orientation", meaning - if a body part has an orientation of 0 it will have its tangent - vector aligned with its parent. The tangent vector has to be - orthogonal to the slot normal. - - We have no specific orientation requirements, so we simply - always return one tangent vector which is orthogonal to both - slot normals, i.e. the vector (0, 0, 1). - """ - self.check_slot(slot) - return Vector3(0, 0, 1) - - -# The second body part is a motorized wheel. For this, we start -# with a cylinder, and extend it to include a thin -# connecting block and a motor. -class Wheel(Cylinder, ColorMixin): - RADIUS = 0.6 - MASS = 0.5 - LENGTH = 0.2 - MOTOR_SIZE = 0.1 - - def _initialize(self, **kwargs): - """ - :param kwargs: - :return: - """ - # Call super to initialize the cylinder part of the wheel - super(Wheel, self)._initialize(**kwargs) - - # Create the small box that serves as the motor - box_size = self.MOTOR_SIZE - self.attachment = self.create_component( - BoxGeom(box_size, box_size, box_size, 0.01), "cylinder-attach") - - # Get attachment position and axis of the motor joint - anchor = Vector3(0, 0, 0.5 * self.LENGTH) - axis = Vector3(0, 0, 1) - - # Size and position the box - self.attachment.set_position(anchor + Vector3(0, 0, 0.5 * box_size)) - - # Create revolute joint. Remember: joint position is in child frame - motor_joint = Joint( - joint_type="revolute", - parent=self.component, - child=self.attachment, - axis=axis) - motor_joint.set_position(Vector3(0, 0, -0.5 * box_size)) - - # Set a force limit on the joint - motor_joint.axis.limit = Limit(effort=0.01) - self.add_joint(motor_joint) - - # Register a joint motor with a maximum velocity of - # 50 rounds per minute (the speed is in radians per second) - # We also give it a simple PID controller - pid = PID(proportional_gain=1.0, integral_gain=0.1) - max_speed = 2 * math.pi * 50.0 / 60 - self.motors.append(VelocityMotor( - part_id=self.id, - motor_id="rotate", - joint=motor_joint, - pid=pid, - min_velocity=-max_speed, - max_velocity=max_speed)) - self.apply_color() - - def get_slot(self, slot_id): - """ - Override get_slot, because we should return the attachment. - :param slot_id: - :return: - """ - return self.attachment - - def get_slot_position(self, slot_id): - """ - Modify `get_slot_position` to return the attachment of the - motor instead. - :param slot_id: - :return: - """ - v = super(Wheel, self).get_slot_position(slot_id) - return v + Vector3(0, 0, self.MOTOR_SIZE) - - -body_spec = BodyImplementation( - { - ("Core", "C"): PartSpec( - body_part=Core, - arity=6, - inputs=6, - params=color_params - ), - ("Wheel", "W"): PartSpec( - body_part=Wheel, - arity=1, - outputs=1, - - # Add color parameters to this part - params=color_params - ), - "Hinge": PartSpec( - body_part=PassiveHinge, - arity=2, - params=color_params + [ - ParamSpec( - name="length", - min_value=0.1, - max_value=1, - default=0.5) - ] - ) - } -) - -# For the brain, we use the default neural network -brain_spec = default_neural_net() - -# Specify a body generator for the specification -body_gen = BodyGenerator( - body_spec, - - # List all parts that can serve as the robot root - root_parts=["Core"], - - # List all parts that can be attached - attach_parts=["Wheel", "Hinge"], - - # Set the maximum number of used parts. The - # actual number will be determined by a - # random pick and some input / output constraints. - max_parts=15, - - # The maximum number of input (i.e. sensory) values - # our robot may have. - max_inputs=8, - - # The maximum number ouf output (i.e. motory) values - # our robot may have. - max_outputs=12 -) - -# Also get a brain generator -brain_gen = NeuralNetworkGenerator( - brain_spec, - max_hidden=10 -) - -# Create a builder to convert the protobuf to SDF -builder = RobotBuilder(BodyBuilder(body_spec), NeuralNetBuilder(brain_spec)) - - -def generate_robot(robot_id=0): - # Create a protobuf robot - robot = Robot() - robot.id = robot_id - - # Generate a body - body = body_gen.generate() - robot.body.CopyFrom(body) - - # The neural network generator can get the interface from the body - brain = brain_gen.generate_from_body(body, body_spec) - robot.brain.CopyFrom(brain) - - return robot - - -def robot_to_sdf(robot, name="test_bot", plugin_controller=None): - model = builder.sdf_robot( - robot=robot, - controller_plugin=plugin_controller, - update_rate=UPDATE_RATE, - name=name) - model_sdf = SDF() - model_sdf.add_element(model) - return model_sdf - - -def generate_sdf_robot(robot_id=0, plugin_controller=None, name="test_bot"): - robot = generate_robot(robot_id) - return robot_to_sdf(robot, name, plugin_controller) - - -if __name__ == "__main__": - # Create SDF and output - sdf = generate_sdf_robot() - logger.info(str(sdf)) diff --git a/pyrevolve/revolve_bot/brain/IMC.py b/pyrevolve/revolve_bot/brain/IMC.py new file mode 100644 index 0000000000..c38753257a --- /dev/null +++ b/pyrevolve/revolve_bot/brain/IMC.py @@ -0,0 +1,64 @@ +""" +Note: Parameters are not set in this file. They are imported from the robot yaml-file, containing the +physical properties of the robot, as well as the brain (learner and controller) and the corresponding +parameters. +""" + +import xml.etree.ElementTree +from .base import Brain + + +class BrainIMC(Brain): + + def __init__(self): + # CPG hyper-parameters + self.active = None + self.restore_checkpoint = None + self.save_checkpoint = None + self.learning_rate = None + self.beta1 = None + self.beta2 = None + self.weight_decay = None + + @staticmethod + def from_yaml(yaml_object): + BIMC = BrainIMC() + try: + for key, value in yaml_object.items(): + try: + setattr(BIMC, key, value) + except: + print(f"Couldn't set {key}, {value}") + except: + print("No IMC") + + for key in vars(BIMC): + if getattr(BIMC, key) is None: + print(f"Didn't load IMC param {key}") + raise RuntimeError(f"Didn't load IMC paramater: {key}") + + return BIMC + + def to_yaml(self): + return { + 'IMC': { + 'active': self.active, + 'restore_checkpoint': self.restore_checkpoint, + 'save_checkpoint': self.save_checkpoint, + 'learning_rate': self.learning_rate, + 'beta1': self.beta1, + 'beta2': self.beta2, + 'weight_decay': self.weight_decay + } + } + + def controller_sdf(self): + return xml.etree.ElementTree.Element('rv:IMC', { + 'active': str(self.active), + 'restore_checkpoint': str(self.restore_checkpoint), + 'save_checkpoint': str(self.save_checkpoint), + 'learning_rate': str(self.learning_rate), + 'beta1': str(self.beta1), + 'beta2': str(self.beta2), + 'weight_decay': str(self.weight_decay) + }) diff --git a/pyrevolve/revolve_bot/brain/__init__.py b/pyrevolve/revolve_bot/brain/__init__.py index aad5efdbfe..a60eb56136 100644 --- a/pyrevolve/revolve_bot/brain/__init__.py +++ b/pyrevolve/revolve_bot/brain/__init__.py @@ -4,3 +4,4 @@ from .bo_cpg import BrainCPGBO from .cpg import BrainCPG from .cppn_cpg import BrainCPPNCPG +from .IMC import BrainIMC diff --git a/pyrevolve/revolve_bot/brain/base.py b/pyrevolve/revolve_bot/brain/base.py index e5e715c212..c16548f96a 100644 --- a/pyrevolve/revolve_bot/brain/base.py +++ b/pyrevolve/revolve_bot/brain/base.py @@ -1,31 +1,45 @@ -import pyrevolve.revolve_bot.brain +from pyrevolve.revolve_bot import brain as brains +from pyrevolve.revolve_bot.brain.learner import Learner class Brain(object): + TYPE = 'NONE' + + def __init__(self): + self.learner = None @staticmethod def from_yaml(yaml_brain): brain_type = yaml_brain['type'] - if brain_type == pyrevolve.revolve_bot.brain.BrainNN.TYPE: - return pyrevolve.revolve_bot.brain.BrainNN.from_yaml(yaml_brain) - elif brain_type == pyrevolve.revolve_bot.brain.BrainRLPowerSplines.TYPE: - return pyrevolve.revolve_bot.brain.BrainRLPowerSplines.from_yaml(yaml_brain) - elif brain_type == pyrevolve.revolve_bot.brain.BrainCPGBO.TYPE: - return pyrevolve.revolve_bot.brain.BrainCPGBO.from_yaml(yaml_brain) - elif brain_type == pyrevolve.revolve_bot.brain.BrainCPG.TYPE: - return pyrevolve.revolve_bot.brain.BrainCPG.from_yaml(yaml_brain) - elif brain_type == pyrevolve.revolve_bot.brain.BrainCPPNCPG.TYPE: - return pyrevolve.revolve_bot.brain.BrainCPPNCPG.from_yaml(yaml_brain) + brain = None + if brain_type == brains.BrainNN.TYPE: + brain = brains.BrainNN.from_yaml(yaml_brain) + elif brain_type == brains.BrainRLPowerSplines.TYPE: + brain = brains.BrainRLPowerSplines.from_yaml(yaml_brain) + elif brain_type == brains.BrainCPGBO.TYPE: + brain = brains.BrainCPGBO.from_yaml(yaml_brain) + elif brain_type == brains.BrainCPG.TYPE: + brain = brains.BrainCPG.from_yaml(yaml_brain) + elif brain_type == brains.BrainCPPNCPG.TYPE: + brain = brains.BrainCPPNCPG.from_yaml(yaml_brain) else: print("No matching brain type defined in yaml file.") - return Brain() + brain = Brain() + + brain.learner = Learner.from_yaml(yaml_brain['learner']) + if 'IMC' in yaml_brain: + brain.IMC = brains.BrainIMC.from_yaml(yaml_brain['IMC']) + return brain def to_yaml(self): - return {} + return { + 'type': self.TYPE, + 'learner': self.learner.to_yaml() + } def learner_sdf(self): - return None + return self.learner.learner_sdf() def controller_sdf(self): return None diff --git a/pyrevolve/revolve_bot/brain/cpg.py b/pyrevolve/revolve_bot/brain/cpg.py index e74123b7f4..d594eb85fb 100644 --- a/pyrevolve/revolve_bot/brain/cpg.py +++ b/pyrevolve/revolve_bot/brain/cpg.py @@ -39,16 +39,15 @@ def __init__(self): @staticmethod def from_yaml(yaml_object): BCPG = BrainCPG() - for my_type in ["controller", "learner"]: #, "meta"]: - try: - my_object = yaml_object[my_type] - for key, value in my_object.items(): - try: - setattr(BCPG, key, value) - except: - print("Couldn't set {}, {}", format(key, value)) - except: - print("Didn't load {} parameters".format(my_type)) + try: + my_object = yaml_object["controller"] + for key, value in my_object.items(): + try: + setattr(BCPG, key, value) + except: + print(f"Couldn't set {key}, {value}") + except: + print("Didn't load \"controller\" parameters") return BCPG @@ -76,11 +75,6 @@ def to_yaml(self): } } - def learner_sdf(self): - return xml.etree.ElementTree.Element('rv:learner', { - 'type': 'offline', - }) - def controller_sdf(self): return xml.etree.ElementTree.Element('rv:controller', { 'type': 'cpg', diff --git a/pyrevolve/revolve_bot/brain/cppn_cpg.py b/pyrevolve/revolve_bot/brain/cppn_cpg.py index 86c4fb07fe..fa9981ab50 100644 --- a/pyrevolve/revolve_bot/brain/cppn_cpg.py +++ b/pyrevolve/revolve_bot/brain/cppn_cpg.py @@ -22,7 +22,9 @@ def to_yaml(self): @staticmethod def from_yaml(yaml_object): cppn_genome = multineat.Genome() - cppn_genome.Deserialize(yaml_object['controller']['cppn'].replace('inf', str(sys.float_info.max))) + cppn_genome_str = yaml_object['controller']['cppn'] + cppn_genome_str_fixed = cppn_genome_str.replace('inf', str(sys.float_info.max)) + cppn_genome.Deserialize(cppn_genome_str_fixed) del yaml_object['controller']['cppn'] BCPG = BrainCPPNCPG(cppn_genome) diff --git a/pyrevolve/revolve_bot/brain/learner/__init__.py b/pyrevolve/revolve_bot/brain/learner/__init__.py new file mode 100644 index 0000000000..41df767a59 --- /dev/null +++ b/pyrevolve/revolve_bot/brain/learner/__init__.py @@ -0,0 +1,5 @@ +from .base import Learner +from .bo import BOLearner +from .hyperneat import HyperNEATLearner +from .nipes import NIPESLearner +from .de import DELearner diff --git a/pyrevolve/revolve_bot/brain/learner/base.py b/pyrevolve/revolve_bot/brain/learner/base.py new file mode 100644 index 0000000000..f7cb5d8cb9 --- /dev/null +++ b/pyrevolve/revolve_bot/brain/learner/base.py @@ -0,0 +1,32 @@ +import xml.etree.ElementTree +from pyrevolve.revolve_bot.brain import learner as learners + + +class Learner(object): + TYPE = 'offline' + + @staticmethod + def from_yaml(yaml_learner): + brain_type = yaml_learner['type'] + + if brain_type == learners.bo.BOLearner.TYPE: + return learners.bo.BOLearner.from_yaml(yaml_learner) + if brain_type == learners.hyperneat.HyperNEATLearner.TYPE: + return learners.hyperneat.HyperNEATLearner.from_yaml(yaml_learner) + if brain_type == learners.nipes.NIPESLearner.TYPE: + return learners.nipes.NIPESLearner.from_yaml(yaml_learner) + if brain_type == learners.de.DELearner.TYPE: + return learners.de.DELearner.from_yaml(yaml_learner) + else: + print("No matching brain/learner type defined in yaml file.") + return Learner() + + def to_yaml(self): + return { + 'type': self.TYPE + } + + def learner_sdf(self): + return xml.etree.ElementTree.Element('rv:learner', { + 'type': 'offline', + }) diff --git a/pyrevolve/revolve_bot/brain/learner/bo.py b/pyrevolve/revolve_bot/brain/learner/bo.py new file mode 100644 index 0000000000..a8687bc278 --- /dev/null +++ b/pyrevolve/revolve_bot/brain/learner/bo.py @@ -0,0 +1,51 @@ +import xml.etree.ElementTree + +from .base import Learner + + +class BOLearner(Learner): + TYPE = 'bo' + + def __init__(self): + self.n_init_samples = 1 + self.n_learning_iterations = 100 + self.evaluation_rate = 15.0 + self.init_method = 'LHS' + self.kernel_noise = 0.00000001 + self.kernel_optimize_noise = False + self.kernel_sigma_sq = 0.222 + self.kernel_l = 0.55 + self.kernel_squared_exp_ard_k = 4 + self.acqui_gpucb_delta = 0.5 + self.acqui_ucb_alpha = 0.44 + self.acqui_ei_jitter = 0.0 + self.acquisition_function = "UCB" + + @staticmethod + def from_yaml(yaml_learner): + # TODO read BO params + return BOLearner() + + # def to_yaml(self): + # #TODO save BO params + # return { + # type: self.TYPE + # } + + def learner_sdf(self): + return xml.etree.ElementTree.Element('rv:learner', { + 'type': 'bo', + 'n_init_samples': str(self.n_init_samples), + 'n_learning_iterations': str(self.n_learning_iterations), + 'evaluation_rate': str(self.evaluation_rate), + 'init_method': str(self.init_method), + 'kernel_noise': str(self.kernel_noise), + 'kernel_optimize_noise': str(self.kernel_optimize_noise), + 'kernel_sigma_sq': str(self.kernel_sigma_sq), + 'kernel_l': str(self.kernel_l), + 'kernel_squared_exp_ard_k': str(self.kernel_squared_exp_ard_k), + 'acqui_gpucb_delta': str(self.acqui_gpucb_delta), + 'acqui_ucb_alpha': str(self.acqui_ucb_alpha), + 'acqui_ei_jitter': str(self.acqui_ei_jitter), + 'acquisition_function': str(self.acquisition_function), + }) diff --git a/pyrevolve/revolve_bot/brain/learner/de.py b/pyrevolve/revolve_bot/brain/learner/de.py new file mode 100644 index 0000000000..0618d98269 --- /dev/null +++ b/pyrevolve/revolve_bot/brain/learner/de.py @@ -0,0 +1,48 @@ +import xml.etree.ElementTree + +from .base import Learner + + +class DELearner(Learner): + TYPE = 'de' + + def __init__(self): + self.subtype = None + self.CR = 0.9 + self.F = 0.3 + self.n_parents = 3 + + self.verbose = False + self.population_size = 10 + self.max_eval = 300 + + @staticmethod + def from_yaml(yaml_learner): + LDE = DELearner() + try: + for key, value in yaml_learner.items(): + try: + setattr(LDE, key, value) + except: + print(f"Couldn't set {key}, {value}") + except: + print("No DE") + + for key in vars(LDE): + if getattr(LDE, key) is None: + raise RuntimeError(f"Didn't load {LDE.TYPE} param {key}") + + return LDE + + def learner_sdf(self): + return xml.etree.ElementTree.Element('rv:learner', { + 'type': 'de', + 'subtype': str(self.subtype), + 'CR': str(self.CR), + 'F': str(self.F), + 'n_parents': str(self.n_parents), + + 'verbose': str(self.verbose), + 'population_size': str(self.population_size), + 'max_eval': str(self.max_eval), + }) diff --git a/pyrevolve/revolve_bot/brain/learner/hyperneat.py b/pyrevolve/revolve_bot/brain/learner/hyperneat.py new file mode 100644 index 0000000000..7e0d5e1806 --- /dev/null +++ b/pyrevolve/revolve_bot/brain/learner/hyperneat.py @@ -0,0 +1,88 @@ +import sys +import xml.etree.ElementTree +import multineat +from .base import Learner + +class HyperNEATLearner(Learner): + TYPE = 'hyperneat' + + def __init__(self): + self.params = multineat.Parameters() + # self.to_yaml() + + + + def to_yaml(self): + obj = super().to_yaml() + obj['learner']['hyperneat'] = self.params + return obj + + @staticmethod + def from_yaml(yaml_object): + HyperNL = HyperNEATLearner() + for yaml_params in ["params"]: + try: + my_object = yaml_object[yaml_params] + for key, value in my_object.items(): + try: + setattr(HyperNL.params, key, value) + except: + print("Couldn't set {}, {}", format(key, value)) + except: + print("Didn't load {} parameters".format(yaml_params)) + + return HyperNL + + def learner_sdf(self): + learner = xml.etree.ElementTree.Element('rv:learner', { + 'type': 'hyperneat', + }) + learner.append(self.params_sdf()) + return learner + + def params_sdf(self): + assert(self.params is not None) + + element = xml.etree.ElementTree.Element('rv:params', { + 'PopulationSize': str(self.params.PopulationSize), + 'DynamicCompatibility': str(self.params.DynamicCompatibility), + 'NormalizeGenomeSize': str(self.params.NormalizeGenomeSize), + 'WeightDiffCoeff': str(self.params.WeightDiffCoeff), + 'CompatTreshold': str(self.params.CompatTreshold), + 'YoungAgeTreshold': str(self.params.YoungAgeTreshold), + # 'SpeciesMaxStagnation ': str(self.params.SpeciesMaxStagnation), # Not present in multineat.Population()? + 'OldAgeTreshold': str(self.params.OldAgeTreshold), + 'MinSpecies': str(self.params.MinSpecies), + 'MaxSpecies': str(self.params.MaxSpecies), + 'RouletteWheelSelection': str(self.params.RouletteWheelSelection), + 'RecurrentProb': str(self.params.RecurrentProb), + 'OverallMutationRate': str(self.params.OverallMutationRate), + 'ArchiveEnforcement': str(self.params.ArchiveEnforcement), + 'MutateWeightsProb': str(self.params.MutateWeightsProb), + 'WeightMutationMaxPower': str(self.params.WeightMutationMaxPower), + 'WeightReplacementMaxPower': str(self.params.WeightReplacementMaxPower), + 'MutateWeightsSevereProb': str(self.params.MutateWeightsSevereProb), + 'WeightMutationRate': str(self.params.WeightMutationRate), + 'WeightReplacementRate': str(self.params.WeightReplacementRate), + 'MaxWeight': str(self.params.MaxWeight), + 'MutateAddNeuronProb': str(self.params.MutateAddNeuronProb), + 'MutateAddLinkProb': str(self.params.MutateAddLinkProb), + 'MutateRemLinkProb': str(self.params.MutateRemLinkProb), + 'MinActivationA': str(self.params.MinActivationA), + 'MaxActivationA': str(self.params.MaxActivationA), + 'ActivationFunction_SignedSigmoid_Prob': str(self.params.ActivationFunction_SignedSigmoid_Prob), + 'ActivationFunction_UnsignedSigmoid_Prob': str(self.params.ActivationFunction_UnsignedSigmoid_Prob), + 'ActivationFunction_Tanh_Prob': str(self.params.ActivationFunction_Tanh_Prob), + 'ActivationFunction_SignedStep_Prob': str(self.params.ActivationFunction_SignedStep_Prob), + 'CrossoverRate': str(self.params.CrossoverRate), + 'MultipointCrossoverRate': str(self.params.MultipointCrossoverRate), + 'SurvivalRate': str(self.params.SurvivalRate), + 'MutateNeuronTraitsProb': str(self.params.MutateNeuronTraitsProb), + 'MutateLinkTraitsProb': str(self.params.MutateLinkTraitsProb), + 'AllowLoops': str(self.params.AllowLoops), + 'AllowClones': str(self.params.AllowClones), + }) + return element + + + diff --git a/pyrevolve/revolve_bot/brain/learner/nipes.py b/pyrevolve/revolve_bot/brain/learner/nipes.py new file mode 100644 index 0000000000..20eb27af07 --- /dev/null +++ b/pyrevolve/revolve_bot/brain/learner/nipes.py @@ -0,0 +1,62 @@ +import xml.etree.ElementTree + +from .base import Learner + + +class NIPESLearner(Learner): + TYPE = 'nipes' + + def __init__(self): + self.stagnation_length = 20 + self.elitist_restart = True + self.CMAES_step = 1.0 + self.novelty_k_value = 15 + self.novelty_ratio = 1. + self.novelty_decrement = 0.05 + self.novelty_threshold = 0.9 + self.novelty_archive_probability = 0.4 + self.population_stagnation_threshold = 0.05 + self.restart = True + self.incremental_population = True + + self.verbose = False + self.population_size = 10 + self.max_eval = 300 + + @staticmethod + def from_yaml(yaml_learner): + LNIPES = NIPESLearner() + try: + for key, value in yaml_learner.items(): + try: + setattr(LNIPES, key, value) + except: + print(f"Couldn't set {key}, {value}") + except: + print("No NIPES") + + for key in vars(LNIPES): + if getattr(LNIPES, key) is None: + raise RuntimeError(f"Didn't load {LNIPES.TYPE} param {key}") + + return LNIPES + + def learner_sdf(self): + return xml.etree.ElementTree.Element('rv:learner', { + 'type': 'nipes', + 'stagnation_length': str(self.stagnation_length), + 'elitist_restart': str(self.elitist_restart), + 'CMAES_step': str(self.CMAES_step), + 'novelty_k_value': str(self.novelty_k_value), + 'novelty_ratio': str(self.novelty_ratio), + 'novelty_decrement': str(self.novelty_decrement), + 'novelty_threshold': str(self.novelty_threshold), + 'novelty_archive_probability': str(self.novelty_archive_probability), + 'population_stagnation_threshold': str(self.population_stagnation_threshold), + 'restart': str(self.restart), + 'incremental_population': str(self.incremental_population), + + 'verbose': str(self.verbose), + 'population_size': str(self.population_size), + 'max_eval': str(self.max_eval), + }) diff --git a/pyrevolve/revolve_bot/revolve_module.py b/pyrevolve/revolve_bot/revolve_module.py index dd21849f8c..7e2601adfc 100644 --- a/pyrevolve/revolve_bot/revolve_module.py +++ b/pyrevolve/revolve_bot/revolve_module.py @@ -245,8 +245,8 @@ class CoreModule(RevolveModule): TYPE = "CoreComponent" VISUAL_MESH = 'model://rg_robot/meshes/CoreComponent.dae' SLOT_COORDINATES = 0.089 / 2.0 - COLLISION_BOX = (0.089, 0.089, 0.045) - MASS = grams(90) + COLLISION_BOX = (0.089, 0.089, 0.0603) + MASS = grams(250) def __init__(self): super().__init__() @@ -273,15 +273,16 @@ class ActiveHingeModule(RevolveModule): TYPE = 'ActiveHinge' VISUAL_MESH_FRAME = 'model://rg_robot/meshes/ActiveHinge_Frame.dae' VISUAL_MESH_SERVO = 'model://rg_robot/meshes/ActiveCardanHinge_Servo_Holder.dae' - COLLISION_BOX_FRAME = (2.20e-02, 3.575e-02, 1.0e-02) - COLLISION_BOX_SERVO = (2.45e-02, 2.575e-02, 1.5e-02) - COLLISION_BOX_SERVO_2 = (1.0e-3, 3.4e-2, 3.4e-02) + + COLLISION_BOX_FRAME = (4.525e-02, 5.3e-02, 1.65891e-02) + COLLISION_BOX_SERVO = (5.83e-02, 5.12e-02, 2.0e-02) + COLLISION_BOX_SERVO_2 = (2.0e-3, 5.3e-2, 5.3e-02) COLLISION_BOX_SERVO_OFFSET = ( SDF.math.Vector3(0, 0, 0), - SDF.math.Vector3(-0.0091, 0, 0), + SDF.math.Vector3(-1.0e-02, 0, 0), ) - MASS_FRAME = grams(1.7) - MASS_SERVO = grams(9) + MASS_FRAME = grams(11) + MASS_SERVO = grams(58) def __init__(self): super().__init__() @@ -318,12 +319,12 @@ def to_sdf(self, tree_depth='', parent_link=None, child_link=None): visual_servo.append(geometry) collision_servo = SDF.Collision(name_servo, self.MASS_SERVO) - collision_servo.translate(SDF.math.Vector3(0.002375, 0, 0)) + collision_servo.translate(SDF.math.Vector3(-0.018, 0, 0)) geometry = SDF.BoxGeometry(self.COLLISION_BOX_SERVO) collision_servo.append(geometry) collision_servo_2 = SDF.Collision(name_servo2, 0) - collision_servo_2.translate(SDF.math.Vector3(0.01175, 0.001, 0)) + collision_servo_2.translate(SDF.math.Vector3(0.02815, 0, 0)) geometry = SDF.BoxGeometry(self.COLLISION_BOX_SERVO_2) collision_servo_2.append(geometry) @@ -335,7 +336,7 @@ def to_sdf(self, tree_depth='', parent_link=None, child_link=None): coordinates=self.substrate_coordinates, motorized=True) - joint.set_position(SDF.math.Vector3(-0.0085, 0, 0)) + joint.set_position(SDF.math.Vector3(-0.0299, 0, 0)) return visual_frame, \ [collision_frame], \ @@ -389,9 +390,9 @@ class BrickModule(RevolveModule): """ TYPE = "FixedBrick" VISUAL_MESH = 'model://rg_robot/meshes/FixedBrick.dae' - SLOT_COORDINATES = 3.8e-2 / 2.0 - COLLISION_BOX = (4.1e-2, 4.1e-2, 3.55e-02) - MASS = grams(10.2) + SLOT_COORDINATES = 0.06288625/ 2.0 + COLLISION_BOX = (0.06288625, 0.06288625, 0.0603) + MASS = grams(30) def __init__(self): super().__init__() diff --git a/pyrevolve/revolve_bot/revolve_moduleV1.py b/pyrevolve/revolve_bot/revolve_moduleV1.py new file mode 100644 index 0000000000..e5522aea21 --- /dev/null +++ b/pyrevolve/revolve_bot/revolve_moduleV1.py @@ -0,0 +1,526 @@ +""" +Class containing the body parts to compose a Robogen robot +""" +from collections import OrderedDict +from enum import Enum + +from pyrevolve import SDF + + +# MEASUREMENT CONVERSION +def mm(x): + return x / 1000.0 + + +def cm(x): + return x / 100.0 + + +def grams(x): + return x / 1000.0 + + +# Module Orientation +class Orientation(Enum): + SOUTH = 0 + NORTH = 1 + EAST = 2 + WEST = 3 + + def short_repr(self): + if self == self.SOUTH: + return 'S' + elif self == self.NORTH: + return 'N' + elif self == self.EAST: + return 'E' + elif self == self.WEST: + return 'W' + else: + assert False + + +class RevolveModule: + """ + Base class allowing for constructing Robogen components in an overviewable manner + """ + DEFAULT_COLOR = (0.5, 0.5, 0.5) + TYPE = None + VISUAL_MESH = None + COLLISION_BOX = None + MASS = None + INERTIA = None + + def __init__(self): + self.id = None + self.orientation = None + self.rgb = None # RevolveModule.DEFAULT_COLOR + self.substrate_coordinates = None + self.children = [None, None, None, None] + self.info = None + + def color(self): + return self.rgb if self.rgb is not None else self.DEFAULT_COLOR + + @staticmethod + def FromYaml(yaml_object): + """ + From a yaml object, creates a data struture of interconnected body modules. + Standard names for modules are: + CoreComponent + ActiveHinge + FixedBrick + FixedBrickSensor + """ + mod_type = yaml_object['type'] + if mod_type == 'CoreComponent' or mod_type == 'Core': + module = CoreModule() + elif mod_type == 'ActiveHinge': + module = ActiveHingeModule() + elif mod_type == 'FixedBrick': + module = BrickModule() + elif mod_type == 'FixedBrickSensor': + module = BrickSensorModule() + elif mod_type == 'TouchSensor': + module = TouchSensorModule() + else: + raise NotImplementedError('"{}" module not yet implemented'.format(mod_type)) + + module.id = yaml_object['id'] + + try: + module.orientation = yaml_object['orientation'] + except KeyError: + module.orientation = 0 + + try: + module.rgb = ( + yaml_object['params']['red'], + yaml_object['params']['green'], + yaml_object['params']['blue'], + ) + except KeyError: + pass + + if 'children' in yaml_object: + for parent_slot in yaml_object['children']: + module.children[parent_slot] = RevolveModule.FromYaml( + yaml_object=yaml_object['children'][parent_slot]) + + return module + + def to_yaml(self): + if self.TYPE is None: + raise RuntimeError('Module TYPE is not implemented for "{}",' + ' this should be defined.'.format(self.__class__)) + + yaml_dict_object = OrderedDict() + yaml_dict_object['id'] = self.id + yaml_dict_object['type'] = self.TYPE + yaml_dict_object['orientation'] = self.orientation + + if self.rgb is not None: + yaml_dict_object['params'] = { + 'red': self.rgb[0], + 'green': self.rgb[1], + 'blue': self.rgb[2], + } + + children = self._generate_yaml_children() + if children is not None: + yaml_dict_object['children'] = children + + return yaml_dict_object + + def iter_children(self): + return enumerate(self.children) + + def _generate_yaml_children(self): + has_children = False + + children = {} + for i, child in self.iter_children(): + if child is not None: + children[i] = child.to_yaml() + has_children = True + + return children if has_children else None + + def validate(self): + """ + Tests if the robot tree is valid (recursively) + :return: True if the robot tree is valid + """ + raise RuntimeError("Robot tree validation not yet implemented") + + def to_sdf(self, tree_depth='', parent_link=None, child_link=None): + """ + Transform the module in sdf elements. + + IMPORTANT: It does not append VISUAL and COLLISION elements to the parent link + automatically. It does append automatically the SENSOR element. + TODO: make the append automatic for VISUAL AND COLLISION AS WELL. + + :param tree_depth: current tree depth as string (for naming) + :param parent_link: link of the parent (may be needed for certain modules) + :param child_link: link of the child (may be needed for certain modules, like hinges) + :return: visual SDF element, collision SDF element, sensor SDF element. + Sensor SDF element may be None. + """ + name = 'component_{}_{}__box'.format(tree_depth, self.TYPE) + visual = SDF.Visual(name, self.rgb) + geometry = SDF.MeshGeometry(self.VISUAL_MESH) + visual.append(geometry) + + collision = SDF.Collision(name, self.MASS) + geometry = SDF.BoxGeometry(self.COLLISION_BOX) + collision.append(geometry) + + return visual, collision, None + + def boxslot(self, orientation=None): + orientation = Orientation.SOUTH if orientation is None else orientation + return BoxSlot(self.possible_slots(), orientation) + + def possible_slots(self): + box_geometry = self.COLLISION_BOX + return ( + (box_geometry[0] / -2.0, box_geometry[0] / 2.0), # X + (box_geometry[1] / -2.0, box_geometry[1] / 2.0), # Y + (box_geometry[2] / -2.0, box_geometry[2] / 2.0), # Z + ) + + def has_children(self): + """ + Check wheter module has children + :return: True if module has children + """ + has_children = False + + if self.children == {1: None}: return False + + for i, child in enumerate(self.children): + if child is not None: + has_children = True + + return has_children + + +class CoreModule(RevolveModule): + """ + Inherits class RevolveModule. Creates Robogen core module + """ + TYPE = "CoreComponent" + VISUAL_MESH = 'model://rg_robot/meshes/CoreComponent.dae' + SLOT_COORDINATES = 0.089 / 2.0 + COLLISION_BOX = (0.089, 0.089, 0.045) + MASS = grams(253) + + def __init__(self): + super().__init__() + self.substrate_coordinates = (0, 0) + + def possible_slots(self): + return ( + (-self.SLOT_COORDINATES, self.SLOT_COORDINATES), # X + (-self.SLOT_COORDINATES, self.SLOT_COORDINATES), # Y + (-self.SLOT_COORDINATES, self.SLOT_COORDINATES), # Z + ) + + def to_sdf(self, tree_depth='', parent_link=None, child_link=None): + imu_sensor = SDF.IMUSensor('core-imu_sensor', parent_link, self) + visual, collision, _ = super().to_sdf(tree_depth, parent_link, child_link) + parent_link.append(imu_sensor) + return visual, collision, imu_sensor + + +class ActiveHingeModule(RevolveModule): + """ + Inherits class RevolveModule. Creates Robogen joint module + """ + TYPE = 'ActiveHinge' + VISUAL_MESH_FRAME = 'model://rg_robot/meshes/ActiveHinge_Frame.dae' + VISUAL_MESH_SERVO = 'model://rg_robot/meshes/ActiveCardanHinge_Servo_Holder.dae' + COLLISION_BOX_FRAME = (2.20e-02, 3.575e-02, 1.0e-02) + COLLISION_BOX_SERVO = (2.45e-02, 2.575e-02, 1.5e-02) + COLLISION_BOX_SERVO_2 = (1.0e-3, 3.4e-2, 3.4e-02) + COLLISION_BOX_SERVO_OFFSET = ( + SDF.math.Vector3(0, 0, 0), + SDF.math.Vector3(-0.0091, 0, 0), + ) + MASS_FRAME = grams(8) + MASS_SERVO = grams(13) + + def __init__(self): + super().__init__() + self.children = {1: None} + + def iter_children(self): + return self.children.items() + + def _generate_yaml_children(self): + child = self.children[1] + if child is None: + return None + else: + return {1: child.to_yaml()} + + def to_sdf(self, tree_depth='', parent_link=None, child_link=None): + assert(parent_link is not None) + assert(child_link is not None) + name_frame = 'component_{}_{}__frame'.format(tree_depth, self.TYPE) + name_joint = 'component_{}_{}__joint'.format(tree_depth, self.TYPE) + name_servo = 'component_{}_{}__servo'.format(tree_depth, self.TYPE) + name_servo2 = 'component_{}_{}__servo2'.format(tree_depth, self.TYPE) + + visual_frame = SDF.Visual(name_frame, self.rgb) + geometry = SDF.MeshGeometry(self.VISUAL_MESH_FRAME) + visual_frame.append(geometry) + + collision_frame = SDF.Collision(name_frame, self.MASS_FRAME) + geometry = SDF.BoxGeometry(self.COLLISION_BOX_FRAME) + collision_frame.append(geometry) + + visual_servo = SDF.Visual(name_servo, self.rgb) + geometry = SDF.MeshGeometry(self.VISUAL_MESH_SERVO) + visual_servo.append(geometry) + + collision_servo = SDF.Collision(name_servo, self.MASS_SERVO) + collision_servo.translate(SDF.math.Vector3(0.002375, 0, 0)) + geometry = SDF.BoxGeometry(self.COLLISION_BOX_SERVO) + collision_servo.append(geometry) + + collision_servo_2 = SDF.Collision(name_servo2, 0) + collision_servo_2.translate(SDF.math.Vector3(0.01175, 0.001, 0)) + geometry = SDF.BoxGeometry(self.COLLISION_BOX_SERVO_2) + collision_servo_2.append(geometry) + + joint = SDF.Joint(self.id, + name_joint, + parent_link, + child_link, + axis=SDF.math.Vector3(0, 1, 0), + coordinates=self.substrate_coordinates, + motorized=True) + + joint.set_position(SDF.math.Vector3(-0.0085, 0, 0)) + + return visual_frame, \ + [collision_frame], \ + visual_servo, \ + [collision_servo, collision_servo_2], \ + joint + + def possible_slots_frame(self): + box_geometry = self.COLLISION_BOX_FRAME + return ( + (box_geometry[0] / -2.0, box_geometry[0] / 2.0 - 0.001), # X + (0, 0), # Y + (0, 0), # Z + ) + + def possible_slots_servo(self): + box_geometry = self.COLLISION_BOX_SERVO + return ( + (box_geometry[0] / -2.0, box_geometry[0] / 2.0), # X + (0, 0), # Y + (0, 0), # Z + ) + + def boxslot_frame(self, orientation=None): + orientation = Orientation.SOUTH if orientation is None else orientation + boundaries = self.possible_slots_frame() + return BoxSlotJoints( + boundaries, + orientation, + self.COLLISION_BOX_SERVO_OFFSET + ) + + def boxslot_servo(self, orientation=None): + orientation = Orientation.SOUTH if orientation is None else orientation + boundaries = self.possible_slots_servo() + return BoxSlotJoints(boundaries, orientation) + + def boxslot(self, orientation=None): + orientation = Orientation.SOUTH if orientation is None else orientation + if orientation is Orientation.SOUTH: + return self.boxslot_frame(orientation) + elif orientation is Orientation.NORTH: + return self.boxslot_servo(orientation) + else: + raise RuntimeError("Invalid orientation") + + +class BrickModule(RevolveModule): + """ + Inherits class RevolveModule. Creates Robogen brick module + """ + TYPE = "FixedBrick" + VISUAL_MESH = 'model://rg_robot/meshes/FixedBrick.dae' + SLOT_COORDINATES = 3.8e-2 / 2.0 + COLLISION_BOX = (4.1e-2, 4.1e-2, 3.55e-02) + MASS = grams(12.0) + + def __init__(self): + super().__init__() + + def possible_slots(self): + return ( + (-self.SLOT_COORDINATES, self.SLOT_COORDINATES), # X + (-self.SLOT_COORDINATES, self.SLOT_COORDINATES), # Y + (-self.SLOT_COORDINATES, self.SLOT_COORDINATES), # Z + ) + + +class BrickSensorModule(RevolveModule): + """ + TODO not finished + Inherits class RevolveModule. Creates Robogen brick sensor module + """ + TYPE = "FixedBrickSensor" + VISUAL_MESH = 'model://rg_robot/meshes/FixedBrick.dae' + COLLISION_BOX = (4.1e-2, 4.1e-2, 3.55e-02) + + def __init__(self): + super().__init__() + raise RuntimeError("Not implemented yet") + + +class TouchSensorModule(RevolveModule): + """ + Inherits class RevolveModule. Creates Robogen sensor module + """ + TYPE = "TouchSensor" + VISUAL_MESH = 'model://rg_robot/meshes/TouchSensor.dae' + SLOT_COORDINATES = 1e-2 / 2.0 + COLLISION_BOX = (4.1e-3, 3.1e-2, 1.55e-02) + MASS = grams(3) + + def __init__(self): + super().__init__() + self.children = {} + + def boxslot(self, orientation=None): + orientation = Orientation.SOUTH if orientation is None else orientation + assert (orientation is Orientation.SOUTH) + return BoxSlotTouchSensor(self.possible_slots()) + + def possible_slots(self): + return ( + (-self.SLOT_COORDINATES, 0), # X + (0, 0), # Y + (0, 0), # Z + ) + + def to_sdf(self, tree_depth='', parent_link=None, child_link=None): + assert(parent_link is not None) + name = 'component_{}_{}'.format(tree_depth, self.TYPE) + name_sensor = 'sensor_{}_{}'.format(tree_depth, self.TYPE) + + visual = SDF.Visual(name, self.rgb) + geometry = SDF.MeshGeometry(self.VISUAL_MESH) + visual.append(geometry) + + collision = SDF.Collision(name, self.MASS) + geometry = SDF.BoxGeometry(self.COLLISION_BOX) + # collision.translate(SDF.math.Vector3(0.01175, 0.001, 0)) + collision.append(geometry) + + sensor = SDF.TouchSensor(name_sensor, collision, parent_link, self) + parent_link.append(sensor) + + return visual, collision, sensor + + +class BoxSlot: + """ + Helper class for modules connection slots + """ + def __init__(self, boundaries, orientation: Orientation): + self.orientation = orientation + self.pos = self._calculate_box_slot_pos(boundaries, orientation) + self.normal = self.pos.normalized() + self.tangent = self._calculate_box_slot_tangent(orientation) + + def _calculate_box_slot_pos(self, boundaries, slot: Orientation): + # boundaries = collision_elem.boundaries + if slot == Orientation.SOUTH: + return SDF.math.Vector3(0, boundaries[1][0], 0) + elif slot == Orientation.NORTH: + return SDF.math.Vector3(0, boundaries[1][1], 0) + elif slot == Orientation.EAST: + return SDF.math.Vector3(boundaries[0][1], 0, 0) + elif slot == Orientation.WEST: + return SDF.math.Vector3(boundaries[0][0], 0, 0) + else: + raise RuntimeError('invalid module orientation: {}'.format(slot)) + + @staticmethod + def _calculate_box_slot_tangent(slot: Orientation): + """ + Return slot tangent + """ + if slot == Orientation.SOUTH: + return SDF.math.Vector3(0, 0, 1) + elif slot == Orientation.NORTH: + return SDF.math.Vector3(0, 0, 1) + elif slot == Orientation.EAST: + return SDF.math.Vector3(0, 0, 1) + elif slot == Orientation.WEST: + return SDF.math.Vector3(0, 0, 1) + # elif slot == 4: + # # Right face tangent: back face + # return SDF.math.Vector3(0, 1, 0) + # elif slot == 5: + # # Left face tangent: back face + # return SDF.math.Vector3(0, 1, 0) + else: + raise RuntimeError("Invalid orientation") + + +class BoxSlotJoints(BoxSlot): + + def __init__(self, boundaries, orientation: Orientation, offset=(SDF.math.Vector3(), SDF.math.Vector3())): + self.offset = offset + super().__init__(boundaries, orientation) + + def _calculate_box_slot_pos(self, boundaries, slot: Orientation): + if slot == Orientation.SOUTH: + return SDF.math.Vector3(boundaries[0][0], 0, 0) + self.offset[0] + elif slot == Orientation.NORTH: + return SDF.math.Vector3(boundaries[0][1], 0, 0) + self.offset[1] + else: + raise RuntimeError('invalid module orientation: {}'.format(slot)) + + @staticmethod + def _calculate_box_slot_tangent(slot: Orientation): + """ + Return slot tangent + """ + if slot == Orientation.SOUTH: + return SDF.math.Vector3(0, 0, 1) + elif slot == Orientation.NORTH: + return SDF.math.Vector3(0, 0, 1) + else: + raise RuntimeError("Invalid orientation") + + +class BoxSlotTouchSensor(BoxSlot): + def __init__(self, boundaries): + super().__init__(boundaries, Orientation.SOUTH) + + def _calculate_box_slot_pos(self, boundaries, slot: Orientation): + if slot == Orientation.SOUTH: + return SDF.math.Vector3(boundaries[0][0], 0, 0) + else: + raise RuntimeError('invalid module orientation: {}'.format(slot)) + + @staticmethod + def _calculate_box_slot_tangent(slot: Orientation): + """ + Return slot tangent + """ + if slot == Orientation.SOUTH: + return SDF.math.Vector3(0, 1, 0) + else: + raise RuntimeError("Invalid orientation") diff --git a/pyrevolve/spec/msgs/__init__.py b/pyrevolve/spec/msgs/__init__.py index 643ad4453a..a15c2f35b3 100644 --- a/pyrevolve/spec/msgs/__init__.py +++ b/pyrevolve/spec/msgs/__init__.py @@ -5,3 +5,4 @@ from .sdf_body_analyze_pb2 import * from .model_inserted_pb2 import * from .robot_states_pb2 import * +from .robot_states_learning_pb2 import * diff --git a/pyrevolve/spec/msgs/robot_states_learning_pb2.py b/pyrevolve/spec/msgs/robot_states_learning_pb2.py new file mode 100644 index 0000000000..fc699f9d67 --- /dev/null +++ b/pyrevolve/spec/msgs/robot_states_learning_pb2.py @@ -0,0 +1,149 @@ +# Generated by the protocol buffer compiler. DO NOT EDIT! +# source: robot_states_learning.proto + +import sys +_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) +from google.protobuf import descriptor as _descriptor +from google.protobuf import message as _message +from google.protobuf import reflection as _reflection +from google.protobuf import symbol_database as _symbol_database +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + +from pygazebo.msg import time_pb2 as time__pb2 +from pygazebo.msg import pose_pb2 as pose__pb2 + + +DESCRIPTOR = _descriptor.FileDescriptor( + name='robot_states_learning.proto', + package='revolve.msgs', + syntax='proto2', + serialized_options=None, + serialized_pb=_b('\n\x1brobot_states_learning.proto\x12\x0crevolve.msgs\x1a\ntime.proto\x1a\npose.proto\"Q\n\rBehaviourData\x12\x1f\n\x04time\x18\x01 \x02(\x0b\x32\x11.gazebo.msgs.Time\x12\x1f\n\x04pose\x18\x02 \x01(\x0b\x32\x11.gazebo.msgs.Pose\"~\n\x13LearningRobotStates\x12\n\n\x02id\x18\x01 \x02(\t\x12\x0c\n\x04\x65val\x18\x02 \x02(\r\x12\x0c\n\x04\x64\x65\x61\x64\x18\x03 \x01(\x08\x12\x0f\n\x07\x66itness\x18\x04 \x02(\x01\x12.\n\tbehaviour\x18\x05 \x03(\x0b\x32\x1b.revolve.msgs.BehaviourData') + , + dependencies=[time__pb2.DESCRIPTOR,pose__pb2.DESCRIPTOR,]) + + + + +_BEHAVIOURDATA = _descriptor.Descriptor( + name='BehaviourData', + full_name='revolve.msgs.BehaviourData', + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[ + _descriptor.FieldDescriptor( + name='time', full_name='revolve.msgs.BehaviourData.time', index=0, + number=1, type=11, cpp_type=10, label=2, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR), + _descriptor.FieldDescriptor( + name='pose', full_name='revolve.msgs.BehaviourData.pose', index=1, + number=2, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto2', + extension_ranges=[], + oneofs=[ + ], + serialized_start=69, + serialized_end=150, +) + + +_LEARNINGROBOTSTATES = _descriptor.Descriptor( + name='LearningRobotStates', + full_name='revolve.msgs.LearningRobotStates', + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[ + _descriptor.FieldDescriptor( + name='id', full_name='revolve.msgs.LearningRobotStates.id', index=0, + number=1, type=9, cpp_type=9, label=2, + has_default_value=False, default_value=_b("").decode('utf-8'), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR), + _descriptor.FieldDescriptor( + name='eval', full_name='revolve.msgs.LearningRobotStates.eval', index=1, + number=2, type=13, cpp_type=3, label=2, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR), + _descriptor.FieldDescriptor( + name='dead', full_name='revolve.msgs.LearningRobotStates.dead', index=2, + number=3, type=8, cpp_type=7, label=1, + has_default_value=False, default_value=False, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR), + _descriptor.FieldDescriptor( + name='fitness', full_name='revolve.msgs.LearningRobotStates.fitness', index=3, + number=4, type=1, cpp_type=5, label=2, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR), + _descriptor.FieldDescriptor( + name='behaviour', full_name='revolve.msgs.LearningRobotStates.behaviour', index=4, + number=5, type=11, cpp_type=10, label=3, + has_default_value=False, default_value=[], + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto2', + extension_ranges=[], + oneofs=[ + ], + serialized_start=152, + serialized_end=278, +) + +_BEHAVIOURDATA.fields_by_name['time'].message_type = time__pb2._TIME +_BEHAVIOURDATA.fields_by_name['pose'].message_type = pose__pb2._POSE +_LEARNINGROBOTSTATES.fields_by_name['behaviour'].message_type = _BEHAVIOURDATA +DESCRIPTOR.message_types_by_name['BehaviourData'] = _BEHAVIOURDATA +DESCRIPTOR.message_types_by_name['LearningRobotStates'] = _LEARNINGROBOTSTATES +_sym_db.RegisterFileDescriptor(DESCRIPTOR) + +BehaviourData = _reflection.GeneratedProtocolMessageType('BehaviourData', (_message.Message,), dict( + DESCRIPTOR = _BEHAVIOURDATA, + __module__ = 'robot_states_learning_pb2' + # @@protoc_insertion_point(class_scope:revolve.msgs.BehaviourData) + )) +_sym_db.RegisterMessage(BehaviourData) + +LearningRobotStates = _reflection.GeneratedProtocolMessageType('LearningRobotStates', (_message.Message,), dict( + DESCRIPTOR = _LEARNINGROBOTSTATES, + __module__ = 'robot_states_learning_pb2' + # @@protoc_insertion_point(class_scope:revolve.msgs.LearningRobotStates) + )) +_sym_db.RegisterMessage(LearningRobotStates) + + +# @@protoc_insertion_point(module_scope) diff --git a/pyrevolve/tol/manage/learningrobotmanager.py b/pyrevolve/tol/manage/learningrobotmanager.py new file mode 100644 index 0000000000..fca112bcc7 --- /dev/null +++ b/pyrevolve/tol/manage/learningrobotmanager.py @@ -0,0 +1,80 @@ +from pyrevolve.util import Time +from pyrevolve.SDF.math import Vector3 +from pyrevolve.spec.msgs.robot_states_learning_pb2 import LearningRobotStates, BehaviourData + + +class Evaluation: + def __init__(self, eval_n: int, fitness: float, behaviour: list): + """ + :param eval_n: + :param fitness: + :param behaviour: + :type behaviour: list(BehaviourData) + """ + self.eval_n = eval_n + self.fitness = fitness + self.behaviour_data = behaviour + + def times(self): + def extract_time(step: BehaviourData): + return step.time + return map(extract_time, self.behaviour_data) + + def poses(self): + def extract_poses(step: BehaviourData): + return step.pose + return map(extract_poses, self.behaviour_data) + + +class LearningRobotManager(object): + """ + Class to manage a single robot + """ + + def __init__( + self, + program_arguments, + robot, + start_position: Vector3, + inserted_time, + ): + """ + :param program_arguments: + :param robot: RevolveBot + :param inserted_time: + :type inserted_time: Time + :return: + """ + + self.robot = robot + self.inserted_time = inserted_time + self.start_position = start_position + self.program_arguments = program_arguments + self.size = robot.size() + + self.dead = False + self.last_fitness = None + self.evaluations = [] + + self.best_evaluation = None + + @property + def name(self): + return str(self.robot.id) + + def learning_step_done(self, report: LearningRobotStates): + """ + :param report: report message sent from the simulator (routed from the WorldManager) + :type report: LearningRobotStates + """ + self.dead = self.dead or report.dead + self.last_fitness = report.fitness + evaluation = Evaluation( + eval_n=report.eval, + fitness=report.fitness, + behaviour=report.behaviour, + ) + self.evaluations.append(evaluation) + + if self.best_evaluation is None or report.fitness > self.best_evaluation.fitness: + self.best_evaluation = evaluation diff --git a/pyrevolve/tol/manage/single_robot_world.py b/pyrevolve/tol/manage/single_robot_world.py new file mode 100644 index 0000000000..426945f36a --- /dev/null +++ b/pyrevolve/tol/manage/single_robot_world.py @@ -0,0 +1,208 @@ +import numbers +import os + +import time + +from pyrevolve.SDF.math import Vector3 +from pyrevolve.gazebo.manage import WorldManager +from .learningrobotmanager import LearningRobotManager +from pyrevolve.spec.msgs import LearningRobotStates +from pyrevolve.spec.msgs import ModelInserted +from pyrevolve.util import Time +from pyrevolve.custom_logging.logger import logger + +# Construct a message base from the time. This should make it unique enough +# for consecutive use when the script is restarted. +_a = time.time() +MSG_BASE = int(_a - 14e8 + (_a - int(_a)) * 1e5) + + +class SingleRobotWorld(WorldManager): + """ + A class that is used to manage the world, meaning it provides methods to + insert / remove robots and request information about where they are. + + The world class contains a number of coroutines, usually from a + request/response perspective. These methods thus work with two futures - + one for the request to complete, one for the response to arrive. The + convention for these methods is to always yield the first future, because it + has proven problematic to send multiple messages over the same channel, + so a request is always sent until completion. The methods then return the + future that resolves when the response is delivered. + """ + + def __init__(self, program_arguments, _private, world_address): + """ + :param program_arguments: + """ + world_address = ("127.0.0.1", 11345) if world_address is None else world_address + + super().__init__( + _private=_private, + world_address=world_address, + ) + + self.robot_managers = {} + self.program_arguments = program_arguments + + @classmethod + async def create(cls, program_arguments, world_address=None): + """ + Coroutine to instantiate a Revolve.Angle WorldManager + :param program_arguments: + :param world_address: + :return: + """ + self = cls(_private=cls._PRIVATE, program_arguments=program_arguments, world_address=world_address) + await self._init() + return self + + async def _init(self): + if self.manager is not None: + return + + await (super()._init()) + + # Subscribe to learning states update + self.learning_states_subscriber = await self.manager.subscribe( + '/gazebo/default/revolve/robot_reports', + 'revolve.msgs.LearningRobotStates', + self._process_learning_state + ) + + # There will be no connection until the first learner starts, so waiting here will result in a deadlock + # await self.learning_states_subscriber.wait_for_connection() + + async def insert_robot( + self, + revolve_bot, + pose=None, + life_timeout=None, + ): + """ + Inserts a robot into the world. This consists of two steps: + + - Sending the insert request message + - Receiving a ModelInfo response + + This method is a coroutine because of the first step, writing + the message must be yielded since PyGazebo doesn't appear to + support writing multiple messages simultaneously. For the response, + i.e. the message that confirms the robot has been inserted, a + future is returned. + + :param revolve_bot: + :type revolve_bot: RevolveBot + :param pose: Insertion pose of a robot + :type pose: Pose|Vector3 + :param life_timeout: Life span of the robot + :type life_timeout: float|None + :return: A future that resolves with the created `Robot` object. + """ + if pose is None: + pose = Vector3(0, 0, self.program_arguments.z_start) + elif isinstance(pose, numbers.Number): + pose = Vector3(0, 0, pose) + elif isinstance(pose, Vector3): + pass + elif hasattr(pose, '__iter__'): + pose = Vector3(pose) + else: + raise RuntimeError(f"pose can only be Vector3 or number, instead {type(pose)} was found") + + # if the ID is digit, when removing the robot, the simulation will try to remove random stuff from the + # environment and give weird crash errors + assert(not str(revolve_bot.id).isdigit()) + + sdf_bot = revolve_bot.to_sdf(pose) + + # To debug and save all SDF files, you can uncomment the following code + # self.output_directory = '/tmp' + # if self.output_directory: + # robot_file_path = os.path.join( + # self.output_directory, + # 'robot_{}.sdf'.format(revolve_bot.id) + # ) + # with open(robot_file_path, 'w') as f: + # f.write(sdf_bot) + + response = await self.insert_model(sdf_bot, life_timeout) + robot_manager = self._robot_inserted( + robot=revolve_bot, + msg=response + ) + return robot_manager + + def _robot_inserted( + self, + robot, + msg + ): + """ + Registers a newly inserted robot and marks the insertion + message response as handled. + + :param robot: RevolveBot + :param msg: + :type msg: pygazebo.msgs.response_pb2.Response + :return: + """ + inserted = ModelInserted() + inserted.ParseFromString(msg.serialized_data) + model = inserted.model + inserted_time = Time(msg=inserted.time) + p = model.pose.position + position = Vector3(p.x, p.y, p.z) + + robot_manager = self.create_robot_manager( + robot, + position, + inserted_time + ) + self.register_robot(robot_manager) + return robot_manager + + def register_robot(self, robot_manager): + """ + Registers a robot with its Gazebo ID in the local array. + :param robot_manager: + :type robot_manager: RobotManager + """ + logger.info("Registering robot {}.".format(robot_manager.name)) + + if robot_manager.name in self.robot_managers: + raise ValueError("Duplicate robot: {}".format(robot_manager.name)) + + self.robot_managers[robot_manager.name] = robot_manager + + def _process_learning_state(self, msg): + """ + Handles the pose info message by updating robot positions. + :param msg: + :return: + """ + report = LearningRobotStates() + report.ParseFromString(msg) + + robot_name = report.id + robot_manager = self.robot_managers[robot_name] + robot_manager.learning_step_done(report) + + def create_robot_manager( + self, + robot, + start_position, + inserted_time, + ): + """ + Overriding with robot manager with more capabilities. + :param robot: + :param inserted_time: + :return: + """ + return LearningRobotManager( + program_arguments=self.program_arguments, + robot=robot, + start_position=start_position, + inserted_time=inserted_time, + ) diff --git a/pyrevolve/tol/manage/world.py b/pyrevolve/tol/manage/world.py index 651ab01553..ab737c80db 100644 --- a/pyrevolve/tol/manage/world.py +++ b/pyrevolve/tol/manage/world.py @@ -37,7 +37,7 @@ class World(WorldManager): future that resolves when the response is delivered. """ - def __init__(self, conf, _private, world_address): + def __init__(self, conf, _private, world_address, listen_to_contacts): """ :param conf: """ @@ -52,7 +52,8 @@ def __init__(self, conf, _private, world_address): builder=None, state_update_frequency=conf.pose_update_frequency, generator=None, - restore=conf.restore_directory + restore=conf.restore_directory, + listen_to_contacts=listen_to_contacts, ) self.conf = conf @@ -83,14 +84,15 @@ def __init__(self, conf, _private, world_address): ) @classmethod - async def create(cls, conf, world_address=None): + async def create(cls, conf, world_address=None, listen_to_contacts=False): """ Coroutine to instantiate a Revolve.Angle WorldManager :param conf: :param world_address: + :param listen_to_contacts: :return: """ - self = cls(_private=cls._PRIVATE, conf=conf, world_address=world_address) + self = cls(_private=cls._PRIVATE, conf=conf, world_address=world_address, listen_to_contacts=listen_to_contacts) await self._init() return self diff --git a/pyrevolve/util/supervisor/supervisor_multi.py b/pyrevolve/util/supervisor/supervisor_multi.py index 7b27ade29a..658e3e5432 100644 --- a/pyrevolve/util/supervisor/supervisor_multi.py +++ b/pyrevolve/util/supervisor/supervisor_multi.py @@ -304,18 +304,23 @@ class SimulatorEnded(Exception): async def read_stdout(): while not ready_str_found.done(): - if process.returncode is None: + if process.returncode is not None: ready_str_found.set_exception(SimulatorEnded()) - out = await stdout.readline() - self._logger.info(f'[starting] {out}') - if ready_str in out: - ready_str_found.set_result(None) + return + out_stream = asyncio.create_task(stdout.readline()) + done, pending = await asyncio.wait({out_stream, ready_str_found}, return_when='FIRST_COMPLETED') + if out_stream in done: + out = out_stream.result() + self._logger.info(f'[starting] {out}') + if ready_str in out: + ready_str_found.set_result(None) async def read_stderr(): while not ready_str_found.done() and process.returncode is None: - err = await stderr.readline() - if err: - self._logger.error(f'[starting] {err}') + err_stream = asyncio.create_task(stderr.readline()) + done, pending = await asyncio.wait({err_stream, ready_str_found}, return_when='FIRST_COMPLETED') + if err_stream in done: + self._logger.error(f'[starting] {err_stream.result()}') stdout_async = asyncio.ensure_future(read_stdout()) stderr_async = asyncio.ensure_future(read_stderr()) diff --git a/src/pygazebo/pygazebo b/src/pygazebo/pygazebo deleted file mode 160000 index e6f03ce47d..0000000000 --- a/src/pygazebo/pygazebo +++ /dev/null @@ -1 +0,0 @@ -Subproject commit e6f03ce47d8e60a2fbf73a142c3182e33a54c7f8 diff --git a/thirdparty/MultiNEAT b/thirdparty/MultiNEAT index a3c32cff53..422bda125a 160000 --- a/thirdparty/MultiNEAT +++ b/thirdparty/MultiNEAT @@ -1 +1 @@ -Subproject commit a3c32cff53abadda74ab28722312a4361309fb3f +Subproject commit 422bda125a427a8c492cf3a40a054a935fa5bff8 diff --git a/thirdparty/PIGPIO/pigpiod_if.c b/thirdparty/PIGPIO/pigpiod_if.c index 7802e57962..fc73aa7255 100644 --- a/thirdparty/PIGPIO/pigpiod_if.c +++ b/thirdparty/PIGPIO/pigpiod_if.c @@ -161,7 +161,7 @@ static int pigpio_command_ext return cmd.res; } -static int pigpioOpenSocket(char *addr, char *port) +static int pigpioOpenSocket(const char *addr, const char *port) { int sock, err, opt; struct addrinfo hints, *res, *rp; diff --git a/thirdparty/PIGPIO/pigpiod_if2.c b/thirdparty/PIGPIO/pigpiod_if2.c index 93efdb08cf..2a16650bea 100644 --- a/thirdparty/PIGPIO/pigpiod_if2.c +++ b/thirdparty/PIGPIO/pigpiod_if2.c @@ -234,7 +234,7 @@ static int pigpio_command_ext return cmd.res; } -static int pigpioOpenSocket(char *addr, char *port) +static int pigpioOpenSocket(const char *addr, const char *port) { int sock, err, opt; struct addrinfo hints, *res, *rp; diff --git a/thirdparty/libtorch/build-hash b/thirdparty/libtorch/build-hash new file mode 100644 index 0000000000..0c079cfcfe --- /dev/null +++ b/thirdparty/libtorch/build-hash @@ -0,0 +1,2 @@ +/pytorch /pytorch ~/project +7f73f1d591afba823daa4a99a939217fb54d7688 diff --git a/thirdparty/libtorch/build-version b/thirdparty/libtorch/build-version new file mode 100644 index 0000000000..a9ca3122a9 --- /dev/null +++ b/thirdparty/libtorch/build-version @@ -0,0 +1 @@ +1.4.0+cpu diff --git a/thirdparty/libtorch/include/ATen/ATen.h b/thirdparty/libtorch/include/ATen/ATen.h new file mode 100644 index 0000000000..669e8a661e --- /dev/null +++ b/thirdparty/libtorch/include/ATen/ATen.h @@ -0,0 +1,27 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include diff --git a/thirdparty/libtorch/include/ATen/AccumulateType.h b/thirdparty/libtorch/include/ATen/AccumulateType.h new file mode 100644 index 0000000000..9f91bcdcdc --- /dev/null +++ b/thirdparty/libtorch/include/ATen/AccumulateType.h @@ -0,0 +1,48 @@ +#pragma once +#include +#include +#include + +// Defines the accumulation type for a scalar type. +// Example: +// using accscalar_t = acc_type; + +#if defined(__CUDACC__) +#include +#include +#elif defined(__HIPCC__) +#include +#include +#endif + +namespace at { + +template +struct AccumulateType { }; + +#if defined(__CUDACC__) || defined(__HIPCC__) +template <> struct AccumulateType { using type = float; }; +#endif +template <> struct AccumulateType { using type = float; }; +template <> struct AccumulateType { using type = float; }; +template <> struct AccumulateType { using type = double; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = float; }; +template <> struct AccumulateType { using type = double; }; +template <> struct AccumulateType { using type = double; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; + +template +using acc_type = typename AccumulateType::type; + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/ArrayRef.h b/thirdparty/libtorch/include/ATen/ArrayRef.h new file mode 100644 index 0000000000..0461d5953e --- /dev/null +++ b/thirdparty/libtorch/include/ATen/ArrayRef.h @@ -0,0 +1,2 @@ +#pragma once +#include diff --git a/thirdparty/libtorch/include/ATen/Backend.h b/thirdparty/libtorch/include/ATen/Backend.h new file mode 100644 index 0000000000..9651469e19 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/Backend.h @@ -0,0 +1,2 @@ +#pragma once +#include diff --git a/thirdparty/libtorch/include/ATen/Backtrace.h b/thirdparty/libtorch/include/ATen/Backtrace.h new file mode 100644 index 0000000000..bdef9f4a9d --- /dev/null +++ b/thirdparty/libtorch/include/ATen/Backtrace.h @@ -0,0 +1,2 @@ +#pragma once +#include diff --git a/thirdparty/libtorch/include/ATen/CPUApplyUtils.h b/thirdparty/libtorch/include/ATen/CPUApplyUtils.h new file mode 100644 index 0000000000..7f5dabea8d --- /dev/null +++ b/thirdparty/libtorch/include/ATen/CPUApplyUtils.h @@ -0,0 +1,439 @@ +#pragma once + +#include +#include +#include +#include +#include + +namespace at { + +/* +[collapse dims] Updates sizes, and strides to reflect a "collapse" of +the info, possibly excluding the optional excludeDim. A "collapsed" version +of the info is the fewest dims that order the tensor's elements in the same +way as the original info. If excludeDim is specified, the collapse is the +fewest dims that order the tensor's elements as the original and preserve the +excluded dimension, unless the tensor collapses to a point. + +This function returns a pair of values. + +1) The (new) index of the preserved dimension if excludeDim is +specified. 0 if the tensor is collapsed to a point. -1 +otherwise. + +2) The new number of dimensions. +*/ +template +inline std::pair collapse_dims( + T* sizes, + T* strides, + int64_t dims, + const int excludeDim = -1) { + TORCH_CHECK( + excludeDim >= -1 && excludeDim < dims, + "expected excluded dim between -1 and dims - 1"); + + int64_t stopDim = (excludeDim == -1) ? dims : excludeDim; + int64_t newIndex = -1; + int64_t oldIndex = 0; + int64_t remappedExcludedDim = -1; + + while (oldIndex < dims) { + // Finds a dimension to collapse into + for (; oldIndex < stopDim; ++oldIndex) { + if (sizes[oldIndex] == 1) { + continue; + } + + ++newIndex; + sizes[newIndex] = sizes[oldIndex]; + strides[newIndex] = strides[oldIndex]; + ++oldIndex; + break; + } + + // Collapses dims + for (; oldIndex < stopDim; ++oldIndex) { + if (sizes[oldIndex] == 1) { + continue; + } + + if (strides[newIndex] == sizes[oldIndex] * strides[oldIndex]) { + sizes[newIndex] *= sizes[oldIndex]; + strides[newIndex] = strides[oldIndex]; + } else { + ++newIndex; + sizes[newIndex] = sizes[oldIndex]; + strides[newIndex] = strides[oldIndex]; + } + } + + // Handles excludeDim being set (oldIndex == excludeDim) + if (oldIndex != dims) { + // Preserves excluded dimension + ++newIndex; + sizes[newIndex] = sizes[oldIndex]; + strides[newIndex] = strides[oldIndex]; + remappedExcludedDim = newIndex; + + // Restarts iteration after excludeDim + ++oldIndex; + stopDim = dims; + } + } + + // Handles special case of all dims size 1 + if (newIndex == -1 || (newIndex == 0 && sizes[0] == 1)) { + dims = 1; + sizes[0] = 1; + strides[0] = 1; + + return std::pair(0, 1); + } + + dims = newIndex + 1; + return std::pair(remappedExcludedDim, dims); +} + +/* + * The basic strategy for apply is as follows: + * + * 1. Starting with the outermost index, loop until we reach a dimension where + * the data is no longer contiguous, i.e. the stride at that dimension is not + * equal to the size of the tensor defined by the outer dimensions. Let's call + * this outer (contiguous) tensor A. Note that if the Tensor is contiguous, then + * A is equal to the entire Tensor. Let's call the inner tensor B. + * + * 2. We loop through the indices in B, starting at its outermost dimension. For + * example, if B is a 2x2 matrix, then we do: + * + * B[0][0] + * B[0][1] + * B[1][0] + * B[1][1] + * + * We set the offset into the underlying storage as (storageOffset + stride_B * + * index_B), i.e. basically we compute the offset into the storage as we would + * normally for a Tensor. But because we are guaranteed the subsequent data is + * contiguous in memory, we can simply loop for sizeof(A) iterations and perform + * the operation, without having to follow the order described by the strides of + * A. + * + * 3. As an optimization, we merge dimensions of A that are contiguous in + * memory. For example, if A is a 3x3x3x3 tensor narrowed from a 3x3x4x3 tensor, + * then the first two dimensions can be merged for the purposes of APPLY, + * reducing the number of nested loops. + */ + +inline Tensor sort_strides(Tensor& tensor_) { + IntArrayRef strides = tensor_.strides(); + std::vector indices; + indices.reserve(tensor_.ndimension()); + for (int64_t i = 0; i < tensor_.ndimension(); i++) { + indices.push_back(i); + } + std::sort(indices.begin(), indices.end(), [&strides](int64_t i1, int64_t i2) { + return strides[i1] > strides[i2]; + }); + Tensor tensor = tensor_.permute(indices); + return tensor; +} + +template +struct strided_tensor_iter_fixed { + public: + T* data_ = NULL; + int64_t dim_ = 0; + + int64_t counter_[N] = {0}; + int64_t sizes_[N] = {0}; + int64_t strides_[N] = {0}; + + strided_tensor_iter_fixed(strided_tensor_iter_fixed const&) = delete; + void operator=(strided_tensor_iter_fixed const& x) = delete; + strided_tensor_iter_fixed(strided_tensor_iter_fixed&&) = default; + strided_tensor_iter_fixed(Tensor& tensor, bool sort_strides = false) + : data_(tensor.data_ptr()) { + std::memset(counter_, 0, sizeof(int64_t) * N); + if (tensor.dim() > 0) { + std::memcpy( + sizes_, tensor.sizes().data(), tensor.dim() * sizeof(int64_t)); + std::memcpy( + strides_, + tensor.strides().data(), + tensor.dim() * sizeof(int64_t)); + } + dim_ = std::get<1>(collapse_dims(sizes_, strides_, tensor.ndimension())); + } +}; + +template +struct strided_tensor_iter { + private: + public: + T* data_ = NULL; + int64_t dim_; + + std::vector counter_; + std::vector sizes_; + std::vector strides_; + + strided_tensor_iter(strided_tensor_iter const&) = delete; + void operator=(strided_tensor_iter const& x) = delete; + strided_tensor_iter(strided_tensor_iter&&) = default; + strided_tensor_iter(Tensor& tensor) + : data_(tensor.data_ptr()), + dim_(tensor.ndimension()), + counter_(dim_, 0), + sizes_(tensor.sizes().vec()), + strides_(tensor.strides().vec()) { + dim_ = std::get<1>(collapse_dims(sizes_.data(), strides_.data(), dim_)); + } +}; + +inline bool _all_equal_numel(at::ArrayRef tensors) { + if (tensors.size() == 0) + return true; + int64_t all_numel = tensors[0].numel(); + for (size_t i = 1; i < tensors.size(); i++) { + if (tensors[i].numel() != all_numel) + return false; + } + return true; +} + +inline std::string _all_equal_numel_error(at::ArrayRef tensors) { + std::ostringstream oss; + oss << "inconsistent tensor size, expected "; + for (size_t i = 0; i < tensors.size() - 1; i++) { + oss << tensors[i].sizes() << ", "; + } + oss << "and " << tensors[tensors.size() - 1].sizes() + << " to have the same number of elements, but got "; + for (size_t i = 0; i < tensors.size() - 1; i++) { + oss << tensors[i].numel() << ", "; + } + oss << "and " << tensors[tensors.size() - 1].numel() + << " elements respectively"; + return oss.str(); +} + +inline bool _apply_preamble(ArrayRef tensors) { + checkDeviceType("CPU_tensor_apply", tensors, kCPU); + checkLayout("CPU_tensor_apply", tensors, kStrided); + if (!_all_equal_numel(tensors)) + AT_ERROR(_all_equal_numel_error(tensors)); + // An empty tensor has no elements + for (auto& t : tensors) + if (t.numel() == 0) + return false; + return true; +} + +inline int64_t _max_dim_tensors(ArrayRef tensors) { + int64_t dim = 0; + for (auto& t : tensors) + dim = std::max(dim, t.ndimension()); + return dim; +} + +inline void iterate(int64_t size){}; + +template +inline void iterate(int64_t size, Arg& iter, Args&... iter_tail) { + iter.counter_[iter.dim_ - 1] += size; + iter.data_ = iter.data_ + size * iter.strides_[iter.dim_ - 1]; + iterate(size, iter_tail...); +} + +inline bool iterate_continue() { + return true; +}; + +template +inline bool iterate_continue(Arg& iter, Args&... iter_tail) { + return iter.counter_[iter.dim_ - 1] < iter.sizes_[iter.dim_ - 1] && + iterate_continue(iter_tail...); +} + +inline int64_t max_iterate_size() { + return std::numeric_limits::max(); +}; + +template +inline int64_t max_iterate_size(Arg& iter, Args&... iter_tail) { + return std::min( + (iter.sizes_[iter.dim_ - 1] - iter.counter_[iter.dim_ - 1]), + max_iterate_size(iter_tail...)); +} + +inline void iterate_overflow(){}; + +template +inline void iterate_overflow(Arg& iter, Args&... iter_tail) { + if (iter.counter_[iter.dim_ - 1] == iter.sizes_[iter.dim_ - 1]) { + for (int64_t i = iter.dim_ - 1; i > 0; i--) { + if (iter.counter_[i] == iter.sizes_[i]) { + iter.counter_[i] = 0; + iter.counter_[i - 1]++; + iter.data_ = iter.data_ - (iter.sizes_[i] * iter.strides_[i]) + + iter.strides_[i - 1]; + } + } + } + iterate_overflow(iter_tail...); +} + +inline void forward(int64_t offset){}; + +template +inline void forward(int64_t offset, Arg& iter, Args&... iter_tail) { + int64_t multi = offset; + for (int64_t i = iter.dim_ - 1; i >= 0; i--) { + int64_t inc = multi % iter.sizes_[i]; + multi = multi / iter.sizes_[i]; + iter.data_ = iter.data_ + inc * iter.strides_[i]; + iter.counter_[i] += inc; + } + forward(offset, iter_tail...); +} + +inline int64_t max_dim() { + return 0; +} + +template +inline int64_t max_dim(Arg& iter, Args&... iter_tail) { + return std::max(iter.dim_, max_dim(iter_tail...)); +} + +inline void apply_op(){}; + +template +inline void +apply_op(int64_t numel, int64_t offset, const Op& op, Args... iters) { + // For 0-dim tensors + if (numel == 1 && max_dim(iters...) == 0) { + op(*iters.data_...); + return; + } + if (offset > 0) + forward(offset, iters...); + // Splitting this into chunks helps the compiler create faster assembly + for (int64_t i = 0; i < numel;) { + for (; iterate_continue(iters...) && i < numel;) { + op(*iters.data_...); + iterate(1, iters...); + i++; + } + iterate_overflow(iters...); + } +} + +/* + Apply a pointwise operator to sequence of tensors + + The calling convention for op is a function/functor that takes the same + number of pointers of type scalar as the number of given tensors. For example, + to compute a = b * c, op would be of the form: + [](scalar* a_val, const scalar* b_val, const scalar* c_val) { a_val[0] = + b_val[0] * c_val[0]; }; +*/ + +template +inline void CPU_tensor_apply1(Tensor tensor1, const Op op) { + if (!_apply_preamble({tensor1})) + return; + if (tensor1.ndimension() < 8) { + apply_op( + tensor1.numel(), + 0, + op, + strided_tensor_iter_fixed(tensor1, true)); + } else { + apply_op(tensor1.numel(), 0, op, strided_tensor_iter(tensor1)); + } +} + +template +inline void CPU_tensor_apply2(Tensor tensor1, Tensor tensor2, const Op op) { + if (!_apply_preamble({tensor1, tensor2})) + return; + if (_max_dim_tensors({tensor1, tensor2}) <= 8) { + apply_op( + tensor1.numel(), + 0, + op, + strided_tensor_iter_fixed(tensor1), + strided_tensor_iter_fixed(tensor2)); + } else { + apply_op( + tensor1.numel(), + 0, + op, + strided_tensor_iter(tensor1), + strided_tensor_iter(tensor2)); + } +} + +template +inline void +CPU_tensor_apply3(Tensor tensor1, Tensor tensor2, Tensor tensor3, const Op op) { + if (!_apply_preamble({tensor1, tensor2, tensor3})) + return; + if (_max_dim_tensors({tensor1, tensor2, tensor3}) <= 8) { + apply_op( + tensor1.numel(), + 0, + op, + strided_tensor_iter_fixed(tensor1), + strided_tensor_iter_fixed(tensor2), + strided_tensor_iter_fixed(tensor3)); + } else { + apply_op( + tensor1.numel(), + 0, + op, + strided_tensor_iter(tensor1), + strided_tensor_iter(tensor2), + strided_tensor_iter(tensor3)); + } +} + +template < + typename scalar1, + typename scalar2, + typename scalar3, + typename scalar4, + typename Op> +inline void CPU_tensor_apply4( + Tensor tensor1, + Tensor tensor2, + Tensor tensor3, + Tensor tensor4, + const Op op) { + if (!_apply_preamble({tensor1, tensor2, tensor3, tensor4})) + return; + if (_max_dim_tensors({tensor1, tensor2, tensor3, tensor4}) <= 8) { + apply_op( + tensor1.numel(), + 0, + op, + strided_tensor_iter_fixed(tensor1), + strided_tensor_iter_fixed(tensor2), + strided_tensor_iter_fixed(tensor3), + strided_tensor_iter_fixed(tensor4)); + } else { + apply_op( + tensor1.numel(), + 0, + op, + strided_tensor_iter(tensor1), + strided_tensor_iter(tensor2), + strided_tensor_iter(tensor3), + strided_tensor_iter(tensor4)); + } +} + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/CPUFixedAllocator.h b/thirdparty/libtorch/include/ATen/CPUFixedAllocator.h new file mode 100644 index 0000000000..bc0918a90d --- /dev/null +++ b/thirdparty/libtorch/include/ATen/CPUFixedAllocator.h @@ -0,0 +1,31 @@ +#pragma once + +#include +#include + +// This file creates a fake allocator that just throws exceptions if +// it is actually used. + +// state passed to the allocator is the std::function called +// when the blob is release by ATen + +namespace at { + +static cpu_fixed_malloc(void *, ptrdiff_t) { + AT_ERROR("attempting to resize a tensor view of an external blob"); +} + +static cpu_fixed_realloc(void *, void*, ptrdiff_t) { + AT_ERROR("attempting to resize a tensor view of an external blob"); +} + +static cpu_fixed_free(void * state, void * allocation) { + auto on_release = static_cast*>(state); + (*on_release)(allocation); + delete on_release; +} + +static Allocator CPU_fixed_allocator = + { cpu_fixed_malloc, cpu_fixed_realloc, cpu_fixed_free }; + +} diff --git a/thirdparty/libtorch/include/ATen/CPUGenerator.h b/thirdparty/libtorch/include/ATen/CPUGenerator.h new file mode 100644 index 0000000000..f41142a395 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/CPUGenerator.h @@ -0,0 +1,44 @@ +#pragma once + +#include +#include +#include +#include + +namespace at { + +struct CAFFE2_API CPUGenerator : public Generator { + // Constructors + CPUGenerator(uint64_t seed_in = default_rng_seed_val); + ~CPUGenerator() = default; + + // CPUGenerator methods + std::shared_ptr clone() const; + void set_current_seed(uint64_t seed) override; + uint64_t current_seed() const override; + uint64_t seed() override; + static DeviceType device_type(); + uint32_t random(); + uint64_t random64(); + c10::optional next_float_normal_sample(); + c10::optional next_double_normal_sample(); + void set_next_float_normal_sample(c10::optional randn); + void set_next_double_normal_sample(c10::optional randn); + at::mt19937 engine(); + void set_engine(at::mt19937 engine); + +private: + CPUGenerator* clone_impl() const override; + at::mt19937 engine_; + c10::optional next_float_normal_sample_; + c10::optional next_double_normal_sample_; +}; + +namespace detail { + +CAFFE2_API CPUGenerator* getDefaultCPUGenerator(); +CAFFE2_API std::shared_ptr createCPUGenerator(uint64_t seed_val = default_rng_seed_val); + +} // namespace detail + +} diff --git a/thirdparty/libtorch/include/ATen/CPUType.h b/thirdparty/libtorch/include/ATen/CPUType.h new file mode 100644 index 0000000000..efda52bf9d --- /dev/null +++ b/thirdparty/libtorch/include/ATen/CPUType.h @@ -0,0 +1,579 @@ +#pragma once + +// @generated by aten/src/ATen/gen.py + +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +namespace c10 { +struct Storage; +} + +namespace at { + +class Tensor; +using TensorList = ArrayRef; + +class Context; +struct Generator; + +struct Quantizer; +// This is temporary typedef to enable Quantizer in aten native function API +// we'll remove them when we are actually exposing Quantizer class +// to frontend +using ConstQuantizerPtr = const c10::intrusive_ptr&; + +#ifdef USE_STATIC_DISPATCH +namespace CPUType { + Tensor & angle_out(Tensor & out, const Tensor & self); + Tensor & real_out(Tensor & out, const Tensor & self); + Tensor & imag_out(Tensor & out, const Tensor & self); + Tensor & conj_out(Tensor & out, const Tensor & self); + Tensor add(const Tensor & self, const Tensor & other, Scalar alpha); + Tensor & add_(Tensor & self, const Tensor & other, Scalar alpha); + Tensor & add_out(Tensor & out, const Tensor & self, const Tensor & other, Scalar alpha); + Tensor addmv(const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha); + Tensor & addmv_(Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha); + Tensor & addmv_out(Tensor & out, const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha); + Tensor & arange_out(Tensor & out, Scalar start, Scalar end, Scalar step); + Tensor as_strided(const Tensor & self, IntArrayRef size, IntArrayRef stride, c10::optional storage_offset); + Tensor & atan_(Tensor & self); + Tensor & atan_out(Tensor & out, const Tensor & self); + Tensor baddbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); + Tensor & baddbmm_(Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); + Tensor & baddbmm_out(Tensor & out, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); + Tensor & bernoulli_(Tensor & self, const Tensor & p, Generator * generator); + Tensor & bernoulli_(Tensor & self, double p, Generator * generator); + Tensor bincount(const Tensor & self, const Tensor & weights, int64_t minlength); + Tensor & bitwise_not_out(Tensor & out, const Tensor & self); + Tensor & logical_not_out(Tensor & out, const Tensor & self); + Tensor & logical_xor_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor bmm(const Tensor & self, const Tensor & mat2); + Tensor & bmm_out(Tensor & out, const Tensor & self, const Tensor & mat2); + Tensor & ceil_out(Tensor & out, const Tensor & self); + Tensor & clamp_(Tensor & self, c10::optional min, c10::optional max); + Tensor & clamp_out(Tensor & out, const Tensor & self, c10::optional min, c10::optional max); + Tensor & clamp_max_(Tensor & self, Scalar max); + Tensor & clamp_max_out(Tensor & out, const Tensor & self, Scalar max); + Tensor & clamp_min_(Tensor & self, Scalar min); + Tensor & clamp_min_out(Tensor & out, const Tensor & self, Scalar min); + Tensor & cos_(Tensor & self); + Tensor & cos_out(Tensor & out, const Tensor & self); + Tensor & cosh_(Tensor & self); + Tensor & cosh_out(Tensor & out, const Tensor & self); + std::tuple _ctc_loss(const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank, bool zero_infinity); + Tensor _ctc_loss_backward(const Tensor & grad, const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, const Tensor & neg_log_likelihood, const Tensor & log_alpha, int64_t blank, bool zero_infinity); + Tensor div(const Tensor & self, const Tensor & other); + Tensor & div_(Tensor & self, const Tensor & other); + Tensor & div_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor dot(const Tensor & self, const Tensor & tensor); + Tensor embedding_dense_backward(const Tensor & grad_output, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq); + Tensor & embedding_renorm_(Tensor & self, const Tensor & indices, double max_norm, double norm_type); + std::tuple _embedding_bag(const Tensor & weight, const Tensor & indices, const Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const Tensor & per_sample_weights); + Tensor _embedding_bag_dense_backward(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, const Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const Tensor & per_sample_weights); + Tensor _embedding_bag_per_sample_weights_backward(const Tensor & grad, const Tensor & weight, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, int64_t mode); + Tensor empty(IntArrayRef size, const TensorOptions & options, c10::optional memory_format); + Tensor _empty_affine_quantized(IntArrayRef size, const TensorOptions & options, double scale, int64_t zero_point, c10::optional memory_format); + Tensor _empty_per_channel_affine_quantized(IntArrayRef size, const Tensor & scales, const Tensor & zero_points, int64_t axis, const TensorOptions & options, c10::optional memory_format); + Tensor & resize_(Tensor & self, IntArrayRef size, c10::optional memory_format); + Tensor empty_strided(IntArrayRef size, IntArrayRef stride, const TensorOptions & options); + Tensor & erf_(Tensor & self); + Tensor & erf_out(Tensor & out, const Tensor & self); + Tensor & erfc_(Tensor & self); + Tensor & erfc_out(Tensor & out, const Tensor & self); + Tensor & exp_(Tensor & self); + Tensor & exp_out(Tensor & out, const Tensor & self); + Tensor & expm1_out(Tensor & out, const Tensor & self); + Tensor & eye_out(Tensor & out, int64_t n); + Tensor & eye_out(Tensor & out, int64_t n, int64_t m); + Tensor & floor_out(Tensor & out, const Tensor & self); + Tensor from_file(std::string filename, c10::optional shared, c10::optional size, const TensorOptions & options); + Tensor grid_sampler_2d(const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); + std::tuple grid_sampler_2d_backward(const Tensor & grad_output, const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); + Tensor grid_sampler_3d(const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); + std::tuple grid_sampler_3d_backward(const Tensor & grad_output, const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); + Tensor ger(const Tensor & self, const Tensor & vec2); + Tensor & ger_out(Tensor & out, const Tensor & self, const Tensor & vec2); + Tensor _fft_with_size(const Tensor & self, int64_t signal_ndim, bool complex_input, bool complex_output, bool inverse, IntArrayRef checked_signal_sizes, bool normalized, bool onesided, IntArrayRef output_sizes); + Tensor _inverse_helper(const Tensor & self); + Tensor kl_div_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); + std::tuple kthvalue_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim, bool keepdim); + std::tuple native_layer_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, int64_t M, int64_t N, double eps); + std::tuple native_layer_norm_backward(const Tensor & grad_out, const Tensor & input, const Tensor & mean, const Tensor & rstd, const Tensor & weight, int64_t M, int64_t N, std::array output_mask); + Tensor & linspace_out(Tensor & out, Scalar start, Scalar end, int64_t steps); + Tensor & log_out(Tensor & out, const Tensor & self); + Tensor & log10_out(Tensor & out, const Tensor & self); + Tensor & log1p_(Tensor & self); + Tensor & log1p_out(Tensor & out, const Tensor & self); + Tensor & log2_out(Tensor & out, const Tensor & self); + Tensor & logspace_out(Tensor & out, Scalar start, Scalar end, int64_t steps, double base); + Tensor _log_softmax(const Tensor & self, int64_t dim, bool half_to_float); + Tensor _log_softmax_backward_data(const Tensor & grad_output, const Tensor & output, int64_t dim, const Tensor & self); + Tensor mean(const Tensor & self, c10::optional dtype); + Tensor mean(const Tensor & self, IntArrayRef dim, bool keepdim, c10::optional dtype); + Tensor & mean_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim, c10::optional dtype); + Tensor mm(const Tensor & self, const Tensor & mat2); + Tensor & mm_out(Tensor & out, const Tensor & self, const Tensor & mat2); + Tensor mul(const Tensor & self, const Tensor & other); + Tensor & mul_(Tensor & self, const Tensor & other); + Tensor & mul_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor mv(const Tensor & self, const Tensor & vec); + Tensor & mv_out(Tensor & out, const Tensor & self, const Tensor & vec); + Tensor narrow_copy(const Tensor & self, int64_t dim, int64_t start, int64_t length); + std::tuple native_batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps); + std::tuple native_batch_norm_backward(const Tensor & grad_out, const Tensor & input, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_invstd, bool train, double eps, std::array output_mask); + std::tuple batch_norm_update_stats(const Tensor & input, const Tensor & running_mean, const Tensor & running_var, double momentum); + Tensor & randperm_out(Tensor & out, int64_t n, Generator * generator); + Tensor & range_out(Tensor & out, Scalar start, Scalar end, Scalar step); + Tensor & reciprocal_(Tensor & self); + Tensor & reciprocal_out(Tensor & out, const Tensor & self); + Tensor & neg_out(Tensor & out, const Tensor & self); + Tensor repeat_interleave(const Tensor & repeats); + Tensor & round_out(Tensor & out, const Tensor & self); + Tensor relu(const Tensor & self); + Tensor & relu_(Tensor & self); + Tensor prelu(const Tensor & self, const Tensor & weight); + std::tuple prelu_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight); + Tensor gelu(const Tensor & self); + Tensor gelu_backward(const Tensor & grad, const Tensor & self); + Tensor hardshrink(const Tensor & self, Scalar lambd); + Tensor hardshrink_backward(const Tensor & grad_out, const Tensor & self, Scalar lambd); + Tensor & rsqrt_out(Tensor & out, const Tensor & self); + Tensor sigmoid(const Tensor & self); + Tensor & sigmoid_(Tensor & self); + Tensor & sin_out(Tensor & out, const Tensor & self); + Tensor _softmax(const Tensor & self, int64_t dim, bool half_to_float); + Tensor _softmax_backward_data(const Tensor & grad_output, const Tensor & output, int64_t dim, const Tensor & self); + Tensor & sspaddmm_out(Tensor & out, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); + Tensor & tan_(Tensor & self); + Tensor & tan_out(Tensor & out, const Tensor & self); + Tensor & tanh_(Tensor & self); + Tensor & tanh_out(Tensor & out, const Tensor & self); + Tensor flip(const Tensor & self, IntArrayRef dims); + Tensor roll(const Tensor & self, IntArrayRef shifts, IntArrayRef dims); + Tensor & trunc_out(Tensor & out, const Tensor & self); + std::tuple _unique(const Tensor & self, bool sorted, bool return_inverse); + std::tuple unique_dim(const Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts); + std::tuple unique_consecutive(const Tensor & self, bool return_inverse, bool return_counts, c10::optional dim); + std::tuple unique_dim_consecutive(const Tensor & self, int64_t dim, bool return_inverse, bool return_counts); + std::tuple _unique2(const Tensor & self, bool sorted, bool return_inverse, bool return_counts); + Tensor _s_where(const Tensor & condition, const Tensor & self, const Tensor & other); + Tensor _standard_gamma_grad(const Tensor & self, const Tensor & output); + Tensor _standard_gamma(const Tensor & self, Generator * generator); + Tensor _dirichlet_grad(const Tensor & x, const Tensor & alpha, const Tensor & total); + Tensor _sample_dirichlet(const Tensor & self, Generator * generator); + Tensor poisson(const Tensor & self, Generator * generator); + Tensor clone(const Tensor & self, c10::optional memory_format); + Tensor & pow_out(Tensor & out, const Tensor & self, Scalar exponent); + Tensor pow(const Tensor & self, Scalar exponent); + Tensor & zero_(Tensor & self); + Tensor & sub_out(Tensor & out, const Tensor & self, const Tensor & other, Scalar alpha); + Tensor sub(const Tensor & self, const Tensor & other, Scalar alpha); + Tensor & sub_(Tensor & self, const Tensor & other, Scalar alpha); + Tensor & addmm_out(Tensor & out, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); + Tensor addmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); + Tensor & addmm_(Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); + Tensor to_sparse(const Tensor & self, int64_t sparse_dim); + Tensor to_sparse(const Tensor & self); + Tensor to_mkldnn(const Tensor & self); + Tensor quantize_per_tensor(const Tensor & self, double scale, int64_t zero_point, ScalarType dtype); + Tensor quantize_per_channel(const Tensor & self, const Tensor & scales, const Tensor & zero_points, int64_t axis, ScalarType dtype); + Tensor _make_per_tensor_quantized_tensor(const Tensor & self, double scale, int64_t zero_point); + Tensor _make_per_channel_quantized_tensor(const Tensor & self, const Tensor & scale, const Tensor & zero_point, int64_t axis); + Tensor fake_quantize_per_tensor_affine(const Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max); + Tensor fake_quantize_per_tensor_affine_backward(const Tensor & grad, const Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max); + Tensor fake_quantize_per_channel_affine(const Tensor & self, const Tensor & scale, const Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max); + Tensor fake_quantize_per_channel_affine_backward(const Tensor & grad, const Tensor & self, const Tensor & scale, const Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max); + Scalar _local_scalar_dense(const Tensor & self); + Tensor & set_(Tensor & self, Storage source); + Tensor & set_(Tensor & self, Storage source, int64_t storage_offset, IntArrayRef size, IntArrayRef stride); + Tensor & set_(Tensor & self, const Tensor & source); + Tensor & set_(Tensor & self); + bool is_set_to(const Tensor & self, const Tensor & tensor); + Tensor & masked_fill_(Tensor & self, const Tensor & mask, Scalar value); + Tensor & masked_fill_(Tensor & self, const Tensor & mask, const Tensor & value); + Tensor & masked_scatter_(Tensor & self, const Tensor & mask, const Tensor & source); + Tensor view(const Tensor & self, IntArrayRef size); + Tensor & put_(Tensor & self, const Tensor & index, const Tensor & source, bool accumulate); + Tensor & index_add_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & source); + Tensor & index_fill_(Tensor & self, int64_t dim, const Tensor & index, Scalar value); + Tensor & index_fill_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & value); + Tensor & scatter_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & src); + Tensor & scatter_(Tensor & self, int64_t dim, const Tensor & index, Scalar value); + Tensor & scatter_add_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & src); + Tensor __and__(const Tensor & self, Scalar other); + Tensor __and__(const Tensor & self, const Tensor & other); + Tensor & __iand__(Tensor & self, Scalar other); + Tensor & __iand__(Tensor & self, const Tensor & other); + Tensor __or__(const Tensor & self, Scalar other); + Tensor __or__(const Tensor & self, const Tensor & other); + Tensor & __ior__(Tensor & self, Scalar other); + Tensor & __ior__(Tensor & self, const Tensor & other); + Tensor & bitwise_xor_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor & bitwise_xor_out(Tensor & out, const Tensor & self, Scalar other); + Tensor __lshift__(const Tensor & self, Scalar other); + Tensor __lshift__(const Tensor & self, const Tensor & other); + Tensor & __ilshift__(Tensor & self, Scalar other); + Tensor & __ilshift__(Tensor & self, const Tensor & other); + Tensor __rshift__(const Tensor & self, Scalar other); + Tensor __rshift__(const Tensor & self, const Tensor & other); + Tensor & __irshift__(Tensor & self, Scalar other); + Tensor & __irshift__(Tensor & self, const Tensor & other); + Tensor & lgamma_(Tensor & self); + Tensor & tril_(Tensor & self, int64_t diagonal); + Tensor & triu_(Tensor & self, int64_t diagonal); + Tensor & renorm_(Tensor & self, Scalar p, int64_t dim, Scalar maxnorm); + Tensor & pow_(Tensor & self, Scalar exponent); + Tensor & pow_(Tensor & self, const Tensor & exponent); + Tensor & lerp_(Tensor & self, const Tensor & end, Scalar weight); + Tensor & lerp_(Tensor & self, const Tensor & end, const Tensor & weight); + Tensor & fmod_(Tensor & self, Scalar other); + Tensor & fmod_(Tensor & self, const Tensor & other); + Tensor & remainder_(Tensor & self, Scalar other); + Tensor & remainder_(Tensor & self, const Tensor & other); + Tensor & addbmm_(Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); + Tensor & addbmm_out(Tensor & out, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); + Tensor addbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); + Tensor & random_(Tensor & self, int64_t from, int64_t to, Generator * generator); + Tensor & random_(Tensor & self, int64_t to, Generator * generator); + Tensor & random_(Tensor & self, Generator * generator); + Tensor & uniform_(Tensor & self, double from, double to, Generator * generator); + Tensor & normal_(Tensor & self, double mean, double std, Generator * generator); + Tensor & cauchy_(Tensor & self, double median, double sigma, Generator * generator); + Tensor & log_normal_(Tensor & self, double mean, double std, Generator * generator); + Tensor & exponential_(Tensor & self, double lambd, Generator * generator); + Tensor & geometric_(Tensor & self, double p, Generator * generator); + Tensor & diag_out(Tensor & out, const Tensor & self, int64_t diagonal); + Tensor diag(const Tensor & self, int64_t diagonal); + Tensor & triu_out(Tensor & out, const Tensor & self, int64_t diagonal); + Tensor & tril_out(Tensor & out, const Tensor & self, int64_t diagonal); + Tensor tril_indices(int64_t row, int64_t col, int64_t offset, const TensorOptions & options); + Tensor triu_indices(int64_t row, int64_t col, int64_t offset, const TensorOptions & options); + Tensor trace(const Tensor & self); + Tensor & ne_out(Tensor & out, const Tensor & self, Scalar other); + Tensor ne(const Tensor & self, Scalar other); + Tensor & ne_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor ne(const Tensor & self, const Tensor & other); + Tensor & eq_out(Tensor & out, const Tensor & self, Scalar other); + Tensor eq(const Tensor & self, Scalar other); + Tensor & eq_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor eq(const Tensor & self, const Tensor & other); + Tensor & ge_out(Tensor & out, const Tensor & self, Scalar other); + Tensor ge(const Tensor & self, Scalar other); + Tensor & ge_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor ge(const Tensor & self, const Tensor & other); + Tensor & le_out(Tensor & out, const Tensor & self, Scalar other); + Tensor le(const Tensor & self, Scalar other); + Tensor & le_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor le(const Tensor & self, const Tensor & other); + Tensor & gt_out(Tensor & out, const Tensor & self, Scalar other); + Tensor gt(const Tensor & self, Scalar other); + Tensor & gt_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor gt(const Tensor & self, const Tensor & other); + Tensor & lt_out(Tensor & out, const Tensor & self, Scalar other); + Tensor lt(const Tensor & self, Scalar other); + Tensor & lt_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor lt(const Tensor & self, const Tensor & other); + Tensor & take_out(Tensor & out, const Tensor & self, const Tensor & index); + Tensor take(const Tensor & self, const Tensor & index); + Tensor & index_select_out(Tensor & out, const Tensor & self, int64_t dim, const Tensor & index); + Tensor index_select(const Tensor & self, int64_t dim, const Tensor & index); + Tensor & masked_select_out(Tensor & out, const Tensor & self, const Tensor & mask); + Tensor masked_select(const Tensor & self, const Tensor & mask); + Tensor & nonzero_out(Tensor & out, const Tensor & self); + Tensor nonzero(const Tensor & self); + Tensor & gather_out(Tensor & out, const Tensor & self, int64_t dim, const Tensor & index, bool sparse_grad); + Tensor gather(const Tensor & self, int64_t dim, const Tensor & index, bool sparse_grad); + std::tuple lstsq_out(Tensor & X, Tensor & qr, const Tensor & self, const Tensor & A); + std::tuple lstsq(const Tensor & self, const Tensor & A); + std::tuple _triangular_solve_helper(const Tensor & self, const Tensor & A, bool upper, bool transpose, bool unitriangular); + std::tuple _symeig_helper(const Tensor & self, bool eigenvectors, bool upper); + std::tuple eig_out(Tensor & e, Tensor & v, const Tensor & self, bool eigenvectors); + std::tuple eig(const Tensor & self, bool eigenvectors); + std::tuple _svd_helper(const Tensor & self, bool some, bool compute_uv); + Tensor _cholesky_helper(const Tensor & self, bool upper); + Tensor _cholesky_solve_helper(const Tensor & self, const Tensor & A, bool upper); + std::tuple _solve_helper(const Tensor & self, const Tensor & A); + Tensor & cholesky_inverse_out(Tensor & out, const Tensor & self, bool upper); + Tensor cholesky_inverse(const Tensor & self, bool upper); + std::tuple _qr_helper(const Tensor & self, bool some); + std::tuple geqrf_out(Tensor & a, Tensor & tau, const Tensor & self); + std::tuple geqrf(const Tensor & self); + Tensor & orgqr_out(Tensor & out, const Tensor & self, const Tensor & input2); + Tensor orgqr(const Tensor & self, const Tensor & input2); + Tensor & ormqr_out(Tensor & out, const Tensor & self, const Tensor & input2, const Tensor & input3, bool left, bool transpose); + Tensor ormqr(const Tensor & self, const Tensor & input2, const Tensor & input3, bool left, bool transpose); + std::tuple _lu_with_info(const Tensor & self, bool pivot, bool check_errors); + Tensor _lu_solve_helper(const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots); + Tensor & multinomial_out(Tensor & out, const Tensor & self, int64_t num_samples, bool replacement, Generator * generator); + Tensor multinomial(const Tensor & self, int64_t num_samples, bool replacement, Generator * generator); + std::tuple _multinomial_alias_setup(const Tensor & probs); + Tensor _multinomial_alias_draw(const Tensor & J, const Tensor & q, int64_t num_samples, Generator * generator); + Tensor & lgamma_out(Tensor & out, const Tensor & self); + Tensor lgamma(const Tensor & self); + Tensor erfinv(const Tensor & self); + Tensor & erfinv_(Tensor & self); + Tensor & erfinv_out(Tensor & out, const Tensor & self); + Tensor & sign_out(Tensor & out, const Tensor & self); + Tensor dist(const Tensor & self, const Tensor & other, Scalar p); + Tensor & lerp_out(Tensor & out, const Tensor & self, const Tensor & end, Scalar weight); + Tensor & lerp_out(Tensor & out, const Tensor & self, const Tensor & end, const Tensor & weight); + Tensor lerp(const Tensor & self, const Tensor & end, Scalar weight); + Tensor lerp(const Tensor & self, const Tensor & end, const Tensor & weight); + Tensor & histc_out(Tensor & out, const Tensor & self, int64_t bins, Scalar min, Scalar max); + Tensor histc(const Tensor & self, int64_t bins, Scalar min, Scalar max); + Tensor & fmod_out(Tensor & out, const Tensor & self, Scalar other); + Tensor fmod(const Tensor & self, Scalar other); + Tensor & fmod_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor fmod(const Tensor & self, const Tensor & other); + Tensor & remainder_out(Tensor & out, const Tensor & self, Scalar other); + Tensor remainder(const Tensor & self, Scalar other); + Tensor & remainder_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor remainder(const Tensor & self, const Tensor & other); + Tensor & min_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor min(const Tensor & self, const Tensor & other); + Tensor min(const Tensor & self); + Tensor & max_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor max(const Tensor & self, const Tensor & other); + Tensor max(const Tensor & self); + Tensor median(const Tensor & self); + std::tuple sort_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool descending); + std::tuple sort(const Tensor & self, int64_t dim, bool descending); + std::tuple topk_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted); + std::tuple topk(const Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted); + Tensor & renorm_out(Tensor & out, const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm); + Tensor renorm(const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm); + Tensor unfold(const Tensor & self, int64_t dimension, int64_t size, int64_t step); + bool equal(const Tensor & self, const Tensor & other); + Tensor & pow_out(Tensor & out, const Tensor & self, const Tensor & exponent); + Tensor pow(const Tensor & self, const Tensor & exponent); + Tensor & pow_out(Tensor & out, Scalar self, const Tensor & exponent); + Tensor pow(Scalar self, const Tensor & exponent); + Tensor & normal_out(Tensor & out, const Tensor & mean, double std, Generator * generator); + Tensor normal(const Tensor & mean, double std, Generator * generator); + Tensor & normal_out(Tensor & out, double mean, const Tensor & std, Generator * generator); + Tensor normal(double mean, const Tensor & std, Generator * generator); + Tensor & normal_out(Tensor & out, const Tensor & mean, const Tensor & std, Generator * generator); + Tensor normal(const Tensor & mean, const Tensor & std, Generator * generator); + Tensor _addr(const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha); + Tensor & _addr_(Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha); + Tensor & _addr_out(Tensor & out, const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha); + Tensor & _index_copy_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & source); + Tensor _cumsum(const Tensor & self, int64_t dim); + Tensor & _cumsum_out(Tensor & out, const Tensor & self, int64_t dim); + Tensor _cumprod(const Tensor & self, int64_t dim); + Tensor & _cumprod_out(Tensor & out, const Tensor & self, int64_t dim); + Tensor _var(const Tensor & self, bool unbiased); + Tensor _std(const Tensor & self, bool unbiased); + Tensor _cat(TensorList tensors, int64_t dim); + Tensor & _cat_out(Tensor & out, TensorList tensors, int64_t dim); + std::tuple _mode(const Tensor & self, int64_t dim, bool keepdim); + std::tuple _mode_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool keepdim); + std::tuple _max(const Tensor & self, int64_t dim, bool keepdim); + std::tuple _max_out(Tensor & max, Tensor & max_indices, const Tensor & self, int64_t dim, bool keepdim); + std::tuple _min(const Tensor & self, int64_t dim, bool keepdim); + std::tuple _min_out(Tensor & min, Tensor & min_indices, const Tensor & self, int64_t dim, bool keepdim); + Tensor & binary_cross_entropy_out(Tensor & out, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction); + Tensor binary_cross_entropy(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction); + Tensor & binary_cross_entropy_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction); + Tensor binary_cross_entropy_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction); + Tensor & mse_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor mse_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor & l1_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor & multi_margin_loss_out(Tensor & out, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction); + Tensor multi_margin_loss(const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction); + Tensor & multi_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction); + Tensor multi_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction); + std::tuple multilabel_margin_loss_forward_out(Tensor & output, Tensor & is_target, const Tensor & self, const Tensor & target, int64_t reduction); + std::tuple multilabel_margin_loss_forward(const Tensor & self, const Tensor & target, int64_t reduction); + Tensor & multilabel_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction, const Tensor & is_target); + Tensor multilabel_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction, const Tensor & is_target); + std::tuple nll_loss_forward_out(Tensor & output, Tensor & total_weight, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); + std::tuple nll_loss_forward(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); + Tensor & nll_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); + Tensor nll_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); + std::tuple nll_loss2d_forward_out(Tensor & output, Tensor & total_weight, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); + std::tuple nll_loss2d_forward(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); + Tensor & nll_loss2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); + Tensor nll_loss2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); + Tensor & smooth_l1_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor & smooth_l1_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor & soft_margin_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor soft_margin_loss(const Tensor & self, const Tensor & target, int64_t reduction); + Tensor & soft_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor soft_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor & elu_out(Tensor & out, const Tensor & self, Scalar alpha, Scalar scale, Scalar input_scale); + Tensor elu(const Tensor & self, Scalar alpha, Scalar scale, Scalar input_scale); + Tensor & elu_backward_out(Tensor & grad_input, const Tensor & grad_output, Scalar alpha, Scalar scale, Scalar input_scale, const Tensor & output); + Tensor elu_backward(const Tensor & grad_output, Scalar alpha, Scalar scale, Scalar input_scale, const Tensor & output); + Tensor & elu_(Tensor & self, Scalar alpha, Scalar scale, Scalar input_scale); + Tensor & glu_out(Tensor & out, const Tensor & self, int64_t dim); + Tensor glu(const Tensor & self, int64_t dim); + Tensor & glu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, int64_t dim); + Tensor glu_backward(const Tensor & grad_output, const Tensor & self, int64_t dim); + Tensor & hardtanh_out(Tensor & out, const Tensor & self, Scalar min_val, Scalar max_val); + Tensor hardtanh(const Tensor & self, Scalar min_val, Scalar max_val); + Tensor & hardtanh_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar min_val, Scalar max_val); + Tensor hardtanh_backward(const Tensor & grad_output, const Tensor & self, Scalar min_val, Scalar max_val); + Tensor & hardtanh_(Tensor & self, Scalar min_val, Scalar max_val); + Tensor & leaky_relu_out(Tensor & out, const Tensor & self, Scalar negative_slope); + Tensor leaky_relu(const Tensor & self, Scalar negative_slope); + Tensor & leaky_relu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar negative_slope); + Tensor leaky_relu_backward(const Tensor & grad_output, const Tensor & self, Scalar negative_slope); + Tensor & leaky_relu_(Tensor & self, Scalar negative_slope); + std::tuple log_sigmoid_forward_out(Tensor & output, Tensor & buffer, const Tensor & self); + std::tuple log_sigmoid_forward(const Tensor & self); + Tensor & log_sigmoid_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & buffer); + Tensor log_sigmoid_backward(const Tensor & grad_output, const Tensor & self, const Tensor & buffer); + Tensor & rrelu_with_noise_out(Tensor & out, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator); + Tensor rrelu_with_noise(const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator); + Tensor & rrelu_with_noise_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training); + Tensor rrelu_with_noise_backward(const Tensor & grad_output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training); + Tensor & rrelu_with_noise_(Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator); + Tensor & softplus_out(Tensor & out, const Tensor & self, Scalar beta, Scalar threshold); + Tensor softplus(const Tensor & self, Scalar beta, Scalar threshold); + Tensor & softplus_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar beta, Scalar threshold, const Tensor & output); + Tensor softplus_backward(const Tensor & grad_output, const Tensor & self, Scalar beta, Scalar threshold, const Tensor & output); + Tensor & softshrink_out(Tensor & out, const Tensor & self, Scalar lambd); + Tensor softshrink(const Tensor & self, Scalar lambd); + Tensor & softshrink_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar lambd); + Tensor softshrink_backward(const Tensor & grad_output, const Tensor & self, Scalar lambd); + Tensor & adaptive_avg_pool2d_out(Tensor & out, const Tensor & self, IntArrayRef output_size); + Tensor _adaptive_avg_pool2d(const Tensor & self, IntArrayRef output_size); + Tensor _adaptive_avg_pool2d_backward(const Tensor & grad_output, const Tensor & self); + Tensor & adaptive_avg_pool3d_out(Tensor & out, const Tensor & self, IntArrayRef output_size); + Tensor adaptive_avg_pool3d(const Tensor & self, IntArrayRef output_size); + Tensor & adaptive_avg_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self); + Tensor adaptive_avg_pool3d_backward(const Tensor & grad_output, const Tensor & self); + std::tuple adaptive_max_pool2d_out(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef output_size); + std::tuple adaptive_max_pool2d(const Tensor & self, IntArrayRef output_size); + Tensor & adaptive_max_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices); + Tensor adaptive_max_pool2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices); + std::tuple adaptive_max_pool3d_out(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef output_size); + std::tuple adaptive_max_pool3d(const Tensor & self, IntArrayRef output_size); + Tensor & adaptive_max_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices); + Tensor adaptive_max_pool3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices); + Tensor & avg_pool2d_out(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); + Tensor avg_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); + Tensor & avg_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); + Tensor avg_pool2d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); + Tensor & avg_pool3d_out(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); + Tensor avg_pool3d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); + Tensor & avg_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); + Tensor avg_pool3d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); + std::tuple fractional_max_pool2d_out(Tensor & output, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); + std::tuple fractional_max_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); + Tensor & fractional_max_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); + Tensor fractional_max_pool2d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); + std::tuple fractional_max_pool3d_out(Tensor & output, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); + std::tuple fractional_max_pool3d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); + Tensor & fractional_max_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); + Tensor fractional_max_pool3d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); + std::tuple max_pool2d_with_indices_out(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode); + std::tuple max_pool2d_with_indices(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode); + Tensor & max_pool2d_with_indices_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); + Tensor max_pool2d_with_indices_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); + std::tuple max_pool3d_with_indices_out(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode); + std::tuple max_pool3d_with_indices(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode); + Tensor & max_pool3d_with_indices_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); + Tensor max_pool3d_with_indices_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); + Tensor & max_unpool2d_out(Tensor & out, const Tensor & self, const Tensor & indices, IntArrayRef output_size); + Tensor max_unpool2d(const Tensor & self, const Tensor & indices, IntArrayRef output_size); + Tensor & max_unpool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size); + Tensor max_unpool2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size); + Tensor & max_unpool3d_out(Tensor & out, const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); + Tensor max_unpool3d(const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); + Tensor & max_unpool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); + Tensor max_unpool3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); + Tensor & reflection_pad1d_out(Tensor & out, const Tensor & self, IntArrayRef padding); + Tensor reflection_pad1d(const Tensor & self, IntArrayRef padding); + Tensor & reflection_pad1d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor reflection_pad1d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor & reflection_pad2d_out(Tensor & out, const Tensor & self, IntArrayRef padding); + Tensor reflection_pad2d(const Tensor & self, IntArrayRef padding); + Tensor & reflection_pad2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor reflection_pad2d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor & replication_pad1d_out(Tensor & out, const Tensor & self, IntArrayRef padding); + Tensor replication_pad1d(const Tensor & self, IntArrayRef padding); + Tensor & replication_pad1d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor replication_pad1d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor & replication_pad2d_out(Tensor & out, const Tensor & self, IntArrayRef padding); + Tensor replication_pad2d(const Tensor & self, IntArrayRef padding); + Tensor & replication_pad2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor replication_pad2d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor & replication_pad3d_out(Tensor & out, const Tensor & self, IntArrayRef padding); + Tensor replication_pad3d(const Tensor & self, IntArrayRef padding); + Tensor & replication_pad3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor replication_pad3d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor & upsample_linear1d_out(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); + Tensor upsample_linear1d(const Tensor & self, IntArrayRef output_size, bool align_corners); + Tensor & upsample_linear1d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); + Tensor upsample_linear1d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); + Tensor & upsample_bilinear2d_out(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); + Tensor upsample_bilinear2d(const Tensor & self, IntArrayRef output_size, bool align_corners); + Tensor & upsample_bilinear2d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); + Tensor upsample_bilinear2d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); + Tensor & upsample_bicubic2d_out(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); + Tensor upsample_bicubic2d(const Tensor & self, IntArrayRef output_size, bool align_corners); + Tensor & upsample_bicubic2d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); + Tensor upsample_bicubic2d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); + Tensor & upsample_trilinear3d_out(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); + Tensor upsample_trilinear3d(const Tensor & self, IntArrayRef output_size, bool align_corners); + Tensor & upsample_trilinear3d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); + Tensor upsample_trilinear3d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); + Tensor & upsample_nearest1d_out(Tensor & out, const Tensor & self, IntArrayRef output_size); + Tensor upsample_nearest1d(const Tensor & self, IntArrayRef output_size); + Tensor & upsample_nearest1d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); + Tensor upsample_nearest1d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); + Tensor & upsample_nearest2d_out(Tensor & out, const Tensor & self, IntArrayRef output_size); + Tensor upsample_nearest2d(const Tensor & self, IntArrayRef output_size); + Tensor & upsample_nearest2d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); + Tensor upsample_nearest2d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); + Tensor & upsample_nearest3d_out(Tensor & out, const Tensor & self, IntArrayRef output_size); + Tensor upsample_nearest3d(const Tensor & self, IntArrayRef output_size); + Tensor & upsample_nearest3d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); + Tensor upsample_nearest3d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); + Tensor & sigmoid_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & output); + Tensor & tanh_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & output); + Tensor tanh_backward(const Tensor & grad_output, const Tensor & output); + Tensor & slow_conv_transpose2d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation); + Tensor slow_conv_transpose2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation); + std::tuple slow_conv_transpose2d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & columns, const Tensor & ones); + std::tuple slow_conv_transpose2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & columns, const Tensor & ones, std::array output_mask); + Tensor & slow_conv_transpose3d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation); + Tensor slow_conv_transpose3d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation); + std::tuple slow_conv_transpose3d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & finput, const Tensor & fgrad_input); + std::tuple slow_conv_transpose3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & finput, const Tensor & fgrad_input, std::array output_mask); + std::tuple thnn_conv2d_forward_out(Tensor & output, Tensor & finput, Tensor & fgrad_input, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); + std::tuple thnn_conv2d_forward(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); + std::tuple thnn_conv2d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & finput, const Tensor & fgrad_input); + std::tuple thnn_conv2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & finput, const Tensor & fgrad_input, std::array output_mask); + std::tuple slow_conv3d_forward_out(Tensor & output, Tensor & finput, Tensor & fgrad_input, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); + std::tuple slow_conv3d_forward(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); + std::tuple slow_conv3d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & finput, const Tensor & fgrad_input); + std::tuple slow_conv3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & finput, const Tensor & fgrad_input, std::array output_mask); + Tensor slow_conv_dilated2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation); + std::tuple slow_conv_dilated2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, std::array output_mask); + Tensor slow_conv_dilated3d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation); + std::tuple slow_conv_dilated3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, std::array output_mask); + Tensor & col2im_out(Tensor & out, const Tensor & self, IntArrayRef output_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); + Tensor col2im(const Tensor & self, IntArrayRef output_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); + Tensor & col2im_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); + Tensor col2im_backward(const Tensor & grad_output, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); + Tensor & im2col_out(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); + Tensor im2col(const Tensor & self, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); + Tensor & im2col_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef input_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); + Tensor im2col_backward(const Tensor & grad_output, IntArrayRef input_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +} +#endif + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/CUDAGenerator.h b/thirdparty/libtorch/include/ATen/CUDAGenerator.h new file mode 100644 index 0000000000..51e4ca916c --- /dev/null +++ b/thirdparty/libtorch/include/ATen/CUDAGenerator.h @@ -0,0 +1,37 @@ +#pragma once + +#include + +namespace at { + +struct CAFFE2_API CUDAGenerator : public Generator { + // Constructors + CUDAGenerator(DeviceIndex device_index = -1); + ~CUDAGenerator() = default; + + // CUDAGenerator methods + std::shared_ptr clone() const; + void set_current_seed(uint64_t seed) override; + uint64_t current_seed() const override; + uint64_t seed() override; + void set_philox_offset_per_thread(uint64_t offset); + uint64_t philox_offset_per_thread(); + std::pair philox_engine_inputs(uint64_t increment); + static DeviceType device_type(); + +private: + CUDAGenerator* clone_impl() const override; + uint64_t seed_ = default_rng_seed_val; + uint64_t philox_offset_per_thread_ = 0; +}; + +namespace cuda { +namespace detail { + + CAFFE2_API CUDAGenerator* getDefaultCUDAGenerator(DeviceIndex device_index = -1); + CAFFE2_API std::shared_ptr createCUDAGenerator(DeviceIndex device_index = -1); + +} // namespace detail +} // namespace cuda +} // namespace at + diff --git a/thirdparty/libtorch/include/ATen/CUDAType.h b/thirdparty/libtorch/include/ATen/CUDAType.h new file mode 100644 index 0000000000..6232d2712b --- /dev/null +++ b/thirdparty/libtorch/include/ATen/CUDAType.h @@ -0,0 +1,620 @@ +#pragma once + +// @generated by aten/src/ATen/gen.py + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include + +namespace c10 { +struct Storage; +} + +namespace at { + +class Tensor; +using TensorList = ArrayRef; + +class Context; +struct Generator; + +struct Quantizer; +// This is temporary typedef to enable Quantizer in aten native function API +// we'll remove them when we are actually exposing Quantizer class +// to frontend +using ConstQuantizerPtr = const c10::intrusive_ptr&; + +#ifdef USE_STATIC_DISPATCH +namespace CUDAType { + bool _use_cudnn_ctc_loss(const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank); + std::tuple _cudnn_ctc_loss(const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank, bool deterministic, bool zero_infinity); + Tensor _cudnn_rnn_flatten_weight(TensorList weight_arr, int64_t weight_stride0, int64_t input_size, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, bool bidirectional); + std::tuple _cudnn_rnn(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & weight_buf, const Tensor & hx, const Tensor & cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntArrayRef batch_sizes, const Tensor & dropout_state); + std::tuple> _cudnn_rnn_backward(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & weight_buf, const Tensor & hx, const Tensor & cx, const Tensor & output, const Tensor & grad_output, const Tensor & grad_hy, const Tensor & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntArrayRef batch_sizes, const Tensor & dropout_state, const Tensor & reserve, std::array output_mask); + Tensor _cudnn_init_dropout_state(double dropout, bool train, int64_t dropout_seed, const TensorOptions & options); + std::tuple _fused_dropout(const Tensor & self, double p, Generator * generator); + Tensor _masked_scale(const Tensor & self, const Tensor & mask, double scale); + Tensor add(const Tensor & self, const Tensor & other, Scalar alpha); + Tensor & add_(Tensor & self, const Tensor & other, Scalar alpha); + Tensor & add_out(Tensor & out, const Tensor & self, const Tensor & other, Scalar alpha); + Tensor addmv(const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha); + Tensor & addmv_(Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha); + Tensor & addmv_out(Tensor & out, const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha); + Tensor & arange_out(Tensor & out, Scalar start, Scalar end, Scalar step); + Tensor as_strided(const Tensor & self, IntArrayRef size, IntArrayRef stride, c10::optional storage_offset); + Tensor & atan_(Tensor & self); + Tensor & atan_out(Tensor & out, const Tensor & self); + Tensor baddbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); + Tensor & baddbmm_(Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); + Tensor & baddbmm_out(Tensor & out, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); + Tensor & bernoulli_(Tensor & self, const Tensor & p, Generator * generator); + Tensor & bernoulli_(Tensor & self, double p, Generator * generator); + Tensor bincount(const Tensor & self, const Tensor & weights, int64_t minlength); + Tensor & bitwise_not_out(Tensor & out, const Tensor & self); + Tensor & logical_not_out(Tensor & out, const Tensor & self); + Tensor & logical_xor_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor bmm(const Tensor & self, const Tensor & mat2); + Tensor & bmm_out(Tensor & out, const Tensor & self, const Tensor & mat2); + Tensor & ceil_out(Tensor & out, const Tensor & self); + Tensor & clamp_(Tensor & self, c10::optional min, c10::optional max); + Tensor & clamp_out(Tensor & out, const Tensor & self, c10::optional min, c10::optional max); + Tensor & clamp_max_(Tensor & self, Scalar max); + Tensor & clamp_max_out(Tensor & out, const Tensor & self, Scalar max); + Tensor & clamp_min_(Tensor & self, Scalar min); + Tensor & clamp_min_out(Tensor & out, const Tensor & self, Scalar min); + Tensor & cos_(Tensor & self); + Tensor & cos_out(Tensor & out, const Tensor & self); + Tensor & cosh_(Tensor & self); + Tensor & cosh_out(Tensor & out, const Tensor & self); + Tensor cudnn_affine_grid_generator(const Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W); + Tensor cudnn_affine_grid_generator_backward(const Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W); + std::tuple cudnn_batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double exponential_average_factor, double epsilon); + std::tuple cudnn_batch_norm_backward(const Tensor & input, const Tensor & grad_output, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_var, double epsilon, const Tensor & reserveSpace); + Tensor cudnn_convolution(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); + Tensor cudnn_convolution_backward_input(IntArrayRef self_size, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); + std::tuple cudnn_convolution_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask); + Tensor cudnn_convolution_backward_bias(const Tensor & grad_output); + Tensor cudnn_convolution_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); + Tensor cudnn_convolution_transpose(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); + std::tuple cudnn_convolution_transpose_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask); + Tensor cudnn_convolution_transpose_backward_bias(const Tensor & grad_output); + Tensor cudnn_convolution_transpose_backward_input(const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); + Tensor cudnn_convolution_transpose_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); + Tensor cudnn_grid_sampler(const Tensor & self, const Tensor & grid); + std::tuple cudnn_grid_sampler_backward(const Tensor & self, const Tensor & grid, const Tensor & grad_output); + std::tuple _ctc_loss(const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank, bool zero_infinity); + Tensor _ctc_loss_backward(const Tensor & grad, const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, const Tensor & neg_log_likelihood, const Tensor & log_alpha, int64_t blank, bool zero_infinity); + Tensor div(const Tensor & self, const Tensor & other); + Tensor & div_(Tensor & self, const Tensor & other); + Tensor & div_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor dot(const Tensor & self, const Tensor & tensor); + Tensor embedding_dense_backward(const Tensor & grad_output, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq); + Tensor & embedding_renorm_(Tensor & self, const Tensor & indices, double max_norm, double norm_type); + std::tuple _embedding_bag(const Tensor & weight, const Tensor & indices, const Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const Tensor & per_sample_weights); + Tensor _embedding_bag_dense_backward(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, const Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const Tensor & per_sample_weights); + Tensor _embedding_bag_per_sample_weights_backward(const Tensor & grad, const Tensor & weight, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, int64_t mode); + Tensor empty(IntArrayRef size, const TensorOptions & options, c10::optional memory_format); + Tensor & resize_(Tensor & self, IntArrayRef size, c10::optional memory_format); + Tensor empty_strided(IntArrayRef size, IntArrayRef stride, const TensorOptions & options); + Tensor & erf_(Tensor & self); + Tensor & erf_out(Tensor & out, const Tensor & self); + Tensor & erfc_(Tensor & self); + Tensor & erfc_out(Tensor & out, const Tensor & self); + Tensor & exp_(Tensor & self); + Tensor & exp_out(Tensor & out, const Tensor & self); + Tensor & expm1_out(Tensor & out, const Tensor & self); + Tensor & eye_out(Tensor & out, int64_t n); + Tensor & eye_out(Tensor & out, int64_t n, int64_t m); + Tensor & floor_out(Tensor & out, const Tensor & self); + Tensor grid_sampler_2d(const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); + std::tuple grid_sampler_2d_backward(const Tensor & grad_output, const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); + Tensor grid_sampler_3d(const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); + std::tuple grid_sampler_3d_backward(const Tensor & grad_output, const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); + Tensor ger(const Tensor & self, const Tensor & vec2); + Tensor & ger_out(Tensor & out, const Tensor & self, const Tensor & vec2); + Tensor _fft_with_size(const Tensor & self, int64_t signal_ndim, bool complex_input, bool complex_output, bool inverse, IntArrayRef checked_signal_sizes, bool normalized, bool onesided, IntArrayRef output_sizes); + Tensor _inverse_helper(const Tensor & self); + Tensor kl_div_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); + std::tuple kthvalue_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim, bool keepdim); + std::tuple native_layer_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, int64_t M, int64_t N, double eps); + std::tuple native_layer_norm_backward(const Tensor & grad_out, const Tensor & input, const Tensor & mean, const Tensor & rstd, const Tensor & weight, int64_t M, int64_t N, std::array output_mask); + Tensor & linspace_out(Tensor & out, Scalar start, Scalar end, int64_t steps); + Tensor & log_out(Tensor & out, const Tensor & self); + Tensor & log10_out(Tensor & out, const Tensor & self); + Tensor & log1p_(Tensor & self); + Tensor & log1p_out(Tensor & out, const Tensor & self); + Tensor & log2_out(Tensor & out, const Tensor & self); + Tensor & logspace_out(Tensor & out, Scalar start, Scalar end, int64_t steps, double base); + Tensor _log_softmax(const Tensor & self, int64_t dim, bool half_to_float); + Tensor _log_softmax_backward_data(const Tensor & grad_output, const Tensor & output, int64_t dim, const Tensor & self); + Tensor mean(const Tensor & self, c10::optional dtype); + Tensor mean(const Tensor & self, IntArrayRef dim, bool keepdim, c10::optional dtype); + Tensor & mean_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim, c10::optional dtype); + std::tuple miopen_batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double exponential_average_factor, double epsilon); + std::tuple miopen_batch_norm_backward(const Tensor & input, const Tensor & grad_output, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_var, double epsilon); + Tensor miopen_convolution(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); + Tensor miopen_convolution_backward_input(IntArrayRef self_size, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); + std::tuple miopen_convolution_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask); + Tensor miopen_convolution_backward_bias(const Tensor & grad_output); + Tensor miopen_convolution_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); + Tensor miopen_convolution_transpose(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); + std::tuple miopen_convolution_transpose_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask); + Tensor miopen_convolution_transpose_backward_input(const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); + Tensor miopen_convolution_transpose_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); + Tensor miopen_depthwise_convolution(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); + Tensor miopen_depthwise_convolution_backward_input(IntArrayRef self_size, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); + std::tuple miopen_depthwise_convolution_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask); + Tensor miopen_depthwise_convolution_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); + std::tuple miopen_rnn(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & hx, const Tensor & cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntArrayRef batch_sizes, const Tensor & dropout_state); + std::tuple> miopen_rnn_backward(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & weight_buf, const Tensor & hx, const Tensor & cx, const Tensor & output, const Tensor & grad_output, const Tensor & grad_hy, const Tensor & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntArrayRef batch_sizes, const Tensor & dropout_state, const Tensor & reserve, std::array output_mask); + Tensor mm(const Tensor & self, const Tensor & mat2); + Tensor & mm_out(Tensor & out, const Tensor & self, const Tensor & mat2); + Tensor mul(const Tensor & self, const Tensor & other); + Tensor & mul_(Tensor & self, const Tensor & other); + Tensor & mul_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor mv(const Tensor & self, const Tensor & vec); + Tensor & mv_out(Tensor & out, const Tensor & self, const Tensor & vec); + Tensor narrow_copy(const Tensor & self, int64_t dim, int64_t start, int64_t length); + std::tuple native_batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps); + std::tuple batch_norm_stats(const Tensor & input, double eps); + Tensor batch_norm_elemt(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & mean, const Tensor & invstd, double eps); + Tensor & batch_norm_elemt_out(Tensor & out, const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & mean, const Tensor & invstd, double eps); + std::tuple batch_norm_gather_stats(const Tensor & input, const Tensor & mean, const Tensor & invstd, const Tensor & running_mean, const Tensor & running_var, double momentum, double eps, int64_t count); + std::tuple batch_norm_gather_stats_with_counts(const Tensor & input, const Tensor & mean, const Tensor & invstd, const Tensor & running_mean, const Tensor & running_var, double momentum, double eps, IntArrayRef counts); + std::tuple native_batch_norm_backward(const Tensor & grad_out, const Tensor & input, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_invstd, bool train, double eps, std::array output_mask); + std::tuple batch_norm_backward_reduce(const Tensor & grad_out, const Tensor & input, const Tensor & mean, const Tensor & invstd, const Tensor & weight, bool input_g, bool weight_g, bool bias_g); + Tensor batch_norm_backward_elemt(const Tensor & grad_out, const Tensor & input, const Tensor & mean, const Tensor & invstd, const Tensor & weight, const Tensor & mean_dy, const Tensor & mean_dy_xmu); + std::tuple batch_norm_update_stats(const Tensor & input, const Tensor & running_mean, const Tensor & running_var, double momentum); + Tensor & randperm_out(Tensor & out, int64_t n, Generator * generator); + Tensor & range_out(Tensor & out, Scalar start, Scalar end, Scalar step); + Tensor & reciprocal_(Tensor & self); + Tensor & reciprocal_out(Tensor & out, const Tensor & self); + Tensor & neg_out(Tensor & out, const Tensor & self); + Tensor repeat_interleave(const Tensor & repeats); + Tensor & round_out(Tensor & out, const Tensor & self); + Tensor relu(const Tensor & self); + Tensor & relu_(Tensor & self); + Tensor prelu(const Tensor & self, const Tensor & weight); + std::tuple prelu_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight); + Tensor gelu(const Tensor & self); + Tensor gelu_backward(const Tensor & grad, const Tensor & self); + Tensor hardshrink(const Tensor & self, Scalar lambd); + Tensor hardshrink_backward(const Tensor & grad_out, const Tensor & self, Scalar lambd); + Tensor & rsqrt_out(Tensor & out, const Tensor & self); + Tensor sigmoid(const Tensor & self); + Tensor & sigmoid_(Tensor & self); + Tensor & sin_out(Tensor & out, const Tensor & self); + Tensor _softmax(const Tensor & self, int64_t dim, bool half_to_float); + Tensor _softmax_backward_data(const Tensor & grad_output, const Tensor & output, int64_t dim, const Tensor & self); + Tensor & sspaddmm_out(Tensor & out, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); + Tensor & tan_(Tensor & self); + Tensor & tan_out(Tensor & out, const Tensor & self); + Tensor & tanh_(Tensor & self); + Tensor & tanh_out(Tensor & out, const Tensor & self); + Tensor flip(const Tensor & self, IntArrayRef dims); + Tensor roll(const Tensor & self, IntArrayRef shifts, IntArrayRef dims); + Tensor & trunc_out(Tensor & out, const Tensor & self); + std::tuple _unique(const Tensor & self, bool sorted, bool return_inverse); + std::tuple unique_dim(const Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts); + std::tuple unique_consecutive(const Tensor & self, bool return_inverse, bool return_counts, c10::optional dim); + std::tuple unique_dim_consecutive(const Tensor & self, int64_t dim, bool return_inverse, bool return_counts); + std::tuple _unique2(const Tensor & self, bool sorted, bool return_inverse, bool return_counts); + Tensor _s_where(const Tensor & condition, const Tensor & self, const Tensor & other); + std::tuple _weight_norm_cuda_interface(const Tensor & v, const Tensor & g, int64_t dim); + std::tuple _weight_norm_cuda_interface_backward(const Tensor & grad_w, const Tensor & saved_v, const Tensor & saved_g, const Tensor & saved_norms, int64_t dim); + Tensor _standard_gamma_grad(const Tensor & self, const Tensor & output); + Tensor _standard_gamma(const Tensor & self, Generator * generator); + Tensor _dirichlet_grad(const Tensor & x, const Tensor & alpha, const Tensor & total); + Tensor _sample_dirichlet(const Tensor & self, Generator * generator); + Tensor poisson(const Tensor & self, Generator * generator); + Tensor clone(const Tensor & self, c10::optional memory_format); + Tensor & pow_out(Tensor & out, const Tensor & self, Scalar exponent); + Tensor pow(const Tensor & self, Scalar exponent); + Tensor & zero_(Tensor & self); + Tensor & sub_out(Tensor & out, const Tensor & self, const Tensor & other, Scalar alpha); + Tensor sub(const Tensor & self, const Tensor & other, Scalar alpha); + Tensor & sub_(Tensor & self, const Tensor & other, Scalar alpha); + Tensor & addmm_out(Tensor & out, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); + Tensor addmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); + Tensor & addmm_(Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); + Tensor to_sparse(const Tensor & self, int64_t sparse_dim); + Tensor to_sparse(const Tensor & self); + Tensor fake_quantize_per_tensor_affine(const Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max); + Tensor fake_quantize_per_tensor_affine_backward(const Tensor & grad, const Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max); + Tensor fake_quantize_per_channel_affine(const Tensor & self, const Tensor & scale, const Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max); + Tensor fake_quantize_per_channel_affine_backward(const Tensor & grad, const Tensor & self, const Tensor & scale, const Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max); + Scalar _local_scalar_dense(const Tensor & self); + std::tuple _thnn_fused_lstm_cell(const Tensor & input_gates, const Tensor & hidden_gates, const Tensor & cx, const Tensor & input_bias, const Tensor & hidden_bias); + std::tuple _thnn_fused_lstm_cell_backward(const Tensor & grad_hy, const Tensor & grad_cy, const Tensor & cx, const Tensor & cy, const Tensor & workspace, bool has_bias); + std::tuple _thnn_fused_gru_cell(const Tensor & input_gates, const Tensor & hidden_gates, const Tensor & hx, const Tensor & input_bias, const Tensor & hidden_bias); + std::tuple _thnn_fused_gru_cell_backward(const Tensor & grad_hy, const Tensor & workspace, bool has_bias); + Tensor & set_(Tensor & self, Storage source); + Tensor & set_(Tensor & self, Storage source, int64_t storage_offset, IntArrayRef size, IntArrayRef stride); + Tensor & set_(Tensor & self, const Tensor & source); + Tensor & set_(Tensor & self); + bool is_set_to(const Tensor & self, const Tensor & tensor); + Tensor & masked_fill_(Tensor & self, const Tensor & mask, Scalar value); + Tensor & masked_fill_(Tensor & self, const Tensor & mask, const Tensor & value); + Tensor & masked_scatter_(Tensor & self, const Tensor & mask, const Tensor & source); + Tensor view(const Tensor & self, IntArrayRef size); + Tensor & put_(Tensor & self, const Tensor & index, const Tensor & source, bool accumulate); + Tensor & index_add_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & source); + Tensor & index_fill_(Tensor & self, int64_t dim, const Tensor & index, Scalar value); + Tensor & index_fill_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & value); + Tensor & scatter_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & src); + Tensor & scatter_(Tensor & self, int64_t dim, const Tensor & index, Scalar value); + Tensor & scatter_add_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & src); + Tensor __and__(const Tensor & self, Scalar other); + Tensor __and__(const Tensor & self, const Tensor & other); + Tensor & __iand__(Tensor & self, Scalar other); + Tensor & __iand__(Tensor & self, const Tensor & other); + Tensor __or__(const Tensor & self, Scalar other); + Tensor __or__(const Tensor & self, const Tensor & other); + Tensor & __ior__(Tensor & self, Scalar other); + Tensor & __ior__(Tensor & self, const Tensor & other); + Tensor & bitwise_xor_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor & bitwise_xor_out(Tensor & out, const Tensor & self, Scalar other); + Tensor __lshift__(const Tensor & self, Scalar other); + Tensor __lshift__(const Tensor & self, const Tensor & other); + Tensor & __ilshift__(Tensor & self, Scalar other); + Tensor & __ilshift__(Tensor & self, const Tensor & other); + Tensor __rshift__(const Tensor & self, Scalar other); + Tensor __rshift__(const Tensor & self, const Tensor & other); + Tensor & __irshift__(Tensor & self, Scalar other); + Tensor & __irshift__(Tensor & self, const Tensor & other); + Tensor & lgamma_(Tensor & self); + Tensor & tril_(Tensor & self, int64_t diagonal); + Tensor & triu_(Tensor & self, int64_t diagonal); + Tensor & renorm_(Tensor & self, Scalar p, int64_t dim, Scalar maxnorm); + Tensor & pow_(Tensor & self, Scalar exponent); + Tensor & pow_(Tensor & self, const Tensor & exponent); + Tensor & lerp_(Tensor & self, const Tensor & end, Scalar weight); + Tensor & lerp_(Tensor & self, const Tensor & end, const Tensor & weight); + Tensor & fmod_(Tensor & self, Scalar other); + Tensor & fmod_(Tensor & self, const Tensor & other); + Tensor & remainder_(Tensor & self, Scalar other); + Tensor & remainder_(Tensor & self, const Tensor & other); + Tensor & addbmm_(Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); + Tensor & addbmm_out(Tensor & out, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); + Tensor addbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); + Tensor & random_(Tensor & self, int64_t from, int64_t to, Generator * generator); + Tensor & random_(Tensor & self, int64_t to, Generator * generator); + Tensor & random_(Tensor & self, Generator * generator); + Tensor & uniform_(Tensor & self, double from, double to, Generator * generator); + Tensor & normal_(Tensor & self, double mean, double std, Generator * generator); + Tensor & cauchy_(Tensor & self, double median, double sigma, Generator * generator); + Tensor & log_normal_(Tensor & self, double mean, double std, Generator * generator); + Tensor & exponential_(Tensor & self, double lambd, Generator * generator); + Tensor & geometric_(Tensor & self, double p, Generator * generator); + Tensor & diag_out(Tensor & out, const Tensor & self, int64_t diagonal); + Tensor diag(const Tensor & self, int64_t diagonal); + Tensor & triu_out(Tensor & out, const Tensor & self, int64_t diagonal); + Tensor & tril_out(Tensor & out, const Tensor & self, int64_t diagonal); + Tensor tril_indices(int64_t row, int64_t col, int64_t offset, const TensorOptions & options); + Tensor triu_indices(int64_t row, int64_t col, int64_t offset, const TensorOptions & options); + Tensor trace(const Tensor & self); + Tensor & ne_out(Tensor & out, const Tensor & self, Scalar other); + Tensor ne(const Tensor & self, Scalar other); + Tensor & ne_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor ne(const Tensor & self, const Tensor & other); + Tensor & eq_out(Tensor & out, const Tensor & self, Scalar other); + Tensor eq(const Tensor & self, Scalar other); + Tensor & eq_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor eq(const Tensor & self, const Tensor & other); + Tensor & ge_out(Tensor & out, const Tensor & self, Scalar other); + Tensor ge(const Tensor & self, Scalar other); + Tensor & ge_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor ge(const Tensor & self, const Tensor & other); + Tensor & le_out(Tensor & out, const Tensor & self, Scalar other); + Tensor le(const Tensor & self, Scalar other); + Tensor & le_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor le(const Tensor & self, const Tensor & other); + Tensor & gt_out(Tensor & out, const Tensor & self, Scalar other); + Tensor gt(const Tensor & self, Scalar other); + Tensor & gt_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor gt(const Tensor & self, const Tensor & other); + Tensor & lt_out(Tensor & out, const Tensor & self, Scalar other); + Tensor lt(const Tensor & self, Scalar other); + Tensor & lt_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor lt(const Tensor & self, const Tensor & other); + Tensor & take_out(Tensor & out, const Tensor & self, const Tensor & index); + Tensor take(const Tensor & self, const Tensor & index); + Tensor & index_select_out(Tensor & out, const Tensor & self, int64_t dim, const Tensor & index); + Tensor index_select(const Tensor & self, int64_t dim, const Tensor & index); + Tensor & masked_select_out(Tensor & out, const Tensor & self, const Tensor & mask); + Tensor masked_select(const Tensor & self, const Tensor & mask); + Tensor & nonzero_out(Tensor & out, const Tensor & self); + Tensor nonzero(const Tensor & self); + Tensor & gather_out(Tensor & out, const Tensor & self, int64_t dim, const Tensor & index, bool sparse_grad); + Tensor gather(const Tensor & self, int64_t dim, const Tensor & index, bool sparse_grad); + std::tuple lstsq_out(Tensor & X, Tensor & qr, const Tensor & self, const Tensor & A); + std::tuple lstsq(const Tensor & self, const Tensor & A); + std::tuple _triangular_solve_helper(const Tensor & self, const Tensor & A, bool upper, bool transpose, bool unitriangular); + std::tuple _symeig_helper(const Tensor & self, bool eigenvectors, bool upper); + std::tuple eig_out(Tensor & e, Tensor & v, const Tensor & self, bool eigenvectors); + std::tuple eig(const Tensor & self, bool eigenvectors); + std::tuple _svd_helper(const Tensor & self, bool some, bool compute_uv); + Tensor _cholesky_helper(const Tensor & self, bool upper); + Tensor _cholesky_solve_helper(const Tensor & self, const Tensor & A, bool upper); + std::tuple _solve_helper(const Tensor & self, const Tensor & A); + Tensor & cholesky_inverse_out(Tensor & out, const Tensor & self, bool upper); + Tensor cholesky_inverse(const Tensor & self, bool upper); + std::tuple _qr_helper(const Tensor & self, bool some); + std::tuple geqrf_out(Tensor & a, Tensor & tau, const Tensor & self); + std::tuple geqrf(const Tensor & self); + std::tuple _lu_with_info(const Tensor & self, bool pivot, bool check_errors); + Tensor _lu_solve_helper(const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots); + Tensor & multinomial_out(Tensor & out, const Tensor & self, int64_t num_samples, bool replacement, Generator * generator); + Tensor multinomial(const Tensor & self, int64_t num_samples, bool replacement, Generator * generator); + std::tuple _multinomial_alias_setup(const Tensor & probs); + Tensor _multinomial_alias_draw(const Tensor & J, const Tensor & q, int64_t num_samples, Generator * generator); + Tensor & lgamma_out(Tensor & out, const Tensor & self); + Tensor lgamma(const Tensor & self); + Tensor erfinv(const Tensor & self); + Tensor & erfinv_(Tensor & self); + Tensor & erfinv_out(Tensor & out, const Tensor & self); + Tensor & sign_out(Tensor & out, const Tensor & self); + Tensor dist(const Tensor & self, const Tensor & other, Scalar p); + Tensor & lerp_out(Tensor & out, const Tensor & self, const Tensor & end, Scalar weight); + Tensor & lerp_out(Tensor & out, const Tensor & self, const Tensor & end, const Tensor & weight); + Tensor lerp(const Tensor & self, const Tensor & end, Scalar weight); + Tensor lerp(const Tensor & self, const Tensor & end, const Tensor & weight); + Tensor & histc_out(Tensor & out, const Tensor & self, int64_t bins, Scalar min, Scalar max); + Tensor histc(const Tensor & self, int64_t bins, Scalar min, Scalar max); + Tensor & fmod_out(Tensor & out, const Tensor & self, Scalar other); + Tensor fmod(const Tensor & self, Scalar other); + Tensor & fmod_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor fmod(const Tensor & self, const Tensor & other); + Tensor & remainder_out(Tensor & out, const Tensor & self, Scalar other); + Tensor remainder(const Tensor & self, Scalar other); + Tensor & remainder_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor remainder(const Tensor & self, const Tensor & other); + Tensor & min_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor min(const Tensor & self, const Tensor & other); + Tensor min(const Tensor & self); + Tensor & max_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor max(const Tensor & self, const Tensor & other); + Tensor max(const Tensor & self); + Tensor median(const Tensor & self); + std::tuple sort_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool descending); + std::tuple sort(const Tensor & self, int64_t dim, bool descending); + std::tuple topk_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted); + std::tuple topk(const Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted); + Tensor & renorm_out(Tensor & out, const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm); + Tensor renorm(const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm); + Tensor unfold(const Tensor & self, int64_t dimension, int64_t size, int64_t step); + bool equal(const Tensor & self, const Tensor & other); + Tensor & pow_out(Tensor & out, const Tensor & self, const Tensor & exponent); + Tensor pow(const Tensor & self, const Tensor & exponent); + Tensor & pow_out(Tensor & out, Scalar self, const Tensor & exponent); + Tensor pow(Scalar self, const Tensor & exponent); + Tensor & normal_out(Tensor & out, const Tensor & mean, double std, Generator * generator); + Tensor normal(const Tensor & mean, double std, Generator * generator); + Tensor & normal_out(Tensor & out, double mean, const Tensor & std, Generator * generator); + Tensor normal(double mean, const Tensor & std, Generator * generator); + Tensor & normal_out(Tensor & out, const Tensor & mean, const Tensor & std, Generator * generator); + Tensor normal(const Tensor & mean, const Tensor & std, Generator * generator); + Tensor _addr(const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha); + Tensor & _addr_(Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha); + Tensor & _addr_out(Tensor & out, const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha); + Tensor & _index_copy_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & source); + Tensor _cumsum(const Tensor & self, int64_t dim); + Tensor & _cumsum_out(Tensor & out, const Tensor & self, int64_t dim); + Tensor _cumprod(const Tensor & self, int64_t dim); + Tensor & _cumprod_out(Tensor & out, const Tensor & self, int64_t dim); + Tensor _var(const Tensor & self, bool unbiased); + Tensor _std(const Tensor & self, bool unbiased); + Tensor _cat(TensorList tensors, int64_t dim); + Tensor & _cat_out(Tensor & out, TensorList tensors, int64_t dim); + std::tuple _mode(const Tensor & self, int64_t dim, bool keepdim); + std::tuple _mode_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool keepdim); + std::tuple _max(const Tensor & self, int64_t dim, bool keepdim); + std::tuple _max_out(Tensor & max, Tensor & max_indices, const Tensor & self, int64_t dim, bool keepdim); + std::tuple _min(const Tensor & self, int64_t dim, bool keepdim); + std::tuple _min_out(Tensor & min, Tensor & min_indices, const Tensor & self, int64_t dim, bool keepdim); + Tensor & binary_cross_entropy_out(Tensor & out, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction); + Tensor binary_cross_entropy(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction); + Tensor & binary_cross_entropy_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction); + Tensor binary_cross_entropy_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction); + Tensor & mse_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor mse_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor & l1_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor & multi_margin_loss_out(Tensor & out, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction); + Tensor multi_margin_loss(const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction); + Tensor & multi_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction); + Tensor multi_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction); + std::tuple multilabel_margin_loss_forward_out(Tensor & output, Tensor & is_target, const Tensor & self, const Tensor & target, int64_t reduction); + std::tuple multilabel_margin_loss_forward(const Tensor & self, const Tensor & target, int64_t reduction); + Tensor & multilabel_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction, const Tensor & is_target); + Tensor multilabel_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction, const Tensor & is_target); + std::tuple nll_loss_forward_out(Tensor & output, Tensor & total_weight, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); + std::tuple nll_loss_forward(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); + Tensor & nll_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); + Tensor nll_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); + std::tuple nll_loss2d_forward_out(Tensor & output, Tensor & total_weight, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); + std::tuple nll_loss2d_forward(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); + Tensor & nll_loss2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); + Tensor nll_loss2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); + Tensor & smooth_l1_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor & smooth_l1_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor & soft_margin_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor soft_margin_loss(const Tensor & self, const Tensor & target, int64_t reduction); + Tensor & soft_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor soft_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor & elu_out(Tensor & out, const Tensor & self, Scalar alpha, Scalar scale, Scalar input_scale); + Tensor elu(const Tensor & self, Scalar alpha, Scalar scale, Scalar input_scale); + Tensor & elu_backward_out(Tensor & grad_input, const Tensor & grad_output, Scalar alpha, Scalar scale, Scalar input_scale, const Tensor & output); + Tensor elu_backward(const Tensor & grad_output, Scalar alpha, Scalar scale, Scalar input_scale, const Tensor & output); + Tensor & elu_(Tensor & self, Scalar alpha, Scalar scale, Scalar input_scale); + Tensor & glu_out(Tensor & out, const Tensor & self, int64_t dim); + Tensor glu(const Tensor & self, int64_t dim); + Tensor & glu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, int64_t dim); + Tensor glu_backward(const Tensor & grad_output, const Tensor & self, int64_t dim); + Tensor & hardtanh_out(Tensor & out, const Tensor & self, Scalar min_val, Scalar max_val); + Tensor hardtanh(const Tensor & self, Scalar min_val, Scalar max_val); + Tensor & hardtanh_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar min_val, Scalar max_val); + Tensor hardtanh_backward(const Tensor & grad_output, const Tensor & self, Scalar min_val, Scalar max_val); + Tensor & hardtanh_(Tensor & self, Scalar min_val, Scalar max_val); + Tensor & leaky_relu_out(Tensor & out, const Tensor & self, Scalar negative_slope); + Tensor leaky_relu(const Tensor & self, Scalar negative_slope); + Tensor & leaky_relu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar negative_slope); + Tensor leaky_relu_backward(const Tensor & grad_output, const Tensor & self, Scalar negative_slope); + Tensor & leaky_relu_(Tensor & self, Scalar negative_slope); + std::tuple log_sigmoid_forward_out(Tensor & output, Tensor & buffer, const Tensor & self); + std::tuple log_sigmoid_forward(const Tensor & self); + Tensor & log_sigmoid_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & buffer); + Tensor log_sigmoid_backward(const Tensor & grad_output, const Tensor & self, const Tensor & buffer); + Tensor & rrelu_with_noise_out(Tensor & out, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator); + Tensor rrelu_with_noise(const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator); + Tensor & rrelu_with_noise_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training); + Tensor rrelu_with_noise_backward(const Tensor & grad_output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training); + Tensor & rrelu_with_noise_(Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator); + Tensor & softplus_out(Tensor & out, const Tensor & self, Scalar beta, Scalar threshold); + Tensor softplus(const Tensor & self, Scalar beta, Scalar threshold); + Tensor & softplus_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar beta, Scalar threshold, const Tensor & output); + Tensor softplus_backward(const Tensor & grad_output, const Tensor & self, Scalar beta, Scalar threshold, const Tensor & output); + Tensor & softshrink_out(Tensor & out, const Tensor & self, Scalar lambd); + Tensor softshrink(const Tensor & self, Scalar lambd); + Tensor & softshrink_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar lambd); + Tensor softshrink_backward(const Tensor & grad_output, const Tensor & self, Scalar lambd); + Tensor & adaptive_avg_pool2d_out(Tensor & out, const Tensor & self, IntArrayRef output_size); + Tensor _adaptive_avg_pool2d(const Tensor & self, IntArrayRef output_size); + Tensor _adaptive_avg_pool2d_backward(const Tensor & grad_output, const Tensor & self); + Tensor & adaptive_avg_pool3d_out(Tensor & out, const Tensor & self, IntArrayRef output_size); + Tensor adaptive_avg_pool3d(const Tensor & self, IntArrayRef output_size); + Tensor & adaptive_avg_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self); + Tensor adaptive_avg_pool3d_backward(const Tensor & grad_output, const Tensor & self); + std::tuple adaptive_max_pool2d_out(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef output_size); + std::tuple adaptive_max_pool2d(const Tensor & self, IntArrayRef output_size); + Tensor & adaptive_max_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices); + Tensor adaptive_max_pool2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices); + std::tuple adaptive_max_pool3d_out(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef output_size); + std::tuple adaptive_max_pool3d(const Tensor & self, IntArrayRef output_size); + Tensor & adaptive_max_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices); + Tensor adaptive_max_pool3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices); + Tensor & avg_pool2d_out(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); + Tensor avg_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); + Tensor & avg_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); + Tensor avg_pool2d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); + Tensor & avg_pool3d_out(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); + Tensor avg_pool3d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); + Tensor & avg_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); + Tensor avg_pool3d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); + std::tuple fractional_max_pool2d_out(Tensor & output, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); + std::tuple fractional_max_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); + Tensor & fractional_max_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); + Tensor fractional_max_pool2d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); + std::tuple fractional_max_pool3d_out(Tensor & output, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); + std::tuple fractional_max_pool3d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); + Tensor & fractional_max_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); + Tensor fractional_max_pool3d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); + std::tuple max_pool2d_with_indices_out(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode); + std::tuple max_pool2d_with_indices(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode); + Tensor & max_pool2d_with_indices_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); + Tensor max_pool2d_with_indices_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); + std::tuple max_pool3d_with_indices_out(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode); + std::tuple max_pool3d_with_indices(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode); + Tensor & max_pool3d_with_indices_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); + Tensor max_pool3d_with_indices_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); + Tensor & max_unpool2d_out(Tensor & out, const Tensor & self, const Tensor & indices, IntArrayRef output_size); + Tensor max_unpool2d(const Tensor & self, const Tensor & indices, IntArrayRef output_size); + Tensor & max_unpool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size); + Tensor max_unpool2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size); + Tensor & max_unpool3d_out(Tensor & out, const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); + Tensor max_unpool3d(const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); + Tensor & max_unpool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); + Tensor max_unpool3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); + Tensor & reflection_pad1d_out(Tensor & out, const Tensor & self, IntArrayRef padding); + Tensor reflection_pad1d(const Tensor & self, IntArrayRef padding); + Tensor & reflection_pad1d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor reflection_pad1d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor & reflection_pad2d_out(Tensor & out, const Tensor & self, IntArrayRef padding); + Tensor reflection_pad2d(const Tensor & self, IntArrayRef padding); + Tensor & reflection_pad2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor reflection_pad2d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor & replication_pad1d_out(Tensor & out, const Tensor & self, IntArrayRef padding); + Tensor replication_pad1d(const Tensor & self, IntArrayRef padding); + Tensor & replication_pad1d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor replication_pad1d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor & replication_pad2d_out(Tensor & out, const Tensor & self, IntArrayRef padding); + Tensor replication_pad2d(const Tensor & self, IntArrayRef padding); + Tensor & replication_pad2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor replication_pad2d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor & replication_pad3d_out(Tensor & out, const Tensor & self, IntArrayRef padding); + Tensor replication_pad3d(const Tensor & self, IntArrayRef padding); + Tensor & replication_pad3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor replication_pad3d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); + Tensor & upsample_linear1d_out(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); + Tensor upsample_linear1d(const Tensor & self, IntArrayRef output_size, bool align_corners); + Tensor & upsample_linear1d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); + Tensor upsample_linear1d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); + Tensor & upsample_bilinear2d_out(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); + Tensor upsample_bilinear2d(const Tensor & self, IntArrayRef output_size, bool align_corners); + Tensor & upsample_bilinear2d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); + Tensor upsample_bilinear2d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); + Tensor & upsample_bicubic2d_out(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); + Tensor upsample_bicubic2d(const Tensor & self, IntArrayRef output_size, bool align_corners); + Tensor & upsample_bicubic2d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); + Tensor upsample_bicubic2d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); + Tensor & upsample_trilinear3d_out(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); + Tensor upsample_trilinear3d(const Tensor & self, IntArrayRef output_size, bool align_corners); + Tensor & upsample_trilinear3d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); + Tensor upsample_trilinear3d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); + Tensor & upsample_nearest1d_out(Tensor & out, const Tensor & self, IntArrayRef output_size); + Tensor upsample_nearest1d(const Tensor & self, IntArrayRef output_size); + Tensor & upsample_nearest1d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); + Tensor upsample_nearest1d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); + Tensor & upsample_nearest2d_out(Tensor & out, const Tensor & self, IntArrayRef output_size); + Tensor upsample_nearest2d(const Tensor & self, IntArrayRef output_size); + Tensor & upsample_nearest2d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); + Tensor upsample_nearest2d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); + Tensor & upsample_nearest3d_out(Tensor & out, const Tensor & self, IntArrayRef output_size); + Tensor upsample_nearest3d(const Tensor & self, IntArrayRef output_size); + Tensor & upsample_nearest3d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); + Tensor upsample_nearest3d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); + Tensor & sigmoid_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & output); + Tensor & tanh_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & output); + Tensor tanh_backward(const Tensor & grad_output, const Tensor & output); + Tensor & slow_conv_transpose2d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation); + Tensor slow_conv_transpose2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation); + std::tuple slow_conv_transpose2d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & columns, const Tensor & ones); + std::tuple slow_conv_transpose2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & columns, const Tensor & ones, std::array output_mask); + Tensor & slow_conv_transpose3d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation); + Tensor slow_conv_transpose3d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation); + std::tuple slow_conv_transpose3d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & finput, const Tensor & fgrad_input); + std::tuple slow_conv_transpose3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & finput, const Tensor & fgrad_input, std::array output_mask); + std::tuple thnn_conv2d_forward_out(Tensor & output, Tensor & finput, Tensor & fgrad_input, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); + std::tuple thnn_conv2d_forward(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); + std::tuple thnn_conv2d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & finput, const Tensor & fgrad_input); + std::tuple thnn_conv2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & finput, const Tensor & fgrad_input, std::array output_mask); + Tensor & thnn_conv_depthwise2d_forward_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation); + Tensor thnn_conv_depthwise2d_forward(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation); + std::tuple thnn_conv_depthwise2d_backward_out(Tensor & grad_input, Tensor & grad_weight, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation); + std::tuple thnn_conv_depthwise2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, std::array output_mask); + Tensor slow_conv_dilated2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation); + std::tuple slow_conv_dilated2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, std::array output_mask); + Tensor slow_conv_dilated3d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation); + std::tuple slow_conv_dilated3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, std::array output_mask); + Tensor & col2im_out(Tensor & out, const Tensor & self, IntArrayRef output_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); + Tensor col2im(const Tensor & self, IntArrayRef output_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); + Tensor & col2im_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); + Tensor col2im_backward(const Tensor & grad_output, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); + Tensor & im2col_out(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); + Tensor im2col(const Tensor & self, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); + Tensor & im2col_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef input_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); + Tensor im2col_backward(const Tensor & grad_output, IntArrayRef input_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +} +#endif + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/Config.h b/thirdparty/libtorch/include/ATen/Config.h new file mode 100644 index 0000000000..2dbea9470f --- /dev/null +++ b/thirdparty/libtorch/include/ATen/Config.h @@ -0,0 +1,12 @@ +#pragma once + +// Test these using #if AT_MKL_ENABLED(), not #ifdef, so that it's +// obvious if you forgot to include Config.h +// c.f. https://stackoverflow.com/questions/33759787/generating-an-error-if-checked-boolean-macro-is-not-defined +// +// DO NOT put the macros for CUDA libraries in this file; they belong in cuda/CUDAConfig.h + +#define AT_MKLDNN_ENABLED() 1 +#define AT_MKL_ENABLED() 1 +#define AT_NNPACK_ENABLED() 1 +#define CAFFE2_STATIC_LINK_CUDA() 0 diff --git a/thirdparty/libtorch/include/ATen/Context.h b/thirdparty/libtorch/include/ATen/Context.h new file mode 100644 index 0000000000..6dc9c10df1 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/Context.h @@ -0,0 +1,237 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include + +namespace at { + +class Tensor; + +class CAFFE2_API Context { + public: + Context(); + + Generator & defaultGenerator(Device device) { + DeviceType device_type = device.type(); + initCUDAIfNeeded(device_type); + initHIPIfNeeded(device_type); + if (device_type == at::kCPU) { + return *at::detail::getDefaultCPUGenerator(); + } else if (device_type == at::kCUDA) { + return *at::detail::getCUDAHooks().getDefaultCUDAGenerator(device.index()); + } else { + AT_ERROR(DeviceTypeName(device_type), " device type not enabled."); + } + } + Device getDeviceFromPtr(void* data, DeviceType device_type) { + initCUDAIfNeeded(device_type); + initHIPIfNeeded(device_type); + if (device_type == at::kCPU) { + return DeviceType::CPU; + } else if (device_type == at::kCUDA) { + return at::detail::getCUDAHooks().getDeviceFromPtr(data); + } else { + AT_ERROR(DeviceTypeName(device_type), " device type not enabled."); + } + } + bool isPinnedPtr(void* data) { + return detail::getCUDAHooks().isPinnedPtr(data); + } + bool hasOpenMP() const; + bool hasMKL() const; + bool hasLAPACK() const; + bool hasMKLDNN() const; + bool hasMAGMA() const { + return detail::getCUDAHooks().hasMAGMA(); + } + bool hasCUDA() const { + return detail::getCUDAHooks().hasCUDA(); + } + bool hasHIP() const { + return detail::getHIPHooks().hasHIP(); + } + bool hasXLA() const { + return c10::impl::hasDeviceGuardImpl(at::DeviceType::XLA); + } + // defined in header so that getNonVariableType has ability to inline + // call_once check. getNonVariableType is called fairly frequently + THCState* lazyInitCUDA() { + std::call_once(thc_init,[&] { + thc_state = detail::getCUDAHooks().initCUDA(); + }); + return thc_state.get(); + } + THHState* lazyInitHIP() { + std::call_once(thh_init,[&] { + thh_state = detail::getHIPHooks().initHIP(); + }); + return thh_state.get(); + } + const at::cuda::NVRTC& getNVRTC() { + return detail::getCUDAHooks().nvrtc(); + } + THCState* getTHCState() { + // AT_ASSERT(thc_state); + return thc_state.get(); + } + THHState* getTHHState() { + return thh_state.get(); + } + + bool setFlushDenormal(bool on); + + // NB: This method is *purely* whether or not a user requested + // that CuDNN was enabled, it doesn't actually say anything about + // whether or not CuDNN is actually usable. Use cudnn_is_acceptable + // to test this instead + bool userEnabledCuDNN() const; + void setUserEnabledCuDNN(bool e); + bool userEnabledMkldnn() const; + void setUserEnabledMkldnn(bool e); + bool benchmarkCuDNN() const; + void setBenchmarkCuDNN(bool); + bool deterministicCuDNN() const; + void setDeterministicCuDNN(bool); + at::QEngine qEngine() const; + void setQEngine(at::QEngine e); + const std::vector& supportedQEngines() const; + + private: + void initCUDAIfNeeded(DeviceType p) { + if (p == DeviceType::CUDA) { + lazyInitCUDA(); + } + } + void initHIPIfNeeded(DeviceType p) { + if (p == DeviceType::HIP) { + lazyInitHIP(); + } + } + std::once_flag thc_init; + std::once_flag thh_init; + bool enabled_cudnn = true; + bool deterministic_cudnn = false; + bool benchmark_cudnn = false; + bool enabled_mkldnn = true; + c10::optional quantized_engine = c10::nullopt; + std::unique_ptr thc_state; + std::unique_ptr thh_state; +}; + +CAFFE2_API Context& globalContext(); + +static inline void init() { + globalContext(); +} + +CAFFE2_API Allocator* getCPUAllocator(); + +static inline DeprecatedTypeProperties& getDeprecatedTypeProperties(Backend p, ScalarType s) { + return globalDeprecatedTypePropertiesRegistry().getDeprecatedTypeProperties( + p, s); +} + +static inline DeprecatedTypeProperties& CPU(ScalarType s) { + return globalDeprecatedTypePropertiesRegistry().getDeprecatedTypeProperties( + Backend::CPU, s); +} + +static inline DeprecatedTypeProperties& CUDA(ScalarType s) { + return globalDeprecatedTypePropertiesRegistry().getDeprecatedTypeProperties( + Backend::CUDA, s); +} + +static inline DeprecatedTypeProperties& HIP(ScalarType s) { + return globalDeprecatedTypePropertiesRegistry().getDeprecatedTypeProperties( + Backend::HIP, s); +} + +static inline bool hasCUDA() { + return globalContext().hasCUDA(); +} + +static inline bool hasHIP() { + return globalContext().hasHIP(); +} + +static inline bool hasXLA() { + return globalContext().hasXLA(); +} + +// Despite its name, this function returns the number of *CUDA* GPUs. +static inline size_t getNumGPUs() { + // WARNING: DO NOT ADD LOGIC TO HANDLE OTHER DEVICE TYPES TO THIS + // FUNCTION. If you are interested in interrogating the number of + // devices for a specific device type, add that function to the + // relevant library (e.g., similar to at::cuda::device_count()) + if (hasCUDA() && hasHIP()) { + throw std::runtime_error( + "Enabling both CUDA and HIP in ATen is not supported, as HIP masquerades " + "to be CUDA (e.g., when you say CUDA, on a HIP build of ATen, this actually " + "means HIP. Rebuild PyTorch with one or the other disabled."); + } else if (hasCUDA()) { + return detail::getCUDAHooks().getNumGPUs(); + } else if (hasHIP()) { + return detail::getHIPHooks().getNumGPUs(); + } else { + return 0; + } +} + +static inline bool hasOpenMP() { + return globalContext().hasOpenMP(); +} + +static inline bool hasMKL() { + return globalContext().hasMKL(); +} + +static inline bool hasLAPACK() { + return globalContext().hasLAPACK(); +} + +static inline bool hasMAGMA() { + return globalContext().hasMAGMA(); +} + +static inline bool hasMKLDNN() { + return globalContext().hasMKLDNN(); +} + +static inline void manual_seed(uint64_t seed) { + auto& gen = globalContext().defaultGenerator(DeviceType::CPU); + { + // See Note [Acquire lock when using random generators] + std::lock_guard lock(gen.mutex_); + gen.set_current_seed(seed); + } + // NB: Sometimes we build with CUDA, but we don't have any GPUs + // available. In that case, we must not seed CUDA; it will fail! + int num_gpus = detail::getCUDAHooks().getNumGPUs(); + if (hasCUDA() && num_gpus > 0) { + for (int i = 0; i < num_gpus; i++) { + auto& cuda_gen = globalContext().defaultGenerator(Device(at::kCUDA, i)); + { + // See Note [Acquire lock when using random generators] + std::lock_guard lock(cuda_gen.mutex_); + cuda_gen.set_current_seed(seed); + } + } + } +} + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/DLConvertor.h b/thirdparty/libtorch/include/ATen/DLConvertor.h new file mode 100644 index 0000000000..8458e6ec2d --- /dev/null +++ b/thirdparty/libtorch/include/ATen/DLConvertor.h @@ -0,0 +1,19 @@ +#pragma once + +#include +#include +#include + +// this convertor will: +// 1) take a Tensor object and wrap it in the DLPack tensor +// 2) take a dlpack tensor and convert it to the ATen Tensor + +namespace at { + +CAFFE2_API ScalarType toScalarType(const DLDataType& dtype); +CAFFE2_API DLManagedTensor* toDLPack(const Tensor& src); +CAFFE2_API Tensor fromDLPack(const DLManagedTensor* src); +CAFFE2_API DLDataType getDLDataType(const Tensor& t); +CAFFE2_API DLContext getDLContext(const Tensor& tensor, const int64_t& device_id); + +} //namespace at diff --git a/thirdparty/libtorch/include/ATen/Device.h b/thirdparty/libtorch/include/ATen/Device.h new file mode 100644 index 0000000000..6c51558036 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/Device.h @@ -0,0 +1,2 @@ +#pragma once +#include diff --git a/thirdparty/libtorch/include/ATen/DeviceGuard.h b/thirdparty/libtorch/include/ATen/DeviceGuard.h new file mode 100644 index 0000000000..2aba6a32ea --- /dev/null +++ b/thirdparty/libtorch/include/ATen/DeviceGuard.h @@ -0,0 +1,36 @@ +#pragma once + +#include +#include +#include // TensorList whyyyyy + +namespace at { + +// Are you here because you're wondering why DeviceGuard(tensor) no +// longer works? For code organization reasons, we have temporarily(?) +// removed this constructor from DeviceGuard. The new way to +// spell it is: +// +// OptionalDeviceGuard guard(device_of(tensor)); + +/// Return the Device of a Tensor, if the Tensor is defined. +inline optional device_of(const Tensor& t) { + if (t.defined()) { + return make_optional(t.device()); + } else { + return nullopt; + } +} + +/// Return the Device of a TensorList, if the list is non-empty and +/// the first Tensor is defined. (This function implicitly assumes +/// that all tensors in the list have the same device.) +inline optional device_of(TensorList t) { + if (!t.empty()) { + return device_of(t.front()); + } else { + return nullopt; + } +} + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/DimVector.h b/thirdparty/libtorch/include/ATen/DimVector.h new file mode 100644 index 0000000000..cb652fffcb --- /dev/null +++ b/thirdparty/libtorch/include/ATen/DimVector.h @@ -0,0 +1,2 @@ +#pragma once +#include diff --git a/thirdparty/libtorch/include/ATen/Dimname.h b/thirdparty/libtorch/include/ATen/Dimname.h new file mode 100644 index 0000000000..71836a9e25 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/Dimname.h @@ -0,0 +1 @@ +#include diff --git a/thirdparty/libtorch/include/ATen/Dispatch.h b/thirdparty/libtorch/include/ATen/Dispatch.h new file mode 100644 index 0000000000..52363ad0ee --- /dev/null +++ b/thirdparty/libtorch/include/ATen/Dispatch.h @@ -0,0 +1,419 @@ +#pragma once + +#include +#include +#include +#include +#include + +#define AT_PRIVATE_CASE_TYPE(enum_type, type, ...) \ + case enum_type: { \ + using scalar_t = type; \ + return __VA_ARGS__(); \ + } + +#define AT_QINT_PRIVATE_CASE_TYPE(enum_type, type, underlying_enum, underlying_type, ...) \ + case enum_type: { \ + const auto& UNDERLYING_TYPE C10_UNUSED = underlying_enum; \ + using scalar_t C10_UNUSED = type; \ + using underlying_t C10_UNUSED = underlying_type; \ + return __VA_ARGS__(); \ + } + +namespace detail { + +inline at::ScalarType scalar_type(at::ScalarType s) { + return s; +} + +C10_DEPRECATED_MESSAGE("passing at::DeprecatedTypeProperties to an AT_DISPATCH macro is deprecated, " \ + "pass an at::ScalarType instead") +inline at::ScalarType scalar_type(const at::DeprecatedTypeProperties &t) { + return t.scalarType(); +} + +C10_DEPRECATED_MESSAGE("AT_DISPATCH_ALL_TYPES_AND_HALF is deprecated, " \ + "use AT_DISPATCH_ALL_TYPES_AND(at::ScalarType::Half, ...) instead") +inline void deprecated_AT_DISPATCH_ALL_TYPES_AND_HALF() {} + +C10_DEPRECATED_MESSAGE("AT_DISPATCH_ALL_TYPES_AND_HALF_AND_COMPLEX is deprecated, " \ + "use AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND(at::ScalarType::Half, ...) " \ + "instead") +inline void deprecated_AT_DISPATCH_ALL_TYPES_AND_HALF_AND_COMPLEX() {} + +} + +// The AT_DISPATCH_* family of macros provides the ability to +// conveniently generate specializations of a kernel over all of the +// dtypes we care about in PyTorch. We call it "dispatch" because +// we are "dispatching" to the correct, dtype-specific kernel. +// +// A standard usage looks like: +// +// AT_DISPATCH_ALL_TYPES(self.scalar_type(), "op_name", [&] { +// // Your code here, with 'scalar_t' now defined to +// // be the dtype in question +// }) +// +// There are many variations of this macro, so it's important to +// understand exactly /which/ dtypes you want to get instantiated, as +// well as what the "default" set is. +// +// The default set of dtypes that are instantiated (e.g., by +// AT_DISPATCH_ALL_TYPES) are floating point types (float, double), +// and integral types (int32_t, int64_t, int16_t, int8_t, uint8_t), +// but NOT booleans (bool), half-precision floats (Half) or +// complex number (std::complex, std::complex). +// This "cut" is somewhat historical (the default types are the +// ones that TH historically supported), but it also reflects the +// fact that the non-default types are "poorly" behaved (booleans +// are NOT integers mod 2, half precision operations ~essentially +// don't exist on CPU, complex numbers are an experimental application). +// +// Here are the questions you should generally ask to decide which +// dispatch you want: +// +// 1. Is this an integral or floating point specific operation? +// (If so, you'll want one of the FLOATING or INTEGRAL macros.) +// +// 2. Should half be supported? (If you're on CPU, the answer is almost +// definitely no. If you do want support, use one of the AND_HALF +// macros) +// +// Much rarer situations: +// +// 3. Should bool be supported? (You often have to write your kernel +// differently if arithmetic operations are involved.) If so, +// Use AT_DISPATCH_ALL_TYPES_AND along with ScalarType::Bool +// +// 4. Should complex be supported? The answer is almost always no, +// unless you are working on "generic" code that should work on +// all dtypes. + + +// NB: the the_type variable is not used, but we have kept it for +// backwards compatibility. It's probably not used by anyone though; +// but we're just being safe (and it doesn't hurt.) Note we must +// use it to shut up warnings about unused store. + +#define AT_DISPATCH_FLOATING_TYPES(TYPE, NAME, ...) \ + [&] { \ + const auto& the_type = TYPE; \ + /* don't use TYPE again in case it is an expensive or side-effect op */ \ + at::ScalarType _st = ::detail::scalar_type(the_type); \ + switch (_st) { \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Double, double, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Float, float, __VA_ARGS__) \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(_st), "'"); \ + } \ + }() + +#define AT_DISPATCH_FLOATING_TYPES_AND_HALF(TYPE, NAME, ...) \ + [&] { \ + const auto& the_type = TYPE; \ + /* don't use TYPE again in case it is an expensive or side-effect op */ \ + at::ScalarType _st = ::detail::scalar_type(the_type); \ + switch (_st) { \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Double, double, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Float, float, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Half, at::Half, __VA_ARGS__) \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(_st), "'"); \ + } \ + }() + +#define AT_DISPATCH_FLOATING_TYPES_AND(SCALARTYPE, TYPE, NAME, ...) \ + [&] { \ + const auto& the_type = TYPE; \ + /* don't use TYPE again in case it is an expensive or side-effect op */ \ + at::ScalarType _st = ::detail::scalar_type(the_type); \ + switch (_st) { \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Double, double, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Float, float, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(SCALARTYPE, \ + decltype(c10::impl::ScalarTypeToCPPType::t), __VA_ARGS__) \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ + } \ + }() + + #define AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES(TYPE, NAME, ...) \ + [&] { \ + const auto& the_type = TYPE; \ + /* don't use TYPE again in case it is an expensive or side-effect op */ \ + at::ScalarType _st = ::detail::scalar_type(the_type); \ + switch (_st) { \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Double, double, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Float, float, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::ComplexDouble, std::complex, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::ComplexFloat, std::complex, __VA_ARGS__) \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(_st), "'"); \ + } \ + }() + + #define AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND1(SCALARTYPE, TYPE, NAME, ...) \ + [&] { \ + const auto& the_type = TYPE; \ + /* don't use TYPE again in case it is an expensive or side-effect op */ \ + at::ScalarType _st = ::detail::scalar_type(the_type); \ + switch (_st) { \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Double, double, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Float, float, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::ComplexDouble, std::complex, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::ComplexFloat, std::complex, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(SCALARTYPE, decltype(c10::impl::ScalarTypeToCPPType::t), __VA_ARGS__) \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(_st), "'"); \ + } \ + }() + +#define AT_DISPATCH_INTEGRAL_TYPES(TYPE, NAME, ...) \ + [&] { \ + const auto& the_type = TYPE; \ + /* don't use TYPE again in case it is an expensive or side-effect op */ \ + at::ScalarType _st = ::detail::scalar_type(the_type); \ + switch (_st) { \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Byte, uint8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Char, int8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Int, int32_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Long, int64_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Short, int16_t, __VA_ARGS__) \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(_st), "'"); \ + } \ + }() + +#define AT_DISPATCH_ALL_TYPES(TYPE, NAME, ...) \ + [&] { \ + const auto& the_type = TYPE; \ + /* don't use TYPE again in case it is an expensive or side-effect op */ \ + at::ScalarType _st = ::detail::scalar_type(the_type); \ + switch (_st) { \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Byte, uint8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Char, int8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Double, double, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Float, float, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Int, int32_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Long, int64_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Short, int16_t, __VA_ARGS__) \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(_st), "'"); \ + } \ + }() + +#define AT_DISPATCH_COMPLEX_TYPES(TYPE, NAME, ...) \ + [&] { \ + const auto& the_type = TYPE; \ + /* don't use TYPE again in case it is an expensive or side-effect op */ \ + at::ScalarType _st = ::detail::scalar_type(the_type); \ + switch (_st) { \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::ComplexFloat, std::complex, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::ComplexDouble, std::complex, __VA_ARGS__) \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(_st), "'"); \ + } \ + }() + +#define AT_DISPATCH_QINT_TYPES(TYPE, NAME, ...) \ + [&] { \ + const auto& SCALAR_TYPE C10_UNUSED = TYPE; \ + switch (TYPE) { \ + AT_QINT_PRIVATE_CASE_TYPE( \ + at::kQInt8, at::qint8, at::kChar, int8_t, __VA_ARGS__) \ + AT_QINT_PRIVATE_CASE_TYPE( \ + at::kQUInt8, at::quint8, at::kByte, uint8_t, __VA_ARGS__) \ + AT_QINT_PRIVATE_CASE_TYPE( \ + at::kQInt32, at::qint32, at::kInt, int, __VA_ARGS__) \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ + } \ + }() + +#define AT_DISPATCH_ALL_TYPES_AND_COMPLEX(TYPE, NAME, ...) \ + [&] { \ + const auto& the_type = TYPE; \ + /* don't use TYPE again in case it is an expensive or side-effect op*/ \ + at::ScalarType _st = ::detail::scalar_type(the_type); \ + switch (_st) { \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Byte, uint8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Char, int8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Double, double, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Float, float, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Int, int32_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Long, int64_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Short, int16_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::ComplexFloat, std::complex, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::ComplexDouble, std::complex, __VA_ARGS__) \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(_st), "'"); \ + } \ + }() + +#define AT_DISPATCH_ALL_TYPES_AND(SCALARTYPE, TYPE, NAME, ...) \ + [&] { \ + switch (TYPE) { \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Byte, uint8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Char, int8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Double, double, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Float, float, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Int, int32_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Long, int64_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Short, int16_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(SCALARTYPE, decltype(c10::impl::ScalarTypeToCPPType::t), __VA_ARGS__) \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ + } \ + }() + +#define AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND(SCALARTYPE, TYPE, NAME, ...) \ + [&] { \ + switch (TYPE) { \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Byte, uint8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Char, int8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Double, double, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Float, float, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Int, int32_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Long, int64_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Short, int16_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::ComplexFloat, std::complex, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::ComplexDouble, std::complex, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(SCALARTYPE, decltype(c10::impl::ScalarTypeToCPPType::t), __VA_ARGS__) \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ + } \ + }() + +#define AT_DISPATCH_COMPLEX_TYPES_AND(SCALARTYPE, TYPE, NAME, ...) \ + [&] { \ + switch (TYPE) { \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::ComplexFloat, std::complex, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::ComplexDouble, std::complex, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(SCALARTYPE, decltype(c10::impl::ScalarTypeToCPPType::t), __VA_ARGS__) \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ + } \ + }() + +#define AT_DISPATCH_ALL_TYPES_AND2(SCALARTYPE1, SCALARTYPE2, TYPE, NAME, ...) \ + [&] { \ + switch (TYPE) { \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Byte, uint8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Char, int8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Double, double, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Float, float, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Int, int32_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Long, int64_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Short, int16_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(SCALARTYPE1, decltype(c10::impl::ScalarTypeToCPPType::t), __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(SCALARTYPE2, decltype(c10::impl::ScalarTypeToCPPType::t), __VA_ARGS__) \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ + } \ + }() + +#define AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(SCALARTYPE1, SCALARTYPE2, TYPE, NAME, ...) \ + [&] { \ + switch (TYPE) { \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Byte, uint8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Char, int8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Double, double, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Float, float, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Int, int32_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Long, int64_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Short, int16_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::ComplexFloat, std::complex, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::ComplexDouble, std::complex, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(SCALARTYPE1, decltype(c10::impl::ScalarTypeToCPPType::t), __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(SCALARTYPE2, decltype(c10::impl::ScalarTypeToCPPType::t), __VA_ARGS__) \ + default: \ + AT_ERROR(#NAME, " not implemented for '", TYPE, "'"); \ + } \ + }() + +#define AT_DISPATCH_ALL_TYPES_AND3(SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, TYPE, NAME, ...) \ + [&] { \ + switch (TYPE) { \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Byte, uint8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Char, int8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Double, double, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Float, float, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Int, int32_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Long, int64_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Short, int16_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(SCALARTYPE1, decltype(c10::impl::ScalarTypeToCPPType::t), __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(SCALARTYPE2, decltype(c10::impl::ScalarTypeToCPPType::t), __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(SCALARTYPE3, decltype(c10::impl::ScalarTypeToCPPType::t), __VA_ARGS__) \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ + } \ + }() + +#define AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND3(SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, TYPE, NAME, ...) \ + [&] { \ + switch (TYPE) { \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Byte, uint8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Char, int8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Double, double, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Float, float, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Int, int32_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Long, int64_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Short, int16_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::ComplexFloat, std::complex, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::ComplexDouble, std::complex, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(SCALARTYPE1, decltype(c10::impl::ScalarTypeToCPPType::t), __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(SCALARTYPE2, decltype(c10::impl::ScalarTypeToCPPType::t), __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(SCALARTYPE3, decltype(c10::impl::ScalarTypeToCPPType::t), __VA_ARGS__) \ + default: \ + AT_ERROR(#NAME, " not implemented for '", TYPE, "'"); \ + } \ + }() + +// ---------------------------------------------------------------------------- +// DEPRECATED MACROS, DON'T USE THESE +// ---------------------------------------------------------------------------- + +#define AT_DISPATCH_ALL_TYPES_AND_HALF(TYPE, NAME, ...) \ + [&] { \ + detail::deprecated_AT_DISPATCH_ALL_TYPES_AND_HALF(); \ + const auto& the_type = TYPE; \ + /* don't use TYPE again in case it is an expensive or side-effect op */ \ + at::ScalarType _st = ::detail::scalar_type(the_type); \ + switch (_st) { \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Byte, uint8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Char, int8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Double, double, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Float, float, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Int, int32_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Long, int64_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Short, int16_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Half, at::Half, __VA_ARGS__) \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(_st), "'"); \ + } \ + }() + +#define AT_DISPATCH_ALL_TYPES_AND_HALF_AND_COMPLEX(TYPE, NAME, ...) \ + [&] { \ + detail::deprecated_AT_DISPATCH_ALL_TYPES_AND_HALF_AND_COMPLEX() \ + const auto& the_type = TYPE; \ + /* don't use TYPE again in case it is an expensive or side-effect op */ \ + at::ScalarType _st = ::detail::scalar_type(the_type); \ + switch (_st) { \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Byte, uint8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Char, int8_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Double, double, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Float, float, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Int, int32_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Long, int64_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Short, int16_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE(at::ScalarType::Half, at::Half, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE( \ + at::ScalarType::ComplexFloat, std::complex, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE( \ + at::ScalarType::ComplexDouble, std::complex, __VA_ARGS__) \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(_st), "'"); \ + } \ + }() diff --git a/thirdparty/libtorch/include/ATen/DynamicLibrary.h b/thirdparty/libtorch/include/ATen/DynamicLibrary.h new file mode 100644 index 0000000000..ea919a79d3 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/DynamicLibrary.h @@ -0,0 +1,21 @@ +#pragma once + +#include +#include + +namespace at { + +struct DynamicLibrary { + AT_DISALLOW_COPY_AND_ASSIGN(DynamicLibrary); + + CAFFE2_API DynamicLibrary(const char* name); + + CAFFE2_API void* sym(const char* name); + + CAFFE2_API ~DynamicLibrary(); + + private: + void* handle = nullptr; +}; + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/ExpandUtils.h b/thirdparty/libtorch/include/ATen/ExpandUtils.h new file mode 100644 index 0000000000..39d2ae5634 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/ExpandUtils.h @@ -0,0 +1,177 @@ +#pragma once + +#include +#include + +#include +#include +#include + +namespace at { + +CAFFE2_API std::vector infer_size(IntArrayRef a, IntArrayRef b); +CAFFE2_API std::tuple, std::vector> +inferExpandGeometry( + IntArrayRef tensor_sizes, + IntArrayRef tensor_strides, + IntArrayRef sizes); + +// avoid copy-construction of Tensor by using a reference_wrapper. +inline void check_defined(std::initializer_list> tensors, const char *api_name) { + for (auto& t : tensors) { + if (!t.get().defined()) { + AT_ERROR(api_name, "(...) called with an undefined Tensor"); + } + } +} + +inline std::tuple expand_inplace(const Tensor &tensor, const Tensor &to_expand) { + if (tensor.sizes().equals(to_expand.sizes())) { + return std::make_tuple(to_expand); + } + + return std::make_tuple(to_expand.expand(tensor.sizes(), /*implicit=*/true)); // see [expand implicit] +} + +inline std::tuple expand_inplace(const Tensor &tensor, const Tensor &to_expand, const char *api_name) { + check_defined({tensor, to_expand}, api_name); + return expand_inplace(tensor, to_expand); +} + +inline std::tuple expand_inplace(const Tensor &tensor, const Tensor &to_expand1, const Tensor &to_expand2) { + if (tensor.sizes().equals(to_expand1.sizes()) && tensor.sizes().equals((to_expand2.sizes()))) { + return std::make_tuple(to_expand1, to_expand2); + } + + return std::make_tuple( + to_expand1.expand(tensor.sizes(), /*implicit=*/true), // see [expand implicit] + to_expand2.expand(tensor.sizes(), /*implicit=*/true)); +} + +inline std::tuple expand_inplace(const Tensor &tensor, const Tensor &to_expand1, const Tensor &to_expand2, + const char *api_name) { + check_defined({tensor, to_expand1, to_expand2}, api_name); + return expand_inplace(tensor, to_expand1, to_expand2); +} + +inline std::tuple expand_outplace(const Tensor &to_expand1, const Tensor &to_expand2) { + if (to_expand1.sizes().equals(to_expand2.sizes())) { + return std::make_tuple(to_expand1, to_expand2); + } + + auto expanded_size = infer_size(to_expand1.sizes(), to_expand2.sizes()); + return std::make_tuple( + to_expand1.expand(expanded_size, /*implicit=*/true), // see [expand implicit] + to_expand2.expand(expanded_size, /*implicit=*/true)); +} + +inline std::tuple expand_outplace(const Tensor &to_expand1, const Tensor &to_expand2, const char *api_name) { + check_defined({to_expand1, to_expand2}, api_name); + return expand_outplace(to_expand1, to_expand2); +} + +inline std::tuple expand_outplace(const Tensor &to_expand1, + const Tensor &to_expand2, + const Tensor &to_expand3) { + if (to_expand1.sizes().equals(to_expand2.sizes()) && to_expand1.sizes().equals(to_expand3.sizes())) { + return std::make_tuple(to_expand1, to_expand2, to_expand3); + } + + auto expanded_size12 = infer_size(to_expand1.sizes(), to_expand2.sizes()); + auto expanded_size = infer_size(expanded_size12, to_expand3.sizes()); + return std::make_tuple( + to_expand1.expand(expanded_size, /*implicit=*/true), // see [expand implicit] + to_expand2.expand(expanded_size, /*implicit=*/true), + to_expand3.expand(expanded_size, /*implicit=*/true)); +} + +inline std::tuple expand_outplace(const Tensor &to_expand1, + const Tensor &to_expand2, + const Tensor &to_expand3, + const char *api_name) { + check_defined({to_expand1, to_expand2, to_expand3}, api_name); + return expand_outplace(to_expand1, to_expand2, to_expand3); +} + +inline std::tuple expand_size(const Tensor &to_expand, IntArrayRef sizes) { + if(to_expand.sizes().equals(sizes)) { + return std::make_tuple(to_expand); + } + + return std::make_tuple(to_expand.expand(sizes, /*implicit=*/true)); // see [expand implicit] +} + +inline std::tuple expand_size(const Tensor &to_expand, IntArrayRef sizes, const char *api_name) { + check_defined({to_expand}, api_name); + return expand_size(to_expand, sizes); +} + +inline std::vector expand_outplace(TensorList to_expand) { + // expands a list of Tensors; ignores undefined (null) tensors + bool first = true; + std::vector sizes; + for (size_t i = 0; i < to_expand.size(); ++i) { + if (!to_expand[i].defined()) { + continue; + } else if (first) { + sizes = to_expand[i].sizes().vec(); + first = false; + } else { + sizes = infer_size(sizes, to_expand[i].sizes()); + } + } + + std::vector result(to_expand.size()); + for (size_t i = 0; i < to_expand.size(); ++i) { + if (!to_expand[i].defined()) { + continue; + } else if (to_expand[i].sizes().equals(sizes)) { + result[i] = to_expand[i]; + } else { + result[i] = to_expand[i].expand(sizes, /*implicit=*/true); // see [expand implicit] + } + } + return result; +} + +// Sums `tensor` repeatedly to produce a tensor of shape `shape`. +// Precondition: is_expandable_to(shape, tensor.sizes()) must be true +static inline Tensor sum_to(Tensor tensor, const IntArrayRef shape) { + if (shape.size() == 0) { + return tensor.sum(); + } + c10::SmallVector reduce_dims; + const at::IntArrayRef sizes = tensor.sizes(); + const int64_t leading_dims = sizes.size() - shape.size(); + for (int64_t i = 0; i < leading_dims; ++i) { + reduce_dims.push_back(i); + } + for (int64_t i = leading_dims; i < static_cast(sizes.size()); ++i) { + if (shape[i - leading_dims] == 1 && sizes[i] != 1) { + reduce_dims.push_back(i); + } + } + if (!reduce_dims.empty()) { + tensor = tensor.sum(reduce_dims, /*keepdim=*/true); + } + return leading_dims > 0 ? tensor.view(shape) : tensor; +} + +// True if `shape` can be broadcasted to `desired` +static inline bool is_expandable_to(IntArrayRef shape, IntArrayRef desired) { + size_t ndim = shape.size(); + size_t target_dim = desired.size(); + if (ndim > target_dim) { + return false; + } + for (size_t i = 0; i < ndim; i++) { + int64_t size = shape[ndim - i - 1]; + int64_t target = desired[target_dim - i - 1]; + if (size != target && size != 1) { + return false; + } + } + return true; +} + +} diff --git a/thirdparty/libtorch/include/ATen/Formatting.h b/thirdparty/libtorch/include/ATen/Formatting.h new file mode 100644 index 0000000000..392e2a27b0 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/Formatting.h @@ -0,0 +1 @@ +#include diff --git a/thirdparty/libtorch/include/ATen/Functions.h b/thirdparty/libtorch/include/ATen/Functions.h new file mode 100644 index 0000000000..0ddc2c5e74 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/Functions.h @@ -0,0 +1,17530 @@ +#pragma once + +// @generated by aten/src/ATen/gen.py + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace at { + +using native::tensor; + +static inline Tensor _cast_Byte(const Tensor & self, bool non_blocking=false); +static inline Tensor _cast_Char(const Tensor & self, bool non_blocking=false); +static inline Tensor _cast_Double(const Tensor & self, bool non_blocking=false); +static inline Tensor _cast_Float(const Tensor & self, bool non_blocking=false); +static inline Tensor _cast_Int(const Tensor & self, bool non_blocking=false); +static inline Tensor _cast_Long(const Tensor & self, bool non_blocking=false); +static inline Tensor _cast_Short(const Tensor & self, bool non_blocking=false); +static inline Tensor _cast_Half(const Tensor & self, bool non_blocking=false); +#ifdef BUILD_NAMEDTENSOR +static inline std::vector align_tensors(TensorList tensors); +#endif +static inline bool _use_cudnn_ctc_loss(const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank); +static inline std::tuple _cudnn_ctc_loss(const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank, bool deterministic, bool zero_infinity); +static inline Tensor _cudnn_rnn_flatten_weight(TensorList weight_arr, int64_t weight_stride0, int64_t input_size, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, bool bidirectional); +static inline std::tuple _cudnn_rnn(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & weight_buf, const Tensor & hx, const Tensor & cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntArrayRef batch_sizes, const Tensor & dropout_state); +static inline std::tuple> _cudnn_rnn_backward(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & weight_buf, const Tensor & hx, const Tensor & cx, const Tensor & output, const Tensor & grad_output, const Tensor & grad_hy, const Tensor & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntArrayRef batch_sizes, const Tensor & dropout_state, const Tensor & reserve, std::array output_mask); +static inline Tensor _cudnn_init_dropout_state(double dropout, bool train, int64_t dropout_seed, const TensorOptions & options); +static inline int64_t _debug_has_internal_overlap(const Tensor & self); +static inline std::tuple _fused_dropout(const Tensor & self, double p, Generator * generator=nullptr); +static inline Tensor _masked_scale(const Tensor & self, const Tensor & mask, double scale); +static inline std::tuple _sobol_engine_draw(const Tensor & quasi, int64_t n, const Tensor & sobolstate, int64_t dimension, int64_t num_generated, c10::optional dtype); +static inline Tensor & _sobol_engine_ff_(Tensor & self, int64_t n, const Tensor & sobolstate, int64_t dimension, int64_t num_generated); +static inline Tensor & _sobol_engine_scramble_(Tensor & self, const Tensor & ltm, int64_t dimension); +static inline Tensor & _sobol_engine_initialize_state_(Tensor & self, int64_t dimension); +static inline Tensor _reshape_from_tensor(const Tensor & self, const Tensor & shape); +static inline Tensor _shape_as_tensor(const Tensor & self); +static inline Tensor dropout(const Tensor & input, double p, bool train); +static inline Tensor & dropout_(Tensor & self, double p, bool train); +static inline Tensor feature_dropout(const Tensor & input, double p, bool train); +static inline Tensor & feature_dropout_(Tensor & self, double p, bool train); +static inline Tensor alpha_dropout(const Tensor & input, double p, bool train); +static inline Tensor & alpha_dropout_(Tensor & self, double p, bool train); +static inline Tensor feature_alpha_dropout(const Tensor & input, double p, bool train); +static inline Tensor & feature_alpha_dropout_(Tensor & self, double p, bool train); +static inline Tensor abs(const Tensor & self); +static inline Tensor & abs_(Tensor & self); +static inline Tensor & abs_out(Tensor & out, const Tensor & self); +static inline Tensor angle(const Tensor & self); +static inline Tensor & angle_out(Tensor & out, const Tensor & self); +static inline Tensor real(const Tensor & self); +static inline Tensor & real_out(Tensor & out, const Tensor & self); +static inline Tensor imag(const Tensor & self); +static inline Tensor & imag_out(Tensor & out, const Tensor & self); +static inline Tensor conj(const Tensor & self); +static inline Tensor & conj_out(Tensor & out, const Tensor & self); +static inline Tensor acos(const Tensor & self); +static inline Tensor & acos_(Tensor & self); +static inline Tensor & acos_out(Tensor & out, const Tensor & self); +static inline Tensor avg_pool1d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true); +static inline Tensor adaptive_avg_pool1d(const Tensor & self, IntArrayRef output_size); +static inline std::tuple adaptive_max_pool1d(const Tensor & self, IntArrayRef output_size); +static inline Tensor add(const Tensor & self, const Tensor & other, Scalar alpha=1); +static inline Tensor & add_out(Tensor & out, const Tensor & self, const Tensor & other, Scalar alpha=1); +static inline Tensor add(const Tensor & self, Scalar other, Scalar alpha=1); +static inline Tensor addmv(const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1); +static inline Tensor & addmv_(Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1); +static inline Tensor & addmv_out(Tensor & out, const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1); +static inline Tensor addr(const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1); +static inline Tensor & addr_out(Tensor & out, const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1); +static inline Tensor affine_grid_generator(const Tensor & theta, IntArrayRef size, bool align_corners); +static inline Tensor affine_grid_generator_backward(const Tensor & grad, IntArrayRef size, bool align_corners); +static inline Tensor all(const Tensor & self, int64_t dim, bool keepdim=false); +static inline Tensor & all_out(Tensor & out, const Tensor & self, int64_t dim, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor all(const Tensor & self, Dimname dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & all_out(Tensor & out, const Tensor & self, Dimname dim, bool keepdim=false); +#endif +static inline bool allclose(const Tensor & self, const Tensor & other, double rtol=1e-05, double atol=1e-08, bool equal_nan=false); +static inline Tensor any(const Tensor & self, int64_t dim, bool keepdim=false); +static inline Tensor & any_out(Tensor & out, const Tensor & self, int64_t dim, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor any(const Tensor & self, Dimname dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & any_out(Tensor & out, const Tensor & self, Dimname dim, bool keepdim=false); +#endif +static inline Tensor arange(Scalar end, const TensorOptions & options={}); +static inline Tensor arange(Scalar start, Scalar end, const TensorOptions & options={}); +static inline Tensor arange(Scalar start, Scalar end, Scalar step, const TensorOptions & options={}); +static inline Tensor & arange_out(Tensor & out, Scalar end); +static inline Tensor & arange_out(Tensor & out, Scalar start, Scalar end, Scalar step=1); +static inline Tensor _dim_arange(const Tensor & like, int64_t dim); +static inline Tensor argmax(const Tensor & self, c10::optional dim=c10::nullopt, bool keepdim=false); +static inline Tensor argmin(const Tensor & self, c10::optional dim=c10::nullopt, bool keepdim=false); +static inline Tensor as_strided(const Tensor & self, IntArrayRef size, IntArrayRef stride, c10::optional storage_offset=c10::nullopt); +static inline Tensor & as_strided_(Tensor & self, IntArrayRef size, IntArrayRef stride, c10::optional storage_offset=c10::nullopt); +static inline Tensor asin(const Tensor & self); +static inline Tensor & asin_(Tensor & self); +static inline Tensor & asin_out(Tensor & out, const Tensor & self); +static inline Tensor atan(const Tensor & self); +static inline Tensor & atan_(Tensor & self); +static inline Tensor & atan_out(Tensor & out, const Tensor & self); +static inline Tensor baddbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1); +static inline Tensor & _baddbmm_mkl_(Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1); +static inline Tensor & baddbmm_out(Tensor & out, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1); +static inline Tensor bartlett_window(int64_t window_length, const TensorOptions & options={}); +static inline Tensor bartlett_window(int64_t window_length, bool periodic, const TensorOptions & options={}); +static inline Tensor batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps, bool cudnn_enabled); +static inline std::tuple _batch_norm_impl_index(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps, bool cudnn_enabled); +static inline std::tuple _batch_norm_impl_index_backward(int64_t impl_index, const Tensor & input, const Tensor & grad_output, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_var_transform, bool train, double eps, std::array output_mask, const Tensor & reservedSpace); +static inline Tensor bernoulli(const Tensor & self, Generator * generator=nullptr); +static inline Tensor & bernoulli_out(Tensor & out, const Tensor & self, Generator * generator=nullptr); +static inline Tensor bernoulli(const Tensor & self, double p, Generator * generator=nullptr); +static inline Tensor bilinear(const Tensor & input1, const Tensor & input2, const Tensor & weight, const Tensor & bias); +static inline Tensor binary_cross_entropy_with_logits(const Tensor & self, const Tensor & target, const Tensor & weight={}, const Tensor & pos_weight={}, int64_t reduction=at::Reduction::Mean); +static inline Tensor binary_cross_entropy_with_logits_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight={}, const Tensor & pos_weight={}, int64_t reduction=at::Reduction::Mean); +static inline Tensor bincount(const Tensor & self, const Tensor & weights={}, int64_t minlength=0); +static inline Tensor bitwise_not(const Tensor & self); +static inline Tensor & bitwise_not_out(Tensor & out, const Tensor & self); +static inline Tensor logical_not(const Tensor & self); +static inline Tensor & logical_not_out(Tensor & out, const Tensor & self); +static inline Tensor logical_xor(const Tensor & self, const Tensor & other); +static inline Tensor & logical_xor_out(Tensor & out, const Tensor & self, const Tensor & other); +static inline Tensor blackman_window(int64_t window_length, const TensorOptions & options={}); +static inline Tensor blackman_window(int64_t window_length, bool periodic, const TensorOptions & options={}); +static inline Tensor bmm(const Tensor & self, const Tensor & mat2); +static inline Tensor & bmm_out(Tensor & out, const Tensor & self, const Tensor & mat2); +static inline std::vector broadcast_tensors(TensorList tensors); +static inline Tensor cat(TensorList tensors, int64_t dim=0); +static inline Tensor & cat_out(Tensor & out, TensorList tensors, int64_t dim=0); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor cat(TensorList tensors, Dimname dim); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & cat_out(Tensor & out, TensorList tensors, Dimname dim); +#endif +static inline Tensor ceil(const Tensor & self); +static inline Tensor & ceil_(Tensor & self); +static inline Tensor & ceil_out(Tensor & out, const Tensor & self); +static inline Tensor chain_matmul(TensorList matrices); +static inline std::vector chunk(const Tensor & self, int64_t chunks, int64_t dim=0); +static inline Tensor clamp(const Tensor & self, c10::optional min=c10::nullopt, c10::optional max=c10::nullopt); +static inline Tensor & clamp_(Tensor & self, c10::optional min=c10::nullopt, c10::optional max=c10::nullopt); +static inline Tensor & clamp_out(Tensor & out, const Tensor & self, c10::optional min=c10::nullopt, c10::optional max=c10::nullopt); +static inline Tensor clamp_max(const Tensor & self, Scalar max); +static inline Tensor & clamp_max_(Tensor & self, Scalar max); +static inline Tensor & clamp_max_out(Tensor & out, const Tensor & self, Scalar max); +static inline Tensor clamp_min(const Tensor & self, Scalar min); +static inline Tensor & clamp_min_(Tensor & self, Scalar min); +static inline Tensor & clamp_min_out(Tensor & out, const Tensor & self, Scalar min); +static inline bool cudnn_is_acceptable(const Tensor & self); +static inline Tensor constant_pad_nd(const Tensor & self, IntArrayRef pad, Scalar value=0); +static inline Tensor convolution(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups); +static inline Tensor convolution_overrideable(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups); +static inline std::tuple convolution_backward_overrideable(const Tensor & grad_output, const Tensor & input, const Tensor & weight, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups, std::array output_mask); +static inline Tensor _convolution(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled); +static inline Tensor _convolution_nogroup(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding); +static inline std::tuple _convolution_double_backward(const Tensor & ggI, const Tensor & ggW, const Tensor & ggb, const Tensor & gO, const Tensor & weight, const Tensor & self, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled, std::array output_mask); +static inline Tensor conv1d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef dilation=1, int64_t groups=1); +static inline Tensor conv2d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef dilation=1, int64_t groups=1); +static inline Tensor conv3d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef dilation=1, int64_t groups=1); +static inline Tensor conv_tbc(const Tensor & self, const Tensor & weight, const Tensor & bias, int64_t pad=0); +static inline std::tuple conv_tbc_backward(const Tensor & self, const Tensor & input, const Tensor & weight, const Tensor & bias, int64_t pad); +static inline Tensor conv_transpose1d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef output_padding=0, int64_t groups=1, IntArrayRef dilation=1); +static inline Tensor conv_transpose2d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef output_padding=0, int64_t groups=1, IntArrayRef dilation=1); +static inline Tensor conv_transpose3d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef output_padding=0, int64_t groups=1, IntArrayRef dilation=1); +static inline Tensor _copy_from(const Tensor & self, const Tensor & dst, bool non_blocking=false); +static inline Tensor cos(const Tensor & self); +static inline Tensor & cos_(Tensor & self); +static inline Tensor & cos_out(Tensor & out, const Tensor & self); +static inline Tensor cosh(const Tensor & self); +static inline Tensor & cosh_(Tensor & self); +static inline Tensor & cosh_out(Tensor & out, const Tensor & self); +static inline Tensor cosine_embedding_loss(const Tensor & input1, const Tensor & input2, const Tensor & target, double margin=0.0, int64_t reduction=at::Reduction::Mean); +static inline Tensor cudnn_affine_grid_generator(const Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W); +static inline Tensor cudnn_affine_grid_generator_backward(const Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W); +static inline std::tuple cudnn_batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double exponential_average_factor, double epsilon); +static inline std::tuple cudnn_batch_norm_backward(const Tensor & input, const Tensor & grad_output, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_var, double epsilon, const Tensor & reserveSpace); +static inline Tensor cudnn_convolution(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +static inline Tensor cudnn_convolution_backward_input(IntArrayRef self_size, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +static inline std::tuple cudnn_convolution_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask); +static inline Tensor cudnn_convolution_backward_bias(const Tensor & grad_output); +static inline Tensor cudnn_convolution_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +static inline Tensor cudnn_convolution_transpose(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +static inline std::tuple cudnn_convolution_transpose_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask); +static inline Tensor cudnn_convolution_transpose_backward_bias(const Tensor & grad_output); +static inline Tensor cudnn_convolution_transpose_backward_input(const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +static inline Tensor cudnn_convolution_transpose_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +static inline Tensor cudnn_grid_sampler(const Tensor & self, const Tensor & grid); +static inline std::tuple cudnn_grid_sampler_backward(const Tensor & self, const Tensor & grid, const Tensor & grad_output); +static inline Tensor cumsum(const Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt); +static inline Tensor & cumsum_out(Tensor & out, const Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor cumsum(const Tensor & self, Dimname dim, c10::optional dtype=c10::nullopt); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & cumsum_out(Tensor & out, const Tensor & self, Dimname dim, c10::optional dtype=c10::nullopt); +#endif +static inline Tensor cumprod(const Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt); +static inline Tensor & cumprod_out(Tensor & out, const Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor cumprod(const Tensor & self, Dimname dim, c10::optional dtype=c10::nullopt); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & cumprod_out(Tensor & out, const Tensor & self, Dimname dim, c10::optional dtype=c10::nullopt); +#endif +static inline Tensor ctc_loss(const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank=0, int64_t reduction=at::Reduction::Mean, bool zero_infinity=false); +static inline Tensor ctc_loss(const Tensor & log_probs, const Tensor & targets, const Tensor & input_lengths, const Tensor & target_lengths, int64_t blank=0, int64_t reduction=at::Reduction::Mean, bool zero_infinity=false); +static inline std::tuple _ctc_loss(const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank=0, bool zero_infinity=false); +static inline Tensor _ctc_loss_backward(const Tensor & grad, const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, const Tensor & neg_log_likelihood, const Tensor & log_alpha, int64_t blank, bool zero_infinity=false); +static inline Tensor det(const Tensor & self); +static inline Tensor diag_embed(const Tensor & self, int64_t offset=0, int64_t dim1=-2, int64_t dim2=-1); +static inline Tensor diagflat(const Tensor & self, int64_t offset=0); +static inline Tensor diagonal(const Tensor & self, int64_t offset=0, int64_t dim1=0, int64_t dim2=1); +static inline Tensor div(const Tensor & self, const Tensor & other); +static inline Tensor & div_out(Tensor & out, const Tensor & self, const Tensor & other); +static inline Tensor div(const Tensor & self, Scalar other); +static inline Tensor dot(const Tensor & self, const Tensor & tensor); +static inline Tensor & dot_out(Tensor & out, const Tensor & self, const Tensor & tensor); +static inline Tensor einsum(std::string equation, TensorList tensors); +static inline Tensor embedding(const Tensor & weight, const Tensor & indices, int64_t padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false); +static inline Tensor embedding_backward(const Tensor & grad, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq, bool sparse); +static inline Tensor embedding_dense_backward(const Tensor & grad_output, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq); +static inline Tensor & embedding_renorm_(Tensor & self, const Tensor & indices, double max_norm, double norm_type); +static inline Tensor embedding_sparse_backward(const Tensor & grad, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq); +static inline std::tuple embedding_bag(const Tensor & weight, const Tensor & indices, const Tensor & offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false, const Tensor & per_sample_weights={}); +static inline std::tuple _embedding_bag(const Tensor & weight, const Tensor & indices, const Tensor & offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false, const Tensor & per_sample_weights={}); +static inline Tensor _embedding_bag_backward(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, const Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse, const Tensor & per_sample_weights); +static inline Tensor _embedding_bag_sparse_backward(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const Tensor & per_sample_weights); +static inline Tensor _embedding_bag_dense_backward(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, const Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const Tensor & per_sample_weights); +static inline Tensor _embedding_bag_per_sample_weights_backward(const Tensor & grad, const Tensor & weight, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, int64_t mode); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor empty(IntArrayRef size, c10::optional names, const TensorOptions & options={}, c10::optional memory_format=c10::nullopt); +#endif +static inline Tensor empty(IntArrayRef size, const TensorOptions & options={}, c10::optional memory_format=c10::nullopt); +static inline Tensor _empty_affine_quantized(IntArrayRef size, const TensorOptions & options={}, double scale=1, int64_t zero_point=0, c10::optional memory_format=MemoryFormat::Contiguous); +static inline Tensor _empty_per_channel_affine_quantized(IntArrayRef size, const Tensor & scales, const Tensor & zero_points, int64_t axis, const TensorOptions & options={}, c10::optional memory_format=MemoryFormat::Contiguous); +static inline Tensor & empty_out(Tensor & out, IntArrayRef size, c10::optional memory_format=c10::nullopt); +static inline Tensor empty_like(const Tensor & self, c10::optional memory_format=c10::nullopt); +static inline Tensor empty_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format=c10::nullopt); +static inline Tensor empty_strided(IntArrayRef size, IntArrayRef stride, const TensorOptions & options={}); +static inline Tensor erf(const Tensor & self); +static inline Tensor & erf_(Tensor & self); +static inline Tensor & erf_out(Tensor & out, const Tensor & self); +static inline Tensor erfc(const Tensor & self); +static inline Tensor & erfc_(Tensor & self); +static inline Tensor & erfc_out(Tensor & out, const Tensor & self); +static inline Tensor exp(const Tensor & self); +static inline Tensor & exp_(Tensor & self); +static inline Tensor & exp_out(Tensor & out, const Tensor & self); +static inline Tensor expm1(const Tensor & self); +static inline Tensor & expm1_(Tensor & self); +static inline Tensor & expm1_out(Tensor & out, const Tensor & self); +static inline Tensor eye(int64_t n, const TensorOptions & options={}); +static inline Tensor eye(int64_t n, int64_t m, const TensorOptions & options={}); +static inline Tensor & eye_out(Tensor & out, int64_t n); +static inline Tensor & eye_out(Tensor & out, int64_t n, int64_t m); +static inline Tensor flatten(const Tensor & self, int64_t start_dim=0, int64_t end_dim=-1); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor flatten(const Tensor & self, int64_t start_dim, int64_t end_dim, Dimname out_dim); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor flatten(const Tensor & self, Dimname start_dim, Dimname end_dim, Dimname out_dim); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor flatten(const Tensor & self, DimnameList dims, Dimname out_dim); +#endif +static inline Tensor & fill_(Tensor & self, Scalar value); +static inline Tensor & fill_(Tensor & self, const Tensor & value); +static inline Tensor floor(const Tensor & self); +static inline Tensor & floor_(Tensor & self); +static inline Tensor & floor_out(Tensor & out, const Tensor & self); +static inline Tensor frac(const Tensor & self); +static inline Tensor & frac_(Tensor & self); +static inline Tensor & frac_out(Tensor & out, const Tensor & self); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor full(IntArrayRef size, Scalar fill_value, c10::optional names, const TensorOptions & options={}); +#endif +static inline Tensor full(IntArrayRef size, Scalar fill_value, const TensorOptions & options={}); +static inline Tensor & full_out(Tensor & out, IntArrayRef size, Scalar fill_value); +static inline Tensor full_like(const Tensor & self, Scalar fill_value, c10::optional memory_format=c10::nullopt); +static inline Tensor full_like(const Tensor & self, Scalar fill_value, const TensorOptions & options, c10::optional memory_format=c10::nullopt); +static inline Tensor from_file(std::string filename, c10::optional shared=c10::nullopt, c10::optional size=0, const TensorOptions & options={}); +static inline Tensor grid_sampler(const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); +static inline Tensor grid_sampler_2d(const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); +static inline std::tuple grid_sampler_2d_backward(const Tensor & grad_output, const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); +static inline Tensor grid_sampler_3d(const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); +static inline std::tuple grid_sampler_3d_backward(const Tensor & grad_output, const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); +static inline Tensor hann_window(int64_t window_length, const TensorOptions & options={}); +static inline Tensor hann_window(int64_t window_length, bool periodic, const TensorOptions & options={}); +static inline Tensor hamming_window(int64_t window_length, const TensorOptions & options={}); +static inline Tensor hamming_window(int64_t window_length, bool periodic, const TensorOptions & options={}); +static inline Tensor hamming_window(int64_t window_length, bool periodic, double alpha, const TensorOptions & options={}); +static inline Tensor hamming_window(int64_t window_length, bool periodic, double alpha, double beta, const TensorOptions & options={}); +static inline Tensor hinge_embedding_loss(const Tensor & self, const Tensor & target, double margin=1.0, int64_t reduction=at::Reduction::Mean); +static inline Tensor ger(const Tensor & self, const Tensor & vec2); +static inline Tensor & ger_out(Tensor & out, const Tensor & self, const Tensor & vec2); +static inline Tensor group_norm(const Tensor & input, int64_t num_groups, const Tensor & weight={}, const Tensor & bias={}, double eps=1e-05, bool cudnn_enabled=true); +static inline Tensor fft(const Tensor & self, int64_t signal_ndim, bool normalized=false); +static inline Tensor ifft(const Tensor & self, int64_t signal_ndim, bool normalized=false); +static inline Tensor rfft(const Tensor & self, int64_t signal_ndim, bool normalized=false, bool onesided=true); +static inline Tensor irfft(const Tensor & self, int64_t signal_ndim, bool normalized=false, bool onesided=true, IntArrayRef signal_sizes={}); +static inline Tensor _fft_with_size(const Tensor & self, int64_t signal_ndim, bool complex_input, bool complex_output, bool inverse, IntArrayRef checked_signal_sizes, bool normalized, bool onesided, IntArrayRef output_sizes); +static inline int64_t _cufft_get_plan_cache_size(int64_t device_index); +static inline int64_t _cufft_get_plan_cache_max_size(int64_t device_index); +static inline void _cufft_set_plan_cache_max_size(int64_t device_index, int64_t max_size); +static inline void _cufft_clear_plan_cache(int64_t device_index); +static inline Tensor index(const Tensor & self, TensorList indices); +static inline Tensor index_copy(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & source); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor index_copy(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & source); +#endif +static inline Tensor & index_put_(Tensor & self, TensorList indices, const Tensor & values, bool accumulate=false); +static inline Tensor index_put(const Tensor & self, TensorList indices, const Tensor & values, bool accumulate=false); +static inline Tensor & _index_put_impl_(Tensor & self, TensorList indices, const Tensor & values, bool accumulate=false, bool unsafe=false); +static inline Tensor instance_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool use_input_stats, double momentum, double eps, bool cudnn_enabled); +static inline Tensor inverse(const Tensor & self); +static inline Tensor & inverse_out(Tensor & out, const Tensor & self); +static inline Tensor _inverse_helper(const Tensor & self); +static inline Tensor isclose(const Tensor & self, const Tensor & other, double rtol=1e-05, double atol=1e-08, bool equal_nan=false); +static inline Tensor isnan(const Tensor & self); +static inline bool is_distributed(const Tensor & self); +static inline bool is_floating_point(const Tensor & self); +static inline bool is_complex(const Tensor & self); +static inline bool is_nonzero(const Tensor & self); +static inline bool is_same_size(const Tensor & self, const Tensor & other); +static inline bool is_signed(const Tensor & self); +static inline Tensor kl_div(const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +static inline Tensor kl_div_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +static inline std::tuple kthvalue(const Tensor & self, int64_t k, int64_t dim=-1, bool keepdim=false); +static inline std::tuple kthvalue_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim=-1, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple kthvalue(const Tensor & self, int64_t k, Dimname dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple kthvalue_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, Dimname dim, bool keepdim=false); +#endif +static inline Tensor layer_norm(const Tensor & input, IntArrayRef normalized_shape, const Tensor & weight={}, const Tensor & bias={}, double eps=1e-05, bool cudnn_enable=true); +static inline std::tuple native_layer_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, int64_t M, int64_t N, double eps); +static inline std::tuple native_layer_norm_backward(const Tensor & grad_out, const Tensor & input, const Tensor & mean, const Tensor & rstd, const Tensor & weight, int64_t M, int64_t N, std::array output_mask); +static inline Tensor linear(const Tensor & input, const Tensor & weight, const Tensor & bias={}); +static inline Tensor mkldnn_linear(const Tensor & input, const Tensor & weight, const Tensor & bias={}); +static inline Tensor fbgemm_linear_int8_weight_fp32_activation(const Tensor & input, const Tensor & weight, const Tensor & packed, const Tensor & col_offsets, Scalar weight_scale, Scalar weight_zero_point, const Tensor & bias); +static inline Tensor fbgemm_linear_int8_weight(const Tensor & input, const Tensor & weight, const Tensor & packed, const Tensor & col_offsets, Scalar weight_scale, Scalar weight_zero_point, const Tensor & bias); +static inline std::tuple fbgemm_linear_quantize_weight(const Tensor & input); +static inline Tensor fbgemm_pack_gemm_matrix_fp16(const Tensor & input); +static inline Tensor fbgemm_linear_fp16_weight_fp32_activation(const Tensor & input, const Tensor & packed_weight, const Tensor & bias); +static inline Tensor fbgemm_linear_fp16_weight(const Tensor & input, const Tensor & packed_weight, const Tensor & bias); +static inline Tensor fbgemm_pack_quantized_matrix(const Tensor & input); +static inline Tensor fbgemm_pack_quantized_matrix(const Tensor & input, int64_t K, int64_t N); +static inline Tensor linspace(Scalar start, Scalar end, int64_t steps=100, const TensorOptions & options={}); +static inline Tensor & linspace_out(Tensor & out, Scalar start, Scalar end, int64_t steps=100); +static inline Tensor log(const Tensor & self); +static inline Tensor & log_(Tensor & self); +static inline Tensor & log_out(Tensor & out, const Tensor & self); +static inline Tensor log10(const Tensor & self); +static inline Tensor & log10_(Tensor & self); +static inline Tensor & log10_out(Tensor & out, const Tensor & self); +static inline Tensor log1p(const Tensor & self); +static inline Tensor & log1p_(Tensor & self); +static inline Tensor & log1p_out(Tensor & out, const Tensor & self); +static inline Tensor log2(const Tensor & self); +static inline Tensor & log2_(Tensor & self); +static inline Tensor & log2_out(Tensor & out, const Tensor & self); +static inline Tensor logdet(const Tensor & self); +static inline Tensor logspace(Scalar start, Scalar end, int64_t steps=100, double base=10.0, const TensorOptions & options={}); +static inline Tensor & logspace_out(Tensor & out, Scalar start, Scalar end, int64_t steps=100, double base=10.0); +static inline Tensor log_softmax(const Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor log_softmax(const Tensor & self, Dimname dim, c10::optional dtype=c10::nullopt); +#endif +static inline Tensor _log_softmax(const Tensor & self, int64_t dim, bool half_to_float); +static inline Tensor _log_softmax_backward_data(const Tensor & grad_output, const Tensor & output, int64_t dim, const Tensor & self); +static inline Tensor logsumexp(const Tensor & self, IntArrayRef dim, bool keepdim=false); +static inline Tensor & logsumexp_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor logsumexp(const Tensor & self, DimnameList dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & logsumexp_out(Tensor & out, const Tensor & self, DimnameList dim, bool keepdim=false); +#endif +static inline Tensor margin_ranking_loss(const Tensor & input1, const Tensor & input2, const Tensor & target, double margin=0.0, int64_t reduction=at::Reduction::Mean); +static inline Tensor matmul(const Tensor & self, const Tensor & other); +static inline Tensor & matmul_out(Tensor & out, const Tensor & self, const Tensor & other); +static inline Tensor matrix_rank(const Tensor & self, double tol, bool symmetric=false); +static inline Tensor matrix_rank(const Tensor & self, bool symmetric=false); +static inline Tensor matrix_power(const Tensor & self, int64_t n); +static inline std::tuple max(const Tensor & self, int64_t dim, bool keepdim=false); +static inline std::tuple max_out(Tensor & max, Tensor & max_values, const Tensor & self, int64_t dim, bool keepdim=false); +static inline Tensor max_values(const Tensor & self, IntArrayRef dim, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple max(const Tensor & self, Dimname dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple max_out(Tensor & max, Tensor & max_values, const Tensor & self, Dimname dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor max_values(const Tensor & self, DimnameList dim, bool keepdim=false); +#endif +static inline std::tuple max_pool1d_with_indices(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +static inline Tensor max_pool1d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +static inline Tensor max_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +static inline Tensor mkldnn_max_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +static inline Tensor quantized_max_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +static inline Tensor max_pool3d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +static inline Tensor mean(const Tensor & self, c10::optional dtype=c10::nullopt); +static inline Tensor mean(const Tensor & self, IntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +static inline Tensor & mean_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor mean(const Tensor & self, DimnameList dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & mean_out(Tensor & out, const Tensor & self, DimnameList dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#endif +static inline std::tuple median(const Tensor & self, int64_t dim, bool keepdim=false); +static inline std::tuple median_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple median(const Tensor & self, Dimname dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple median_out(Tensor & values, Tensor & indices, const Tensor & self, Dimname dim, bool keepdim=false); +#endif +static inline std::tuple min(const Tensor & self, int64_t dim, bool keepdim=false); +static inline std::tuple min_out(Tensor & min, Tensor & min_indices, const Tensor & self, int64_t dim, bool keepdim=false); +static inline Tensor min_values(const Tensor & self, IntArrayRef dim, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple min(const Tensor & self, Dimname dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple min_out(Tensor & min, Tensor & min_indices, const Tensor & self, Dimname dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor min_values(const Tensor & self, DimnameList dim, bool keepdim=false); +#endif +static inline Tensor mkldnn_convolution(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups); +static inline Tensor mkldnn_convolution_backward_input(IntArrayRef self_size, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool bias_defined); +static inline std::tuple mkldnn_convolution_backward_weights(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool bias_defined); +static inline std::tuple mkldnn_convolution_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, std::array output_mask); +static inline std::tuple miopen_batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double exponential_average_factor, double epsilon); +static inline std::tuple miopen_batch_norm_backward(const Tensor & input, const Tensor & grad_output, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_var, double epsilon); +static inline Tensor miopen_convolution(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +static inline Tensor miopen_convolution_backward_input(IntArrayRef self_size, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +static inline std::tuple miopen_convolution_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask); +static inline Tensor miopen_convolution_backward_bias(const Tensor & grad_output); +static inline Tensor miopen_convolution_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +static inline Tensor miopen_convolution_transpose(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +static inline std::tuple miopen_convolution_transpose_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask); +static inline Tensor miopen_convolution_transpose_backward_input(const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +static inline Tensor miopen_convolution_transpose_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +static inline Tensor miopen_depthwise_convolution(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +static inline Tensor miopen_depthwise_convolution_backward_input(IntArrayRef self_size, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +static inline std::tuple miopen_depthwise_convolution_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask); +static inline Tensor miopen_depthwise_convolution_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +static inline std::tuple miopen_rnn(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & hx, const Tensor & cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntArrayRef batch_sizes, const Tensor & dropout_state); +static inline std::tuple> miopen_rnn_backward(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & weight_buf, const Tensor & hx, const Tensor & cx, const Tensor & output, const Tensor & grad_output, const Tensor & grad_hy, const Tensor & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntArrayRef batch_sizes, const Tensor & dropout_state, const Tensor & reserve, std::array output_mask); +static inline Tensor mm(const Tensor & self, const Tensor & mat2); +static inline Tensor & mm_out(Tensor & out, const Tensor & self, const Tensor & mat2); +static inline Tensor _sparse_mm(const Tensor & sparse, const Tensor & dense); +static inline std::tuple mode(const Tensor & self, int64_t dim=-1, bool keepdim=false); +static inline std::tuple mode_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim=-1, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple mode(const Tensor & self, Dimname dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple mode_out(Tensor & values, Tensor & indices, const Tensor & self, Dimname dim, bool keepdim=false); +#endif +static inline Tensor mul(const Tensor & self, const Tensor & other); +static inline Tensor & mul_out(Tensor & out, const Tensor & self, const Tensor & other); +static inline Tensor mul(const Tensor & self, Scalar other); +static inline Tensor mv(const Tensor & self, const Tensor & vec); +static inline Tensor & mv_out(Tensor & out, const Tensor & self, const Tensor & vec); +static inline Tensor mvlgamma(const Tensor & self, int64_t p); +static inline Tensor narrow(const Tensor & self, int64_t dim, int64_t start, int64_t length); +static inline std::tuple native_batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps); +static inline std::tuple batch_norm_stats(const Tensor & input, double eps); +static inline Tensor batch_norm_elemt(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & mean, const Tensor & invstd, double eps); +static inline Tensor & batch_norm_elemt_out(Tensor & out, const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & mean, const Tensor & invstd, double eps); +static inline std::tuple batch_norm_gather_stats(const Tensor & input, const Tensor & mean, const Tensor & invstd, const Tensor & running_mean, const Tensor & running_var, double momentum, double eps, int64_t count); +static inline std::tuple batch_norm_gather_stats_with_counts(const Tensor & input, const Tensor & mean, const Tensor & invstd, const Tensor & running_mean, const Tensor & running_var, double momentum, double eps, IntArrayRef counts); +static inline std::tuple native_batch_norm_backward(const Tensor & grad_out, const Tensor & input, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_invstd, bool train, double eps, std::array output_mask); +static inline std::tuple batch_norm_backward_reduce(const Tensor & grad_out, const Tensor & input, const Tensor & mean, const Tensor & invstd, const Tensor & weight, bool input_g, bool weight_g, bool bias_g); +static inline Tensor batch_norm_backward_elemt(const Tensor & grad_out, const Tensor & input, const Tensor & mean, const Tensor & invstd, const Tensor & weight, const Tensor & mean_dy, const Tensor & mean_dy_xmu); +static inline std::tuple batch_norm_update_stats(const Tensor & input, const Tensor & running_mean, const Tensor & running_var, double momentum); +static inline bool _nnpack_available(); +static inline Tensor _nnpack_spatial_convolution(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride=1); +static inline std::tuple _nnpack_spatial_convolution_backward(const Tensor & input, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, std::array output_mask); +static inline Tensor _nnpack_spatial_convolution_backward_input(const Tensor & input, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding); +static inline Tensor _nnpack_spatial_convolution_backward_weight(const Tensor & input, IntArrayRef weightsize, const Tensor & grad_output, IntArrayRef padding); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor ones(IntArrayRef size, c10::optional names, const TensorOptions & options={}); +#endif +static inline Tensor ones(IntArrayRef size, const TensorOptions & options={}); +static inline Tensor & ones_out(Tensor & out, IntArrayRef size); +static inline Tensor ones_like(const Tensor & self, c10::optional memory_format=c10::nullopt); +static inline Tensor ones_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format=c10::nullopt); +static inline Tensor pairwise_distance(const Tensor & x1, const Tensor & x2, double p=2, double eps=1e-06, bool keepdim=false); +static inline Tensor cdist(const Tensor & x1, const Tensor & x2, double p=2, c10::optional compute_mode=c10::nullopt); +static inline Tensor _cdist_backward(const Tensor & grad, const Tensor & x1, const Tensor & x2, double p, const Tensor & cdist); +static inline Tensor pdist(const Tensor & self, double p=2); +static inline Tensor _pdist_forward(const Tensor & self, double p=2); +static inline Tensor _pdist_backward(const Tensor & grad, const Tensor & self, double p, const Tensor & pdist); +static inline Tensor cosine_similarity(const Tensor & x1, const Tensor & x2, int64_t dim=1, double eps=1e-08); +static inline Tensor pixel_shuffle(const Tensor & self, int64_t upscale_factor); +static inline Tensor pinverse(const Tensor & self, double rcond=1e-15); +static inline Tensor poisson_nll_loss(const Tensor & input, const Tensor & target, bool log_input, bool full, double eps, int64_t reduction); +static inline Tensor scalar_tensor(Scalar s, const TensorOptions & options={}); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor rand(IntArrayRef size, c10::optional names, const TensorOptions & options={}); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor rand(IntArrayRef size, Generator * generator, c10::optional names, const TensorOptions & options={}); +#endif +static inline Tensor rand(IntArrayRef size, const TensorOptions & options={}); +static inline Tensor rand(IntArrayRef size, Generator * generator, const TensorOptions & options={}); +static inline Tensor & rand_out(Tensor & out, IntArrayRef size); +static inline Tensor & rand_out(Tensor & out, IntArrayRef size, Generator * generator); +static inline Tensor rand_like(const Tensor & self, c10::optional memory_format=c10::nullopt); +static inline Tensor rand_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format=c10::nullopt); +static inline Tensor randint(int64_t high, IntArrayRef size, const TensorOptions & options={}); +static inline Tensor randint(int64_t high, IntArrayRef size, Generator * generator, const TensorOptions & options={}); +static inline Tensor randint(int64_t low, int64_t high, IntArrayRef size, const TensorOptions & options={}); +static inline Tensor randint(int64_t low, int64_t high, IntArrayRef size, Generator * generator, const TensorOptions & options={}); +static inline Tensor & randint_out(Tensor & out, int64_t high, IntArrayRef size); +static inline Tensor & randint_out(Tensor & out, int64_t high, IntArrayRef size, Generator * generator); +static inline Tensor & randint_out(Tensor & out, int64_t low, int64_t high, IntArrayRef size); +static inline Tensor & randint_out(Tensor & out, int64_t low, int64_t high, IntArrayRef size, Generator * generator); +static inline Tensor randint_like(const Tensor & self, int64_t high, c10::optional memory_format=c10::nullopt); +static inline Tensor randint_like(const Tensor & self, int64_t low, int64_t high, c10::optional memory_format=c10::nullopt); +static inline Tensor randint_like(const Tensor & self, int64_t high, const TensorOptions & options, c10::optional memory_format=c10::nullopt); +static inline Tensor randint_like(const Tensor & self, int64_t low, int64_t high, const TensorOptions & options, c10::optional memory_format=c10::nullopt); +static inline Tensor randn(IntArrayRef size, const TensorOptions & options={}); +static inline Tensor randn(IntArrayRef size, Generator * generator, const TensorOptions & options={}); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor randn(IntArrayRef size, c10::optional names, const TensorOptions & options={}); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor randn(IntArrayRef size, Generator * generator, c10::optional names, const TensorOptions & options={}); +#endif +static inline Tensor & randn_out(Tensor & out, IntArrayRef size); +static inline Tensor & randn_out(Tensor & out, IntArrayRef size, Generator * generator); +static inline Tensor randn_like(const Tensor & self, c10::optional memory_format=c10::nullopt); +static inline Tensor randn_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format=c10::nullopt); +static inline Tensor randperm(int64_t n, const TensorOptions & options={}); +static inline Tensor randperm(int64_t n, Generator * generator, const TensorOptions & options={}); +static inline Tensor & randperm_out(Tensor & out, int64_t n); +static inline Tensor & randperm_out(Tensor & out, int64_t n, Generator * generator); +static inline Tensor range(Scalar start, Scalar end, Scalar step=1, const TensorOptions & options={}); +static inline Tensor range(Scalar start, Scalar end, const TensorOptions & options={}); +static inline Tensor & range_out(Tensor & out, Scalar start, Scalar end, Scalar step=1); +static inline Tensor reciprocal(const Tensor & self); +static inline Tensor & reciprocal_(Tensor & self); +static inline Tensor & reciprocal_out(Tensor & out, const Tensor & self); +static inline Tensor neg(const Tensor & self); +static inline Tensor & neg_(Tensor & self); +static inline Tensor & neg_out(Tensor & out, const Tensor & self); +static inline Tensor repeat_interleave(const Tensor & repeats); +static inline Tensor repeat_interleave(const Tensor & self, const Tensor & repeats, c10::optional dim=c10::nullopt); +static inline Tensor repeat_interleave(const Tensor & self, int64_t repeats, c10::optional dim=c10::nullopt); +static inline Tensor reshape(const Tensor & self, IntArrayRef shape); +static inline Tensor _mkldnn_reshape(const Tensor & self, IntArrayRef shape); +static inline Tensor round(const Tensor & self); +static inline Tensor & round_(Tensor & self); +static inline Tensor & round_out(Tensor & out, const Tensor & self); +static inline Tensor rrelu(const Tensor & self, Scalar lower=0.125, Scalar upper=0.333333333333, bool training=false, Generator * generator=nullptr); +static inline Tensor & rrelu_(Tensor & self, Scalar lower=0.125, Scalar upper=0.333333333333, bool training=false, Generator * generator=nullptr); +static inline Tensor relu(const Tensor & self); +static inline Tensor & relu_(Tensor & self); +static inline Tensor prelu(const Tensor & self, const Tensor & weight); +static inline std::tuple prelu_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight); +static inline Tensor gelu(const Tensor & self); +static inline Tensor gelu_backward(const Tensor & grad, const Tensor & self); +static inline Tensor hardshrink(const Tensor & self, Scalar lambd=0.5); +static inline Tensor hardshrink_backward(const Tensor & grad_out, const Tensor & self, Scalar lambd); +static inline Tensor rsqrt(const Tensor & self); +static inline Tensor & rsqrt_(Tensor & self); +static inline Tensor & rsqrt_out(Tensor & out, const Tensor & self); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor select(const Tensor & self, Dimname dim, int64_t index); +#endif +static inline Tensor select(const Tensor & self, int64_t dim, int64_t index); +static inline Tensor selu(const Tensor & self); +static inline Tensor & selu_(Tensor & self); +static inline Tensor celu(const Tensor & self, Scalar alpha=1.0); +static inline Tensor & celu_(Tensor & self, Scalar alpha=1.0); +static inline Tensor sigmoid(const Tensor & self); +static inline Tensor & sigmoid_(Tensor & self); +static inline Tensor & sigmoid_out(Tensor & out, const Tensor & self); +static inline Tensor sin(const Tensor & self); +static inline Tensor & sin_(Tensor & self); +static inline Tensor & sin_out(Tensor & out, const Tensor & self); +static inline Tensor sinh(const Tensor & self); +static inline Tensor & sinh_(Tensor & self); +static inline Tensor & sinh_out(Tensor & out, const Tensor & self); +static inline Tensor detach(const Tensor & self); +static inline Tensor & detach_(Tensor & self); +static inline int64_t size(const Tensor & self, int64_t dim); +#ifdef BUILD_NAMEDTENSOR +static inline int64_t size(const Tensor & self, Dimname dim); +#endif +static inline Tensor slice(const Tensor & self, int64_t dim=0, int64_t start=0, int64_t end=9223372036854775807, int64_t step=1); +static inline std::tuple slogdet(const Tensor & self); +static inline Tensor smm(const Tensor & self, const Tensor & mat2); +static inline Tensor softmax(const Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor softmax(const Tensor & self, Dimname dim, c10::optional dtype=c10::nullopt); +#endif +static inline Tensor _softmax(const Tensor & self, int64_t dim, bool half_to_float); +static inline Tensor _softmax_backward_data(const Tensor & grad_output, const Tensor & output, int64_t dim, const Tensor & self); +static inline std::vector split(const Tensor & self, int64_t split_size, int64_t dim=0); +static inline std::vector split_with_sizes(const Tensor & self, IntArrayRef split_sizes, int64_t dim=0); +static inline Tensor squeeze(const Tensor & self); +static inline Tensor squeeze(const Tensor & self, int64_t dim); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor squeeze(const Tensor & self, Dimname dim); +#endif +static inline Tensor sspaddmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1); +static inline Tensor & sspaddmm_out(Tensor & out, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1); +static inline Tensor stack(TensorList tensors, int64_t dim=0); +static inline Tensor & stack_out(Tensor & out, TensorList tensors, int64_t dim=0); +static inline Tensor stft(const Tensor & self, int64_t n_fft, c10::optional hop_length=c10::nullopt, c10::optional win_length=c10::nullopt, const Tensor & window={}, bool normalized=false, bool onesided=true); +static inline int64_t stride(const Tensor & self, int64_t dim); +#ifdef BUILD_NAMEDTENSOR +static inline int64_t stride(const Tensor & self, Dimname dim); +#endif +static inline Tensor sum(const Tensor & self, c10::optional dtype=c10::nullopt); +static inline Tensor sum(const Tensor & self, IntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor sum(const Tensor & self, DimnameList dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#endif +static inline Tensor & sum_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & sum_out(Tensor & out, const Tensor & self, DimnameList dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#endif +static inline Tensor sqrt(const Tensor & self); +static inline Tensor & sqrt_(Tensor & self); +static inline Tensor & sqrt_out(Tensor & out, const Tensor & self); +static inline Tensor std(const Tensor & self, bool unbiased=true); +static inline Tensor std(const Tensor & self, IntArrayRef dim, bool unbiased=true, bool keepdim=false); +static inline std::tuple std_mean(const Tensor & self, bool unbiased=true); +static inline std::tuple std_mean(const Tensor & self, IntArrayRef dim, bool unbiased=true, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple std_mean(const Tensor & self, DimnameList dim, bool unbiased=true, bool keepdim=false); +#endif +static inline Tensor & std_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool unbiased=true, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor std(const Tensor & self, DimnameList dim, bool unbiased=true, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & std_out(Tensor & out, const Tensor & self, DimnameList dim, bool unbiased=true, bool keepdim=false); +#endif +static inline Tensor prod(const Tensor & self, c10::optional dtype=c10::nullopt); +static inline Tensor prod(const Tensor & self, int64_t dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +static inline Tensor & prod_out(Tensor & out, const Tensor & self, int64_t dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor prod(const Tensor & self, Dimname dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & prod_out(Tensor & out, const Tensor & self, Dimname dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#endif +static inline Tensor t(const Tensor & self); +static inline Tensor tan(const Tensor & self); +static inline Tensor & tan_(Tensor & self); +static inline Tensor & tan_out(Tensor & out, const Tensor & self); +static inline Tensor tanh(const Tensor & self); +static inline Tensor & tanh_(Tensor & self); +static inline Tensor & tanh_out(Tensor & out, const Tensor & self); +static inline Tensor tensordot(const Tensor & self, const Tensor & other, IntArrayRef dims_self, IntArrayRef dims_other); +static inline Tensor threshold(const Tensor & self, Scalar threshold, Scalar value); +static inline Tensor & threshold_(Tensor & self, Scalar threshold, Scalar value); +static inline Tensor & threshold_out(Tensor & out, const Tensor & self, Scalar threshold, Scalar value); +static inline Tensor threshold_backward(const Tensor & grad_output, const Tensor & self, Scalar threshold); +static inline Tensor transpose(const Tensor & self, int64_t dim0, int64_t dim1); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor transpose(const Tensor & self, Dimname dim0, Dimname dim1); +#endif +static inline Tensor _mkldnn_transpose(const Tensor & self, int64_t dim0, int64_t dim1); +static inline Tensor & _mkldnn_transpose_(Tensor & self, int64_t dim0, int64_t dim1); +static inline Tensor one_hot(const Tensor & self, int64_t num_classes=-1); +static inline Tensor flip(const Tensor & self, IntArrayRef dims); +static inline Tensor roll(const Tensor & self, IntArrayRef shifts, IntArrayRef dims={}); +static inline Tensor rot90(const Tensor & self, int64_t k=1, IntArrayRef dims={0,1}); +static inline Tensor trapz(const Tensor & y, const Tensor & x, int64_t dim=-1); +static inline Tensor trapz(const Tensor & y, double dx=1, int64_t dim=-1); +static inline Tensor _trilinear(const Tensor & i1, const Tensor & i2, const Tensor & i3, IntArrayRef expand1, IntArrayRef expand2, IntArrayRef expand3, IntArrayRef sumdim, int64_t unroll_dim=1); +static inline Tensor triplet_margin_loss(const Tensor & anchor, const Tensor & positive, const Tensor & negative, double margin=1.0, double p=2, double eps=1e-06, bool swap=false, int64_t reduction=at::Reduction::Mean); +static inline Tensor trunc(const Tensor & self); +static inline Tensor & trunc_(Tensor & self); +static inline Tensor & trunc_out(Tensor & out, const Tensor & self); +static inline bool _has_compatible_shallow_copy_type(const Tensor & self, const Tensor & from); +static inline std::tuple _unique(const Tensor & self, bool sorted=true, bool return_inverse=false); +static inline std::tuple unique_dim(const Tensor & self, int64_t dim, bool sorted=true, bool return_inverse=false, bool return_counts=false); +static inline std::tuple unique_consecutive(const Tensor & self, bool return_inverse=false, bool return_counts=false, c10::optional dim=c10::nullopt); +static inline std::tuple unique_dim_consecutive(const Tensor & self, int64_t dim, bool return_inverse=false, bool return_counts=false); +static inline std::tuple _unique2(const Tensor & self, bool sorted=true, bool return_inverse=false, bool return_counts=false); +static inline Tensor _unsafe_view(const Tensor & self, IntArrayRef size); +static inline Tensor unsqueeze(const Tensor & self, int64_t dim); +static inline Tensor var(const Tensor & self, bool unbiased=true); +static inline Tensor var(const Tensor & self, IntArrayRef dim, bool unbiased=true, bool keepdim=false); +static inline Tensor & var_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool unbiased=true, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor var(const Tensor & self, DimnameList dim, bool unbiased=true, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & var_out(Tensor & out, const Tensor & self, DimnameList dim, bool unbiased=true, bool keepdim=false); +#endif +static inline std::tuple var_mean(const Tensor & self, bool unbiased=true); +static inline std::tuple var_mean(const Tensor & self, IntArrayRef dim, bool unbiased=true, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple var_mean(const Tensor & self, DimnameList dim, bool unbiased=true, bool keepdim=false); +#endif +static inline Tensor where(const Tensor & condition, const Tensor & self, const Tensor & other); +static inline std::vector where(const Tensor & condition); +static inline Tensor _s_where(const Tensor & condition, const Tensor & self, const Tensor & other); +static inline Tensor norm_except_dim(const Tensor & v, int64_t pow=2, int64_t dim=0); +static inline Tensor _weight_norm(const Tensor & v, const Tensor & g, int64_t dim=0); +static inline std::tuple _weight_norm_cuda_interface(const Tensor & v, const Tensor & g, int64_t dim=0); +static inline std::tuple _weight_norm_cuda_interface_backward(const Tensor & grad_w, const Tensor & saved_v, const Tensor & saved_g, const Tensor & saved_norms, int64_t dim); +static inline std::tuple _weight_norm_differentiable_backward(const Tensor & grad_w, const Tensor & saved_v, const Tensor & saved_g, const Tensor & saved_norms, int64_t dim); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor zeros(IntArrayRef size, c10::optional names, const TensorOptions & options={}); +#endif +static inline Tensor zeros(IntArrayRef size, const TensorOptions & options={}); +static inline Tensor & zeros_out(Tensor & out, IntArrayRef size); +static inline Tensor zeros_like(const Tensor & self, c10::optional memory_format=c10::nullopt); +static inline Tensor zeros_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format=c10::nullopt); +static inline Tensor _standard_gamma_grad(const Tensor & self, const Tensor & output); +static inline Tensor _standard_gamma(const Tensor & self, Generator * generator=nullptr); +static inline Tensor _dirichlet_grad(const Tensor & x, const Tensor & alpha, const Tensor & total); +static inline Tensor _sample_dirichlet(const Tensor & self, Generator * generator=nullptr); +static inline Tensor poisson(const Tensor & self, Generator * generator=nullptr); +static inline Tensor native_norm(const Tensor & self, Scalar p=2); +static inline Tensor _sparse_sum(const Tensor & self); +static inline Tensor _sparse_sum(const Tensor & self, ScalarType dtype); +static inline Tensor _sparse_sum(const Tensor & self, IntArrayRef dim); +static inline Tensor _sparse_sum(const Tensor & self, IntArrayRef dim, ScalarType dtype); +static inline Tensor _sparse_sum_backward(const Tensor & grad, const Tensor & self, IntArrayRef dim); +static inline Tensor norm(const Tensor & self, c10::optional p, ScalarType dtype); +static inline Tensor norm(const Tensor & self, Scalar p=2); +static inline Tensor norm(const Tensor & self, c10::optional p, IntArrayRef dim, bool keepdim, ScalarType dtype); +static inline Tensor norm(const Tensor & self, c10::optional p, IntArrayRef dim, bool keepdim=false); +static inline Tensor & norm_out(Tensor & out, const Tensor & self, c10::optional p, IntArrayRef dim, bool keepdim, ScalarType dtype); +static inline Tensor & norm_out(Tensor & out, const Tensor & self, c10::optional p, IntArrayRef dim, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor norm(const Tensor & self, c10::optional p, DimnameList dim, bool keepdim, ScalarType dtype); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor norm(const Tensor & self, c10::optional p, DimnameList dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & norm_out(Tensor & out, const Tensor & self, c10::optional p, DimnameList dim, bool keepdim, ScalarType dtype); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & norm_out(Tensor & out, const Tensor & self, c10::optional p, DimnameList dim, bool keepdim=false); +#endif +static inline Tensor frobenius_norm(const Tensor & self); +static inline Tensor frobenius_norm(const Tensor & self, IntArrayRef dim, bool keepdim=false); +static inline Tensor & frobenius_norm_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim=false); +static inline Tensor nuclear_norm(const Tensor & self, bool keepdim=false); +static inline Tensor & nuclear_norm_out(Tensor & out, const Tensor & self, bool keepdim=false); +static inline Tensor nuclear_norm(const Tensor & self, IntArrayRef dim, bool keepdim=false); +static inline Tensor & nuclear_norm_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim=false); +static inline Tensor clone(const Tensor & self, c10::optional memory_format=c10::nullopt); +static inline Tensor & resize_as_(Tensor & self, const Tensor & the_template, c10::optional memory_format=c10::nullopt); +static inline Tensor & pow_out(Tensor & out, const Tensor & self, Scalar exponent); +static inline Tensor pow(const Tensor & self, Scalar exponent); +static inline Tensor & zero_(Tensor & self); +static inline Tensor & sub_out(Tensor & out, const Tensor & self, const Tensor & other, Scalar alpha=1); +static inline Tensor sub(const Tensor & self, const Tensor & other, Scalar alpha=1); +static inline Tensor sub(const Tensor & self, Scalar other, Scalar alpha=1); +static inline Tensor rsub(const Tensor & self, const Tensor & other, Scalar alpha=1); +static inline Tensor rsub(const Tensor & self, Scalar other, Scalar alpha=1); +static inline Tensor _sparse_addmm(const Tensor & self, const Tensor & sparse, const Tensor & dense, Scalar beta=1, Scalar alpha=1); +static inline Tensor & addmm_out(Tensor & out, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1); +static inline Tensor addmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1); +static inline Tensor sparse_coo_tensor(IntArrayRef size, const TensorOptions & options); +static inline Tensor sparse_coo_tensor(const Tensor & indices, const Tensor & values, const TensorOptions & options={}); +static inline Tensor sparse_coo_tensor(const Tensor & indices, const Tensor & values, IntArrayRef size, const TensorOptions & options={}); +static inline Tensor _sparse_coo_tensor_unsafe(const Tensor & indices, const Tensor & values, IntArrayRef size, const TensorOptions & options={}); +static inline Tensor _sparse_coo_tensor_with_dims(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size, const TensorOptions & options); +static inline Tensor _sparse_coo_tensor_with_dims_and_tensors(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size, const Tensor & indices, const Tensor & values, const TensorOptions & options); +static inline Tensor to_dense_backward(const Tensor & grad, const Tensor & input); +static inline Tensor & hspmm_out(Tensor & out, const Tensor & mat1, const Tensor & mat2); +static inline Tensor hspmm(const Tensor & mat1, const Tensor & mat2); +static inline Tensor & copy_sparse_to_sparse_(Tensor & self, const Tensor & src, bool non_blocking=false); +static inline std::vector unbind(const Tensor & self, int64_t dim=0); +#ifdef BUILD_NAMEDTENSOR +static inline std::vector unbind(const Tensor & self, Dimname dim); +#endif +static inline Tensor mkldnn_reorder_conv2d_weight(const Tensor & self, IntArrayRef padding=0, IntArrayRef stride=1, IntArrayRef dilation=1, int64_t groups=1); +static inline Tensor to_mkldnn_backward(const Tensor & grad, const Tensor & input); +static inline Tensor quantize_per_tensor(const Tensor & self, double scale, int64_t zero_point, ScalarType dtype); +static inline Tensor quantize_per_channel(const Tensor & self, const Tensor & scales, const Tensor & zero_points, int64_t axis, ScalarType dtype); +static inline Tensor dequantize(const Tensor & self); +static inline double q_scale(const Tensor & self); +static inline int64_t q_zero_point(const Tensor & self); +static inline Tensor q_per_channel_scales(const Tensor & self); +static inline Tensor q_per_channel_zero_points(const Tensor & self); +static inline int64_t q_per_channel_axis(const Tensor & self); +static inline Tensor int_repr(const Tensor & self); +static inline Tensor _make_per_tensor_quantized_tensor(const Tensor & self, double scale, int64_t zero_point); +static inline Tensor _make_per_channel_quantized_tensor(const Tensor & self, const Tensor & scale, const Tensor & zero_point, int64_t axis); +static inline Tensor fake_quantize_per_tensor_affine(const Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max); +static inline Tensor fake_quantize_per_tensor_affine_backward(const Tensor & grad, const Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max); +static inline Tensor fake_quantize_per_channel_affine(const Tensor & self, const Tensor & scale, const Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max); +static inline Tensor fake_quantize_per_channel_affine_backward(const Tensor & grad, const Tensor & self, const Tensor & scale, const Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max); +static inline std::vector meshgrid(TensorList tensors); +static inline Tensor cartesian_prod(TensorList tensors); +static inline Tensor combinations(const Tensor & self, int64_t r=2, bool with_replacement=false); +static inline ScalarType result_type(const Tensor & tensor, const Tensor & other); +static inline ScalarType result_type(const Tensor & tensor, Scalar other); +static inline ScalarType result_type(Scalar scalar, const Tensor & tensor); +static inline ScalarType result_type(Scalar scalar1, Scalar scalar2); +static inline bool can_cast(ScalarType from, ScalarType to); +static inline ScalarType promote_types(ScalarType type1, ScalarType type2); +static inline Scalar _local_scalar_dense(const Tensor & self); +static inline std::tuple _thnn_fused_lstm_cell(const Tensor & input_gates, const Tensor & hidden_gates, const Tensor & cx, const Tensor & input_bias={}, const Tensor & hidden_bias={}); +static inline std::tuple _thnn_fused_lstm_cell_backward(const Tensor & grad_hy, const Tensor & grad_cy, const Tensor & cx, const Tensor & cy, const Tensor & workspace, bool has_bias); +static inline std::tuple _thnn_differentiable_lstm_cell_backward(const Tensor & grad_hy, const Tensor & grad_cy, const Tensor & input_gates, const Tensor & hidden_gates, const Tensor & input_bias, const Tensor & hidden_bias, const Tensor & cx, const Tensor & cy); +static inline std::tuple _thnn_fused_gru_cell(const Tensor & input_gates, const Tensor & hidden_gates, const Tensor & hx, const Tensor & input_bias={}, const Tensor & hidden_bias={}); +static inline std::tuple _thnn_fused_gru_cell_backward(const Tensor & grad_hy, const Tensor & workspace, bool has_bias); +static inline std::tuple _thnn_differentiable_gru_cell_backward(const Tensor & grad_hy, const Tensor & input_gates, const Tensor & hidden_gates, const Tensor & hx, const Tensor & input_bias, const Tensor & hidden_bias); +static inline std::tuple lstm(const Tensor & input, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); +static inline std::tuple lstm(const Tensor & data, const Tensor & batch_sizes, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); +static inline std::tuple gru(const Tensor & input, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); +static inline std::tuple gru(const Tensor & data, const Tensor & batch_sizes, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); +static inline std::tuple rnn_tanh(const Tensor & input, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); +static inline std::tuple rnn_tanh(const Tensor & data, const Tensor & batch_sizes, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); +static inline std::tuple rnn_relu(const Tensor & input, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); +static inline std::tuple rnn_relu(const Tensor & data, const Tensor & batch_sizes, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); +static inline std::tuple lstm_cell(const Tensor & input, TensorList hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih={}, const Tensor & b_hh={}); +static inline Tensor gru_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih={}, const Tensor & b_hh={}); +static inline Tensor rnn_tanh_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih={}, const Tensor & b_hh={}); +static inline Tensor rnn_relu_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih={}, const Tensor & b_hh={}); +static inline std::tuple quantized_lstm(const Tensor & input, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first, c10::optional dtype=c10::nullopt, bool use_dynamic=false); +static inline std::tuple quantized_lstm(const Tensor & data, const Tensor & batch_sizes, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, c10::optional dtype=c10::nullopt, bool use_dynamic=false); +static inline std::tuple quantized_gru(const Tensor & input, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); +static inline std::tuple quantized_gru(const Tensor & data, const Tensor & batch_sizes, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); +static inline std::tuple quantized_lstm_cell(const Tensor & input, TensorList hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh, const Tensor & packed_ih, const Tensor & packed_hh, const Tensor & col_offsets_ih, const Tensor & col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh); +static inline Tensor quantized_gru_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh, const Tensor & packed_ih, const Tensor & packed_hh, const Tensor & col_offsets_ih, const Tensor & col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh); +static inline Tensor quantized_rnn_relu_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh, const Tensor & packed_ih, const Tensor & packed_hh, const Tensor & col_offsets_ih, const Tensor & col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh); +static inline Tensor quantized_rnn_tanh_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh, const Tensor & packed_ih, const Tensor & packed_hh, const Tensor & col_offsets_ih, const Tensor & col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh); +static inline std::tuple _pack_padded_sequence(const Tensor & input, const Tensor & lengths, bool batch_first); +static inline Tensor _pack_padded_sequence_backward(const Tensor & grad, IntArrayRef input_size, const Tensor & batch_sizes, bool batch_first); +static inline std::tuple _pad_packed_sequence(const Tensor & data, const Tensor & batch_sizes, bool batch_first, Scalar padding_value, int64_t total_length); +static inline Tensor masked_fill(const Tensor & self, const Tensor & mask, Scalar value); +static inline Tensor masked_fill(const Tensor & self, const Tensor & mask, const Tensor & value); +static inline Tensor masked_scatter(const Tensor & self, const Tensor & mask, const Tensor & source); +static inline Tensor index_add(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & source); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor index_add(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & source); +#endif +static inline Tensor index_fill(const Tensor & self, int64_t dim, const Tensor & index, Scalar value); +static inline Tensor index_fill(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & value); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor index_fill(const Tensor & self, Dimname dim, const Tensor & index, Scalar value); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor index_fill(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & value); +#endif +static inline Tensor scatter(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & src); +static inline Tensor scatter(const Tensor & self, int64_t dim, const Tensor & index, Scalar value); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor scatter(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & src); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor scatter(const Tensor & self, Dimname dim, const Tensor & index, Scalar value); +#endif +static inline Tensor scatter_add(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & src); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor scatter_add(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & src); +#endif +static inline Tensor __and__(const Tensor & self, Scalar other); +static inline Tensor __and__(const Tensor & self, const Tensor & other); +static inline Tensor __or__(const Tensor & self, Scalar other); +static inline Tensor __or__(const Tensor & self, const Tensor & other); +static inline Tensor & bitwise_xor_out(Tensor & out, const Tensor & self, const Tensor & other); +static inline Tensor & bitwise_xor_out(Tensor & out, const Tensor & self, Scalar other); +static inline Tensor bitwise_xor(const Tensor & self, Scalar other); +static inline Tensor bitwise_xor(const Tensor & self, const Tensor & other); +static inline Tensor __xor__(const Tensor & self, Scalar other); +static inline Tensor __xor__(const Tensor & self, const Tensor & other); +static inline Tensor __lshift__(const Tensor & self, Scalar other); +static inline Tensor __lshift__(const Tensor & self, const Tensor & other); +static inline Tensor __rshift__(const Tensor & self, Scalar other); +static inline Tensor __rshift__(const Tensor & self, const Tensor & other); +static inline Tensor & addbmm_out(Tensor & out, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1); +static inline Tensor addbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1); +static inline Tensor & diag_out(Tensor & out, const Tensor & self, int64_t diagonal=0); +static inline Tensor diag(const Tensor & self, int64_t diagonal=0); +static inline Tensor & cross_out(Tensor & out, const Tensor & self, const Tensor & other, c10::optional dim=c10::nullopt); +static inline Tensor cross(const Tensor & self, const Tensor & other, c10::optional dim=c10::nullopt); +static inline Tensor & triu_out(Tensor & out, const Tensor & self, int64_t diagonal=0); +static inline Tensor triu(const Tensor & self, int64_t diagonal=0); +static inline Tensor & tril_out(Tensor & out, const Tensor & self, int64_t diagonal=0); +static inline Tensor tril(const Tensor & self, int64_t diagonal=0); +static inline Tensor tril_indices(int64_t row, int64_t col, int64_t offset=0, const TensorOptions & options=at::kLong); +static inline Tensor triu_indices(int64_t row, int64_t col, int64_t offset=0, const TensorOptions & options=at::kLong); +static inline Tensor trace(const Tensor & self); +static inline Tensor & ne_out(Tensor & out, const Tensor & self, Scalar other); +static inline Tensor ne(const Tensor & self, Scalar other); +static inline Tensor & ne_out(Tensor & out, const Tensor & self, const Tensor & other); +static inline Tensor ne(const Tensor & self, const Tensor & other); +static inline Tensor & eq_out(Tensor & out, const Tensor & self, Scalar other); +static inline Tensor eq(const Tensor & self, Scalar other); +static inline Tensor & eq_out(Tensor & out, const Tensor & self, const Tensor & other); +static inline Tensor eq(const Tensor & self, const Tensor & other); +static inline Tensor & ge_out(Tensor & out, const Tensor & self, Scalar other); +static inline Tensor ge(const Tensor & self, Scalar other); +static inline Tensor & ge_out(Tensor & out, const Tensor & self, const Tensor & other); +static inline Tensor ge(const Tensor & self, const Tensor & other); +static inline Tensor & le_out(Tensor & out, const Tensor & self, Scalar other); +static inline Tensor le(const Tensor & self, Scalar other); +static inline Tensor & le_out(Tensor & out, const Tensor & self, const Tensor & other); +static inline Tensor le(const Tensor & self, const Tensor & other); +static inline Tensor & gt_out(Tensor & out, const Tensor & self, Scalar other); +static inline Tensor gt(const Tensor & self, Scalar other); +static inline Tensor & gt_out(Tensor & out, const Tensor & self, const Tensor & other); +static inline Tensor gt(const Tensor & self, const Tensor & other); +static inline Tensor & lt_out(Tensor & out, const Tensor & self, Scalar other); +static inline Tensor lt(const Tensor & self, Scalar other); +static inline Tensor & lt_out(Tensor & out, const Tensor & self, const Tensor & other); +static inline Tensor lt(const Tensor & self, const Tensor & other); +static inline Tensor & take_out(Tensor & out, const Tensor & self, const Tensor & index); +static inline Tensor take(const Tensor & self, const Tensor & index); +static inline Tensor & index_select_out(Tensor & out, const Tensor & self, int64_t dim, const Tensor & index); +static inline Tensor index_select(const Tensor & self, int64_t dim, const Tensor & index); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & index_select_out(Tensor & out, const Tensor & self, Dimname dim, const Tensor & index); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor index_select(const Tensor & self, Dimname dim, const Tensor & index); +#endif +static inline Tensor & masked_select_out(Tensor & out, const Tensor & self, const Tensor & mask); +static inline Tensor masked_select(const Tensor & self, const Tensor & mask); +static inline Tensor & nonzero_out(Tensor & out, const Tensor & self); +static inline Tensor nonzero(const Tensor & self); +static inline std::vector nonzero_numpy(const Tensor & self); +static inline Tensor & gather_out(Tensor & out, const Tensor & self, int64_t dim, const Tensor & index, bool sparse_grad=false); +static inline Tensor gather(const Tensor & self, int64_t dim, const Tensor & index, bool sparse_grad=false); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & gather_out(Tensor & out, const Tensor & self, Dimname dim, const Tensor & index, bool sparse_grad=false); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor gather(const Tensor & self, Dimname dim, const Tensor & index, bool sparse_grad=false); +#endif +static inline Tensor _gather_sparse_backward(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & grad); +static inline Tensor & addcmul_out(Tensor & out, const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1); +static inline Tensor addcmul(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1); +static inline Tensor & addcdiv_out(Tensor & out, const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1); +static inline Tensor addcdiv(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1); +static inline std::tuple lstsq_out(Tensor & X, Tensor & qr, const Tensor & self, const Tensor & A); +static inline std::tuple lstsq(const Tensor & self, const Tensor & A); +static inline std::tuple triangular_solve_out(Tensor & X, Tensor & M, const Tensor & self, const Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false); +static inline std::tuple triangular_solve(const Tensor & self, const Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false); +static inline std::tuple _triangular_solve_helper(const Tensor & self, const Tensor & A, bool upper, bool transpose, bool unitriangular); +static inline std::tuple symeig_out(Tensor & e, Tensor & V, const Tensor & self, bool eigenvectors=false, bool upper=true); +static inline std::tuple symeig(const Tensor & self, bool eigenvectors=false, bool upper=true); +static inline std::tuple _symeig_helper(const Tensor & self, bool eigenvectors, bool upper); +static inline std::tuple eig_out(Tensor & e, Tensor & v, const Tensor & self, bool eigenvectors=false); +static inline std::tuple eig(const Tensor & self, bool eigenvectors=false); +static inline std::tuple svd_out(Tensor & U, Tensor & S, Tensor & V, const Tensor & self, bool some=true, bool compute_uv=true); +static inline std::tuple svd(const Tensor & self, bool some=true, bool compute_uv=true); +static inline std::tuple _svd_helper(const Tensor & self, bool some, bool compute_uv); +static inline Tensor & cholesky_out(Tensor & out, const Tensor & self, bool upper=false); +static inline Tensor cholesky(const Tensor & self, bool upper=false); +static inline Tensor _cholesky_helper(const Tensor & self, bool upper); +static inline Tensor & cholesky_solve_out(Tensor & out, const Tensor & self, const Tensor & input2, bool upper=false); +static inline Tensor cholesky_solve(const Tensor & self, const Tensor & input2, bool upper=false); +static inline Tensor _cholesky_solve_helper(const Tensor & self, const Tensor & A, bool upper); +static inline std::tuple solve(const Tensor & self, const Tensor & A); +static inline std::tuple solve_out(Tensor & solution, Tensor & lu, const Tensor & self, const Tensor & A); +static inline std::tuple _solve_helper(const Tensor & self, const Tensor & A); +static inline Tensor & cholesky_inverse_out(Tensor & out, const Tensor & self, bool upper=false); +static inline Tensor cholesky_inverse(const Tensor & self, bool upper=false); +static inline std::tuple qr_out(Tensor & Q, Tensor & R, const Tensor & self, bool some=true); +static inline std::tuple qr(const Tensor & self, bool some=true); +static inline std::tuple _qr_helper(const Tensor & self, bool some); +static inline std::tuple geqrf_out(Tensor & a, Tensor & tau, const Tensor & self); +static inline std::tuple geqrf(const Tensor & self); +static inline Tensor & orgqr_out(Tensor & out, const Tensor & self, const Tensor & input2); +static inline Tensor orgqr(const Tensor & self, const Tensor & input2); +static inline Tensor & ormqr_out(Tensor & out, const Tensor & self, const Tensor & input2, const Tensor & input3, bool left=true, bool transpose=false); +static inline Tensor ormqr(const Tensor & self, const Tensor & input2, const Tensor & input3, bool left=true, bool transpose=false); +static inline std::tuple _lu_with_info(const Tensor & self, bool pivot=true, bool check_errors=true); +static inline Tensor & lu_solve_out(Tensor & out, const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots); +static inline Tensor lu_solve(const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots); +static inline Tensor _lu_solve_helper(const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots); +static inline Tensor & multinomial_out(Tensor & out, const Tensor & self, int64_t num_samples, bool replacement=false, Generator * generator=nullptr); +static inline Tensor multinomial(const Tensor & self, int64_t num_samples, bool replacement=false, Generator * generator=nullptr); +static inline std::tuple _multinomial_alias_setup(const Tensor & probs); +static inline Tensor _multinomial_alias_draw(const Tensor & J, const Tensor & q, int64_t num_samples, Generator * generator=nullptr); +static inline Tensor & lgamma_out(Tensor & out, const Tensor & self); +static inline Tensor lgamma(const Tensor & self); +static inline Tensor & digamma_out(Tensor & out, const Tensor & self); +static inline Tensor digamma(const Tensor & self); +static inline Tensor & polygamma_out(Tensor & out, int64_t n, const Tensor & self); +static inline Tensor polygamma(int64_t n, const Tensor & self); +static inline Tensor erfinv(const Tensor & self); +static inline Tensor & erfinv_out(Tensor & out, const Tensor & self); +static inline Tensor sign(const Tensor & self); +static inline Tensor & sign_out(Tensor & out, const Tensor & self); +static inline Tensor dist(const Tensor & self, const Tensor & other, Scalar p=2); +static inline Tensor & atan2_out(Tensor & out, const Tensor & self, const Tensor & other); +static inline Tensor atan2(const Tensor & self, const Tensor & other); +static inline Tensor & lerp_out(Tensor & out, const Tensor & self, const Tensor & end, Scalar weight); +static inline Tensor & lerp_out(Tensor & out, const Tensor & self, const Tensor & end, const Tensor & weight); +static inline Tensor lerp(const Tensor & self, const Tensor & end, Scalar weight); +static inline Tensor lerp(const Tensor & self, const Tensor & end, const Tensor & weight); +static inline Tensor & histc_out(Tensor & out, const Tensor & self, int64_t bins=100, Scalar min=0, Scalar max=0); +static inline Tensor histc(const Tensor & self, int64_t bins=100, Scalar min=0, Scalar max=0); +static inline Tensor & fmod_out(Tensor & out, const Tensor & self, Scalar other); +static inline Tensor fmod(const Tensor & self, Scalar other); +static inline Tensor & fmod_out(Tensor & out, const Tensor & self, const Tensor & other); +static inline Tensor fmod(const Tensor & self, const Tensor & other); +static inline Tensor & remainder_out(Tensor & out, const Tensor & self, Scalar other); +static inline Tensor remainder(const Tensor & self, Scalar other); +static inline Tensor & remainder_out(Tensor & out, const Tensor & self, const Tensor & other); +static inline Tensor remainder(const Tensor & self, const Tensor & other); +static inline Tensor & min_out(Tensor & out, const Tensor & self, const Tensor & other); +static inline Tensor min(const Tensor & self, const Tensor & other); +static inline Tensor min(const Tensor & self); +static inline Tensor & max_out(Tensor & out, const Tensor & self, const Tensor & other); +static inline Tensor max(const Tensor & self, const Tensor & other); +static inline Tensor max(const Tensor & self); +static inline Tensor median(const Tensor & self); +static inline std::tuple sort_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim=-1, bool descending=false); +static inline std::tuple sort(const Tensor & self, int64_t dim=-1, bool descending=false); +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple sort_out(Tensor & values, Tensor & indices, const Tensor & self, Dimname dim, bool descending=false); +#endif +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple sort(const Tensor & self, Dimname dim, bool descending=false); +#endif +static inline Tensor argsort(const Tensor & self, int64_t dim=-1, bool descending=false); +#ifdef BUILD_NAMEDTENSOR +static inline Tensor argsort(const Tensor & self, Dimname dim, bool descending=false); +#endif +static inline std::tuple topk_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true); +static inline std::tuple topk(const Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true); +static inline Tensor all(const Tensor & self); +static inline Tensor any(const Tensor & self); +static inline Tensor & renorm_out(Tensor & out, const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm); +static inline Tensor renorm(const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm); +static inline bool equal(const Tensor & self, const Tensor & other); +static inline Tensor & pow_out(Tensor & out, const Tensor & self, const Tensor & exponent); +static inline Tensor pow(const Tensor & self, const Tensor & exponent); +static inline Tensor & pow_out(Tensor & out, Scalar self, const Tensor & exponent); +static inline Tensor pow(Scalar self, const Tensor & exponent); +static inline Tensor & normal_out(Tensor & out, const Tensor & mean, double std=1, Generator * generator=nullptr); +static inline Tensor normal(const Tensor & mean, double std=1, Generator * generator=nullptr); +static inline Tensor & normal_out(Tensor & out, double mean, const Tensor & std, Generator * generator=nullptr); +static inline Tensor normal(double mean, const Tensor & std, Generator * generator=nullptr); +static inline Tensor & normal_out(Tensor & out, const Tensor & mean, const Tensor & std, Generator * generator=nullptr); +static inline Tensor normal(const Tensor & mean, const Tensor & std, Generator * generator=nullptr); +static inline Tensor normal(double mean, double std, IntArrayRef size, Generator * generator=nullptr, const TensorOptions & options={}); +static inline Tensor & normal_out(Tensor & out, double mean, double std, IntArrayRef size, Generator * generator=nullptr); +static inline Tensor alias(const Tensor & self); +static inline Tensor _addr(const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1); +static inline Tensor & _addr_(Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1); +static inline Tensor & _addr_out(Tensor & out, const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1); +static inline Tensor & _index_copy_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & source); +static inline Tensor _cumsum(const Tensor & self, int64_t dim); +static inline Tensor & _cumsum_out(Tensor & out, const Tensor & self, int64_t dim); +static inline Tensor _cumprod(const Tensor & self, int64_t dim); +static inline Tensor & _cumprod_out(Tensor & out, const Tensor & self, int64_t dim); +static inline Tensor _var(const Tensor & self, bool unbiased=true); +static inline Tensor _std(const Tensor & self, bool unbiased=true); +static inline Tensor _cat(TensorList tensors, int64_t dim=0); +static inline Tensor & _cat_out(Tensor & out, TensorList tensors, int64_t dim=0); +static inline std::tuple _mode(const Tensor & self, int64_t dim=-1, bool keepdim=false); +static inline std::tuple _mode_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim=-1, bool keepdim=false); +static inline std::tuple _max(const Tensor & self, int64_t dim, bool keepdim=false); +static inline std::tuple _max_out(Tensor & max, Tensor & max_indices, const Tensor & self, int64_t dim, bool keepdim=false); +static inline std::tuple _min(const Tensor & self, int64_t dim, bool keepdim=false); +static inline std::tuple _min_out(Tensor & min, Tensor & min_indices, const Tensor & self, int64_t dim, bool keepdim=false); +static inline Tensor & binary_cross_entropy_out(Tensor & out, const Tensor & self, const Tensor & target, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean); +static inline Tensor binary_cross_entropy(const Tensor & self, const Tensor & target, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean); +static inline Tensor & binary_cross_entropy_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean); +static inline Tensor binary_cross_entropy_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean); +static inline Tensor & mse_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +static inline Tensor mse_loss(const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +static inline Tensor & mse_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); +static inline Tensor mse_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); +static inline Tensor & l1_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +static inline Tensor l1_loss(const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +static inline Tensor & l1_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); +static inline Tensor l1_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); +static inline Tensor & multi_margin_loss_out(Tensor & out, const Tensor & self, const Tensor & target, Scalar p=1, Scalar margin=1, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean); +static inline Tensor multi_margin_loss(const Tensor & self, const Tensor & target, Scalar p=1, Scalar margin=1, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean); +static inline Tensor & multi_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean); +static inline Tensor multi_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean); +static inline Tensor & multilabel_margin_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +static inline Tensor multilabel_margin_loss(const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +static inline std::tuple multilabel_margin_loss_forward_out(Tensor & output, Tensor & is_target, const Tensor & self, const Tensor & target, int64_t reduction); +static inline std::tuple multilabel_margin_loss_forward(const Tensor & self, const Tensor & target, int64_t reduction); +static inline Tensor & multilabel_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction, const Tensor & is_target); +static inline Tensor multilabel_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction, const Tensor & is_target); +static inline Tensor & nll_loss_out(Tensor & out, const Tensor & self, const Tensor & target, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100); +static inline Tensor nll_loss(const Tensor & self, const Tensor & target, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100); +static inline std::tuple nll_loss_forward_out(Tensor & output, Tensor & total_weight, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); +static inline std::tuple nll_loss_forward(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); +static inline Tensor & nll_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); +static inline Tensor nll_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); +static inline Tensor & nll_loss2d_out(Tensor & out, const Tensor & self, const Tensor & target, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100); +static inline Tensor nll_loss2d(const Tensor & self, const Tensor & target, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100); +static inline std::tuple nll_loss2d_forward_out(Tensor & output, Tensor & total_weight, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); +static inline std::tuple nll_loss2d_forward(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); +static inline Tensor & nll_loss2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); +static inline Tensor nll_loss2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); +static inline Tensor & smooth_l1_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +static inline Tensor smooth_l1_loss(const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +static inline Tensor & smooth_l1_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); +static inline Tensor smooth_l1_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); +static inline Tensor & soft_margin_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +static inline Tensor soft_margin_loss(const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +static inline Tensor & soft_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); +static inline Tensor soft_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); +static inline Tensor & elu_out(Tensor & out, const Tensor & self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1); +static inline Tensor elu(const Tensor & self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1); +static inline Tensor & elu_backward_out(Tensor & grad_input, const Tensor & grad_output, Scalar alpha, Scalar scale, Scalar input_scale, const Tensor & output); +static inline Tensor elu_backward(const Tensor & grad_output, Scalar alpha, Scalar scale, Scalar input_scale, const Tensor & output); +static inline Tensor & elu_(Tensor & self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1); +static inline Tensor & glu_out(Tensor & out, const Tensor & self, int64_t dim=-1); +static inline Tensor glu(const Tensor & self, int64_t dim=-1); +static inline Tensor & glu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, int64_t dim); +static inline Tensor glu_backward(const Tensor & grad_output, const Tensor & self, int64_t dim); +static inline Tensor & hardtanh_out(Tensor & out, const Tensor & self, Scalar min_val=-1, Scalar max_val=1); +static inline Tensor hardtanh(const Tensor & self, Scalar min_val=-1, Scalar max_val=1); +static inline Tensor & hardtanh_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar min_val, Scalar max_val); +static inline Tensor hardtanh_backward(const Tensor & grad_output, const Tensor & self, Scalar min_val, Scalar max_val); +static inline Tensor & hardtanh_(Tensor & self, Scalar min_val=-1, Scalar max_val=1); +static inline Tensor & leaky_relu_out(Tensor & out, const Tensor & self, Scalar negative_slope=0.01); +static inline Tensor leaky_relu(const Tensor & self, Scalar negative_slope=0.01); +static inline Tensor & leaky_relu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar negative_slope); +static inline Tensor leaky_relu_backward(const Tensor & grad_output, const Tensor & self, Scalar negative_slope); +static inline Tensor & leaky_relu_(Tensor & self, Scalar negative_slope=0.01); +static inline Tensor & log_sigmoid_out(Tensor & out, const Tensor & self); +static inline Tensor log_sigmoid(const Tensor & self); +static inline std::tuple log_sigmoid_forward_out(Tensor & output, Tensor & buffer, const Tensor & self); +static inline std::tuple log_sigmoid_forward(const Tensor & self); +static inline Tensor & log_sigmoid_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & buffer); +static inline Tensor log_sigmoid_backward(const Tensor & grad_output, const Tensor & self, const Tensor & buffer); +static inline Tensor & rrelu_with_noise_out(Tensor & out, const Tensor & self, const Tensor & noise, Scalar lower=0.125, Scalar upper=0.333333333333, bool training=false, Generator * generator=nullptr); +static inline Tensor rrelu_with_noise(const Tensor & self, const Tensor & noise, Scalar lower=0.125, Scalar upper=0.333333333333, bool training=false, Generator * generator=nullptr); +static inline Tensor & rrelu_with_noise_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training); +static inline Tensor rrelu_with_noise_backward(const Tensor & grad_output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training); +static inline Tensor & rrelu_with_noise_(Tensor & self, const Tensor & noise, Scalar lower=0.125, Scalar upper=0.333333333333, bool training=false, Generator * generator=nullptr); +static inline Tensor & softplus_out(Tensor & out, const Tensor & self, Scalar beta=1, Scalar threshold=20); +static inline Tensor softplus(const Tensor & self, Scalar beta=1, Scalar threshold=20); +static inline Tensor & softplus_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar beta, Scalar threshold, const Tensor & output); +static inline Tensor softplus_backward(const Tensor & grad_output, const Tensor & self, Scalar beta, Scalar threshold, const Tensor & output); +static inline Tensor & softshrink_out(Tensor & out, const Tensor & self, Scalar lambd=0.5); +static inline Tensor softshrink(const Tensor & self, Scalar lambd=0.5); +static inline Tensor & softshrink_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar lambd); +static inline Tensor softshrink_backward(const Tensor & grad_output, const Tensor & self, Scalar lambd); +static inline Tensor & adaptive_avg_pool2d_out(Tensor & out, const Tensor & self, IntArrayRef output_size); +static inline Tensor adaptive_avg_pool2d(const Tensor & self, IntArrayRef output_size); +static inline Tensor mkldnn_adaptive_avg_pool2d(const Tensor & self, IntArrayRef output_size); +static inline Tensor _adaptive_avg_pool2d(const Tensor & self, IntArrayRef output_size); +static inline Tensor _adaptive_avg_pool2d_backward(const Tensor & grad_output, const Tensor & self); +static inline Tensor & adaptive_avg_pool3d_out(Tensor & out, const Tensor & self, IntArrayRef output_size); +static inline Tensor adaptive_avg_pool3d(const Tensor & self, IntArrayRef output_size); +static inline Tensor & adaptive_avg_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self); +static inline Tensor adaptive_avg_pool3d_backward(const Tensor & grad_output, const Tensor & self); +static inline std::tuple adaptive_max_pool2d_out(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef output_size); +static inline std::tuple adaptive_max_pool2d(const Tensor & self, IntArrayRef output_size); +static inline Tensor & adaptive_max_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices); +static inline Tensor adaptive_max_pool2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices); +static inline std::tuple adaptive_max_pool3d_out(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef output_size); +static inline std::tuple adaptive_max_pool3d(const Tensor & self, IntArrayRef output_size); +static inline Tensor & adaptive_max_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices); +static inline Tensor adaptive_max_pool3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices); +static inline Tensor & avg_pool2d_out(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, c10::optional divisor_override=c10::nullopt); +static inline Tensor avg_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, c10::optional divisor_override=c10::nullopt); +static inline Tensor & avg_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); +static inline Tensor avg_pool2d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); +static inline Tensor & avg_pool3d_out(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, c10::optional divisor_override=c10::nullopt); +static inline Tensor avg_pool3d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, c10::optional divisor_override=c10::nullopt); +static inline Tensor & avg_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); +static inline Tensor avg_pool3d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); +static inline std::tuple fractional_max_pool2d_out(Tensor & output, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); +static inline std::tuple fractional_max_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); +static inline Tensor & fractional_max_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); +static inline Tensor fractional_max_pool2d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); +static inline std::tuple fractional_max_pool3d_out(Tensor & output, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); +static inline std::tuple fractional_max_pool3d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); +static inline Tensor & fractional_max_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); +static inline Tensor fractional_max_pool3d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); +static inline std::tuple max_pool2d_with_indices_out(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +static inline std::tuple max_pool2d_with_indices(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +static inline Tensor & max_pool2d_with_indices_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); +static inline Tensor max_pool2d_with_indices_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); +static inline std::tuple max_pool3d_with_indices_out(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +static inline std::tuple max_pool3d_with_indices(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +static inline Tensor & max_pool3d_with_indices_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); +static inline Tensor max_pool3d_with_indices_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); +static inline Tensor & max_unpool2d_out(Tensor & out, const Tensor & self, const Tensor & indices, IntArrayRef output_size); +static inline Tensor max_unpool2d(const Tensor & self, const Tensor & indices, IntArrayRef output_size); +static inline Tensor & max_unpool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size); +static inline Tensor max_unpool2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size); +static inline Tensor & max_unpool3d_out(Tensor & out, const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); +static inline Tensor max_unpool3d(const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); +static inline Tensor & max_unpool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); +static inline Tensor max_unpool3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); +static inline Tensor & reflection_pad1d_out(Tensor & out, const Tensor & self, IntArrayRef padding); +static inline Tensor reflection_pad1d(const Tensor & self, IntArrayRef padding); +static inline Tensor & reflection_pad1d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +static inline Tensor reflection_pad1d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +static inline Tensor & reflection_pad2d_out(Tensor & out, const Tensor & self, IntArrayRef padding); +static inline Tensor reflection_pad2d(const Tensor & self, IntArrayRef padding); +static inline Tensor & reflection_pad2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +static inline Tensor reflection_pad2d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +static inline Tensor & replication_pad1d_out(Tensor & out, const Tensor & self, IntArrayRef padding); +static inline Tensor replication_pad1d(const Tensor & self, IntArrayRef padding); +static inline Tensor & replication_pad1d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +static inline Tensor replication_pad1d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +static inline Tensor & replication_pad2d_out(Tensor & out, const Tensor & self, IntArrayRef padding); +static inline Tensor replication_pad2d(const Tensor & self, IntArrayRef padding); +static inline Tensor & replication_pad2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +static inline Tensor replication_pad2d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +static inline Tensor & replication_pad3d_out(Tensor & out, const Tensor & self, IntArrayRef padding); +static inline Tensor replication_pad3d(const Tensor & self, IntArrayRef padding); +static inline Tensor & replication_pad3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +static inline Tensor replication_pad3d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +static inline Tensor _test_optional_float(const Tensor & self, c10::optional scale=c10::nullopt); +static inline Tensor & upsample_linear1d_out(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); +static inline Tensor upsample_linear1d(const Tensor & self, IntArrayRef output_size, bool align_corners); +static inline Tensor & upsample_linear1d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +static inline Tensor upsample_linear1d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +static inline Tensor & upsample_bilinear2d_out(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); +static inline Tensor upsample_bilinear2d(const Tensor & self, IntArrayRef output_size, bool align_corners); +static inline Tensor & upsample_bilinear2d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +static inline Tensor upsample_bilinear2d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +static inline Tensor & upsample_bicubic2d_out(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); +static inline Tensor upsample_bicubic2d(const Tensor & self, IntArrayRef output_size, bool align_corners); +static inline Tensor & upsample_bicubic2d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +static inline Tensor upsample_bicubic2d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +static inline Tensor & upsample_trilinear3d_out(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); +static inline Tensor upsample_trilinear3d(const Tensor & self, IntArrayRef output_size, bool align_corners); +static inline Tensor & upsample_trilinear3d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +static inline Tensor upsample_trilinear3d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +static inline Tensor & upsample_nearest1d_out(Tensor & out, const Tensor & self, IntArrayRef output_size); +static inline Tensor upsample_nearest1d(const Tensor & self, IntArrayRef output_size); +static inline Tensor & upsample_nearest1d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); +static inline Tensor upsample_nearest1d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); +static inline Tensor & upsample_nearest2d_out(Tensor & out, const Tensor & self, IntArrayRef output_size); +static inline Tensor upsample_nearest2d(const Tensor & self, IntArrayRef output_size); +static inline Tensor & upsample_nearest2d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); +static inline Tensor upsample_nearest2d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); +static inline Tensor & upsample_nearest3d_out(Tensor & out, const Tensor & self, IntArrayRef output_size); +static inline Tensor upsample_nearest3d(const Tensor & self, IntArrayRef output_size); +static inline Tensor & upsample_nearest3d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); +static inline Tensor upsample_nearest3d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); +static inline Tensor & sigmoid_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & output); +static inline Tensor sigmoid_backward(const Tensor & grad_output, const Tensor & output); +static inline Tensor & tanh_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & output); +static inline Tensor tanh_backward(const Tensor & grad_output, const Tensor & output); +static inline Tensor & slow_conv_transpose2d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef output_padding=0, IntArrayRef dilation=1); +static inline Tensor slow_conv_transpose2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef output_padding=0, IntArrayRef dilation=1); +static inline std::tuple slow_conv_transpose2d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & columns, const Tensor & ones); +static inline std::tuple slow_conv_transpose2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & columns, const Tensor & ones, std::array output_mask); +static inline Tensor & slow_conv_transpose3d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef output_padding=0, IntArrayRef dilation=1); +static inline Tensor slow_conv_transpose3d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef output_padding=0, IntArrayRef dilation=1); +static inline std::tuple slow_conv_transpose3d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & finput, const Tensor & fgrad_input); +static inline std::tuple slow_conv_transpose3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & finput, const Tensor & fgrad_input, std::array output_mask); +static inline Tensor & thnn_conv2d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0); +static inline Tensor thnn_conv2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0); +static inline std::tuple thnn_conv2d_forward_out(Tensor & output, Tensor & finput, Tensor & fgrad_input, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); +static inline std::tuple thnn_conv2d_forward(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); +static inline std::tuple thnn_conv2d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & finput, const Tensor & fgrad_input); +static inline std::tuple thnn_conv2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & finput, const Tensor & fgrad_input, std::array output_mask); +static inline Tensor & thnn_conv_depthwise2d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef dilation=1); +static inline Tensor thnn_conv_depthwise2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef dilation=1); +static inline Tensor & thnn_conv_depthwise2d_forward_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation); +static inline Tensor thnn_conv_depthwise2d_forward(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation); +static inline std::tuple thnn_conv_depthwise2d_backward_out(Tensor & grad_input, Tensor & grad_weight, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation); +static inline std::tuple thnn_conv_depthwise2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, std::array output_mask); +static inline Tensor & slow_conv3d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0); +static inline Tensor slow_conv3d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0); +static inline std::tuple slow_conv3d_forward_out(Tensor & output, Tensor & finput, Tensor & fgrad_input, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); +static inline std::tuple slow_conv3d_forward(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); +static inline std::tuple slow_conv3d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & finput, const Tensor & fgrad_input); +static inline std::tuple slow_conv3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & finput, const Tensor & fgrad_input, std::array output_mask); +static inline Tensor slow_conv_dilated2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef dilation=1); +static inline std::tuple slow_conv_dilated2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, std::array output_mask); +static inline Tensor slow_conv_dilated3d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef dilation=1); +static inline std::tuple slow_conv_dilated3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, std::array output_mask); +static inline Tensor & col2im_out(Tensor & out, const Tensor & self, IntArrayRef output_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +static inline Tensor col2im(const Tensor & self, IntArrayRef output_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +static inline Tensor & col2im_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +static inline Tensor col2im_backward(const Tensor & grad_output, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +static inline Tensor & im2col_out(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +static inline Tensor im2col(const Tensor & self, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +static inline Tensor & im2col_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef input_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +static inline Tensor im2col_backward(const Tensor & grad_output, IntArrayRef input_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +static inline Tensor isfinite(const Tensor & self); + +inline Tensor from_blob( + void* data, + IntArrayRef sizes, + IntArrayRef strides, + const std::function& deleter, + const TensorOptions& options = {}) { + AutoNonVariableTypeMode guard; + auto device = globalContext().getDeviceFromPtr(data, options.device().type()); + if (options.device().has_index()) { + TORCH_CHECK( + options.device() == device, + "Specified device ", options.device(), + " does not match device of data ", device); + } + auto storage = Storage( + options.dtype(), + detail::computeStorageSize(sizes, strides), + InefficientStdFunctionContext::makeDataPtr( + data, deleter, device), + /*allocator=*/nullptr, + /*resizable=*/false); + return empty({0}, options).set_(storage, 0, sizes, strides); +} + +inline Tensor from_blob( + void* data, + IntArrayRef sizes, + const std::function& deleter, + const TensorOptions& options = {}) { + return from_blob(data, sizes, detail::defaultStrides(sizes), deleter, options); +} + +inline Tensor from_blob( + void* data, + IntArrayRef sizes, + IntArrayRef strides, + const TensorOptions& options = {}) { + return from_blob(data, sizes, strides, [](void*) {}, options); +} + +inline Tensor from_blob( + void* data, + IntArrayRef sizes, + const TensorOptions& options = {}) { + return from_blob(data, sizes, detail::defaultStrides(sizes), [](void*) {}, options); +} + +inline int64_t numel(const Tensor& tensor) { + return tensor.numel(); +} + +// function definitions are all static inline because +// they are one-line statically dispatched functions that +// invoke the actual dynamic dispatch on the correct argument +static inline Tensor _cast_Byte(const Tensor & self, bool non_blocking) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_cast_Byte(self, non_blocking); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cast_Byte", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, non_blocking); +#endif +} +static inline Tensor _cast_Char(const Tensor & self, bool non_blocking) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_cast_Char(self, non_blocking); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cast_Char", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, non_blocking); +#endif +} +static inline Tensor _cast_Double(const Tensor & self, bool non_blocking) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_cast_Double(self, non_blocking); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cast_Double", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, non_blocking); +#endif +} +static inline Tensor _cast_Float(const Tensor & self, bool non_blocking) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_cast_Float(self, non_blocking); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cast_Float", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, non_blocking); +#endif +} +static inline Tensor _cast_Int(const Tensor & self, bool non_blocking) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_cast_Int(self, non_blocking); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cast_Int", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, non_blocking); +#endif +} +static inline Tensor _cast_Long(const Tensor & self, bool non_blocking) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_cast_Long(self, non_blocking); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cast_Long", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, non_blocking); +#endif +} +static inline Tensor _cast_Short(const Tensor & self, bool non_blocking) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_cast_Short(self, non_blocking); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cast_Short", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, non_blocking); +#endif +} +static inline Tensor _cast_Half(const Tensor & self, bool non_blocking) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_cast_Half(self, non_blocking); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cast_Half", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, non_blocking); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline std::vector align_tensors(TensorList tensors) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::align_tensors(tensors); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::align_tensors", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, TensorList>( + op, tensors); +#endif +} +#endif +static inline bool _use_cudnn_ctc_loss(const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(log_probs, targets)))) { + + default: + AT_ERROR("_use_cudnn_ctc_loss not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(log_probs, targets))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_use_cudnn_ctc_loss", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, log_probs, targets, input_lengths, target_lengths, blank); +#endif +} +static inline std::tuple _cudnn_ctc_loss(const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank, bool deterministic, bool zero_infinity) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(log_probs, targets)))) { + + default: + AT_ERROR("_cudnn_ctc_loss not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(log_probs, targets))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cudnn_ctc_loss", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, int64_t, bool, bool>( + op, log_probs, targets, input_lengths, target_lengths, blank, deterministic, zero_infinity); +#endif +} +static inline Tensor _cudnn_rnn_flatten_weight(TensorList weight_arr, int64_t weight_stride0, int64_t input_size, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, bool bidirectional) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(weight_arr)))) { + + default: + AT_ERROR("_cudnn_rnn_flatten_weight not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(weight_arr))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cudnn_rnn_flatten_weight", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, weight_arr, weight_stride0, input_size, mode, hidden_size, num_layers, batch_first, bidirectional); +#endif +} +static inline std::tuple _cudnn_rnn(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & weight_buf, const Tensor & hx, const Tensor & cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntArrayRef batch_sizes, const Tensor & dropout_state) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input, weight, weight_buf, hx, cx, dropout_state)))) { + + default: + AT_ERROR("_cudnn_rnn not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input, weight, weight_buf, hx, cx, dropout_state))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cudnn_rnn", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, TensorList, int64_t, const Tensor &, const Tensor &, const Tensor &, int64_t, int64_t, int64_t, bool, double, bool, bool, IntArrayRef, const Tensor &>( + op, input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state); +#endif +} +static inline std::tuple> _cudnn_rnn_backward(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & weight_buf, const Tensor & hx, const Tensor & cx, const Tensor & output, const Tensor & grad_output, const Tensor & grad_hy, const Tensor & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntArrayRef batch_sizes, const Tensor & dropout_state, const Tensor & reserve, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input, weight, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, dropout_state, reserve)))) { + + default: + AT_ERROR("_cudnn_rnn_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input, weight, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, dropout_state, reserve))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cudnn_rnn_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>, const Tensor &, TensorList, int64_t, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, int64_t, int64_t, int64_t, bool, double, bool, bool, IntArrayRef, const Tensor &, const Tensor &, std::array>( + op, input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask); +#endif +} +static inline Tensor _cudnn_init_dropout_state(double dropout, bool train, int64_t dropout_seed, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(options)))) { + + default: + AT_ERROR("_cudnn_init_dropout_state not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(options))); + } +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cudnn_init_dropout_state", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, dropout, train, dropout_seed, options); +#endif +} +static inline int64_t _debug_has_internal_overlap(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_debug_has_internal_overlap(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_debug_has_internal_overlap", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline std::tuple _fused_dropout(const Tensor & self, double p, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + + default: + AT_ERROR("_fused_dropout not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_fused_dropout", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, double, Generator *>( + op, self, p, generator); +#endif +} +static inline Tensor _masked_scale(const Tensor & self, const Tensor & mask, double scale) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, mask)))) { + + default: + AT_ERROR("_masked_scale not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, mask))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_masked_scale", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, mask, scale); +#endif +} +static inline std::tuple _sobol_engine_draw(const Tensor & quasi, int64_t n, const Tensor & sobolstate, int64_t dimension, int64_t num_generated, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_sobol_engine_draw(quasi, n, sobolstate, dimension, num_generated, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_sobol_engine_draw", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, const Tensor &, int64_t, int64_t, c10::optional>( + op, quasi, n, sobolstate, dimension, num_generated, dtype); +#endif +} +static inline Tensor & _sobol_engine_ff_(Tensor & self, int64_t n, const Tensor & sobolstate, int64_t dimension, int64_t num_generated) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_sobol_engine_ff_(self, n, sobolstate, dimension, num_generated); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_sobol_engine_ff_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, n, sobolstate, dimension, num_generated); +#endif +} +static inline Tensor & _sobol_engine_scramble_(Tensor & self, const Tensor & ltm, int64_t dimension) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_sobol_engine_scramble_(self, ltm, dimension); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_sobol_engine_scramble_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, ltm, dimension); +#endif +} +static inline Tensor & _sobol_engine_initialize_state_(Tensor & self, int64_t dimension) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_sobol_engine_initialize_state_(self, dimension); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_sobol_engine_initialize_state_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dimension); +#endif +} +static inline Tensor _reshape_from_tensor(const Tensor & self, const Tensor & shape) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_reshape_from_tensor(self, shape); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_reshape_from_tensor", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, shape); +#endif +} +static inline Tensor _shape_as_tensor(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_shape_as_tensor(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_shape_as_tensor", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor dropout(const Tensor & input, double p, bool train) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::dropout(input, p, train); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::dropout", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, input, p, train); +#endif +} +static inline Tensor & dropout_(Tensor & self, double p, bool train) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::dropout_(self, p, train); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::dropout_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, p, train); +#endif +} +static inline Tensor feature_dropout(const Tensor & input, double p, bool train) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::feature_dropout(input, p, train); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::feature_dropout", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, input, p, train); +#endif +} +static inline Tensor & feature_dropout_(Tensor & self, double p, bool train) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::feature_dropout_(self, p, train); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::feature_dropout_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, p, train); +#endif +} +static inline Tensor alpha_dropout(const Tensor & input, double p, bool train) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::alpha_dropout(input, p, train); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::alpha_dropout", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, input, p, train); +#endif +} +static inline Tensor & alpha_dropout_(Tensor & self, double p, bool train) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::alpha_dropout_(self, p, train); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::alpha_dropout_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, p, train); +#endif +} +static inline Tensor feature_alpha_dropout(const Tensor & input, double p, bool train) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::feature_alpha_dropout(input, p, train); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::feature_alpha_dropout", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, input, p, train); +#endif +} +static inline Tensor & feature_alpha_dropout_(Tensor & self, double p, bool train) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::feature_alpha_dropout_(self, p, train); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::feature_alpha_dropout_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, p, train); +#endif +} +static inline Tensor abs(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::abs(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::abs", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & abs_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::abs_(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::abs_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & abs_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::abs_out(out, self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::abs", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor angle(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::angle(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::angle", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & angle_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::angle_out(out, self); + break; + default: + AT_ERROR("angle_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::angle", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor real(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::real(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::real", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & real_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::real_out(out, self); + break; + default: + AT_ERROR("real_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::real", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor imag(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::imag(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::imag", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & imag_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::imag_out(out, self); + break; + default: + AT_ERROR("imag_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::imag", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor conj(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::conj(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::conj", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & conj_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::conj_out(out, self); + break; + default: + AT_ERROR("conj_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::conj", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor acos(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::acos(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::acos", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & acos_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::acos_(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::acos_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & acos_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::acos_out(out, self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::acos", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor avg_pool1d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::avg_pool1d(self, kernel_size, stride, padding, ceil_mode, count_include_pad); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::avg_pool1d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, kernel_size, stride, padding, ceil_mode, count_include_pad); +#endif +} +static inline Tensor adaptive_avg_pool1d(const Tensor & self, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::adaptive_avg_pool1d(self, output_size); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::adaptive_avg_pool1d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, output_size); +#endif +} +static inline std::tuple adaptive_max_pool1d(const Tensor & self, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::adaptive_max_pool1d(self, output_size); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::adaptive_max_pool1d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, IntArrayRef>( + op, self, output_size); +#endif +} +static inline Tensor add(const Tensor & self, const Tensor & other, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::add(self, other, alpha); + break; + case Backend::SparseCPU: + return SparseCPUType::add(self, other, alpha); + break; + default: + AT_ERROR("add not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::add", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other, alpha); +#endif +} +static inline Tensor & add_out(Tensor & out, const Tensor & self, const Tensor & other, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, other)))) { + case Backend::CPU: + return CPUType::add_out(out, self, other, alpha); + break; + case Backend::SparseCPU: + return SparseCPUType::add_out(out, self, other, alpha); + break; + default: + AT_ERROR("add_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::add", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other, alpha); +#endif +} +static inline Tensor add(const Tensor & self, Scalar other, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::add(self, other, alpha); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::add", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other, alpha); +#endif +} +static inline Tensor addmv(const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, mat, vec)))) { + case Backend::CPU: + return CPUType::addmv(self, mat, vec, beta, alpha); + break; + default: + AT_ERROR("addmv not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, mat, vec))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::addmv", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, mat, vec, beta, alpha); +#endif +} +static inline Tensor & addmv_(Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, mat, vec)))) { + case Backend::CPU: + return CPUType::addmv_(self, mat, vec, beta, alpha); + break; + default: + AT_ERROR("addmv_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, mat, vec))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::addmv_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, mat, vec, beta, alpha); +#endif +} +static inline Tensor & addmv_out(Tensor & out, const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, mat, vec)))) { + case Backend::CPU: + return CPUType::addmv_out(out, self, mat, vec, beta, alpha); + break; + default: + AT_ERROR("addmv_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, mat, vec))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::addmv", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, mat, vec, beta, alpha); +#endif +} +static inline Tensor addr(const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::addr(self, vec1, vec2, beta, alpha); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::addr", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, vec1, vec2, beta, alpha); +#endif +} +static inline Tensor & addr_out(Tensor & out, const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::addr_out(out, self, vec1, vec2, beta, alpha); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::addr", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, vec1, vec2, beta, alpha); +#endif +} +static inline Tensor affine_grid_generator(const Tensor & theta, IntArrayRef size, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::affine_grid_generator(theta, size, align_corners); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::affine_grid_generator", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, theta, size, align_corners); +#endif +} +static inline Tensor affine_grid_generator_backward(const Tensor & grad, IntArrayRef size, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::affine_grid_generator_backward(grad, size, align_corners); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::affine_grid_generator_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad, size, align_corners); +#endif +} +static inline Tensor all(const Tensor & self, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::all(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::all", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim, keepdim); +#endif +} +static inline Tensor & all_out(Tensor & out, const Tensor & self, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::all_out(out, self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::all", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, dim, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor all(const Tensor & self, Dimname dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::all(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::all", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, keepdim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & all_out(Tensor & out, const Tensor & self, Dimname dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::all_out(out, self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::all", "dimname_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, dim, keepdim); +#endif +} +#endif +static inline bool allclose(const Tensor & self, const Tensor & other, double rtol, double atol, bool equal_nan) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::allclose(self, other, rtol, atol, equal_nan); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::allclose", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other, rtol, atol, equal_nan); +#endif +} +static inline Tensor any(const Tensor & self, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::any(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::any", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim, keepdim); +#endif +} +static inline Tensor & any_out(Tensor & out, const Tensor & self, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::any_out(out, self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::any", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, dim, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor any(const Tensor & self, Dimname dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::any(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::any", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, keepdim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & any_out(Tensor & out, const Tensor & self, Dimname dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::any_out(out, self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::any", "dimname_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, dim, keepdim); +#endif +} +#endif +static inline Tensor arange(Scalar end, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::arange(end, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::arange", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, end, options); +#endif +} +static inline Tensor arange(Scalar start, Scalar end, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::arange(start, end, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::arange", "start"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, start, end, options); +#endif +} +static inline Tensor arange(Scalar start, Scalar end, Scalar step, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::arange(start, end, step, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::arange", "start_step"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, start, end, step, options); +#endif +} +static inline Tensor & arange_out(Tensor & out, Scalar end) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::arange_out(out, end); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::arange", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, end); +#endif +} +static inline Tensor & arange_out(Tensor & out, Scalar start, Scalar end, Scalar step) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out)))) { + case Backend::CPU: + return CPUType::arange_out(out, start, end, step); + break; + default: + AT_ERROR("arange_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::arange", "start_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, start, end, step); +#endif +} +static inline Tensor _dim_arange(const Tensor & like, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_dim_arange(like, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_dim_arange", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, like, dim); +#endif +} +static inline Tensor argmax(const Tensor & self, c10::optional dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::argmax(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::argmax", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed, bool>( + op, self, dim, keepdim); +#endif +} +static inline Tensor argmin(const Tensor & self, c10::optional dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::argmin(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::argmin", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed, bool>( + op, self, dim, keepdim); +#endif +} +static inline Tensor as_strided(const Tensor & self, IntArrayRef size, IntArrayRef stride, c10::optional storage_offset) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::as_strided(self, size, stride, storage_offset); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::as_strided(self, size, stride, storage_offset); + break; + default: + AT_ERROR("as_strided not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::as_strided", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, size, stride, storage_offset); +#endif +} +static inline Tensor & as_strided_(Tensor & self, IntArrayRef size, IntArrayRef stride, c10::optional storage_offset) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::as_strided_(self, size, stride, storage_offset); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::as_strided_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, size, stride, storage_offset); +#endif +} +static inline Tensor asin(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::asin(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::asin", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & asin_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::asin_(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::asin_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & asin_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::asin_out(out, self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::asin", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor atan(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::atan(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::atan", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & atan_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::atan_(self); + break; + default: + AT_ERROR("atan_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::atan_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & atan_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::atan_out(out, self); + break; + default: + AT_ERROR("atan_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::atan", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor baddbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, batch1, batch2)))) { + case Backend::CPU: + return CPUType::baddbmm(self, batch1, batch2, beta, alpha); + break; + default: + AT_ERROR("baddbmm not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, batch1, batch2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::baddbmm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, batch1, batch2, beta, alpha); +#endif +} +static inline Tensor & _baddbmm_mkl_(Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_baddbmm_mkl_(self, batch1, batch2, beta, alpha); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_baddbmm_mkl_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, batch1, batch2, beta, alpha); +#endif +} +static inline Tensor & baddbmm_out(Tensor & out, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, batch1, batch2)))) { + case Backend::CPU: + return CPUType::baddbmm_out(out, self, batch1, batch2, beta, alpha); + break; + default: + AT_ERROR("baddbmm_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, batch1, batch2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::baddbmm", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, batch1, batch2, beta, alpha); +#endif +} +static inline Tensor bartlett_window(int64_t window_length, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::bartlett_window(window_length, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::bartlett_window", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, window_length, options); +#endif +} +static inline Tensor bartlett_window(int64_t window_length, bool periodic, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::bartlett_window(window_length, periodic, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::bartlett_window", "periodic"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, window_length, periodic, options); +#endif +} +static inline Tensor batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps, bool cudnn_enabled) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::batch_norm(input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::batch_norm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled); +#endif +} +static inline std::tuple _batch_norm_impl_index(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps, bool cudnn_enabled) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_batch_norm_impl_index(input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_batch_norm_impl_index", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, bool, double, double, bool>( + op, input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled); +#endif +} +static inline std::tuple _batch_norm_impl_index_backward(int64_t impl_index, const Tensor & input, const Tensor & grad_output, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_var_transform, bool train, double eps, std::array output_mask, const Tensor & reservedSpace) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_batch_norm_impl_index_backward(impl_index, input, grad_output, weight, running_mean, running_var, save_mean, save_var_transform, train, eps, output_mask, reservedSpace); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_batch_norm_impl_index_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, int64_t, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, bool, double, std::array, const Tensor &>( + op, impl_index, input, grad_output, weight, running_mean, running_var, save_mean, save_var_transform, train, eps, output_mask, reservedSpace); +#endif +} +static inline Tensor bernoulli(const Tensor & self, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::bernoulli(self, generator); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::bernoulli", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, generator); +#endif +} +static inline Tensor & bernoulli_out(Tensor & out, const Tensor & self, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::bernoulli_out(out, self, generator); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::bernoulli", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, generator); +#endif +} +static inline Tensor bernoulli(const Tensor & self, double p, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::bernoulli(self, p, generator); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::bernoulli", "p"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, p, generator); +#endif +} +static inline Tensor bilinear(const Tensor & input1, const Tensor & input2, const Tensor & weight, const Tensor & bias) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::bilinear(input1, input2, weight, bias); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::bilinear", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input1, input2, weight, bias); +#endif +} +static inline Tensor binary_cross_entropy_with_logits(const Tensor & self, const Tensor & target, const Tensor & weight, const Tensor & pos_weight, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::binary_cross_entropy_with_logits(self, target, weight, pos_weight, reduction); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::binary_cross_entropy_with_logits", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, target, weight, pos_weight, reduction); +#endif +} +static inline Tensor binary_cross_entropy_with_logits_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, const Tensor & pos_weight, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::binary_cross_entropy_with_logits_backward(grad_output, self, target, weight, pos_weight, reduction); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::binary_cross_entropy_with_logits_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, self, target, weight, pos_weight, reduction); +#endif +} +static inline Tensor bincount(const Tensor & self, const Tensor & weights, int64_t minlength) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, weights)))) { + case Backend::CPU: + return CPUType::bincount(self, weights, minlength); + break; + default: + AT_ERROR("bincount not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, weights))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::bincount", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, weights, minlength); +#endif +} +static inline Tensor bitwise_not(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::bitwise_not(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::bitwise_not", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & bitwise_not_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::bitwise_not_out(out, self); + break; + default: + AT_ERROR("bitwise_not_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::bitwise_not", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor logical_not(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::logical_not(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::logical_not", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & logical_not_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::logical_not_out(out, self); + break; + default: + AT_ERROR("logical_not_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::logical_not", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor logical_xor(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::logical_xor(self, other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::logical_xor", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, other); +#endif +} +static inline Tensor & logical_xor_out(Tensor & out, const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, other)))) { + case Backend::CPU: + return CPUType::logical_xor_out(out, self, other); + break; + default: + AT_ERROR("logical_xor_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::logical_xor", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor blackman_window(int64_t window_length, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::blackman_window(window_length, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::blackman_window", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, window_length, options); +#endif +} +static inline Tensor blackman_window(int64_t window_length, bool periodic, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::blackman_window(window_length, periodic, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::blackman_window", "periodic"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, window_length, periodic, options); +#endif +} +static inline Tensor bmm(const Tensor & self, const Tensor & mat2) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, mat2)))) { + case Backend::CPU: + return CPUType::bmm(self, mat2); + break; + default: + AT_ERROR("bmm not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, mat2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::bmm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, mat2); +#endif +} +static inline Tensor & bmm_out(Tensor & out, const Tensor & self, const Tensor & mat2) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, mat2)))) { + case Backend::CPU: + return CPUType::bmm_out(out, self, mat2); + break; + default: + AT_ERROR("bmm_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, mat2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::bmm", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, mat2); +#endif +} +static inline std::vector broadcast_tensors(TensorList tensors) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::broadcast_tensors(tensors); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::broadcast_tensors", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, TensorList>( + op, tensors); +#endif +} +static inline Tensor cat(TensorList tensors, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cat(tensors, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cat", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, tensors, dim); +#endif +} +static inline Tensor & cat_out(Tensor & out, TensorList tensors, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cat_out(out, tensors, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cat", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, tensors, dim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor cat(TensorList tensors, Dimname dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cat(tensors, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cat", "names"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, tensors, dim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & cat_out(Tensor & out, TensorList tensors, Dimname dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cat_out(out, tensors, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cat", "names_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, tensors, dim); +#endif +} +#endif +static inline Tensor ceil(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::ceil(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ceil", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & ceil_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::ceil_(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ceil_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & ceil_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::ceil_out(out, self); + break; + default: + AT_ERROR("ceil_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ceil", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor chain_matmul(TensorList matrices) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::chain_matmul(matrices); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::chain_matmul", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, matrices); +#endif +} +static inline std::vector chunk(const Tensor & self, int64_t chunks, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::chunk(self, chunks, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::chunk", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, int64_t>( + op, self, chunks, dim); +#endif +} +static inline Tensor clamp(const Tensor & self, c10::optional min, c10::optional max) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::clamp(self, min, max); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::clamp", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed, c10::optional>( + op, self, min, max); +#endif +} +static inline Tensor & clamp_(Tensor & self, c10::optional min, c10::optional max) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::clamp_(self, min, max); + break; + default: + AT_ERROR("clamp_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::clamp_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, c10::optional>( + op, self, min, max); +#endif +} +static inline Tensor & clamp_out(Tensor & out, const Tensor & self, c10::optional min, c10::optional max) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::clamp_out(out, self, min, max); + break; + default: + AT_ERROR("clamp_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::clamp", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, c10::optional>( + op, out, self, min, max); +#endif +} +static inline Tensor clamp_max(const Tensor & self, Scalar max) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::clamp_max(self, max); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::clamp_max", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, max); +#endif +} +static inline Tensor & clamp_max_(Tensor & self, Scalar max) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::clamp_max_(self, max); + break; + default: + AT_ERROR("clamp_max_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::clamp_max_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, max); +#endif +} +static inline Tensor & clamp_max_out(Tensor & out, const Tensor & self, Scalar max) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::clamp_max_out(out, self, max); + break; + default: + AT_ERROR("clamp_max_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::clamp_max", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, max); +#endif +} +static inline Tensor clamp_min(const Tensor & self, Scalar min) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::clamp_min(self, min); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::clamp_min", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, min); +#endif +} +static inline Tensor & clamp_min_(Tensor & self, Scalar min) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::clamp_min_(self, min); + break; + default: + AT_ERROR("clamp_min_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::clamp_min_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, min); +#endif +} +static inline Tensor & clamp_min_out(Tensor & out, const Tensor & self, Scalar min) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::clamp_min_out(out, self, min); + break; + default: + AT_ERROR("clamp_min_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::clamp_min", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, min); +#endif +} +static inline bool cudnn_is_acceptable(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cudnn_is_acceptable(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cudnn_is_acceptable", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor constant_pad_nd(const Tensor & self, IntArrayRef pad, Scalar value) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::constant_pad_nd(self, pad, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::constant_pad_nd", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, pad, value); +#endif +} +static inline Tensor convolution(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::convolution(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::convolution", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, weight, bias, stride, padding, dilation, transposed, output_padding, groups); +#endif +} +static inline Tensor convolution_overrideable(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::convolution_overrideable(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::convolution_overrideable", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, weight, bias, stride, padding, dilation, transposed, output_padding, groups); +#endif +} +static inline std::tuple convolution_backward_overrideable(const Tensor & grad_output, const Tensor & input, const Tensor & weight, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::convolution_backward_overrideable(grad_output, input, weight, stride, padding, dilation, transposed, output_padding, groups, output_mask); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::convolution_backward_overrideable", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, bool, IntArrayRef, int64_t, std::array>( + op, grad_output, input, weight, stride, padding, dilation, transposed, output_padding, groups, output_mask); +#endif +} +static inline Tensor _convolution(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_convolution(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_convolution", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled); +#endif +} +static inline Tensor _convolution_nogroup(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_convolution_nogroup(input, weight, bias, stride, padding, dilation, transposed, output_padding); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_convolution_nogroup", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, weight, bias, stride, padding, dilation, transposed, output_padding); +#endif +} +static inline std::tuple _convolution_double_backward(const Tensor & ggI, const Tensor & ggW, const Tensor & ggb, const Tensor & gO, const Tensor & weight, const Tensor & self, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_convolution_double_backward(ggI, ggW, ggb, gO, weight, self, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, output_mask); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_convolution_double_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, bool, IntArrayRef, int64_t, bool, bool, bool, std::array>( + op, ggI, ggW, ggb, gO, weight, self, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, output_mask); +#endif +} +static inline Tensor conv1d(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, int64_t groups) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::conv1d(input, weight, bias, stride, padding, dilation, groups); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::conv1d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, weight, bias, stride, padding, dilation, groups); +#endif +} +static inline Tensor conv2d(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, int64_t groups) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::conv2d(input, weight, bias, stride, padding, dilation, groups); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::conv2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, weight, bias, stride, padding, dilation, groups); +#endif +} +static inline Tensor conv3d(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, int64_t groups) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::conv3d(input, weight, bias, stride, padding, dilation, groups); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::conv3d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, weight, bias, stride, padding, dilation, groups); +#endif +} +static inline Tensor conv_tbc(const Tensor & self, const Tensor & weight, const Tensor & bias, int64_t pad) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::conv_tbc(self, weight, bias, pad); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::conv_tbc", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, weight, bias, pad); +#endif +} +static inline std::tuple conv_tbc_backward(const Tensor & self, const Tensor & input, const Tensor & weight, const Tensor & bias, int64_t pad) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::conv_tbc_backward(self, input, weight, bias, pad); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::conv_tbc_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, int64_t>( + op, self, input, weight, bias, pad); +#endif +} +static inline Tensor conv_transpose1d(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, int64_t groups, IntArrayRef dilation) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::conv_transpose1d(input, weight, bias, stride, padding, output_padding, groups, dilation); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::conv_transpose1d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, weight, bias, stride, padding, output_padding, groups, dilation); +#endif +} +static inline Tensor conv_transpose2d(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, int64_t groups, IntArrayRef dilation) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::conv_transpose2d(input, weight, bias, stride, padding, output_padding, groups, dilation); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::conv_transpose2d", "input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, weight, bias, stride, padding, output_padding, groups, dilation); +#endif +} +static inline Tensor conv_transpose3d(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, int64_t groups, IntArrayRef dilation) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::conv_transpose3d(input, weight, bias, stride, padding, output_padding, groups, dilation); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::conv_transpose3d", "input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, weight, bias, stride, padding, output_padding, groups, dilation); +#endif +} +static inline Tensor _copy_from(const Tensor & self, const Tensor & dst, bool non_blocking) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, dst)))) { + + default: + AT_ERROR("_copy_from not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, dst))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_copy_from", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dst, non_blocking); +#endif +} +static inline Tensor cos(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cos(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cos", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & cos_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::cos_(self); + break; + default: + AT_ERROR("cos_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cos_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & cos_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::cos_out(out, self); + break; + default: + AT_ERROR("cos_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cos", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor cosh(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cosh(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cosh", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & cosh_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::cosh_(self); + break; + default: + AT_ERROR("cosh_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cosh_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & cosh_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::cosh_out(out, self); + break; + default: + AT_ERROR("cosh_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cosh", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor cosine_embedding_loss(const Tensor & input1, const Tensor & input2, const Tensor & target, double margin, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cosine_embedding_loss(input1, input2, target, margin, reduction); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cosine_embedding_loss", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, input1, input2, target, margin, reduction); +#endif +} +static inline Tensor cudnn_affine_grid_generator(const Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(theta)))) { + + default: + AT_ERROR("cudnn_affine_grid_generator not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(theta))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cudnn_affine_grid_generator", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, theta, N, C, H, W); +#endif +} +static inline Tensor cudnn_affine_grid_generator_backward(const Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad)))) { + + default: + AT_ERROR("cudnn_affine_grid_generator_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cudnn_affine_grid_generator_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad, N, C, H, W); +#endif +} +static inline std::tuple cudnn_batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double exponential_average_factor, double epsilon) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input, weight, bias, running_mean, running_var)))) { + + default: + AT_ERROR("cudnn_batch_norm not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input, weight, bias, running_mean, running_var))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cudnn_batch_norm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, bool, double, double>( + op, input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon); +#endif +} +static inline std::tuple cudnn_batch_norm_backward(const Tensor & input, const Tensor & grad_output, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_var, double epsilon, const Tensor & reserveSpace) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input, grad_output, weight, running_mean, running_var, save_mean, save_var, reserveSpace)))) { + + default: + AT_ERROR("cudnn_batch_norm_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input, grad_output, weight, running_mean, running_var, save_mean, save_var, reserveSpace))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cudnn_batch_norm_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, double, const Tensor &>( + op, input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon, reserveSpace); +#endif +} +static inline Tensor cudnn_convolution(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias)))) { + + default: + AT_ERROR("cudnn_convolution not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cudnn_convolution", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic); +#endif +} +static inline Tensor cudnn_convolution_backward_input(IntArrayRef self_size, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, weight)))) { + + default: + AT_ERROR("cudnn_convolution_backward_input not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cudnn_convolution_backward_input", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self_size, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic); +#endif +} +static inline std::tuple cudnn_convolution_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, grad_output, weight)))) { + + default: + AT_ERROR("cudnn_convolution_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, grad_output, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cudnn_convolution_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, int64_t, bool, bool, std::array>( + op, self, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic, output_mask); +#endif +} +static inline Tensor cudnn_convolution_backward_bias(const Tensor & grad_output) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output)))) { + + default: + AT_ERROR("cudnn_convolution_backward_bias not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cudnn_convolution_backward_bias", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output); +#endif +} +static inline Tensor cudnn_convolution_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self)))) { + + default: + AT_ERROR("cudnn_convolution_backward_weight not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cudnn_convolution_backward_weight", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic); +#endif +} +static inline Tensor cudnn_convolution_transpose(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias)))) { + + default: + AT_ERROR("cudnn_convolution_transpose not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cudnn_convolution_transpose", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic); +#endif +} +static inline std::tuple cudnn_convolution_transpose_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, grad_output, weight)))) { + + default: + AT_ERROR("cudnn_convolution_transpose_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, grad_output, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cudnn_convolution_transpose_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, IntArrayRef, int64_t, bool, bool, std::array>( + op, self, grad_output, weight, padding, output_padding, stride, dilation, groups, benchmark, deterministic, output_mask); +#endif +} +static inline Tensor cudnn_convolution_transpose_backward_bias(const Tensor & grad_output) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output)))) { + + default: + AT_ERROR("cudnn_convolution_transpose_backward_bias not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cudnn_convolution_transpose_backward_bias", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output); +#endif +} +static inline Tensor cudnn_convolution_transpose_backward_input(const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, weight)))) { + + default: + AT_ERROR("cudnn_convolution_transpose_backward_input not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cudnn_convolution_transpose_backward_input", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic); +#endif +} +static inline Tensor cudnn_convolution_transpose_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self)))) { + + default: + AT_ERROR("cudnn_convolution_transpose_backward_weight not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cudnn_convolution_transpose_backward_weight", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic); +#endif +} +static inline Tensor cudnn_grid_sampler(const Tensor & self, const Tensor & grid) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, grid)))) { + + default: + AT_ERROR("cudnn_grid_sampler not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, grid))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cudnn_grid_sampler", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, grid); +#endif +} +static inline std::tuple cudnn_grid_sampler_backward(const Tensor & self, const Tensor & grid, const Tensor & grad_output) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, grid, grad_output)))) { + + default: + AT_ERROR("cudnn_grid_sampler_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, grid, grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cudnn_grid_sampler_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &>( + op, self, grid, grad_output); +#endif +} +static inline Tensor cumsum(const Tensor & self, int64_t dim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cumsum(self, dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cumsum", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, dim, dtype); +#endif +} +static inline Tensor & cumsum_out(Tensor & out, const Tensor & self, int64_t dim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cumsum_out(out, self, dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cumsum", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, out, self, dim, dtype); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor cumsum(const Tensor & self, Dimname dim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cumsum(self, dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cumsum", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, dim, dtype); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & cumsum_out(Tensor & out, const Tensor & self, Dimname dim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cumsum_out(out, self, dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cumsum", "dimname_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, out, self, dim, dtype); +#endif +} +#endif +static inline Tensor cumprod(const Tensor & self, int64_t dim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cumprod(self, dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cumprod", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, dim, dtype); +#endif +} +static inline Tensor & cumprod_out(Tensor & out, const Tensor & self, int64_t dim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cumprod_out(out, self, dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cumprod", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, out, self, dim, dtype); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor cumprod(const Tensor & self, Dimname dim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cumprod(self, dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cumprod", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, dim, dtype); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & cumprod_out(Tensor & out, const Tensor & self, Dimname dim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cumprod_out(out, self, dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cumprod", "dimname_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, out, self, dim, dtype); +#endif +} +#endif +static inline Tensor ctc_loss(const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank, int64_t reduction, bool zero_infinity) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::ctc_loss(log_probs, targets, input_lengths, target_lengths, blank, reduction, zero_infinity); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ctc_loss", "IntList"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, log_probs, targets, input_lengths, target_lengths, blank, reduction, zero_infinity); +#endif +} +static inline Tensor ctc_loss(const Tensor & log_probs, const Tensor & targets, const Tensor & input_lengths, const Tensor & target_lengths, int64_t blank, int64_t reduction, bool zero_infinity) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::ctc_loss(log_probs, targets, input_lengths, target_lengths, blank, reduction, zero_infinity); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ctc_loss", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, log_probs, targets, input_lengths, target_lengths, blank, reduction, zero_infinity); +#endif +} +static inline std::tuple _ctc_loss(const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank, bool zero_infinity) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(log_probs, targets)))) { + case Backend::CPU: + return CPUType::_ctc_loss(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity); + break; + default: + AT_ERROR("_ctc_loss not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(log_probs, targets))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_ctc_loss", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, int64_t, bool>( + op, log_probs, targets, input_lengths, target_lengths, blank, zero_infinity); +#endif +} +static inline Tensor _ctc_loss_backward(const Tensor & grad, const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, const Tensor & neg_log_likelihood, const Tensor & log_alpha, int64_t blank, bool zero_infinity) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad, log_probs, targets, neg_log_likelihood, log_alpha)))) { + case Backend::CPU: + return CPUType::_ctc_loss_backward(grad, log_probs, targets, input_lengths, target_lengths, neg_log_likelihood, log_alpha, blank, zero_infinity); + break; + default: + AT_ERROR("_ctc_loss_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad, log_probs, targets, neg_log_likelihood, log_alpha))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_ctc_loss_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad, log_probs, targets, input_lengths, target_lengths, neg_log_likelihood, log_alpha, blank, zero_infinity); +#endif +} +static inline Tensor det(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::det(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::det", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor diag_embed(const Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::diag_embed(self, offset, dim1, dim2); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::diag_embed", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, offset, dim1, dim2); +#endif +} +static inline Tensor diagflat(const Tensor & self, int64_t offset) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::diagflat(self, offset); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::diagflat", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, offset); +#endif +} +static inline Tensor diagonal(const Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::diagonal(self, offset, dim1, dim2); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::diagonal", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, offset, dim1, dim2); +#endif +} +static inline Tensor div(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::div(self, other); + break; + case Backend::SparseCPU: + return SparseCPUType::div(self, other); + break; + default: + AT_ERROR("div not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::div", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & div_out(Tensor & out, const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, other)))) { + case Backend::CPU: + return CPUType::div_out(out, self, other); + break; + case Backend::SparseCPU: + return SparseCPUType::div_out(out, self, other); + break; + default: + AT_ERROR("div_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::div", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor div(const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::div(self, other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::div", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor dot(const Tensor & self, const Tensor & tensor) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, tensor)))) { + case Backend::CPU: + return CPUType::dot(self, tensor); + break; + default: + AT_ERROR("dot not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, tensor))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::dot", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, tensor); +#endif +} +static inline Tensor & dot_out(Tensor & out, const Tensor & self, const Tensor & tensor) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::dot_out(out, self, tensor); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::dot", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, tensor); +#endif +} +static inline Tensor einsum(std::string equation, TensorList tensors) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::einsum(equation, tensors); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::einsum", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, equation, tensors); +#endif +} +static inline Tensor embedding(const Tensor & weight, const Tensor & indices, int64_t padding_idx, bool scale_grad_by_freq, bool sparse) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::embedding(weight, indices, padding_idx, scale_grad_by_freq, sparse); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::embedding", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, weight, indices, padding_idx, scale_grad_by_freq, sparse); +#endif +} +static inline Tensor embedding_backward(const Tensor & grad, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq, bool sparse) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::embedding_backward(grad, indices, num_weights, padding_idx, scale_grad_by_freq, sparse); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::embedding_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad, indices, num_weights, padding_idx, scale_grad_by_freq, sparse); +#endif +} +static inline Tensor embedding_dense_backward(const Tensor & grad_output, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, indices)))) { + case Backend::CPU: + return CPUType::embedding_dense_backward(grad_output, indices, num_weights, padding_idx, scale_grad_by_freq); + break; + default: + AT_ERROR("embedding_dense_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::embedding_dense_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, indices, num_weights, padding_idx, scale_grad_by_freq); +#endif +} +static inline Tensor & embedding_renorm_(Tensor & self, const Tensor & indices, double max_norm, double norm_type) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, indices)))) { + case Backend::CPU: + return CPUType::embedding_renorm_(self, indices, max_norm, norm_type); + break; + default: + AT_ERROR("embedding_renorm_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::embedding_renorm_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, indices, max_norm, norm_type); +#endif +} +static inline Tensor embedding_sparse_backward(const Tensor & grad, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::embedding_sparse_backward(grad, indices, num_weights, padding_idx, scale_grad_by_freq); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::embedding_sparse_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad, indices, num_weights, padding_idx, scale_grad_by_freq); +#endif +} +static inline std::tuple embedding_bag(const Tensor & weight, const Tensor & indices, const Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const Tensor & per_sample_weights) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::embedding_bag(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::embedding_bag", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, bool, int64_t, bool, const Tensor &>( + op, weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights); +#endif +} +static inline std::tuple _embedding_bag(const Tensor & weight, const Tensor & indices, const Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const Tensor & per_sample_weights) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(weight, indices, offsets, per_sample_weights)))) { + case Backend::CPU: + return CPUType::_embedding_bag(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights); + break; + default: + AT_ERROR("_embedding_bag not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(weight, indices, offsets, per_sample_weights))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_embedding_bag", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, bool, int64_t, bool, const Tensor &>( + op, weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights); +#endif +} +static inline Tensor _embedding_bag_backward(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, const Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse, const Tensor & per_sample_weights) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_embedding_bag_backward(grad, indices, offsets, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, sparse, per_sample_weights); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_embedding_bag_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad, indices, offsets, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, sparse, per_sample_weights); +#endif +} +static inline Tensor _embedding_bag_sparse_backward(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const Tensor & per_sample_weights) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_embedding_bag_sparse_backward(grad, indices, offsets, offset2bag, bag_size, num_weights, scale_grad_by_freq, mode, per_sample_weights); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_embedding_bag_sparse_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad, indices, offsets, offset2bag, bag_size, num_weights, scale_grad_by_freq, mode, per_sample_weights); +#endif +} +static inline Tensor _embedding_bag_dense_backward(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, const Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const Tensor & per_sample_weights) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad, indices, offsets, offset2bag, bag_size, maximum_indices, per_sample_weights)))) { + case Backend::CPU: + return CPUType::_embedding_bag_dense_backward(grad, indices, offsets, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights); + break; + default: + AT_ERROR("_embedding_bag_dense_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad, indices, offsets, offset2bag, bag_size, maximum_indices, per_sample_weights))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_embedding_bag_dense_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad, indices, offsets, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights); +#endif +} +static inline Tensor _embedding_bag_per_sample_weights_backward(const Tensor & grad, const Tensor & weight, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, int64_t mode) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad, weight, indices, offsets, offset2bag)))) { + case Backend::CPU: + return CPUType::_embedding_bag_per_sample_weights_backward(grad, weight, indices, offsets, offset2bag, mode); + break; + default: + AT_ERROR("_embedding_bag_per_sample_weights_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad, weight, indices, offsets, offset2bag))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_embedding_bag_per_sample_weights_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad, weight, indices, offsets, offset2bag, mode); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor empty(IntArrayRef size, c10::optional names, const TensorOptions & options, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::empty(size, names, options, memory_format); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::empty", "names"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const TensorOptions &, c10::optional>( + op, size, names, options, memory_format); +#endif +} +#endif +static inline Tensor empty(IntArrayRef size, const TensorOptions & options, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(options)))) { + case Backend::CPU: + return CPUType::empty(size, options, memory_format); + break; + case Backend::SparseCPU: + return SparseCPUType::empty(size, options, memory_format); + break; + default: + AT_ERROR("empty not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(options))); + } +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::empty", "memory_format"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, size, options, memory_format); +#endif +} +static inline Tensor _empty_affine_quantized(IntArrayRef size, const TensorOptions & options, double scale, int64_t zero_point, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(options)))) { + case Backend::CPU: + return CPUType::_empty_affine_quantized(size, options, scale, zero_point, memory_format); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::_empty_affine_quantized(size, options, scale, zero_point, memory_format); + break; + default: + AT_ERROR("_empty_affine_quantized not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(options))); + } +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_empty_affine_quantized", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, size, options, scale, zero_point, memory_format); +#endif +} +static inline Tensor _empty_per_channel_affine_quantized(IntArrayRef size, const Tensor & scales, const Tensor & zero_points, int64_t axis, const TensorOptions & options, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(scales, zero_points, options)))) { + case Backend::CPU: + return CPUType::_empty_per_channel_affine_quantized(size, scales, zero_points, axis, options, memory_format); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::_empty_per_channel_affine_quantized(size, scales, zero_points, axis, options, memory_format); + break; + default: + AT_ERROR("_empty_per_channel_affine_quantized not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(scales, zero_points, options))); + } +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(scales, zero_points, options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_empty_per_channel_affine_quantized", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, size, scales, zero_points, axis, options, memory_format); +#endif +} +static inline Tensor & empty_out(Tensor & out, IntArrayRef size, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::empty_out(out, size, memory_format); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::empty", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, out, size, memory_format); +#endif +} +static inline Tensor empty_like(const Tensor & self, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::empty_like(self, memory_format); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::empty_like", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, memory_format); +#endif +} +static inline Tensor empty_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::empty_like(self, options, memory_format); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(self, options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::empty_like", "dtype"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, options, memory_format); +#endif +} +static inline Tensor empty_strided(IntArrayRef size, IntArrayRef stride, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(options)))) { + case Backend::CPU: + return CPUType::empty_strided(size, stride, options); + break; + default: + AT_ERROR("empty_strided not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(options))); + } +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::empty_strided", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, size, stride, options); +#endif +} +static inline Tensor erf(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::erf(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::erf", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & erf_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::erf_(self); + break; + default: + AT_ERROR("erf_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::erf_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & erf_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::erf_out(out, self); + break; + default: + AT_ERROR("erf_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::erf", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor erfc(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::erfc(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::erfc", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & erfc_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::erfc_(self); + break; + default: + AT_ERROR("erfc_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::erfc_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & erfc_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::erfc_out(out, self); + break; + default: + AT_ERROR("erfc_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::erfc", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor exp(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::exp(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::exp", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & exp_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::exp_(self); + break; + default: + AT_ERROR("exp_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::exp_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & exp_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::exp_out(out, self); + break; + default: + AT_ERROR("exp_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::exp", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor expm1(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::expm1(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::expm1", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & expm1_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::expm1_(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::expm1_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & expm1_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::expm1_out(out, self); + break; + default: + AT_ERROR("expm1_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::expm1", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor eye(int64_t n, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::eye(n, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::eye", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, n, options); +#endif +} +static inline Tensor eye(int64_t n, int64_t m, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::eye(n, m, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::eye", "m"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, n, m, options); +#endif +} +static inline Tensor & eye_out(Tensor & out, int64_t n) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out)))) { + case Backend::CPU: + return CPUType::eye_out(out, n); + break; + default: + AT_ERROR("eye_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::eye", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, n); +#endif +} +static inline Tensor & eye_out(Tensor & out, int64_t n, int64_t m) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out)))) { + case Backend::CPU: + return CPUType::eye_out(out, n, m); + break; + default: + AT_ERROR("eye_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::eye", "m_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, n, m); +#endif +} +static inline Tensor flatten(const Tensor & self, int64_t start_dim, int64_t end_dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::flatten(self, start_dim, end_dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::flatten", "using_ints"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, start_dim, end_dim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor flatten(const Tensor & self, int64_t start_dim, int64_t end_dim, Dimname out_dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::flatten(self, start_dim, end_dim, out_dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::flatten", "named_out_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, start_dim, end_dim, out_dim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor flatten(const Tensor & self, Dimname start_dim, Dimname end_dim, Dimname out_dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::flatten(self, start_dim, end_dim, out_dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::flatten", "using_names"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, start_dim, end_dim, out_dim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor flatten(const Tensor & self, DimnameList dims, Dimname out_dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::flatten(self, dims, out_dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::flatten", "DimnameList"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dims, out_dim); +#endif +} +#endif +static inline Tensor & fill_(Tensor & self, Scalar value) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::fill_(self, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fill_", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, value); +#endif +} +static inline Tensor & fill_(Tensor & self, const Tensor & value) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::fill_(self, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fill_", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, value); +#endif +} +static inline Tensor floor(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::floor(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::floor", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & floor_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::floor_(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::floor_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & floor_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::floor_out(out, self); + break; + default: + AT_ERROR("floor_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::floor", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor frac(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::frac(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::frac", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & frac_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::frac_(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::frac_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & frac_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::frac_out(out, self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::frac", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor full(IntArrayRef size, Scalar fill_value, c10::optional names, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::full(size, fill_value, names, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::full", "names"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const TensorOptions &>( + op, size, fill_value, names, options); +#endif +} +#endif +static inline Tensor full(IntArrayRef size, Scalar fill_value, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::full(size, fill_value, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::full", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, size, fill_value, options); +#endif +} +static inline Tensor & full_out(Tensor & out, IntArrayRef size, Scalar fill_value) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::full_out(out, size, fill_value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::full", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, size, fill_value); +#endif +} +static inline Tensor full_like(const Tensor & self, Scalar fill_value, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::full_like(self, fill_value, memory_format); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::full_like", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, fill_value, memory_format); +#endif +} +static inline Tensor full_like(const Tensor & self, Scalar fill_value, const TensorOptions & options, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::full_like(self, fill_value, options, memory_format); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(self, options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::full_like", "dtype"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, fill_value, options, memory_format); +#endif +} +static inline Tensor from_file(std::string filename, c10::optional shared, c10::optional size, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(options)))) { + case Backend::CPU: + return CPUType::from_file(filename, shared, size, options); + break; + default: + AT_ERROR("from_file not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(options))); + } +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::from_file", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, c10::optional, const TensorOptions &>( + op, filename, shared, size, options); +#endif +} +static inline Tensor grid_sampler(const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::grid_sampler(input, grid, interpolation_mode, padding_mode, align_corners); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::grid_sampler", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, input, grid, interpolation_mode, padding_mode, align_corners); +#endif +} +static inline Tensor grid_sampler_2d(const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input, grid)))) { + case Backend::CPU: + return CPUType::grid_sampler_2d(input, grid, interpolation_mode, padding_mode, align_corners); + break; + default: + AT_ERROR("grid_sampler_2d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input, grid))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::grid_sampler_2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, input, grid, interpolation_mode, padding_mode, align_corners); +#endif +} +static inline std::tuple grid_sampler_2d_backward(const Tensor & grad_output, const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, input, grid)))) { + case Backend::CPU: + return CPUType::grid_sampler_2d_backward(grad_output, input, grid, interpolation_mode, padding_mode, align_corners); + break; + default: + AT_ERROR("grid_sampler_2d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, input, grid))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::grid_sampler_2d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, int64_t, int64_t, bool>( + op, grad_output, input, grid, interpolation_mode, padding_mode, align_corners); +#endif +} +static inline Tensor grid_sampler_3d(const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input, grid)))) { + case Backend::CPU: + return CPUType::grid_sampler_3d(input, grid, interpolation_mode, padding_mode, align_corners); + break; + default: + AT_ERROR("grid_sampler_3d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input, grid))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::grid_sampler_3d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, input, grid, interpolation_mode, padding_mode, align_corners); +#endif +} +static inline std::tuple grid_sampler_3d_backward(const Tensor & grad_output, const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, input, grid)))) { + case Backend::CPU: + return CPUType::grid_sampler_3d_backward(grad_output, input, grid, interpolation_mode, padding_mode, align_corners); + break; + default: + AT_ERROR("grid_sampler_3d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, input, grid))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::grid_sampler_3d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, int64_t, int64_t, bool>( + op, grad_output, input, grid, interpolation_mode, padding_mode, align_corners); +#endif +} +static inline Tensor hann_window(int64_t window_length, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::hann_window(window_length, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::hann_window", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, window_length, options); +#endif +} +static inline Tensor hann_window(int64_t window_length, bool periodic, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::hann_window(window_length, periodic, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::hann_window", "periodic"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, window_length, periodic, options); +#endif +} +static inline Tensor hamming_window(int64_t window_length, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::hamming_window(window_length, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::hamming_window", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, window_length, options); +#endif +} +static inline Tensor hamming_window(int64_t window_length, bool periodic, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::hamming_window(window_length, periodic, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::hamming_window", "periodic"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, window_length, periodic, options); +#endif +} +static inline Tensor hamming_window(int64_t window_length, bool periodic, double alpha, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::hamming_window(window_length, periodic, alpha, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::hamming_window", "periodic_alpha"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, window_length, periodic, alpha, options); +#endif +} +static inline Tensor hamming_window(int64_t window_length, bool periodic, double alpha, double beta, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::hamming_window(window_length, periodic, alpha, beta, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::hamming_window", "periodic_alpha_beta"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, window_length, periodic, alpha, beta, options); +#endif +} +static inline Tensor hinge_embedding_loss(const Tensor & self, const Tensor & target, double margin, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::hinge_embedding_loss(self, target, margin, reduction); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::hinge_embedding_loss", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, target, margin, reduction); +#endif +} +static inline Tensor ger(const Tensor & self, const Tensor & vec2) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, vec2)))) { + case Backend::CPU: + return CPUType::ger(self, vec2); + break; + default: + AT_ERROR("ger not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, vec2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ger", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, vec2); +#endif +} +static inline Tensor & ger_out(Tensor & out, const Tensor & self, const Tensor & vec2) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, vec2)))) { + case Backend::CPU: + return CPUType::ger_out(out, self, vec2); + break; + default: + AT_ERROR("ger_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, vec2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ger", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, vec2); +#endif +} +static inline Tensor group_norm(const Tensor & input, int64_t num_groups, const Tensor & weight, const Tensor & bias, double eps, bool cudnn_enabled) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::group_norm(input, num_groups, weight, bias, eps, cudnn_enabled); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::group_norm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, num_groups, weight, bias, eps, cudnn_enabled); +#endif +} +static inline Tensor fft(const Tensor & self, int64_t signal_ndim, bool normalized) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::fft(self, signal_ndim, normalized); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fft", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, signal_ndim, normalized); +#endif +} +static inline Tensor ifft(const Tensor & self, int64_t signal_ndim, bool normalized) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::ifft(self, signal_ndim, normalized); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ifft", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, signal_ndim, normalized); +#endif +} +static inline Tensor rfft(const Tensor & self, int64_t signal_ndim, bool normalized, bool onesided) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rfft(self, signal_ndim, normalized, onesided); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rfft", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, signal_ndim, normalized, onesided); +#endif +} +static inline Tensor irfft(const Tensor & self, int64_t signal_ndim, bool normalized, bool onesided, IntArrayRef signal_sizes) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::irfft(self, signal_ndim, normalized, onesided, signal_sizes); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::irfft", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, signal_ndim, normalized, onesided, signal_sizes); +#endif +} +static inline Tensor _fft_with_size(const Tensor & self, int64_t signal_ndim, bool complex_input, bool complex_output, bool inverse, IntArrayRef checked_signal_sizes, bool normalized, bool onesided, IntArrayRef output_sizes) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_fft_with_size(self, signal_ndim, complex_input, complex_output, inverse, checked_signal_sizes, normalized, onesided, output_sizes); + break; + default: + AT_ERROR("_fft_with_size not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_fft_with_size", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, signal_ndim, complex_input, complex_output, inverse, checked_signal_sizes, normalized, onesided, output_sizes); +#endif +} +static inline int64_t _cufft_get_plan_cache_size(int64_t device_index) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_cufft_get_plan_cache_size(device_index); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cufft_get_plan_cache_size", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, device_index); +#endif +} +static inline int64_t _cufft_get_plan_cache_max_size(int64_t device_index) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_cufft_get_plan_cache_max_size(device_index); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cufft_get_plan_cache_max_size", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, device_index); +#endif +} +static inline void _cufft_set_plan_cache_max_size(int64_t device_index, int64_t max_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + TypeDefault::_cufft_set_plan_cache_max_size(device_index, max_size); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cufft_set_plan_cache_max_size", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, device_index, max_size); +#endif +} +static inline void _cufft_clear_plan_cache(int64_t device_index) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + TypeDefault::_cufft_clear_plan_cache(device_index); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cufft_clear_plan_cache", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, device_index); +#endif +} +static inline Tensor index(const Tensor & self, TensorList indices) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index(self, indices); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::index", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, indices); +#endif +} +static inline Tensor index_copy(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & source) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_copy(self, dim, index, source); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::index_copy", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim, index, source); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor index_copy(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & source) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_copy(self, dim, index, source); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::index_copy", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, index, source); +#endif +} +#endif +static inline Tensor & index_put_(Tensor & self, TensorList indices, const Tensor & values, bool accumulate) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_put_(self, indices, values, accumulate); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::index_put_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, indices, values, accumulate); +#endif +} +static inline Tensor index_put(const Tensor & self, TensorList indices, const Tensor & values, bool accumulate) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_put(self, indices, values, accumulate); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::index_put", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, indices, values, accumulate); +#endif +} +static inline Tensor & _index_put_impl_(Tensor & self, TensorList indices, const Tensor & values, bool accumulate, bool unsafe) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_index_put_impl_(self, indices, values, accumulate, unsafe); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_index_put_impl_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, indices, values, accumulate, unsafe); +#endif +} +static inline Tensor instance_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool use_input_stats, double momentum, double eps, bool cudnn_enabled) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::instance_norm(input, weight, bias, running_mean, running_var, use_input_stats, momentum, eps, cudnn_enabled); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::instance_norm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, weight, bias, running_mean, running_var, use_input_stats, momentum, eps, cudnn_enabled); +#endif +} +static inline Tensor inverse(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::inverse(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::inverse", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & inverse_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::inverse_out(out, self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::inverse", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor _inverse_helper(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_inverse_helper(self); + break; + default: + AT_ERROR("_inverse_helper not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_inverse_helper", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor isclose(const Tensor & self, const Tensor & other, double rtol, double atol, bool equal_nan) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::isclose(self, other, rtol, atol, equal_nan); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::isclose", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other, rtol, atol, equal_nan); +#endif +} +static inline Tensor isnan(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::isnan(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::isnan", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline bool is_distributed(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::is_distributed(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::is_distributed", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline bool is_floating_point(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::is_floating_point(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::is_floating_point", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline bool is_complex(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::is_complex(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::is_complex", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline bool is_nonzero(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::is_nonzero(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::is_nonzero", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline bool is_same_size(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::is_same_size(self, other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::is_same_size", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline bool is_signed(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::is_signed(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::is_signed", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor kl_div(const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::kl_div(self, target, reduction); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::kl_div", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, target, reduction); +#endif +} +static inline Tensor kl_div_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, target)))) { + case Backend::CPU: + return CPUType::kl_div_backward(grad_output, self, target, reduction); + break; + default: + AT_ERROR("kl_div_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, target))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::kl_div_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, self, target, reduction); +#endif +} +static inline std::tuple kthvalue(const Tensor & self, int64_t k, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::kthvalue(self, k, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::kthvalue", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, int64_t, bool>( + op, self, k, dim, keepdim); +#endif +} +static inline std::tuple kthvalue_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(values, indices, self)))) { + case Backend::CPU: + return CPUType::kthvalue_out(values, indices, self, k, dim, keepdim); + break; + default: + AT_ERROR("kthvalue_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(values, indices, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::kthvalue", "values"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, int64_t, int64_t, bool>( + op, values, indices, self, k, dim, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple kthvalue(const Tensor & self, int64_t k, Dimname dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::kthvalue(self, k, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::kthvalue", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, Dimname, bool>( + op, self, k, dim, keepdim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple kthvalue_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, Dimname dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::kthvalue_out(values, indices, self, k, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::kthvalue", "dimname_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, int64_t, Dimname, bool>( + op, values, indices, self, k, dim, keepdim); +#endif +} +#endif +static inline Tensor layer_norm(const Tensor & input, IntArrayRef normalized_shape, const Tensor & weight, const Tensor & bias, double eps, bool cudnn_enable) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::layer_norm(input, normalized_shape, weight, bias, eps, cudnn_enable); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::layer_norm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, normalized_shape, weight, bias, eps, cudnn_enable); +#endif +} +static inline std::tuple native_layer_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, int64_t M, int64_t N, double eps) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input, weight, bias)))) { + case Backend::CPU: + return CPUType::native_layer_norm(input, weight, bias, M, N, eps); + break; + default: + AT_ERROR("native_layer_norm not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input, weight, bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::native_layer_norm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, int64_t, int64_t, double>( + op, input, weight, bias, M, N, eps); +#endif +} +static inline std::tuple native_layer_norm_backward(const Tensor & grad_out, const Tensor & input, const Tensor & mean, const Tensor & rstd, const Tensor & weight, int64_t M, int64_t N, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_out, input, mean, rstd, weight)))) { + case Backend::CPU: + return CPUType::native_layer_norm_backward(grad_out, input, mean, rstd, weight, M, N, output_mask); + break; + default: + AT_ERROR("native_layer_norm_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_out, input, mean, rstd, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::native_layer_norm_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, int64_t, int64_t, std::array>( + op, grad_out, input, mean, rstd, weight, M, N, output_mask); +#endif +} +static inline Tensor linear(const Tensor & input, const Tensor & weight, const Tensor & bias) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::linear(input, weight, bias); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::linear", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, weight, bias); +#endif +} +static inline Tensor mkldnn_linear(const Tensor & input, const Tensor & weight, const Tensor & bias) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input, weight, bias)))) { + + default: + AT_ERROR("mkldnn_linear not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input, weight, bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mkldnn_linear", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, weight, bias); +#endif +} +static inline Tensor fbgemm_linear_int8_weight_fp32_activation(const Tensor & input, const Tensor & weight, const Tensor & packed, const Tensor & col_offsets, Scalar weight_scale, Scalar weight_zero_point, const Tensor & bias) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::fbgemm_linear_int8_weight_fp32_activation(input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fbgemm_linear_int8_weight_fp32_activation", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias); +#endif +} +static inline Tensor fbgemm_linear_int8_weight(const Tensor & input, const Tensor & weight, const Tensor & packed, const Tensor & col_offsets, Scalar weight_scale, Scalar weight_zero_point, const Tensor & bias) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::fbgemm_linear_int8_weight(input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fbgemm_linear_int8_weight", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias); +#endif +} +static inline std::tuple fbgemm_linear_quantize_weight(const Tensor & input) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::fbgemm_linear_quantize_weight(input); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fbgemm_linear_quantize_weight", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &>( + op, input); +#endif +} +static inline Tensor fbgemm_pack_gemm_matrix_fp16(const Tensor & input) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::fbgemm_pack_gemm_matrix_fp16(input); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fbgemm_pack_gemm_matrix_fp16", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, input); +#endif +} +static inline Tensor fbgemm_linear_fp16_weight_fp32_activation(const Tensor & input, const Tensor & packed_weight, const Tensor & bias) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::fbgemm_linear_fp16_weight_fp32_activation(input, packed_weight, bias); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fbgemm_linear_fp16_weight_fp32_activation", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, packed_weight, bias); +#endif +} +static inline Tensor fbgemm_linear_fp16_weight(const Tensor & input, const Tensor & packed_weight, const Tensor & bias) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::fbgemm_linear_fp16_weight(input, packed_weight, bias); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fbgemm_linear_fp16_weight", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, input, packed_weight, bias); +#endif +} +static inline Tensor fbgemm_pack_quantized_matrix(const Tensor & input) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::fbgemm_pack_quantized_matrix(input); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fbgemm_pack_quantized_matrix", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, input); +#endif +} +static inline Tensor fbgemm_pack_quantized_matrix(const Tensor & input, int64_t K, int64_t N) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::fbgemm_pack_quantized_matrix(input, K, N); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fbgemm_pack_quantized_matrix", "KN"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, input, K, N); +#endif +} +static inline Tensor linspace(Scalar start, Scalar end, int64_t steps, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::linspace(start, end, steps, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::linspace", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, start, end, steps, options); +#endif +} +static inline Tensor & linspace_out(Tensor & out, Scalar start, Scalar end, int64_t steps) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out)))) { + case Backend::CPU: + return CPUType::linspace_out(out, start, end, steps); + break; + default: + AT_ERROR("linspace_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::linspace", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, start, end, steps); +#endif +} +static inline Tensor log(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & log_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log_(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & log_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::log_out(out, self); + break; + default: + AT_ERROR("log_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor log10(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log10(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log10", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & log10_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log10_(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log10_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & log10_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::log10_out(out, self); + break; + default: + AT_ERROR("log10_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log10", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor log1p(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log1p(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log1p", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & log1p_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::log1p_(self); + break; + case Backend::SparseCPU: + return SparseCPUType::log1p_(self); + break; + default: + AT_ERROR("log1p_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log1p_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & log1p_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::log1p_out(out, self); + break; + case Backend::SparseCPU: + return SparseCPUType::log1p_out(out, self); + break; + default: + AT_ERROR("log1p_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log1p", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor log2(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log2(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log2", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & log2_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log2_(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log2_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & log2_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::log2_out(out, self); + break; + default: + AT_ERROR("log2_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log2", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor logdet(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::logdet(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::logdet", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor logspace(Scalar start, Scalar end, int64_t steps, double base, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::logspace(start, end, steps, base, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::logspace", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, start, end, steps, base, options); +#endif +} +static inline Tensor & logspace_out(Tensor & out, Scalar start, Scalar end, int64_t steps, double base) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out)))) { + case Backend::CPU: + return CPUType::logspace_out(out, start, end, steps, base); + break; + default: + AT_ERROR("logspace_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::logspace", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, start, end, steps, base); +#endif +} +static inline Tensor log_softmax(const Tensor & self, int64_t dim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log_softmax(self, dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log_softmax", "int"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, dim, dtype); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor log_softmax(const Tensor & self, Dimname dim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log_softmax(self, dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log_softmax", "Dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, dim, dtype); +#endif +} +#endif +static inline Tensor _log_softmax(const Tensor & self, int64_t dim, bool half_to_float) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_log_softmax(self, dim, half_to_float); + break; + default: + AT_ERROR("_log_softmax not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_log_softmax", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim, half_to_float); +#endif +} +static inline Tensor _log_softmax_backward_data(const Tensor & grad_output, const Tensor & output, int64_t dim, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, output, self)))) { + case Backend::CPU: + return CPUType::_log_softmax_backward_data(grad_output, output, dim, self); + break; + default: + AT_ERROR("_log_softmax_backward_data not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_log_softmax_backward_data", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, output, dim, self); +#endif +} +static inline Tensor logsumexp(const Tensor & self, IntArrayRef dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::logsumexp(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::logsumexp", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, keepdim); +#endif +} +static inline Tensor & logsumexp_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::logsumexp_out(out, self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::logsumexp", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, dim, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor logsumexp(const Tensor & self, DimnameList dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::logsumexp(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::logsumexp", "names"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, keepdim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & logsumexp_out(Tensor & out, const Tensor & self, DimnameList dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::logsumexp_out(out, self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::logsumexp", "names_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, dim, keepdim); +#endif +} +#endif +static inline Tensor margin_ranking_loss(const Tensor & input1, const Tensor & input2, const Tensor & target, double margin, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::margin_ranking_loss(input1, input2, target, margin, reduction); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::margin_ranking_loss", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, input1, input2, target, margin, reduction); +#endif +} +static inline Tensor matmul(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::matmul(self, other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::matmul", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & matmul_out(Tensor & out, const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::matmul_out(out, self, other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::matmul", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor matrix_rank(const Tensor & self, double tol, bool symmetric) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::matrix_rank(self, tol, symmetric); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::matrix_rank", "tol"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, tol, symmetric); +#endif +} +static inline Tensor matrix_rank(const Tensor & self, bool symmetric) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::matrix_rank(self, symmetric); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::matrix_rank", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, symmetric); +#endif +} +static inline Tensor matrix_power(const Tensor & self, int64_t n) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::matrix_power(self, n); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::matrix_power", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, n); +#endif +} +static inline std::tuple max(const Tensor & self, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::max(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, bool>( + op, self, dim, keepdim); +#endif +} +static inline std::tuple max_out(Tensor & max, Tensor & max_values, const Tensor & self, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::max_out(max, max_values, self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max", "dim_max"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, int64_t, bool>( + op, max, max_values, self, dim, keepdim); +#endif +} +static inline Tensor max_values(const Tensor & self, IntArrayRef dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::max_values(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_values", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple max(const Tensor & self, Dimname dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::max(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max", "names_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, Dimname, bool>( + op, self, dim, keepdim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple max_out(Tensor & max, Tensor & max_values, const Tensor & self, Dimname dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::max_out(max, max_values, self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max", "names_dim_max"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, Dimname, bool>( + op, max, max_values, self, dim, keepdim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor max_values(const Tensor & self, DimnameList dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::max_values(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_values", "names"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, keepdim); +#endif +} +#endif +static inline std::tuple max_pool1d_with_indices(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::max_pool1d_with_indices(self, kernel_size, stride, padding, dilation, ceil_mode); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_pool1d_with_indices", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, IntArrayRef, bool>( + op, self, kernel_size, stride, padding, dilation, ceil_mode); +#endif +} +static inline Tensor max_pool1d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::max_pool1d(self, kernel_size, stride, padding, dilation, ceil_mode); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_pool1d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, kernel_size, stride, padding, dilation, ceil_mode); +#endif +} +static inline Tensor max_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::max_pool2d(self, kernel_size, stride, padding, dilation, ceil_mode); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_pool2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, kernel_size, stride, padding, dilation, ceil_mode); +#endif +} +static inline Tensor mkldnn_max_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + + default: + AT_ERROR("mkldnn_max_pool2d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mkldnn_max_pool2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, kernel_size, stride, padding, dilation, ceil_mode); +#endif +} +static inline Tensor quantized_max_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::QuantizedCPU: + return QuantizedCPUType::quantized_max_pool2d(self, kernel_size, stride, padding, dilation, ceil_mode); + break; + default: + AT_ERROR("quantized_max_pool2d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::quantized_max_pool2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, kernel_size, stride, padding, dilation, ceil_mode); +#endif +} +static inline Tensor max_pool3d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::max_pool3d(self, kernel_size, stride, padding, dilation, ceil_mode); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_pool3d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, kernel_size, stride, padding, dilation, ceil_mode); +#endif +} +static inline Tensor mean(const Tensor & self, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::mean(self, dtype); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::mean(self, dtype); + break; + default: + AT_ERROR("mean not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mean", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, dtype); +#endif +} +static inline Tensor mean(const Tensor & self, IntArrayRef dim, bool keepdim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::mean(self, dim, keepdim, dtype); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::mean(self, dim, keepdim, dtype); + break; + default: + AT_ERROR("mean not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mean", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, dim, keepdim, dtype); +#endif +} +static inline Tensor & mean_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::mean_out(out, self, dim, keepdim, dtype); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::mean_out(out, self, dim, keepdim, dtype); + break; + default: + AT_ERROR("mean_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mean", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, out, self, dim, keepdim, dtype); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor mean(const Tensor & self, DimnameList dim, bool keepdim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mean(self, dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mean", "names_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, dim, keepdim, dtype); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & mean_out(Tensor & out, const Tensor & self, DimnameList dim, bool keepdim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mean_out(out, self, dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mean", "names_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, out, self, dim, keepdim, dtype); +#endif +} +#endif +static inline std::tuple median(const Tensor & self, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::median(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::median", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, bool>( + op, self, dim, keepdim); +#endif +} +static inline std::tuple median_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::median_out(values, indices, self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::median", "dim_values"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, int64_t, bool>( + op, values, indices, self, dim, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple median(const Tensor & self, Dimname dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::median(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::median", "names_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, Dimname, bool>( + op, self, dim, keepdim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple median_out(Tensor & values, Tensor & indices, const Tensor & self, Dimname dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::median_out(values, indices, self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::median", "names_dim_values"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, Dimname, bool>( + op, values, indices, self, dim, keepdim); +#endif +} +#endif +static inline std::tuple min(const Tensor & self, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::min(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::min", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, bool>( + op, self, dim, keepdim); +#endif +} +static inline std::tuple min_out(Tensor & min, Tensor & min_indices, const Tensor & self, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::min_out(min, min_indices, self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::min", "dim_min"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, int64_t, bool>( + op, min, min_indices, self, dim, keepdim); +#endif +} +static inline Tensor min_values(const Tensor & self, IntArrayRef dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::min_values(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::min_values", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple min(const Tensor & self, Dimname dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::min(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::min", "names_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, Dimname, bool>( + op, self, dim, keepdim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple min_out(Tensor & min, Tensor & min_indices, const Tensor & self, Dimname dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::min_out(min, min_indices, self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::min", "names_dim_min"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, Dimname, bool>( + op, min, min_indices, self, dim, keepdim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor min_values(const Tensor & self, DimnameList dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::min_values(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::min_values", "names"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, keepdim); +#endif +} +#endif +static inline Tensor mkldnn_convolution(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mkldnn_convolution(self, weight, bias, padding, stride, dilation, groups); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mkldnn_convolution", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, weight, bias, padding, stride, dilation, groups); +#endif +} +static inline Tensor mkldnn_convolution_backward_input(IntArrayRef self_size, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool bias_defined) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mkldnn_convolution_backward_input(self_size, grad_output, weight, padding, stride, dilation, groups, bias_defined); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mkldnn_convolution_backward_input", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self_size, grad_output, weight, padding, stride, dilation, groups, bias_defined); +#endif +} +static inline std::tuple mkldnn_convolution_backward_weights(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool bias_defined) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mkldnn_convolution_backward_weights(weight_size, grad_output, self, padding, stride, dilation, groups, bias_defined); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mkldnn_convolution_backward_weights", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, IntArrayRef, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, int64_t, bool>( + op, weight_size, grad_output, self, padding, stride, dilation, groups, bias_defined); +#endif +} +static inline std::tuple mkldnn_convolution_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mkldnn_convolution_backward(self, grad_output, weight, padding, stride, dilation, groups, output_mask); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mkldnn_convolution_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, int64_t, std::array>( + op, self, grad_output, weight, padding, stride, dilation, groups, output_mask); +#endif +} +static inline std::tuple miopen_batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double exponential_average_factor, double epsilon) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input, weight, bias, running_mean, running_var)))) { + + default: + AT_ERROR("miopen_batch_norm not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input, weight, bias, running_mean, running_var))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::miopen_batch_norm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, bool, double, double>( + op, input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon); +#endif +} +static inline std::tuple miopen_batch_norm_backward(const Tensor & input, const Tensor & grad_output, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_var, double epsilon) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input, grad_output, weight, running_mean, running_var, save_mean, save_var)))) { + + default: + AT_ERROR("miopen_batch_norm_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input, grad_output, weight, running_mean, running_var, save_mean, save_var))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::miopen_batch_norm_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, double>( + op, input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon); +#endif +} +static inline Tensor miopen_convolution(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias)))) { + + default: + AT_ERROR("miopen_convolution not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::miopen_convolution", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic); +#endif +} +static inline Tensor miopen_convolution_backward_input(IntArrayRef self_size, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, weight)))) { + + default: + AT_ERROR("miopen_convolution_backward_input not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::miopen_convolution_backward_input", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self_size, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic); +#endif +} +static inline std::tuple miopen_convolution_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, grad_output, weight)))) { + + default: + AT_ERROR("miopen_convolution_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, grad_output, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::miopen_convolution_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, int64_t, bool, bool, std::array>( + op, self, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic, output_mask); +#endif +} +static inline Tensor miopen_convolution_backward_bias(const Tensor & grad_output) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output)))) { + + default: + AT_ERROR("miopen_convolution_backward_bias not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::miopen_convolution_backward_bias", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output); +#endif +} +static inline Tensor miopen_convolution_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self)))) { + + default: + AT_ERROR("miopen_convolution_backward_weight not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::miopen_convolution_backward_weight", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic); +#endif +} +static inline Tensor miopen_convolution_transpose(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias)))) { + + default: + AT_ERROR("miopen_convolution_transpose not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::miopen_convolution_transpose", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic); +#endif +} +static inline std::tuple miopen_convolution_transpose_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, grad_output, weight)))) { + + default: + AT_ERROR("miopen_convolution_transpose_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, grad_output, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::miopen_convolution_transpose_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, IntArrayRef, int64_t, bool, bool, std::array>( + op, self, grad_output, weight, padding, output_padding, stride, dilation, groups, benchmark, deterministic, output_mask); +#endif +} +static inline Tensor miopen_convolution_transpose_backward_input(const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, weight)))) { + + default: + AT_ERROR("miopen_convolution_transpose_backward_input not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::miopen_convolution_transpose_backward_input", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic); +#endif +} +static inline Tensor miopen_convolution_transpose_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self)))) { + + default: + AT_ERROR("miopen_convolution_transpose_backward_weight not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::miopen_convolution_transpose_backward_weight", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic); +#endif +} +static inline Tensor miopen_depthwise_convolution(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias)))) { + + default: + AT_ERROR("miopen_depthwise_convolution not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::miopen_depthwise_convolution", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic); +#endif +} +static inline Tensor miopen_depthwise_convolution_backward_input(IntArrayRef self_size, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, weight)))) { + + default: + AT_ERROR("miopen_depthwise_convolution_backward_input not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::miopen_depthwise_convolution_backward_input", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self_size, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic); +#endif +} +static inline std::tuple miopen_depthwise_convolution_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, grad_output, weight)))) { + + default: + AT_ERROR("miopen_depthwise_convolution_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, grad_output, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::miopen_depthwise_convolution_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, int64_t, bool, bool, std::array>( + op, self, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic, output_mask); +#endif +} +static inline Tensor miopen_depthwise_convolution_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self)))) { + + default: + AT_ERROR("miopen_depthwise_convolution_backward_weight not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::miopen_depthwise_convolution_backward_weight", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic); +#endif +} +static inline std::tuple miopen_rnn(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & hx, const Tensor & cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntArrayRef batch_sizes, const Tensor & dropout_state) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input, weight, hx, cx, dropout_state)))) { + + default: + AT_ERROR("miopen_rnn not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input, weight, hx, cx, dropout_state))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::miopen_rnn", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, TensorList, int64_t, const Tensor &, const Tensor &, int64_t, int64_t, int64_t, bool, double, bool, bool, IntArrayRef, const Tensor &>( + op, input, weight, weight_stride0, hx, cx, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state); +#endif +} +static inline std::tuple> miopen_rnn_backward(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & weight_buf, const Tensor & hx, const Tensor & cx, const Tensor & output, const Tensor & grad_output, const Tensor & grad_hy, const Tensor & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntArrayRef batch_sizes, const Tensor & dropout_state, const Tensor & reserve, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input, weight, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, dropout_state, reserve)))) { + + default: + AT_ERROR("miopen_rnn_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input, weight, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, dropout_state, reserve))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::miopen_rnn_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>, const Tensor &, TensorList, int64_t, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, int64_t, int64_t, int64_t, bool, double, bool, bool, IntArrayRef, const Tensor &, const Tensor &, std::array>( + op, input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask); +#endif +} +static inline Tensor mm(const Tensor & self, const Tensor & mat2) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, mat2)))) { + case Backend::CPU: + return CPUType::mm(self, mat2); + break; + case Backend::SparseCPU: + return SparseCPUType::mm(self, mat2); + break; + default: + AT_ERROR("mm not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, mat2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, mat2); +#endif +} +static inline Tensor & mm_out(Tensor & out, const Tensor & self, const Tensor & mat2) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, mat2)))) { + case Backend::CPU: + return CPUType::mm_out(out, self, mat2); + break; + case Backend::SparseCPU: + return SparseCPUType::mm_out(out, self, mat2); + break; + default: + AT_ERROR("mm_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, mat2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mm", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, mat2); +#endif +} +static inline Tensor _sparse_mm(const Tensor & sparse, const Tensor & dense) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_sparse_mm(sparse, dense); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_sparse_mm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, sparse, dense); +#endif +} +static inline std::tuple mode(const Tensor & self, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mode(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mode", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, bool>( + op, self, dim, keepdim); +#endif +} +static inline std::tuple mode_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mode_out(values, indices, self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mode", "values"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, int64_t, bool>( + op, values, indices, self, dim, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple mode(const Tensor & self, Dimname dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mode(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mode", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, Dimname, bool>( + op, self, dim, keepdim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple mode_out(Tensor & values, Tensor & indices, const Tensor & self, Dimname dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mode_out(values, indices, self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mode", "dimname_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, Dimname, bool>( + op, values, indices, self, dim, keepdim); +#endif +} +#endif +static inline Tensor mul(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::mul(self, other); + break; + case Backend::SparseCPU: + return SparseCPUType::mul(self, other); + break; + default: + AT_ERROR("mul not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mul", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & mul_out(Tensor & out, const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, other)))) { + case Backend::CPU: + return CPUType::mul_out(out, self, other); + break; + case Backend::SparseCPU: + return SparseCPUType::mul_out(out, self, other); + break; + default: + AT_ERROR("mul_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mul", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor mul(const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mul(self, other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mul", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor mv(const Tensor & self, const Tensor & vec) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, vec)))) { + case Backend::CPU: + return CPUType::mv(self, vec); + break; + default: + AT_ERROR("mv not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, vec))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mv", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, vec); +#endif +} +static inline Tensor & mv_out(Tensor & out, const Tensor & self, const Tensor & vec) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, vec)))) { + case Backend::CPU: + return CPUType::mv_out(out, self, vec); + break; + default: + AT_ERROR("mv_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, vec))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mv", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, vec); +#endif +} +static inline Tensor mvlgamma(const Tensor & self, int64_t p) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mvlgamma(self, p); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mvlgamma", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, p); +#endif +} +static inline Tensor narrow(const Tensor & self, int64_t dim, int64_t start, int64_t length) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::narrow(self, dim, start, length); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::narrow", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, start, length); +#endif +} +static inline std::tuple native_batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input, weight, bias, running_mean, running_var)))) { + case Backend::CPU: + return CPUType::native_batch_norm(input, weight, bias, running_mean, running_var, training, momentum, eps); + break; + default: + AT_ERROR("native_batch_norm not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input, weight, bias, running_mean, running_var))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::native_batch_norm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, bool, double, double>( + op, input, weight, bias, running_mean, running_var, training, momentum, eps); +#endif +} +static inline std::tuple batch_norm_stats(const Tensor & input, double eps) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input)))) { + + default: + AT_ERROR("batch_norm_stats not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::batch_norm_stats", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, double>( + op, input, eps); +#endif +} +static inline Tensor batch_norm_elemt(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & mean, const Tensor & invstd, double eps) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input, weight, bias, mean, invstd)))) { + + default: + AT_ERROR("batch_norm_elemt not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input, weight, bias, mean, invstd))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::batch_norm_elemt", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, weight, bias, mean, invstd, eps); +#endif +} +static inline Tensor & batch_norm_elemt_out(Tensor & out, const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & mean, const Tensor & invstd, double eps) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, input, weight, bias, mean, invstd)))) { + + default: + AT_ERROR("batch_norm_elemt_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, input, weight, bias, mean, invstd))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::batch_norm_elemt", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, input, weight, bias, mean, invstd, eps); +#endif +} +static inline std::tuple batch_norm_gather_stats(const Tensor & input, const Tensor & mean, const Tensor & invstd, const Tensor & running_mean, const Tensor & running_var, double momentum, double eps, int64_t count) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input, mean, invstd, running_mean, running_var)))) { + + default: + AT_ERROR("batch_norm_gather_stats not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input, mean, invstd, running_mean, running_var))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::batch_norm_gather_stats", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, double, double, int64_t>( + op, input, mean, invstd, running_mean, running_var, momentum, eps, count); +#endif +} +static inline std::tuple batch_norm_gather_stats_with_counts(const Tensor & input, const Tensor & mean, const Tensor & invstd, const Tensor & running_mean, const Tensor & running_var, double momentum, double eps, IntArrayRef counts) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input, mean, invstd, running_mean, running_var)))) { + + default: + AT_ERROR("batch_norm_gather_stats_with_counts not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input, mean, invstd, running_mean, running_var))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::batch_norm_gather_stats_with_counts", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, double, double, IntArrayRef>( + op, input, mean, invstd, running_mean, running_var, momentum, eps, counts); +#endif +} +static inline std::tuple native_batch_norm_backward(const Tensor & grad_out, const Tensor & input, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_invstd, bool train, double eps, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_out, input, weight, running_mean, running_var, save_mean, save_invstd)))) { + case Backend::CPU: + return CPUType::native_batch_norm_backward(grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask); + break; + default: + AT_ERROR("native_batch_norm_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_out, input, weight, running_mean, running_var, save_mean, save_invstd))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::native_batch_norm_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, bool, double, std::array>( + op, grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask); +#endif +} +static inline std::tuple batch_norm_backward_reduce(const Tensor & grad_out, const Tensor & input, const Tensor & mean, const Tensor & invstd, const Tensor & weight, bool input_g, bool weight_g, bool bias_g) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_out, input, mean, invstd, weight)))) { + + default: + AT_ERROR("batch_norm_backward_reduce not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_out, input, mean, invstd, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::batch_norm_backward_reduce", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, bool, bool, bool>( + op, grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g); +#endif +} +static inline Tensor batch_norm_backward_elemt(const Tensor & grad_out, const Tensor & input, const Tensor & mean, const Tensor & invstd, const Tensor & weight, const Tensor & mean_dy, const Tensor & mean_dy_xmu) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_out, input, mean, invstd, weight, mean_dy, mean_dy_xmu)))) { + + default: + AT_ERROR("batch_norm_backward_elemt not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_out, input, mean, invstd, weight, mean_dy, mean_dy_xmu))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::batch_norm_backward_elemt", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_out, input, mean, invstd, weight, mean_dy, mean_dy_xmu); +#endif +} +static inline std::tuple batch_norm_update_stats(const Tensor & input, const Tensor & running_mean, const Tensor & running_var, double momentum) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input, running_mean, running_var)))) { + case Backend::CPU: + return CPUType::batch_norm_update_stats(input, running_mean, running_var, momentum); + break; + default: + AT_ERROR("batch_norm_update_stats not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input, running_mean, running_var))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::batch_norm_update_stats", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, double>( + op, input, running_mean, running_var, momentum); +#endif +} +static inline bool _nnpack_available() { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_nnpack_available(); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_nnpack_available", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op); +#endif +} +static inline Tensor _nnpack_spatial_convolution(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_nnpack_spatial_convolution(input, weight, bias, padding, stride); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_nnpack_spatial_convolution", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, weight, bias, padding, stride); +#endif +} +static inline std::tuple _nnpack_spatial_convolution_backward(const Tensor & input, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_nnpack_spatial_convolution_backward(input, grad_output, weight, padding, output_mask); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_nnpack_spatial_convolution_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, std::array>( + op, input, grad_output, weight, padding, output_mask); +#endif +} +static inline Tensor _nnpack_spatial_convolution_backward_input(const Tensor & input, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_nnpack_spatial_convolution_backward_input(input, grad_output, weight, padding); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_nnpack_spatial_convolution_backward_input", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, grad_output, weight, padding); +#endif +} +static inline Tensor _nnpack_spatial_convolution_backward_weight(const Tensor & input, IntArrayRef weightsize, const Tensor & grad_output, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_nnpack_spatial_convolution_backward_weight(input, weightsize, grad_output, padding); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_nnpack_spatial_convolution_backward_weight", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, weightsize, grad_output, padding); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor ones(IntArrayRef size, c10::optional names, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::ones(size, names, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ones", "names"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const TensorOptions &>( + op, size, names, options); +#endif +} +#endif +static inline Tensor ones(IntArrayRef size, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::ones(size, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ones", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, size, options); +#endif +} +static inline Tensor & ones_out(Tensor & out, IntArrayRef size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::ones_out(out, size); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ones", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, size); +#endif +} +static inline Tensor ones_like(const Tensor & self, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::ones_like(self, memory_format); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ones_like", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, memory_format); +#endif +} +static inline Tensor ones_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::ones_like(self, options, memory_format); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(self, options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ones_like", "dtype"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, options, memory_format); +#endif +} +static inline Tensor pairwise_distance(const Tensor & x1, const Tensor & x2, double p, double eps, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::pairwise_distance(x1, x2, p, eps, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::pairwise_distance", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, x1, x2, p, eps, keepdim); +#endif +} +static inline Tensor cdist(const Tensor & x1, const Tensor & x2, double p, c10::optional compute_mode) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cdist(x1, x2, p, compute_mode); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cdist", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed>( + op, x1, x2, p, compute_mode); +#endif +} +static inline Tensor _cdist_backward(const Tensor & grad, const Tensor & x1, const Tensor & x2, double p, const Tensor & cdist) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_cdist_backward(grad, x1, x2, p, cdist); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cdist_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad, x1, x2, p, cdist); +#endif +} +static inline Tensor pdist(const Tensor & self, double p) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::pdist(self, p); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::pdist", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, p); +#endif +} +static inline Tensor _pdist_forward(const Tensor & self, double p) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_pdist_forward(self, p); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_pdist_forward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, p); +#endif +} +static inline Tensor _pdist_backward(const Tensor & grad, const Tensor & self, double p, const Tensor & pdist) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_pdist_backward(grad, self, p, pdist); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_pdist_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad, self, p, pdist); +#endif +} +static inline Tensor cosine_similarity(const Tensor & x1, const Tensor & x2, int64_t dim, double eps) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cosine_similarity(x1, x2, dim, eps); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cosine_similarity", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, x1, x2, dim, eps); +#endif +} +static inline Tensor pixel_shuffle(const Tensor & self, int64_t upscale_factor) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::pixel_shuffle(self, upscale_factor); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::pixel_shuffle", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, upscale_factor); +#endif +} +static inline Tensor pinverse(const Tensor & self, double rcond) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::pinverse(self, rcond); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::pinverse", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, rcond); +#endif +} +static inline Tensor poisson_nll_loss(const Tensor & input, const Tensor & target, bool log_input, bool full, double eps, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::poisson_nll_loss(input, target, log_input, full, eps, reduction); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::poisson_nll_loss", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, input, target, log_input, full, eps, reduction); +#endif +} +static inline Tensor scalar_tensor(Scalar s, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::scalar_tensor(s, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::scalar_tensor", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, s, options); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor rand(IntArrayRef size, c10::optional names, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rand(size, names, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rand", "names"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const TensorOptions &>( + op, size, names, options); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor rand(IntArrayRef size, Generator * generator, c10::optional names, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rand(size, generator, names, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rand", "generator_with_names"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const TensorOptions &>( + op, size, generator, names, options); +#endif +} +#endif +static inline Tensor rand(IntArrayRef size, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rand(size, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rand", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, size, options); +#endif +} +static inline Tensor rand(IntArrayRef size, Generator * generator, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rand(size, generator, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rand", "generator"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, size, generator, options); +#endif +} +static inline Tensor & rand_out(Tensor & out, IntArrayRef size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rand_out(out, size); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rand", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, size); +#endif +} +static inline Tensor & rand_out(Tensor & out, IntArrayRef size, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rand_out(out, size, generator); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rand", "generator_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, size, generator); +#endif +} +static inline Tensor rand_like(const Tensor & self, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rand_like(self, memory_format); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rand_like", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, memory_format); +#endif +} +static inline Tensor rand_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rand_like(self, options, memory_format); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(self, options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rand_like", "dtype"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, options, memory_format); +#endif +} +static inline Tensor randint(int64_t high, IntArrayRef size, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randint(high, size, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randint", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, high, size, options); +#endif +} +static inline Tensor randint(int64_t high, IntArrayRef size, Generator * generator, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randint(high, size, generator, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randint", "generator"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, high, size, generator, options); +#endif +} +static inline Tensor randint(int64_t low, int64_t high, IntArrayRef size, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randint(low, high, size, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randint", "low"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, low, high, size, options); +#endif +} +static inline Tensor randint(int64_t low, int64_t high, IntArrayRef size, Generator * generator, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randint(low, high, size, generator, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randint", "low_generator"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, low, high, size, generator, options); +#endif +} +static inline Tensor & randint_out(Tensor & out, int64_t high, IntArrayRef size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randint_out(out, high, size); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randint", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, high, size); +#endif +} +static inline Tensor & randint_out(Tensor & out, int64_t high, IntArrayRef size, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randint_out(out, high, size, generator); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randint", "generator_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, high, size, generator); +#endif +} +static inline Tensor & randint_out(Tensor & out, int64_t low, int64_t high, IntArrayRef size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randint_out(out, low, high, size); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randint", "low_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, low, high, size); +#endif +} +static inline Tensor & randint_out(Tensor & out, int64_t low, int64_t high, IntArrayRef size, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randint_out(out, low, high, size, generator); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randint", "low_generator_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, low, high, size, generator); +#endif +} +static inline Tensor randint_like(const Tensor & self, int64_t high, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randint_like(self, high, memory_format); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randint_like", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, high, memory_format); +#endif +} +static inline Tensor randint_like(const Tensor & self, int64_t low, int64_t high, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randint_like(self, low, high, memory_format); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randint_like", "low"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, low, high, memory_format); +#endif +} +static inline Tensor randint_like(const Tensor & self, int64_t high, const TensorOptions & options, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randint_like(self, high, options, memory_format); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(self, options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randint_like", "dtype"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, high, options, memory_format); +#endif +} +static inline Tensor randint_like(const Tensor & self, int64_t low, int64_t high, const TensorOptions & options, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randint_like(self, low, high, options, memory_format); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(self, options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randint_like", "low_dtype"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, low, high, options, memory_format); +#endif +} +static inline Tensor randn(IntArrayRef size, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randn(size, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randn", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, size, options); +#endif +} +static inline Tensor randn(IntArrayRef size, Generator * generator, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randn(size, generator, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randn", "generator"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, size, generator, options); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor randn(IntArrayRef size, c10::optional names, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randn(size, names, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randn", "names"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const TensorOptions &>( + op, size, names, options); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor randn(IntArrayRef size, Generator * generator, c10::optional names, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randn(size, generator, names, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randn", "generator_with_names"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const TensorOptions &>( + op, size, generator, names, options); +#endif +} +#endif +static inline Tensor & randn_out(Tensor & out, IntArrayRef size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randn_out(out, size); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randn", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, size); +#endif +} +static inline Tensor & randn_out(Tensor & out, IntArrayRef size, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randn_out(out, size, generator); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randn", "generator_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, size, generator); +#endif +} +static inline Tensor randn_like(const Tensor & self, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randn_like(self, memory_format); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randn_like", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, memory_format); +#endif +} +static inline Tensor randn_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randn_like(self, options, memory_format); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(self, options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randn_like", "dtype"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, options, memory_format); +#endif +} +static inline Tensor randperm(int64_t n, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randperm(n, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randperm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, n, options); +#endif +} +static inline Tensor randperm(int64_t n, Generator * generator, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randperm(n, generator, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randperm", "generator"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, n, generator, options); +#endif +} +static inline Tensor & randperm_out(Tensor & out, int64_t n) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::randperm_out(out, n); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randperm", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, n); +#endif +} +static inline Tensor & randperm_out(Tensor & out, int64_t n, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out)))) { + case Backend::CPU: + return CPUType::randperm_out(out, n, generator); + break; + default: + AT_ERROR("randperm_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::randperm", "generator_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, n, generator); +#endif +} +static inline Tensor range(Scalar start, Scalar end, Scalar step, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::range(start, end, step, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::range", "step"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, start, end, step, options); +#endif +} +static inline Tensor range(Scalar start, Scalar end, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::range(start, end, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::range", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, start, end, options); +#endif +} +static inline Tensor & range_out(Tensor & out, Scalar start, Scalar end, Scalar step) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out)))) { + case Backend::CPU: + return CPUType::range_out(out, start, end, step); + break; + default: + AT_ERROR("range_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::range", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, start, end, step); +#endif +} +static inline Tensor reciprocal(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::reciprocal(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::reciprocal", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & reciprocal_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::reciprocal_(self); + break; + default: + AT_ERROR("reciprocal_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::reciprocal_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & reciprocal_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::reciprocal_out(out, self); + break; + default: + AT_ERROR("reciprocal_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::reciprocal", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor neg(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::neg(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::neg", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & neg_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::neg_(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::neg_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & neg_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::neg_out(out, self); + break; + default: + AT_ERROR("neg_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::neg", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor repeat_interleave(const Tensor & repeats) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(repeats)))) { + case Backend::CPU: + return CPUType::repeat_interleave(repeats); + break; + default: + AT_ERROR("repeat_interleave not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(repeats))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::repeat_interleave", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, repeats); +#endif +} +static inline Tensor repeat_interleave(const Tensor & self, const Tensor & repeats, c10::optional dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::repeat_interleave(self, repeats, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::repeat_interleave", "self_Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed>( + op, self, repeats, dim); +#endif +} +static inline Tensor repeat_interleave(const Tensor & self, int64_t repeats, c10::optional dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::repeat_interleave(self, repeats, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::repeat_interleave", "self_int"}).value(); + return c10::Dispatcher::singleton().callUnboxed>( + op, self, repeats, dim); +#endif +} +static inline Tensor reshape(const Tensor & self, IntArrayRef shape) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::reshape(self, shape); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::reshape", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, shape); +#endif +} +static inline Tensor _mkldnn_reshape(const Tensor & self, IntArrayRef shape) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + + default: + AT_ERROR("_mkldnn_reshape not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_mkldnn_reshape", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, shape); +#endif +} +static inline Tensor round(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::round(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::round", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & round_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::round_(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::round_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & round_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::round_out(out, self); + break; + default: + AT_ERROR("round_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::round", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor rrelu(const Tensor & self, Scalar lower, Scalar upper, bool training, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rrelu(self, lower, upper, training, generator); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rrelu", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, lower, upper, training, generator); +#endif +} +static inline Tensor & rrelu_(Tensor & self, Scalar lower, Scalar upper, bool training, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rrelu_(self, lower, upper, training, generator); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rrelu_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, lower, upper, training, generator); +#endif +} +static inline Tensor relu(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::relu(self); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::relu(self); + break; + default: + AT_ERROR("relu not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::relu", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & relu_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::relu_(self); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::relu_(self); + break; + default: + AT_ERROR("relu_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::relu_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor prelu(const Tensor & self, const Tensor & weight) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, weight)))) { + case Backend::CPU: + return CPUType::prelu(self, weight); + break; + default: + AT_ERROR("prelu not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::prelu", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, weight); +#endif +} +static inline std::tuple prelu_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, weight)))) { + case Backend::CPU: + return CPUType::prelu_backward(grad_output, self, weight); + break; + default: + AT_ERROR("prelu_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::prelu_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &>( + op, grad_output, self, weight); +#endif +} +static inline Tensor gelu(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::gelu(self); + break; + default: + AT_ERROR("gelu not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::gelu", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor gelu_backward(const Tensor & grad, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad, self)))) { + case Backend::CPU: + return CPUType::gelu_backward(grad, self); + break; + default: + AT_ERROR("gelu_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::gelu_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad, self); +#endif +} +static inline Tensor hardshrink(const Tensor & self, Scalar lambd) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::hardshrink(self, lambd); + break; + default: + AT_ERROR("hardshrink not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::hardshrink", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, lambd); +#endif +} +static inline Tensor hardshrink_backward(const Tensor & grad_out, const Tensor & self, Scalar lambd) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_out, self)))) { + case Backend::CPU: + return CPUType::hardshrink_backward(grad_out, self, lambd); + break; + default: + AT_ERROR("hardshrink_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::hardshrink_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_out, self, lambd); +#endif +} +static inline Tensor rsqrt(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rsqrt(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rsqrt", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & rsqrt_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rsqrt_(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rsqrt_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & rsqrt_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::rsqrt_out(out, self); + break; + default: + AT_ERROR("rsqrt_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rsqrt", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor select(const Tensor & self, Dimname dim, int64_t index) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::select(self, dim, index); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::select", "Dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, index); +#endif +} +#endif +static inline Tensor select(const Tensor & self, int64_t dim, int64_t index) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::select(self, dim, index); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::select", "int"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, index); +#endif +} +static inline Tensor selu(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::selu(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::selu", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & selu_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::selu_(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::selu_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor celu(const Tensor & self, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::celu(self, alpha); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::celu", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, alpha); +#endif +} +static inline Tensor & celu_(Tensor & self, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::celu_(self, alpha); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::celu_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, alpha); +#endif +} +static inline Tensor sigmoid(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::sigmoid(self); + break; + default: + AT_ERROR("sigmoid not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sigmoid", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & sigmoid_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::sigmoid_(self); + break; + default: + AT_ERROR("sigmoid_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sigmoid_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & sigmoid_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sigmoid_out(out, self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sigmoid", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor sin(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sin(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sin", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & sin_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sin_(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sin_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & sin_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::sin_out(out, self); + break; + default: + AT_ERROR("sin_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sin", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor sinh(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sinh(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sinh", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & sinh_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sinh_(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sinh_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & sinh_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sinh_out(out, self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sinh", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor detach(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::detach(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::detach", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & detach_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::detach_(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::detach_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline int64_t size(const Tensor & self, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::size(self, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::size", "int"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline int64_t size(const Tensor & self, Dimname dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::size(self, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::size", "Dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim); +#endif +} +#endif +static inline Tensor slice(const Tensor & self, int64_t dim, int64_t start, int64_t end, int64_t step) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::slice(self, dim, start, end, step); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slice", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, start, end, step); +#endif +} +static inline std::tuple slogdet(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::slogdet(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slogdet", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &>( + op, self); +#endif +} +static inline Tensor smm(const Tensor & self, const Tensor & mat2) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::smm(self, mat2); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::smm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, mat2); +#endif +} +static inline Tensor softmax(const Tensor & self, int64_t dim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::softmax(self, dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::softmax", "int"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, dim, dtype); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor softmax(const Tensor & self, Dimname dim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::softmax(self, dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::softmax", "Dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, dim, dtype); +#endif +} +#endif +static inline Tensor _softmax(const Tensor & self, int64_t dim, bool half_to_float) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_softmax(self, dim, half_to_float); + break; + default: + AT_ERROR("_softmax not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_softmax", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim, half_to_float); +#endif +} +static inline Tensor _softmax_backward_data(const Tensor & grad_output, const Tensor & output, int64_t dim, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, output, self)))) { + case Backend::CPU: + return CPUType::_softmax_backward_data(grad_output, output, dim, self); + break; + default: + AT_ERROR("_softmax_backward_data not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_softmax_backward_data", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, output, dim, self); +#endif +} +static inline std::vector split(const Tensor & self, int64_t split_size, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::split(self, split_size, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::split", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, int64_t>( + op, self, split_size, dim); +#endif +} +static inline std::vector split_with_sizes(const Tensor & self, IntArrayRef split_sizes, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::split_with_sizes(self, split_sizes, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::split_with_sizes", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, IntArrayRef, int64_t>( + op, self, split_sizes, dim); +#endif +} +static inline Tensor squeeze(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::squeeze(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::squeeze", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor squeeze(const Tensor & self, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::squeeze(self, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::squeeze", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor squeeze(const Tensor & self, Dimname dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::squeeze(self, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::squeeze", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim); +#endif +} +#endif +static inline Tensor sspaddmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sspaddmm(self, mat1, mat2, beta, alpha); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sspaddmm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, mat1, mat2, beta, alpha); +#endif +} +static inline Tensor & sspaddmm_out(Tensor & out, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, mat1, mat2)))) { + case Backend::CPU: + return CPUType::sspaddmm_out(out, self, mat1, mat2, beta, alpha); + break; + case Backend::SparseCPU: + return SparseCPUType::sspaddmm_out(out, self, mat1, mat2, beta, alpha); + break; + default: + AT_ERROR("sspaddmm_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, mat1, mat2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sspaddmm", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, mat1, mat2, beta, alpha); +#endif +} +static inline Tensor stack(TensorList tensors, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::stack(tensors, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::stack", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, tensors, dim); +#endif +} +static inline Tensor & stack_out(Tensor & out, TensorList tensors, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::stack_out(out, tensors, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::stack", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, tensors, dim); +#endif +} +static inline Tensor stft(const Tensor & self, int64_t n_fft, c10::optional hop_length, c10::optional win_length, const Tensor & window, bool normalized, bool onesided) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::stft(self, n_fft, hop_length, win_length, window, normalized, onesided); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::stft", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, c10::optional, const Tensor &, bool, bool>( + op, self, n_fft, hop_length, win_length, window, normalized, onesided); +#endif +} +static inline int64_t stride(const Tensor & self, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::stride(self, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::stride", "int"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline int64_t stride(const Tensor & self, Dimname dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::stride(self, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::stride", "Dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim); +#endif +} +#endif +static inline Tensor sum(const Tensor & self, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sum(self, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sum", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, dtype); +#endif +} +static inline Tensor sum(const Tensor & self, IntArrayRef dim, bool keepdim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sum(self, dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sum", "dim_IntList"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, dim, keepdim, dtype); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor sum(const Tensor & self, DimnameList dim, bool keepdim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sum(self, dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sum", "dim_DimnameList"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, dim, keepdim, dtype); +#endif +} +#endif +static inline Tensor & sum_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sum_out(out, self, dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sum", "IntList_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, out, self, dim, keepdim, dtype); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & sum_out(Tensor & out, const Tensor & self, DimnameList dim, bool keepdim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sum_out(out, self, dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sum", "DimnameList_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, out, self, dim, keepdim, dtype); +#endif +} +#endif +static inline Tensor sqrt(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sqrt(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sqrt", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & sqrt_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sqrt_(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sqrt_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & sqrt_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sqrt_out(out, self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sqrt", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor std(const Tensor & self, bool unbiased) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::std(self, unbiased); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::std", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, unbiased); +#endif +} +static inline Tensor std(const Tensor & self, IntArrayRef dim, bool unbiased, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::std(self, dim, unbiased, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::std", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, unbiased, keepdim); +#endif +} +static inline std::tuple std_mean(const Tensor & self, bool unbiased) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::std_mean(self, unbiased); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::std_mean", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, bool>( + op, self, unbiased); +#endif +} +static inline std::tuple std_mean(const Tensor & self, IntArrayRef dim, bool unbiased, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::std_mean(self, dim, unbiased, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::std_mean", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, IntArrayRef, bool, bool>( + op, self, dim, unbiased, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple std_mean(const Tensor & self, DimnameList dim, bool unbiased, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::std_mean(self, dim, unbiased, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::std_mean", "names_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, DimnameList, bool, bool>( + op, self, dim, unbiased, keepdim); +#endif +} +#endif +static inline Tensor & std_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool unbiased, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::std_out(out, self, dim, unbiased, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::std", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, dim, unbiased, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor std(const Tensor & self, DimnameList dim, bool unbiased, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::std(self, dim, unbiased, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::std", "names_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, unbiased, keepdim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & std_out(Tensor & out, const Tensor & self, DimnameList dim, bool unbiased, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::std_out(out, self, dim, unbiased, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::std", "names_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, dim, unbiased, keepdim); +#endif +} +#endif +static inline Tensor prod(const Tensor & self, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::prod(self, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::prod", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, dtype); +#endif +} +static inline Tensor prod(const Tensor & self, int64_t dim, bool keepdim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::prod(self, dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::prod", "dim_int"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, dim, keepdim, dtype); +#endif +} +static inline Tensor & prod_out(Tensor & out, const Tensor & self, int64_t dim, bool keepdim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::prod_out(out, self, dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::prod", "int_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, out, self, dim, keepdim, dtype); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor prod(const Tensor & self, Dimname dim, bool keepdim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::prod(self, dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::prod", "dim_Dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, dim, keepdim, dtype); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & prod_out(Tensor & out, const Tensor & self, Dimname dim, bool keepdim, c10::optional dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::prod_out(out, self, dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::prod", "Dimname_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, out, self, dim, keepdim, dtype); +#endif +} +#endif +static inline Tensor t(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::t(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::t", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor tan(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::tan(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::tan", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & tan_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::tan_(self); + break; + default: + AT_ERROR("tan_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::tan_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & tan_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::tan_out(out, self); + break; + default: + AT_ERROR("tan_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::tan", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor tanh(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::tanh(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::tanh", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & tanh_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::tanh_(self); + break; + default: + AT_ERROR("tanh_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::tanh_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & tanh_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::tanh_out(out, self); + break; + default: + AT_ERROR("tanh_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::tanh", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor tensordot(const Tensor & self, const Tensor & other, IntArrayRef dims_self, IntArrayRef dims_other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::tensordot(self, other, dims_self, dims_other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::tensordot", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, other, dims_self, dims_other); +#endif +} +static inline Tensor threshold(const Tensor & self, Scalar threshold, Scalar value) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::threshold(self, threshold, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::threshold", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, threshold, value); +#endif +} +static inline Tensor & threshold_(Tensor & self, Scalar threshold, Scalar value) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::threshold_(self, threshold, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::threshold_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, threshold, value); +#endif +} +static inline Tensor & threshold_out(Tensor & out, const Tensor & self, Scalar threshold, Scalar value) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::threshold_out(out, self, threshold, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::threshold", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, threshold, value); +#endif +} +static inline Tensor threshold_backward(const Tensor & grad_output, const Tensor & self, Scalar threshold) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::threshold_backward(grad_output, self, threshold); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::threshold_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, self, threshold); +#endif +} +static inline Tensor transpose(const Tensor & self, int64_t dim0, int64_t dim1) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::transpose(self, dim0, dim1); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::transpose", "int"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim0, dim1); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor transpose(const Tensor & self, Dimname dim0, Dimname dim1) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::transpose(self, dim0, dim1); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::transpose", "Dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim0, dim1); +#endif +} +#endif +static inline Tensor _mkldnn_transpose(const Tensor & self, int64_t dim0, int64_t dim1) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + + default: + AT_ERROR("_mkldnn_transpose not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_mkldnn_transpose", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim0, dim1); +#endif +} +static inline Tensor & _mkldnn_transpose_(Tensor & self, int64_t dim0, int64_t dim1) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + + default: + AT_ERROR("_mkldnn_transpose_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_mkldnn_transpose_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim0, dim1); +#endif +} +static inline Tensor one_hot(const Tensor & self, int64_t num_classes) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::one_hot(self, num_classes); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::one_hot", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, num_classes); +#endif +} +static inline Tensor flip(const Tensor & self, IntArrayRef dims) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::flip(self, dims); + break; + default: + AT_ERROR("flip not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::flip", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dims); +#endif +} +static inline Tensor roll(const Tensor & self, IntArrayRef shifts, IntArrayRef dims) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::roll(self, shifts, dims); + break; + default: + AT_ERROR("roll not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::roll", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, shifts, dims); +#endif +} +static inline Tensor rot90(const Tensor & self, int64_t k, IntArrayRef dims) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rot90(self, k, dims); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rot90", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, k, dims); +#endif +} +static inline Tensor trapz(const Tensor & y, const Tensor & x, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::trapz(y, x, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::trapz", "x"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, y, x, dim); +#endif +} +static inline Tensor trapz(const Tensor & y, double dx, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::trapz(y, dx, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::trapz", "dx"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, y, dx, dim); +#endif +} +static inline Tensor _trilinear(const Tensor & i1, const Tensor & i2, const Tensor & i3, IntArrayRef expand1, IntArrayRef expand2, IntArrayRef expand3, IntArrayRef sumdim, int64_t unroll_dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_trilinear(i1, i2, i3, expand1, expand2, expand3, sumdim, unroll_dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_trilinear", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, i1, i2, i3, expand1, expand2, expand3, sumdim, unroll_dim); +#endif +} +static inline Tensor triplet_margin_loss(const Tensor & anchor, const Tensor & positive, const Tensor & negative, double margin, double p, double eps, bool swap, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::triplet_margin_loss(anchor, positive, negative, margin, p, eps, swap, reduction); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::triplet_margin_loss", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, anchor, positive, negative, margin, p, eps, swap, reduction); +#endif +} +static inline Tensor trunc(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::trunc(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::trunc", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & trunc_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::trunc_(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::trunc_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & trunc_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::trunc_out(out, self); + break; + default: + AT_ERROR("trunc_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::trunc", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline bool _has_compatible_shallow_copy_type(const Tensor & self, const Tensor & from) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_has_compatible_shallow_copy_type(self, from); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_has_compatible_shallow_copy_type", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, from); +#endif +} +static inline std::tuple _unique(const Tensor & self, bool sorted, bool return_inverse) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_unique(self, sorted, return_inverse); + break; + default: + AT_ERROR("_unique not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_unique", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, bool, bool>( + op, self, sorted, return_inverse); +#endif +} +static inline std::tuple unique_dim(const Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::unique_dim(self, dim, sorted, return_inverse, return_counts); + break; + default: + AT_ERROR("unique_dim not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::unique_dim", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, bool, bool, bool>( + op, self, dim, sorted, return_inverse, return_counts); +#endif +} +static inline std::tuple unique_consecutive(const Tensor & self, bool return_inverse, bool return_counts, c10::optional dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::unique_consecutive(self, return_inverse, return_counts, dim); + break; + default: + AT_ERROR("unique_consecutive not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::unique_consecutive", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, bool, bool, c10::optional>( + op, self, return_inverse, return_counts, dim); +#endif +} +static inline std::tuple unique_dim_consecutive(const Tensor & self, int64_t dim, bool return_inverse, bool return_counts) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::unique_dim_consecutive(self, dim, return_inverse, return_counts); + break; + default: + AT_ERROR("unique_dim_consecutive not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::unique_dim_consecutive", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, bool, bool>( + op, self, dim, return_inverse, return_counts); +#endif +} +static inline std::tuple _unique2(const Tensor & self, bool sorted, bool return_inverse, bool return_counts) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_unique2(self, sorted, return_inverse, return_counts); + break; + default: + AT_ERROR("_unique2 not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_unique2", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, bool, bool, bool>( + op, self, sorted, return_inverse, return_counts); +#endif +} +static inline Tensor _unsafe_view(const Tensor & self, IntArrayRef size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_unsafe_view(self, size); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_unsafe_view", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, size); +#endif +} +static inline Tensor unsqueeze(const Tensor & self, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::unsqueeze(self, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::unsqueeze", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim); +#endif +} +static inline Tensor var(const Tensor & self, bool unbiased) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::var(self, unbiased); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::var", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, unbiased); +#endif +} +static inline Tensor var(const Tensor & self, IntArrayRef dim, bool unbiased, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::var(self, dim, unbiased, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::var", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, unbiased, keepdim); +#endif +} +static inline Tensor & var_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool unbiased, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::var_out(out, self, dim, unbiased, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::var", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, dim, unbiased, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor var(const Tensor & self, DimnameList dim, bool unbiased, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::var(self, dim, unbiased, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::var", "names_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, unbiased, keepdim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & var_out(Tensor & out, const Tensor & self, DimnameList dim, bool unbiased, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::var_out(out, self, dim, unbiased, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::var", "names_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, dim, unbiased, keepdim); +#endif +} +#endif +static inline std::tuple var_mean(const Tensor & self, bool unbiased) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::var_mean(self, unbiased); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::var_mean", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, bool>( + op, self, unbiased); +#endif +} +static inline std::tuple var_mean(const Tensor & self, IntArrayRef dim, bool unbiased, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::var_mean(self, dim, unbiased, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::var_mean", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, IntArrayRef, bool, bool>( + op, self, dim, unbiased, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple var_mean(const Tensor & self, DimnameList dim, bool unbiased, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::var_mean(self, dim, unbiased, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::var_mean", "names_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, DimnameList, bool, bool>( + op, self, dim, unbiased, keepdim); +#endif +} +#endif +static inline Tensor where(const Tensor & condition, const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::where(condition, self, other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::where", "self"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, condition, self, other); +#endif +} +static inline std::vector where(const Tensor & condition) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::where(condition); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::where", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &>( + op, condition); +#endif +} +static inline Tensor _s_where(const Tensor & condition, const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(condition, self, other)))) { + case Backend::CPU: + return CPUType::_s_where(condition, self, other); + break; + default: + AT_ERROR("_s_where not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(condition, self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_s_where", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, condition, self, other); +#endif +} +static inline Tensor norm_except_dim(const Tensor & v, int64_t pow, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::norm_except_dim(v, pow, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::norm_except_dim", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, v, pow, dim); +#endif +} +static inline Tensor _weight_norm(const Tensor & v, const Tensor & g, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_weight_norm(v, g, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_weight_norm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, v, g, dim); +#endif +} +static inline std::tuple _weight_norm_cuda_interface(const Tensor & v, const Tensor & g, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(v, g)))) { + + default: + AT_ERROR("_weight_norm_cuda_interface not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(v, g))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_weight_norm_cuda_interface", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, int64_t>( + op, v, g, dim); +#endif +} +static inline std::tuple _weight_norm_cuda_interface_backward(const Tensor & grad_w, const Tensor & saved_v, const Tensor & saved_g, const Tensor & saved_norms, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_w, saved_v, saved_g, saved_norms)))) { + + default: + AT_ERROR("_weight_norm_cuda_interface_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_w, saved_v, saved_g, saved_norms))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_weight_norm_cuda_interface_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, int64_t>( + op, grad_w, saved_v, saved_g, saved_norms, dim); +#endif +} +static inline std::tuple _weight_norm_differentiable_backward(const Tensor & grad_w, const Tensor & saved_v, const Tensor & saved_g, const Tensor & saved_norms, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_weight_norm_differentiable_backward(grad_w, saved_v, saved_g, saved_norms, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_weight_norm_differentiable_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, int64_t>( + op, grad_w, saved_v, saved_g, saved_norms, dim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor zeros(IntArrayRef size, c10::optional names, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::zeros(size, names, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::zeros", "names"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const TensorOptions &>( + op, size, names, options); +#endif +} +#endif +static inline Tensor zeros(IntArrayRef size, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::zeros(size, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::zeros", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, size, options); +#endif +} +static inline Tensor & zeros_out(Tensor & out, IntArrayRef size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::zeros_out(out, size); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::zeros", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, size); +#endif +} +static inline Tensor zeros_like(const Tensor & self, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::zeros_like(self, memory_format); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::zeros_like", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, memory_format); +#endif +} +static inline Tensor zeros_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::zeros_like(self, options, memory_format); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(self, options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::zeros_like", "dtype"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, options, memory_format); +#endif +} +static inline Tensor _standard_gamma_grad(const Tensor & self, const Tensor & output) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, output)))) { + case Backend::CPU: + return CPUType::_standard_gamma_grad(self, output); + break; + default: + AT_ERROR("_standard_gamma_grad not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_standard_gamma_grad", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, output); +#endif +} +static inline Tensor _standard_gamma(const Tensor & self, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_standard_gamma(self, generator); + break; + default: + AT_ERROR("_standard_gamma not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_standard_gamma", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, generator); +#endif +} +static inline Tensor _dirichlet_grad(const Tensor & x, const Tensor & alpha, const Tensor & total) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(x, alpha, total)))) { + case Backend::CPU: + return CPUType::_dirichlet_grad(x, alpha, total); + break; + default: + AT_ERROR("_dirichlet_grad not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(x, alpha, total))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_dirichlet_grad", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, x, alpha, total); +#endif +} +static inline Tensor _sample_dirichlet(const Tensor & self, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_sample_dirichlet(self, generator); + break; + default: + AT_ERROR("_sample_dirichlet not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_sample_dirichlet", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, generator); +#endif +} +static inline Tensor poisson(const Tensor & self, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::poisson(self, generator); + break; + default: + AT_ERROR("poisson not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::poisson", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, generator); +#endif +} +static inline Tensor native_norm(const Tensor & self, Scalar p) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::SparseCPU: + return SparseCPUType::native_norm(self, p); + break; + default: + AT_ERROR("native_norm not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::native_norm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, p); +#endif +} +static inline Tensor _sparse_sum(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_sparse_sum(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_sparse_sum", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor _sparse_sum(const Tensor & self, ScalarType dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_sparse_sum(self, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_sparse_sum", "dtype"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dtype); +#endif +} +static inline Tensor _sparse_sum(const Tensor & self, IntArrayRef dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_sparse_sum(self, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_sparse_sum", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim); +#endif +} +static inline Tensor _sparse_sum(const Tensor & self, IntArrayRef dim, ScalarType dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_sparse_sum(self, dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_sparse_sum", "dim_dtype"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, dtype); +#endif +} +static inline Tensor _sparse_sum_backward(const Tensor & grad, const Tensor & self, IntArrayRef dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad, self)))) { + case Backend::SparseCPU: + return SparseCPUType::_sparse_sum_backward(grad, self, dim); + break; + default: + AT_ERROR("_sparse_sum_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_sparse_sum_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad, self, dim); +#endif +} +static inline Tensor norm(const Tensor & self, c10::optional p, ScalarType dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::norm(self, p, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::norm", "ScalarOpt_dtype"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, ScalarType>( + op, self, p, dtype); +#endif +} +static inline Tensor norm(const Tensor & self, Scalar p) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::norm(self, p); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::norm", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, p); +#endif +} +static inline Tensor norm(const Tensor & self, c10::optional p, IntArrayRef dim, bool keepdim, ScalarType dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::norm(self, p, dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::norm", "ScalarOpt_dim_dtype"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, IntArrayRef, bool, ScalarType>( + op, self, p, dim, keepdim, dtype); +#endif +} +static inline Tensor norm(const Tensor & self, c10::optional p, IntArrayRef dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::norm(self, p, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::norm", "ScalarOpt_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, IntArrayRef, bool>( + op, self, p, dim, keepdim); +#endif +} +static inline Tensor & norm_out(Tensor & out, const Tensor & self, c10::optional p, IntArrayRef dim, bool keepdim, ScalarType dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::norm_out(out, self, p, dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::norm", "dtype_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, IntArrayRef, bool, ScalarType>( + op, out, self, p, dim, keepdim, dtype); +#endif +} +static inline Tensor & norm_out(Tensor & out, const Tensor & self, c10::optional p, IntArrayRef dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::norm_out(out, self, p, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::norm", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, IntArrayRef, bool>( + op, out, self, p, dim, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor norm(const Tensor & self, c10::optional p, DimnameList dim, bool keepdim, ScalarType dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::norm(self, p, dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::norm", "names_ScalarOpt_dim_dtype"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, DimnameList, bool, ScalarType>( + op, self, p, dim, keepdim, dtype); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor norm(const Tensor & self, c10::optional p, DimnameList dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::norm(self, p, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::norm", "names_ScalarOpt_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, DimnameList, bool>( + op, self, p, dim, keepdim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & norm_out(Tensor & out, const Tensor & self, c10::optional p, DimnameList dim, bool keepdim, ScalarType dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::norm_out(out, self, p, dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::norm", "names_dtype_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, DimnameList, bool, ScalarType>( + op, out, self, p, dim, keepdim, dtype); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & norm_out(Tensor & out, const Tensor & self, c10::optional p, DimnameList dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::norm_out(out, self, p, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::norm", "names_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, DimnameList, bool>( + op, out, self, p, dim, keepdim); +#endif +} +#endif +static inline Tensor frobenius_norm(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::frobenius_norm(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::frobenius_norm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor frobenius_norm(const Tensor & self, IntArrayRef dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::frobenius_norm(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::frobenius_norm", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, keepdim); +#endif +} +static inline Tensor & frobenius_norm_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::frobenius_norm_out(out, self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::frobenius_norm", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, dim, keepdim); +#endif +} +static inline Tensor nuclear_norm(const Tensor & self, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::nuclear_norm(self, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::nuclear_norm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, keepdim); +#endif +} +static inline Tensor & nuclear_norm_out(Tensor & out, const Tensor & self, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::nuclear_norm_out(out, self, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::nuclear_norm", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, keepdim); +#endif +} +static inline Tensor nuclear_norm(const Tensor & self, IntArrayRef dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::nuclear_norm(self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::nuclear_norm", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, keepdim); +#endif +} +static inline Tensor & nuclear_norm_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::nuclear_norm_out(out, self, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::nuclear_norm", "dim_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, dim, keepdim); +#endif +} +static inline Tensor clone(const Tensor & self, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::clone(self, memory_format); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::clone(self, memory_format); + break; + case Backend::SparseCPU: + return SparseCPUType::clone(self, memory_format); + break; + default: + AT_ERROR("clone not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::clone", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, memory_format); +#endif +} +static inline Tensor & resize_as_(Tensor & self, const Tensor & the_template, c10::optional memory_format) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::resize_as_(self, the_template, memory_format); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::resize_as_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, the_template, memory_format); +#endif +} +static inline Tensor & pow_out(Tensor & out, const Tensor & self, Scalar exponent) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::pow_out(out, self, exponent); + break; + case Backend::SparseCPU: + return SparseCPUType::pow_out(out, self, exponent); + break; + default: + AT_ERROR("pow_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::pow", "Tensor_Scalar_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, exponent); +#endif +} +static inline Tensor pow(const Tensor & self, Scalar exponent) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::pow(self, exponent); + break; + case Backend::SparseCPU: + return SparseCPUType::pow(self, exponent); + break; + default: + AT_ERROR("pow not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::pow", "Tensor_Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, exponent); +#endif +} +static inline Tensor & zero_(Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::zero_(self); + break; + case Backend::SparseCPU: + return SparseCPUType::zero_(self); + break; + default: + AT_ERROR("zero_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::zero_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & sub_out(Tensor & out, const Tensor & self, const Tensor & other, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, other)))) { + case Backend::CPU: + return CPUType::sub_out(out, self, other, alpha); + break; + case Backend::SparseCPU: + return SparseCPUType::sub_out(out, self, other, alpha); + break; + default: + AT_ERROR("sub_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sub", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other, alpha); +#endif +} +static inline Tensor sub(const Tensor & self, const Tensor & other, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::sub(self, other, alpha); + break; + case Backend::SparseCPU: + return SparseCPUType::sub(self, other, alpha); + break; + default: + AT_ERROR("sub not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sub", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other, alpha); +#endif +} +static inline Tensor sub(const Tensor & self, Scalar other, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sub(self, other, alpha); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sub", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other, alpha); +#endif +} +static inline Tensor rsub(const Tensor & self, const Tensor & other, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rsub(self, other, alpha); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rsub", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other, alpha); +#endif +} +static inline Tensor rsub(const Tensor & self, Scalar other, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rsub(self, other, alpha); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rsub", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other, alpha); +#endif +} +static inline Tensor _sparse_addmm(const Tensor & self, const Tensor & sparse, const Tensor & dense, Scalar beta, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_sparse_addmm(self, sparse, dense, beta, alpha); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_sparse_addmm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, sparse, dense, beta, alpha); +#endif +} +static inline Tensor & addmm_out(Tensor & out, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, mat1, mat2)))) { + case Backend::CPU: + return CPUType::addmm_out(out, self, mat1, mat2, beta, alpha); + break; + case Backend::SparseCPU: + return SparseCPUType::addmm_out(out, self, mat1, mat2, beta, alpha); + break; + default: + AT_ERROR("addmm_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, mat1, mat2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::addmm", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, mat1, mat2, beta, alpha); +#endif +} +static inline Tensor addmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, mat1, mat2)))) { + case Backend::CPU: + return CPUType::addmm(self, mat1, mat2, beta, alpha); + break; + case Backend::SparseCPU: + return SparseCPUType::addmm(self, mat1, mat2, beta, alpha); + break; + default: + AT_ERROR("addmm not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, mat1, mat2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::addmm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, mat1, mat2, beta, alpha); +#endif +} +static inline Tensor sparse_coo_tensor(IntArrayRef size, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sparse_coo_tensor(size, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sparse_coo_tensor", "size"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, size, options); +#endif +} +static inline Tensor sparse_coo_tensor(const Tensor & indices, const Tensor & values, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sparse_coo_tensor(indices, values, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(indices, values, options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sparse_coo_tensor", "indices"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, indices, values, options); +#endif +} +static inline Tensor sparse_coo_tensor(const Tensor & indices, const Tensor & values, IntArrayRef size, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sparse_coo_tensor(indices, values, size, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(indices, values, options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sparse_coo_tensor", "indices_size"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, indices, values, size, options); +#endif +} +static inline Tensor _sparse_coo_tensor_unsafe(const Tensor & indices, const Tensor & values, IntArrayRef size, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_sparse_coo_tensor_unsafe(indices, values, size, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(indices, values, options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_sparse_coo_tensor_unsafe", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, indices, values, size, options); +#endif +} +static inline Tensor _sparse_coo_tensor_with_dims(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(options)))) { + case Backend::SparseCPU: + return SparseCPUType::_sparse_coo_tensor_with_dims(sparse_dim, dense_dim, size, options); + break; + default: + AT_ERROR("_sparse_coo_tensor_with_dims not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(options))); + } +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_sparse_coo_tensor_with_dims", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, sparse_dim, dense_dim, size, options); +#endif +} +static inline Tensor _sparse_coo_tensor_with_dims_and_tensors(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size, const Tensor & indices, const Tensor & values, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(indices, values, options)))) { + case Backend::SparseCPU: + return SparseCPUType::_sparse_coo_tensor_with_dims_and_tensors(sparse_dim, dense_dim, size, indices, values, options); + break; + default: + AT_ERROR("_sparse_coo_tensor_with_dims_and_tensors not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(indices, values, options))); + } +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(indices, values, options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_sparse_coo_tensor_with_dims_and_tensors", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, sparse_dim, dense_dim, size, indices, values, options); +#endif +} +static inline Tensor to_dense_backward(const Tensor & grad, const Tensor & input) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::to_dense_backward(grad, input); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::to_dense_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad, input); +#endif +} +static inline Tensor & hspmm_out(Tensor & out, const Tensor & mat1, const Tensor & mat2) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, mat1, mat2)))) { + case Backend::SparseCPU: + return SparseCPUType::hspmm_out(out, mat1, mat2); + break; + default: + AT_ERROR("hspmm_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, mat1, mat2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::hspmm", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, mat1, mat2); +#endif +} +static inline Tensor hspmm(const Tensor & mat1, const Tensor & mat2) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(mat1, mat2)))) { + case Backend::SparseCPU: + return SparseCPUType::hspmm(mat1, mat2); + break; + default: + AT_ERROR("hspmm not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(mat1, mat2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::hspmm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, mat1, mat2); +#endif +} +static inline Tensor & copy_sparse_to_sparse_(Tensor & self, const Tensor & src, bool non_blocking) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, src)))) { + case Backend::SparseCPU: + return SparseCPUType::copy_sparse_to_sparse_(self, src, non_blocking); + break; + default: + AT_ERROR("copy_sparse_to_sparse_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, src))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::copy_sparse_to_sparse_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, src, non_blocking); +#endif +} +static inline std::vector unbind(const Tensor & self, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::unbind(self, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::unbind", "int"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t>( + op, self, dim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline std::vector unbind(const Tensor & self, Dimname dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::unbind(self, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::unbind", "Dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, Dimname>( + op, self, dim); +#endif +} +#endif +static inline Tensor mkldnn_reorder_conv2d_weight(const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + + default: + AT_ERROR("mkldnn_reorder_conv2d_weight not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mkldnn_reorder_conv2d_weight", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, padding, stride, dilation, groups); +#endif +} +static inline Tensor to_mkldnn_backward(const Tensor & grad, const Tensor & input) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::to_mkldnn_backward(grad, input); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::to_mkldnn_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad, input); +#endif +} +static inline Tensor quantize_per_tensor(const Tensor & self, double scale, int64_t zero_point, ScalarType dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::quantize_per_tensor(self, scale, zero_point, dtype); + break; + default: + AT_ERROR("quantize_per_tensor not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::quantize_per_tensor", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, scale, zero_point, dtype); +#endif +} +static inline Tensor quantize_per_channel(const Tensor & self, const Tensor & scales, const Tensor & zero_points, int64_t axis, ScalarType dtype) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, scales, zero_points)))) { + case Backend::CPU: + return CPUType::quantize_per_channel(self, scales, zero_points, axis, dtype); + break; + default: + AT_ERROR("quantize_per_channel not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, scales, zero_points))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::quantize_per_channel", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, scales, zero_points, axis, dtype); +#endif +} +static inline Tensor dequantize(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::QuantizedCPU: + return QuantizedCPUType::dequantize(self); + break; + default: + AT_ERROR("dequantize not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::dequantize", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline double q_scale(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::QuantizedCPU: + return QuantizedCPUType::q_scale(self); + break; + default: + AT_ERROR("q_scale not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::q_scale", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline int64_t q_zero_point(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::QuantizedCPU: + return QuantizedCPUType::q_zero_point(self); + break; + default: + AT_ERROR("q_zero_point not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::q_zero_point", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor q_per_channel_scales(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::QuantizedCPU: + return QuantizedCPUType::q_per_channel_scales(self); + break; + default: + AT_ERROR("q_per_channel_scales not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::q_per_channel_scales", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor q_per_channel_zero_points(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::QuantizedCPU: + return QuantizedCPUType::q_per_channel_zero_points(self); + break; + default: + AT_ERROR("q_per_channel_zero_points not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::q_per_channel_zero_points", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline int64_t q_per_channel_axis(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::QuantizedCPU: + return QuantizedCPUType::q_per_channel_axis(self); + break; + default: + AT_ERROR("q_per_channel_axis not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::q_per_channel_axis", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor int_repr(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::QuantizedCPU: + return QuantizedCPUType::int_repr(self); + break; + default: + AT_ERROR("int_repr not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::int_repr", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor _make_per_tensor_quantized_tensor(const Tensor & self, double scale, int64_t zero_point) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_make_per_tensor_quantized_tensor(self, scale, zero_point); + break; + default: + AT_ERROR("_make_per_tensor_quantized_tensor not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_make_per_tensor_quantized_tensor", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, scale, zero_point); +#endif +} +static inline Tensor _make_per_channel_quantized_tensor(const Tensor & self, const Tensor & scale, const Tensor & zero_point, int64_t axis) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, scale, zero_point)))) { + case Backend::CPU: + return CPUType::_make_per_channel_quantized_tensor(self, scale, zero_point, axis); + break; + default: + AT_ERROR("_make_per_channel_quantized_tensor not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, scale, zero_point))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_make_per_channel_quantized_tensor", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, scale, zero_point, axis); +#endif +} +static inline Tensor fake_quantize_per_tensor_affine(const Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::fake_quantize_per_tensor_affine(self, scale, zero_point, quant_min, quant_max); + break; + default: + AT_ERROR("fake_quantize_per_tensor_affine not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fake_quantize_per_tensor_affine", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, scale, zero_point, quant_min, quant_max); +#endif +} +static inline Tensor fake_quantize_per_tensor_affine_backward(const Tensor & grad, const Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad, self)))) { + case Backend::CPU: + return CPUType::fake_quantize_per_tensor_affine_backward(grad, self, scale, zero_point, quant_min, quant_max); + break; + default: + AT_ERROR("fake_quantize_per_tensor_affine_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fake_quantize_per_tensor_affine_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad, self, scale, zero_point, quant_min, quant_max); +#endif +} +static inline Tensor fake_quantize_per_channel_affine(const Tensor & self, const Tensor & scale, const Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, scale, zero_point)))) { + case Backend::CPU: + return CPUType::fake_quantize_per_channel_affine(self, scale, zero_point, axis, quant_min, quant_max); + break; + default: + AT_ERROR("fake_quantize_per_channel_affine not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, scale, zero_point))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fake_quantize_per_channel_affine", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, scale, zero_point, axis, quant_min, quant_max); +#endif +} +static inline Tensor fake_quantize_per_channel_affine_backward(const Tensor & grad, const Tensor & self, const Tensor & scale, const Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad, self, scale, zero_point)))) { + case Backend::CPU: + return CPUType::fake_quantize_per_channel_affine_backward(grad, self, scale, zero_point, axis, quant_min, quant_max); + break; + default: + AT_ERROR("fake_quantize_per_channel_affine_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad, self, scale, zero_point))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fake_quantize_per_channel_affine_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad, self, scale, zero_point, axis, quant_min, quant_max); +#endif +} +static inline std::vector meshgrid(TensorList tensors) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::meshgrid(tensors); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::meshgrid", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, TensorList>( + op, tensors); +#endif +} +static inline Tensor cartesian_prod(TensorList tensors) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cartesian_prod(tensors); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cartesian_prod", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, tensors); +#endif +} +static inline Tensor combinations(const Tensor & self, int64_t r, bool with_replacement) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::combinations(self, r, with_replacement); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::combinations", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, r, with_replacement); +#endif +} +static inline ScalarType result_type(const Tensor & tensor, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::result_type(tensor, other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::result_type", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, tensor, other); +#endif +} +static inline ScalarType result_type(const Tensor & tensor, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::result_type(tensor, other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::result_type", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, tensor, other); +#endif +} +static inline ScalarType result_type(Scalar scalar, const Tensor & tensor) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::result_type(scalar, tensor); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::result_type", "Scalar_Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, scalar, tensor); +#endif +} +static inline ScalarType result_type(Scalar scalar1, Scalar scalar2) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::result_type(scalar1, scalar2); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::result_type", "Scalar_Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, scalar1, scalar2); +#endif +} +static inline bool can_cast(ScalarType from, ScalarType to) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::can_cast(from, to); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::can_cast", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, from, to); +#endif +} +static inline ScalarType promote_types(ScalarType type1, ScalarType type2) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::promote_types(type1, type2); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::promote_types", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, type1, type2); +#endif +} +static inline Scalar _local_scalar_dense(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_local_scalar_dense(self); + break; + default: + AT_ERROR("_local_scalar_dense not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_local_scalar_dense", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline std::tuple _thnn_fused_lstm_cell(const Tensor & input_gates, const Tensor & hidden_gates, const Tensor & cx, const Tensor & input_bias, const Tensor & hidden_bias) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input_gates, hidden_gates, cx, input_bias, hidden_bias)))) { + + default: + AT_ERROR("_thnn_fused_lstm_cell not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input_gates, hidden_gates, cx, input_bias, hidden_bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_thnn_fused_lstm_cell", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &>( + op, input_gates, hidden_gates, cx, input_bias, hidden_bias); +#endif +} +static inline std::tuple _thnn_fused_lstm_cell_backward(const Tensor & grad_hy, const Tensor & grad_cy, const Tensor & cx, const Tensor & cy, const Tensor & workspace, bool has_bias) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_hy, grad_cy, cx, cy, workspace)))) { + + default: + AT_ERROR("_thnn_fused_lstm_cell_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_hy, grad_cy, cx, cy, workspace))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_thnn_fused_lstm_cell_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, bool>( + op, grad_hy, grad_cy, cx, cy, workspace, has_bias); +#endif +} +static inline std::tuple _thnn_differentiable_lstm_cell_backward(const Tensor & grad_hy, const Tensor & grad_cy, const Tensor & input_gates, const Tensor & hidden_gates, const Tensor & input_bias, const Tensor & hidden_bias, const Tensor & cx, const Tensor & cy) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_thnn_differentiable_lstm_cell_backward(grad_hy, grad_cy, input_gates, hidden_gates, input_bias, hidden_bias, cx, cy); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_thnn_differentiable_lstm_cell_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &>( + op, grad_hy, grad_cy, input_gates, hidden_gates, input_bias, hidden_bias, cx, cy); +#endif +} +static inline std::tuple _thnn_fused_gru_cell(const Tensor & input_gates, const Tensor & hidden_gates, const Tensor & hx, const Tensor & input_bias, const Tensor & hidden_bias) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(input_gates, hidden_gates, hx, input_bias, hidden_bias)))) { + + default: + AT_ERROR("_thnn_fused_gru_cell not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(input_gates, hidden_gates, hx, input_bias, hidden_bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_thnn_fused_gru_cell", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &>( + op, input_gates, hidden_gates, hx, input_bias, hidden_bias); +#endif +} +static inline std::tuple _thnn_fused_gru_cell_backward(const Tensor & grad_hy, const Tensor & workspace, bool has_bias) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_hy, workspace)))) { + + default: + AT_ERROR("_thnn_fused_gru_cell_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_hy, workspace))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_thnn_fused_gru_cell_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, bool>( + op, grad_hy, workspace, has_bias); +#endif +} +static inline std::tuple _thnn_differentiable_gru_cell_backward(const Tensor & grad_hy, const Tensor & input_gates, const Tensor & hidden_gates, const Tensor & hx, const Tensor & input_bias, const Tensor & hidden_bias) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_thnn_differentiable_gru_cell_backward(grad_hy, input_gates, hidden_gates, hx, input_bias, hidden_bias); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_thnn_differentiable_gru_cell_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &>( + op, grad_hy, input_gates, hidden_gates, hx, input_bias, hidden_bias); +#endif +} +static inline std::tuple lstm(const Tensor & input, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::lstm(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::lstm", "input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, TensorList, TensorList, bool, int64_t, double, bool, bool, bool>( + op, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); +#endif +} +static inline std::tuple lstm(const Tensor & data, const Tensor & batch_sizes, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::lstm(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::lstm", "data"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, TensorList, TensorList, bool, int64_t, double, bool, bool>( + op, data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); +#endif +} +static inline std::tuple gru(const Tensor & input, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::gru(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::gru", "input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, TensorList, bool, int64_t, double, bool, bool, bool>( + op, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); +#endif +} +static inline std::tuple gru(const Tensor & data, const Tensor & batch_sizes, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::gru(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::gru", "data"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, TensorList, bool, int64_t, double, bool, bool>( + op, data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); +#endif +} +static inline std::tuple rnn_tanh(const Tensor & input, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rnn_tanh(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rnn_tanh", "input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, TensorList, bool, int64_t, double, bool, bool, bool>( + op, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); +#endif +} +static inline std::tuple rnn_tanh(const Tensor & data, const Tensor & batch_sizes, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rnn_tanh(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rnn_tanh", "data"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, TensorList, bool, int64_t, double, bool, bool>( + op, data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); +#endif +} +static inline std::tuple rnn_relu(const Tensor & input, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rnn_relu(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rnn_relu", "input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, TensorList, bool, int64_t, double, bool, bool, bool>( + op, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); +#endif +} +static inline std::tuple rnn_relu(const Tensor & data, const Tensor & batch_sizes, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rnn_relu(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rnn_relu", "data"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, TensorList, bool, int64_t, double, bool, bool>( + op, data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); +#endif +} +static inline std::tuple lstm_cell(const Tensor & input, TensorList hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::lstm_cell(input, hx, w_ih, w_hh, b_ih, b_hh); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::lstm_cell", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, TensorList, const Tensor &, const Tensor &, const Tensor &, const Tensor &>( + op, input, hx, w_ih, w_hh, b_ih, b_hh); +#endif +} +static inline Tensor gru_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::gru_cell(input, hx, w_ih, w_hh, b_ih, b_hh); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::gru_cell", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, hx, w_ih, w_hh, b_ih, b_hh); +#endif +} +static inline Tensor rnn_tanh_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rnn_tanh_cell(input, hx, w_ih, w_hh, b_ih, b_hh); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rnn_tanh_cell", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, hx, w_ih, w_hh, b_ih, b_hh); +#endif +} +static inline Tensor rnn_relu_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rnn_relu_cell(input, hx, w_ih, w_hh, b_ih, b_hh); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rnn_relu_cell", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, input, hx, w_ih, w_hh, b_ih, b_hh); +#endif +} +static inline std::tuple quantized_lstm(const Tensor & input, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first, c10::optional dtype, bool use_dynamic) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::quantized_lstm(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first, dtype, use_dynamic); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::quantized_lstm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, TensorList, TensorList, bool, int64_t, double, bool, bool, bool, c10::optional, bool>( + op, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first, dtype, use_dynamic); +#endif +} +static inline std::tuple quantized_lstm(const Tensor & data, const Tensor & batch_sizes, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, c10::optional dtype, bool use_dynamic) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::quantized_lstm(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional, dtype, use_dynamic); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::quantized_lstm", "data"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, TensorList, TensorList, bool, int64_t, double, bool, bool, c10::optional, bool>( + op, data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional, dtype, use_dynamic); +#endif +} +static inline std::tuple quantized_gru(const Tensor & input, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::quantized_gru(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::quantized_gru", "input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, TensorList, bool, int64_t, double, bool, bool, bool>( + op, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); +#endif +} +static inline std::tuple quantized_gru(const Tensor & data, const Tensor & batch_sizes, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::quantized_gru(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::quantized_gru", "data"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, TensorList, bool, int64_t, double, bool, bool>( + op, data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); +#endif +} +static inline std::tuple quantized_lstm_cell(const Tensor & input, TensorList hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh, const Tensor & packed_ih, const Tensor & packed_hh, const Tensor & col_offsets_ih, const Tensor & col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::quantized_lstm_cell(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::quantized_lstm_cell", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, TensorList, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, const Tensor &, Scalar, Scalar, Scalar, Scalar>( + op, input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); +#endif +} +static inline Tensor quantized_gru_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh, const Tensor & packed_ih, const Tensor & packed_hh, const Tensor & col_offsets_ih, const Tensor & col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::quantized_gru_cell(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::quantized_gru_cell", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); +#endif +} +static inline Tensor quantized_rnn_relu_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh, const Tensor & packed_ih, const Tensor & packed_hh, const Tensor & col_offsets_ih, const Tensor & col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::quantized_rnn_relu_cell(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::quantized_rnn_relu_cell", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); +#endif +} +static inline Tensor quantized_rnn_tanh_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh, const Tensor & packed_ih, const Tensor & packed_hh, const Tensor & col_offsets_ih, const Tensor & col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::quantized_rnn_tanh_cell(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::quantized_rnn_tanh_cell", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); +#endif +} +static inline std::tuple _pack_padded_sequence(const Tensor & input, const Tensor & lengths, bool batch_first) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_pack_padded_sequence(input, lengths, batch_first); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_pack_padded_sequence", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, bool>( + op, input, lengths, batch_first); +#endif +} +static inline Tensor _pack_padded_sequence_backward(const Tensor & grad, IntArrayRef input_size, const Tensor & batch_sizes, bool batch_first) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_pack_padded_sequence_backward(grad, input_size, batch_sizes, batch_first); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_pack_padded_sequence_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad, input_size, batch_sizes, batch_first); +#endif +} +static inline std::tuple _pad_packed_sequence(const Tensor & data, const Tensor & batch_sizes, bool batch_first, Scalar padding_value, int64_t total_length) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_pad_packed_sequence(data, batch_sizes, batch_first, padding_value, total_length); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_pad_packed_sequence", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, bool, Scalar, int64_t>( + op, data, batch_sizes, batch_first, padding_value, total_length); +#endif +} +static inline Tensor masked_fill(const Tensor & self, const Tensor & mask, Scalar value) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::masked_fill(self, mask, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::masked_fill", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, mask, value); +#endif +} +static inline Tensor masked_fill(const Tensor & self, const Tensor & mask, const Tensor & value) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::masked_fill(self, mask, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::masked_fill", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, mask, value); +#endif +} +static inline Tensor masked_scatter(const Tensor & self, const Tensor & mask, const Tensor & source) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::masked_scatter(self, mask, source); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::masked_scatter", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, mask, source); +#endif +} +static inline Tensor index_add(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & source) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_add(self, dim, index, source); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::index_add", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim, index, source); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor index_add(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & source) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_add(self, dim, index, source); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::index_add", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, index, source); +#endif +} +#endif +static inline Tensor index_fill(const Tensor & self, int64_t dim, const Tensor & index, Scalar value) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_fill(self, dim, index, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::index_fill", "int_Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim, index, value); +#endif +} +static inline Tensor index_fill(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & value) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_fill(self, dim, index, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::index_fill", "int_Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim, index, value); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor index_fill(const Tensor & self, Dimname dim, const Tensor & index, Scalar value) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_fill(self, dim, index, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::index_fill", "Dimname_Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, index, value); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor index_fill(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & value) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_fill(self, dim, index, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::index_fill", "Dimname_Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, index, value); +#endif +} +#endif +static inline Tensor scatter(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & src) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::scatter(self, dim, index, src); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::scatter", "src"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim, index, src); +#endif +} +static inline Tensor scatter(const Tensor & self, int64_t dim, const Tensor & index, Scalar value) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::scatter(self, dim, index, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::scatter", "value"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim, index, value); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor scatter(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & src) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::scatter(self, dim, index, src); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::scatter", "dimname_src"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, index, src); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor scatter(const Tensor & self, Dimname dim, const Tensor & index, Scalar value) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::scatter(self, dim, index, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::scatter", "dimname_value"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, index, value); +#endif +} +#endif +static inline Tensor scatter_add(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & src) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::scatter_add(self, dim, index, src); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::scatter_add", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim, index, src); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor scatter_add(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & src) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::scatter_add(self, dim, index, src); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::scatter_add", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, index, src); +#endif +} +#endif +static inline Tensor __and__(const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::__and__(self, other); + break; + default: + AT_ERROR("__and__ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::__and__", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor __and__(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::__and__(self, other); + break; + default: + AT_ERROR("__and__ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::__and__", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor __or__(const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::__or__(self, other); + break; + default: + AT_ERROR("__or__ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::__or__", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor __or__(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::__or__(self, other); + break; + default: + AT_ERROR("__or__ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::__or__", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & bitwise_xor_out(Tensor & out, const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, other)))) { + case Backend::CPU: + return CPUType::bitwise_xor_out(out, self, other); + break; + default: + AT_ERROR("bitwise_xor_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::bitwise_xor", "Tensor_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor & bitwise_xor_out(Tensor & out, const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::bitwise_xor_out(out, self, other); + break; + default: + AT_ERROR("bitwise_xor_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::bitwise_xor", "Scalar_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor bitwise_xor(const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::bitwise_xor(self, other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::bitwise_xor", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, other); +#endif +} +static inline Tensor bitwise_xor(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::bitwise_xor(self, other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::bitwise_xor", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, other); +#endif +} +static inline Tensor __xor__(const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::__xor__(self, other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::__xor__", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor __xor__(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::__xor__(self, other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::__xor__", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor __lshift__(const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::__lshift__(self, other); + break; + default: + AT_ERROR("__lshift__ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::__lshift__", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor __lshift__(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::__lshift__(self, other); + break; + default: + AT_ERROR("__lshift__ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::__lshift__", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor __rshift__(const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::__rshift__(self, other); + break; + default: + AT_ERROR("__rshift__ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::__rshift__", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor __rshift__(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::__rshift__(self, other); + break; + default: + AT_ERROR("__rshift__ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::__rshift__", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & addbmm_out(Tensor & out, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, batch1, batch2)))) { + case Backend::CPU: + return CPUType::addbmm_out(out, self, batch1, batch2, beta, alpha); + break; + default: + AT_ERROR("addbmm_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, batch1, batch2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::addbmm", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, batch1, batch2, beta, alpha); +#endif +} +static inline Tensor addbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, batch1, batch2)))) { + case Backend::CPU: + return CPUType::addbmm(self, batch1, batch2, beta, alpha); + break; + default: + AT_ERROR("addbmm not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, batch1, batch2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::addbmm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, batch1, batch2, beta, alpha); +#endif +} +static inline Tensor & diag_out(Tensor & out, const Tensor & self, int64_t diagonal) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::diag_out(out, self, diagonal); + break; + default: + AT_ERROR("diag_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::diag", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, diagonal); +#endif +} +static inline Tensor diag(const Tensor & self, int64_t diagonal) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::diag(self, diagonal); + break; + default: + AT_ERROR("diag not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::diag", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, diagonal); +#endif +} +static inline Tensor & cross_out(Tensor & out, const Tensor & self, const Tensor & other, c10::optional dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cross_out(out, self, other, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cross", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, out, self, other, dim); +#endif +} +static inline Tensor cross(const Tensor & self, const Tensor & other, c10::optional dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cross(self, other, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cross", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed>( + op, self, other, dim); +#endif +} +static inline Tensor & triu_out(Tensor & out, const Tensor & self, int64_t diagonal) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::triu_out(out, self, diagonal); + break; + default: + AT_ERROR("triu_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::triu", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, diagonal); +#endif +} +static inline Tensor triu(const Tensor & self, int64_t diagonal) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::triu(self, diagonal); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::triu", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, diagonal); +#endif +} +static inline Tensor & tril_out(Tensor & out, const Tensor & self, int64_t diagonal) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::tril_out(out, self, diagonal); + break; + default: + AT_ERROR("tril_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::tril", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, diagonal); +#endif +} +static inline Tensor tril(const Tensor & self, int64_t diagonal) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::tril(self, diagonal); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::tril", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, diagonal); +#endif +} +static inline Tensor tril_indices(int64_t row, int64_t col, int64_t offset, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(options)))) { + case Backend::CPU: + return CPUType::tril_indices(row, col, offset, options); + break; + default: + AT_ERROR("tril_indices not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(options))); + } +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::tril_indices", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, row, col, offset, options); +#endif +} +static inline Tensor triu_indices(int64_t row, int64_t col, int64_t offset, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(options)))) { + case Backend::CPU: + return CPUType::triu_indices(row, col, offset, options); + break; + default: + AT_ERROR("triu_indices not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(options))); + } +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::triu_indices", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, row, col, offset, options); +#endif +} +static inline Tensor trace(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::trace(self); + break; + default: + AT_ERROR("trace not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::trace", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & ne_out(Tensor & out, const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::ne_out(out, self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::ne_out(out, self, other); + break; + default: + AT_ERROR("ne_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ne", "Scalar_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor ne(const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::ne(self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::ne(self, other); + break; + default: + AT_ERROR("ne not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ne", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & ne_out(Tensor & out, const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, other)))) { + case Backend::CPU: + return CPUType::ne_out(out, self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::ne_out(out, self, other); + break; + default: + AT_ERROR("ne_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ne", "Tensor_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor ne(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::ne(self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::ne(self, other); + break; + default: + AT_ERROR("ne not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ne", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & eq_out(Tensor & out, const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::eq_out(out, self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::eq_out(out, self, other); + break; + default: + AT_ERROR("eq_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::eq", "Scalar_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor eq(const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::eq(self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::eq(self, other); + break; + default: + AT_ERROR("eq not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::eq", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & eq_out(Tensor & out, const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, other)))) { + case Backend::CPU: + return CPUType::eq_out(out, self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::eq_out(out, self, other); + break; + default: + AT_ERROR("eq_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::eq", "Tensor_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor eq(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::eq(self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::eq(self, other); + break; + default: + AT_ERROR("eq not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::eq", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & ge_out(Tensor & out, const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::ge_out(out, self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::ge_out(out, self, other); + break; + default: + AT_ERROR("ge_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ge", "Scalar_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor ge(const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::ge(self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::ge(self, other); + break; + default: + AT_ERROR("ge not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ge", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & ge_out(Tensor & out, const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, other)))) { + case Backend::CPU: + return CPUType::ge_out(out, self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::ge_out(out, self, other); + break; + default: + AT_ERROR("ge_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ge", "Tensor_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor ge(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::ge(self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::ge(self, other); + break; + default: + AT_ERROR("ge not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ge", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & le_out(Tensor & out, const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::le_out(out, self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::le_out(out, self, other); + break; + default: + AT_ERROR("le_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::le", "Scalar_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor le(const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::le(self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::le(self, other); + break; + default: + AT_ERROR("le not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::le", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & le_out(Tensor & out, const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, other)))) { + case Backend::CPU: + return CPUType::le_out(out, self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::le_out(out, self, other); + break; + default: + AT_ERROR("le_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::le", "Tensor_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor le(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::le(self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::le(self, other); + break; + default: + AT_ERROR("le not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::le", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & gt_out(Tensor & out, const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::gt_out(out, self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::gt_out(out, self, other); + break; + default: + AT_ERROR("gt_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::gt", "Scalar_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor gt(const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::gt(self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::gt(self, other); + break; + default: + AT_ERROR("gt not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::gt", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & gt_out(Tensor & out, const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, other)))) { + case Backend::CPU: + return CPUType::gt_out(out, self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::gt_out(out, self, other); + break; + default: + AT_ERROR("gt_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::gt", "Tensor_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor gt(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::gt(self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::gt(self, other); + break; + default: + AT_ERROR("gt not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::gt", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & lt_out(Tensor & out, const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::lt_out(out, self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::lt_out(out, self, other); + break; + default: + AT_ERROR("lt_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::lt", "Scalar_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor lt(const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::lt(self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::lt(self, other); + break; + default: + AT_ERROR("lt not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::lt", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & lt_out(Tensor & out, const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, other)))) { + case Backend::CPU: + return CPUType::lt_out(out, self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::lt_out(out, self, other); + break; + default: + AT_ERROR("lt_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::lt", "Tensor_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor lt(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::lt(self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::lt(self, other); + break; + default: + AT_ERROR("lt not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::lt", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & take_out(Tensor & out, const Tensor & self, const Tensor & index) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, index)))) { + case Backend::CPU: + return CPUType::take_out(out, self, index); + break; + default: + AT_ERROR("take_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, index))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::take", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, index); +#endif +} +static inline Tensor take(const Tensor & self, const Tensor & index) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, index)))) { + case Backend::CPU: + return CPUType::take(self, index); + break; + default: + AT_ERROR("take not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, index))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::take", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, index); +#endif +} +static inline Tensor & index_select_out(Tensor & out, const Tensor & self, int64_t dim, const Tensor & index) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, index)))) { + case Backend::CPU: + return CPUType::index_select_out(out, self, dim, index); + break; + default: + AT_ERROR("index_select_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, index))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::index_select", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, dim, index); +#endif +} +static inline Tensor index_select(const Tensor & self, int64_t dim, const Tensor & index) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, index)))) { + case Backend::CPU: + return CPUType::index_select(self, dim, index); + break; + case Backend::SparseCPU: + return SparseCPUType::index_select(self, dim, index); + break; + default: + AT_ERROR("index_select not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, index))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::index_select", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim, index); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & index_select_out(Tensor & out, const Tensor & self, Dimname dim, const Tensor & index) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_select_out(out, self, dim, index); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::index_select", "dimname_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, dim, index); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor index_select(const Tensor & self, Dimname dim, const Tensor & index) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_select(self, dim, index); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::index_select", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, index); +#endif +} +#endif +static inline Tensor & masked_select_out(Tensor & out, const Tensor & self, const Tensor & mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, mask)))) { + case Backend::CPU: + return CPUType::masked_select_out(out, self, mask); + break; + default: + AT_ERROR("masked_select_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, mask))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::masked_select", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, mask); +#endif +} +static inline Tensor masked_select(const Tensor & self, const Tensor & mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, mask)))) { + case Backend::CPU: + return CPUType::masked_select(self, mask); + break; + default: + AT_ERROR("masked_select not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, mask))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::masked_select", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, mask); +#endif +} +static inline Tensor & nonzero_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::nonzero_out(out, self); + break; + default: + AT_ERROR("nonzero_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::nonzero", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor nonzero(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::nonzero(self); + break; + default: + AT_ERROR("nonzero not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::nonzero", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline std::vector nonzero_numpy(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::nonzero_numpy(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::nonzero_numpy", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &>( + op, self); +#endif +} +static inline Tensor & gather_out(Tensor & out, const Tensor & self, int64_t dim, const Tensor & index, bool sparse_grad) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, index)))) { + case Backend::CPU: + return CPUType::gather_out(out, self, dim, index, sparse_grad); + break; + default: + AT_ERROR("gather_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, index))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::gather", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, dim, index, sparse_grad); +#endif +} +static inline Tensor gather(const Tensor & self, int64_t dim, const Tensor & index, bool sparse_grad) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, index)))) { + case Backend::CPU: + return CPUType::gather(self, dim, index, sparse_grad); + break; + default: + AT_ERROR("gather not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, index))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::gather", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim, index, sparse_grad); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor & gather_out(Tensor & out, const Tensor & self, Dimname dim, const Tensor & index, bool sparse_grad) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::gather_out(out, self, dim, index, sparse_grad); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::gather", "dimname_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, dim, index, sparse_grad); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline Tensor gather(const Tensor & self, Dimname dim, const Tensor & index, bool sparse_grad) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::gather(self, dim, index, sparse_grad); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::gather", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, index, sparse_grad); +#endif +} +#endif +static inline Tensor _gather_sparse_backward(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & grad) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_gather_sparse_backward(self, dim, index, grad); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_gather_sparse_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim, index, grad); +#endif +} +static inline Tensor & addcmul_out(Tensor & out, const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::addcmul_out(out, self, tensor1, tensor2, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::addcmul", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, tensor1, tensor2, value); +#endif +} +static inline Tensor addcmul(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::addcmul(self, tensor1, tensor2, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::addcmul", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, tensor1, tensor2, value); +#endif +} +static inline Tensor & addcdiv_out(Tensor & out, const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::addcdiv_out(out, self, tensor1, tensor2, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::addcdiv", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, tensor1, tensor2, value); +#endif +} +static inline Tensor addcdiv(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::addcdiv(self, tensor1, tensor2, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::addcdiv", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, tensor1, tensor2, value); +#endif +} +static inline std::tuple lstsq_out(Tensor & X, Tensor & qr, const Tensor & self, const Tensor & A) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(X, qr, self, A)))) { + case Backend::CPU: + return CPUType::lstsq_out(X, qr, self, A); + break; + default: + AT_ERROR("lstsq_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(X, qr, self, A))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::lstsq", "X"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, const Tensor &>( + op, X, qr, self, A); +#endif +} +static inline std::tuple lstsq(const Tensor & self, const Tensor & A) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, A)))) { + case Backend::CPU: + return CPUType::lstsq(self, A); + break; + default: + AT_ERROR("lstsq not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, A))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::lstsq", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &>( + op, self, A); +#endif +} +static inline std::tuple triangular_solve_out(Tensor & X, Tensor & M, const Tensor & self, const Tensor & A, bool upper, bool transpose, bool unitriangular) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::triangular_solve_out(X, M, self, A, upper, transpose, unitriangular); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::triangular_solve", "X"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, const Tensor &, bool, bool, bool>( + op, X, M, self, A, upper, transpose, unitriangular); +#endif +} +static inline std::tuple triangular_solve(const Tensor & self, const Tensor & A, bool upper, bool transpose, bool unitriangular) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::triangular_solve(self, A, upper, transpose, unitriangular); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::triangular_solve", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, bool, bool, bool>( + op, self, A, upper, transpose, unitriangular); +#endif +} +static inline std::tuple _triangular_solve_helper(const Tensor & self, const Tensor & A, bool upper, bool transpose, bool unitriangular) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, A)))) { + case Backend::CPU: + return CPUType::_triangular_solve_helper(self, A, upper, transpose, unitriangular); + break; + default: + AT_ERROR("_triangular_solve_helper not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, A))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_triangular_solve_helper", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, bool, bool, bool>( + op, self, A, upper, transpose, unitriangular); +#endif +} +static inline std::tuple symeig_out(Tensor & e, Tensor & V, const Tensor & self, bool eigenvectors, bool upper) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::symeig_out(e, V, self, eigenvectors, upper); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::symeig", "e"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, bool, bool>( + op, e, V, self, eigenvectors, upper); +#endif +} +static inline std::tuple symeig(const Tensor & self, bool eigenvectors, bool upper) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::symeig(self, eigenvectors, upper); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::symeig", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, bool, bool>( + op, self, eigenvectors, upper); +#endif +} +static inline std::tuple _symeig_helper(const Tensor & self, bool eigenvectors, bool upper) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_symeig_helper(self, eigenvectors, upper); + break; + default: + AT_ERROR("_symeig_helper not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_symeig_helper", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, bool, bool>( + op, self, eigenvectors, upper); +#endif +} +static inline std::tuple eig_out(Tensor & e, Tensor & v, const Tensor & self, bool eigenvectors) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(e, v, self)))) { + case Backend::CPU: + return CPUType::eig_out(e, v, self, eigenvectors); + break; + default: + AT_ERROR("eig_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(e, v, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::eig", "e"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, bool>( + op, e, v, self, eigenvectors); +#endif +} +static inline std::tuple eig(const Tensor & self, bool eigenvectors) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::eig(self, eigenvectors); + break; + default: + AT_ERROR("eig not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::eig", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, bool>( + op, self, eigenvectors); +#endif +} +static inline std::tuple svd_out(Tensor & U, Tensor & S, Tensor & V, const Tensor & self, bool some, bool compute_uv) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::svd_out(U, S, V, self, some, compute_uv); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::svd", "U"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, Tensor &, const Tensor &, bool, bool>( + op, U, S, V, self, some, compute_uv); +#endif +} +static inline std::tuple svd(const Tensor & self, bool some, bool compute_uv) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::svd(self, some, compute_uv); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::svd", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, bool, bool>( + op, self, some, compute_uv); +#endif +} +static inline std::tuple _svd_helper(const Tensor & self, bool some, bool compute_uv) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_svd_helper(self, some, compute_uv); + break; + default: + AT_ERROR("_svd_helper not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_svd_helper", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, bool, bool>( + op, self, some, compute_uv); +#endif +} +static inline Tensor & cholesky_out(Tensor & out, const Tensor & self, bool upper) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cholesky_out(out, self, upper); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cholesky", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, upper); +#endif +} +static inline Tensor cholesky(const Tensor & self, bool upper) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cholesky(self, upper); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cholesky", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, upper); +#endif +} +static inline Tensor _cholesky_helper(const Tensor & self, bool upper) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_cholesky_helper(self, upper); + break; + default: + AT_ERROR("_cholesky_helper not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cholesky_helper", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, upper); +#endif +} +static inline Tensor & cholesky_solve_out(Tensor & out, const Tensor & self, const Tensor & input2, bool upper) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cholesky_solve_out(out, self, input2, upper); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cholesky_solve", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, input2, upper); +#endif +} +static inline Tensor cholesky_solve(const Tensor & self, const Tensor & input2, bool upper) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cholesky_solve(self, input2, upper); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cholesky_solve", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, input2, upper); +#endif +} +static inline Tensor _cholesky_solve_helper(const Tensor & self, const Tensor & A, bool upper) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, A)))) { + case Backend::CPU: + return CPUType::_cholesky_solve_helper(self, A, upper); + break; + default: + AT_ERROR("_cholesky_solve_helper not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, A))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cholesky_solve_helper", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, A, upper); +#endif +} +static inline std::tuple solve(const Tensor & self, const Tensor & A) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::solve(self, A); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::solve", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &>( + op, self, A); +#endif +} +static inline std::tuple solve_out(Tensor & solution, Tensor & lu, const Tensor & self, const Tensor & A) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::solve_out(solution, lu, self, A); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::solve", "solution"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, const Tensor &>( + op, solution, lu, self, A); +#endif +} +static inline std::tuple _solve_helper(const Tensor & self, const Tensor & A) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, A)))) { + case Backend::CPU: + return CPUType::_solve_helper(self, A); + break; + default: + AT_ERROR("_solve_helper not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, A))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_solve_helper", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &>( + op, self, A); +#endif +} +static inline Tensor & cholesky_inverse_out(Tensor & out, const Tensor & self, bool upper) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::cholesky_inverse_out(out, self, upper); + break; + default: + AT_ERROR("cholesky_inverse_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cholesky_inverse", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, upper); +#endif +} +static inline Tensor cholesky_inverse(const Tensor & self, bool upper) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::cholesky_inverse(self, upper); + break; + default: + AT_ERROR("cholesky_inverse not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::cholesky_inverse", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, upper); +#endif +} +static inline std::tuple qr_out(Tensor & Q, Tensor & R, const Tensor & self, bool some) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::qr_out(Q, R, self, some); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::qr", "Q"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, bool>( + op, Q, R, self, some); +#endif +} +static inline std::tuple qr(const Tensor & self, bool some) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::qr(self, some); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::qr", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, bool>( + op, self, some); +#endif +} +static inline std::tuple _qr_helper(const Tensor & self, bool some) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_qr_helper(self, some); + break; + default: + AT_ERROR("_qr_helper not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_qr_helper", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, bool>( + op, self, some); +#endif +} +static inline std::tuple geqrf_out(Tensor & a, Tensor & tau, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(a, tau, self)))) { + case Backend::CPU: + return CPUType::geqrf_out(a, tau, self); + break; + default: + AT_ERROR("geqrf_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(a, tau, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::geqrf", "a"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &>( + op, a, tau, self); +#endif +} +static inline std::tuple geqrf(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::geqrf(self); + break; + default: + AT_ERROR("geqrf not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::geqrf", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &>( + op, self); +#endif +} +static inline Tensor & orgqr_out(Tensor & out, const Tensor & self, const Tensor & input2) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, input2)))) { + case Backend::CPU: + return CPUType::orgqr_out(out, self, input2); + break; + default: + AT_ERROR("orgqr_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, input2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::orgqr", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, input2); +#endif +} +static inline Tensor orgqr(const Tensor & self, const Tensor & input2) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, input2)))) { + case Backend::CPU: + return CPUType::orgqr(self, input2); + break; + default: + AT_ERROR("orgqr not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, input2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::orgqr", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, input2); +#endif +} +static inline Tensor & ormqr_out(Tensor & out, const Tensor & self, const Tensor & input2, const Tensor & input3, bool left, bool transpose) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, input2, input3)))) { + case Backend::CPU: + return CPUType::ormqr_out(out, self, input2, input3, left, transpose); + break; + default: + AT_ERROR("ormqr_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, input2, input3))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ormqr", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, input2, input3, left, transpose); +#endif +} +static inline Tensor ormqr(const Tensor & self, const Tensor & input2, const Tensor & input3, bool left, bool transpose) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, input2, input3)))) { + case Backend::CPU: + return CPUType::ormqr(self, input2, input3, left, transpose); + break; + default: + AT_ERROR("ormqr not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, input2, input3))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::ormqr", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, input2, input3, left, transpose); +#endif +} +static inline std::tuple _lu_with_info(const Tensor & self, bool pivot, bool check_errors) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_lu_with_info(self, pivot, check_errors); + break; + default: + AT_ERROR("_lu_with_info not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_lu_with_info", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, bool, bool>( + op, self, pivot, check_errors); +#endif +} +static inline Tensor & lu_solve_out(Tensor & out, const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::lu_solve_out(out, self, LU_data, LU_pivots); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::lu_solve", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, LU_data, LU_pivots); +#endif +} +static inline Tensor lu_solve(const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::lu_solve(self, LU_data, LU_pivots); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::lu_solve", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, LU_data, LU_pivots); +#endif +} +static inline Tensor _lu_solve_helper(const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, LU_data, LU_pivots)))) { + case Backend::CPU: + return CPUType::_lu_solve_helper(self, LU_data, LU_pivots); + break; + default: + AT_ERROR("_lu_solve_helper not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, LU_data, LU_pivots))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_lu_solve_helper", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, LU_data, LU_pivots); +#endif +} +static inline Tensor & multinomial_out(Tensor & out, const Tensor & self, int64_t num_samples, bool replacement, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::multinomial_out(out, self, num_samples, replacement, generator); + break; + default: + AT_ERROR("multinomial_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::multinomial", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, num_samples, replacement, generator); +#endif +} +static inline Tensor multinomial(const Tensor & self, int64_t num_samples, bool replacement, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::multinomial(self, num_samples, replacement, generator); + break; + default: + AT_ERROR("multinomial not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::multinomial", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, num_samples, replacement, generator); +#endif +} +static inline std::tuple _multinomial_alias_setup(const Tensor & probs) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(probs)))) { + case Backend::CPU: + return CPUType::_multinomial_alias_setup(probs); + break; + default: + AT_ERROR("_multinomial_alias_setup not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(probs))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_multinomial_alias_setup", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &>( + op, probs); +#endif +} +static inline Tensor _multinomial_alias_draw(const Tensor & J, const Tensor & q, int64_t num_samples, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(J, q)))) { + case Backend::CPU: + return CPUType::_multinomial_alias_draw(J, q, num_samples, generator); + break; + default: + AT_ERROR("_multinomial_alias_draw not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(J, q))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_multinomial_alias_draw", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, J, q, num_samples, generator); +#endif +} +static inline Tensor & lgamma_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::lgamma_out(out, self); + break; + default: + AT_ERROR("lgamma_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::lgamma", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor lgamma(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::lgamma(self); + break; + default: + AT_ERROR("lgamma not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::lgamma", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & digamma_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::digamma_out(out, self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::digamma", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor digamma(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::digamma(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::digamma", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & polygamma_out(Tensor & out, int64_t n, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::polygamma_out(out, n, self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::polygamma", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, n, self); +#endif +} +static inline Tensor polygamma(int64_t n, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::polygamma(n, self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::polygamma", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, n, self); +#endif +} +static inline Tensor erfinv(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::erfinv(self); + break; + default: + AT_ERROR("erfinv not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::erfinv", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & erfinv_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::erfinv_out(out, self); + break; + default: + AT_ERROR("erfinv_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::erfinv", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor sign(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sign(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sign", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor & sign_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::sign_out(out, self); + break; + default: + AT_ERROR("sign_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sign", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor dist(const Tensor & self, const Tensor & other, Scalar p) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::dist(self, other, p); + break; + default: + AT_ERROR("dist not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::dist", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other, p); +#endif +} +static inline Tensor & atan2_out(Tensor & out, const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::atan2_out(out, self, other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::atan2", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor atan2(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::atan2(self, other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::atan2", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & lerp_out(Tensor & out, const Tensor & self, const Tensor & end, Scalar weight) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, end)))) { + case Backend::CPU: + return CPUType::lerp_out(out, self, end, weight); + break; + default: + AT_ERROR("lerp_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, end))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::lerp", "Scalar_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, end, weight); +#endif +} +static inline Tensor & lerp_out(Tensor & out, const Tensor & self, const Tensor & end, const Tensor & weight) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, end, weight)))) { + case Backend::CPU: + return CPUType::lerp_out(out, self, end, weight); + break; + default: + AT_ERROR("lerp_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, end, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::lerp", "Tensor_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, end, weight); +#endif +} +static inline Tensor lerp(const Tensor & self, const Tensor & end, Scalar weight) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, end)))) { + case Backend::CPU: + return CPUType::lerp(self, end, weight); + break; + default: + AT_ERROR("lerp not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, end))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::lerp", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, end, weight); +#endif +} +static inline Tensor lerp(const Tensor & self, const Tensor & end, const Tensor & weight) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, end, weight)))) { + case Backend::CPU: + return CPUType::lerp(self, end, weight); + break; + default: + AT_ERROR("lerp not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, end, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::lerp", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, end, weight); +#endif +} +static inline Tensor & histc_out(Tensor & out, const Tensor & self, int64_t bins, Scalar min, Scalar max) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::histc_out(out, self, bins, min, max); + break; + default: + AT_ERROR("histc_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::histc", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, bins, min, max); +#endif +} +static inline Tensor histc(const Tensor & self, int64_t bins, Scalar min, Scalar max) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::histc(self, bins, min, max); + break; + default: + AT_ERROR("histc not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::histc", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, bins, min, max); +#endif +} +static inline Tensor & fmod_out(Tensor & out, const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::fmod_out(out, self, other); + break; + default: + AT_ERROR("fmod_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fmod", "Scalar_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor fmod(const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::fmod(self, other); + break; + default: + AT_ERROR("fmod not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fmod", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & fmod_out(Tensor & out, const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, other)))) { + case Backend::CPU: + return CPUType::fmod_out(out, self, other); + break; + default: + AT_ERROR("fmod_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fmod", "Tensor_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor fmod(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::fmod(self, other); + break; + default: + AT_ERROR("fmod not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fmod", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & remainder_out(Tensor & out, const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::remainder_out(out, self, other); + break; + default: + AT_ERROR("remainder_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::remainder", "Scalar_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor remainder(const Tensor & self, Scalar other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::remainder(self, other); + break; + default: + AT_ERROR("remainder not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::remainder", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & remainder_out(Tensor & out, const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, other)))) { + case Backend::CPU: + return CPUType::remainder_out(out, self, other); + break; + default: + AT_ERROR("remainder_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::remainder", "Tensor_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor remainder(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::remainder(self, other); + break; + default: + AT_ERROR("remainder not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::remainder", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & min_out(Tensor & out, const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, other)))) { + case Backend::CPU: + return CPUType::min_out(out, self, other); + break; + default: + AT_ERROR("min_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::min", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor min(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::min(self, other); + break; + default: + AT_ERROR("min not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::min", "other"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor min(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::min(self); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::min(self); + break; + default: + AT_ERROR("min not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::min", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & max_out(Tensor & out, const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, other)))) { + case Backend::CPU: + return CPUType::max_out(out, self, other); + break; + default: + AT_ERROR("max_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, other); +#endif +} +static inline Tensor max(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::max(self, other); + break; + default: + AT_ERROR("max not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max", "other"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor max(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::max(self); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::max(self); + break; + default: + AT_ERROR("max not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor median(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::median(self); + break; + default: + AT_ERROR("median not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::median", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline std::tuple sort_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool descending) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(values, indices, self)))) { + case Backend::CPU: + return CPUType::sort_out(values, indices, self, dim, descending); + break; + default: + AT_ERROR("sort_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(values, indices, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sort", "values"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, int64_t, bool>( + op, values, indices, self, dim, descending); +#endif +} +static inline std::tuple sort(const Tensor & self, int64_t dim, bool descending) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::sort(self, dim, descending); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::sort(self, dim, descending); + break; + default: + AT_ERROR("sort not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sort", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, bool>( + op, self, dim, descending); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple sort_out(Tensor & values, Tensor & indices, const Tensor & self, Dimname dim, bool descending) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sort_out(values, indices, self, dim, descending); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sort", "dimname_values"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, Dimname, bool>( + op, values, indices, self, dim, descending); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +static inline std::tuple sort(const Tensor & self, Dimname dim, bool descending) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sort(self, dim, descending); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sort", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, Dimname, bool>( + op, self, dim, descending); +#endif +} +#endif +static inline Tensor argsort(const Tensor & self, int64_t dim, bool descending) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::argsort(self, dim, descending); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::argsort", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim, descending); +#endif +} +#ifdef BUILD_NAMEDTENSOR +static inline Tensor argsort(const Tensor & self, Dimname dim, bool descending) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::argsort(self, dim, descending); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::argsort", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, descending); +#endif +} +#endif +static inline std::tuple topk_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(values, indices, self)))) { + case Backend::CPU: + return CPUType::topk_out(values, indices, self, k, dim, largest, sorted); + break; + default: + AT_ERROR("topk_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(values, indices, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::topk", "values"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, int64_t, int64_t, bool, bool>( + op, values, indices, self, k, dim, largest, sorted); +#endif +} +static inline std::tuple topk(const Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::topk(self, k, dim, largest, sorted); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::topk(self, k, dim, largest, sorted); + break; + default: + AT_ERROR("topk not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::topk", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, int64_t, bool, bool>( + op, self, k, dim, largest, sorted); +#endif +} +static inline Tensor all(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::all(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::all", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor any(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::any(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::any", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline Tensor & renorm_out(Tensor & out, const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::renorm_out(out, self, p, dim, maxnorm); + break; + default: + AT_ERROR("renorm_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::renorm", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, p, dim, maxnorm); +#endif +} +static inline Tensor renorm(const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::renorm(self, p, dim, maxnorm); + break; + default: + AT_ERROR("renorm not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::renorm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, p, dim, maxnorm); +#endif +} +static inline bool equal(const Tensor & self, const Tensor & other) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, other)))) { + case Backend::CPU: + return CPUType::equal(self, other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::equal(self, other); + break; + default: + AT_ERROR("equal not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, other))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::equal", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, other); +#endif +} +static inline Tensor & pow_out(Tensor & out, const Tensor & self, const Tensor & exponent) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, exponent)))) { + case Backend::CPU: + return CPUType::pow_out(out, self, exponent); + break; + default: + AT_ERROR("pow_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, exponent))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::pow", "Tensor_Tensor_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, exponent); +#endif +} +static inline Tensor pow(const Tensor & self, const Tensor & exponent) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, exponent)))) { + case Backend::CPU: + return CPUType::pow(self, exponent); + break; + default: + AT_ERROR("pow not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, exponent))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::pow", "Tensor_Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, exponent); +#endif +} +static inline Tensor & pow_out(Tensor & out, Scalar self, const Tensor & exponent) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, exponent)))) { + case Backend::CPU: + return CPUType::pow_out(out, self, exponent); + break; + default: + AT_ERROR("pow_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, exponent))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::pow", "Scalar_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, exponent); +#endif +} +static inline Tensor pow(Scalar self, const Tensor & exponent) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(exponent)))) { + case Backend::CPU: + return CPUType::pow(self, exponent); + break; + default: + AT_ERROR("pow not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(exponent))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::pow", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, exponent); +#endif +} +static inline Tensor & normal_out(Tensor & out, const Tensor & mean, double std, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, mean)))) { + case Backend::CPU: + return CPUType::normal_out(out, mean, std, generator); + break; + default: + AT_ERROR("normal_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, mean))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::normal", "Tensor_float_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, mean, std, generator); +#endif +} +static inline Tensor normal(const Tensor & mean, double std, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(mean)))) { + case Backend::CPU: + return CPUType::normal(mean, std, generator); + break; + default: + AT_ERROR("normal not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(mean))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::normal", "Tensor_float"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, mean, std, generator); +#endif +} +static inline Tensor & normal_out(Tensor & out, double mean, const Tensor & std, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, std)))) { + case Backend::CPU: + return CPUType::normal_out(out, mean, std, generator); + break; + default: + AT_ERROR("normal_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, std))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::normal", "float_Tensor_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, mean, std, generator); +#endif +} +static inline Tensor normal(double mean, const Tensor & std, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(std)))) { + case Backend::CPU: + return CPUType::normal(mean, std, generator); + break; + default: + AT_ERROR("normal not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(std))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::normal", "float_Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, mean, std, generator); +#endif +} +static inline Tensor & normal_out(Tensor & out, const Tensor & mean, const Tensor & std, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, mean, std)))) { + case Backend::CPU: + return CPUType::normal_out(out, mean, std, generator); + break; + default: + AT_ERROR("normal_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, mean, std))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::normal", "Tensor_Tensor_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, mean, std, generator); +#endif +} +static inline Tensor normal(const Tensor & mean, const Tensor & std, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(mean, std)))) { + case Backend::CPU: + return CPUType::normal(mean, std, generator); + break; + default: + AT_ERROR("normal not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(mean, std))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::normal", "Tensor_Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, mean, std, generator); +#endif +} +static inline Tensor normal(double mean, double std, IntArrayRef size, Generator * generator, const TensorOptions & options) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::normal(mean, std, size, generator, options); +#else + globalLegacyTypeDispatch().initForTensorTypeSet(c10::detail::multi_dispatch_tensor_type_set(options)); + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::normal", "float_float"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, mean, std, size, generator, options); +#endif +} +static inline Tensor & normal_out(Tensor & out, double mean, double std, IntArrayRef size, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::normal_out(out, mean, std, size, generator); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::normal", "float_float_out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, mean, std, size, generator); +#endif +} +static inline Tensor alias(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::alias(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::alias", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self); +#endif +} +static inline Tensor _addr(const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, vec1, vec2)))) { + case Backend::CPU: + return CPUType::_addr(self, vec1, vec2, beta, alpha); + break; + default: + AT_ERROR("_addr not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, vec1, vec2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_addr", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, vec1, vec2, beta, alpha); +#endif +} +static inline Tensor & _addr_(Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, vec1, vec2)))) { + case Backend::CPU: + return CPUType::_addr_(self, vec1, vec2, beta, alpha); + break; + default: + AT_ERROR("_addr_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, vec1, vec2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_addr_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, vec1, vec2, beta, alpha); +#endif +} +static inline Tensor & _addr_out(Tensor & out, const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, vec1, vec2)))) { + case Backend::CPU: + return CPUType::_addr_out(out, self, vec1, vec2, beta, alpha); + break; + default: + AT_ERROR("_addr_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, vec1, vec2))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_addr", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, vec1, vec2, beta, alpha); +#endif +} +static inline Tensor & _index_copy_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & source) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, index, source)))) { + case Backend::CPU: + return CPUType::_index_copy_(self, dim, index, source); + break; + default: + AT_ERROR("_index_copy_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, index, source))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_index_copy_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, dim, index, source); +#endif +} +static inline Tensor _cumsum(const Tensor & self, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_cumsum(self, dim); + break; + default: + AT_ERROR("_cumsum not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cumsum", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim); +#endif +} +static inline Tensor & _cumsum_out(Tensor & out, const Tensor & self, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::_cumsum_out(out, self, dim); + break; + default: + AT_ERROR("_cumsum_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cumsum", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, dim); +#endif +} +static inline Tensor _cumprod(const Tensor & self, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_cumprod(self, dim); + break; + default: + AT_ERROR("_cumprod not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cumprod", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim); +#endif +} +static inline Tensor & _cumprod_out(Tensor & out, const Tensor & self, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::_cumprod_out(out, self, dim); + break; + default: + AT_ERROR("_cumprod_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cumprod", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, dim); +#endif +} +static inline Tensor _var(const Tensor & self, bool unbiased) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_var(self, unbiased); + break; + default: + AT_ERROR("_var not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_var", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, unbiased); +#endif +} +static inline Tensor _std(const Tensor & self, bool unbiased) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_std(self, unbiased); + break; + default: + AT_ERROR("_std not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_std", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, unbiased); +#endif +} +static inline Tensor _cat(TensorList tensors, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(tensors)))) { + case Backend::CPU: + return CPUType::_cat(tensors, dim); + break; + default: + AT_ERROR("_cat not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(tensors))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cat", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, tensors, dim); +#endif +} +static inline Tensor & _cat_out(Tensor & out, TensorList tensors, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, tensors)))) { + case Backend::CPU: + return CPUType::_cat_out(out, tensors, dim); + break; + default: + AT_ERROR("_cat_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, tensors))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_cat", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, tensors, dim); +#endif +} +static inline std::tuple _mode(const Tensor & self, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_mode(self, dim, keepdim); + break; + default: + AT_ERROR("_mode not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_mode", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, bool>( + op, self, dim, keepdim); +#endif +} +static inline std::tuple _mode_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(values, indices, self)))) { + case Backend::CPU: + return CPUType::_mode_out(values, indices, self, dim, keepdim); + break; + default: + AT_ERROR("_mode_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(values, indices, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_mode", "values"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, int64_t, bool>( + op, values, indices, self, dim, keepdim); +#endif +} +static inline std::tuple _max(const Tensor & self, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_max(self, dim, keepdim); + break; + default: + AT_ERROR("_max not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_max", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, bool>( + op, self, dim, keepdim); +#endif +} +static inline std::tuple _max_out(Tensor & max, Tensor & max_indices, const Tensor & self, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(max, max_indices, self)))) { + case Backend::CPU: + return CPUType::_max_out(max, max_indices, self, dim, keepdim); + break; + default: + AT_ERROR("_max_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(max, max_indices, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_max", "max"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, int64_t, bool>( + op, max, max_indices, self, dim, keepdim); +#endif +} +static inline std::tuple _min(const Tensor & self, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_min(self, dim, keepdim); + break; + default: + AT_ERROR("_min not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_min", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, bool>( + op, self, dim, keepdim); +#endif +} +static inline std::tuple _min_out(Tensor & min, Tensor & min_indices, const Tensor & self, int64_t dim, bool keepdim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(min, min_indices, self)))) { + case Backend::CPU: + return CPUType::_min_out(min, min_indices, self, dim, keepdim); + break; + default: + AT_ERROR("_min_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(min, min_indices, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_min", "min"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, int64_t, bool>( + op, min, min_indices, self, dim, keepdim); +#endif +} +static inline Tensor & binary_cross_entropy_out(Tensor & out, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, target, weight)))) { + case Backend::CPU: + return CPUType::binary_cross_entropy_out(out, self, target, weight, reduction); + break; + default: + AT_ERROR("binary_cross_entropy_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, target, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::binary_cross_entropy", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, target, weight, reduction); +#endif +} +static inline Tensor binary_cross_entropy(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, target, weight)))) { + case Backend::CPU: + return CPUType::binary_cross_entropy(self, target, weight, reduction); + break; + default: + AT_ERROR("binary_cross_entropy not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, target, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::binary_cross_entropy", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, target, weight, reduction); +#endif +} +static inline Tensor & binary_cross_entropy_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, target, weight)))) { + case Backend::CPU: + return CPUType::binary_cross_entropy_backward_out(grad_input, grad_output, self, target, weight, reduction); + break; + default: + AT_ERROR("binary_cross_entropy_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, target, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::binary_cross_entropy_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, target, weight, reduction); +#endif +} +static inline Tensor binary_cross_entropy_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, target, weight)))) { + case Backend::CPU: + return CPUType::binary_cross_entropy_backward(grad_output, self, target, weight, reduction); + break; + default: + AT_ERROR("binary_cross_entropy_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, target, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::binary_cross_entropy_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, self, target, weight, reduction); +#endif +} +static inline Tensor & mse_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mse_loss_out(out, self, target, reduction); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mse_loss", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, target, reduction); +#endif +} +static inline Tensor mse_loss(const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mse_loss(self, target, reduction); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mse_loss", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, target, reduction); +#endif +} +static inline Tensor & mse_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, target)))) { + case Backend::CPU: + return CPUType::mse_loss_backward_out(grad_input, grad_output, self, target, reduction); + break; + default: + AT_ERROR("mse_loss_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, target))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mse_loss_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, target, reduction); +#endif +} +static inline Tensor mse_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, target)))) { + case Backend::CPU: + return CPUType::mse_loss_backward(grad_output, self, target, reduction); + break; + default: + AT_ERROR("mse_loss_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, target))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mse_loss_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, self, target, reduction); +#endif +} +static inline Tensor & l1_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::l1_loss_out(out, self, target, reduction); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::l1_loss", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, target, reduction); +#endif +} +static inline Tensor l1_loss(const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::l1_loss(self, target, reduction); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::l1_loss", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, target, reduction); +#endif +} +static inline Tensor & l1_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, target)))) { + case Backend::CPU: + return CPUType::l1_loss_backward_out(grad_input, grad_output, self, target, reduction); + break; + default: + AT_ERROR("l1_loss_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, target))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::l1_loss_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, target, reduction); +#endif +} +static inline Tensor l1_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::l1_loss_backward(grad_output, self, target, reduction); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::l1_loss_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, self, target, reduction); +#endif +} +static inline Tensor & multi_margin_loss_out(Tensor & out, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, target, weight)))) { + case Backend::CPU: + return CPUType::multi_margin_loss_out(out, self, target, p, margin, weight, reduction); + break; + default: + AT_ERROR("multi_margin_loss_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, target, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::multi_margin_loss", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, target, p, margin, weight, reduction); +#endif +} +static inline Tensor multi_margin_loss(const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, target, weight)))) { + case Backend::CPU: + return CPUType::multi_margin_loss(self, target, p, margin, weight, reduction); + break; + default: + AT_ERROR("multi_margin_loss not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, target, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::multi_margin_loss", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, target, p, margin, weight, reduction); +#endif +} +static inline Tensor & multi_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, target, weight)))) { + case Backend::CPU: + return CPUType::multi_margin_loss_backward_out(grad_input, grad_output, self, target, p, margin, weight, reduction); + break; + default: + AT_ERROR("multi_margin_loss_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, target, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::multi_margin_loss_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, target, p, margin, weight, reduction); +#endif +} +static inline Tensor multi_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, target, weight)))) { + case Backend::CPU: + return CPUType::multi_margin_loss_backward(grad_output, self, target, p, margin, weight, reduction); + break; + default: + AT_ERROR("multi_margin_loss_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, target, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::multi_margin_loss_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, self, target, p, margin, weight, reduction); +#endif +} +static inline Tensor & multilabel_margin_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::multilabel_margin_loss_out(out, self, target, reduction); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::multilabel_margin_loss", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, target, reduction); +#endif +} +static inline Tensor multilabel_margin_loss(const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::multilabel_margin_loss(self, target, reduction); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::multilabel_margin_loss", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, target, reduction); +#endif +} +static inline std::tuple multilabel_margin_loss_forward_out(Tensor & output, Tensor & is_target, const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(output, is_target, self, target)))) { + case Backend::CPU: + return CPUType::multilabel_margin_loss_forward_out(output, is_target, self, target, reduction); + break; + default: + AT_ERROR("multilabel_margin_loss_forward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(output, is_target, self, target))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::multilabel_margin_loss_forward", "output"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, const Tensor &, int64_t>( + op, output, is_target, self, target, reduction); +#endif +} +static inline std::tuple multilabel_margin_loss_forward(const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, target)))) { + case Backend::CPU: + return CPUType::multilabel_margin_loss_forward(self, target, reduction); + break; + default: + AT_ERROR("multilabel_margin_loss_forward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, target))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::multilabel_margin_loss_forward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, int64_t>( + op, self, target, reduction); +#endif +} +static inline Tensor & multilabel_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction, const Tensor & is_target) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, target, is_target)))) { + case Backend::CPU: + return CPUType::multilabel_margin_loss_backward_out(grad_input, grad_output, self, target, reduction, is_target); + break; + default: + AT_ERROR("multilabel_margin_loss_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, target, is_target))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::multilabel_margin_loss_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, target, reduction, is_target); +#endif +} +static inline Tensor multilabel_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction, const Tensor & is_target) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, target, is_target)))) { + case Backend::CPU: + return CPUType::multilabel_margin_loss_backward(grad_output, self, target, reduction, is_target); + break; + default: + AT_ERROR("multilabel_margin_loss_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, target, is_target))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::multilabel_margin_loss_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, self, target, reduction, is_target); +#endif +} +static inline Tensor & nll_loss_out(Tensor & out, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::nll_loss_out(out, self, target, weight, reduction, ignore_index); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::nll_loss", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, target, weight, reduction, ignore_index); +#endif +} +static inline Tensor nll_loss(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::nll_loss(self, target, weight, reduction, ignore_index); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::nll_loss", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, target, weight, reduction, ignore_index); +#endif +} +static inline std::tuple nll_loss_forward_out(Tensor & output, Tensor & total_weight, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(output, total_weight, self, target, weight)))) { + case Backend::CPU: + return CPUType::nll_loss_forward_out(output, total_weight, self, target, weight, reduction, ignore_index); + break; + default: + AT_ERROR("nll_loss_forward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(output, total_weight, self, target, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::nll_loss_forward", "output"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, const Tensor &, const Tensor &, int64_t, int64_t>( + op, output, total_weight, self, target, weight, reduction, ignore_index); +#endif +} +static inline std::tuple nll_loss_forward(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, target, weight)))) { + case Backend::CPU: + return CPUType::nll_loss_forward(self, target, weight, reduction, ignore_index); + break; + default: + AT_ERROR("nll_loss_forward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, target, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::nll_loss_forward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, int64_t, int64_t>( + op, self, target, weight, reduction, ignore_index); +#endif +} +static inline Tensor & nll_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, target, weight, total_weight)))) { + case Backend::CPU: + return CPUType::nll_loss_backward_out(grad_input, grad_output, self, target, weight, reduction, ignore_index, total_weight); + break; + default: + AT_ERROR("nll_loss_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, target, weight, total_weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::nll_loss_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, target, weight, reduction, ignore_index, total_weight); +#endif +} +static inline Tensor nll_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, target, weight, total_weight)))) { + case Backend::CPU: + return CPUType::nll_loss_backward(grad_output, self, target, weight, reduction, ignore_index, total_weight); + break; + default: + AT_ERROR("nll_loss_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, target, weight, total_weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::nll_loss_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, self, target, weight, reduction, ignore_index, total_weight); +#endif +} +static inline Tensor & nll_loss2d_out(Tensor & out, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::nll_loss2d_out(out, self, target, weight, reduction, ignore_index); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::nll_loss2d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, target, weight, reduction, ignore_index); +#endif +} +static inline Tensor nll_loss2d(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::nll_loss2d(self, target, weight, reduction, ignore_index); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::nll_loss2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, target, weight, reduction, ignore_index); +#endif +} +static inline std::tuple nll_loss2d_forward_out(Tensor & output, Tensor & total_weight, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(output, total_weight, self, target, weight)))) { + case Backend::CPU: + return CPUType::nll_loss2d_forward_out(output, total_weight, self, target, weight, reduction, ignore_index); + break; + default: + AT_ERROR("nll_loss2d_forward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(output, total_weight, self, target, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::nll_loss2d_forward", "output"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, const Tensor &, const Tensor &, int64_t, int64_t>( + op, output, total_weight, self, target, weight, reduction, ignore_index); +#endif +} +static inline std::tuple nll_loss2d_forward(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, target, weight)))) { + case Backend::CPU: + return CPUType::nll_loss2d_forward(self, target, weight, reduction, ignore_index); + break; + default: + AT_ERROR("nll_loss2d_forward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, target, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::nll_loss2d_forward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, int64_t, int64_t>( + op, self, target, weight, reduction, ignore_index); +#endif +} +static inline Tensor & nll_loss2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, target, weight, total_weight)))) { + case Backend::CPU: + return CPUType::nll_loss2d_backward_out(grad_input, grad_output, self, target, weight, reduction, ignore_index, total_weight); + break; + default: + AT_ERROR("nll_loss2d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, target, weight, total_weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::nll_loss2d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, target, weight, reduction, ignore_index, total_weight); +#endif +} +static inline Tensor nll_loss2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, target, weight, total_weight)))) { + case Backend::CPU: + return CPUType::nll_loss2d_backward(grad_output, self, target, weight, reduction, ignore_index, total_weight); + break; + default: + AT_ERROR("nll_loss2d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, target, weight, total_weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::nll_loss2d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, self, target, weight, reduction, ignore_index, total_weight); +#endif +} +static inline Tensor & smooth_l1_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, target)))) { + case Backend::CPU: + return CPUType::smooth_l1_loss_out(out, self, target, reduction); + break; + default: + AT_ERROR("smooth_l1_loss_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, target))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::smooth_l1_loss", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, target, reduction); +#endif +} +static inline Tensor smooth_l1_loss(const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::smooth_l1_loss(self, target, reduction); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::smooth_l1_loss", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, target, reduction); +#endif +} +static inline Tensor & smooth_l1_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, target)))) { + case Backend::CPU: + return CPUType::smooth_l1_loss_backward_out(grad_input, grad_output, self, target, reduction); + break; + default: + AT_ERROR("smooth_l1_loss_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, target))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::smooth_l1_loss_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, target, reduction); +#endif +} +static inline Tensor smooth_l1_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::smooth_l1_loss_backward(grad_output, self, target, reduction); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::smooth_l1_loss_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, self, target, reduction); +#endif +} +static inline Tensor & soft_margin_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, target)))) { + case Backend::CPU: + return CPUType::soft_margin_loss_out(out, self, target, reduction); + break; + default: + AT_ERROR("soft_margin_loss_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, target))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::soft_margin_loss", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, target, reduction); +#endif +} +static inline Tensor soft_margin_loss(const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, target)))) { + case Backend::CPU: + return CPUType::soft_margin_loss(self, target, reduction); + break; + default: + AT_ERROR("soft_margin_loss not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, target))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::soft_margin_loss", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, target, reduction); +#endif +} +static inline Tensor & soft_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, target)))) { + case Backend::CPU: + return CPUType::soft_margin_loss_backward_out(grad_input, grad_output, self, target, reduction); + break; + default: + AT_ERROR("soft_margin_loss_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, target))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::soft_margin_loss_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, target, reduction); +#endif +} +static inline Tensor soft_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, target)))) { + case Backend::CPU: + return CPUType::soft_margin_loss_backward(grad_output, self, target, reduction); + break; + default: + AT_ERROR("soft_margin_loss_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, target))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::soft_margin_loss_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, self, target, reduction); +#endif +} +static inline Tensor & elu_out(Tensor & out, const Tensor & self, Scalar alpha, Scalar scale, Scalar input_scale) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::elu_out(out, self, alpha, scale, input_scale); + break; + default: + AT_ERROR("elu_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::elu", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, alpha, scale, input_scale); +#endif +} +static inline Tensor elu(const Tensor & self, Scalar alpha, Scalar scale, Scalar input_scale) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::elu(self, alpha, scale, input_scale); + break; + default: + AT_ERROR("elu not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::elu", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, alpha, scale, input_scale); +#endif +} +static inline Tensor & elu_backward_out(Tensor & grad_input, const Tensor & grad_output, Scalar alpha, Scalar scale, Scalar input_scale, const Tensor & output) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, output)))) { + case Backend::CPU: + return CPUType::elu_backward_out(grad_input, grad_output, alpha, scale, input_scale, output); + break; + default: + AT_ERROR("elu_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::elu_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, alpha, scale, input_scale, output); +#endif +} +static inline Tensor elu_backward(const Tensor & grad_output, Scalar alpha, Scalar scale, Scalar input_scale, const Tensor & output) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, output)))) { + case Backend::CPU: + return CPUType::elu_backward(grad_output, alpha, scale, input_scale, output); + break; + default: + AT_ERROR("elu_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::elu_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, alpha, scale, input_scale, output); +#endif +} +static inline Tensor & elu_(Tensor & self, Scalar alpha, Scalar scale, Scalar input_scale) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::elu_(self, alpha, scale, input_scale); + break; + default: + AT_ERROR("elu_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::elu_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, alpha, scale, input_scale); +#endif +} +static inline Tensor & glu_out(Tensor & out, const Tensor & self, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::glu_out(out, self, dim); + break; + default: + AT_ERROR("glu_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::glu", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, dim); +#endif +} +static inline Tensor glu(const Tensor & self, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::glu(self, dim); + break; + default: + AT_ERROR("glu not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::glu", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, dim); +#endif +} +static inline Tensor & glu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self)))) { + case Backend::CPU: + return CPUType::glu_backward_out(grad_input, grad_output, self, dim); + break; + default: + AT_ERROR("glu_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::glu_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, dim); +#endif +} +static inline Tensor glu_backward(const Tensor & grad_output, const Tensor & self, int64_t dim) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self)))) { + case Backend::CPU: + return CPUType::glu_backward(grad_output, self, dim); + break; + default: + AT_ERROR("glu_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::glu_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, self, dim); +#endif +} +static inline Tensor & hardtanh_out(Tensor & out, const Tensor & self, Scalar min_val, Scalar max_val) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::hardtanh_out(out, self, min_val, max_val); + break; + default: + AT_ERROR("hardtanh_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::hardtanh", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, min_val, max_val); +#endif +} +static inline Tensor hardtanh(const Tensor & self, Scalar min_val, Scalar max_val) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::hardtanh(self, min_val, max_val); + break; + default: + AT_ERROR("hardtanh not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::hardtanh", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, min_val, max_val); +#endif +} +static inline Tensor & hardtanh_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar min_val, Scalar max_val) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self)))) { + case Backend::CPU: + return CPUType::hardtanh_backward_out(grad_input, grad_output, self, min_val, max_val); + break; + default: + AT_ERROR("hardtanh_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::hardtanh_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, min_val, max_val); +#endif +} +static inline Tensor hardtanh_backward(const Tensor & grad_output, const Tensor & self, Scalar min_val, Scalar max_val) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self)))) { + case Backend::CPU: + return CPUType::hardtanh_backward(grad_output, self, min_val, max_val); + break; + default: + AT_ERROR("hardtanh_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::hardtanh_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, self, min_val, max_val); +#endif +} +static inline Tensor & hardtanh_(Tensor & self, Scalar min_val, Scalar max_val) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::hardtanh_(self, min_val, max_val); + break; + default: + AT_ERROR("hardtanh_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::hardtanh_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, min_val, max_val); +#endif +} +static inline Tensor & leaky_relu_out(Tensor & out, const Tensor & self, Scalar negative_slope) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::leaky_relu_out(out, self, negative_slope); + break; + default: + AT_ERROR("leaky_relu_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::leaky_relu", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, negative_slope); +#endif +} +static inline Tensor leaky_relu(const Tensor & self, Scalar negative_slope) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::leaky_relu(self, negative_slope); + break; + default: + AT_ERROR("leaky_relu not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::leaky_relu", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, negative_slope); +#endif +} +static inline Tensor & leaky_relu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar negative_slope) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self)))) { + case Backend::CPU: + return CPUType::leaky_relu_backward_out(grad_input, grad_output, self, negative_slope); + break; + default: + AT_ERROR("leaky_relu_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::leaky_relu_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, negative_slope); +#endif +} +static inline Tensor leaky_relu_backward(const Tensor & grad_output, const Tensor & self, Scalar negative_slope) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self)))) { + case Backend::CPU: + return CPUType::leaky_relu_backward(grad_output, self, negative_slope); + break; + default: + AT_ERROR("leaky_relu_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::leaky_relu_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, self, negative_slope); +#endif +} +static inline Tensor & leaky_relu_(Tensor & self, Scalar negative_slope) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::leaky_relu_(self, negative_slope); + break; + default: + AT_ERROR("leaky_relu_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::leaky_relu_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, negative_slope); +#endif +} +static inline Tensor & log_sigmoid_out(Tensor & out, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log_sigmoid_out(out, self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log_sigmoid", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self); +#endif +} +static inline Tensor log_sigmoid(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log_sigmoid(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log_sigmoid", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} +static inline std::tuple log_sigmoid_forward_out(Tensor & output, Tensor & buffer, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(output, buffer, self)))) { + case Backend::CPU: + return CPUType::log_sigmoid_forward_out(output, buffer, self); + break; + default: + AT_ERROR("log_sigmoid_forward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(output, buffer, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log_sigmoid_forward", "output"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &>( + op, output, buffer, self); +#endif +} +static inline std::tuple log_sigmoid_forward(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::log_sigmoid_forward(self); + break; + default: + AT_ERROR("log_sigmoid_forward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log_sigmoid_forward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &>( + op, self); +#endif +} +static inline Tensor & log_sigmoid_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & buffer) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, buffer)))) { + case Backend::CPU: + return CPUType::log_sigmoid_backward_out(grad_input, grad_output, self, buffer); + break; + default: + AT_ERROR("log_sigmoid_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, buffer))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log_sigmoid_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, buffer); +#endif +} +static inline Tensor log_sigmoid_backward(const Tensor & grad_output, const Tensor & self, const Tensor & buffer) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, buffer)))) { + case Backend::CPU: + return CPUType::log_sigmoid_backward(grad_output, self, buffer); + break; + default: + AT_ERROR("log_sigmoid_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, buffer))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::log_sigmoid_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, self, buffer); +#endif +} +static inline Tensor & rrelu_with_noise_out(Tensor & out, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, noise)))) { + case Backend::CPU: + return CPUType::rrelu_with_noise_out(out, self, noise, lower, upper, training, generator); + break; + default: + AT_ERROR("rrelu_with_noise_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, noise))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rrelu_with_noise", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, noise, lower, upper, training, generator); +#endif +} +static inline Tensor rrelu_with_noise(const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, noise)))) { + case Backend::CPU: + return CPUType::rrelu_with_noise(self, noise, lower, upper, training, generator); + break; + default: + AT_ERROR("rrelu_with_noise not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, noise))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rrelu_with_noise", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, noise, lower, upper, training, generator); +#endif +} +static inline Tensor & rrelu_with_noise_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, noise)))) { + case Backend::CPU: + return CPUType::rrelu_with_noise_backward_out(grad_input, grad_output, self, noise, lower, upper, training); + break; + default: + AT_ERROR("rrelu_with_noise_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, noise))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rrelu_with_noise_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, noise, lower, upper, training); +#endif +} +static inline Tensor rrelu_with_noise_backward(const Tensor & grad_output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, noise)))) { + case Backend::CPU: + return CPUType::rrelu_with_noise_backward(grad_output, self, noise, lower, upper, training); + break; + default: + AT_ERROR("rrelu_with_noise_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, noise))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rrelu_with_noise_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, self, noise, lower, upper, training); +#endif +} +static inline Tensor & rrelu_with_noise_(Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, noise)))) { + case Backend::CPU: + return CPUType::rrelu_with_noise_(self, noise, lower, upper, training, generator); + break; + default: + AT_ERROR("rrelu_with_noise_ not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, noise))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::rrelu_with_noise_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, noise, lower, upper, training, generator); +#endif +} +static inline Tensor & softplus_out(Tensor & out, const Tensor & self, Scalar beta, Scalar threshold) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::softplus_out(out, self, beta, threshold); + break; + default: + AT_ERROR("softplus_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::softplus", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, beta, threshold); +#endif +} +static inline Tensor softplus(const Tensor & self, Scalar beta, Scalar threshold) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::softplus(self, beta, threshold); + break; + default: + AT_ERROR("softplus not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::softplus", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, beta, threshold); +#endif +} +static inline Tensor & softplus_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar beta, Scalar threshold, const Tensor & output) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, output)))) { + case Backend::CPU: + return CPUType::softplus_backward_out(grad_input, grad_output, self, beta, threshold, output); + break; + default: + AT_ERROR("softplus_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::softplus_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, beta, threshold, output); +#endif +} +static inline Tensor softplus_backward(const Tensor & grad_output, const Tensor & self, Scalar beta, Scalar threshold, const Tensor & output) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, output)))) { + case Backend::CPU: + return CPUType::softplus_backward(grad_output, self, beta, threshold, output); + break; + default: + AT_ERROR("softplus_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::softplus_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, self, beta, threshold, output); +#endif +} +static inline Tensor & softshrink_out(Tensor & out, const Tensor & self, Scalar lambd) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::softshrink_out(out, self, lambd); + break; + default: + AT_ERROR("softshrink_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::softshrink", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, lambd); +#endif +} +static inline Tensor softshrink(const Tensor & self, Scalar lambd) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::softshrink(self, lambd); + break; + default: + AT_ERROR("softshrink not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::softshrink", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self, lambd); +#endif +} +static inline Tensor & softshrink_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar lambd) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self)))) { + case Backend::CPU: + return CPUType::softshrink_backward_out(grad_input, grad_output, self, lambd); + break; + default: + AT_ERROR("softshrink_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::softshrink_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, lambd); +#endif +} +static inline Tensor softshrink_backward(const Tensor & grad_output, const Tensor & self, Scalar lambd) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self)))) { + case Backend::CPU: + return CPUType::softshrink_backward(grad_output, self, lambd); + break; + default: + AT_ERROR("softshrink_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::softshrink_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, self, lambd); +#endif +} +static inline Tensor & adaptive_avg_pool2d_out(Tensor & out, const Tensor & self, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::adaptive_avg_pool2d_out(out, self, output_size); + break; + default: + AT_ERROR("adaptive_avg_pool2d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::adaptive_avg_pool2d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, output_size); +#endif +} +static inline Tensor adaptive_avg_pool2d(const Tensor & self, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::adaptive_avg_pool2d(self, output_size); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::adaptive_avg_pool2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, output_size); +#endif +} +static inline Tensor mkldnn_adaptive_avg_pool2d(const Tensor & self, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + + default: + AT_ERROR("mkldnn_adaptive_avg_pool2d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::mkldnn_adaptive_avg_pool2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, output_size); +#endif +} +static inline Tensor _adaptive_avg_pool2d(const Tensor & self, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::_adaptive_avg_pool2d(self, output_size); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::_adaptive_avg_pool2d(self, output_size); + break; + default: + AT_ERROR("_adaptive_avg_pool2d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_adaptive_avg_pool2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, output_size); +#endif +} +static inline Tensor _adaptive_avg_pool2d_backward(const Tensor & grad_output, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self)))) { + case Backend::CPU: + return CPUType::_adaptive_avg_pool2d_backward(grad_output, self); + break; + default: + AT_ERROR("_adaptive_avg_pool2d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_adaptive_avg_pool2d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, self); +#endif +} +static inline Tensor & adaptive_avg_pool3d_out(Tensor & out, const Tensor & self, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::adaptive_avg_pool3d_out(out, self, output_size); + break; + default: + AT_ERROR("adaptive_avg_pool3d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::adaptive_avg_pool3d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, output_size); +#endif +} +static inline Tensor adaptive_avg_pool3d(const Tensor & self, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::adaptive_avg_pool3d(self, output_size); + break; + default: + AT_ERROR("adaptive_avg_pool3d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::adaptive_avg_pool3d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, output_size); +#endif +} +static inline Tensor & adaptive_avg_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self)))) { + case Backend::CPU: + return CPUType::adaptive_avg_pool3d_backward_out(grad_input, grad_output, self); + break; + default: + AT_ERROR("adaptive_avg_pool3d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::adaptive_avg_pool3d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self); +#endif +} +static inline Tensor adaptive_avg_pool3d_backward(const Tensor & grad_output, const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self)))) { + case Backend::CPU: + return CPUType::adaptive_avg_pool3d_backward(grad_output, self); + break; + default: + AT_ERROR("adaptive_avg_pool3d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::adaptive_avg_pool3d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, self); +#endif +} +static inline std::tuple adaptive_max_pool2d_out(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, indices, self)))) { + case Backend::CPU: + return CPUType::adaptive_max_pool2d_out(out, indices, self, output_size); + break; + default: + AT_ERROR("adaptive_max_pool2d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, indices, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::adaptive_max_pool2d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, IntArrayRef>( + op, out, indices, self, output_size); +#endif +} +static inline std::tuple adaptive_max_pool2d(const Tensor & self, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::adaptive_max_pool2d(self, output_size); + break; + default: + AT_ERROR("adaptive_max_pool2d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::adaptive_max_pool2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, IntArrayRef>( + op, self, output_size); +#endif +} +static inline Tensor & adaptive_max_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, indices)))) { + case Backend::CPU: + return CPUType::adaptive_max_pool2d_backward_out(grad_input, grad_output, self, indices); + break; + default: + AT_ERROR("adaptive_max_pool2d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::adaptive_max_pool2d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, indices); +#endif +} +static inline Tensor adaptive_max_pool2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, indices)))) { + case Backend::CPU: + return CPUType::adaptive_max_pool2d_backward(grad_output, self, indices); + break; + default: + AT_ERROR("adaptive_max_pool2d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::adaptive_max_pool2d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, self, indices); +#endif +} +static inline std::tuple adaptive_max_pool3d_out(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, indices, self)))) { + case Backend::CPU: + return CPUType::adaptive_max_pool3d_out(out, indices, self, output_size); + break; + default: + AT_ERROR("adaptive_max_pool3d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, indices, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::adaptive_max_pool3d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, IntArrayRef>( + op, out, indices, self, output_size); +#endif +} +static inline std::tuple adaptive_max_pool3d(const Tensor & self, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::adaptive_max_pool3d(self, output_size); + break; + default: + AT_ERROR("adaptive_max_pool3d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::adaptive_max_pool3d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, IntArrayRef>( + op, self, output_size); +#endif +} +static inline Tensor & adaptive_max_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, indices)))) { + case Backend::CPU: + return CPUType::adaptive_max_pool3d_backward_out(grad_input, grad_output, self, indices); + break; + default: + AT_ERROR("adaptive_max_pool3d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::adaptive_max_pool3d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, indices); +#endif +} +static inline Tensor adaptive_max_pool3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, indices)))) { + case Backend::CPU: + return CPUType::adaptive_max_pool3d_backward(grad_output, self, indices); + break; + default: + AT_ERROR("adaptive_max_pool3d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::adaptive_max_pool3d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, self, indices); +#endif +} +static inline Tensor & avg_pool2d_out(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::avg_pool2d_out(out, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + break; + default: + AT_ERROR("avg_pool2d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::avg_pool2d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, out, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); +#endif +} +static inline Tensor avg_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::avg_pool2d(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::avg_pool2d(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + break; + default: + AT_ERROR("avg_pool2d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::avg_pool2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); +#endif +} +static inline Tensor & avg_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self)))) { + case Backend::CPU: + return CPUType::avg_pool2d_backward_out(grad_input, grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + break; + default: + AT_ERROR("avg_pool2d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::avg_pool2d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, grad_input, grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); +#endif +} +static inline Tensor avg_pool2d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self)))) { + case Backend::CPU: + return CPUType::avg_pool2d_backward(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + break; + default: + AT_ERROR("avg_pool2d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::avg_pool2d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); +#endif +} +static inline Tensor & avg_pool3d_out(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::avg_pool3d_out(out, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + break; + default: + AT_ERROR("avg_pool3d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::avg_pool3d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, out, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); +#endif +} +static inline Tensor avg_pool3d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::avg_pool3d(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + break; + default: + AT_ERROR("avg_pool3d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::avg_pool3d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); +#endif +} +static inline Tensor & avg_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self)))) { + case Backend::CPU: + return CPUType::avg_pool3d_backward_out(grad_input, grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + break; + default: + AT_ERROR("avg_pool3d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::avg_pool3d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, grad_input, grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); +#endif +} +static inline Tensor avg_pool3d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self)))) { + case Backend::CPU: + return CPUType::avg_pool3d_backward(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + break; + default: + AT_ERROR("avg_pool3d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::avg_pool3d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); +#endif +} +static inline std::tuple fractional_max_pool2d_out(Tensor & output, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(output, indices, self, random_samples)))) { + case Backend::CPU: + return CPUType::fractional_max_pool2d_out(output, indices, self, kernel_size, output_size, random_samples); + break; + default: + AT_ERROR("fractional_max_pool2d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(output, indices, self, random_samples))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fractional_max_pool2d", "output"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, IntArrayRef, IntArrayRef, const Tensor &>( + op, output, indices, self, kernel_size, output_size, random_samples); +#endif +} +static inline std::tuple fractional_max_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, random_samples)))) { + case Backend::CPU: + return CPUType::fractional_max_pool2d(self, kernel_size, output_size, random_samples); + break; + default: + AT_ERROR("fractional_max_pool2d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, random_samples))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fractional_max_pool2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, IntArrayRef, IntArrayRef, const Tensor &>( + op, self, kernel_size, output_size, random_samples); +#endif +} +static inline Tensor & fractional_max_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, indices)))) { + case Backend::CPU: + return CPUType::fractional_max_pool2d_backward_out(grad_input, grad_output, self, kernel_size, output_size, indices); + break; + default: + AT_ERROR("fractional_max_pool2d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fractional_max_pool2d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, kernel_size, output_size, indices); +#endif +} +static inline Tensor fractional_max_pool2d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, indices)))) { + case Backend::CPU: + return CPUType::fractional_max_pool2d_backward(grad_output, self, kernel_size, output_size, indices); + break; + default: + AT_ERROR("fractional_max_pool2d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fractional_max_pool2d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, self, kernel_size, output_size, indices); +#endif +} +static inline std::tuple fractional_max_pool3d_out(Tensor & output, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(output, indices, self, random_samples)))) { + case Backend::CPU: + return CPUType::fractional_max_pool3d_out(output, indices, self, kernel_size, output_size, random_samples); + break; + default: + AT_ERROR("fractional_max_pool3d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(output, indices, self, random_samples))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fractional_max_pool3d", "output"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, IntArrayRef, IntArrayRef, const Tensor &>( + op, output, indices, self, kernel_size, output_size, random_samples); +#endif +} +static inline std::tuple fractional_max_pool3d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, random_samples)))) { + case Backend::CPU: + return CPUType::fractional_max_pool3d(self, kernel_size, output_size, random_samples); + break; + default: + AT_ERROR("fractional_max_pool3d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, random_samples))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fractional_max_pool3d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, IntArrayRef, IntArrayRef, const Tensor &>( + op, self, kernel_size, output_size, random_samples); +#endif +} +static inline Tensor & fractional_max_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, indices)))) { + case Backend::CPU: + return CPUType::fractional_max_pool3d_backward_out(grad_input, grad_output, self, kernel_size, output_size, indices); + break; + default: + AT_ERROR("fractional_max_pool3d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fractional_max_pool3d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, kernel_size, output_size, indices); +#endif +} +static inline Tensor fractional_max_pool3d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, indices)))) { + case Backend::CPU: + return CPUType::fractional_max_pool3d_backward(grad_output, self, kernel_size, output_size, indices); + break; + default: + AT_ERROR("fractional_max_pool3d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::fractional_max_pool3d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, self, kernel_size, output_size, indices); +#endif +} +static inline std::tuple max_pool2d_with_indices_out(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, indices, self)))) { + case Backend::CPU: + return CPUType::max_pool2d_with_indices_out(out, indices, self, kernel_size, stride, padding, dilation, ceil_mode); + break; + default: + AT_ERROR("max_pool2d_with_indices_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, indices, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_pool2d_with_indices", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, IntArrayRef, bool>( + op, out, indices, self, kernel_size, stride, padding, dilation, ceil_mode); +#endif +} +static inline std::tuple max_pool2d_with_indices(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::max_pool2d_with_indices(self, kernel_size, stride, padding, dilation, ceil_mode); + break; + default: + AT_ERROR("max_pool2d_with_indices not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_pool2d_with_indices", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, IntArrayRef, bool>( + op, self, kernel_size, stride, padding, dilation, ceil_mode); +#endif +} +static inline Tensor & max_pool2d_with_indices_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, indices)))) { + case Backend::CPU: + return CPUType::max_pool2d_with_indices_backward_out(grad_input, grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices); + break; + default: + AT_ERROR("max_pool2d_with_indices_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_pool2d_with_indices_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices); +#endif +} +static inline Tensor max_pool2d_with_indices_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, indices)))) { + case Backend::CPU: + return CPUType::max_pool2d_with_indices_backward(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices); + break; + default: + AT_ERROR("max_pool2d_with_indices_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_pool2d_with_indices_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices); +#endif +} +static inline std::tuple max_pool3d_with_indices_out(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, indices, self)))) { + case Backend::CPU: + return CPUType::max_pool3d_with_indices_out(out, indices, self, kernel_size, stride, padding, dilation, ceil_mode); + break; + default: + AT_ERROR("max_pool3d_with_indices_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, indices, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_pool3d_with_indices", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, IntArrayRef, bool>( + op, out, indices, self, kernel_size, stride, padding, dilation, ceil_mode); +#endif +} +static inline std::tuple max_pool3d_with_indices(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::max_pool3d_with_indices(self, kernel_size, stride, padding, dilation, ceil_mode); + break; + default: + AT_ERROR("max_pool3d_with_indices not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_pool3d_with_indices", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, IntArrayRef, bool>( + op, self, kernel_size, stride, padding, dilation, ceil_mode); +#endif +} +static inline Tensor & max_pool3d_with_indices_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, indices)))) { + case Backend::CPU: + return CPUType::max_pool3d_with_indices_backward_out(grad_input, grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices); + break; + default: + AT_ERROR("max_pool3d_with_indices_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_pool3d_with_indices_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices); +#endif +} +static inline Tensor max_pool3d_with_indices_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, indices)))) { + case Backend::CPU: + return CPUType::max_pool3d_with_indices_backward(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices); + break; + default: + AT_ERROR("max_pool3d_with_indices_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_pool3d_with_indices_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices); +#endif +} +static inline Tensor & max_unpool2d_out(Tensor & out, const Tensor & self, const Tensor & indices, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, indices)))) { + case Backend::CPU: + return CPUType::max_unpool2d_out(out, self, indices, output_size); + break; + default: + AT_ERROR("max_unpool2d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_unpool2d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, indices, output_size); +#endif +} +static inline Tensor max_unpool2d(const Tensor & self, const Tensor & indices, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, indices)))) { + case Backend::CPU: + return CPUType::max_unpool2d(self, indices, output_size); + break; + default: + AT_ERROR("max_unpool2d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_unpool2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, indices, output_size); +#endif +} +static inline Tensor & max_unpool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, indices)))) { + case Backend::CPU: + return CPUType::max_unpool2d_backward_out(grad_input, grad_output, self, indices, output_size); + break; + default: + AT_ERROR("max_unpool2d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_unpool2d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, indices, output_size); +#endif +} +static inline Tensor max_unpool2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, indices)))) { + case Backend::CPU: + return CPUType::max_unpool2d_backward(grad_output, self, indices, output_size); + break; + default: + AT_ERROR("max_unpool2d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_unpool2d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, self, indices, output_size); +#endif +} +static inline Tensor & max_unpool3d_out(Tensor & out, const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, indices)))) { + case Backend::CPU: + return CPUType::max_unpool3d_out(out, self, indices, output_size, stride, padding); + break; + default: + AT_ERROR("max_unpool3d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_unpool3d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, indices, output_size, stride, padding); +#endif +} +static inline Tensor max_unpool3d(const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, indices)))) { + case Backend::CPU: + return CPUType::max_unpool3d(self, indices, output_size, stride, padding); + break; + default: + AT_ERROR("max_unpool3d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_unpool3d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, indices, output_size, stride, padding); +#endif +} +static inline Tensor & max_unpool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, indices)))) { + case Backend::CPU: + return CPUType::max_unpool3d_backward_out(grad_input, grad_output, self, indices, output_size, stride, padding); + break; + default: + AT_ERROR("max_unpool3d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_unpool3d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, indices, output_size, stride, padding); +#endif +} +static inline Tensor max_unpool3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, indices)))) { + case Backend::CPU: + return CPUType::max_unpool3d_backward(grad_output, self, indices, output_size, stride, padding); + break; + default: + AT_ERROR("max_unpool3d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, indices))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::max_unpool3d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, self, indices, output_size, stride, padding); +#endif +} +static inline Tensor & reflection_pad1d_out(Tensor & out, const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::reflection_pad1d_out(out, self, padding); + break; + default: + AT_ERROR("reflection_pad1d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::reflection_pad1d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, padding); +#endif +} +static inline Tensor reflection_pad1d(const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::reflection_pad1d(self, padding); + break; + default: + AT_ERROR("reflection_pad1d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::reflection_pad1d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, padding); +#endif +} +static inline Tensor & reflection_pad1d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self)))) { + case Backend::CPU: + return CPUType::reflection_pad1d_backward_out(grad_input, grad_output, self, padding); + break; + default: + AT_ERROR("reflection_pad1d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::reflection_pad1d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, padding); +#endif +} +static inline Tensor reflection_pad1d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self)))) { + case Backend::CPU: + return CPUType::reflection_pad1d_backward(grad_output, self, padding); + break; + default: + AT_ERROR("reflection_pad1d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::reflection_pad1d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, self, padding); +#endif +} +static inline Tensor & reflection_pad2d_out(Tensor & out, const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::reflection_pad2d_out(out, self, padding); + break; + default: + AT_ERROR("reflection_pad2d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::reflection_pad2d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, padding); +#endif +} +static inline Tensor reflection_pad2d(const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::reflection_pad2d(self, padding); + break; + default: + AT_ERROR("reflection_pad2d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::reflection_pad2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, padding); +#endif +} +static inline Tensor & reflection_pad2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self)))) { + case Backend::CPU: + return CPUType::reflection_pad2d_backward_out(grad_input, grad_output, self, padding); + break; + default: + AT_ERROR("reflection_pad2d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::reflection_pad2d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, padding); +#endif +} +static inline Tensor reflection_pad2d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self)))) { + case Backend::CPU: + return CPUType::reflection_pad2d_backward(grad_output, self, padding); + break; + default: + AT_ERROR("reflection_pad2d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::reflection_pad2d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, self, padding); +#endif +} +static inline Tensor & replication_pad1d_out(Tensor & out, const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::replication_pad1d_out(out, self, padding); + break; + default: + AT_ERROR("replication_pad1d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::replication_pad1d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, padding); +#endif +} +static inline Tensor replication_pad1d(const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::replication_pad1d(self, padding); + break; + default: + AT_ERROR("replication_pad1d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::replication_pad1d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, padding); +#endif +} +static inline Tensor & replication_pad1d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self)))) { + case Backend::CPU: + return CPUType::replication_pad1d_backward_out(grad_input, grad_output, self, padding); + break; + default: + AT_ERROR("replication_pad1d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::replication_pad1d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, padding); +#endif +} +static inline Tensor replication_pad1d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self)))) { + case Backend::CPU: + return CPUType::replication_pad1d_backward(grad_output, self, padding); + break; + default: + AT_ERROR("replication_pad1d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::replication_pad1d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, self, padding); +#endif +} +static inline Tensor & replication_pad2d_out(Tensor & out, const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::replication_pad2d_out(out, self, padding); + break; + default: + AT_ERROR("replication_pad2d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::replication_pad2d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, padding); +#endif +} +static inline Tensor replication_pad2d(const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::replication_pad2d(self, padding); + break; + default: + AT_ERROR("replication_pad2d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::replication_pad2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, padding); +#endif +} +static inline Tensor & replication_pad2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self)))) { + case Backend::CPU: + return CPUType::replication_pad2d_backward_out(grad_input, grad_output, self, padding); + break; + default: + AT_ERROR("replication_pad2d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::replication_pad2d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, padding); +#endif +} +static inline Tensor replication_pad2d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self)))) { + case Backend::CPU: + return CPUType::replication_pad2d_backward(grad_output, self, padding); + break; + default: + AT_ERROR("replication_pad2d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::replication_pad2d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, self, padding); +#endif +} +static inline Tensor & replication_pad3d_out(Tensor & out, const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::replication_pad3d_out(out, self, padding); + break; + default: + AT_ERROR("replication_pad3d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::replication_pad3d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, padding); +#endif +} +static inline Tensor replication_pad3d(const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::replication_pad3d(self, padding); + break; + default: + AT_ERROR("replication_pad3d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::replication_pad3d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, padding); +#endif +} +static inline Tensor & replication_pad3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self)))) { + case Backend::CPU: + return CPUType::replication_pad3d_backward_out(grad_input, grad_output, self, padding); + break; + default: + AT_ERROR("replication_pad3d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::replication_pad3d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, self, padding); +#endif +} +static inline Tensor replication_pad3d_backward(const Tensor & grad_output, const Tensor & self, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self)))) { + case Backend::CPU: + return CPUType::replication_pad3d_backward(grad_output, self, padding); + break; + default: + AT_ERROR("replication_pad3d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::replication_pad3d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, self, padding); +#endif +} +static inline Tensor _test_optional_float(const Tensor & self, c10::optional scale) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_test_optional_float(self, scale); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::_test_optional_float", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, self, scale); +#endif +} +static inline Tensor & upsample_linear1d_out(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::upsample_linear1d_out(out, self, output_size, align_corners); + break; + default: + AT_ERROR("upsample_linear1d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_linear1d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, output_size, align_corners); +#endif +} +static inline Tensor upsample_linear1d(const Tensor & self, IntArrayRef output_size, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::upsample_linear1d(self, output_size, align_corners); + break; + default: + AT_ERROR("upsample_linear1d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_linear1d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, output_size, align_corners); +#endif +} +static inline Tensor & upsample_linear1d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output)))) { + case Backend::CPU: + return CPUType::upsample_linear1d_backward_out(grad_input, grad_output, output_size, input_size, align_corners); + break; + default: + AT_ERROR("upsample_linear1d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_linear1d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, output_size, input_size, align_corners); +#endif +} +static inline Tensor upsample_linear1d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output)))) { + case Backend::CPU: + return CPUType::upsample_linear1d_backward(grad_output, output_size, input_size, align_corners); + break; + default: + AT_ERROR("upsample_linear1d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_linear1d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, output_size, input_size, align_corners); +#endif +} +static inline Tensor & upsample_bilinear2d_out(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::upsample_bilinear2d_out(out, self, output_size, align_corners); + break; + default: + AT_ERROR("upsample_bilinear2d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_bilinear2d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, output_size, align_corners); +#endif +} +static inline Tensor upsample_bilinear2d(const Tensor & self, IntArrayRef output_size, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::upsample_bilinear2d(self, output_size, align_corners); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::upsample_bilinear2d(self, output_size, align_corners); + break; + default: + AT_ERROR("upsample_bilinear2d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_bilinear2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, output_size, align_corners); +#endif +} +static inline Tensor & upsample_bilinear2d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output)))) { + case Backend::CPU: + return CPUType::upsample_bilinear2d_backward_out(grad_input, grad_output, output_size, input_size, align_corners); + break; + default: + AT_ERROR("upsample_bilinear2d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_bilinear2d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, output_size, input_size, align_corners); +#endif +} +static inline Tensor upsample_bilinear2d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output)))) { + case Backend::CPU: + return CPUType::upsample_bilinear2d_backward(grad_output, output_size, input_size, align_corners); + break; + default: + AT_ERROR("upsample_bilinear2d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_bilinear2d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, output_size, input_size, align_corners); +#endif +} +static inline Tensor & upsample_bicubic2d_out(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::upsample_bicubic2d_out(out, self, output_size, align_corners); + break; + default: + AT_ERROR("upsample_bicubic2d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_bicubic2d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, output_size, align_corners); +#endif +} +static inline Tensor upsample_bicubic2d(const Tensor & self, IntArrayRef output_size, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::upsample_bicubic2d(self, output_size, align_corners); + break; + default: + AT_ERROR("upsample_bicubic2d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_bicubic2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, output_size, align_corners); +#endif +} +static inline Tensor & upsample_bicubic2d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output)))) { + case Backend::CPU: + return CPUType::upsample_bicubic2d_backward_out(grad_input, grad_output, output_size, input_size, align_corners); + break; + default: + AT_ERROR("upsample_bicubic2d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_bicubic2d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, output_size, input_size, align_corners); +#endif +} +static inline Tensor upsample_bicubic2d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output)))) { + case Backend::CPU: + return CPUType::upsample_bicubic2d_backward(grad_output, output_size, input_size, align_corners); + break; + default: + AT_ERROR("upsample_bicubic2d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_bicubic2d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, output_size, input_size, align_corners); +#endif +} +static inline Tensor & upsample_trilinear3d_out(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::upsample_trilinear3d_out(out, self, output_size, align_corners); + break; + default: + AT_ERROR("upsample_trilinear3d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_trilinear3d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, output_size, align_corners); +#endif +} +static inline Tensor upsample_trilinear3d(const Tensor & self, IntArrayRef output_size, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::upsample_trilinear3d(self, output_size, align_corners); + break; + default: + AT_ERROR("upsample_trilinear3d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_trilinear3d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, output_size, align_corners); +#endif +} +static inline Tensor & upsample_trilinear3d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output)))) { + case Backend::CPU: + return CPUType::upsample_trilinear3d_backward_out(grad_input, grad_output, output_size, input_size, align_corners); + break; + default: + AT_ERROR("upsample_trilinear3d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_trilinear3d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, output_size, input_size, align_corners); +#endif +} +static inline Tensor upsample_trilinear3d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output)))) { + case Backend::CPU: + return CPUType::upsample_trilinear3d_backward(grad_output, output_size, input_size, align_corners); + break; + default: + AT_ERROR("upsample_trilinear3d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_trilinear3d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, output_size, input_size, align_corners); +#endif +} +static inline Tensor & upsample_nearest1d_out(Tensor & out, const Tensor & self, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::upsample_nearest1d_out(out, self, output_size); + break; + default: + AT_ERROR("upsample_nearest1d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_nearest1d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, output_size); +#endif +} +static inline Tensor upsample_nearest1d(const Tensor & self, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::upsample_nearest1d(self, output_size); + break; + default: + AT_ERROR("upsample_nearest1d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_nearest1d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, output_size); +#endif +} +static inline Tensor & upsample_nearest1d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output)))) { + case Backend::CPU: + return CPUType::upsample_nearest1d_backward_out(grad_input, grad_output, output_size, input_size); + break; + default: + AT_ERROR("upsample_nearest1d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_nearest1d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, output_size, input_size); +#endif +} +static inline Tensor upsample_nearest1d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output)))) { + case Backend::CPU: + return CPUType::upsample_nearest1d_backward(grad_output, output_size, input_size); + break; + default: + AT_ERROR("upsample_nearest1d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_nearest1d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, output_size, input_size); +#endif +} +static inline Tensor & upsample_nearest2d_out(Tensor & out, const Tensor & self, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::upsample_nearest2d_out(out, self, output_size); + break; + default: + AT_ERROR("upsample_nearest2d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_nearest2d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, output_size); +#endif +} +static inline Tensor upsample_nearest2d(const Tensor & self, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::upsample_nearest2d(self, output_size); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::upsample_nearest2d(self, output_size); + break; + default: + AT_ERROR("upsample_nearest2d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_nearest2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, output_size); +#endif +} +static inline Tensor & upsample_nearest2d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output)))) { + case Backend::CPU: + return CPUType::upsample_nearest2d_backward_out(grad_input, grad_output, output_size, input_size); + break; + default: + AT_ERROR("upsample_nearest2d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_nearest2d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, output_size, input_size); +#endif +} +static inline Tensor upsample_nearest2d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output)))) { + case Backend::CPU: + return CPUType::upsample_nearest2d_backward(grad_output, output_size, input_size); + break; + default: + AT_ERROR("upsample_nearest2d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_nearest2d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, output_size, input_size); +#endif +} +static inline Tensor & upsample_nearest3d_out(Tensor & out, const Tensor & self, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::upsample_nearest3d_out(out, self, output_size); + break; + default: + AT_ERROR("upsample_nearest3d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_nearest3d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, output_size); +#endif +} +static inline Tensor upsample_nearest3d(const Tensor & self, IntArrayRef output_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::upsample_nearest3d(self, output_size); + break; + default: + AT_ERROR("upsample_nearest3d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_nearest3d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, output_size); +#endif +} +static inline Tensor & upsample_nearest3d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output)))) { + case Backend::CPU: + return CPUType::upsample_nearest3d_backward_out(grad_input, grad_output, output_size, input_size); + break; + default: + AT_ERROR("upsample_nearest3d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_nearest3d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, output_size, input_size); +#endif +} +static inline Tensor upsample_nearest3d_backward(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output)))) { + case Backend::CPU: + return CPUType::upsample_nearest3d_backward(grad_output, output_size, input_size); + break; + default: + AT_ERROR("upsample_nearest3d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::upsample_nearest3d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, output_size, input_size); +#endif +} +static inline Tensor & sigmoid_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & output) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, output)))) { + case Backend::CPU: + return CPUType::sigmoid_backward_out(grad_input, grad_output, output); + break; + default: + AT_ERROR("sigmoid_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sigmoid_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, output); +#endif +} +static inline Tensor sigmoid_backward(const Tensor & grad_output, const Tensor & output) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sigmoid_backward(grad_output, output); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::sigmoid_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, output); +#endif +} +static inline Tensor & tanh_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & output) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, output)))) { + case Backend::CPU: + return CPUType::tanh_backward_out(grad_input, grad_output, output); + break; + default: + AT_ERROR("tanh_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output, output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::tanh_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, output); +#endif +} +static inline Tensor tanh_backward(const Tensor & grad_output, const Tensor & output) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, output)))) { + case Backend::CPU: + return CPUType::tanh_backward(grad_output, output); + break; + default: + AT_ERROR("tanh_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::tanh_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_output, output); +#endif +} +static inline Tensor & slow_conv_transpose2d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, weight, bias)))) { + case Backend::CPU: + return CPUType::slow_conv_transpose2d_out(out, self, weight, kernel_size, bias, stride, padding, output_padding, dilation); + break; + default: + AT_ERROR("slow_conv_transpose2d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, weight, bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slow_conv_transpose2d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, weight, kernel_size, bias, stride, padding, output_padding, dilation); +#endif +} +static inline Tensor slow_conv_transpose2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias)))) { + case Backend::CPU: + return CPUType::slow_conv_transpose2d(self, weight, kernel_size, bias, stride, padding, output_padding, dilation); + break; + default: + AT_ERROR("slow_conv_transpose2d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slow_conv_transpose2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, weight, kernel_size, bias, stride, padding, output_padding, dilation); +#endif +} +static inline std::tuple slow_conv_transpose2d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & columns, const Tensor & ones) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_weight, grad_bias, grad_output, self, weight, columns, ones)))) { + case Backend::CPU: + return CPUType::slow_conv_transpose2d_backward_out(grad_input, grad_weight, grad_bias, grad_output, self, weight, kernel_size, stride, padding, output_padding, dilation, columns, ones); + break; + default: + AT_ERROR("slow_conv_transpose2d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_weight, grad_bias, grad_output, self, weight, columns, ones))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slow_conv_transpose2d_backward", "grad_output"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, Tensor &, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, IntArrayRef, IntArrayRef, const Tensor &, const Tensor &>( + op, grad_input, grad_weight, grad_bias, grad_output, self, weight, kernel_size, stride, padding, output_padding, dilation, columns, ones); +#endif +} +static inline std::tuple slow_conv_transpose2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & columns, const Tensor & ones, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, weight, columns, ones)))) { + case Backend::CPU: + return CPUType::slow_conv_transpose2d_backward(grad_output, self, weight, kernel_size, stride, padding, output_padding, dilation, columns, ones, output_mask); + break; + default: + AT_ERROR("slow_conv_transpose2d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, weight, columns, ones))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slow_conv_transpose2d_backward", "output_mask"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, IntArrayRef, IntArrayRef, const Tensor &, const Tensor &, std::array>( + op, grad_output, self, weight, kernel_size, stride, padding, output_padding, dilation, columns, ones, output_mask); +#endif +} +static inline Tensor & slow_conv_transpose3d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, weight, bias)))) { + case Backend::CPU: + return CPUType::slow_conv_transpose3d_out(out, self, weight, kernel_size, bias, stride, padding, output_padding, dilation); + break; + default: + AT_ERROR("slow_conv_transpose3d_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, weight, bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slow_conv_transpose3d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, weight, kernel_size, bias, stride, padding, output_padding, dilation); +#endif +} +static inline Tensor slow_conv_transpose3d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias)))) { + case Backend::CPU: + return CPUType::slow_conv_transpose3d(self, weight, kernel_size, bias, stride, padding, output_padding, dilation); + break; + default: + AT_ERROR("slow_conv_transpose3d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slow_conv_transpose3d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, weight, kernel_size, bias, stride, padding, output_padding, dilation); +#endif +} +static inline std::tuple slow_conv_transpose3d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & finput, const Tensor & fgrad_input) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_weight, grad_bias, grad_output, self, weight, finput, fgrad_input)))) { + case Backend::CPU: + return CPUType::slow_conv_transpose3d_backward_out(grad_input, grad_weight, grad_bias, grad_output, self, weight, kernel_size, stride, padding, output_padding, dilation, finput, fgrad_input); + break; + default: + AT_ERROR("slow_conv_transpose3d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_weight, grad_bias, grad_output, self, weight, finput, fgrad_input))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slow_conv_transpose3d_backward", "grad_output"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, Tensor &, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, IntArrayRef, IntArrayRef, const Tensor &, const Tensor &>( + op, grad_input, grad_weight, grad_bias, grad_output, self, weight, kernel_size, stride, padding, output_padding, dilation, finput, fgrad_input); +#endif +} +static inline std::tuple slow_conv_transpose3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & finput, const Tensor & fgrad_input, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, weight, finput, fgrad_input)))) { + case Backend::CPU: + return CPUType::slow_conv_transpose3d_backward(grad_output, self, weight, kernel_size, stride, padding, output_padding, dilation, finput, fgrad_input, output_mask); + break; + default: + AT_ERROR("slow_conv_transpose3d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, weight, finput, fgrad_input))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slow_conv_transpose3d_backward", "output_mask"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, IntArrayRef, IntArrayRef, const Tensor &, const Tensor &, std::array>( + op, grad_output, self, weight, kernel_size, stride, padding, output_padding, dilation, finput, fgrad_input, output_mask); +#endif +} +static inline Tensor & thnn_conv2d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::thnn_conv2d_out(out, self, weight, kernel_size, bias, stride, padding); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::thnn_conv2d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, weight, kernel_size, bias, stride, padding); +#endif +} +static inline Tensor thnn_conv2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::thnn_conv2d(self, weight, kernel_size, bias, stride, padding); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::thnn_conv2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, weight, kernel_size, bias, stride, padding); +#endif +} +static inline std::tuple thnn_conv2d_forward_out(Tensor & output, Tensor & finput, Tensor & fgrad_input, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(output, finput, fgrad_input, self, weight, bias)))) { + case Backend::CPU: + return CPUType::thnn_conv2d_forward_out(output, finput, fgrad_input, self, weight, kernel_size, bias, stride, padding); + break; + default: + AT_ERROR("thnn_conv2d_forward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(output, finput, fgrad_input, self, weight, bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::thnn_conv2d_forward", "output"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, Tensor &, const Tensor &, const Tensor &, IntArrayRef, const Tensor &, IntArrayRef, IntArrayRef>( + op, output, finput, fgrad_input, self, weight, kernel_size, bias, stride, padding); +#endif +} +static inline std::tuple thnn_conv2d_forward(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias)))) { + case Backend::CPU: + return CPUType::thnn_conv2d_forward(self, weight, kernel_size, bias, stride, padding); + break; + default: + AT_ERROR("thnn_conv2d_forward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::thnn_conv2d_forward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, IntArrayRef, const Tensor &, IntArrayRef, IntArrayRef>( + op, self, weight, kernel_size, bias, stride, padding); +#endif +} +static inline std::tuple thnn_conv2d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & finput, const Tensor & fgrad_input) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_weight, grad_bias, grad_output, self, weight, finput, fgrad_input)))) { + case Backend::CPU: + return CPUType::thnn_conv2d_backward_out(grad_input, grad_weight, grad_bias, grad_output, self, weight, kernel_size, stride, padding, finput, fgrad_input); + break; + default: + AT_ERROR("thnn_conv2d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_weight, grad_bias, grad_output, self, weight, finput, fgrad_input))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::thnn_conv2d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, Tensor &, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, const Tensor &, const Tensor &>( + op, grad_input, grad_weight, grad_bias, grad_output, self, weight, kernel_size, stride, padding, finput, fgrad_input); +#endif +} +static inline std::tuple thnn_conv2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & finput, const Tensor & fgrad_input, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, weight, finput, fgrad_input)))) { + case Backend::CPU: + return CPUType::thnn_conv2d_backward(grad_output, self, weight, kernel_size, stride, padding, finput, fgrad_input, output_mask); + break; + default: + AT_ERROR("thnn_conv2d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, weight, finput, fgrad_input))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::thnn_conv2d_backward", "output_mask"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, const Tensor &, const Tensor &, std::array>( + op, grad_output, self, weight, kernel_size, stride, padding, finput, fgrad_input, output_mask); +#endif +} +static inline Tensor & thnn_conv_depthwise2d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::thnn_conv_depthwise2d_out(out, self, weight, kernel_size, bias, stride, padding, dilation); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::thnn_conv_depthwise2d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, weight, kernel_size, bias, stride, padding, dilation); +#endif +} +static inline Tensor thnn_conv_depthwise2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::thnn_conv_depthwise2d(self, weight, kernel_size, bias, stride, padding, dilation); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::thnn_conv_depthwise2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, weight, kernel_size, bias, stride, padding, dilation); +#endif +} +static inline Tensor & thnn_conv_depthwise2d_forward_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self, weight, bias)))) { + + default: + AT_ERROR("thnn_conv_depthwise2d_forward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self, weight, bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::thnn_conv_depthwise2d_forward", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, weight, kernel_size, bias, stride, padding, dilation); +#endif +} +static inline Tensor thnn_conv_depthwise2d_forward(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias)))) { + + default: + AT_ERROR("thnn_conv_depthwise2d_forward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::thnn_conv_depthwise2d_forward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, weight, kernel_size, bias, stride, padding, dilation); +#endif +} +static inline std::tuple thnn_conv_depthwise2d_backward_out(Tensor & grad_input, Tensor & grad_weight, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_weight, grad_output, self, weight)))) { + + default: + AT_ERROR("thnn_conv_depthwise2d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_weight, grad_output, self, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::thnn_conv_depthwise2d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, IntArrayRef>( + op, grad_input, grad_weight, grad_output, self, weight, kernel_size, stride, padding, dilation); +#endif +} +static inline std::tuple thnn_conv_depthwise2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, weight)))) { + + default: + AT_ERROR("thnn_conv_depthwise2d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::thnn_conv_depthwise2d_backward", "output_mask"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, IntArrayRef, std::array>( + op, grad_output, self, weight, kernel_size, stride, padding, dilation, output_mask); +#endif +} +static inline Tensor & slow_conv3d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::slow_conv3d_out(out, self, weight, kernel_size, bias, stride, padding); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slow_conv3d", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, weight, kernel_size, bias, stride, padding); +#endif +} +static inline Tensor slow_conv3d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::slow_conv3d(self, weight, kernel_size, bias, stride, padding); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slow_conv3d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, weight, kernel_size, bias, stride, padding); +#endif +} +static inline std::tuple slow_conv3d_forward_out(Tensor & output, Tensor & finput, Tensor & fgrad_input, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(output, finput, fgrad_input, self, weight, bias)))) { + case Backend::CPU: + return CPUType::slow_conv3d_forward_out(output, finput, fgrad_input, self, weight, kernel_size, bias, stride, padding); + break; + default: + AT_ERROR("slow_conv3d_forward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(output, finput, fgrad_input, self, weight, bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slow_conv3d_forward", "output"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, Tensor &, const Tensor &, const Tensor &, IntArrayRef, const Tensor &, IntArrayRef, IntArrayRef>( + op, output, finput, fgrad_input, self, weight, kernel_size, bias, stride, padding); +#endif +} +static inline std::tuple slow_conv3d_forward(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias)))) { + case Backend::CPU: + return CPUType::slow_conv3d_forward(self, weight, kernel_size, bias, stride, padding); + break; + default: + AT_ERROR("slow_conv3d_forward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slow_conv3d_forward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, IntArrayRef, const Tensor &, IntArrayRef, IntArrayRef>( + op, self, weight, kernel_size, bias, stride, padding); +#endif +} +static inline std::tuple slow_conv3d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & finput, const Tensor & fgrad_input) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_weight, grad_bias, grad_output, self, weight, finput, fgrad_input)))) { + case Backend::CPU: + return CPUType::slow_conv3d_backward_out(grad_input, grad_weight, grad_bias, grad_output, self, weight, kernel_size, stride, padding, finput, fgrad_input); + break; + default: + AT_ERROR("slow_conv3d_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_weight, grad_bias, grad_output, self, weight, finput, fgrad_input))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slow_conv3d_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, Tensor &, Tensor &, Tensor &, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, const Tensor &, const Tensor &>( + op, grad_input, grad_weight, grad_bias, grad_output, self, weight, kernel_size, stride, padding, finput, fgrad_input); +#endif +} +static inline std::tuple slow_conv3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & finput, const Tensor & fgrad_input, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, weight, finput, fgrad_input)))) { + case Backend::CPU: + return CPUType::slow_conv3d_backward(grad_output, self, weight, kernel_size, stride, padding, finput, fgrad_input, output_mask); + break; + default: + AT_ERROR("slow_conv3d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, weight, finput, fgrad_input))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slow_conv3d_backward", "output_mask"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, const Tensor &, const Tensor &, std::array>( + op, grad_output, self, weight, kernel_size, stride, padding, finput, fgrad_input, output_mask); +#endif +} +static inline Tensor slow_conv_dilated2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias)))) { + case Backend::CPU: + return CPUType::slow_conv_dilated2d(self, weight, kernel_size, bias, stride, padding, dilation); + break; + default: + AT_ERROR("slow_conv_dilated2d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slow_conv_dilated2d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, weight, kernel_size, bias, stride, padding, dilation); +#endif +} +static inline std::tuple slow_conv_dilated2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, weight)))) { + case Backend::CPU: + return CPUType::slow_conv_dilated2d_backward(grad_output, self, weight, kernel_size, stride, padding, dilation, output_mask); + break; + default: + AT_ERROR("slow_conv_dilated2d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slow_conv_dilated2d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, IntArrayRef, std::array>( + op, grad_output, self, weight, kernel_size, stride, padding, dilation, output_mask); +#endif +} +static inline Tensor slow_conv_dilated3d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias)))) { + case Backend::CPU: + return CPUType::slow_conv_dilated3d(self, weight, kernel_size, bias, stride, padding, dilation); + break; + default: + AT_ERROR("slow_conv_dilated3d not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self, weight, bias))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slow_conv_dilated3d", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, weight, kernel_size, bias, stride, padding, dilation); +#endif +} +static inline std::tuple slow_conv_dilated3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, std::array output_mask) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, weight)))) { + case Backend::CPU: + return CPUType::slow_conv_dilated3d_backward(grad_output, self, weight, kernel_size, stride, padding, dilation, output_mask); + break; + default: + AT_ERROR("slow_conv_dilated3d_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output, self, weight))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::slow_conv_dilated3d_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &, IntArrayRef, IntArrayRef, IntArrayRef, IntArrayRef, std::array>( + op, grad_output, self, weight, kernel_size, stride, padding, dilation, output_mask); +#endif +} +static inline Tensor & col2im_out(Tensor & out, const Tensor & self, IntArrayRef output_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::col2im_out(out, self, output_size, kernel_size, dilation, padding, stride); + break; + default: + AT_ERROR("col2im_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::col2im", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, output_size, kernel_size, dilation, padding, stride); +#endif +} +static inline Tensor col2im(const Tensor & self, IntArrayRef output_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::col2im(self, output_size, kernel_size, dilation, padding, stride); + break; + default: + AT_ERROR("col2im not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::col2im", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, output_size, kernel_size, dilation, padding, stride); +#endif +} +static inline Tensor & col2im_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output)))) { + case Backend::CPU: + return CPUType::col2im_backward_out(grad_input, grad_output, kernel_size, dilation, padding, stride); + break; + default: + AT_ERROR("col2im_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::col2im_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, kernel_size, dilation, padding, stride); +#endif +} +static inline Tensor col2im_backward(const Tensor & grad_output, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output)))) { + case Backend::CPU: + return CPUType::col2im_backward(grad_output, kernel_size, dilation, padding, stride); + break; + default: + AT_ERROR("col2im_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::col2im_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, kernel_size, dilation, padding, stride); +#endif +} +static inline Tensor & im2col_out(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(out, self)))) { + case Backend::CPU: + return CPUType::im2col_out(out, self, kernel_size, dilation, padding, stride); + break; + default: + AT_ERROR("im2col_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(out, self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::im2col", "out"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, out, self, kernel_size, dilation, padding, stride); +#endif +} +static inline Tensor im2col(const Tensor & self, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(self)))) { + case Backend::CPU: + return CPUType::im2col(self, kernel_size, dilation, padding, stride); + break; + default: + AT_ERROR("im2col not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(self))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::im2col", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, self, kernel_size, dilation, padding, stride); +#endif +} +static inline Tensor & im2col_backward_out(Tensor & grad_input, const Tensor & grad_output, IntArrayRef input_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output)))) { + case Backend::CPU: + return CPUType::im2col_backward_out(grad_input, grad_output, input_size, kernel_size, dilation, padding, stride); + break; + default: + AT_ERROR("im2col_backward_out not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_input, grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::im2col_backward", "grad_input"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_input, grad_output, input_size, kernel_size, dilation, padding, stride); +#endif +} +static inline Tensor im2col_backward(const Tensor & grad_output, IntArrayRef input_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(c10::detail::multi_dispatch_tensor_type_set(grad_output)))) { + case Backend::CPU: + return CPUType::im2col_backward(grad_output, input_size, kernel_size, dilation, padding, stride); + break; + default: + AT_ERROR("im2col_backward not implemented for ", at::toString(c10::detail::multi_dispatch_tensor_type_set(grad_output))); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::im2col_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, grad_output, input_size, kernel_size, dilation, padding, stride); +#endif +} +static inline Tensor isfinite(const Tensor & self) { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::isfinite(self); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton() + .findSchema({"aten::isfinite", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, self); +#endif +} + +} diff --git a/thirdparty/libtorch/include/ATen/Generator.h b/thirdparty/libtorch/include/ATen/Generator.h new file mode 100644 index 0000000000..48c25e141d --- /dev/null +++ b/thirdparty/libtorch/include/ATen/Generator.h @@ -0,0 +1,2 @@ +#pragma once +#include diff --git a/thirdparty/libtorch/include/ATen/InferSize.h b/thirdparty/libtorch/include/ATen/InferSize.h new file mode 100644 index 0000000000..301ad9bb20 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/InferSize.h @@ -0,0 +1,52 @@ +#pragma once + +#include +#include +#include +#include + +namespace at { + +// Infers the size of a dim with size -1, if it exists. Also checks that new +// shape is compatible with the number of elements. +inline std::vector infer_size(IntArrayRef shape, int64_t numel) { + auto res = shape.vec(); + int64_t newsize = 1; + auto infer_dim = c10::optional(); + for (int64_t dim = 0, ndim = shape.size(); dim != ndim; dim++) { + if (shape[dim] == -1) { + if (infer_dim) { + throw std::runtime_error("only one dimension can be inferred"); + } + infer_dim = dim; + } else if (shape[dim] >= 0) { + newsize *= shape[dim]; + } else { + AT_ERROR("invalid shape dimension ", shape[dim]); + } + } + + if (numel == newsize || (infer_dim && newsize > 0 && numel % newsize == 0)) { + if (infer_dim) { + // We have a degree of freedom here to select the dimension size; follow + // NumPy semantics and just bail. However, a nice error message is needed + // because users often use `view` as a way to flatten & unflatten + // dimensions and will otherwise be confused why + // empty_tensor.view( 0, 0) + // works yet + // empty_tensor.view(-1, 0) + // doesn't. + TORCH_CHECK(newsize != 0, "cannot reshape tensor of 0 elements into shape ", + shape, " because the unspecified dimension size -1 can be any " + "value and is ambiguous"); + res[*infer_dim] = numel / newsize; + } + return res; + } + + std::ostringstream ss; + ss << "shape '" << shape << "' is invalid for input of size " << numel; + throw std::runtime_error(ss.str()); +} + +} diff --git a/thirdparty/libtorch/include/ATen/InitialTensorOptions.h b/thirdparty/libtorch/include/ATen/InitialTensorOptions.h new file mode 100644 index 0000000000..00438563af --- /dev/null +++ b/thirdparty/libtorch/include/ATen/InitialTensorOptions.h @@ -0,0 +1,15 @@ +#pragma once + +#include + +namespace at { + +// Represents the initial TensorOptions, before the "defaults" are ever changed. +// This is designed to be used in library code, where the explicit devices, dtypes, etc. are known. +// NOTE: this is not a stable API. +inline TensorOptions initialTensorOptions() { + return TensorOptions(kCPU).dtype(kFloat).layout(kStrided) + .requires_grad(false); +} + +} diff --git a/thirdparty/libtorch/include/ATen/Layout.h b/thirdparty/libtorch/include/ATen/Layout.h new file mode 100644 index 0000000000..ea71e2b469 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/Layout.h @@ -0,0 +1,2 @@ +#pragma once +#include diff --git a/thirdparty/libtorch/include/ATen/LegacyTHFunctionsCPU.h b/thirdparty/libtorch/include/ATen/LegacyTHFunctionsCPU.h new file mode 100644 index 0000000000..57ff1d9647 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/LegacyTHFunctionsCPU.h @@ -0,0 +1,261 @@ +#pragma once + +// @generated by aten/src/ATen/gen.py + +#include +#include +#include + +namespace c10 { +class Scalar; +} +namespace at { +struct Generator; +class Tensor; +struct Type; +} // namespace at + +namespace at { +namespace native { +namespace legacy { +namespace cpu { + +Tensor & _th_set_(Tensor & self, Storage source, int64_t storage_offset, IntArrayRef size, IntArrayRef stride); +Tensor & _th_set_(Tensor & self, const Tensor & source); +Tensor & _th_masked_fill_(Tensor & self, const Tensor & mask, Scalar value); +Tensor & s__th_masked_fill_(Tensor & self, const Tensor & mask, Scalar value); +Tensor & _th_masked_fill_bool_(Tensor & self, const Tensor & mask, Scalar value); +Tensor & s__th_masked_fill_bool_(Tensor & self, const Tensor & mask, Scalar value); +Tensor & _th_masked_scatter_(Tensor & self, const Tensor & mask, const Tensor & source); +Tensor & s__th_masked_scatter_(Tensor & self, const Tensor & mask, const Tensor & source); +Tensor & _th_masked_scatter_bool_(Tensor & self, const Tensor & mask, const Tensor & source); +Tensor & s__th_masked_scatter_bool_(Tensor & self, const Tensor & mask, const Tensor & source); +Tensor & _th_masked_select_out(Tensor & result, const Tensor & self, const Tensor & mask); +Tensor & s__th_masked_select_out(Tensor & result, const Tensor & self, const Tensor & mask); +Tensor _th_masked_select(const Tensor & self, const Tensor & mask); +Tensor s__th_masked_select(const Tensor & self, const Tensor & mask); +Tensor & _th_masked_select_bool_out(Tensor & result, const Tensor & self, const Tensor & mask); +Tensor & s__th_masked_select_bool_out(Tensor & result, const Tensor & self, const Tensor & mask); +Tensor _th_masked_select_bool(const Tensor & self, const Tensor & mask); +Tensor s__th_masked_select_bool(const Tensor & self, const Tensor & mask); +Tensor & _th_nonzero_out(Tensor & result, const Tensor & self); +Tensor _th_nonzero(const Tensor & self); +Tensor _th_clone(const Tensor & self); +Tensor & _th_index_select_out(Tensor & result, const Tensor & self, int64_t dim, const Tensor & index); +Tensor _th_index_select(const Tensor & self, int64_t dim, const Tensor & index); +Tensor & _th_index_copy_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & source); +Tensor & _th_take_out(Tensor & result, const Tensor & self, const Tensor & index); +Tensor _th_take(const Tensor & self, const Tensor & index); +Tensor & _th_put_(Tensor & self, const Tensor & index, const Tensor & source, bool accumulate); +Tensor & _th_index_fill_(Tensor & self, int64_t dim, const Tensor & index, Scalar value); +Tensor & _th_scatter_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & src); +Tensor & _th_scatter_(Tensor & self, int64_t dim, const Tensor & index, Scalar value); +Tensor & _th_scatter_add_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & src); +Tensor & _th_gather_out(Tensor & result, const Tensor & self, int64_t dim, const Tensor & index); +Tensor _th_gather(const Tensor & self, int64_t dim, const Tensor & index); +bool _th_equal(const Tensor & self, const Tensor & other); +Tensor & _th_and_out(Tensor & result, const Tensor & self, Scalar other); +Tensor _th_and(const Tensor & self, Scalar other); +Tensor & _th_and_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor & s__th_and_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor _th_and(const Tensor & self, const Tensor & other); +Tensor s__th_and(const Tensor & self, const Tensor & other); +Tensor & _th_iand_(Tensor & self, Scalar other); +Tensor & _th_iand_(Tensor & self, const Tensor & other); +Tensor & s__th_iand_(Tensor & self, const Tensor & other); +Tensor & _th_or_out(Tensor & result, const Tensor & self, Scalar other); +Tensor _th_or(const Tensor & self, Scalar other); +Tensor & _th_or_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor & s__th_or_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor _th_or(const Tensor & self, const Tensor & other); +Tensor s__th_or(const Tensor & self, const Tensor & other); +Tensor & _th_ior_(Tensor & self, Scalar other); +Tensor & _th_ior_(Tensor & self, const Tensor & other); +Tensor & s__th_ior_(Tensor & self, const Tensor & other); +Tensor & _th_lshift_out(Tensor & result, const Tensor & self, Scalar other); +Tensor _th_lshift(const Tensor & self, Scalar other); +Tensor & _th_lshift_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor & s__th_lshift_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor _th_lshift(const Tensor & self, const Tensor & other); +Tensor s__th_lshift(const Tensor & self, const Tensor & other); +Tensor & _th_ilshift_(Tensor & self, Scalar other); +Tensor & _th_ilshift_(Tensor & self, const Tensor & other); +Tensor & s__th_ilshift_(Tensor & self, const Tensor & other); +Tensor & _th_rshift_out(Tensor & result, const Tensor & self, Scalar other); +Tensor _th_rshift(const Tensor & self, Scalar other); +Tensor & _th_rshift_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor & s__th_rshift_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor _th_rshift(const Tensor & self, const Tensor & other); +Tensor s__th_rshift(const Tensor & self, const Tensor & other); +Tensor & _th_irshift_(Tensor & self, Scalar other); +Tensor & _th_irshift_(Tensor & self, const Tensor & other); +Tensor & s__th_irshift_(Tensor & self, const Tensor & other); +Tensor & _th_min_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor & s__th_min_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor _th_min(const Tensor & self, const Tensor & other); +Tensor s__th_min(const Tensor & self, const Tensor & other); +Tensor _th_min(const Tensor & self); +std::tuple _th_min_out(Tensor & min, Tensor & min_indices, const Tensor & self, int64_t dim, bool keepdim); +std::tuple _th_min(const Tensor & self, int64_t dim, bool keepdim); +Tensor & _th_max_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor & s__th_max_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor _th_max(const Tensor & self, const Tensor & other); +Tensor s__th_max(const Tensor & self, const Tensor & other); +Tensor _th_max(const Tensor & self); +std::tuple _th_max_out(Tensor & max, Tensor & max_indices, const Tensor & self, int64_t dim, bool keepdim); +std::tuple _th_max(const Tensor & self, int64_t dim, bool keepdim); +std::tuple _th_mode_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool keepdim); +std::tuple _th_mode(const Tensor & self, int64_t dim, bool keepdim); +std::tuple _th_sort_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool descending); +std::tuple _th_sort(const Tensor & self, int64_t dim, bool descending); +Tensor & _th_var_out(Tensor & result, const Tensor & self, int64_t dim, bool unbiased, bool keepdim); +Tensor _th_var(const Tensor & self, int64_t dim, bool unbiased, bool keepdim); +Tensor _th_var(const Tensor & self, bool unbiased); +Tensor & _th_std_out(Tensor & result, const Tensor & self, int64_t dim, bool unbiased, bool keepdim); +Tensor _th_std(const Tensor & self, int64_t dim, bool unbiased, bool keepdim); +Tensor _th_std(const Tensor & self, bool unbiased); +Tensor & _th_renorm_out(Tensor & result, const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm); +Tensor _th_renorm(const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm); +Tensor & _th_renorm_(Tensor & self, Scalar p, int64_t dim, Scalar maxnorm); +Tensor _th_dist(const Tensor & self, const Tensor & other, Scalar p); +Tensor s__th_dist(const Tensor & self, const Tensor & other, Scalar p); +Tensor & _th_histc_out(Tensor & result, const Tensor & self, int64_t bins, Scalar min, Scalar max); +Tensor _th_histc(const Tensor & self, int64_t bins, Scalar min, Scalar max); +Tensor & _th_cumsum_out(Tensor & result, const Tensor & self, int64_t dim); +Tensor _th_cumsum(const Tensor & self, int64_t dim); +Tensor & _th_cumprod_out(Tensor & result, const Tensor & self, int64_t dim); +Tensor _th_cumprod(const Tensor & self, int64_t dim); +Tensor _th_trace(const Tensor & self); +Tensor & _th_fmod_out(Tensor & result, const Tensor & self, Scalar other); +Tensor _th_fmod(const Tensor & self, Scalar other); +Tensor & _th_fmod_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor & s__th_fmod_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor _th_fmod(const Tensor & self, const Tensor & other); +Tensor s__th_fmod(const Tensor & self, const Tensor & other); +Tensor & _th_fmod_(Tensor & self, Scalar other); +Tensor & _th_fmod_(Tensor & self, const Tensor & other); +Tensor & s__th_fmod_(Tensor & self, const Tensor & other); +Tensor & _th_remainder_out(Tensor & result, const Tensor & self, Scalar other); +Tensor _th_remainder(const Tensor & self, Scalar other); +Tensor & _th_remainder_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor & s__th_remainder_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor _th_remainder(const Tensor & self, const Tensor & other); +Tensor s__th_remainder(const Tensor & self, const Tensor & other); +Tensor & _th_remainder_(Tensor & self, Scalar other); +Tensor & _th_remainder_(Tensor & self, const Tensor & other); +Tensor & s__th_remainder_(Tensor & self, const Tensor & other); +Tensor _th_dot(const Tensor & self, const Tensor & tensor); +Tensor & _th_diag_out(Tensor & result, const Tensor & self, int64_t diagonal); +Tensor _th_diag(const Tensor & self, int64_t diagonal); +Tensor & _th_addmm_out(Tensor & result, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); +Tensor & s__th_addmm_out(Tensor & result, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); +Tensor _th_addmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); +Tensor s__th_addmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); +Tensor & _th_addmm_(Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); +Tensor & _th_addmv_out(Tensor & result, const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha); +Tensor & s__th_addmv_out(Tensor & result, const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha); +Tensor _th_addmv(const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha); +Tensor s__th_addmv(const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha); +Tensor & _th_addmv_(Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha); +Tensor & _th_addr_out(Tensor & result, const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha); +Tensor & s__th_addr_out(Tensor & result, const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha); +Tensor _th_addr(const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha); +Tensor s__th_addr(const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha); +Tensor & _th_addr_(Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha); +Tensor & _th_ger_out(Tensor & result, const Tensor & self, const Tensor & vec2); +Tensor _th_ger(const Tensor & self, const Tensor & vec2); +Tensor & _th_mv_out(Tensor & result, const Tensor & self, const Tensor & vec); +Tensor _th_mv(const Tensor & self, const Tensor & vec); +Tensor & _th_mm_out(Tensor & result, const Tensor & self, const Tensor & mat2); +Tensor _th_mm(const Tensor & self, const Tensor & mat2); +Tensor & _th_addbmm_out(Tensor & result, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); +Tensor & s__th_addbmm_out(Tensor & result, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); +Tensor _th_addbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); +Tensor s__th_addbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); +Tensor & _th_addbmm_(Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); +std::tuple _th_gels_out(Tensor & res1, Tensor & res2, const Tensor & self, const Tensor & A); +std::tuple _th_gels(const Tensor & self, const Tensor & A); +std::tuple _th_eig_out(Tensor & res1, Tensor & res2, const Tensor & self, bool eigenvectors); +std::tuple _th_eig(const Tensor & self, bool eigenvectors); +Tensor & _th_potri_out(Tensor & output, const Tensor & self, bool upper); +Tensor _th_potri(const Tensor & self, bool upper); +std::tuple _th_geqrf_out(Tensor & res1, Tensor & res2, const Tensor & self); +std::tuple _th_geqrf(const Tensor & self); +Tensor & _th_orgqr_out(Tensor & result, const Tensor & self, const Tensor & input2); +Tensor _th_orgqr(const Tensor & self, const Tensor & input2); +Tensor & _th_ormqr_out(Tensor & result, const Tensor & self, const Tensor & input2, const Tensor & input3, bool left, bool transpose); +Tensor _th_ormqr(const Tensor & self, const Tensor & input2, const Tensor & input3, bool left, bool transpose); +Tensor & _th_random_(Tensor & self, int64_t from, int64_t to, Generator * generator); +Tensor & _th_random_(Tensor & self, int64_t to, Generator * generator); +Tensor & _th_random_(Tensor & self, Generator * generator); +std::tuple _th_multinomial_alias_setup_out(Tensor & J, Tensor & q, const Tensor & probs); +std::tuple _th_multinomial_alias_setup(const Tensor & probs); +Tensor & _th_multinomial_alias_draw_out(Tensor & result, const Tensor & q, const Tensor & J, int64_t num_samples, Generator * generator); +Tensor _th_multinomial_alias_draw(const Tensor & q, const Tensor & J, int64_t num_samples, Generator * generator); +Tensor & _th_uniform_(Tensor & self, double from, double to, Generator * generator); +Tensor & _th_normal_out(Tensor & output, const Tensor & mean, double std, Generator * generator); +Tensor _th_normal(const Tensor & mean, double std, Generator * generator); +Tensor & _th_normal_out(Tensor & output, double mean, const Tensor & std, Generator * generator); +Tensor _th_normal(double mean, const Tensor & std, Generator * generator); +Tensor & _th_normal_out(Tensor & output, const Tensor & mean, const Tensor & std, Generator * generator); +Tensor _th_normal(const Tensor & mean, const Tensor & std, Generator * generator); +Tensor & _th_normal_(Tensor & self, double mean, double std, Generator * generator); +Tensor & _th_cauchy_(Tensor & self, double median, double sigma, Generator * generator); +Tensor & _th_log_normal_(Tensor & self, double mean, double std, Generator * generator); +Tensor & _th_exponential_(Tensor & self, double lambd, Generator * generator); +Tensor & _th_geometric_(Tensor & self, double p, Generator * generator); +Tensor & _th_cat_out(Tensor & self, TensorList tensors, int64_t dim); +Tensor _th_cat(TensorList tensors, int64_t dim); +Tensor & _thnn_binary_cross_entropy_forward_out(Tensor & output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction); +Tensor _thnn_binary_cross_entropy_forward(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction); +Tensor & _thnn_binary_cross_entropy_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction); +Tensor _thnn_binary_cross_entropy_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction); +Tensor & _thnn_soft_margin_loss_forward_out(Tensor & output, const Tensor & self, const Tensor & target, int64_t reduction); +Tensor _thnn_soft_margin_loss_forward(const Tensor & self, const Tensor & target, int64_t reduction); +Tensor & _thnn_soft_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); +Tensor _thnn_soft_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); +Tensor & _thnn_elu_forward_out(Tensor & output, const Tensor & self, Scalar alpha, Scalar scale, Scalar input_scale); +Tensor _thnn_elu_forward(const Tensor & self, Scalar alpha, Scalar scale, Scalar input_scale); +Tensor & _thnn_elu_backward_out(Tensor & grad_input, const Tensor & grad_output, Scalar alpha, Scalar scale, Scalar input_scale, const Tensor & output); +Tensor _thnn_elu_backward(const Tensor & grad_output, Scalar alpha, Scalar scale, Scalar input_scale, const Tensor & output); +Tensor & _thnn_elu_forward_(Tensor & self, Scalar alpha, Scalar scale, Scalar input_scale); +Tensor & _thnn_glu_forward_out(Tensor & output, const Tensor & self, int64_t dim); +Tensor _thnn_glu_forward(const Tensor & self, int64_t dim); +Tensor & _thnn_glu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, int64_t dim); +Tensor _thnn_glu_backward(const Tensor & grad_output, const Tensor & self, int64_t dim); +Tensor & _thnn_hardtanh_forward_out(Tensor & output, const Tensor & self, Scalar min_val, Scalar max_val); +Tensor _thnn_hardtanh_forward(const Tensor & self, Scalar min_val, Scalar max_val); +Tensor & _thnn_hardtanh_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar min_val, Scalar max_val); +Tensor _thnn_hardtanh_backward(const Tensor & grad_output, const Tensor & self, Scalar min_val, Scalar max_val); +Tensor & _thnn_hardtanh_forward_(Tensor & self, Scalar min_val, Scalar max_val); +Tensor & _thnn_leaky_relu_forward_out(Tensor & output, const Tensor & self, Scalar negative_slope); +Tensor _thnn_leaky_relu_forward(const Tensor & self, Scalar negative_slope); +Tensor & _thnn_leaky_relu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar negative_slope); +Tensor _thnn_leaky_relu_backward(const Tensor & grad_output, const Tensor & self, Scalar negative_slope); +Tensor & _thnn_leaky_relu_forward_(Tensor & self, Scalar negative_slope); +std::tuple _thnn_log_sigmoid_forward_out(Tensor & output, Tensor & buffer, const Tensor & self); +std::tuple _thnn_log_sigmoid_forward(const Tensor & self); +Tensor & _thnn_log_sigmoid_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & buffer); +Tensor _thnn_log_sigmoid_backward(const Tensor & grad_output, const Tensor & self, const Tensor & buffer); +Tensor & _thnn_rrelu_with_noise_forward_out(Tensor & output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator); +Tensor _thnn_rrelu_with_noise_forward(const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator); +Tensor & _thnn_rrelu_with_noise_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training); +Tensor _thnn_rrelu_with_noise_backward(const Tensor & grad_output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training); +Tensor & _thnn_rrelu_with_noise_forward_(Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator); +Tensor & _thnn_softplus_forward_out(Tensor & output, const Tensor & self, Scalar beta, Scalar threshold); +Tensor _thnn_softplus_forward(const Tensor & self, Scalar beta, Scalar threshold); +Tensor & _thnn_softplus_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar beta, Scalar threshold, const Tensor & output); +Tensor _thnn_softplus_backward(const Tensor & grad_output, const Tensor & self, Scalar beta, Scalar threshold, const Tensor & output); +Tensor & _thnn_softshrink_forward_out(Tensor & output, const Tensor & self, Scalar lambd); +Tensor _thnn_softshrink_forward(const Tensor & self, Scalar lambd); +Tensor & _thnn_softshrink_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar lambd); +Tensor _thnn_softshrink_backward(const Tensor & grad_output, const Tensor & self, Scalar lambd); +Tensor & _thnn_tanh_forward_out(Tensor & output, const Tensor & self); +Tensor _thnn_tanh_forward(const Tensor & self); +Tensor & _thnn_tanh_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & output); +Tensor _thnn_tanh_backward(const Tensor & grad_output, const Tensor & output); + +} // namespace th +} // namespace legacy +} // namespace native +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/LegacyTHFunctionsCUDA.h b/thirdparty/libtorch/include/ATen/LegacyTHFunctionsCUDA.h new file mode 100644 index 0000000000..6534446241 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/LegacyTHFunctionsCUDA.h @@ -0,0 +1,300 @@ +#pragma once + +// @generated by aten/src/ATen/gen.py + +#include +#include +#include + +namespace c10 { +class Scalar; +} +namespace at { +struct Generator; +class Tensor; +struct Type; +} // namespace at + +namespace at { +namespace native { +namespace legacy { +namespace cuda { + +Tensor & _th_set_(Tensor & self, Storage source, int64_t storage_offset, IntArrayRef size, IntArrayRef stride); +Tensor & _th_set_(Tensor & self, const Tensor & source); +Tensor & _th_masked_fill_(Tensor & self, const Tensor & mask, Scalar value); +Tensor & s__th_masked_fill_(Tensor & self, const Tensor & mask, Scalar value); +Tensor & _th_masked_fill_bool_(Tensor & self, const Tensor & mask, Scalar value); +Tensor & s__th_masked_fill_bool_(Tensor & self, const Tensor & mask, Scalar value); +Tensor & _th_masked_scatter_(Tensor & self, const Tensor & mask, const Tensor & source); +Tensor & s__th_masked_scatter_(Tensor & self, const Tensor & mask, const Tensor & source); +Tensor & _th_masked_scatter_bool_(Tensor & self, const Tensor & mask, const Tensor & source); +Tensor & s__th_masked_scatter_bool_(Tensor & self, const Tensor & mask, const Tensor & source); +Tensor & _th_masked_select_out(Tensor & result, const Tensor & self, const Tensor & mask); +Tensor & s__th_masked_select_out(Tensor & result, const Tensor & self, const Tensor & mask); +Tensor _th_masked_select(const Tensor & self, const Tensor & mask); +Tensor s__th_masked_select(const Tensor & self, const Tensor & mask); +Tensor & _th_masked_select_bool_out(Tensor & result, const Tensor & self, const Tensor & mask); +Tensor & s__th_masked_select_bool_out(Tensor & result, const Tensor & self, const Tensor & mask); +Tensor _th_masked_select_bool(const Tensor & self, const Tensor & mask); +Tensor s__th_masked_select_bool(const Tensor & self, const Tensor & mask); +Tensor & _th_nonzero_out(Tensor & result, const Tensor & self); +Tensor _th_nonzero(const Tensor & self); +Tensor _th_clone(const Tensor & self); +Tensor & _th_index_select_out(Tensor & result, const Tensor & self, int64_t dim, const Tensor & index); +Tensor _th_index_select(const Tensor & self, int64_t dim, const Tensor & index); +Tensor & _th_index_copy_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & source); +Tensor & _th_take_out(Tensor & result, const Tensor & self, const Tensor & index); +Tensor _th_take(const Tensor & self, const Tensor & index); +Tensor & _th_put_(Tensor & self, const Tensor & index, const Tensor & source, bool accumulate); +Tensor & _th_index_add_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & source); +Tensor & _th_index_fill_(Tensor & self, int64_t dim, const Tensor & index, Scalar value); +Tensor & _th_scatter_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & src); +Tensor & _th_scatter_(Tensor & self, int64_t dim, const Tensor & index, Scalar value); +Tensor & _th_scatter_add_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & src); +Tensor & _th_gather_out(Tensor & result, const Tensor & self, int64_t dim, const Tensor & index); +Tensor _th_gather(const Tensor & self, int64_t dim, const Tensor & index); +bool _th_equal(const Tensor & self, const Tensor & other); +Tensor & _th_and_out(Tensor & result, const Tensor & self, Scalar other); +Tensor _th_and(const Tensor & self, Scalar other); +Tensor & _th_and_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor & s__th_and_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor _th_and(const Tensor & self, const Tensor & other); +Tensor s__th_and(const Tensor & self, const Tensor & other); +Tensor & _th_iand_(Tensor & self, Scalar other); +Tensor & _th_iand_(Tensor & self, const Tensor & other); +Tensor & s__th_iand_(Tensor & self, const Tensor & other); +Tensor & _th_or_out(Tensor & result, const Tensor & self, Scalar other); +Tensor _th_or(const Tensor & self, Scalar other); +Tensor & _th_or_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor & s__th_or_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor _th_or(const Tensor & self, const Tensor & other); +Tensor s__th_or(const Tensor & self, const Tensor & other); +Tensor & _th_ior_(Tensor & self, Scalar other); +Tensor & _th_ior_(Tensor & self, const Tensor & other); +Tensor & s__th_ior_(Tensor & self, const Tensor & other); +Tensor & _th_lshift_out(Tensor & result, const Tensor & self, Scalar other); +Tensor _th_lshift(const Tensor & self, Scalar other); +Tensor & _th_lshift_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor & s__th_lshift_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor _th_lshift(const Tensor & self, const Tensor & other); +Tensor s__th_lshift(const Tensor & self, const Tensor & other); +Tensor & _th_ilshift_(Tensor & self, Scalar other); +Tensor & _th_ilshift_(Tensor & self, const Tensor & other); +Tensor & s__th_ilshift_(Tensor & self, const Tensor & other); +Tensor & _th_rshift_out(Tensor & result, const Tensor & self, Scalar other); +Tensor _th_rshift(const Tensor & self, Scalar other); +Tensor & _th_rshift_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor & s__th_rshift_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor _th_rshift(const Tensor & self, const Tensor & other); +Tensor s__th_rshift(const Tensor & self, const Tensor & other); +Tensor & _th_irshift_(Tensor & self, Scalar other); +Tensor & _th_irshift_(Tensor & self, const Tensor & other); +Tensor & s__th_irshift_(Tensor & self, const Tensor & other); +Tensor & _th_min_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor & s__th_min_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor _th_min(const Tensor & self, const Tensor & other); +Tensor s__th_min(const Tensor & self, const Tensor & other); +Tensor _th_min(const Tensor & self); +std::tuple _th_min_out(Tensor & min, Tensor & min_indices, const Tensor & self, int64_t dim, bool keepdim); +std::tuple _th_min(const Tensor & self, int64_t dim, bool keepdim); +Tensor & _th_max_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor & s__th_max_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor _th_max(const Tensor & self, const Tensor & other); +Tensor s__th_max(const Tensor & self, const Tensor & other); +Tensor _th_max(const Tensor & self); +std::tuple _th_max_out(Tensor & max, Tensor & max_indices, const Tensor & self, int64_t dim, bool keepdim); +std::tuple _th_max(const Tensor & self, int64_t dim, bool keepdim); +std::tuple _th_mode_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool keepdim); +std::tuple _th_mode(const Tensor & self, int64_t dim, bool keepdim); +std::tuple _th_sort_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool descending); +std::tuple _th_sort(const Tensor & self, int64_t dim, bool descending); +std::tuple _th_topk_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted); +std::tuple _th_topk(const Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted); +Tensor & _th_exp_out(Tensor & result, const Tensor & self); +Tensor _th_exp(const Tensor & self); +Tensor & _th_cos_out(Tensor & result, const Tensor & self); +Tensor _th_cos(const Tensor & self); +Tensor & _th_cosh_out(Tensor & result, const Tensor & self); +Tensor _th_cosh(const Tensor & self); +Tensor & _th_tan_out(Tensor & result, const Tensor & self); +Tensor _th_tan(const Tensor & self); +Tensor & _th_atan_out(Tensor & result, const Tensor & self); +Tensor _th_atan(const Tensor & self); +Tensor & _th_tanh_out(Tensor & result, const Tensor & self); +Tensor _th_tanh(const Tensor & self); +Tensor & _th_erf_out(Tensor & result, const Tensor & self); +Tensor _th_erf(const Tensor & self); +Tensor & _th_erfc_out(Tensor & result, const Tensor & self); +Tensor _th_erfc(const Tensor & self); +Tensor & _th_var_out(Tensor & result, const Tensor & self, int64_t dim, bool unbiased, bool keepdim); +Tensor _th_var(const Tensor & self, int64_t dim, bool unbiased, bool keepdim); +Tensor _th_var(const Tensor & self, bool unbiased); +Tensor & _th_std_out(Tensor & result, const Tensor & self, int64_t dim, bool unbiased, bool keepdim); +Tensor _th_std(const Tensor & self, int64_t dim, bool unbiased, bool keepdim); +Tensor _th_std(const Tensor & self, bool unbiased); +Tensor & _th_renorm_out(Tensor & result, const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm); +Tensor _th_renorm(const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm); +Tensor & _th_renorm_(Tensor & self, Scalar p, int64_t dim, Scalar maxnorm); +Tensor _th_dist(const Tensor & self, const Tensor & other, Scalar p); +Tensor s__th_dist(const Tensor & self, const Tensor & other, Scalar p); +Tensor & _th_reciprocal_out(Tensor & result, const Tensor & self); +Tensor _th_reciprocal(const Tensor & self); +Tensor & _th_cumsum_out(Tensor & result, const Tensor & self, int64_t dim); +Tensor _th_cumsum(const Tensor & self, int64_t dim); +Tensor & _th_cumprod_out(Tensor & result, const Tensor & self, int64_t dim); +Tensor _th_cumprod(const Tensor & self, int64_t dim); +Tensor _th_trace(const Tensor & self); +Tensor & _th_fmod_out(Tensor & result, const Tensor & self, Scalar other); +Tensor _th_fmod(const Tensor & self, Scalar other); +Tensor & _th_fmod_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor & s__th_fmod_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor _th_fmod(const Tensor & self, const Tensor & other); +Tensor s__th_fmod(const Tensor & self, const Tensor & other); +Tensor & _th_fmod_(Tensor & self, Scalar other); +Tensor & _th_fmod_(Tensor & self, const Tensor & other); +Tensor & s__th_fmod_(Tensor & self, const Tensor & other); +Tensor & _th_remainder_out(Tensor & result, const Tensor & self, Scalar other); +Tensor _th_remainder(const Tensor & self, Scalar other); +Tensor & _th_remainder_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor & s__th_remainder_out(Tensor & result, const Tensor & self, const Tensor & other); +Tensor _th_remainder(const Tensor & self, const Tensor & other); +Tensor s__th_remainder(const Tensor & self, const Tensor & other); +Tensor & _th_remainder_(Tensor & self, Scalar other); +Tensor & _th_remainder_(Tensor & self, const Tensor & other); +Tensor & s__th_remainder_(Tensor & self, const Tensor & other); +Tensor & _th_clamp_out(Tensor & result, const Tensor & self, Scalar min, Scalar max); +Tensor _th_clamp(const Tensor & self, Scalar min, Scalar max); +Tensor & _th_clamp_min_out(Tensor & result, const Tensor & self, Scalar min); +Tensor _th_clamp_min(const Tensor & self, Scalar min); +Tensor & _th_clamp_max_out(Tensor & result, const Tensor & self, Scalar max); +Tensor _th_clamp_max(const Tensor & self, Scalar max); +Tensor _th_dot(const Tensor & self, const Tensor & tensor); +Tensor & _th_cross_kernel_out(Tensor & result, const Tensor & self, const Tensor & other, int64_t dim); +Tensor _th_cross_kernel(const Tensor & self, const Tensor & other, int64_t dim); +Tensor & _th_diag_out(Tensor & result, const Tensor & self, int64_t diagonal); +Tensor _th_diag(const Tensor & self, int64_t diagonal); +Tensor & _th_addmm_out(Tensor & result, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); +Tensor & s__th_addmm_out(Tensor & result, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); +Tensor _th_addmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); +Tensor s__th_addmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); +Tensor & _th_addmm_(Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); +Tensor & _th_addmv_out(Tensor & result, const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha); +Tensor & s__th_addmv_out(Tensor & result, const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha); +Tensor _th_addmv(const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha); +Tensor s__th_addmv(const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha); +Tensor & _th_addmv_(Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha); +Tensor & _th_addr_out(Tensor & result, const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha); +Tensor & s__th_addr_out(Tensor & result, const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha); +Tensor _th_addr(const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha); +Tensor s__th_addr(const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha); +Tensor & _th_addr_(Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha); +Tensor & _th_ger_out(Tensor & result, const Tensor & self, const Tensor & vec2); +Tensor _th_ger(const Tensor & self, const Tensor & vec2); +Tensor & _th_mv_out(Tensor & result, const Tensor & self, const Tensor & vec); +Tensor _th_mv(const Tensor & self, const Tensor & vec); +Tensor & _th_mm_out(Tensor & result, const Tensor & self, const Tensor & mat2); +Tensor _th_mm(const Tensor & self, const Tensor & mat2); +Tensor & _th_bmm_out(Tensor & result, const Tensor & self, const Tensor & mat2); +Tensor _th_bmm(const Tensor & self, const Tensor & mat2); +Tensor & _th_addbmm_out(Tensor & result, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); +Tensor & s__th_addbmm_out(Tensor & result, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); +Tensor _th_addbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); +Tensor s__th_addbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); +Tensor & _th_addbmm_(Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); +Tensor & _th_baddbmm_out(Tensor & result, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); +Tensor & s__th_baddbmm_out(Tensor & result, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); +Tensor _th_baddbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); +Tensor s__th_baddbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); +std::tuple _th_gels_out(Tensor & res1, Tensor & res2, const Tensor & self, const Tensor & A); +std::tuple _th_gels(const Tensor & self, const Tensor & A); +std::tuple _th_eig_out(Tensor & res1, Tensor & res2, const Tensor & self, bool eigenvectors); +std::tuple _th_eig(const Tensor & self, bool eigenvectors); +Tensor & _th_potri_out(Tensor & output, const Tensor & self, bool upper); +Tensor _th_potri(const Tensor & self, bool upper); +std::tuple _th_geqrf_out(Tensor & res1, Tensor & res2, const Tensor & self); +std::tuple _th_geqrf(const Tensor & self); +std::tuple _th_multinomial_alias_setup_out(Tensor & J, Tensor & q, const Tensor & probs); +std::tuple _th_multinomial_alias_setup(const Tensor & probs); +Tensor & _th_multinomial_alias_draw_out(Tensor & result, const Tensor & q, const Tensor & J, int64_t num_samples, Generator * generator); +Tensor _th_multinomial_alias_draw(const Tensor & q, const Tensor & J, int64_t num_samples, Generator * generator); +Tensor & _th_copy_ignoring_overlaps_(Tensor & self, const Tensor & src); +Tensor & _th_cat_out(Tensor & self, TensorList tensors, int64_t dim); +Tensor _th_cat(TensorList tensors, int64_t dim); +Tensor & _thnn_binary_cross_entropy_forward_out(Tensor & output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction); +Tensor _thnn_binary_cross_entropy_forward(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction); +Tensor & _thnn_binary_cross_entropy_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction); +Tensor _thnn_binary_cross_entropy_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction); +Tensor & _thnn_multi_margin_loss_forward_out(Tensor & output, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction); +Tensor _thnn_multi_margin_loss_forward(const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction); +Tensor & _thnn_multi_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction); +Tensor _thnn_multi_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction); +std::tuple _thnn_multilabel_margin_loss_forward_out(Tensor & output, Tensor & is_target, const Tensor & self, const Tensor & target, int64_t reduction); +std::tuple _thnn_multilabel_margin_loss_forward(const Tensor & self, const Tensor & target, int64_t reduction); +Tensor & _thnn_multilabel_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction, const Tensor & is_target); +Tensor _thnn_multilabel_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction, const Tensor & is_target); +std::tuple _thnn_nll_loss_forward_out(Tensor & output, Tensor & total_weight, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); +std::tuple _thnn_nll_loss_forward(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); +Tensor & _thnn_nll_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); +Tensor _thnn_nll_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); +std::tuple _thnn_nll_loss2d_forward_out(Tensor & output, Tensor & total_weight, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); +std::tuple _thnn_nll_loss2d_forward(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); +Tensor & _thnn_nll_loss2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); +Tensor _thnn_nll_loss2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); +Tensor & _thnn_soft_margin_loss_forward_out(Tensor & output, const Tensor & self, const Tensor & target, int64_t reduction); +Tensor _thnn_soft_margin_loss_forward(const Tensor & self, const Tensor & target, int64_t reduction); +Tensor & _thnn_soft_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); +Tensor _thnn_soft_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); +Tensor & _thnn_elu_forward_out(Tensor & output, const Tensor & self, Scalar alpha, Scalar scale, Scalar input_scale); +Tensor _thnn_elu_forward(const Tensor & self, Scalar alpha, Scalar scale, Scalar input_scale); +Tensor & _thnn_elu_backward_out(Tensor & grad_input, const Tensor & grad_output, Scalar alpha, Scalar scale, Scalar input_scale, const Tensor & output); +Tensor _thnn_elu_backward(const Tensor & grad_output, Scalar alpha, Scalar scale, Scalar input_scale, const Tensor & output); +Tensor & _thnn_elu_forward_(Tensor & self, Scalar alpha, Scalar scale, Scalar input_scale); +Tensor & _thnn_glu_forward_out(Tensor & output, const Tensor & self, int64_t dim); +Tensor _thnn_glu_forward(const Tensor & self, int64_t dim); +Tensor & _thnn_glu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, int64_t dim); +Tensor _thnn_glu_backward(const Tensor & grad_output, const Tensor & self, int64_t dim); +Tensor & _thnn_hardtanh_forward_out(Tensor & output, const Tensor & self, Scalar min_val, Scalar max_val); +Tensor _thnn_hardtanh_forward(const Tensor & self, Scalar min_val, Scalar max_val); +Tensor & _thnn_hardtanh_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar min_val, Scalar max_val); +Tensor _thnn_hardtanh_backward(const Tensor & grad_output, const Tensor & self, Scalar min_val, Scalar max_val); +Tensor & _thnn_hardtanh_forward_(Tensor & self, Scalar min_val, Scalar max_val); +Tensor & _thnn_leaky_relu_forward_out(Tensor & output, const Tensor & self, Scalar negative_slope); +Tensor _thnn_leaky_relu_forward(const Tensor & self, Scalar negative_slope); +Tensor & _thnn_leaky_relu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar negative_slope); +Tensor _thnn_leaky_relu_backward(const Tensor & grad_output, const Tensor & self, Scalar negative_slope); +Tensor & _thnn_leaky_relu_forward_(Tensor & self, Scalar negative_slope); +std::tuple _thnn_log_sigmoid_forward_out(Tensor & output, Tensor & buffer, const Tensor & self); +std::tuple _thnn_log_sigmoid_forward(const Tensor & self); +Tensor & _thnn_log_sigmoid_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & buffer); +Tensor _thnn_log_sigmoid_backward(const Tensor & grad_output, const Tensor & self, const Tensor & buffer); +Tensor & _thnn_rrelu_with_noise_forward_out(Tensor & output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator); +Tensor _thnn_rrelu_with_noise_forward(const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator); +Tensor & _thnn_rrelu_with_noise_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training); +Tensor _thnn_rrelu_with_noise_backward(const Tensor & grad_output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training); +Tensor & _thnn_rrelu_with_noise_forward_(Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator); +Tensor & _thnn_softplus_forward_out(Tensor & output, const Tensor & self, Scalar beta, Scalar threshold); +Tensor _thnn_softplus_forward(const Tensor & self, Scalar beta, Scalar threshold); +Tensor & _thnn_softplus_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar beta, Scalar threshold, const Tensor & output); +Tensor _thnn_softplus_backward(const Tensor & grad_output, const Tensor & self, Scalar beta, Scalar threshold, const Tensor & output); +Tensor & _thnn_softshrink_forward_out(Tensor & output, const Tensor & self, Scalar lambd); +Tensor _thnn_softshrink_forward(const Tensor & self, Scalar lambd); +Tensor & _thnn_softshrink_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar lambd); +Tensor _thnn_softshrink_backward(const Tensor & grad_output, const Tensor & self, Scalar lambd); +Tensor & _thnn_tanh_forward_out(Tensor & output, const Tensor & self); +Tensor _thnn_tanh_forward(const Tensor & self); +Tensor & _thnn_tanh_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & output); +Tensor _thnn_tanh_backward(const Tensor & grad_output, const Tensor & output); +std::tuple _thnn_conv2d_forward_out(Tensor & output, Tensor & columns, Tensor & ones, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); +std::tuple _thnn_conv2d_forward(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); +std::tuple _thnn_conv2d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & columns, const Tensor & ones); +std::tuple _thnn_conv2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & columns, const Tensor & ones, std::array output_mask); +Tensor & _thnn_conv_depthwise2d_forward_out(Tensor & output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation); +Tensor _thnn_conv_depthwise2d_forward(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation); +std::tuple _thnn_conv_depthwise2d_backward_out(Tensor & grad_input, Tensor & grad_weight, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation); +std::tuple _thnn_conv_depthwise2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, std::array output_mask); + +} // namespace th +} // namespace legacy +} // namespace native +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/MatrixRef.h b/thirdparty/libtorch/include/ATen/MatrixRef.h new file mode 100644 index 0000000000..d6bcc08add --- /dev/null +++ b/thirdparty/libtorch/include/ATen/MatrixRef.h @@ -0,0 +1,100 @@ +#pragma once +#include +#include + +#include + +namespace at { + /// MatrixRef - Like an ArrayRef, but with an extra recorded strides so that + /// we can easily view it as a multidimensional array. + /// + /// Like ArrayRef, this class does not own the underlying data, it is expected + /// to be used in situations where the data resides in some other buffer. + /// + /// This is intended to be trivially copyable, so it should be passed by + /// value. + /// + /// For now, 2D only (so the copies are actually cheap, without having + /// to write a SmallVector class) and contiguous only (so we can + /// return non-strided ArrayRef on index). + /// + /// P.S. dimension 0 indexes rows, dimension 1 indexes columns + template + class MatrixRef { + public: + typedef size_t size_type; + + private: + /// Underlying ArrayRef + ArrayRef arr; + + /// Stride of dim 0 (outer dimension) + size_type stride0; + + // Stride of dim 1 is assumed to be 1 + + public: + /// Construct an empty Matrixref. + /*implicit*/ MatrixRef() : arr(nullptr), stride0(0) {} + + /// Construct an MatrixRef from an ArrayRef and outer stride. + /*implicit*/ MatrixRef(ArrayRef arr, size_type stride0) + : arr(arr), stride0(stride0) { + TORCH_CHECK(arr.size() % stride0 == 0, "MatrixRef: ArrayRef size ", arr.size(), " not divisible by stride ", stride0) + } + + /// @} + /// @name Simple Operations + /// @{ + + /// empty - Check if the matrix is empty. + bool empty() const { return arr.empty(); } + + const T *data() const { return arr.data(); } + + /// size - Get size a dimension + size_t size(size_t dim) const { + if (dim == 0) { + return arr.size() / stride0; + } else if (dim == 1) { + return stride0; + } else { + TORCH_CHECK(0, "MatrixRef: out of bounds dimension ", dim, "; expected 0 or 1"); + } + } + + size_t numel() const { + return arr.size(); + } + + /// equals - Check for element-wise equality. + bool equals(MatrixRef RHS) const { + return stride0 == RHS.stride0 && arr.equals(RHS.arr); + } + + /// @} + /// @name Operator Overloads + /// @{ + ArrayRef operator[](size_t Index) const { + return arr.slice(Index*stride0, stride0); + } + + /// Disallow accidental assignment from a temporary. + /// + /// The declaration here is extra complicated so that "arrayRef = {}" + /// continues to select the move assignment operator. + template + typename std::enable_if::value, MatrixRef>::type & + operator=(U &&Temporary) = delete; + + /// Disallow accidental assignment from a temporary. + /// + /// The declaration here is extra complicated so that "arrayRef = {}" + /// continues to select the move assignment operator. + template + typename std::enable_if::value, MatrixRef>::type & + operator=(std::initializer_list) = delete; + + }; + +} // end namespace at diff --git a/thirdparty/libtorch/include/ATen/MemoryOverlap.h b/thirdparty/libtorch/include/ATen/MemoryOverlap.h new file mode 100644 index 0000000000..e997869c52 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/MemoryOverlap.h @@ -0,0 +1,30 @@ +#pragma once + +#include + +namespace at { + +// MemOverlap: Whether or not there is memory overlap +// +// NO: Absolutely no memory overlap +// YES: Absolutely yes memory overlap +// TOO_HARD: There might be memory overlap, but it was too expensive to compute. +// +// NB: Please update the python test for these if you renumber them. +enum class MemOverlap { NO, YES, TOO_HARD }; + +enum class MemOverlapStatus { FULL, PARTIAL, NO, TOO_HARD }; + +CAFFE2_API MemOverlap has_internal_overlap(const Tensor& t); +CAFFE2_API MemOverlap has_internal_overlap(TensorImpl* t); + +CAFFE2_API void assert_no_internal_overlap(const Tensor& t); +CAFFE2_API void assert_no_internal_overlap(TensorImpl* t); + +MemOverlapStatus get_overlap_status(const Tensor& a, const Tensor& b); +MemOverlapStatus get_overlap_status(TensorImpl* a, TensorImpl* b); + +void assert_no_partial_overlap(const Tensor& a, const Tensor& b); +void assert_no_partial_overlap(TensorImpl* a, TensorImpl* b); + +} diff --git a/thirdparty/libtorch/include/ATen/MkldnnCPUType.h b/thirdparty/libtorch/include/ATen/MkldnnCPUType.h new file mode 100644 index 0000000000..5087963838 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/MkldnnCPUType.h @@ -0,0 +1,67 @@ +#pragma once + +// @generated by aten/src/ATen/gen.py + +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +namespace c10 { +struct Storage; +} + +namespace at { + +class Tensor; +using TensorList = ArrayRef; + +class Context; +struct Generator; + +struct Quantizer; +// This is temporary typedef to enable Quantizer in aten native function API +// we'll remove them when we are actually exposing Quantizer class +// to frontend +using ConstQuantizerPtr = const c10::intrusive_ptr&; + +#ifdef USE_STATIC_DISPATCH +namespace MkldnnCPUType { + Tensor add(const Tensor & self, const Tensor & other, Scalar alpha); + Tensor & add_(Tensor & self, const Tensor & other, Scalar alpha); + Tensor & add_out(Tensor & out, const Tensor & self, const Tensor & other, Scalar alpha); + Tensor empty(IntArrayRef size, const TensorOptions & options, c10::optional memory_format); + Tensor mkldnn_linear(const Tensor & input, const Tensor & weight, const Tensor & bias); + Tensor mkldnn_max_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode); + Tensor mul(const Tensor & self, const Tensor & other); + Tensor & mul_(Tensor & self, const Tensor & other); + Tensor & mul_out(Tensor & out, const Tensor & self, const Tensor & other); + std::tuple native_batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps); + Tensor _mkldnn_reshape(const Tensor & self, IntArrayRef shape); + Tensor relu(const Tensor & self); + Tensor & relu_(Tensor & self); + Tensor sigmoid(const Tensor & self); + Tensor & sigmoid_(Tensor & self); + Tensor _softmax(const Tensor & self, int64_t dim, bool half_to_float); + Tensor _mkldnn_transpose(const Tensor & self, int64_t dim0, int64_t dim1); + Tensor & _mkldnn_transpose_(Tensor & self, int64_t dim0, int64_t dim1); + Tensor clone(const Tensor & self, c10::optional memory_format); + Tensor & zero_(Tensor & self); + Tensor to_dense(const Tensor & self); + Tensor mkldnn_reorder_conv2d_weight(const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups); + Tensor view(const Tensor & self, IntArrayRef size); + Tensor & adaptive_avg_pool2d_out(Tensor & out, const Tensor & self, IntArrayRef output_size); + Tensor mkldnn_adaptive_avg_pool2d(const Tensor & self, IntArrayRef output_size); + Tensor & avg_pool2d_out(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); + Tensor avg_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); +} +#endif + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/NamedTensor.h b/thirdparty/libtorch/include/ATen/NamedTensor.h new file mode 100644 index 0000000000..a7606b0a66 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/NamedTensor.h @@ -0,0 +1 @@ +#include diff --git a/thirdparty/libtorch/include/ATen/NamedTensorUtils.h b/thirdparty/libtorch/include/ATen/NamedTensorUtils.h new file mode 100644 index 0000000000..4b75420935 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/NamedTensorUtils.h @@ -0,0 +1,165 @@ +#pragma once +#include +#include +#include + +#include +#include +#include + +#ifdef BUILD_NAMEDTENSOR +namespace at { + +using NameVector = SmallVector; + +inline bool has_names(TensorList tensors) { + return std::any_of( + tensors.begin(), tensors.end(), [](const Tensor& t) { return t.has_names(); }); +} + +// Converts dim to an positional index. Errors if `dim` cannot be used to +// refer to any dimension of tensor. +CAFFE2_API int64_t dimname_to_position(const Tensor& tensor, Dimname dim); +CAFFE2_API std::vector dimnames_to_positions(const Tensor& tensor, DimnameList dims); + +// Unifies two DimnameList to produce a third. This is useful for implementing +// the named inference rule for binary broadcasting operations like add. +// +// There are three main constraints: +// 1) Check matching: Names must match positionally from the right. +// 2) Check misaligned: If a name `n` is in `names`, then it must appear at +// the same index from the right in other. +// 3) The output names are obtained by unifying the names individually from the right. +CAFFE2_API std::vector +unify_from_right(DimnameList names, DimnameList other, const char* action = "broadcast"); + +[[noreturn]] inline void reportNYIDimnameOverload(const char* op_name) { + TORCH_CHECK( + false, + op_name, ": You passed a dimname (string) to this op in place of a dimension " + "index but it does not yet support this behavior. Please pass a dimension " + "index to work around this."); +} + +// [NOTE] Writing name inference rules +// +// Operators that support named tensors are either composed of operations that +// support named tensors or implement some name inference rule. An op that +// implements its own name inference rule generally looks like the following: +// +// Tensor op(...) { +// perform_shape_checks(...); +// # (1) +// auto maybe_outnames = compute_outnames(...); +// auto result = [&]() { +// NoNamesGuard guard; +// return op_impl(...); +// }(); +// # (2) +// propagate_names_if_nonempty(result, maybe_outnames); +// +// Each op has (1) a compute outnames step and (2) a propagate names step. +// +// compute_outnames is responsible for checking that input names match and +// determining what the output names should be. It returns either: +// - {} (if the inputs tensors are all unnamed) +// - non-empty outnames. +// +// propagate_names_if_nonempty propagates the outnames if they exist to the result +// tensors. +// +// The {} case is an optimization; if the user does not use named tensors they +// pay no perf cost for it. + +namespace namedinference { + +// Propagates `names` to `result` if `names` is not empty. +// `names` can be empty; see [NOTE] Writing name inference rules +// If `names` is not empty, `names.size()` should equal `result.dim()`. +// When in doubt, use this overload instead of the others. +CAFFE2_API Tensor& propagate_names_if_nonempty( + Tensor& result, + DimnameList maybe_names, + bool validate_names = false); + +// Propagates `names` to `result`. Only use this if we are certain that there are +// names to propagate (that names is not empty). +CAFFE2_API Tensor& propagate_names( + Tensor& result, + DimnameList names, + bool validate_names = false); + +// Propagates all names from src to result. +CAFFE2_API void propagate_names(Tensor& result, const Tensor& src); + +// Propagates all names except for those at the excluded_idxs. +void propagate_names_except(Tensor& result, const Tensor& src, IntArrayRef excluded_idxs); + +// Used for reduction ops that have a `keepdim` arg. +void propagate_names_for_reduction(Tensor& result, const Tensor& src, IntArrayRef excluded_idxs, bool keepdim); + +void propagate_names_for_expand(Tensor& result, const Tensor& self); + +std::vector compute_cat_outnames(TensorList tensors); + +std::vector compute_broadcast_outnames( + const Tensor& self, + const Tensor& other); + +std::vector broadcast_to_outnames( + const Tensor& tensor, + const Tensor& reference_tensor, + const char* op_name); + +std::vector compute_matmul_outnames(const Tensor& self, const Tensor& other); + +std::vector compute_cdist_outnames(const Tensor& self, const Tensor& other); + +std::vector compute_bmm_outnames( + Tensor& result, + const Tensor& self, + const Tensor& other); + +std::vector compute_squeeze_outnames(const Tensor& tensor); + +// TensorImpl* overloads for Legacy TH/THC code. Use these sparingly. + +TensorImpl* propagate_names_if_nonempty( + TensorImpl* result, + DimnameList maybe_names, + bool validate_names = false); + +TensorImpl* propagate_names( + TensorImpl* result, + DimnameList names, + bool validate_names = false); + +void propagate_names(TensorImpl* result, /*const */TensorImpl* src); + +// result = m1 @ m2 + bias +void propagate_names_for_addmm( + TensorImpl* result, + /*const*/TensorImpl* m1, + /*const*/TensorImpl* m2, + /*const*/TensorImpl* bias); + +void propagate_names_for_addmv( + TensorImpl* result, + TensorImpl* mat, + TensorImpl* vec, + TensorImpl* bias); + +void check_names_for_dot(TensorImpl* vec1, TensorImpl* vec2); + +std::vector compute_baddbmm_outnames( + TensorImpl* result, + TensorImpl* self, + TensorImpl* other, + TensorImpl* bias); + +bool are_names_equal(TensorImpl* self, TensorImpl* other); + +} // namespace namedinference + +} // namespace at +#endif diff --git a/thirdparty/libtorch/include/ATen/NativeFunctions.h b/thirdparty/libtorch/include/ATen/NativeFunctions.h new file mode 100644 index 0000000000..9ccbe8151d --- /dev/null +++ b/thirdparty/libtorch/include/ATen/NativeFunctions.h @@ -0,0 +1,1709 @@ +#pragma once + +// @generated by aten/src/ATen/gen.py + +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +namespace c10 { +class Scalar; +} +namespace at { +struct Generator; +class Tensor; +struct Type; +} // namespace at + +namespace at { +namespace native { + +// These functions are defined in native/TensorFactories.cpp. +#define TENSOR(T, S) \ + CAFFE2_API Tensor tensor(ArrayRef values, const TensorOptions& options); \ + inline Tensor tensor( \ + std::initializer_list values, const TensorOptions& options) { \ + return native::tensor(ArrayRef(values), options); \ + } \ + inline Tensor tensor(T value, const TensorOptions& options) { \ + return native::tensor(ArrayRef(value), options); \ + } \ + inline Tensor tensor(ArrayRef values) { \ + return native::tensor(std::move(values), at::dtype(k##S)); \ + } \ + inline Tensor tensor(std::initializer_list values) { \ + return native::tensor(ArrayRef(values)); \ + } \ + inline Tensor tensor(T value) { \ + return native::tensor(ArrayRef(value)); \ + } +AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, TENSOR) +#undef TENSOR + +CAFFE2_API Tensor _cast_Byte(const Tensor & self, bool non_blocking=false); +CAFFE2_API Tensor _cast_Char(const Tensor & self, bool non_blocking=false); +CAFFE2_API Tensor _cast_Double(const Tensor & self, bool non_blocking=false); +CAFFE2_API Tensor _cast_Float(const Tensor & self, bool non_blocking=false); +CAFFE2_API Tensor _cast_Int(const Tensor & self, bool non_blocking=false); +CAFFE2_API Tensor _cast_Long(const Tensor & self, bool non_blocking=false); +CAFFE2_API Tensor _cast_Short(const Tensor & self, bool non_blocking=false); +CAFFE2_API Tensor _cast_Half(const Tensor & self, bool non_blocking=false); +CAFFE2_API void backward(const Tensor & self, const Tensor & gradient={}, bool keep_graph=false, bool create_graph=false); +CAFFE2_API void set_data(const Tensor & self, const Tensor & new_data); +CAFFE2_API Tensor data(const Tensor & self); +CAFFE2_API bool is_leaf(const Tensor & self); +CAFFE2_API int64_t output_nr(const Tensor & self); +CAFFE2_API int64_t _version(const Tensor & self); +CAFFE2_API Tensor & requires_grad_(Tensor & self, bool _requires_grad=true); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & rename_(Tensor & self, c10::optional names); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor rename(const Tensor & self, c10::optional names); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor align_to(const Tensor & self, DimnameList names); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor align_to(const Tensor & self, DimnameList order, int64_t ellipsis_idx); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor align_as(const Tensor & self, const Tensor & other); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API std::vector align_tensors(TensorList tensors); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor refine_names(const Tensor & self, DimnameList names); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor unflatten(const Tensor & self, Dimname dim, IntArrayRef sizes, DimnameList names); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor unflatten(const Tensor & self, int64_t dim, IntArrayRef sizes, DimnameList names); +#endif +CAFFE2_API bool _use_cudnn_ctc_loss(const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank); +CAFFE2_API std::tuple _cudnn_ctc_loss(const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank, bool deterministic, bool zero_infinity); +CAFFE2_API Tensor _cudnn_rnn_flatten_weight(TensorList weight_arr, int64_t weight_stride0, int64_t input_size, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, bool bidirectional); +CAFFE2_API std::tuple _cudnn_rnn(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & weight_buf, const Tensor & hx, const Tensor & cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntArrayRef batch_sizes, const Tensor & dropout_state); +CAFFE2_API std::tuple> _cudnn_rnn_backward(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & weight_buf, const Tensor & hx, const Tensor & cx, const Tensor & output, const Tensor & grad_output, const Tensor & grad_hy, const Tensor & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntArrayRef batch_sizes, const Tensor & dropout_state, const Tensor & reserve, std::array output_mask); +CAFFE2_API Tensor _cudnn_init_dropout_state(double dropout, bool train, int64_t dropout_seed, const TensorOptions & options); +CAFFE2_API int64_t _debug_has_internal_overlap(const Tensor & self); +CAFFE2_API std::tuple fused_dropout_cuda(const Tensor & self, double p, Generator * generator=nullptr); +CAFFE2_API Tensor masked_scale_cuda(const Tensor & self, const Tensor & mask, double scale); +CAFFE2_API std::tuple _sobol_engine_draw(const Tensor & quasi, int64_t n, const Tensor & sobolstate, int64_t dimension, int64_t num_generated, c10::optional dtype); +CAFFE2_API Tensor & _sobol_engine_ff_(Tensor & self, int64_t n, const Tensor & sobolstate, int64_t dimension, int64_t num_generated); +CAFFE2_API Tensor & _sobol_engine_scramble_(Tensor & self, const Tensor & ltm, int64_t dimension); +CAFFE2_API Tensor & _sobol_engine_initialize_state_(Tensor & self, int64_t dimension); +CAFFE2_API Tensor _reshape_from_tensor(const Tensor & self, const Tensor & shape); +CAFFE2_API Tensor _shape_as_tensor(const Tensor & self); +CAFFE2_API Tensor dropout(const Tensor & input, double p, bool train); +CAFFE2_API Tensor & dropout_(Tensor & self, double p, bool train); +CAFFE2_API Tensor feature_dropout(const Tensor & input, double p, bool train); +CAFFE2_API Tensor & feature_dropout_(Tensor & self, double p, bool train); +CAFFE2_API Tensor alpha_dropout(const Tensor & input, double p, bool train); +CAFFE2_API Tensor & alpha_dropout_(Tensor & self, double p, bool train); +CAFFE2_API Tensor feature_alpha_dropout(const Tensor & input, double p, bool train); +CAFFE2_API Tensor & feature_alpha_dropout_(Tensor & self, double p, bool train); +CAFFE2_API Tensor abs(const Tensor & self); +CAFFE2_API Tensor & abs_(Tensor & self); +CAFFE2_API Tensor & abs_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor angle(const Tensor & self); +CAFFE2_API Tensor & _angle_out_cpu(Tensor & out, const Tensor & self); +CAFFE2_API Tensor real(const Tensor & self); +CAFFE2_API Tensor & _real_out_cpu(Tensor & out, const Tensor & self); +CAFFE2_API Tensor imag(const Tensor & self); +CAFFE2_API Tensor & _imag_out_cpu(Tensor & out, const Tensor & self); +CAFFE2_API Tensor conj(const Tensor & self); +CAFFE2_API Tensor & _conj_out_cpu(Tensor & out, const Tensor & self); +CAFFE2_API Tensor acos(const Tensor & self); +CAFFE2_API Tensor & acos_(Tensor & self); +CAFFE2_API Tensor & acos_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor avg_pool1d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true); +CAFFE2_API Tensor adaptive_avg_pool1d(const Tensor & self, IntArrayRef output_size); +CAFFE2_API std::tuple adaptive_max_pool1d(const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor add(const Tensor & self, const Tensor & other, Scalar alpha=1); +CAFFE2_API Tensor mkldnn_add(const Tensor & self, const Tensor & other, Scalar alpha=1); +CAFFE2_API Tensor add_sparse(const Tensor & self, const Tensor & other, Scalar alpha=1); +CAFFE2_API Tensor & add_(Tensor & self, const Tensor & other, Scalar alpha=1); +CAFFE2_API Tensor & mkldnn_add_(Tensor & self, const Tensor & other, Scalar alpha=1); +CAFFE2_API Tensor & add_sparse_(Tensor & self, const Tensor & other, Scalar alpha=1); +CAFFE2_API Tensor & add_out(Tensor & out, const Tensor & self, const Tensor & other, Scalar alpha=1); +CAFFE2_API Tensor & mkldnn_add_out(Tensor & out, const Tensor & self, const Tensor & other, Scalar alpha=1); +CAFFE2_API Tensor & add_out_sparse_cpu(Tensor & out, const Tensor & self, const Tensor & other, Scalar alpha=1); +CAFFE2_API Tensor & add_out_sparse_cuda(Tensor & out, const Tensor & self, const Tensor & other, Scalar alpha=1); +CAFFE2_API Tensor add(const Tensor & self, Scalar other, Scalar alpha=1); +CAFFE2_API Tensor & add_(Tensor & self, Scalar other, Scalar alpha=1); +CAFFE2_API Tensor addr(const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor & addr_(Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor & addr_out(Tensor & out, const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor affine_grid_generator(const Tensor & theta, IntArrayRef size, bool align_corners); +CAFFE2_API Tensor affine_grid_generator_backward(const Tensor & grad, IntArrayRef size, bool align_corners); +CAFFE2_API Tensor all(const Tensor & self, int64_t dim, bool keepdim=false); +CAFFE2_API Tensor & all_out(Tensor & out, const Tensor & self, int64_t dim, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor all(const Tensor & self, Dimname dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & all_out(Tensor & out, const Tensor & self, Dimname dim, bool keepdim=false); +#endif +CAFFE2_API bool allclose(const Tensor & self, const Tensor & other, double rtol=1e-05, double atol=1e-08, bool equal_nan=false); +CAFFE2_API Tensor any(const Tensor & self, int64_t dim, bool keepdim=false); +CAFFE2_API Tensor & any_out(Tensor & out, const Tensor & self, int64_t dim, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor any(const Tensor & self, Dimname dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & any_out(Tensor & out, const Tensor & self, Dimname dim, bool keepdim=false); +#endif +CAFFE2_API Tensor arange(Scalar end, const TensorOptions & options={}); +CAFFE2_API Tensor arange(Scalar start, Scalar end, const TensorOptions & options={}); +CAFFE2_API Tensor arange(Scalar start, Scalar end, Scalar step, const TensorOptions & options={}); +CAFFE2_API Tensor & arange_out(Tensor & out, Scalar end); +CAFFE2_API Tensor & arange_cpu_out(Tensor & out, Scalar start, Scalar end, Scalar step=1); +CAFFE2_API Tensor & arange_cuda_out(Tensor & out, Scalar start, Scalar end, Scalar step=1); +CAFFE2_API Tensor _dim_arange(const Tensor & like, int64_t dim); +CAFFE2_API Tensor argmax(const Tensor & self, c10::optional dim=c10::nullopt, bool keepdim=false); +CAFFE2_API Tensor argmin(const Tensor & self, c10::optional dim=c10::nullopt, bool keepdim=false); +CAFFE2_API Tensor as_strided_tensorimpl(const Tensor & self, IntArrayRef size, IntArrayRef stride, c10::optional storage_offset=c10::nullopt); +CAFFE2_API Tensor as_strided_qtensorimpl(const Tensor & self, IntArrayRef size, IntArrayRef stride, c10::optional storage_offset=c10::nullopt); +CAFFE2_API Tensor & as_strided_(Tensor & self, IntArrayRef size, IntArrayRef stride, c10::optional storage_offset=c10::nullopt); +CAFFE2_API Tensor asin(const Tensor & self); +CAFFE2_API Tensor & asin_(Tensor & self); +CAFFE2_API Tensor & asin_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor atan(const Tensor & self); +CAFFE2_API Tensor & _atan__cpu(Tensor & self); +CAFFE2_API Tensor & _atan__cuda(Tensor & self); +CAFFE2_API Tensor & _atan_out_cpu(Tensor & out, const Tensor & self); +CAFFE2_API Tensor & _atan_out_cuda(Tensor & out, const Tensor & self); +CAFFE2_API Tensor baddbmm_cpu(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor baddbmm_cuda(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor & baddbmm__cpu(Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor & baddbmm__cuda(Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor & _baddbmm_mkl_(Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor & baddbmm_out_cpu(Tensor & out, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor & baddbmm_out_cuda(Tensor & out, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor bartlett_window(int64_t window_length, const TensorOptions & options={}); +CAFFE2_API Tensor bartlett_window(int64_t window_length, bool periodic, const TensorOptions & options={}); +CAFFE2_API Tensor batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps, bool cudnn_enabled); +CAFFE2_API std::tuple _batch_norm_impl_index(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps, bool cudnn_enabled); +CAFFE2_API std::tuple _batch_norm_impl_index_backward(int64_t impl_index, const Tensor & input, const Tensor & grad_output, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_var_transform, bool train, double eps, std::array output_mask, const Tensor & reservedSpace); +CAFFE2_API Tensor bernoulli(const Tensor & self, Generator * generator=nullptr); +CAFFE2_API Tensor & bernoulli_out(Tensor & out, const Tensor & self, Generator * generator=nullptr); +CAFFE2_API Tensor & bernoulli_tensor_cpu_(Tensor & self, const Tensor & p, Generator * generator=nullptr); +CAFFE2_API Tensor & bernoulli_tensor_cuda_(Tensor & self, const Tensor & p, Generator * generator=nullptr); +CAFFE2_API Tensor & bernoulli_scalar_cpu_(Tensor & self, double p=0.5, Generator * generator=nullptr); +CAFFE2_API Tensor & bernoulli_scalar_cuda_(Tensor & self, double p=0.5, Generator * generator=nullptr); +CAFFE2_API Tensor bernoulli(const Tensor & self, double p, Generator * generator=nullptr); +CAFFE2_API Tensor bilinear(const Tensor & input1, const Tensor & input2, const Tensor & weight, const Tensor & bias); +CAFFE2_API Tensor binary_cross_entropy_with_logits(const Tensor & self, const Tensor & target, const Tensor & weight={}, const Tensor & pos_weight={}, int64_t reduction=at::Reduction::Mean); +CAFFE2_API Tensor binary_cross_entropy_with_logits_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight={}, const Tensor & pos_weight={}, int64_t reduction=at::Reduction::Mean); +CAFFE2_API Tensor _bincount_cpu(const Tensor & self, const Tensor & weights={}, int64_t minlength=0); +CAFFE2_API Tensor _bincount_cuda(const Tensor & self, const Tensor & weights={}, int64_t minlength=0); +CAFFE2_API Tensor bitwise_not(const Tensor & self); +CAFFE2_API Tensor & bitwise_not_(Tensor & self); +CAFFE2_API Tensor & bitwise_not_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor logical_not(const Tensor & self); +CAFFE2_API Tensor & logical_not_(Tensor & self); +CAFFE2_API Tensor & logical_not_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor logical_xor(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & logical_xor_(Tensor & self, const Tensor & other); +CAFFE2_API Tensor & logical_xor_out(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor blackman_window(int64_t window_length, const TensorOptions & options={}); +CAFFE2_API Tensor blackman_window(int64_t window_length, bool periodic, const TensorOptions & options={}); +CAFFE2_API Tensor bmm_cpu(const Tensor & self, const Tensor & mat2); +CAFFE2_API Tensor bmm_cuda(const Tensor & self, const Tensor & mat2); +CAFFE2_API Tensor & bmm_out_cpu(Tensor & out, const Tensor & self, const Tensor & mat2); +CAFFE2_API Tensor & bmm_out_cuda(Tensor & out, const Tensor & self, const Tensor & mat2); +CAFFE2_API std::vector broadcast_tensors(TensorList tensors); +CAFFE2_API Tensor cat(TensorList tensors, int64_t dim=0); +CAFFE2_API Tensor & cat_out(Tensor & out, TensorList tensors, int64_t dim=0); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor cat(TensorList tensors, Dimname dim); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & cat_out(Tensor & out, TensorList tensors, Dimname dim); +#endif +CAFFE2_API Tensor ceil(const Tensor & self); +CAFFE2_API Tensor & ceil_(Tensor & self); +CAFFE2_API Tensor & ceil_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor chain_matmul(TensorList matrices); +CAFFE2_API std::vector chunk(const Tensor & self, int64_t chunks, int64_t dim=0); +CAFFE2_API Tensor clamp(const Tensor & self, c10::optional min=c10::nullopt, c10::optional max=c10::nullopt); +CAFFE2_API Tensor & _clamp__cpu(Tensor & self, c10::optional min=c10::nullopt, c10::optional max=c10::nullopt); +CAFFE2_API Tensor & _clamp__cuda(Tensor & self, c10::optional min=c10::nullopt, c10::optional max=c10::nullopt); +CAFFE2_API Tensor & _clamp_out_cpu(Tensor & out, const Tensor & self, c10::optional min=c10::nullopt, c10::optional max=c10::nullopt); +CAFFE2_API Tensor & _clamp_out_cuda(Tensor & out, const Tensor & self, c10::optional min=c10::nullopt, c10::optional max=c10::nullopt); +CAFFE2_API Tensor clamp_max(const Tensor & self, Scalar max); +CAFFE2_API Tensor & _clamp_max__cpu(Tensor & self, Scalar max); +CAFFE2_API Tensor & _clamp_max__cuda(Tensor & self, Scalar max); +CAFFE2_API Tensor & _clamp_max_out_cpu(Tensor & out, const Tensor & self, Scalar max); +CAFFE2_API Tensor & _clamp_max_out_cuda(Tensor & out, const Tensor & self, Scalar max); +CAFFE2_API Tensor clamp_min(const Tensor & self, Scalar min); +CAFFE2_API Tensor & _clamp_min__cpu(Tensor & self, Scalar min); +CAFFE2_API Tensor & _clamp_min__cuda(Tensor & self, Scalar min); +CAFFE2_API Tensor & _clamp_min_out_cpu(Tensor & out, const Tensor & self, Scalar min); +CAFFE2_API Tensor & _clamp_min_out_cuda(Tensor & out, const Tensor & self, Scalar min); +CAFFE2_API bool cudnn_is_acceptable(const Tensor & self); +CAFFE2_API Tensor constant_pad_nd(const Tensor & self, IntArrayRef pad, Scalar value=0); +CAFFE2_API Tensor contiguous(const Tensor & self, MemoryFormat memory_format=MemoryFormat::Contiguous); +CAFFE2_API Tensor convolution(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups); +CAFFE2_API Tensor convolution_overrideable(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups); +CAFFE2_API std::tuple convolution_backward_overrideable(const Tensor & grad_output, const Tensor & input, const Tensor & weight, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups, std::array output_mask); +CAFFE2_API Tensor _convolution(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled); +CAFFE2_API Tensor _convolution_nogroup(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding); +CAFFE2_API std::tuple _convolution_double_backward(const Tensor & ggI, const Tensor & ggW, const Tensor & ggb, const Tensor & gO, const Tensor & weight, const Tensor & self, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled, std::array output_mask); +CAFFE2_API Tensor conv1d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef dilation=1, int64_t groups=1); +CAFFE2_API Tensor conv2d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef dilation=1, int64_t groups=1); +CAFFE2_API Tensor conv3d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef dilation=1, int64_t groups=1); +CAFFE2_API Tensor conv_tbc(const Tensor & self, const Tensor & weight, const Tensor & bias, int64_t pad=0); +CAFFE2_API std::tuple conv_tbc_backward(const Tensor & self, const Tensor & input, const Tensor & weight, const Tensor & bias, int64_t pad); +CAFFE2_API Tensor conv_transpose1d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef output_padding=0, int64_t groups=1, IntArrayRef dilation=1); +CAFFE2_API Tensor conv_transpose2d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef output_padding=0, int64_t groups=1, IntArrayRef dilation=1); +CAFFE2_API Tensor conv_transpose3d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef output_padding=0, int64_t groups=1, IntArrayRef dilation=1); +CAFFE2_API Tensor & copy_(Tensor & self, const Tensor & src, bool non_blocking=false); +CAFFE2_API Tensor cos(const Tensor & self); +CAFFE2_API Tensor & _cos__cpu(Tensor & self); +CAFFE2_API Tensor & _cos__cuda(Tensor & self); +CAFFE2_API Tensor & _cos_out_cpu(Tensor & out, const Tensor & self); +CAFFE2_API Tensor & _cos_out_cuda(Tensor & out, const Tensor & self); +CAFFE2_API Tensor cosh(const Tensor & self); +CAFFE2_API Tensor & _cosh__cpu(Tensor & self); +CAFFE2_API Tensor & _cosh__cuda(Tensor & self); +CAFFE2_API Tensor & _cosh_out_cpu(Tensor & out, const Tensor & self); +CAFFE2_API Tensor & _cosh_out_cuda(Tensor & out, const Tensor & self); +CAFFE2_API Tensor cosine_embedding_loss(const Tensor & input1, const Tensor & input2, const Tensor & target, double margin=0.0, int64_t reduction=at::Reduction::Mean); +CAFFE2_API Tensor cudnn_affine_grid_generator_forward(const Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W); +CAFFE2_API Tensor cudnn_affine_grid_generator_backward(const Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W); +CAFFE2_API std::tuple cudnn_batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double exponential_average_factor, double epsilon); +CAFFE2_API std::tuple cudnn_batch_norm_backward(const Tensor & input, const Tensor & grad_output, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_var, double epsilon, const Tensor & reserveSpace); +CAFFE2_API Tensor cudnn_convolution(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +CAFFE2_API Tensor cudnn_convolution_backward_input(IntArrayRef self_size, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +CAFFE2_API std::tuple cudnn_convolution_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask); +CAFFE2_API Tensor cudnn_convolution_backward_bias(const Tensor & grad_output); +CAFFE2_API Tensor cudnn_convolution_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +CAFFE2_API Tensor cudnn_convolution_transpose(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +CAFFE2_API std::tuple cudnn_convolution_transpose_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask); +CAFFE2_API Tensor cudnn_convolution_backward_bias(const Tensor & grad_output); +CAFFE2_API Tensor cudnn_convolution_transpose_backward_input(const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +CAFFE2_API Tensor cudnn_convolution_transpose_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +CAFFE2_API Tensor cudnn_grid_sampler_forward(const Tensor & self, const Tensor & grid); +CAFFE2_API std::tuple cudnn_grid_sampler_backward(const Tensor & self, const Tensor & grid, const Tensor & grad_output); +CAFFE2_API Tensor cumsum(const Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt); +CAFFE2_API Tensor & cumsum_out(Tensor & out, const Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor cumsum(const Tensor & self, Dimname dim, c10::optional dtype=c10::nullopt); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & cumsum_out(Tensor & out, const Tensor & self, Dimname dim, c10::optional dtype=c10::nullopt); +#endif +CAFFE2_API Tensor cumprod(const Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt); +CAFFE2_API Tensor & cumprod_out(Tensor & out, const Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor cumprod(const Tensor & self, Dimname dim, c10::optional dtype=c10::nullopt); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & cumprod_out(Tensor & out, const Tensor & self, Dimname dim, c10::optional dtype=c10::nullopt); +#endif +CAFFE2_API Tensor ctc_loss(const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank=0, int64_t reduction=at::Reduction::Mean, bool zero_infinity=false); +CAFFE2_API Tensor ctc_loss(const Tensor & log_probs, const Tensor & targets, const Tensor & input_lengths, const Tensor & target_lengths, int64_t blank=0, int64_t reduction=at::Reduction::Mean, bool zero_infinity=false); +CAFFE2_API std::tuple ctc_loss_cpu(const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank=0, bool zero_infinity=false); +CAFFE2_API std::tuple ctc_loss_gpu(const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank=0, bool zero_infinity=false); +CAFFE2_API Tensor ctc_loss_backward_cpu(const Tensor & grad, const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, const Tensor & neg_log_likelihood, const Tensor & log_alpha, int64_t blank, bool zero_infinity=false); +CAFFE2_API Tensor ctc_loss_backward_gpu(const Tensor & grad, const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, const Tensor & neg_log_likelihood, const Tensor & log_alpha, int64_t blank, bool zero_infinity=false); +CAFFE2_API Tensor det(const Tensor & self); +CAFFE2_API Tensor diag_embed(const Tensor & self, int64_t offset=0, int64_t dim1=-2, int64_t dim2=-1); +CAFFE2_API Tensor diagflat(const Tensor & self, int64_t offset=0); +CAFFE2_API Tensor diagonal(const Tensor & self, int64_t offset=0, int64_t dim1=0, int64_t dim2=1); +CAFFE2_API Tensor & fill_diagonal_(Tensor & self, Scalar fill_value, bool wrap=false); +CAFFE2_API Tensor div(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor div_sparse(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & div_(Tensor & self, const Tensor & other); +CAFFE2_API Tensor & div_sparse_(Tensor & self, const Tensor & other); +CAFFE2_API Tensor & div_out(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & div_out_sparse_zerodim(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor div(const Tensor & self, Scalar other); +CAFFE2_API Tensor & div_(Tensor & self, Scalar other); +CAFFE2_API Tensor & dot_out(Tensor & out, const Tensor & self, const Tensor & tensor); +CAFFE2_API Tensor einsum(std::string equation, TensorList tensors); +CAFFE2_API Tensor embedding(const Tensor & weight, const Tensor & indices, int64_t padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false); +CAFFE2_API Tensor embedding_backward(const Tensor & grad, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq, bool sparse); +CAFFE2_API Tensor embedding_dense_backward_cpu(const Tensor & grad_output, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq); +CAFFE2_API Tensor embedding_dense_backward_cuda(const Tensor & grad_output, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq); +CAFFE2_API Tensor & embedding_renorm_cpu_(Tensor & self, const Tensor & indices, double max_norm, double norm_type); +CAFFE2_API Tensor & embedding_renorm_cuda_(Tensor & self, const Tensor & indices, double max_norm, double norm_type); +CAFFE2_API Tensor embedding_sparse_backward(const Tensor & grad, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq); +CAFFE2_API std::tuple embedding_bag(const Tensor & weight, const Tensor & indices, const Tensor & offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false, const Tensor & per_sample_weights={}); +CAFFE2_API std::tuple _embedding_bag_cpu(const Tensor & weight, const Tensor & indices, const Tensor & offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false, const Tensor & per_sample_weights={}); +CAFFE2_API std::tuple _embedding_bag_cuda(const Tensor & weight, const Tensor & indices, const Tensor & offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false, const Tensor & per_sample_weights={}); +CAFFE2_API Tensor _embedding_bag_backward(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, const Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse, const Tensor & per_sample_weights); +CAFFE2_API Tensor _embedding_bag_sparse_backward(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const Tensor & per_sample_weights); +CAFFE2_API Tensor _embedding_bag_dense_backward_cpu(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, const Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const Tensor & per_sample_weights); +CAFFE2_API Tensor _embedding_bag_dense_backward_cuda(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, const Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const Tensor & per_sample_weights); +CAFFE2_API Tensor _embedding_bag_per_sample_weights_backward_cpu(const Tensor & grad, const Tensor & weight, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, int64_t mode); +CAFFE2_API Tensor _embedding_bag_per_sample_weights_backward_cuda(const Tensor & grad, const Tensor & weight, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, int64_t mode); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor empty(IntArrayRef size, c10::optional names, const TensorOptions & options={}, c10::optional memory_format=c10::nullopt); +#endif +CAFFE2_API Tensor empty_cpu(IntArrayRef size, const TensorOptions & options={}, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor empty_cuda(IntArrayRef size, const TensorOptions & options={}, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor empty_mkldnn(IntArrayRef size, const TensorOptions & options={}, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor empty_sparse(IntArrayRef size, const TensorOptions & options={}, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor new_empty(const Tensor & self, IntArrayRef size, const TensorOptions & options={}); +CAFFE2_API Tensor new_full(const Tensor & self, IntArrayRef size, Scalar fill_value, const TensorOptions & options={}); +CAFFE2_API Tensor new_zeros(const Tensor & self, IntArrayRef size, const TensorOptions & options={}); +CAFFE2_API Tensor empty_affine_quantized_other_backends_stub(IntArrayRef size, const TensorOptions & options={}, double scale=1, int64_t zero_point=0, c10::optional memory_format=MemoryFormat::Contiguous); +CAFFE2_API Tensor empty_affine_quantized_cpu(IntArrayRef size, const TensorOptions & options={}, double scale=1, int64_t zero_point=0, c10::optional memory_format=MemoryFormat::Contiguous); +CAFFE2_API Tensor empty_per_channel_affine_quantized_other_backends_stub(IntArrayRef size, const Tensor & scales, const Tensor & zero_points, int64_t axis, const TensorOptions & options={}, c10::optional memory_format=MemoryFormat::Contiguous); +CAFFE2_API Tensor empty_per_channel_affine_quantized_cpu(IntArrayRef size, const Tensor & scales, const Tensor & zero_points, int64_t axis, const TensorOptions & options={}, c10::optional memory_format=MemoryFormat::Contiguous); +CAFFE2_API Tensor & resize_cpu_(Tensor & self, IntArrayRef size, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor & resize_cuda_(Tensor & self, IntArrayRef size, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor & quantized_resize_cpu_(Tensor & self, IntArrayRef size, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor & empty_out(Tensor & out, IntArrayRef size, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor empty_like(const Tensor & self, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor empty_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor empty_strided_cpu(IntArrayRef size, IntArrayRef stride, const TensorOptions & options={}); +CAFFE2_API Tensor empty_strided_cuda(IntArrayRef size, IntArrayRef stride, const TensorOptions & options={}); +CAFFE2_API Tensor erf(const Tensor & self); +CAFFE2_API Tensor & _erf__cpu(Tensor & self); +CAFFE2_API Tensor & _erf__cuda(Tensor & self); +CAFFE2_API Tensor & _erf_out_cpu(Tensor & out, const Tensor & self); +CAFFE2_API Tensor & _erf_out_cuda(Tensor & out, const Tensor & self); +CAFFE2_API Tensor erfc(const Tensor & self); +CAFFE2_API Tensor & _erfc__cpu(Tensor & self); +CAFFE2_API Tensor & _erfc__cuda(Tensor & self); +CAFFE2_API Tensor & _erfc_out_cpu(Tensor & out, const Tensor & self); +CAFFE2_API Tensor & _erfc_out_cuda(Tensor & out, const Tensor & self); +CAFFE2_API Tensor exp(const Tensor & self); +CAFFE2_API Tensor & _exp__cpu(Tensor & self); +CAFFE2_API Tensor & _exp__cuda(Tensor & self); +CAFFE2_API Tensor & _exp_out_cpu(Tensor & out, const Tensor & self); +CAFFE2_API Tensor & _exp_out_cuda(Tensor & out, const Tensor & self); +CAFFE2_API Tensor expm1(const Tensor & self); +CAFFE2_API Tensor & expm1_(Tensor & self); +CAFFE2_API Tensor & expm1_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor expand(const Tensor & self, IntArrayRef size, bool implicit=false); +CAFFE2_API Tensor expand_as(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor eye(int64_t n, const TensorOptions & options={}); +CAFFE2_API Tensor eye(int64_t n, int64_t m, const TensorOptions & options={}); +CAFFE2_API Tensor & eye_out_cpu(Tensor & out, int64_t n); +CAFFE2_API Tensor & eye_out_cuda(Tensor & out, int64_t n); +CAFFE2_API Tensor & eye_out_cpu(Tensor & out, int64_t n, int64_t m); +CAFFE2_API Tensor & eye_out_cuda(Tensor & out, int64_t n, int64_t m); +CAFFE2_API Tensor flatten(const Tensor & self, int64_t start_dim=0, int64_t end_dim=-1); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor flatten(const Tensor & self, int64_t start_dim, int64_t end_dim, Dimname out_dim); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor flatten(const Tensor & self, Dimname start_dim, Dimname end_dim, Dimname out_dim); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor flatten(const Tensor & self, DimnameList dims, Dimname out_dim); +#endif +CAFFE2_API Tensor & fill_(Tensor & self, Scalar value); +CAFFE2_API Tensor & fill_(Tensor & self, const Tensor & value); +CAFFE2_API Tensor floor(const Tensor & self); +CAFFE2_API Tensor & floor_(Tensor & self); +CAFFE2_API Tensor & floor_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor frac(const Tensor & self); +CAFFE2_API Tensor & frac_(Tensor & self); +CAFFE2_API Tensor & frac_out(Tensor & out, const Tensor & self); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor full(IntArrayRef size, Scalar fill_value, c10::optional names, const TensorOptions & options={}); +#endif +CAFFE2_API Tensor full(IntArrayRef size, Scalar fill_value, const TensorOptions & options={}); +CAFFE2_API Tensor & full_out(Tensor & out, IntArrayRef size, Scalar fill_value); +CAFFE2_API Tensor full_like(const Tensor & self, Scalar fill_value, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor full_like(const Tensor & self, Scalar fill_value, const TensorOptions & options, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor from_file(std::string filename, c10::optional shared=c10::nullopt, c10::optional size=0, const TensorOptions & options={}); +CAFFE2_API Tensor grid_sampler(const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); +CAFFE2_API Tensor grid_sampler_2d_cpu(const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); +CAFFE2_API Tensor grid_sampler_2d_cuda(const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); +CAFFE2_API std::tuple grid_sampler_2d_backward_cpu(const Tensor & grad_output, const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); +CAFFE2_API std::tuple grid_sampler_2d_backward_cuda(const Tensor & grad_output, const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); +CAFFE2_API Tensor grid_sampler_3d_cpu(const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); +CAFFE2_API Tensor grid_sampler_3d_cuda(const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); +CAFFE2_API std::tuple grid_sampler_3d_backward_cpu(const Tensor & grad_output, const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); +CAFFE2_API std::tuple grid_sampler_3d_backward_cuda(const Tensor & grad_output, const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); +CAFFE2_API Tensor hann_window(int64_t window_length, const TensorOptions & options={}); +CAFFE2_API Tensor hann_window(int64_t window_length, bool periodic, const TensorOptions & options={}); +CAFFE2_API Tensor hamming_window(int64_t window_length, const TensorOptions & options={}); +CAFFE2_API Tensor hamming_window(int64_t window_length, bool periodic, const TensorOptions & options={}); +CAFFE2_API Tensor hamming_window(int64_t window_length, bool periodic, double alpha, const TensorOptions & options={}); +CAFFE2_API Tensor hamming_window(int64_t window_length, bool periodic, double alpha, double beta, const TensorOptions & options={}); +CAFFE2_API Tensor hinge_embedding_loss(const Tensor & self, const Tensor & target, double margin=1.0, int64_t reduction=at::Reduction::Mean); +CAFFE2_API Tensor group_norm(const Tensor & input, int64_t num_groups, const Tensor & weight={}, const Tensor & bias={}, double eps=1e-05, bool cudnn_enabled=true); +CAFFE2_API Tensor fft(const Tensor & self, int64_t signal_ndim, bool normalized=false); +CAFFE2_API Tensor ifft(const Tensor & self, int64_t signal_ndim, bool normalized=false); +CAFFE2_API Tensor rfft(const Tensor & self, int64_t signal_ndim, bool normalized=false, bool onesided=true); +CAFFE2_API Tensor irfft(const Tensor & self, int64_t signal_ndim, bool normalized=false, bool onesided=true, IntArrayRef signal_sizes={}); +CAFFE2_API Tensor _fft_mkl(const Tensor & self, int64_t signal_ndim, bool complex_input, bool complex_output, bool inverse, IntArrayRef checked_signal_sizes, bool normalized, bool onesided, IntArrayRef output_sizes); +CAFFE2_API Tensor _fft_cufft(const Tensor & self, int64_t signal_ndim, bool complex_input, bool complex_output, bool inverse, IntArrayRef checked_signal_sizes, bool normalized, bool onesided, IntArrayRef output_sizes); +CAFFE2_API int64_t _cufft_get_plan_cache_size(int64_t device_index); +CAFFE2_API int64_t _cufft_get_plan_cache_max_size(int64_t device_index); +CAFFE2_API void _cufft_set_plan_cache_max_size(int64_t device_index, int64_t max_size); +CAFFE2_API void _cufft_clear_plan_cache(int64_t device_index); +CAFFE2_API Tensor index(const Tensor & self, TensorList indices); +CAFFE2_API Tensor & index_copy_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & source); +CAFFE2_API Tensor index_copy(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & source); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & index_copy_(Tensor & self, Dimname dim, const Tensor & index, const Tensor & source); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor index_copy(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & source); +#endif +CAFFE2_API Tensor & index_put_(Tensor & self, TensorList indices, const Tensor & values, bool accumulate=false); +CAFFE2_API Tensor index_put(const Tensor & self, TensorList indices, const Tensor & values, bool accumulate=false); +CAFFE2_API Tensor & _index_put_impl_(Tensor & self, TensorList indices, const Tensor & values, bool accumulate=false, bool unsafe=false); +CAFFE2_API Tensor instance_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool use_input_stats, double momentum, double eps, bool cudnn_enabled); +CAFFE2_API Tensor inverse(const Tensor & self); +CAFFE2_API Tensor & inverse_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor _inverse_helper_cpu(const Tensor & self); +CAFFE2_API Tensor _inverse_helper_cuda(const Tensor & self); +CAFFE2_API Tensor isclose(const Tensor & self, const Tensor & other, double rtol=1e-05, double atol=1e-08, bool equal_nan=false); +CAFFE2_API Tensor isnan(const Tensor & self); +CAFFE2_API bool is_distributed(const Tensor & self); +CAFFE2_API bool is_floating_point(const Tensor & self); +CAFFE2_API bool is_complex(const Tensor & self); +CAFFE2_API bool is_nonzero(const Tensor & self); +CAFFE2_API bool is_same_size(const Tensor & self, const Tensor & other); +CAFFE2_API bool is_signed(const Tensor & self); +CAFFE2_API Tensor kl_div(const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +CAFFE2_API Tensor kl_div_backward_cpu(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +CAFFE2_API Tensor kl_div_backward_cuda(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +CAFFE2_API std::tuple kthvalue(const Tensor & self, int64_t k, int64_t dim=-1, bool keepdim=false); +CAFFE2_API std::tuple kthvalue_out_cpu(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim=-1, bool keepdim=false); +CAFFE2_API std::tuple kthvalue_out_cuda(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim=-1, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API std::tuple kthvalue(const Tensor & self, int64_t k, Dimname dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API std::tuple kthvalue_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, Dimname dim, bool keepdim=false); +#endif +CAFFE2_API Tensor layer_norm(const Tensor & input, IntArrayRef normalized_shape, const Tensor & weight={}, const Tensor & bias={}, double eps=1e-05, bool cudnn_enable=true); +CAFFE2_API std::tuple layer_norm_cpu(const Tensor & input, const Tensor & weight, const Tensor & bias, int64_t M, int64_t N, double eps); +CAFFE2_API std::tuple layer_norm_cuda(const Tensor & input, const Tensor & weight, const Tensor & bias, int64_t M, int64_t N, double eps); +CAFFE2_API std::tuple layer_norm_backward_cpu(const Tensor & grad_out, const Tensor & input, const Tensor & mean, const Tensor & rstd, const Tensor & weight, int64_t M, int64_t N, std::array output_mask); +CAFFE2_API std::tuple layer_norm_backward_cuda(const Tensor & grad_out, const Tensor & input, const Tensor & mean, const Tensor & rstd, const Tensor & weight, int64_t M, int64_t N, std::array output_mask); +CAFFE2_API Tensor linear(const Tensor & input, const Tensor & weight, const Tensor & bias={}); +CAFFE2_API Tensor mkldnn_linear(const Tensor & input, const Tensor & weight, const Tensor & bias={}); +CAFFE2_API Tensor fbgemm_linear_int8_weight_fp32_activation(const Tensor & input, const Tensor & weight, const Tensor & packed, const Tensor & col_offsets, Scalar weight_scale, Scalar weight_zero_point, const Tensor & bias); +CAFFE2_API Tensor fbgemm_linear_int8_weight(const Tensor & input, const Tensor & weight, const Tensor & packed, const Tensor & col_offsets, Scalar weight_scale, Scalar weight_zero_point, const Tensor & bias); +CAFFE2_API std::tuple fbgemm_linear_quantize_weight(const Tensor & input); +CAFFE2_API Tensor fbgemm_pack_gemm_matrix_fp16(const Tensor & input); +CAFFE2_API Tensor fbgemm_linear_fp16_weight_fp32_activation(const Tensor & input, const Tensor & packed_weight, const Tensor & bias); +CAFFE2_API Tensor fbgemm_linear_fp16_weight(const Tensor & input, const Tensor & packed_weight, const Tensor & bias); +CAFFE2_API Tensor fbgemm_pack_quantized_matrix(const Tensor & input); +CAFFE2_API Tensor fbgemm_pack_quantized_matrix(const Tensor & input, int64_t K, int64_t N); +CAFFE2_API Tensor linspace(Scalar start, Scalar end, int64_t steps=100, const TensorOptions & options={}); +CAFFE2_API Tensor & linspace_cpu_out(Tensor & out, Scalar start, Scalar end, int64_t steps=100); +CAFFE2_API Tensor & linspace_cuda_out(Tensor & out, Scalar start, Scalar end, int64_t steps=100); +CAFFE2_API Tensor log(const Tensor & self); +CAFFE2_API Tensor & log_(Tensor & self); +CAFFE2_API Tensor & log_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor log10(const Tensor & self); +CAFFE2_API Tensor & log10_(Tensor & self); +CAFFE2_API Tensor & log10_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor log1p(const Tensor & self); +CAFFE2_API Tensor & log1p_(Tensor & self); +CAFFE2_API Tensor & log1p_sparse_(Tensor & self); +CAFFE2_API Tensor & log1p_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor & log1p_out_sparse(Tensor & out, const Tensor & self); +CAFFE2_API Tensor log2(const Tensor & self); +CAFFE2_API Tensor & log2_(Tensor & self); +CAFFE2_API Tensor & log2_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor logdet(const Tensor & self); +CAFFE2_API Tensor logspace(Scalar start, Scalar end, int64_t steps=100, double base=10.0, const TensorOptions & options={}); +CAFFE2_API Tensor & logspace_cpu_out(Tensor & out, Scalar start, Scalar end, int64_t steps=100, double base=10.0); +CAFFE2_API Tensor & logspace_cuda_out(Tensor & out, Scalar start, Scalar end, int64_t steps=100, double base=10.0); +CAFFE2_API Tensor log_softmax(const Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor log_softmax(const Tensor & self, Dimname dim, c10::optional dtype=c10::nullopt); +#endif +CAFFE2_API Tensor log_softmax_cpu(const Tensor & self, int64_t dim, bool half_to_float); +CAFFE2_API Tensor log_softmax_cuda(const Tensor & self, int64_t dim, bool half_to_float); +CAFFE2_API Tensor log_softmax_backward_cpu(const Tensor & grad_output, const Tensor & output, int64_t dim, const Tensor & self); +CAFFE2_API Tensor log_softmax_backward_cuda(const Tensor & grad_output, const Tensor & output, int64_t dim, const Tensor & self); +CAFFE2_API Tensor logsumexp(const Tensor & self, IntArrayRef dim, bool keepdim=false); +CAFFE2_API Tensor & logsumexp_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor logsumexp(const Tensor & self, DimnameList dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & logsumexp_out(Tensor & out, const Tensor & self, DimnameList dim, bool keepdim=false); +#endif +CAFFE2_API Tensor margin_ranking_loss(const Tensor & input1, const Tensor & input2, const Tensor & target, double margin=0.0, int64_t reduction=at::Reduction::Mean); +CAFFE2_API Tensor matmul(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & matmul_out(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor matrix_rank(const Tensor & self, double tol, bool symmetric=false); +CAFFE2_API Tensor matrix_rank(const Tensor & self, bool symmetric=false); +CAFFE2_API Tensor matrix_power(const Tensor & self, int64_t n); +CAFFE2_API std::tuple max(const Tensor & self, int64_t dim, bool keepdim=false); +CAFFE2_API std::tuple max_out(Tensor & max, Tensor & max_values, const Tensor & self, int64_t dim, bool keepdim=false); +CAFFE2_API Tensor max_values(const Tensor & self, IntArrayRef dim, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API std::tuple max(const Tensor & self, Dimname dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API std::tuple max_out(Tensor & max, Tensor & max_values, const Tensor & self, Dimname dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor max_values(const Tensor & self, DimnameList dim, bool keepdim=false); +#endif +CAFFE2_API std::tuple max_pool1d_with_indices(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +CAFFE2_API Tensor max_pool1d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +CAFFE2_API Tensor max_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +CAFFE2_API Tensor mkldnn_max_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +CAFFE2_API Tensor quantized_max_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +CAFFE2_API Tensor max_pool3d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +CAFFE2_API Tensor mean_cpu_gpu(const Tensor & self, c10::optional dtype=c10::nullopt); +CAFFE2_API Tensor quantized_mean_cpu(const Tensor & self, c10::optional dtype=c10::nullopt); +CAFFE2_API Tensor mean_cpu_gpu(const Tensor & self, IntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +CAFFE2_API Tensor quantized_mean_cpu(const Tensor & self, IntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +CAFFE2_API Tensor & mean_out_cpu_gpu(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +CAFFE2_API Tensor & quantized_mean_out_cpu(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor mean(const Tensor & self, DimnameList dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & mean_out(Tensor & out, const Tensor & self, DimnameList dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#endif +CAFFE2_API std::tuple median(const Tensor & self, int64_t dim, bool keepdim=false); +CAFFE2_API std::tuple median_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API std::tuple median(const Tensor & self, Dimname dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API std::tuple median_out(Tensor & values, Tensor & indices, const Tensor & self, Dimname dim, bool keepdim=false); +#endif +CAFFE2_API std::tuple min(const Tensor & self, int64_t dim, bool keepdim=false); +CAFFE2_API std::tuple min_out(Tensor & min, Tensor & min_indices, const Tensor & self, int64_t dim, bool keepdim=false); +CAFFE2_API Tensor min_values(const Tensor & self, IntArrayRef dim, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API std::tuple min(const Tensor & self, Dimname dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API std::tuple min_out(Tensor & min, Tensor & min_indices, const Tensor & self, Dimname dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor min_values(const Tensor & self, DimnameList dim, bool keepdim=false); +#endif +CAFFE2_API Tensor mkldnn_convolution(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups); +CAFFE2_API Tensor mkldnn_convolution_backward_input(IntArrayRef self_size, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool bias_defined); +CAFFE2_API std::tuple mkldnn_convolution_backward_weights(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool bias_defined); +CAFFE2_API std::tuple mkldnn_convolution_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, std::array output_mask); +CAFFE2_API std::tuple miopen_batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double exponential_average_factor, double epsilon); +CAFFE2_API std::tuple miopen_batch_norm_backward(const Tensor & input, const Tensor & grad_output, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_var, double epsilon); +CAFFE2_API Tensor miopen_convolution(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +CAFFE2_API Tensor miopen_convolution_backward_input(IntArrayRef self_size, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +CAFFE2_API std::tuple miopen_convolution_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask); +CAFFE2_API Tensor miopen_convolution_backward_bias(const Tensor & grad_output); +CAFFE2_API Tensor miopen_convolution_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +CAFFE2_API Tensor miopen_convolution_transpose(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +CAFFE2_API std::tuple miopen_convolution_transpose_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask); +CAFFE2_API Tensor miopen_convolution_transpose_backward_input(const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +CAFFE2_API Tensor miopen_convolution_transpose_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +CAFFE2_API Tensor miopen_depthwise_convolution(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +CAFFE2_API Tensor miopen_depthwise_convolution_backward_input(IntArrayRef self_size, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +CAFFE2_API std::tuple miopen_depthwise_convolution_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, std::array output_mask); +CAFFE2_API Tensor miopen_depthwise_convolution_backward_weight(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +CAFFE2_API std::tuple miopen_rnn(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & hx, const Tensor & cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntArrayRef batch_sizes, const Tensor & dropout_state); +CAFFE2_API std::tuple> miopen_rnn_backward(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & weight_buf, const Tensor & hx, const Tensor & cx, const Tensor & output, const Tensor & grad_output, const Tensor & grad_hy, const Tensor & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntArrayRef batch_sizes, const Tensor & dropout_state, const Tensor & reserve, std::array output_mask); +CAFFE2_API Tensor _sparse_mm(const Tensor & self, const Tensor & mat2); +CAFFE2_API Tensor & _sparse_mm_out(Tensor & out, const Tensor & self, const Tensor & mat2); +CAFFE2_API Tensor _sparse_mm(const Tensor & sparse, const Tensor & dense); +CAFFE2_API std::tuple mode(const Tensor & self, int64_t dim=-1, bool keepdim=false); +CAFFE2_API std::tuple mode_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim=-1, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API std::tuple mode(const Tensor & self, Dimname dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API std::tuple mode_out(Tensor & values, Tensor & indices, const Tensor & self, Dimname dim, bool keepdim=false); +#endif +CAFFE2_API Tensor mul(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor mkldnn_mul(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor mul_sparse(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & mul_(Tensor & self, const Tensor & other); +CAFFE2_API Tensor & mkldnn_mul_(Tensor & self, const Tensor & other); +CAFFE2_API Tensor & mul_sparse_(Tensor & self, const Tensor & other); +CAFFE2_API Tensor & mul_out(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & mkldnn_mul_out(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & mul_out_sparse_cpu(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & mul_out_sparse_cuda(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor mul(const Tensor & self, Scalar other); +CAFFE2_API Tensor & mul_(Tensor & self, Scalar other); +CAFFE2_API Tensor mvlgamma(const Tensor & self, int64_t p); +CAFFE2_API Tensor & mvlgamma_(Tensor & self, int64_t p); +CAFFE2_API Tensor narrow_copy_dense(const Tensor & self, int64_t dim, int64_t start, int64_t length); +CAFFE2_API Tensor narrow_copy_sparse(const Tensor & self, int64_t dim, int64_t start, int64_t length); +CAFFE2_API Tensor narrow(const Tensor & self, int64_t dim, int64_t start, int64_t length); +CAFFE2_API std::tuple batch_norm_cpu(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps); +CAFFE2_API std::tuple batch_norm_cuda(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps); +CAFFE2_API std::tuple mkldnn_batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps); +CAFFE2_API std::tuple batch_norm_stats_cuda(const Tensor & input, double eps); +CAFFE2_API Tensor batch_norm_elemt_cuda(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & mean, const Tensor & invstd, double eps); +CAFFE2_API Tensor & batch_norm_elemt_cuda_out(Tensor & out, const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & mean, const Tensor & invstd, double eps); +CAFFE2_API std::tuple batch_norm_gather_stats_cuda(const Tensor & input, const Tensor & mean, const Tensor & invstd, const Tensor & running_mean, const Tensor & running_var, double momentum, double eps, int64_t count); +CAFFE2_API std::tuple batch_norm_gather_stats_with_counts_cuda(const Tensor & input, const Tensor & mean, const Tensor & invstd, const Tensor & running_mean, const Tensor & running_var, double momentum, double eps, IntArrayRef counts); +CAFFE2_API std::tuple batch_norm_backward_cpu(const Tensor & grad_out, const Tensor & input, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_invstd, bool train, double eps, std::array output_mask); +CAFFE2_API std::tuple batch_norm_backward_cuda(const Tensor & grad_out, const Tensor & input, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_invstd, bool train, double eps, std::array output_mask); +CAFFE2_API std::tuple batch_norm_backward_reduce_cuda(const Tensor & grad_out, const Tensor & input, const Tensor & mean, const Tensor & invstd, const Tensor & weight, bool input_g, bool weight_g, bool bias_g); +CAFFE2_API Tensor batch_norm_backward_elemt_cuda(const Tensor & grad_out, const Tensor & input, const Tensor & mean, const Tensor & invstd, const Tensor & weight, const Tensor & mean_dy, const Tensor & mean_dy_xmu); +CAFFE2_API std::tuple batch_norm_update_stats_cpu(const Tensor & input, const Tensor & running_mean, const Tensor & running_var, double momentum); +CAFFE2_API std::tuple batch_norm_update_stats_cuda(const Tensor & input, const Tensor & running_mean, const Tensor & running_var, double momentum); +CAFFE2_API bool _nnpack_available(); +CAFFE2_API Tensor _nnpack_spatial_convolution(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride=1); +CAFFE2_API std::tuple _nnpack_spatial_convolution_backward(const Tensor & input, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, std::array output_mask); +CAFFE2_API Tensor _nnpack_spatial_convolution_backward_input(const Tensor & input, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding); +CAFFE2_API Tensor _nnpack_spatial_convolution_backward_weight(const Tensor & input, IntArrayRef weightsize, const Tensor & grad_output, IntArrayRef padding); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor ones(IntArrayRef size, c10::optional names, const TensorOptions & options={}); +#endif +CAFFE2_API Tensor ones(IntArrayRef size, const TensorOptions & options={}); +CAFFE2_API Tensor & ones_out(Tensor & out, IntArrayRef size); +CAFFE2_API Tensor ones_like(const Tensor & self, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor ones_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor pairwise_distance(const Tensor & x1, const Tensor & x2, double p=2, double eps=1e-06, bool keepdim=false); +CAFFE2_API Tensor cdist(const Tensor & x1, const Tensor & x2, double p=2, c10::optional compute_mode=c10::nullopt); +CAFFE2_API Tensor _cdist_backward(const Tensor & grad, const Tensor & x1, const Tensor & x2, double p, const Tensor & cdist); +CAFFE2_API Tensor pdist(const Tensor & self, double p=2); +CAFFE2_API Tensor _pdist_forward(const Tensor & self, double p=2); +CAFFE2_API Tensor _pdist_backward(const Tensor & grad, const Tensor & self, double p, const Tensor & pdist); +CAFFE2_API Tensor cosine_similarity(const Tensor & x1, const Tensor & x2, int64_t dim=1, double eps=1e-08); +CAFFE2_API Tensor permute(const Tensor & self, IntArrayRef dims); +CAFFE2_API Tensor numpy_T(const Tensor & self); +CAFFE2_API Tensor pixel_shuffle(const Tensor & self, int64_t upscale_factor); +CAFFE2_API bool is_pinned(const Tensor & self); +CAFFE2_API Tensor pin_memory(const Tensor & self); +CAFFE2_API Tensor pinverse(const Tensor & self, double rcond=1e-15); +CAFFE2_API Tensor poisson_nll_loss(const Tensor & input, const Tensor & target, bool log_input, bool full, double eps, int64_t reduction); +CAFFE2_API Tensor scalar_tensor(Scalar s, const TensorOptions & options={}); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor rand(IntArrayRef size, c10::optional names, const TensorOptions & options={}); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor rand(IntArrayRef size, Generator * generator, c10::optional names, const TensorOptions & options={}); +#endif +CAFFE2_API Tensor rand(IntArrayRef size, const TensorOptions & options={}); +CAFFE2_API Tensor rand(IntArrayRef size, Generator * generator, const TensorOptions & options={}); +CAFFE2_API Tensor & rand_out(Tensor & out, IntArrayRef size); +CAFFE2_API Tensor & rand_out(Tensor & out, IntArrayRef size, Generator * generator); +CAFFE2_API Tensor rand_like(const Tensor & self, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor rand_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor randint(int64_t high, IntArrayRef size, const TensorOptions & options={}); +CAFFE2_API Tensor randint(int64_t high, IntArrayRef size, Generator * generator, const TensorOptions & options={}); +CAFFE2_API Tensor randint(int64_t low, int64_t high, IntArrayRef size, const TensorOptions & options={}); +CAFFE2_API Tensor randint(int64_t low, int64_t high, IntArrayRef size, Generator * generator, const TensorOptions & options={}); +CAFFE2_API Tensor & randint_out(Tensor & out, int64_t high, IntArrayRef size); +CAFFE2_API Tensor & randint_out(Tensor & out, int64_t high, IntArrayRef size, Generator * generator); +CAFFE2_API Tensor & randint_out(Tensor & out, int64_t low, int64_t high, IntArrayRef size); +CAFFE2_API Tensor & randint_out(Tensor & out, int64_t low, int64_t high, IntArrayRef size, Generator * generator); +CAFFE2_API Tensor randint_like(const Tensor & self, int64_t high, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor randint_like(const Tensor & self, int64_t low, int64_t high, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor randint_like(const Tensor & self, int64_t high, const TensorOptions & options, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor randint_like(const Tensor & self, int64_t low, int64_t high, const TensorOptions & options, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor randn(IntArrayRef size, const TensorOptions & options={}); +CAFFE2_API Tensor randn(IntArrayRef size, Generator * generator, const TensorOptions & options={}); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor randn(IntArrayRef size, c10::optional names, const TensorOptions & options={}); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor randn(IntArrayRef size, Generator * generator, c10::optional names, const TensorOptions & options={}); +#endif +CAFFE2_API Tensor & randn_out(Tensor & out, IntArrayRef size); +CAFFE2_API Tensor & randn_out(Tensor & out, IntArrayRef size, Generator * generator); +CAFFE2_API Tensor randn_like(const Tensor & self, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor randn_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor randperm(int64_t n, const TensorOptions & options={}); +CAFFE2_API Tensor randperm(int64_t n, Generator * generator, const TensorOptions & options={}); +CAFFE2_API Tensor & randperm_out(Tensor & out, int64_t n); +CAFFE2_API Tensor & randperm_out_cpu(Tensor & out, int64_t n, Generator * generator); +CAFFE2_API Tensor & randperm_out_cuda(Tensor & out, int64_t n, Generator * generator); +CAFFE2_API Tensor range(Scalar start, Scalar end, Scalar step=1, const TensorOptions & options={}); +CAFFE2_API Tensor range(Scalar start, Scalar end, const TensorOptions & options={}); +CAFFE2_API Tensor & range_cpu_out(Tensor & out, Scalar start, Scalar end, Scalar step=1); +CAFFE2_API Tensor & range_cuda_out(Tensor & out, Scalar start, Scalar end, Scalar step=1); +CAFFE2_API Tensor reciprocal(const Tensor & self); +CAFFE2_API Tensor & _reciprocal__cpu(Tensor & self); +CAFFE2_API Tensor & _reciprocal__cuda(Tensor & self); +CAFFE2_API Tensor & _reciprocal_out_cpu(Tensor & out, const Tensor & self); +CAFFE2_API Tensor & _reciprocal_out_cuda(Tensor & out, const Tensor & self); +CAFFE2_API Tensor neg(const Tensor & self); +CAFFE2_API Tensor & neg_(Tensor & self); +CAFFE2_API Tensor & neg_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor repeat(const Tensor & self, IntArrayRef repeats); +CAFFE2_API Tensor repeat_interleave_cpu(const Tensor & repeats); +CAFFE2_API Tensor repeat_interleave_cuda(const Tensor & repeats); +CAFFE2_API Tensor repeat_interleave(const Tensor & self, const Tensor & repeats, c10::optional dim=c10::nullopt); +CAFFE2_API Tensor repeat_interleave(const Tensor & self, int64_t repeats, c10::optional dim=c10::nullopt); +CAFFE2_API Tensor reshape(const Tensor & self, IntArrayRef shape); +CAFFE2_API Tensor mkldnn_reshape(const Tensor & self, IntArrayRef shape); +CAFFE2_API Tensor reshape_as(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor round(const Tensor & self); +CAFFE2_API Tensor & round_(Tensor & self); +CAFFE2_API Tensor & round_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor rrelu(const Tensor & self, Scalar lower=0.125, Scalar upper=0.333333333333, bool training=false, Generator * generator=nullptr); +CAFFE2_API Tensor & rrelu_(Tensor & self, Scalar lower=0.125, Scalar upper=0.333333333333, bool training=false, Generator * generator=nullptr); +CAFFE2_API Tensor relu(const Tensor & self); +CAFFE2_API Tensor mkldnn_relu(const Tensor & self); +CAFFE2_API Tensor quantized_relu(const Tensor & self); +CAFFE2_API Tensor & relu_(Tensor & self); +CAFFE2_API Tensor & mkldnn_relu_(Tensor & self); +CAFFE2_API Tensor & quantized_relu_(Tensor & self); +CAFFE2_API Tensor prelu_cpu(const Tensor & self, const Tensor & weight); +CAFFE2_API Tensor prelu_cuda(const Tensor & self, const Tensor & weight); +CAFFE2_API std::tuple prelu_backward_cpu(const Tensor & grad_output, const Tensor & self, const Tensor & weight); +CAFFE2_API std::tuple prelu_backward_cuda(const Tensor & grad_output, const Tensor & self, const Tensor & weight); +CAFFE2_API Tensor gelu_cpu(const Tensor & self); +CAFFE2_API Tensor gelu_cuda(const Tensor & self); +CAFFE2_API Tensor gelu_backward_cpu(const Tensor & grad, const Tensor & self); +CAFFE2_API Tensor gelu_backward_cuda(const Tensor & grad, const Tensor & self); +CAFFE2_API Tensor hardshrink_cpu(const Tensor & self, Scalar lambd=0.5); +CAFFE2_API Tensor hardshrink_cuda(const Tensor & self, Scalar lambd=0.5); +CAFFE2_API Tensor hardshrink_backward_cpu(const Tensor & grad_out, const Tensor & self, Scalar lambd); +CAFFE2_API Tensor hardshrink_backward_cuda(const Tensor & grad_out, const Tensor & self, Scalar lambd); +CAFFE2_API Tensor rsqrt(const Tensor & self); +CAFFE2_API Tensor & rsqrt_(Tensor & self); +CAFFE2_API Tensor & rsqrt_out(Tensor & out, const Tensor & self); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor select(const Tensor & self, Dimname dim, int64_t index); +#endif +CAFFE2_API Tensor select(const Tensor & self, int64_t dim, int64_t index); +CAFFE2_API Tensor selu(const Tensor & self); +CAFFE2_API Tensor & selu_(Tensor & self); +CAFFE2_API Tensor celu(const Tensor & self, Scalar alpha=1.0); +CAFFE2_API Tensor & celu_(Tensor & self, Scalar alpha=1.0); +CAFFE2_API Tensor sigmoid(const Tensor & self); +CAFFE2_API Tensor mkldnn_sigmoid(const Tensor & self); +CAFFE2_API Tensor & sigmoid_(Tensor & self); +CAFFE2_API Tensor & mkldnn_sigmoid_(Tensor & self); +CAFFE2_API Tensor & sigmoid_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor sin(const Tensor & self); +CAFFE2_API Tensor & sin_(Tensor & self); +CAFFE2_API Tensor & sin_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor sinh(const Tensor & self); +CAFFE2_API Tensor & sinh_(Tensor & self); +CAFFE2_API Tensor & sinh_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor detach(const Tensor & self); +CAFFE2_API Tensor & detach_(Tensor & self); +CAFFE2_API int64_t size(const Tensor & self, int64_t dim); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API int64_t size(const Tensor & self, Dimname dim); +#endif +CAFFE2_API Tensor slice(const Tensor & self, int64_t dim=0, int64_t start=0, int64_t end=9223372036854775807, int64_t step=1); +CAFFE2_API std::tuple slogdet(const Tensor & self); +CAFFE2_API Tensor smm(const Tensor & self, const Tensor & mat2); +CAFFE2_API Tensor softmax(const Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor softmax(const Tensor & self, Dimname dim, c10::optional dtype=c10::nullopt); +#endif +CAFFE2_API Tensor softmax_cpu(const Tensor & self, int64_t dim, bool half_to_float); +CAFFE2_API Tensor softmax_cuda(const Tensor & self, int64_t dim, bool half_to_float); +CAFFE2_API Tensor mkldnn_softmax(const Tensor & self, int64_t dim, bool half_to_float); +CAFFE2_API Tensor softmax_backward_cpu(const Tensor & grad_output, const Tensor & output, int64_t dim, const Tensor & self); +CAFFE2_API Tensor softmax_backward_cuda(const Tensor & grad_output, const Tensor & output, int64_t dim, const Tensor & self); +CAFFE2_API std::vector split(const Tensor & self, int64_t split_size, int64_t dim=0); +CAFFE2_API std::vector split_with_sizes(const Tensor & self, IntArrayRef split_sizes, int64_t dim=0); +CAFFE2_API Tensor squeeze(const Tensor & self); +CAFFE2_API Tensor squeeze(const Tensor & self, int64_t dim); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor squeeze(const Tensor & self, Dimname dim); +#endif +CAFFE2_API Tensor & squeeze_(Tensor & self); +CAFFE2_API Tensor & squeeze_(Tensor & self, int64_t dim); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & squeeze_(Tensor & self, Dimname dim); +#endif +CAFFE2_API Tensor sspaddmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor & _sspaddmm_out_only_sparse(Tensor & out, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor & _sspaddmm_out_only_sparse_cuda(Tensor & out, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor & _sspaddmm_out_cpu(Tensor & out, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor & _sspaddmm_out_cuda(Tensor & out, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor stack(TensorList tensors, int64_t dim=0); +CAFFE2_API Tensor & stack_out(Tensor & out, TensorList tensors, int64_t dim=0); +CAFFE2_API Tensor stft(const Tensor & self, int64_t n_fft, c10::optional hop_length=c10::nullopt, c10::optional win_length=c10::nullopt, const Tensor & window={}, bool normalized=false, bool onesided=true); +CAFFE2_API int64_t stride(const Tensor & self, int64_t dim); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API int64_t stride(const Tensor & self, Dimname dim); +#endif +CAFFE2_API Tensor sum(const Tensor & self, c10::optional dtype=c10::nullopt); +CAFFE2_API Tensor sum(const Tensor & self, IntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor sum(const Tensor & self, DimnameList dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#endif +CAFFE2_API Tensor & sum_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & sum_out(Tensor & out, const Tensor & self, DimnameList dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#endif +CAFFE2_API Tensor sum_to_size(const Tensor & self, IntArrayRef size); +CAFFE2_API Tensor sqrt(const Tensor & self); +CAFFE2_API Tensor & sqrt_(Tensor & self); +CAFFE2_API Tensor & sqrt_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor std(const Tensor & self, bool unbiased=true); +CAFFE2_API Tensor std(const Tensor & self, IntArrayRef dim, bool unbiased=true, bool keepdim=false); +CAFFE2_API std::tuple std_mean(const Tensor & self, bool unbiased=true); +CAFFE2_API std::tuple std_mean(const Tensor & self, IntArrayRef dim, bool unbiased=true, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API std::tuple std_mean(const Tensor & self, DimnameList dim, bool unbiased=true, bool keepdim=false); +#endif +CAFFE2_API Tensor & std_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool unbiased=true, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor std(const Tensor & self, DimnameList dim, bool unbiased=true, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & std_out(Tensor & out, const Tensor & self, DimnameList dim, bool unbiased=true, bool keepdim=false); +#endif +CAFFE2_API Tensor prod(const Tensor & self, c10::optional dtype=c10::nullopt); +CAFFE2_API Tensor prod(const Tensor & self, int64_t dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +CAFFE2_API Tensor & prod_out(Tensor & out, const Tensor & self, int64_t dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor prod(const Tensor & self, Dimname dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & prod_out(Tensor & out, const Tensor & self, Dimname dim, bool keepdim=false, c10::optional dtype=c10::nullopt); +#endif +CAFFE2_API Tensor t(const Tensor & self); +CAFFE2_API Tensor & t_(Tensor & self); +CAFFE2_API Tensor tan(const Tensor & self); +CAFFE2_API Tensor & _tan__cpu(Tensor & self); +CAFFE2_API Tensor & _tan__cuda(Tensor & self); +CAFFE2_API Tensor & _tan_out_cpu(Tensor & out, const Tensor & self); +CAFFE2_API Tensor & _tan_out_cuda(Tensor & out, const Tensor & self); +CAFFE2_API Tensor tanh(const Tensor & self); +CAFFE2_API Tensor & _tanh__cpu(Tensor & self); +CAFFE2_API Tensor & _tanh__cuda(Tensor & self); +CAFFE2_API Tensor & _tanh_out_cpu(Tensor & out, const Tensor & self); +CAFFE2_API Tensor & _tanh_out_cuda(Tensor & out, const Tensor & self); +CAFFE2_API Tensor tensordot(const Tensor & self, const Tensor & other, IntArrayRef dims_self, IntArrayRef dims_other); +CAFFE2_API Tensor threshold(const Tensor & self, Scalar threshold, Scalar value); +CAFFE2_API Tensor & threshold_(Tensor & self, Scalar threshold, Scalar value); +CAFFE2_API Tensor & threshold_out(Tensor & out, const Tensor & self, Scalar threshold, Scalar value); +CAFFE2_API Tensor threshold_backward(const Tensor & grad_output, const Tensor & self, Scalar threshold); +CAFFE2_API Tensor transpose(const Tensor & self, int64_t dim0, int64_t dim1); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor transpose(const Tensor & self, Dimname dim0, Dimname dim1); +#endif +CAFFE2_API Tensor mkldnn_transpose(const Tensor & self, int64_t dim0, int64_t dim1); +CAFFE2_API Tensor & transpose_(Tensor & self, int64_t dim0, int64_t dim1); +CAFFE2_API Tensor & mkldnn_transpose_(Tensor & self, int64_t dim0, int64_t dim1); +CAFFE2_API Tensor one_hot(const Tensor & self, int64_t num_classes=-1); +CAFFE2_API Tensor flip_cpu(const Tensor & self, IntArrayRef dims); +CAFFE2_API Tensor flip_cuda(const Tensor & self, IntArrayRef dims); +CAFFE2_API Tensor roll_cpu(const Tensor & self, IntArrayRef shifts, IntArrayRef dims={}); +CAFFE2_API Tensor roll_cuda(const Tensor & self, IntArrayRef shifts, IntArrayRef dims={}); +CAFFE2_API Tensor rot90(const Tensor & self, int64_t k=1, IntArrayRef dims={0,1}); +CAFFE2_API Tensor trapz(const Tensor & y, const Tensor & x, int64_t dim=-1); +CAFFE2_API Tensor trapz(const Tensor & y, double dx=1, int64_t dim=-1); +CAFFE2_API Tensor _trilinear(const Tensor & i1, const Tensor & i2, const Tensor & i3, IntArrayRef expand1, IntArrayRef expand2, IntArrayRef expand3, IntArrayRef sumdim, int64_t unroll_dim=1); +CAFFE2_API Tensor triplet_margin_loss(const Tensor & anchor, const Tensor & positive, const Tensor & negative, double margin=1.0, double p=2, double eps=1e-06, bool swap=false, int64_t reduction=at::Reduction::Mean); +CAFFE2_API Tensor trunc(const Tensor & self); +CAFFE2_API Tensor & trunc_(Tensor & self); +CAFFE2_API Tensor & trunc_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor type_as(const Tensor & self, const Tensor & other); +CAFFE2_API bool _has_compatible_shallow_copy_type(const Tensor & self, const Tensor & from); +CAFFE2_API std::tuple _unique_cpu(const Tensor & self, bool sorted=true, bool return_inverse=false); +CAFFE2_API std::tuple _unique_cuda(const Tensor & self, bool sorted=true, bool return_inverse=false); +CAFFE2_API std::tuple unique_dim_cpu(const Tensor & self, int64_t dim, bool sorted=true, bool return_inverse=false, bool return_counts=false); +CAFFE2_API std::tuple unique_dim_cuda(const Tensor & self, int64_t dim, bool sorted=true, bool return_inverse=false, bool return_counts=false); +CAFFE2_API std::tuple unique_consecutive_cpu(const Tensor & self, bool return_inverse=false, bool return_counts=false, c10::optional dim=c10::nullopt); +CAFFE2_API std::tuple unique_consecutive_cuda(const Tensor & self, bool return_inverse=false, bool return_counts=false, c10::optional dim=c10::nullopt); +CAFFE2_API std::tuple unique_dim_consecutive_cpu(const Tensor & self, int64_t dim, bool return_inverse=false, bool return_counts=false); +CAFFE2_API std::tuple unique_dim_consecutive_cuda(const Tensor & self, int64_t dim, bool return_inverse=false, bool return_counts=false); +CAFFE2_API std::tuple _unique2_cpu(const Tensor & self, bool sorted=true, bool return_inverse=false, bool return_counts=false); +CAFFE2_API std::tuple _unique2_cuda(const Tensor & self, bool sorted=true, bool return_inverse=false, bool return_counts=false); +CAFFE2_API Tensor _unsafe_view(const Tensor & self, IntArrayRef size); +CAFFE2_API Tensor unsqueeze(const Tensor & self, int64_t dim); +CAFFE2_API Tensor & unsqueeze_(Tensor & self, int64_t dim); +CAFFE2_API Tensor var(const Tensor & self, bool unbiased=true); +CAFFE2_API Tensor var(const Tensor & self, IntArrayRef dim, bool unbiased=true, bool keepdim=false); +CAFFE2_API Tensor & var_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool unbiased=true, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor var(const Tensor & self, DimnameList dim, bool unbiased=true, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & var_out(Tensor & out, const Tensor & self, DimnameList dim, bool unbiased=true, bool keepdim=false); +#endif +CAFFE2_API std::tuple var_mean(const Tensor & self, bool unbiased=true); +CAFFE2_API std::tuple var_mean(const Tensor & self, IntArrayRef dim, bool unbiased=true, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API std::tuple var_mean(const Tensor & self, DimnameList dim, bool unbiased=true, bool keepdim=false); +#endif +CAFFE2_API Tensor view_as(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor where(const Tensor & condition, const Tensor & self, const Tensor & other); +CAFFE2_API std::vector where(const Tensor & condition); +CAFFE2_API Tensor _s_where_cpu(const Tensor & condition, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor _s_where_cuda(const Tensor & condition, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor norm_except_dim(const Tensor & v, int64_t pow=2, int64_t dim=0); +CAFFE2_API Tensor _weight_norm(const Tensor & v, const Tensor & g, int64_t dim=0); +CAFFE2_API std::tuple weight_norm_cuda(const Tensor & v, const Tensor & g, int64_t dim=0); +CAFFE2_API std::tuple weight_norm_cuda_backward(const Tensor & grad_w, const Tensor & saved_v, const Tensor & saved_g, const Tensor & saved_norms, int64_t dim); +CAFFE2_API std::tuple _weight_norm_differentiable_backward(const Tensor & grad_w, const Tensor & saved_v, const Tensor & saved_g, const Tensor & saved_norms, int64_t dim); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor zeros(IntArrayRef size, c10::optional names, const TensorOptions & options={}); +#endif +CAFFE2_API Tensor zeros(IntArrayRef size, const TensorOptions & options={}); +CAFFE2_API Tensor & zeros_out(Tensor & out, IntArrayRef size); +CAFFE2_API Tensor zeros_like(const Tensor & self, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor zeros_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor _standard_gamma_grad_cpu(const Tensor & self, const Tensor & output); +CAFFE2_API Tensor _standard_gamma_grad_cuda(const Tensor & self, const Tensor & output); +CAFFE2_API Tensor _s_gamma_cpu(const Tensor & self, Generator * generator=nullptr); +CAFFE2_API Tensor _s_gamma_cuda(const Tensor & self, Generator * generator=nullptr); +CAFFE2_API Tensor _dirichlet_grad_cpu(const Tensor & x, const Tensor & alpha, const Tensor & total); +CAFFE2_API Tensor _dirichlet_grad_cuda(const Tensor & x, const Tensor & alpha, const Tensor & total); +CAFFE2_API Tensor _s_dirichlet_cpu(const Tensor & self, Generator * generator=nullptr); +CAFFE2_API Tensor _s_dirichlet_cuda(const Tensor & self, Generator * generator=nullptr); +CAFFE2_API Tensor _s_poisson_cpu(const Tensor & self, Generator * generator=nullptr); +CAFFE2_API Tensor _s_poisson_cuda(const Tensor & self, Generator * generator=nullptr); +CAFFE2_API Tensor norm_sparse(const Tensor & self, Scalar p=2); +CAFFE2_API Tensor _sparse_sum(const Tensor & self); +CAFFE2_API Tensor _sparse_sum(const Tensor & self, ScalarType dtype); +CAFFE2_API Tensor _sparse_sum(const Tensor & self, IntArrayRef dim); +CAFFE2_API Tensor _sparse_sum(const Tensor & self, IntArrayRef dim, ScalarType dtype); +CAFFE2_API Tensor _sparse_sum_backward_cpu(const Tensor & grad, const Tensor & self, IntArrayRef dim); +CAFFE2_API Tensor _sparse_sum_backward_cuda(const Tensor & grad, const Tensor & self, IntArrayRef dim); +CAFFE2_API Tensor norm(const Tensor & self, c10::optional p, ScalarType dtype); +CAFFE2_API Tensor norm(const Tensor & self, Scalar p=2); +CAFFE2_API Tensor norm(const Tensor & self, c10::optional p, IntArrayRef dim, bool keepdim, ScalarType dtype); +CAFFE2_API Tensor norm(const Tensor & self, c10::optional p, IntArrayRef dim, bool keepdim=false); +CAFFE2_API Tensor & norm_out(Tensor & out, const Tensor & self, c10::optional p, IntArrayRef dim, bool keepdim, ScalarType dtype); +CAFFE2_API Tensor & norm_out(Tensor & out, const Tensor & self, c10::optional p, IntArrayRef dim, bool keepdim=false); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor norm(const Tensor & self, c10::optional p, DimnameList dim, bool keepdim, ScalarType dtype); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor norm(const Tensor & self, c10::optional p, DimnameList dim, bool keepdim=false); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & norm_out(Tensor & out, const Tensor & self, c10::optional p, DimnameList dim, bool keepdim, ScalarType dtype); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & norm_out(Tensor & out, const Tensor & self, c10::optional p, DimnameList dim, bool keepdim=false); +#endif +CAFFE2_API Tensor frobenius_norm(const Tensor & self); +CAFFE2_API Tensor frobenius_norm(const Tensor & self, IntArrayRef dim, bool keepdim=false); +CAFFE2_API Tensor & frobenius_norm_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim=false); +CAFFE2_API Tensor nuclear_norm(const Tensor & self, bool keepdim=false); +CAFFE2_API Tensor & nuclear_norm_out(Tensor & out, const Tensor & self, bool keepdim=false); +CAFFE2_API Tensor nuclear_norm(const Tensor & self, IntArrayRef dim, bool keepdim=false); +CAFFE2_API Tensor & nuclear_norm_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim=false); +CAFFE2_API Tensor clone(const Tensor & self, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor mkldnn_clone(const Tensor & self, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor quantized_clone(const Tensor & self, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor clone_sparse(const Tensor & self, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor & resize_as_(Tensor & self, const Tensor & the_template, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor & pow_out(Tensor & out, const Tensor & self, Scalar exponent); +CAFFE2_API Tensor & pow_out_sparse_scalar(Tensor & out, const Tensor & self, Scalar exponent); +CAFFE2_API Tensor pow(const Tensor & self, Scalar exponent); +CAFFE2_API Tensor pow_sparse_scalar(const Tensor & self, Scalar exponent); +CAFFE2_API Tensor & zero_(Tensor & self); +CAFFE2_API Tensor & mkldnn_zero_(Tensor & self); +CAFFE2_API Tensor & zero_sparse_(Tensor & self); +CAFFE2_API Tensor & sub_out(Tensor & out, const Tensor & self, const Tensor & other, Scalar alpha=1); +CAFFE2_API Tensor & sub_out_sparse(Tensor & out, const Tensor & self, const Tensor & other, Scalar alpha=1); +CAFFE2_API Tensor sub(const Tensor & self, const Tensor & other, Scalar alpha=1); +CAFFE2_API Tensor sub_sparse(const Tensor & self, const Tensor & other, Scalar alpha=1); +CAFFE2_API Tensor & sub_(Tensor & self, const Tensor & other, Scalar alpha=1); +CAFFE2_API Tensor & sub_sparse_(Tensor & self, const Tensor & other, Scalar alpha=1); +CAFFE2_API Tensor sub(const Tensor & self, Scalar other, Scalar alpha=1); +CAFFE2_API Tensor & sub_(Tensor & self, Scalar other, Scalar alpha=1); +CAFFE2_API Tensor rsub(const Tensor & self, const Tensor & other, Scalar alpha=1); +CAFFE2_API Tensor rsub(const Tensor & self, Scalar other, Scalar alpha=1); +CAFFE2_API Tensor _sparse_addmm(const Tensor & self, const Tensor & sparse, const Tensor & dense, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor & addmm_out_sparse_dense_cpu(Tensor & out, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor & addmm_out_sparse_dense_cuda(Tensor & out, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor addmm_sparse_dense_cpu(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor addmm_sparse_dense_cuda(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor & s_addmm_sparse_dense_cpu_(Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor & s_addmm_sparse_dense_cuda_(Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1); +CAFFE2_API Tensor sparse_coo_tensor(IntArrayRef size, const TensorOptions & options); +CAFFE2_API Tensor sparse_coo_tensor(const Tensor & indices, const Tensor & values, const TensorOptions & options={}); +CAFFE2_API Tensor sparse_coo_tensor(const Tensor & indices, const Tensor & values, IntArrayRef size, const TensorOptions & options={}); +CAFFE2_API Tensor _sparse_coo_tensor_unsafe(const Tensor & indices, const Tensor & values, IntArrayRef size, const TensorOptions & options={}); +CAFFE2_API Tensor new_with_dims_sparse(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size, const TensorOptions & options); +CAFFE2_API Tensor new_with_dims_and_tensor_sparse(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size, const Tensor & indices, const Tensor & values, const TensorOptions & options); +CAFFE2_API Tensor & sparse_resize_(Tensor & self, IntArrayRef size, int64_t sparse_dim, int64_t dense_dim); +CAFFE2_API Tensor & sparse_resize_and_clear_(Tensor & self, IntArrayRef size, int64_t sparse_dim, int64_t dense_dim); +CAFFE2_API Tensor sparse_mask_cpu(const Tensor & self, const Tensor & mask); +CAFFE2_API Tensor sparse_mask_cuda(const Tensor & self, const Tensor & mask); +CAFFE2_API Tensor mkldnn_to_dense(const Tensor & self); +CAFFE2_API Tensor sparse_to_dense(const Tensor & self); +CAFFE2_API Tensor to_dense_backward(const Tensor & grad, const Tensor & input); +CAFFE2_API int64_t sparse_dim_sparse(const Tensor & self); +CAFFE2_API int64_t sparse_dim_sparse(const Tensor & self); +CAFFE2_API int64_t dense_dim_sparse(const Tensor & self); +CAFFE2_API int64_t dense_dim_sparse(const Tensor & self); +CAFFE2_API int64_t _nnz_sparse(const Tensor & self); +CAFFE2_API Tensor coalesce_sparse_cpu(const Tensor & self); +CAFFE2_API Tensor coalesce_sparse_cuda(const Tensor & self); +CAFFE2_API bool is_coalesced_sparse(const Tensor & self); +CAFFE2_API Tensor _indices_sparse(const Tensor & self); +CAFFE2_API Tensor _values_sparse(const Tensor & self); +CAFFE2_API Tensor & _coalesced_sparse_(Tensor & self, bool coalesced); +CAFFE2_API Tensor indices_sparse(const Tensor & self); +CAFFE2_API Tensor values_sparse(const Tensor & self); +CAFFE2_API Tensor & hspmm_out_sparse_cpu(Tensor & out, const Tensor & mat1, const Tensor & mat2); +CAFFE2_API Tensor & hspmm_out_sparse_cuda(Tensor & out, const Tensor & mat1, const Tensor & mat2); +CAFFE2_API Tensor hspmm_sparse_cpu(const Tensor & mat1, const Tensor & mat2); +CAFFE2_API Tensor hspmm_sparse_cuda(const Tensor & mat1, const Tensor & mat2); +CAFFE2_API Tensor & copy_sparse_(Tensor & self, const Tensor & src, bool non_blocking=false); +CAFFE2_API std::vector unbind(const Tensor & self, int64_t dim=0); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API std::vector unbind(const Tensor & self, Dimname dim); +#endif +CAFFE2_API Tensor dense_to_sparse(const Tensor & self, int64_t sparse_dim); +CAFFE2_API Tensor dense_to_sparse(const Tensor & self); +CAFFE2_API Tensor dense_to_mkldnn(const Tensor & self); +CAFFE2_API Tensor mkldnn_reorder_conv2d_weight(const Tensor & self, IntArrayRef padding=0, IntArrayRef stride=1, IntArrayRef dilation=1, int64_t groups=1); +CAFFE2_API Tensor to_mkldnn_backward(const Tensor & grad, const Tensor & input); +CAFFE2_API Tensor quantize_per_tensor_cpu(const Tensor & self, double scale, int64_t zero_point, ScalarType dtype); +CAFFE2_API Tensor quantize_per_channel_cpu(const Tensor & self, const Tensor & scales, const Tensor & zero_points, int64_t axis, ScalarType dtype); +CAFFE2_API Tensor dequantize_quant(const Tensor & self); +CAFFE2_API double q_scale_quant(const Tensor & self); +CAFFE2_API int64_t q_zero_point_quant(const Tensor & self); +CAFFE2_API Tensor q_per_channel_scales_quant(const Tensor & self); +CAFFE2_API Tensor q_per_channel_zero_points_quant(const Tensor & self); +CAFFE2_API int64_t q_per_channel_axis_quant(const Tensor & self); +CAFFE2_API Tensor int_repr_quant(const Tensor & self); +CAFFE2_API Tensor make_per_tensor_quantized_tensor_cpu(const Tensor & self, double scale, int64_t zero_point); +CAFFE2_API Tensor make_per_channel_quantized_tensor_cpu(const Tensor & self, const Tensor & scale, const Tensor & zero_point, int64_t axis); +CAFFE2_API QScheme qscheme_quant(const Tensor & self); +CAFFE2_API Tensor fake_quantize_per_tensor_affine_cpu(const Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max); +CAFFE2_API Tensor fake_quantize_per_tensor_affine_cuda(const Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max); +CAFFE2_API Tensor fake_quantize_per_tensor_affine_backward_cpu(const Tensor & grad, const Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max); +CAFFE2_API Tensor fake_quantize_per_tensor_affine_backward_cuda(const Tensor & grad, const Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max); +CAFFE2_API Tensor fake_quantize_per_channel_affine_cpu(const Tensor & self, const Tensor & scale, const Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max); +CAFFE2_API Tensor fake_quantize_per_channel_affine_cuda(const Tensor & self, const Tensor & scale, const Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max); +CAFFE2_API Tensor fake_quantize_per_channel_affine_backward_cpu(const Tensor & grad, const Tensor & self, const Tensor & scale, const Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max); +CAFFE2_API Tensor fake_quantize_per_channel_affine_backward_cuda(const Tensor & grad, const Tensor & self, const Tensor & scale, const Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max); +CAFFE2_API Tensor to(const Tensor & self, const TensorOptions & options, bool non_blocking=false, bool copy=false, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor to(const Tensor & self, Device device, ScalarType dtype, bool non_blocking=false, bool copy=false, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor to(const Tensor & self, ScalarType dtype, bool non_blocking=false, bool copy=false, c10::optional memory_format=c10::nullopt); +CAFFE2_API Tensor to(const Tensor & self, const Tensor & other, bool non_blocking=false, bool copy=false, c10::optional memory_format=c10::nullopt); +CAFFE2_API std::vector meshgrid(TensorList tensors); +CAFFE2_API Tensor cartesian_prod(TensorList tensors); +CAFFE2_API Tensor combinations(const Tensor & self, int64_t r=2, bool with_replacement=false); +CAFFE2_API Scalar item(const Tensor & self); +CAFFE2_API ScalarType result_type(const Tensor & tensor, const Tensor & other); +CAFFE2_API ScalarType result_type(const Tensor & tensor, Scalar other); +CAFFE2_API ScalarType result_type(Scalar scalar, const Tensor & tensor); +CAFFE2_API ScalarType result_type(Scalar scalar1, Scalar scalar2); +CAFFE2_API bool can_cast(ScalarType from, ScalarType to); +CAFFE2_API ScalarType promote_types(ScalarType type1, ScalarType type2); +CAFFE2_API Scalar _local_scalar_dense_cpu(const Tensor & self); +CAFFE2_API Scalar _local_scalar_dense_cuda(const Tensor & self); +CAFFE2_API std::tuple _thnn_fused_lstm_cell_cuda(const Tensor & input_gates, const Tensor & hidden_gates, const Tensor & cx, const Tensor & input_bias={}, const Tensor & hidden_bias={}); +CAFFE2_API std::tuple _thnn_fused_lstm_cell_backward_cuda(const Tensor & grad_hy, const Tensor & grad_cy, const Tensor & cx, const Tensor & cy, const Tensor & workspace, bool has_bias); +CAFFE2_API std::tuple _thnn_differentiable_lstm_cell_backward(const Tensor & grad_hy, const Tensor & grad_cy, const Tensor & input_gates, const Tensor & hidden_gates, const Tensor & input_bias, const Tensor & hidden_bias, const Tensor & cx, const Tensor & cy); +CAFFE2_API std::tuple _thnn_fused_gru_cell_cuda(const Tensor & input_gates, const Tensor & hidden_gates, const Tensor & hx, const Tensor & input_bias={}, const Tensor & hidden_bias={}); +CAFFE2_API std::tuple _thnn_fused_gru_cell_backward_cuda(const Tensor & grad_hy, const Tensor & workspace, bool has_bias); +CAFFE2_API std::tuple _thnn_differentiable_gru_cell_backward(const Tensor & grad_hy, const Tensor & input_gates, const Tensor & hidden_gates, const Tensor & hx, const Tensor & input_bias, const Tensor & hidden_bias); +CAFFE2_API std::tuple lstm(const Tensor & input, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); +CAFFE2_API std::tuple lstm(const Tensor & data, const Tensor & batch_sizes, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); +CAFFE2_API std::tuple gru(const Tensor & input, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); +CAFFE2_API std::tuple gru(const Tensor & data, const Tensor & batch_sizes, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); +CAFFE2_API std::tuple rnn_tanh(const Tensor & input, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); +CAFFE2_API std::tuple rnn_tanh(const Tensor & data, const Tensor & batch_sizes, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); +CAFFE2_API std::tuple rnn_relu(const Tensor & input, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); +CAFFE2_API std::tuple rnn_relu(const Tensor & data, const Tensor & batch_sizes, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); +CAFFE2_API std::tuple lstm_cell(const Tensor & input, TensorList hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih={}, const Tensor & b_hh={}); +CAFFE2_API Tensor gru_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih={}, const Tensor & b_hh={}); +CAFFE2_API Tensor rnn_tanh_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih={}, const Tensor & b_hh={}); +CAFFE2_API Tensor rnn_relu_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih={}, const Tensor & b_hh={}); +CAFFE2_API std::tuple quantized_lstm(const Tensor & input, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first, c10::optional dtype=c10::nullopt, bool use_dynamic=false); +CAFFE2_API std::tuple quantized_lstm(const Tensor & data, const Tensor & batch_sizes, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, c10::optional dtype=c10::nullopt, bool use_dynamic=false); +CAFFE2_API std::tuple quantized_gru(const Tensor & input, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); +CAFFE2_API std::tuple quantized_gru(const Tensor & data, const Tensor & batch_sizes, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); +CAFFE2_API std::tuple quantized_lstm_cell(const Tensor & input, TensorList hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh, const Tensor & packed_ih, const Tensor & packed_hh, const Tensor & col_offsets_ih, const Tensor & col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh); +CAFFE2_API Tensor quantized_gru_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh, const Tensor & packed_ih, const Tensor & packed_hh, const Tensor & col_offsets_ih, const Tensor & col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh); +CAFFE2_API Tensor quantized_rnn_relu_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh, const Tensor & packed_ih, const Tensor & packed_hh, const Tensor & col_offsets_ih, const Tensor & col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh); +CAFFE2_API Tensor quantized_rnn_tanh_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh, const Tensor & packed_ih, const Tensor & packed_hh, const Tensor & col_offsets_ih, const Tensor & col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh); +CAFFE2_API std::tuple _pack_padded_sequence(const Tensor & input, const Tensor & lengths, bool batch_first); +CAFFE2_API Tensor _pack_padded_sequence_backward(const Tensor & grad, IntArrayRef input_size, const Tensor & batch_sizes, bool batch_first); +CAFFE2_API std::tuple _pad_packed_sequence(const Tensor & data, const Tensor & batch_sizes, bool batch_first, Scalar padding_value, int64_t total_length); +CAFFE2_API Tensor & set_(Tensor & self, Storage source); +CAFFE2_API Tensor & set_storage(Tensor & self, Storage source, int64_t storage_offset, IntArrayRef size, IntArrayRef stride={}); +CAFFE2_API Tensor & set_cpu_(Tensor & self); +CAFFE2_API Tensor & set_cuda_(Tensor & self); +CAFFE2_API Tensor & set_quantizer_(Tensor & self, ConstQuantizerPtr quantizer); +CAFFE2_API bool is_set_to(const Tensor & self, const Tensor & tensor); +CAFFE2_API Tensor & masked_fill__cpu(Tensor & self, const Tensor & mask, Scalar value); +CAFFE2_API Tensor & masked_fill__cuda(Tensor & self, const Tensor & mask, Scalar value); +CAFFE2_API Tensor masked_fill(const Tensor & self, const Tensor & mask, Scalar value); +CAFFE2_API Tensor & masked_fill__cpu(Tensor & self, const Tensor & mask, const Tensor & value); +CAFFE2_API Tensor & masked_fill__cuda(Tensor & self, const Tensor & mask, const Tensor & value); +CAFFE2_API Tensor masked_fill(const Tensor & self, const Tensor & mask, const Tensor & value); +CAFFE2_API Tensor & masked_scatter__cpu(Tensor & self, const Tensor & mask, const Tensor & source); +CAFFE2_API Tensor & masked_scatter__cuda(Tensor & self, const Tensor & mask, const Tensor & source); +CAFFE2_API Tensor masked_scatter(const Tensor & self, const Tensor & mask, const Tensor & source); +CAFFE2_API Tensor view(const Tensor & self, IntArrayRef size); +CAFFE2_API Tensor mkldnn_view(const Tensor & self, IntArrayRef size); +CAFFE2_API Tensor & index_add_cpu_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & source); +CAFFE2_API Tensor index_add(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & source); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor index_add(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & source); +#endif +CAFFE2_API Tensor index_fill(const Tensor & self, int64_t dim, const Tensor & index, Scalar value); +CAFFE2_API Tensor & index_fill_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & value); +CAFFE2_API Tensor index_fill(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & value); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & index_fill_(Tensor & self, Dimname dim, const Tensor & index, Scalar value); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & index_fill_(Tensor & self, Dimname dim, const Tensor & index, const Tensor & value); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor index_fill(const Tensor & self, Dimname dim, const Tensor & index, Scalar value); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor index_fill(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & value); +#endif +CAFFE2_API Tensor scatter(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & src); +CAFFE2_API Tensor scatter(const Tensor & self, int64_t dim, const Tensor & index, Scalar value); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor scatter(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & src); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor scatter(const Tensor & self, Dimname dim, const Tensor & index, Scalar value); +#endif +CAFFE2_API Tensor scatter_add(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & src); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor scatter_add(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & src); +#endif +CAFFE2_API Tensor & lt_(Tensor & self, Scalar other); +CAFFE2_API Tensor & lt_(Tensor & self, const Tensor & other); +CAFFE2_API Tensor & gt_(Tensor & self, Scalar other); +CAFFE2_API Tensor & gt_(Tensor & self, const Tensor & other); +CAFFE2_API Tensor & le_(Tensor & self, Scalar other); +CAFFE2_API Tensor & le_(Tensor & self, const Tensor & other); +CAFFE2_API Tensor & ge_(Tensor & self, Scalar other); +CAFFE2_API Tensor & ge_(Tensor & self, const Tensor & other); +CAFFE2_API Tensor & eq_(Tensor & self, Scalar other); +CAFFE2_API Tensor & eq_(Tensor & self, const Tensor & other); +CAFFE2_API Tensor & ne_(Tensor & self, Scalar other); +CAFFE2_API Tensor & ne_(Tensor & self, const Tensor & other); +CAFFE2_API Tensor & bitwise_xor_out(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & bitwise_xor_out(Tensor & out, const Tensor & self, Scalar other); +CAFFE2_API Tensor bitwise_xor(const Tensor & self, Scalar other); +CAFFE2_API Tensor bitwise_xor(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & bitwise_xor_(Tensor & self, Scalar other); +CAFFE2_API Tensor & bitwise_xor_(Tensor & self, const Tensor & other); +CAFFE2_API Tensor __xor__(const Tensor & self, Scalar other); +CAFFE2_API Tensor __xor__(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & __ixor__(Tensor & self, Scalar other); +CAFFE2_API Tensor & __ixor__(Tensor & self, const Tensor & other); +CAFFE2_API Tensor & _lgamma__cpu(Tensor & self); +CAFFE2_API Tensor & _lgamma__cuda(Tensor & self); +CAFFE2_API Tensor & atan2_(Tensor & self, const Tensor & other); +CAFFE2_API Tensor & tril_cpu_(Tensor & self, int64_t diagonal=0); +CAFFE2_API Tensor & tril_cuda_(Tensor & self, int64_t diagonal=0); +CAFFE2_API Tensor & triu_cpu_(Tensor & self, int64_t diagonal=0); +CAFFE2_API Tensor & triu_cuda_(Tensor & self, int64_t diagonal=0); +CAFFE2_API Tensor & digamma_(Tensor & self); +CAFFE2_API Tensor & polygamma_(Tensor & self, int64_t n); +CAFFE2_API Tensor & pow_(Tensor & self, Scalar exponent); +CAFFE2_API Tensor & pow_(Tensor & self, const Tensor & exponent); +CAFFE2_API Tensor & lerp_cpu_scalar_(Tensor & self, const Tensor & end, Scalar weight); +CAFFE2_API Tensor & lerp_cuda_scalar_(Tensor & self, const Tensor & end, Scalar weight); +CAFFE2_API Tensor & lerp_cpu_tensor_(Tensor & self, const Tensor & end, const Tensor & weight); +CAFFE2_API Tensor & lerp_cuda_tensor_(Tensor & self, const Tensor & end, const Tensor & weight); +CAFFE2_API Tensor & addcdiv_(Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1); +CAFFE2_API Tensor & clamped_random_cuda_(Tensor & self, int64_t from, int64_t to, Generator * generator=nullptr); +CAFFE2_API Tensor & capped_random_cuda_(Tensor & self, int64_t to, Generator * generator=nullptr); +CAFFE2_API Tensor & random_cuda_(Tensor & self, Generator * generator=nullptr); +CAFFE2_API Tensor & uniform_cuda_(Tensor & self, double from=0, double to=1, Generator * generator=nullptr); +CAFFE2_API Tensor & normal_cuda_(Tensor & self, double mean=0, double std=1, Generator * generator=nullptr); +CAFFE2_API Tensor & cauchy_cuda_(Tensor & self, double median=0, double sigma=1, Generator * generator=nullptr); +CAFFE2_API Tensor & log_normal_cuda_(Tensor & self, double mean=1, double std=2, Generator * generator=nullptr); +CAFFE2_API Tensor & exponential_cuda_(Tensor & self, double lambd=1, Generator * generator=nullptr); +CAFFE2_API Tensor & geometric_cuda_(Tensor & self, double p, Generator * generator=nullptr); +CAFFE2_API Tensor & cross_out(Tensor & out, const Tensor & self, const Tensor & other, c10::optional dim=c10::nullopt); +CAFFE2_API Tensor cross(const Tensor & self, const Tensor & other, c10::optional dim=c10::nullopt); +CAFFE2_API Tensor & triu_cpu_out(Tensor & out, const Tensor & self, int64_t diagonal=0); +CAFFE2_API Tensor & triu_cuda_out(Tensor & out, const Tensor & self, int64_t diagonal=0); +CAFFE2_API Tensor triu(const Tensor & self, int64_t diagonal=0); +CAFFE2_API Tensor & tril_cpu_out(Tensor & out, const Tensor & self, int64_t diagonal=0); +CAFFE2_API Tensor & tril_cuda_out(Tensor & out, const Tensor & self, int64_t diagonal=0); +CAFFE2_API Tensor tril(const Tensor & self, int64_t diagonal=0); +CAFFE2_API Tensor tril_indices_cpu(int64_t row, int64_t col, int64_t offset=0, const TensorOptions & options=at::kLong); +CAFFE2_API Tensor tril_indices_cuda(int64_t row, int64_t col, int64_t offset=0, const TensorOptions & options=at::kLong); +CAFFE2_API Tensor triu_indices_cpu(int64_t row, int64_t col, int64_t offset=0, const TensorOptions & options=at::kLong); +CAFFE2_API Tensor triu_indices_cuda(int64_t row, int64_t col, int64_t offset=0, const TensorOptions & options=at::kLong); +CAFFE2_API Tensor & ne_out(Tensor & out, const Tensor & self, Scalar other); +CAFFE2_API Tensor & ne_out_quantized_cpu(Tensor & out, const Tensor & self, Scalar other); +CAFFE2_API Tensor ne(const Tensor & self, Scalar other); +CAFFE2_API Tensor ne_quantized_cpu(const Tensor & self, Scalar other); +CAFFE2_API Tensor & ne_out(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & ne_out_quantized_cpu(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor ne(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor ne_quantized_cpu(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & eq_out(Tensor & out, const Tensor & self, Scalar other); +CAFFE2_API Tensor & eq_out_quantized_cpu(Tensor & out, const Tensor & self, Scalar other); +CAFFE2_API Tensor eq(const Tensor & self, Scalar other); +CAFFE2_API Tensor eq_quantized_cpu(const Tensor & self, Scalar other); +CAFFE2_API Tensor & eq_out(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & eq_out_quantized_cpu(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor eq(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor eq_quantized_cpu(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & ge_out(Tensor & out, const Tensor & self, Scalar other); +CAFFE2_API Tensor & ge_out_quantized_cpu(Tensor & out, const Tensor & self, Scalar other); +CAFFE2_API Tensor ge(const Tensor & self, Scalar other); +CAFFE2_API Tensor ge_quantized_cpu(const Tensor & self, Scalar other); +CAFFE2_API Tensor & ge_out(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & ge_out_quantized_cpu(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor ge(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor ge_quantized_cpu(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & le_out(Tensor & out, const Tensor & self, Scalar other); +CAFFE2_API Tensor & le_out_quantized_cpu(Tensor & out, const Tensor & self, Scalar other); +CAFFE2_API Tensor le(const Tensor & self, Scalar other); +CAFFE2_API Tensor le_quantized_cpu(const Tensor & self, Scalar other); +CAFFE2_API Tensor & le_out(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & le_out_quantized_cpu(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor le(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor le_quantized_cpu(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & gt_out(Tensor & out, const Tensor & self, Scalar other); +CAFFE2_API Tensor & gt_out_quantized_cpu(Tensor & out, const Tensor & self, Scalar other); +CAFFE2_API Tensor gt(const Tensor & self, Scalar other); +CAFFE2_API Tensor gt_quantized_cpu(const Tensor & self, Scalar other); +CAFFE2_API Tensor & gt_out(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & gt_out_quantized_cpu(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor gt(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor gt_quantized_cpu(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & lt_out(Tensor & out, const Tensor & self, Scalar other); +CAFFE2_API Tensor & lt_out_quantized_cpu(Tensor & out, const Tensor & self, Scalar other); +CAFFE2_API Tensor lt(const Tensor & self, Scalar other); +CAFFE2_API Tensor lt_quantized_cpu(const Tensor & self, Scalar other); +CAFFE2_API Tensor & lt_out(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & lt_out_quantized_cpu(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor lt(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor lt_quantized_cpu(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor index_select_sparse(const Tensor & self, int64_t dim, const Tensor & index); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & index_select_out(Tensor & out, const Tensor & self, Dimname dim, const Tensor & index); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor index_select(const Tensor & self, Dimname dim, const Tensor & index); +#endif +CAFFE2_API Tensor & masked_select_out_cpu(Tensor & out, const Tensor & self, const Tensor & mask); +CAFFE2_API Tensor & masked_select_out_cuda(Tensor & out, const Tensor & self, const Tensor & mask); +CAFFE2_API Tensor masked_select_cpu(const Tensor & self, const Tensor & mask); +CAFFE2_API Tensor masked_select_cuda(const Tensor & self, const Tensor & mask); +CAFFE2_API std::vector nonzero_numpy(const Tensor & self); +CAFFE2_API Tensor & gather_out_cpu(Tensor & out, const Tensor & self, int64_t dim, const Tensor & index, bool sparse_grad=false); +CAFFE2_API Tensor & gather_out_cuda(Tensor & out, const Tensor & self, int64_t dim, const Tensor & index, bool sparse_grad=false); +CAFFE2_API Tensor gather_cpu(const Tensor & self, int64_t dim, const Tensor & index, bool sparse_grad=false); +CAFFE2_API Tensor gather_cuda(const Tensor & self, int64_t dim, const Tensor & index, bool sparse_grad=false); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor & gather_out(Tensor & out, const Tensor & self, Dimname dim, const Tensor & index, bool sparse_grad=false); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor gather(const Tensor & self, Dimname dim, const Tensor & index, bool sparse_grad=false); +#endif +CAFFE2_API Tensor _gather_sparse_backward(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & grad); +CAFFE2_API Tensor & addcmul_out(Tensor & out, const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1); +CAFFE2_API Tensor addcmul(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1); +CAFFE2_API Tensor & addcmul_(Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1); +CAFFE2_API Tensor & addcdiv_out(Tensor & out, const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1); +CAFFE2_API Tensor addcdiv(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1); +CAFFE2_API std::tuple triangular_solve_out(Tensor & X, Tensor & M, const Tensor & self, const Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false); +CAFFE2_API std::tuple triangular_solve(const Tensor & self, const Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false); +CAFFE2_API std::tuple _triangular_solve_helper_cpu(const Tensor & self, const Tensor & A, bool upper, bool transpose, bool unitriangular); +CAFFE2_API std::tuple _triangular_solve_helper_cuda(const Tensor & self, const Tensor & A, bool upper, bool transpose, bool unitriangular); +CAFFE2_API std::tuple symeig_out(Tensor & e, Tensor & V, const Tensor & self, bool eigenvectors=false, bool upper=true); +CAFFE2_API std::tuple symeig(const Tensor & self, bool eigenvectors=false, bool upper=true); +CAFFE2_API std::tuple _symeig_helper_cpu(const Tensor & self, bool eigenvectors, bool upper); +CAFFE2_API std::tuple _symeig_helper_cuda(const Tensor & self, bool eigenvectors, bool upper); +CAFFE2_API std::tuple svd_out(Tensor & U, Tensor & S, Tensor & V, const Tensor & self, bool some=true, bool compute_uv=true); +CAFFE2_API std::tuple svd(const Tensor & self, bool some=true, bool compute_uv=true); +CAFFE2_API std::tuple _svd_helper_cpu(const Tensor & self, bool some, bool compute_uv); +CAFFE2_API std::tuple _svd_helper_cuda(const Tensor & self, bool some, bool compute_uv); +CAFFE2_API Tensor & cholesky_out(Tensor & out, const Tensor & self, bool upper=false); +CAFFE2_API Tensor cholesky(const Tensor & self, bool upper=false); +CAFFE2_API Tensor _cholesky_helper_cpu(const Tensor & self, bool upper); +CAFFE2_API Tensor _cholesky_helper_cuda(const Tensor & self, bool upper); +CAFFE2_API Tensor & cholesky_solve_out(Tensor & out, const Tensor & self, const Tensor & input2, bool upper=false); +CAFFE2_API Tensor cholesky_solve(const Tensor & self, const Tensor & input2, bool upper=false); +CAFFE2_API Tensor _cholesky_solve_helper_cpu(const Tensor & self, const Tensor & A, bool upper); +CAFFE2_API Tensor _cholesky_solve_helper_cuda(const Tensor & self, const Tensor & A, bool upper); +CAFFE2_API std::tuple solve(const Tensor & self, const Tensor & A); +CAFFE2_API std::tuple solve_out(Tensor & solution, Tensor & lu, const Tensor & self, const Tensor & A); +CAFFE2_API std::tuple _solve_helper_cpu(const Tensor & self, const Tensor & A); +CAFFE2_API std::tuple _solve_helper_cuda(const Tensor & self, const Tensor & A); +CAFFE2_API std::tuple qr_out(Tensor & Q, Tensor & R, const Tensor & self, bool some=true); +CAFFE2_API std::tuple qr(const Tensor & self, bool some=true); +CAFFE2_API std::tuple _qr_helper_cpu(const Tensor & self, bool some); +CAFFE2_API std::tuple _qr_helper_cuda(const Tensor & self, bool some); +CAFFE2_API std::tuple _lu_with_info_cpu(const Tensor & self, bool pivot=true, bool check_errors=true); +CAFFE2_API std::tuple _lu_with_info_cuda(const Tensor & self, bool pivot=true, bool check_errors=true); +CAFFE2_API Tensor & lu_solve_out(Tensor & out, const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots); +CAFFE2_API Tensor lu_solve(const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots); +CAFFE2_API Tensor _lu_solve_helper_cpu(const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots); +CAFFE2_API Tensor _lu_solve_helper_cuda(const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots); +CAFFE2_API Tensor & multinomial_out(Tensor & out, const Tensor & self, int64_t num_samples, bool replacement=false, Generator * generator=nullptr); +CAFFE2_API Tensor multinomial(const Tensor & self, int64_t num_samples, bool replacement=false, Generator * generator=nullptr); +CAFFE2_API Tensor & _lgamma_out_cpu(Tensor & out, const Tensor & self); +CAFFE2_API Tensor & _lgamma_out_cuda(Tensor & out, const Tensor & self); +CAFFE2_API Tensor lgamma(const Tensor & self); +CAFFE2_API Tensor & digamma_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor digamma(const Tensor & self); +CAFFE2_API Tensor & polygamma_out(Tensor & out, int64_t n, const Tensor & self); +CAFFE2_API Tensor polygamma(int64_t n, const Tensor & self); +CAFFE2_API Tensor erfinv(const Tensor & self); +CAFFE2_API Tensor & _erfinv__cpu(Tensor & self); +CAFFE2_API Tensor & _erfinv__cuda(Tensor & self); +CAFFE2_API Tensor & _erfinv_out_cpu(Tensor & out, const Tensor & self); +CAFFE2_API Tensor & _erfinv_out_cuda(Tensor & out, const Tensor & self); +CAFFE2_API Tensor sign(const Tensor & self); +CAFFE2_API Tensor & sign_(Tensor & self); +CAFFE2_API Tensor & sign_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor & atan2_out(Tensor & out, const Tensor & self, const Tensor & other); +CAFFE2_API Tensor atan2(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & lerp_cpu_scalar_out(Tensor & out, const Tensor & self, const Tensor & end, Scalar weight); +CAFFE2_API Tensor & lerp_cuda_scalar_out(Tensor & out, const Tensor & self, const Tensor & end, Scalar weight); +CAFFE2_API Tensor & lerp_cpu_tensor_out(Tensor & out, const Tensor & self, const Tensor & end, const Tensor & weight); +CAFFE2_API Tensor & lerp_cuda_tensor_out(Tensor & out, const Tensor & self, const Tensor & end, const Tensor & weight); +CAFFE2_API Tensor lerp_cpu_scalar(const Tensor & self, const Tensor & end, Scalar weight); +CAFFE2_API Tensor lerp_cuda_scalar(const Tensor & self, const Tensor & end, Scalar weight); +CAFFE2_API Tensor lerp_cpu_tensor(const Tensor & self, const Tensor & end, const Tensor & weight); +CAFFE2_API Tensor lerp_cuda_tensor(const Tensor & self, const Tensor & end, const Tensor & weight); +CAFFE2_API Tensor & _histc_out_cuda(Tensor & out, const Tensor & self, int64_t bins=100, Scalar min=0, Scalar max=0); +CAFFE2_API Tensor _histc_cuda(const Tensor & self, int64_t bins=100, Scalar min=0, Scalar max=0); +CAFFE2_API Tensor min_quant(const Tensor & self); +CAFFE2_API Tensor max_quant(const Tensor & self); +CAFFE2_API Tensor median_cpu(const Tensor & self); +CAFFE2_API Tensor median_cuda(const Tensor & self); +CAFFE2_API std::tuple sort_quant(const Tensor & self, int64_t dim=-1, bool descending=false); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API std::tuple sort_out(Tensor & values, Tensor & indices, const Tensor & self, Dimname dim, bool descending=false); +#endif +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API std::tuple sort(const Tensor & self, Dimname dim, bool descending=false); +#endif +CAFFE2_API Tensor argsort(const Tensor & self, int64_t dim=-1, bool descending=false); +#ifdef BUILD_NAMEDTENSOR +CAFFE2_API Tensor argsort(const Tensor & self, Dimname dim, bool descending=false); +#endif +CAFFE2_API std::tuple topk_out_cpu(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true); +CAFFE2_API std::tuple topk(const Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true); +CAFFE2_API std::tuple quantized_topk_cpu(const Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true); +CAFFE2_API Tensor all(const Tensor & self); +CAFFE2_API Tensor any(const Tensor & self); +CAFFE2_API Tensor unfold(const Tensor & self, int64_t dimension, int64_t size, int64_t step); +CAFFE2_API bool quantized_equal(const Tensor & self, const Tensor & other); +CAFFE2_API Tensor & pow_out(Tensor & out, const Tensor & self, const Tensor & exponent); +CAFFE2_API Tensor pow(const Tensor & self, const Tensor & exponent); +CAFFE2_API Tensor & pow_out(Tensor & out, Scalar self, const Tensor & exponent); +CAFFE2_API Tensor pow(Scalar self, const Tensor & exponent); +CAFFE2_API Tensor & normal_out_cuda(Tensor & out, const Tensor & mean, double std=1, Generator * generator=nullptr); +CAFFE2_API Tensor normal_cuda(const Tensor & mean, double std=1, Generator * generator=nullptr); +CAFFE2_API Tensor & normal_out_cuda(Tensor & out, double mean, const Tensor & std, Generator * generator=nullptr); +CAFFE2_API Tensor normal_cuda(double mean, const Tensor & std, Generator * generator=nullptr); +CAFFE2_API Tensor & normal_out_cuda(Tensor & out, const Tensor & mean, const Tensor & std, Generator * generator=nullptr); +CAFFE2_API Tensor normal_cuda(const Tensor & mean, const Tensor & std, Generator * generator=nullptr); +CAFFE2_API Tensor normal(double mean, double std, IntArrayRef size, Generator * generator=nullptr, const TensorOptions & options={}); +CAFFE2_API Tensor & normal_out(Tensor & out, double mean, double std, IntArrayRef size, Generator * generator=nullptr); +CAFFE2_API Tensor alias(const Tensor & self); +CAFFE2_API Tensor & mse_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +CAFFE2_API Tensor mse_loss(const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +CAFFE2_API Tensor & mse_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); +CAFFE2_API Tensor mse_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); +CAFFE2_API Tensor & l1_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +CAFFE2_API Tensor l1_loss(const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +CAFFE2_API Tensor & l1_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); +CAFFE2_API Tensor l1_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); +CAFFE2_API Tensor & multi_margin_loss_cpu_out(Tensor & out, const Tensor & self, const Tensor & target, Scalar p=1, Scalar margin=1, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean); +CAFFE2_API Tensor multi_margin_loss_cpu(const Tensor & self, const Tensor & target, Scalar p=1, Scalar margin=1, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean); +CAFFE2_API Tensor & multi_margin_loss_cpu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean); +CAFFE2_API Tensor multi_margin_loss_cpu_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean); +CAFFE2_API Tensor & multilabel_margin_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +CAFFE2_API Tensor multilabel_margin_loss(const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +CAFFE2_API std::tuple multilabel_margin_loss_forward_out_cpu(Tensor & output, Tensor & is_target, const Tensor & self, const Tensor & target, int64_t reduction); +CAFFE2_API std::tuple multilabel_margin_loss_forward_cpu(const Tensor & self, const Tensor & target, int64_t reduction); +CAFFE2_API Tensor & multilabel_margin_loss_backward_cpu_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction, const Tensor & is_target); +CAFFE2_API Tensor multilabel_margin_loss_backward_cpu(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction, const Tensor & is_target); +CAFFE2_API Tensor & nll_loss_out(Tensor & out, const Tensor & self, const Tensor & target, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100); +CAFFE2_API Tensor nll_loss(const Tensor & self, const Tensor & target, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100); +CAFFE2_API std::tuple nll_loss_forward_out_cpu(Tensor & output, Tensor & total_weight, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); +CAFFE2_API std::tuple nll_loss_forward_cpu(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); +CAFFE2_API Tensor & nll_loss_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); +CAFFE2_API Tensor nll_loss_backward_cpu(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); +CAFFE2_API Tensor & nll_loss2d_out(Tensor & out, const Tensor & self, const Tensor & target, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100); +CAFFE2_API Tensor nll_loss2d(const Tensor & self, const Tensor & target, const Tensor & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100); +CAFFE2_API std::tuple nll_loss2d_forward_out_cpu(Tensor & output, Tensor & total_weight, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); +CAFFE2_API std::tuple nll_loss2d_forward_cpu(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); +CAFFE2_API Tensor & nll_loss2d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); +CAFFE2_API Tensor nll_loss2d_backward_cpu(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index, const Tensor & total_weight); +CAFFE2_API Tensor & smooth_l1_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +CAFFE2_API Tensor smooth_l1_loss(const Tensor & self, const Tensor & target, int64_t reduction=at::Reduction::Mean); +CAFFE2_API Tensor & smooth_l1_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); +CAFFE2_API Tensor smooth_l1_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); +CAFFE2_API Tensor & log_sigmoid_out(Tensor & out, const Tensor & self); +CAFFE2_API Tensor log_sigmoid(const Tensor & self); +CAFFE2_API Tensor & adaptive_avg_pool2d_out_cpu(Tensor & out, const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor & adaptive_avg_pool2d_out_cuda(Tensor & out, const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor & mkldnn_adaptive_avg_pool2d_out(Tensor & out, const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor adaptive_avg_pool2d(const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor mkldnn_adaptive_avg_pool2d(const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor adaptive_avg_pool2d_cpu(const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor adaptive_avg_pool2d_cuda(const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor quantized_adaptive_avg_pool2d(const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor adaptive_avg_pool2d_backward_cpu(const Tensor & grad_output, const Tensor & self); +CAFFE2_API Tensor adaptive_avg_pool2d_backward_cuda(const Tensor & grad_output, const Tensor & self); +CAFFE2_API Tensor & adaptive_avg_pool3d_out_cpu(Tensor & out, const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor & adaptive_avg_pool3d_out_cuda(Tensor & out, const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor adaptive_avg_pool3d_cpu(const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor adaptive_avg_pool3d_cuda(const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor & adaptive_avg_pool3d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, const Tensor & self); +CAFFE2_API Tensor & adaptive_avg_pool3d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, const Tensor & self); +CAFFE2_API Tensor adaptive_avg_pool3d_backward_cpu(const Tensor & grad_output, const Tensor & self); +CAFFE2_API Tensor adaptive_avg_pool3d_backward_cuda(const Tensor & grad_output, const Tensor & self); +CAFFE2_API std::tuple adaptive_max_pool2d_out_cpu(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef output_size); +CAFFE2_API std::tuple adaptive_max_pool2d_out_cuda(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef output_size); +CAFFE2_API std::tuple adaptive_max_pool2d_cpu(const Tensor & self, IntArrayRef output_size); +CAFFE2_API std::tuple adaptive_max_pool2d_cuda(const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor & adaptive_max_pool2d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices); +CAFFE2_API Tensor & adaptive_max_pool2d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices); +CAFFE2_API Tensor adaptive_max_pool2d_backward_cpu(const Tensor & grad_output, const Tensor & self, const Tensor & indices); +CAFFE2_API Tensor adaptive_max_pool2d_backward_cuda(const Tensor & grad_output, const Tensor & self, const Tensor & indices); +CAFFE2_API std::tuple adaptive_max_pool3d_out_cpu(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef output_size); +CAFFE2_API std::tuple adaptive_max_pool3d_out_cuda(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef output_size); +CAFFE2_API std::tuple adaptive_max_pool3d_cpu(const Tensor & self, IntArrayRef output_size); +CAFFE2_API std::tuple adaptive_max_pool3d_cuda(const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor & adaptive_max_pool3d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices); +CAFFE2_API Tensor & adaptive_max_pool3d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices); +CAFFE2_API Tensor adaptive_max_pool3d_backward_cpu(const Tensor & grad_output, const Tensor & self, const Tensor & indices); +CAFFE2_API Tensor adaptive_max_pool3d_backward_cuda(const Tensor & grad_output, const Tensor & self, const Tensor & indices); +CAFFE2_API Tensor & avg_pool2d_out_cpu(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, c10::optional divisor_override=c10::nullopt); +CAFFE2_API Tensor & avg_pool2d_out_cuda(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, c10::optional divisor_override=c10::nullopt); +CAFFE2_API Tensor & mkldnn_avg_pool2d_out(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, c10::optional divisor_override=c10::nullopt); +CAFFE2_API Tensor avg_pool2d_cpu(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, c10::optional divisor_override=c10::nullopt); +CAFFE2_API Tensor avg_pool2d_cuda(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, c10::optional divisor_override=c10::nullopt); +CAFFE2_API Tensor mkldnn_avg_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, c10::optional divisor_override=c10::nullopt); +CAFFE2_API Tensor quantized_avg_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, c10::optional divisor_override=c10::nullopt); +CAFFE2_API Tensor & avg_pool2d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); +CAFFE2_API Tensor & avg_pool2d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); +CAFFE2_API Tensor avg_pool2d_backward_cpu(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); +CAFFE2_API Tensor avg_pool2d_backward_cuda(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); +CAFFE2_API Tensor & avg_pool3d_out_cpu(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, c10::optional divisor_override=c10::nullopt); +CAFFE2_API Tensor & avg_pool3d_out_cuda(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, c10::optional divisor_override=c10::nullopt); +CAFFE2_API Tensor avg_pool3d_cpu(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, c10::optional divisor_override=c10::nullopt); +CAFFE2_API Tensor avg_pool3d_cuda(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, c10::optional divisor_override=c10::nullopt); +CAFFE2_API Tensor & avg_pool3d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); +CAFFE2_API Tensor & avg_pool3d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); +CAFFE2_API Tensor avg_pool3d_backward_cpu(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); +CAFFE2_API Tensor avg_pool3d_backward_cuda(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); +CAFFE2_API std::tuple fractional_max_pool2d_out_cpu(Tensor & output, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); +CAFFE2_API std::tuple fractional_max_pool2d_out_cuda(Tensor & output, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); +CAFFE2_API std::tuple fractional_max_pool2d_cpu(const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); +CAFFE2_API std::tuple fractional_max_pool2d_cuda(const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); +CAFFE2_API Tensor & fractional_max_pool2d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); +CAFFE2_API Tensor & fractional_max_pool2d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); +CAFFE2_API Tensor fractional_max_pool2d_backward_cpu(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); +CAFFE2_API Tensor fractional_max_pool2d_backward_cuda(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); +CAFFE2_API std::tuple fractional_max_pool3d_out_cpu(Tensor & output, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); +CAFFE2_API std::tuple fractional_max_pool3d_out_cuda(Tensor & output, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); +CAFFE2_API std::tuple fractional_max_pool3d_cpu(const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); +CAFFE2_API std::tuple fractional_max_pool3d_cuda(const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & random_samples); +CAFFE2_API Tensor & fractional_max_pool3d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); +CAFFE2_API Tensor & fractional_max_pool3d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); +CAFFE2_API Tensor fractional_max_pool3d_backward_cpu(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); +CAFFE2_API Tensor fractional_max_pool3d_backward_cuda(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor & indices); +CAFFE2_API std::tuple max_pool2d_with_indices_out_cpu(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +CAFFE2_API std::tuple max_pool2d_with_indices_out_cuda(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +CAFFE2_API std::tuple max_pool2d_with_indices_cpu(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +CAFFE2_API std::tuple max_pool2d_with_indices_cuda(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +CAFFE2_API Tensor & max_pool2d_with_indices_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); +CAFFE2_API Tensor & max_pool2d_with_indices_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); +CAFFE2_API Tensor max_pool2d_with_indices_backward_cpu(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); +CAFFE2_API Tensor max_pool2d_with_indices_backward_cuda(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); +CAFFE2_API std::tuple max_pool3d_with_indices_out_cpu(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +CAFFE2_API std::tuple max_pool3d_with_indices_out_cuda(Tensor & out, Tensor & indices, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +CAFFE2_API std::tuple max_pool3d_with_indices_cpu(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +CAFFE2_API std::tuple max_pool3d_with_indices_cuda(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride={}, IntArrayRef padding=0, IntArrayRef dilation=1, bool ceil_mode=false); +CAFFE2_API Tensor & max_pool3d_with_indices_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); +CAFFE2_API Tensor & max_pool3d_with_indices_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); +CAFFE2_API Tensor max_pool3d_with_indices_backward_cpu(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); +CAFFE2_API Tensor max_pool3d_with_indices_backward_cuda(const Tensor & grad_output, const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, const Tensor & indices); +CAFFE2_API Tensor & max_unpooling2d_forward_out_cpu(Tensor & out, const Tensor & self, const Tensor & indices, IntArrayRef output_size); +CAFFE2_API Tensor & max_unpooling2d_forward_out_cuda(Tensor & out, const Tensor & self, const Tensor & indices, IntArrayRef output_size); +CAFFE2_API Tensor max_unpooling2d_forward_cpu(const Tensor & self, const Tensor & indices, IntArrayRef output_size); +CAFFE2_API Tensor max_unpooling2d_forward_cuda(const Tensor & self, const Tensor & indices, IntArrayRef output_size); +CAFFE2_API Tensor & max_unpooling2d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size); +CAFFE2_API Tensor & max_unpooling2d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size); +CAFFE2_API Tensor max_unpooling2d_backward_cpu(const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size); +CAFFE2_API Tensor max_unpooling2d_backward_cuda(const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size); +CAFFE2_API Tensor & max_unpooling3d_forward_out_cpu(Tensor & out, const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); +CAFFE2_API Tensor & max_unpooling3d_forward_out_cuda(Tensor & out, const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); +CAFFE2_API Tensor max_unpooling3d_forward_cpu(const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); +CAFFE2_API Tensor max_unpooling3d_forward_cuda(const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); +CAFFE2_API Tensor & max_unpooling3d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); +CAFFE2_API Tensor & max_unpooling3d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); +CAFFE2_API Tensor max_unpooling3d_backward_cpu(const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); +CAFFE2_API Tensor max_unpooling3d_backward_cuda(const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding); +CAFFE2_API Tensor & reflection_pad1d_out_cpu(Tensor & out, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor & reflection_pad1d_out_cuda(Tensor & out, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor reflection_pad1d_cpu(const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor reflection_pad1d_cuda(const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor & reflection_pad1d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor & reflection_pad1d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor reflection_pad1d_backward_cpu(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor reflection_pad1d_backward_cuda(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor & reflection_pad2d_out_cpu(Tensor & out, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor & reflection_pad2d_out_cuda(Tensor & out, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor reflection_pad2d_cpu(const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor reflection_pad2d_cuda(const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor & reflection_pad2d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor & reflection_pad2d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor reflection_pad2d_backward_cpu(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor reflection_pad2d_backward_cuda(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor & replication_pad1d_out_cpu(Tensor & out, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor & replication_pad1d_out_cuda(Tensor & out, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor replication_pad1d_cpu(const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor replication_pad1d_cuda(const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor & replication_pad1d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor & replication_pad1d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor replication_pad1d_backward_cpu(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor replication_pad1d_backward_cuda(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor & replication_pad2d_out_cpu(Tensor & out, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor & replication_pad2d_out_cuda(Tensor & out, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor replication_pad2d_cpu(const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor replication_pad2d_cuda(const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor & replication_pad2d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor & replication_pad2d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor replication_pad2d_backward_cpu(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor replication_pad2d_backward_cuda(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor & replication_pad3d_out_cpu(Tensor & out, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor & replication_pad3d_out_cuda(Tensor & out, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor replication_pad3d_cpu(const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor replication_pad3d_cuda(const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor & replication_pad3d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor & replication_pad3d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor replication_pad3d_backward_cpu(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor replication_pad3d_backward_cuda(const Tensor & grad_output, const Tensor & self, IntArrayRef padding); +CAFFE2_API Tensor _test_optional_float(const Tensor & self, c10::optional scale=c10::nullopt); +CAFFE2_API Tensor & upsample_linear1d_out_cpu(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); +CAFFE2_API Tensor & upsample_linear1d_out_cuda(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); +CAFFE2_API Tensor upsample_linear1d_cpu(const Tensor & self, IntArrayRef output_size, bool align_corners); +CAFFE2_API Tensor upsample_linear1d_cuda(const Tensor & self, IntArrayRef output_size, bool align_corners); +CAFFE2_API Tensor & upsample_linear1d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +CAFFE2_API Tensor & upsample_linear1d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +CAFFE2_API Tensor upsample_linear1d_backward_cpu(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +CAFFE2_API Tensor upsample_linear1d_backward_cuda(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +CAFFE2_API Tensor & upsample_bilinear2d_out_cpu(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); +CAFFE2_API Tensor & upsample_bilinear2d_out_cuda(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); +CAFFE2_API Tensor upsample_bilinear2d_cpu(const Tensor & self, IntArrayRef output_size, bool align_corners); +CAFFE2_API Tensor upsample_bilinear2d_cuda(const Tensor & self, IntArrayRef output_size, bool align_corners); +CAFFE2_API Tensor quantized_upsample_bilinear2d_cpu(const Tensor & self, IntArrayRef output_size, bool align_corners); +CAFFE2_API Tensor & upsample_bilinear2d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +CAFFE2_API Tensor & upsample_bilinear2d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +CAFFE2_API Tensor upsample_bilinear2d_backward_cpu(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +CAFFE2_API Tensor upsample_bilinear2d_backward_cuda(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +CAFFE2_API Tensor & upsample_bicubic2d_out_cpu(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); +CAFFE2_API Tensor & upsample_bicubic2d_out_cuda(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); +CAFFE2_API Tensor upsample_bicubic2d_cpu(const Tensor & self, IntArrayRef output_size, bool align_corners); +CAFFE2_API Tensor upsample_bicubic2d_cuda(const Tensor & self, IntArrayRef output_size, bool align_corners); +CAFFE2_API Tensor & upsample_bicubic2d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +CAFFE2_API Tensor & upsample_bicubic2d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +CAFFE2_API Tensor upsample_bicubic2d_backward_cpu(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +CAFFE2_API Tensor upsample_bicubic2d_backward_cuda(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +CAFFE2_API Tensor & upsample_trilinear3d_out_cpu(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); +CAFFE2_API Tensor & upsample_trilinear3d_out_cuda(Tensor & out, const Tensor & self, IntArrayRef output_size, bool align_corners); +CAFFE2_API Tensor upsample_trilinear3d_cpu(const Tensor & self, IntArrayRef output_size, bool align_corners); +CAFFE2_API Tensor upsample_trilinear3d_cuda(const Tensor & self, IntArrayRef output_size, bool align_corners); +CAFFE2_API Tensor & upsample_trilinear3d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +CAFFE2_API Tensor & upsample_trilinear3d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +CAFFE2_API Tensor upsample_trilinear3d_backward_cpu(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +CAFFE2_API Tensor upsample_trilinear3d_backward_cuda(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size, bool align_corners); +CAFFE2_API Tensor & upsample_nearest1d_out_cpu(Tensor & out, const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor & upsample_nearest1d_out_cuda(Tensor & out, const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor upsample_nearest1d_cpu(const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor upsample_nearest1d_cuda(const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor & upsample_nearest1d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); +CAFFE2_API Tensor & upsample_nearest1d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); +CAFFE2_API Tensor upsample_nearest1d_backward_cpu(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); +CAFFE2_API Tensor upsample_nearest1d_backward_cuda(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); +CAFFE2_API Tensor & upsample_nearest2d_out_cpu(Tensor & out, const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor & upsample_nearest2d_out_cuda(Tensor & out, const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor upsample_nearest2d_cpu(const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor upsample_nearest2d_cuda(const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor quantized_upsample_nearest2d_cpu(const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor & upsample_nearest2d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); +CAFFE2_API Tensor & upsample_nearest2d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); +CAFFE2_API Tensor upsample_nearest2d_backward_cpu(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); +CAFFE2_API Tensor upsample_nearest2d_backward_cuda(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); +CAFFE2_API Tensor & upsample_nearest3d_out_cpu(Tensor & out, const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor & upsample_nearest3d_out_cuda(Tensor & out, const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor upsample_nearest3d_cpu(const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor upsample_nearest3d_cuda(const Tensor & self, IntArrayRef output_size); +CAFFE2_API Tensor & upsample_nearest3d_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); +CAFFE2_API Tensor & upsample_nearest3d_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); +CAFFE2_API Tensor upsample_nearest3d_backward_cpu(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); +CAFFE2_API Tensor upsample_nearest3d_backward_cuda(const Tensor & grad_output, IntArrayRef output_size, IntArrayRef input_size); +CAFFE2_API Tensor & sigmoid_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & output); +CAFFE2_API Tensor sigmoid_backward(const Tensor & grad_output, const Tensor & output); +CAFFE2_API Tensor & slow_conv_transpose2d_out_cpu(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef output_padding=0, IntArrayRef dilation=1); +CAFFE2_API Tensor & slow_conv_transpose2d_out_cuda(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef output_padding=0, IntArrayRef dilation=1); +CAFFE2_API Tensor slow_conv_transpose2d_cpu(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef output_padding=0, IntArrayRef dilation=1); +CAFFE2_API Tensor slow_conv_transpose2d_cuda(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef output_padding=0, IntArrayRef dilation=1); +CAFFE2_API std::tuple slow_conv_transpose2d_backward_out_cpu(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & columns, const Tensor & ones); +CAFFE2_API std::tuple slow_conv_transpose2d_backward_out_cuda(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & columns, const Tensor & ones); +CAFFE2_API std::tuple slow_conv_transpose2d_backward_cpu(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & columns, const Tensor & ones, std::array output_mask); +CAFFE2_API std::tuple slow_conv_transpose2d_backward_cuda(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & columns, const Tensor & ones, std::array output_mask); +CAFFE2_API Tensor & slow_conv_transpose3d_out_cpu(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef output_padding=0, IntArrayRef dilation=1); +CAFFE2_API Tensor & slow_conv_transpose3d_out_cuda(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef output_padding=0, IntArrayRef dilation=1); +CAFFE2_API Tensor slow_conv_transpose3d_cpu(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef output_padding=0, IntArrayRef dilation=1); +CAFFE2_API Tensor slow_conv_transpose3d_cuda(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef output_padding=0, IntArrayRef dilation=1); +CAFFE2_API std::tuple slow_conv_transpose3d_backward_out_cpu(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & finput, const Tensor & fgrad_input); +CAFFE2_API std::tuple slow_conv_transpose3d_backward_out_cuda(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & finput, const Tensor & fgrad_input); +CAFFE2_API std::tuple slow_conv_transpose3d_backward_cpu(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & finput, const Tensor & fgrad_input, std::array output_mask); +CAFFE2_API std::tuple slow_conv_transpose3d_backward_cuda(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, const Tensor & finput, const Tensor & fgrad_input, std::array output_mask); +CAFFE2_API Tensor & thnn_conv2d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0); +CAFFE2_API Tensor thnn_conv2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0); +CAFFE2_API std::tuple slow_conv2d_forward_out_cpu(Tensor & output, Tensor & finput, Tensor & fgrad_input, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); +CAFFE2_API std::tuple slow_conv2d_forward_cpu(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); +CAFFE2_API std::tuple slow_conv2d_backward_out_cpu(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & finput, const Tensor & fgrad_input); +CAFFE2_API std::tuple slow_conv2d_backward_cpu(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & finput, const Tensor & fgrad_input, std::array output_mask); +CAFFE2_API Tensor & thnn_conv_depthwise2d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef dilation=1); +CAFFE2_API Tensor thnn_conv_depthwise2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef dilation=1); +CAFFE2_API Tensor & slow_conv3d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0); +CAFFE2_API Tensor slow_conv3d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0); +CAFFE2_API std::tuple slow_conv3d_forward_out_cpu(Tensor & output, Tensor & finput, Tensor & fgrad_input, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); +CAFFE2_API std::tuple slow_conv3d_forward_cpu(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); +CAFFE2_API std::tuple slow_conv3d_backward_out_cpu(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & finput, const Tensor & fgrad_input); +CAFFE2_API std::tuple slow_conv3d_backward_cpu(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & finput, const Tensor & fgrad_input, std::array output_mask); +CAFFE2_API Tensor slow_conv_dilated2d_cpu(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef dilation=1); +CAFFE2_API Tensor slow_conv_dilated2d_cuda(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef dilation=1); +CAFFE2_API std::tuple slow_conv_dilated2d_backward_cpu(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, std::array output_mask); +CAFFE2_API std::tuple slow_conv_dilated2d_backward_cuda(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, std::array output_mask); +CAFFE2_API Tensor slow_conv_dilated3d_cpu(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef dilation=1); +CAFFE2_API Tensor slow_conv_dilated3d_cuda(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias={}, IntArrayRef stride=1, IntArrayRef padding=0, IntArrayRef dilation=1); +CAFFE2_API std::tuple slow_conv_dilated3d_backward_cpu(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, std::array output_mask); +CAFFE2_API std::tuple slow_conv_dilated3d_backward_cuda(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, std::array output_mask); +CAFFE2_API Tensor & col2im_out_cpu(Tensor & out, const Tensor & self, IntArrayRef output_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +CAFFE2_API Tensor & col2im_out_cuda(Tensor & out, const Tensor & self, IntArrayRef output_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +CAFFE2_API Tensor col2im_cpu(const Tensor & self, IntArrayRef output_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +CAFFE2_API Tensor col2im_cuda(const Tensor & self, IntArrayRef output_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +CAFFE2_API Tensor & col2im_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +CAFFE2_API Tensor & col2im_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +CAFFE2_API Tensor col2im_backward_cpu(const Tensor & grad_output, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +CAFFE2_API Tensor col2im_backward_cuda(const Tensor & grad_output, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +CAFFE2_API Tensor & im2col_out_cpu(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +CAFFE2_API Tensor & im2col_out_cuda(Tensor & out, const Tensor & self, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +CAFFE2_API Tensor im2col_cpu(const Tensor & self, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +CAFFE2_API Tensor im2col_cuda(const Tensor & self, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +CAFFE2_API Tensor & im2col_backward_out_cpu(Tensor & grad_input, const Tensor & grad_output, IntArrayRef input_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +CAFFE2_API Tensor & im2col_backward_out_cuda(Tensor & grad_input, const Tensor & grad_output, IntArrayRef input_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +CAFFE2_API Tensor im2col_backward_cpu(const Tensor & grad_output, IntArrayRef input_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +CAFFE2_API Tensor im2col_backward_cuda(const Tensor & grad_output, IntArrayRef input_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride); +CAFFE2_API Tensor isfinite(const Tensor & self); + +} // namespace native +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/NumericUtils.h b/thirdparty/libtorch/include/ATen/NumericUtils.h new file mode 100644 index 0000000000..3b002ee8e7 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/NumericUtils.h @@ -0,0 +1,46 @@ +#pragma once + +#ifdef __HIPCC__ +#include +#endif + +#include +#include +#include +#include +#include +#include + +namespace at { + +// std::isnan isn't performant to use on integral types; it will +// (uselessly) convert to floating point and then do the test. +// This function is. + +template ::value, int>::type = 0> +inline C10_HOST_DEVICE bool _isnan(T val) { + return false; +} + +template ::value, int>::type = 0> +inline C10_HOST_DEVICE bool _isnan(T val) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return ::isnan(val); +#else + return std::isnan(val); +#endif +} + +template ::value, int>::type = 0> +inline bool _isnan(T val) { + return std::isnan(val.real()) || std::isnan(val.imag()); +} + +inline C10_HOST_DEVICE bool _isnan(at::BFloat16 val) { + return at::_isnan(float(val)); +} + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/OpaqueTensorImpl.h b/thirdparty/libtorch/include/ATen/OpaqueTensorImpl.h new file mode 100644 index 0000000000..dd467d9398 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/OpaqueTensorImpl.h @@ -0,0 +1,142 @@ +#pragma once + +#include +#include +#include + +namespace at { + +// An "Opaque" TensorImpl -- there are no strides and (for now) +// even data() is not supported (thus no pointer arithmetic). + +// NOTE: We could allow data() in the future, but would have to ensure pointer +// arithmetic code is properly guarded. +// +// NOTE: This does not support resize_ (and other metadata-changing ops) because of +// `shallow_copy_and_detach`. We would need to define an interface to "shallow copy" +// in order to add support. + +template +struct CAFFE2_API OpaqueTensorImpl : public TensorImpl { + // public constructor for now... + OpaqueTensorImpl(at::TensorTypeSet type_set, const caffe2::TypeMeta& data_type, c10::Device device, + OpaqueHandle opaque_handle, c10::IntArrayRef sizes) + : TensorImpl(type_set, data_type, device), + opaque_handle_(std::move(opaque_handle)) + { + sizes_ = sizes.vec(); + refresh_numel(); + } + + void release_resources() override { + TensorImpl::release_resources(); + opaque_handle_ = {}; + } + + IntArrayRef strides() const override { + AT_ERROR("opaque tensors do not have strides"); + } + + bool is_contiguous(c10::MemoryFormat memory_format=c10::MemoryFormat::Contiguous) const override { + AT_ERROR("opaque tensors do not have is_contiguous"); + } + + int64_t stride(int64_t d) const override { + AT_ERROR("opaque tensors do not have strides"); + } + + void resize_dim(int64_t ndim) override { + AT_ERROR("opaque tensors do not have resize_dim"); + } + + void set_size(int64_t dim, int64_t new_size) override { + AT_ERROR("opaque tensors do not have set_size"); + } + + void set_stride(int64_t dim, int64_t new_stride) override { + AT_ERROR("opaque tensors do not have set_stride"); + } + + void set_storage_offset(int64_t storage_offset) override { + AT_ERROR("opaque tensors do not have set_storage_offset"); + } + + TensorImpl* maybe_zero_dim(bool condition_when_zero_dim) override { + AT_ERROR("opaque tensors do not support maybe_zero_dim"); + } + + bool has_storage() const override { + return false; + } + + const Storage& storage() const override{ + AT_ERROR("opaque tensors do not have storage"); + } + + int64_t storage_offset() const override { + AT_ERROR("opaque tensors do not have storage"); + } + + /** + * Return a TensorImpl that is a shallow-copy of this TensorImpl. + * + * For usage of `version_counter` and `allow_tensor_metadata_change`, + * see NOTE [ TensorImpl Shallow-Copying ]. + */ + c10::intrusive_ptr shallow_copy_and_detach( + const c10::VariableVersion& version_counter, + bool allow_tensor_metadata_change) const override { + auto impl = c10::make_intrusive>( + type_set(), dtype(), device(), opaque_handle_, sizes_); + copy_tensor_metadata( + /*src_impl=*/this, + /*dest_impl=*/impl.get(), + /*version_counter=*/version_counter, + /*allow_tensor_metadata_change=*/allow_tensor_metadata_change); + impl->refresh_numel(); + return impl; + } + + /** + * Shallow-copies data from another TensorImpl into this TensorImpl. + * + * For why this function doesn't check this TensorImpl's `allow_tensor_metadata_change_`, + * see NOTE [ TensorImpl Shallow-Copying ]. + */ + void shallow_copy_from(const c10::intrusive_ptr& impl) override { + AT_ASSERT(has_compatible_shallow_copy_type(impl->type_set())); + auto opaque_impl = static_cast*>(impl.get()); + copy_tensor_metadata( + /*src_impl=*/opaque_impl, + /*dest_impl=*/this, + /*version_counter=*/version_counter(), + /*allow_tensor_metadata_change=*/allow_tensor_metadata_change()); + refresh_numel(); + } + + OpaqueHandle& unsafe_opaque_handle() { + return opaque_handle_; + } + +private: + OpaqueHandle opaque_handle_; + + /** + * Copy the tensor metadata fields (e.g. sizes / strides / storage pointer / storage_offset) + * from one TensorImpl to another TensorImpl. + * + * For usage of `version_counter` and `allow_tensor_metadata_change`, see NOTE [ TensorImpl Shallow-Copying ]. + */ + static void copy_tensor_metadata( + const OpaqueTensorImpl* src_opaque_impl, + OpaqueTensorImpl* dest_opaque_impl, + const c10::VariableVersion& version_counter, + bool allow_tensor_metadata_change) { + TensorImpl::copy_tensor_metadata(src_opaque_impl, dest_opaque_impl, version_counter, allow_tensor_metadata_change); + + // OpaqueTensorImpl-specific fields. + dest_opaque_impl->opaque_handle_ = src_opaque_impl->opaque_handle_; + } +}; + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/PTThreadPool.h b/thirdparty/libtorch/include/ATen/PTThreadPool.h new file mode 100644 index 0000000000..f5e8a1a182 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/PTThreadPool.h @@ -0,0 +1,19 @@ +#pragma once + +#include +#include + +namespace at { + +class CAFFE2_API PTThreadPool : public c10::ThreadPool { +public: + explicit PTThreadPool( + int pool_size, + int numa_node_id = -1) + : c10::ThreadPool(pool_size, numa_node_id, [](){ + c10::setThreadName("PTThreadPool"); + at::init_num_threads(); + }) {} +}; + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/Parallel.h b/thirdparty/libtorch/include/ATen/Parallel.h new file mode 100644 index 0000000000..eb30946ab2 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/Parallel.h @@ -0,0 +1,136 @@ +#pragma once +#include +#include + +namespace at { +namespace internal { +// This parameter is heuristically chosen to determine the minimum number of +// work that warrants parallelism. For example, when summing an array, it is +// deemed inefficient to parallelise over arrays shorter than 32768. Further, +// no parallel algorithm (such as parallel_reduce) should split work into +// smaller than GRAIN_SIZE chunks. +constexpr int64_t GRAIN_SIZE = 32768; +} // namespace internal + +inline int64_t divup(int64_t x, int64_t y) { + return (x + y - 1) / y; +} + +// Called during new thread initialization +CAFFE2_API void init_num_threads(); + +// Sets the number of threads to be used in parallel region +CAFFE2_API void set_num_threads(int); + +// Returns the number of threads used in parallel region +CAFFE2_API int get_num_threads(); + +// Returns the current thread number (starting from 0) +// in the current parallel region, or 0 in the sequential region +CAFFE2_API int get_thread_num(); + +// Checks whether the code runs in parallel region +CAFFE2_API bool in_parallel_region(); + +/* +parallel_for + +begin: index at which to start applying user function + +end: index at which to stop applying user function + +grain_size: number of elements per chunk. impacts the degree of parallelization + +f: user function applied in parallel to the chunks, signature: + void f(int64_t begin, int64_t end) + +Warning: parallel_for does NOT copy thread local +states from the current thread to the worker threads. +This means for example that Tensor operations CANNOT be used in the +body of your function, only data pointers. +*/ +template +inline void parallel_for( + const int64_t begin, + const int64_t end, + const int64_t grain_size, + const F& f); + +/* +parallel_reduce + +begin: index at which to start applying reduction + +end: index at which to stop applying reduction + +grain_size: number of elements per chunk. impacts number of elements in +intermediate results tensor and degree of parallelization. + +ident: identity for binary combination function sf. sf(ident, x) needs to return +x. + +f: function for reduction over a chunk. f needs to be of signature scalar_t +f(int64_t partial_begin, int64_t partial_end, scalar_t identifiy) + +sf: function to combine two partial results. sf needs to be of signature +scalar_t sf(scalar_t x, scalar_t y) + +For example, you might have a tensor of 10000 entires and want to sum together +all the elements. Parallel_reduce with a grain_size of 2500 will then allocate +an intermediate result tensor with 4 elements. Then it will execute the function +"f" you provide and pass the beginning and end index of these chunks, so +0-2499, 2500-4999, etc. and the combination identity. It will then write out +the result from each of these chunks into the intermediate result tensor. After +that it'll reduce the partial results from each chunk into a single number using +the combination function sf and the identity ident. For a total summation this +would be "+" and 0 respectively. This is similar to tbb's approach [1], where +you need to provide a function to accumulate a subrange, a function to combine +two partial results and an identity. + +Warning: parallel_reduce does NOT copy thread local +states from the current thread to the worker threads. +This means for example that Tensor operations CANNOT be used in the +body of your function, only data pointers. + +[1] https://software.intel.com/en-us/node/506154 +*/ +template +inline scalar_t parallel_reduce( + const int64_t begin, + const int64_t end, + const int64_t grain_size, + const scalar_t ident, + const F& f, + const SF& sf); + +// Returns a detailed string describing parallelization settings +CAFFE2_API std::string get_parallel_info(); + +// Sets number of threads used for inter-op parallelism +CAFFE2_API void set_num_interop_threads(int); + +// Returns the number of threads used for inter-op parallelism +CAFFE2_API int get_num_interop_threads(); + +// Launches inter-op parallel task +CAFFE2_API void launch(std::function func); + +// Launches intra-op parallel task +CAFFE2_API void intraop_launch(std::function func); + +// Launches intra-op parallel task, returns a future +CAFFE2_API std::shared_ptr intraop_launch_future( + std::function func); + +// Returns number of intra-op threads used by default +CAFFE2_API int intraop_default_num_threads(); + +} // namespace at + +#if AT_PARALLEL_OPENMP +#include +#elif AT_PARALLEL_NATIVE +#include +#elif AT_PARALLEL_NATIVE_TBB +#include +#endif diff --git a/thirdparty/libtorch/include/ATen/ParallelNative.h b/thirdparty/libtorch/include/ATen/ParallelNative.h new file mode 100644 index 0000000000..58d3445cc5 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/ParallelNative.h @@ -0,0 +1,92 @@ +#pragma once + +#include +#include +#include + +#define INTRA_OP_PARALLEL + +namespace at { +namespace internal { + +inline std::tuple calc_num_tasks_and_chunk_size( + int64_t begin, int64_t end, int64_t grain_size) { + if ((end - begin) < grain_size) { + return std::make_tuple(1, std::max((int64_t)0, end - begin)); + } + // Choose number of tasks based on grain size and number of threads. + size_t chunk_size = divup((end - begin), get_num_threads()); + // Make sure each task is at least grain_size size. + chunk_size = std::max((size_t)grain_size, chunk_size); + size_t num_tasks = divup((end - begin), chunk_size); + return std::make_tuple(num_tasks, chunk_size); +} + +CAFFE2_API void _parallel_run( + const int64_t begin, + const int64_t end, + const int64_t grain_size, + const std::function& f); + +} // namespace internal + +template +inline void parallel_for( + const int64_t begin, + const int64_t end, + const int64_t grain_size, + const F& f) { + TORCH_CHECK(grain_size >= 0); + if (begin >= end) { + return; + } + if ((end - begin) < grain_size || in_parallel_region()) { + f(begin, end); + return; + } + internal::_parallel_run( + begin, + end, + grain_size, + [f](int64_t start, int64_t end, size_t /* unused */) { + f(start, end); + } + ); +} + +template +inline scalar_t parallel_reduce( + const int64_t begin, + const int64_t end, + const int64_t grain_size, + const scalar_t ident, + const F& f, + const SF& sf) { + TORCH_CHECK(grain_size >= 0); + if (begin >= end) { + return ident; + } + if ((end - begin) < grain_size || in_parallel_region()) { + return f(begin, end, ident); + } + size_t num_tasks, chunk_size; + std::tie(num_tasks, chunk_size) = + internal::calc_num_tasks_and_chunk_size(begin, end, grain_size); + std::vector results(num_tasks); + scalar_t* results_data = results.data(); + internal::_parallel_run( + begin, + end, + grain_size, + [f, ident, results_data](int64_t start, int64_t end, size_t task_id) { + results_data[task_id] = f(start, end, ident); + } + ); + scalar_t result = ident; + for (auto partial_result : results) { + result = sf(result, partial_result); + } + return result; +} + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/ParallelNativeTBB.h b/thirdparty/libtorch/include/ATen/ParallelNativeTBB.h new file mode 100644 index 0000000000..aa412320e9 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/ParallelNativeTBB.h @@ -0,0 +1,91 @@ +#pragma once +#include + +#include +#include + +#ifdef _WIN32 +#define WIN32_LEAN_AND_MEAN +#endif +#include "tbb/tbb.h" + +#define INTRA_OP_PARALLEL + +namespace at { + +template +inline void parallel_for( + const int64_t begin, + const int64_t end, + const int64_t grain_size, + const F& f) { + TORCH_CHECK(grain_size >= 0); + if (begin >= end) { + return; + } + if ((end - begin) < grain_size || get_num_threads() == 1) { + f(begin, end); + return; + } + std::atomic_flag err_flag = ATOMIC_FLAG_INIT; + std::exception_ptr eptr; + tbb::parallel_for(tbb::blocked_range(begin, end, grain_size), + [&eptr, &err_flag, f](const tbb::blocked_range& r) { + try { + f(r.begin(), r.end()); + } catch (...) { + if (!err_flag.test_and_set()) { + eptr = std::current_exception(); + } + } + }); + if (eptr) { + std::rethrow_exception(eptr); + } +} + +template +inline scalar_t parallel_reduce( + const int64_t begin, + const int64_t end, + const int64_t grain_size, + const scalar_t ident, + const F& f, + const SF& sf) { + TORCH_CHECK(grain_size >= 0); + if (begin >= end) { + return ident; + } + if ((end - begin) < grain_size || get_num_threads() == 1) { + return f(begin, end, ident); + } + scalar_t result; + std::atomic_flag err_flag = ATOMIC_FLAG_INIT; + std::exception_ptr eptr; + result = tbb::parallel_reduce( + tbb::blocked_range(begin, end, grain_size), ident, + [&eptr, &err_flag, f, ident] + (const tbb::blocked_range& r, scalar_t ident) { + try { + return f(r.begin(), r.end(), ident); + } catch (...) { + if (!err_flag.test_and_set()) { + eptr = std::current_exception(); + } + return ident; + } + }, + sf + ); + if (eptr) { + std::rethrow_exception(eptr); + } + return result; +} + +template +void intraop_invoke(const F0& f0, const F1& f1) { + tbb::parallel_invoke(f0, f1); +} + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/ParallelOpenMP.h b/thirdparty/libtorch/include/ATen/ParallelOpenMP.h new file mode 100644 index 0000000000..fecb9858d3 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/ParallelOpenMP.h @@ -0,0 +1,99 @@ +#pragma once +#include + +#include +#include + +#ifdef _OPENMP +#define INTRA_OP_PARALLEL + +#include +#endif + +namespace at { + +template +inline void parallel_for( + const int64_t begin, + const int64_t end, + const int64_t grain_size, + const F& f) { + TORCH_CHECK(grain_size >= 0); + if (begin >= end) { + return; + } +#ifdef _OPENMP + std::atomic_flag err_flag = ATOMIC_FLAG_INIT; + std::exception_ptr eptr; + // choose number of tasks based on grain size and number of threads + int64_t num_threads = omp_in_parallel() ? 1 : omp_get_max_threads(); + if (grain_size > 0) { + num_threads = std::min(num_threads, divup((end - begin), grain_size)); + } + +#pragma omp parallel num_threads(num_threads) + { + int64_t num_threads = omp_get_num_threads(); + int64_t tid = omp_get_thread_num(); + int64_t chunk_size = divup((end - begin), num_threads); + int64_t begin_tid = begin + tid * chunk_size; + if (begin_tid < end) { + try { + f(begin_tid, std::min(end, chunk_size + begin_tid)); + } catch (...) { + if (!err_flag.test_and_set()) { + eptr = std::current_exception(); + } + } + } + } + if (eptr) { + std::rethrow_exception(eptr); + } +#else + f(begin, end); +#endif +} + +template +inline scalar_t parallel_reduce( + const int64_t begin, + const int64_t end, + const int64_t grain_size, + const scalar_t ident, + const F& f, + const SF& sf) { + TORCH_CHECK(grain_size >= 0); + if (begin >= end) { + return ident; + } else if (in_parallel_region() || get_num_threads() == 1) { + return f(begin, end, ident); + } else { + const int64_t num_results = divup((end - begin), grain_size); + std::vector results(num_results); + scalar_t* results_data = results.data(); + std::atomic_flag err_flag = ATOMIC_FLAG_INIT; + std::exception_ptr eptr; +#pragma omp parallel for if ((end - begin) >= grain_size) + for (int64_t id = 0; id < num_results; id++) { + int64_t i = begin + id * grain_size; + try { + results_data[id] = f(i, i + std::min(end - i, grain_size), ident); + } catch (...) { + if (!err_flag.test_and_set()) { + eptr = std::current_exception(); + } + } + } + if (eptr) { + std::rethrow_exception(eptr); + } + scalar_t result = ident; + for (auto partial_result : results) { + result = sf(result, partial_result); + } + return result; + } +} + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/QuantizedCPUType.h b/thirdparty/libtorch/include/ATen/QuantizedCPUType.h new file mode 100644 index 0000000000..980e4c1f10 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/QuantizedCPUType.h @@ -0,0 +1,95 @@ +#pragma once + +// @generated by aten/src/ATen/gen.py + +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +namespace c10 { +struct Storage; +} + +namespace at { + +class Tensor; +using TensorList = ArrayRef; + +class Context; +struct Generator; + +struct Quantizer; +// This is temporary typedef to enable Quantizer in aten native function API +// we'll remove them when we are actually exposing Quantizer class +// to frontend +using ConstQuantizerPtr = const c10::intrusive_ptr&; + +#ifdef USE_STATIC_DISPATCH +namespace QuantizedCPUType { + Tensor as_strided(const Tensor & self, IntArrayRef size, IntArrayRef stride, c10::optional storage_offset); + Tensor _empty_affine_quantized(IntArrayRef size, const TensorOptions & options, double scale, int64_t zero_point, c10::optional memory_format); + Tensor _empty_per_channel_affine_quantized(IntArrayRef size, const Tensor & scales, const Tensor & zero_points, int64_t axis, const TensorOptions & options, c10::optional memory_format); + Tensor & resize_(Tensor & self, IntArrayRef size, c10::optional memory_format); + Tensor quantized_max_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode); + Tensor mean(const Tensor & self, c10::optional dtype); + Tensor mean(const Tensor & self, IntArrayRef dim, bool keepdim, c10::optional dtype); + Tensor & mean_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim, c10::optional dtype); + Tensor relu(const Tensor & self); + Tensor & relu_(Tensor & self); + Tensor clone(const Tensor & self, c10::optional memory_format); + Tensor dequantize(const Tensor & self); + double q_scale(const Tensor & self); + int64_t q_zero_point(const Tensor & self); + Tensor q_per_channel_scales(const Tensor & self); + Tensor q_per_channel_zero_points(const Tensor & self); + int64_t q_per_channel_axis(const Tensor & self); + Tensor int_repr(const Tensor & self); + QScheme qscheme(const Tensor & self); + Tensor & set_(Tensor & self, Storage source, int64_t storage_offset, IntArrayRef size, IntArrayRef stride); + Tensor & set_quantizer_(Tensor & self, ConstQuantizerPtr quantizer); + Tensor view(const Tensor & self, IntArrayRef size); + Tensor & ne_out(Tensor & out, const Tensor & self, Scalar other); + Tensor ne(const Tensor & self, Scalar other); + Tensor & ne_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor ne(const Tensor & self, const Tensor & other); + Tensor & eq_out(Tensor & out, const Tensor & self, Scalar other); + Tensor eq(const Tensor & self, Scalar other); + Tensor & eq_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor eq(const Tensor & self, const Tensor & other); + Tensor & ge_out(Tensor & out, const Tensor & self, Scalar other); + Tensor ge(const Tensor & self, Scalar other); + Tensor & ge_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor ge(const Tensor & self, const Tensor & other); + Tensor & le_out(Tensor & out, const Tensor & self, Scalar other); + Tensor le(const Tensor & self, Scalar other); + Tensor & le_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor le(const Tensor & self, const Tensor & other); + Tensor & gt_out(Tensor & out, const Tensor & self, Scalar other); + Tensor gt(const Tensor & self, Scalar other); + Tensor & gt_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor gt(const Tensor & self, const Tensor & other); + Tensor & lt_out(Tensor & out, const Tensor & self, Scalar other); + Tensor lt(const Tensor & self, Scalar other); + Tensor & lt_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor lt(const Tensor & self, const Tensor & other); + Tensor min(const Tensor & self); + Tensor max(const Tensor & self); + std::tuple sort(const Tensor & self, int64_t dim, bool descending); + std::tuple topk(const Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted); + bool equal(const Tensor & self, const Tensor & other); + Tensor _adaptive_avg_pool2d(const Tensor & self, IntArrayRef output_size); + Tensor avg_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override); + Tensor upsample_bilinear2d(const Tensor & self, IntArrayRef output_size, bool align_corners); + Tensor upsample_nearest2d(const Tensor & self, IntArrayRef output_size); +} +#endif + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/Scalar.h b/thirdparty/libtorch/include/ATen/Scalar.h new file mode 100644 index 0000000000..e12557428f --- /dev/null +++ b/thirdparty/libtorch/include/ATen/Scalar.h @@ -0,0 +1,3 @@ +#pragma once + +#include diff --git a/thirdparty/libtorch/include/ATen/ScalarOps.h b/thirdparty/libtorch/include/ATen/ScalarOps.h new file mode 100644 index 0000000000..8b26ed1d51 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/ScalarOps.h @@ -0,0 +1,38 @@ +#pragma once + +#include +#include +#include + +// This is in the c10 namespace because we use ADL to find the functions in it. +namespace c10 { + +// FIXME: this should be (and was) Scalar::toTensor, but there is currently no way +// to implement this without going through Derived Types (which are not part of core). +inline at::Tensor scalar_to_tensor(Scalar s, const Device device = at::kCPU) { + // This is the fast track we have for CPU scalar tensors. + if (device == at::kCPU) { + if (s.isFloatingPoint()) { + return at::native::scalar_tensor(s, at::device(at::kCPU).dtype(at::kDouble)); + } else if (s.isBoolean()) { + return at::native::scalar_tensor(s, at::device(at::kCPU).dtype(at::kBool)); + } else if (s.isComplex()) { + return at::native::scalar_tensor(s, at::device(at::kCPU).dtype(at::kComplexDouble)); + } else { + AT_ASSERT(s.isIntegral(false)); + return at::native::scalar_tensor(s, at::device(at::kCPU).dtype(at::kLong)); + } + } + if (s.isFloatingPoint()) { + return at::scalar_tensor(s, at::device(device).dtype(at::kDouble)); + } else if (s.isBoolean()) { + return at::scalar_tensor(s, at::device(device).dtype(at::kBool)); + } else if (s.isComplex()) { + return at::scalar_tensor(s, at::device(device).dtype(at::kComplexDouble)); + } else { + AT_ASSERT(s.isIntegral(false)); + return at::scalar_tensor(s, at::device(device).dtype(at::kLong)); + } +} + +} diff --git a/thirdparty/libtorch/include/ATen/ScalarType.h b/thirdparty/libtorch/include/ATen/ScalarType.h new file mode 100644 index 0000000000..2181250740 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/ScalarType.h @@ -0,0 +1,4 @@ +#pragma once +#include // for BC reasons +#include +#include diff --git a/thirdparty/libtorch/include/ATen/SmallVector.h b/thirdparty/libtorch/include/ATen/SmallVector.h new file mode 100644 index 0000000000..fabfa44190 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/SmallVector.h @@ -0,0 +1,2 @@ +#pragma once +#include diff --git a/thirdparty/libtorch/include/ATen/SparseCPUType.h b/thirdparty/libtorch/include/ATen/SparseCPUType.h new file mode 100644 index 0000000000..244fc483dc --- /dev/null +++ b/thirdparty/libtorch/include/ATen/SparseCPUType.h @@ -0,0 +1,90 @@ +#pragma once + +// @generated by aten/src/ATen/gen.py + +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +namespace c10 { +struct Storage; +} + +namespace at { + +class Tensor; +using TensorList = ArrayRef; + +class Context; +struct Generator; + +struct Quantizer; +// This is temporary typedef to enable Quantizer in aten native function API +// we'll remove them when we are actually exposing Quantizer class +// to frontend +using ConstQuantizerPtr = const c10::intrusive_ptr&; + +#ifdef USE_STATIC_DISPATCH +namespace SparseCPUType { + Tensor add(const Tensor & self, const Tensor & other, Scalar alpha); + Tensor & add_(Tensor & self, const Tensor & other, Scalar alpha); + Tensor & add_out(Tensor & out, const Tensor & self, const Tensor & other, Scalar alpha); + Tensor div(const Tensor & self, const Tensor & other); + Tensor & div_(Tensor & self, const Tensor & other); + Tensor & div_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor empty(IntArrayRef size, const TensorOptions & options, c10::optional memory_format); + Tensor & log1p_(Tensor & self); + Tensor & log1p_out(Tensor & out, const Tensor & self); + Tensor mm(const Tensor & self, const Tensor & mat2); + Tensor & mm_out(Tensor & out, const Tensor & self, const Tensor & mat2); + Tensor mul(const Tensor & self, const Tensor & other); + Tensor & mul_(Tensor & self, const Tensor & other); + Tensor & mul_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor narrow_copy(const Tensor & self, int64_t dim, int64_t start, int64_t length); + Tensor & sspaddmm_out(Tensor & out, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); + Tensor native_norm(const Tensor & self, Scalar p); + Tensor _sparse_sum_backward(const Tensor & grad, const Tensor & self, IntArrayRef dim); + Tensor clone(const Tensor & self, c10::optional memory_format); + Tensor & pow_out(Tensor & out, const Tensor & self, Scalar exponent); + Tensor pow(const Tensor & self, Scalar exponent); + Tensor & zero_(Tensor & self); + Tensor & sub_out(Tensor & out, const Tensor & self, const Tensor & other, Scalar alpha); + Tensor sub(const Tensor & self, const Tensor & other, Scalar alpha); + Tensor & sub_(Tensor & self, const Tensor & other, Scalar alpha); + Tensor & addmm_out(Tensor & out, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); + Tensor addmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); + Tensor & addmm_(Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); + Tensor _sparse_coo_tensor_with_dims(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size, const TensorOptions & options); + Tensor _sparse_coo_tensor_with_dims_and_tensors(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size, const Tensor & indices, const Tensor & values, const TensorOptions & options); + Tensor & sparse_resize_(Tensor & self, IntArrayRef size, int64_t sparse_dim, int64_t dense_dim); + Tensor & sparse_resize_and_clear_(Tensor & self, IntArrayRef size, int64_t sparse_dim, int64_t dense_dim); + Tensor sparse_mask(const Tensor & self, const Tensor & mask); + Tensor to_dense(const Tensor & self); + int64_t sparse_dim(const Tensor & self); + int64_t _dimI(const Tensor & self); + int64_t dense_dim(const Tensor & self); + int64_t _dimV(const Tensor & self); + int64_t _nnz(const Tensor & self); + Tensor coalesce(const Tensor & self); + bool is_coalesced(const Tensor & self); + Tensor _indices(const Tensor & self); + Tensor _values(const Tensor & self); + Tensor & _coalesced_(Tensor & self, bool coalesced); + Tensor indices(const Tensor & self); + Tensor values(const Tensor & self); + Tensor & hspmm_out(Tensor & out, const Tensor & mat1, const Tensor & mat2); + Tensor hspmm(const Tensor & mat1, const Tensor & mat2); + Tensor & copy_sparse_to_sparse_(Tensor & self, const Tensor & src, bool non_blocking); + Tensor index_select(const Tensor & self, int64_t dim, const Tensor & index); +} +#endif + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/SparseCUDAType.h b/thirdparty/libtorch/include/ATen/SparseCUDAType.h new file mode 100644 index 0000000000..afa7f43af3 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/SparseCUDAType.h @@ -0,0 +1,93 @@ +#pragma once + +// @generated by aten/src/ATen/gen.py + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include + +namespace c10 { +struct Storage; +} + +namespace at { + +class Tensor; +using TensorList = ArrayRef; + +class Context; +struct Generator; + +struct Quantizer; +// This is temporary typedef to enable Quantizer in aten native function API +// we'll remove them when we are actually exposing Quantizer class +// to frontend +using ConstQuantizerPtr = const c10::intrusive_ptr&; + +#ifdef USE_STATIC_DISPATCH +namespace SparseCUDAType { + Tensor add(const Tensor & self, const Tensor & other, Scalar alpha); + Tensor & add_(Tensor & self, const Tensor & other, Scalar alpha); + Tensor & add_out(Tensor & out, const Tensor & self, const Tensor & other, Scalar alpha); + Tensor div(const Tensor & self, const Tensor & other); + Tensor & div_(Tensor & self, const Tensor & other); + Tensor & div_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor empty(IntArrayRef size, const TensorOptions & options, c10::optional memory_format); + Tensor & log1p_(Tensor & self); + Tensor & log1p_out(Tensor & out, const Tensor & self); + Tensor mm(const Tensor & self, const Tensor & mat2); + Tensor & mm_out(Tensor & out, const Tensor & self, const Tensor & mat2); + Tensor mul(const Tensor & self, const Tensor & other); + Tensor & mul_(Tensor & self, const Tensor & other); + Tensor & mul_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor narrow_copy(const Tensor & self, int64_t dim, int64_t start, int64_t length); + Tensor & sspaddmm_out(Tensor & out, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); + Tensor native_norm(const Tensor & self, Scalar p); + Tensor _sparse_sum_backward(const Tensor & grad, const Tensor & self, IntArrayRef dim); + Tensor clone(const Tensor & self, c10::optional memory_format); + Tensor & pow_out(Tensor & out, const Tensor & self, Scalar exponent); + Tensor pow(const Tensor & self, Scalar exponent); + Tensor & zero_(Tensor & self); + Tensor & sub_out(Tensor & out, const Tensor & self, const Tensor & other, Scalar alpha); + Tensor sub(const Tensor & self, const Tensor & other, Scalar alpha); + Tensor & sub_(Tensor & self, const Tensor & other, Scalar alpha); + Tensor & addmm_out(Tensor & out, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); + Tensor addmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); + Tensor & addmm_(Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); + Tensor _sparse_coo_tensor_with_dims(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size, const TensorOptions & options); + Tensor _sparse_coo_tensor_with_dims_and_tensors(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size, const Tensor & indices, const Tensor & values, const TensorOptions & options); + Tensor & sparse_resize_(Tensor & self, IntArrayRef size, int64_t sparse_dim, int64_t dense_dim); + Tensor & sparse_resize_and_clear_(Tensor & self, IntArrayRef size, int64_t sparse_dim, int64_t dense_dim); + Tensor sparse_mask(const Tensor & self, const Tensor & mask); + Tensor to_dense(const Tensor & self); + int64_t sparse_dim(const Tensor & self); + int64_t _dimI(const Tensor & self); + int64_t dense_dim(const Tensor & self); + int64_t _dimV(const Tensor & self); + int64_t _nnz(const Tensor & self); + Tensor coalesce(const Tensor & self); + bool is_coalesced(const Tensor & self); + Tensor _indices(const Tensor & self); + Tensor _values(const Tensor & self); + Tensor & _coalesced_(Tensor & self, bool coalesced); + Tensor indices(const Tensor & self); + Tensor values(const Tensor & self); + Tensor & hspmm_out(Tensor & out, const Tensor & mat1, const Tensor & mat2); + Tensor hspmm(const Tensor & mat1, const Tensor & mat2); + Tensor & copy_sparse_to_sparse_(Tensor & self, const Tensor & src, bool non_blocking); + Tensor index_select(const Tensor & self, int64_t dim, const Tensor & index); +} +#endif + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/SparseTensorImpl.h b/thirdparty/libtorch/include/ATen/SparseTensorImpl.h new file mode 100644 index 0000000000..0a52bcab9c --- /dev/null +++ b/thirdparty/libtorch/include/ATen/SparseTensorImpl.h @@ -0,0 +1,246 @@ +#pragma once + +#include +#include +#include + +namespace at { +struct CAFFE2_API SparseTensorImpl : public TensorImpl { + // Stored in COO format, indices + values. + + // INVARIANTS: + // sparse_dim: range [0, len(shape)]; sparse_dim + dense_dim = len(shape) + // dense_dim : range [0, len(shape)]; sparse_dim + dense_dim = len(shape) + // _indices.shape: dimensionality: 2, shape: (sparse_dim, nnz) + // _values.shape: dimensionality: 1 + dense_dim. shape: (nnz, shape[sparse_dim:]) + + int64_t sparse_dim_ = 0; // number of sparse dimensions + int64_t dense_dim_ = 0; // number of dense dimensions + + Tensor indices_; // always a LongTensor + Tensor values_; + + // A sparse tensor is 'coalesced' if every index occurs at most once in + // the indices tensor, and the indices are in sorted order. (This means + // that it is very easy to convert a coalesced tensor to CSR format: you + // need only compute CSR format indices.) + // + // Most math operations can only be performed on coalesced sparse tensors, + // because many algorithms proceed by merging two sorted lists (of indices). + bool coalesced_ = false; + +public: + // Public for now... + explicit SparseTensorImpl(at::TensorTypeSet, const caffe2::TypeMeta&); + + int64_t nnz() const { return values_.size(0); } + int64_t sparse_dim() const { return sparse_dim_; } + int64_t dense_dim() const { return dense_dim_; } + bool coalesced() const { return coalesced_; } + Tensor indices() const { return indices_; } + Tensor values() const { return values_; } + + IntArrayRef strides() const override; + bool is_contiguous(at::MemoryFormat memory_format=at::MemoryFormat::Contiguous) const override; + int64_t stride(int64_t d) const override; + void resize_dim(int64_t ndim) override; + void set_size(int64_t dim, int64_t new_size) override; + void set_stride(int64_t dim, int64_t new_stride) override; + void set_storage_offset(int64_t storage_offset) override; + + int64_t dim() const override; + TensorImpl* maybe_zero_dim(bool condition_when_zero_dim) override; + bool has_storage() const override; + const Storage& storage() const override; + int64_t storage_offset() const override; + + // WARNING: This function does NOT preserve invariants of sparse_dim/dense_dim with + // respect to indices and values + void raw_resize_(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size) { + TORCH_CHECK(allow_tensor_metadata_change(), "raw_resize_ ", err_msg_tensor_metadata_change_not_allowed); + sizes_ = size.vec(); + sparse_dim_ = sparse_dim; + dense_dim_ = dense_dim; + refresh_numel(); + } + + // NOTE: This function preserves invariants of sparse_dim/dense_dim with respect to + // indices and values. + // + // NOTE: This function supports the following cases: + // 1. When we keep the number of dense dimensions unchanged, and NOT shrinking the size of + // any of the dense dimensions. + // 2. When we keep the number of sparse dimensions unchanged, and NOT shrinking the size of + // any of the sparse dimensions. + // 3. When the sparse tensor has zero nnz, in which case we are free to change the shapes of + // both its sparse and dense dimensions. + // + // This function DOESN'T support (and will throw an error) the following cases: + // 1. When we attempt to change the number of sparse dimensions on a non-empty sparse tensor + // (such an operation will invalidate the indices stored). + // 2. When we attempt to change the number of dense dimensions on a non-empty sparse tensor + // (such an operation will behave differently from an equivalent dense tensor's resize method, + // and for API consistency we don't support it). + // 3. When we attempt to shrink the size of any of the dense dimensions on a non-empty sparse tensor + // (such an operation will behave differently from an equivalent dense tensor's resize method, + // and for API consistency we don't support it). + // 4. When we attempt to shrink the size of any of the sparse dimensions on a non-empty sparse tensor + // (this could make some of the stored indices out-of-bound and thus unsafe). + void resize_(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size) { + TORCH_CHECK(allow_tensor_metadata_change(), "resize_ ", err_msg_tensor_metadata_change_not_allowed); + TORCH_CHECK(sparse_dim + dense_dim == static_cast(size.size()), "number of dimensions must be sparse_dim (", sparse_dim, ") + dense_dim (", dense_dim, "), but got ", size.size()); + if (nnz() > 0) { + auto alt_options_msg = "You could try the following options:\n\ +1. If you need an empty sparse tensor of this size, call `x = torch.sparse_coo_tensor(size)`.\n\ +2. If you need to resize this tensor, you have the following options:\n\ + 1. For both sparse and dense dimensions, keep the number of them constant and the size of them non-shrinking, and then try the same call again.\n\ + 2. Or, create a new sparse tensor with the correct indices and values from this sparse tensor."; + + TORCH_CHECK(sparse_dim == sparse_dim_, + "changing the number of sparse dimensions (from ", sparse_dim_, " to ", sparse_dim, ") on a non-empty sparse tensor is not supported.\n", alt_options_msg); + + TORCH_CHECK(dense_dim == dense_dim_, + "changing the number of dense dimensions (from ", dense_dim_, " to ", dense_dim, ") on a non-empty sparse tensor is not supported.\n", alt_options_msg); + + bool shrinking_sparse_dims = false; + bool shrinking_dense_dim = false; + auto sparse_size_original = sizes().slice(0, sparse_dim); + auto sparse_size_new = size.slice(0, sparse_dim); + for (int64_t i = 0; i < sparse_dim; i++) { + if (sparse_size_new[i] < sparse_size_original[i]) { + shrinking_sparse_dims = true; + break; + } + } + auto dense_size_original = sizes().slice(sparse_dim); + auto dense_size_new = size.slice(sparse_dim); + for (int64_t i = 0; i < dense_dim; i++) { + if (dense_size_new[i] < dense_size_original[i]) { + shrinking_dense_dim = true; + break; + } + } + + TORCH_CHECK(!shrinking_sparse_dims, + "shrinking the size of sparse dimensions (from ", sparse_size_original, " to ", sparse_size_new, ") on a non-empty sparse tensor is not supported.\n", alt_options_msg); + + TORCH_CHECK(!shrinking_dense_dim, + "shrinking the size of dense dimensions (from ", dense_size_original, " to ", dense_size_new, ") on a non-empty sparse tensor is not supported.\n", alt_options_msg); + } + + if ((!size.equals(sizes_)) || (sparse_dim != sparse_dim_) || (dense_dim != dense_dim_)) { + auto nnz = values().size(0); + std::vector values_size = {nnz}; + auto dense_size = size.slice(sparse_dim); + values_size.insert(values_size.end(), dense_size.begin(), dense_size.end()); + values_.resize_(values_size); + indices_.resize_({sparse_dim, nnz}); + } + + sizes_ = size.vec(); + sparse_dim_ = sparse_dim; + dense_dim_ = dense_dim; + refresh_numel(); + } + + // NOTE: this function will resize the sparse tensor and also set `indices` and `values` to empty. + void resize_and_clear_(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size) { + TORCH_CHECK(allow_tensor_metadata_change(), "resize_and_clear_ ", err_msg_tensor_metadata_change_not_allowed); + TORCH_CHECK(sparse_dim + dense_dim == static_cast(size.size()), "number of dimensions must be sparse_dim (", sparse_dim, ") + dense_dim (", dense_dim, "), but got ", size.size()); + + sizes_ = size.vec(); + sparse_dim_ = sparse_dim; + dense_dim_ = dense_dim; + + auto empty_indices = at::empty({sparse_dim, 0}, indices().options()); + std::vector values_size = {0}; + auto dense_size = sizes().slice(sparse_dim); + values_size.insert(values_size.end(), dense_size.begin(), dense_size.end()); + auto empty_values = at::empty(values_size, values().options()); + set_indices_and_values_unsafe(empty_indices, empty_values); + refresh_numel(); + } + + void set_coalesced(bool coalesced) { + TORCH_CHECK(allow_tensor_metadata_change(), "set_coalesced ", err_msg_tensor_metadata_change_not_allowed); + coalesced_ = coalesced; + } + + // NOTE: this function is only used internally and not exposed to Python frontend + void set_nnz_and_narrow(int64_t new_nnz) { + TORCH_CHECK(allow_tensor_metadata_change(), "set_nnz_and_narrow ", err_msg_tensor_metadata_change_not_allowed); + AT_ASSERT(new_nnz <= nnz()); + indices_ = indices_.narrow(1, 0, new_nnz); + values_ = values_.narrow(0, 0, new_nnz); + } + + // Takes indices and values and directly puts them into the sparse tensor, no copy. + // NOTE: this function is unsafe because it doesn't check whether any indices are + // out of boundaries of `sizes`, so it should ONLY be used where we know that the + // indices are guaranteed to be within bounds. + // This used to be called THSTensor_(_move) + // NB: This used to be able to avoid a refcount bump, but I was too lazy to + // make it happen + void set_indices_and_values_unsafe(const Tensor& indices, const Tensor& values); + + /** + * Return a TensorImpl that is a shallow-copy of this TensorImpl. + * + * For usage of `version_counter` and `allow_tensor_metadata_change`, + * see NOTE [ TensorImpl Shallow-Copying ]. + */ + c10::intrusive_ptr shallow_copy_and_detach( + const c10::VariableVersion& version_counter, + bool allow_tensor_metadata_change) const override { + auto impl = c10::make_intrusive(type_set(), dtype()); + copy_tensor_metadata( + /*src_impl=*/this, + /*dest_impl=*/impl.get(), + /*version_counter=*/version_counter, + /*allow_tensor_metadata_change=*/allow_tensor_metadata_change); + impl->refresh_numel(); + return impl; + } + + /** + * Shallow-copies data from another TensorImpl into this TensorImpl. + * + * For why this function doesn't check this TensorImpl's `allow_tensor_metadata_change_`, + * see NOTE [ TensorImpl Shallow-Copying ]. + */ + void shallow_copy_from(const c10::intrusive_ptr& impl) override { + AT_ASSERT(has_compatible_shallow_copy_type(impl->type_set())); + auto sparse_impl = static_cast(impl.get()); + copy_tensor_metadata( + /*src_impl=*/sparse_impl, + /*dest_impl=*/this, + /*version_counter=*/version_counter(), + /*allow_tensor_metadata_change=*/allow_tensor_metadata_change()); + refresh_numel(); + } +private: + explicit SparseTensorImpl(at::TensorTypeSet, const caffe2::TypeMeta&, at::Tensor indices, at::Tensor values); + + /** + * Copy the tensor metadata fields (e.g. sizes / strides / storage pointer / storage_offset) + * from one TensorImpl to another TensorImpl. + * + * For usage of `version_counter` and `allow_tensor_metadata_change`, see NOTE [ TensorImpl Shallow-Copying ]. + */ + static void copy_tensor_metadata( + const SparseTensorImpl* src_sparse_impl, + SparseTensorImpl* dest_sparse_impl, + const c10::VariableVersion& version_counter, + bool allow_tensor_metadata_change) { + TensorImpl::copy_tensor_metadata(src_sparse_impl, dest_sparse_impl, version_counter, allow_tensor_metadata_change); + + // Sparse-specific fields + dest_sparse_impl->sparse_dim_ = src_sparse_impl->sparse_dim(); + dest_sparse_impl->dense_dim_ = src_sparse_impl->dense_dim(); + dest_sparse_impl->indices_ = src_sparse_impl->indices(); + dest_sparse_impl->values_ = src_sparse_impl->values(); + dest_sparse_impl->coalesced_ = src_sparse_impl->coalesced(); + } +}; + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/SparseTensorUtils.h b/thirdparty/libtorch/include/ATen/SparseTensorUtils.h new file mode 100644 index 0000000000..2ed5588126 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/SparseTensorUtils.h @@ -0,0 +1,132 @@ +#pragma once + +#include +#include + +namespace at { namespace sparse { + +// Just for documentary purposes +using SparseTensor = Tensor; +using LongTensor = Tensor; +using IntTensor = Tensor; +using SparseType = Type; + +// This is an internal utility function for getting at the SparseTensorImpl, +// so that we can write sparse tensor specific accessors for special fields +// in SparseTensor. You should only use this for writing low level +// setters/getters for SparseTensorImpl fields; otherwise, you should use +// the low level setters/getters that were implemented using this. +// +// This may be called repeatedly, so make sure it's pretty cheap. +inline SparseTensorImpl* get_sparse_impl(const SparseTensor& self) { + TORCH_INTERNAL_ASSERT(at::impl::variable_excluded_from_dispatch()); + AT_ASSERTM(self.is_sparse(), "_internal_get_SparseTensorImpl: not a sparse tensor"); + return static_cast(self.unsafeGetTensorImpl()); +} + +// Takes indices and values and directly puts them into the sparse tensor, no +// copy. This used to be called THSTensor_(_move) +inline void alias_into_sparse(const SparseTensor& self, const LongTensor& indices, const Tensor& values) { + get_sparse_impl(self)->set_indices_and_values_unsafe(indices, values); +} + +// Take indices and values and makes a (data) copy of them to put into the sparse +// indices/values. This used to be called THSTensor_(_set) +inline void copy_into_sparse(const SparseTensor& self, const LongTensor& indices, const Tensor& values, bool non_blocking) { + alias_into_sparse( + self, + indices.to(self._indices().options(), non_blocking, /*copy=*/true), + values.to(self._values().options(), non_blocking, /*copy=*/true)); +} + +// TODO: put this into the public API +inline bool is_same_tensor(const Tensor& lhs, const Tensor& rhs) { + return lhs.unsafeGetTensorImpl() == rhs.unsafeGetTensorImpl(); +} + +inline bool is_same_density(const SparseTensor& self, const SparseTensor& src) { + return self.sparse_dim() == src.sparse_dim() && self.dense_dim() == src.dense_dim(); +} + +// Give us a new values tensor, with the same dimensionality +// as 'values' but with a new number of non-zero elements. +// TODO: Expose this for real in ATen, some day? +// NB: Doesn't preserve data. +inline Tensor new_values_with_size_of(const Tensor& values, int64_t nnz) { + std::vector size = values.sizes().vec(); + size[0] = nnz; + return at::empty(size, values.options()); +} + +// NOTE [ Flatten Sparse Indices ] +// This helper function flattens a sparse indices tensor (a LongTensor) into a 1D +// indices tensor. E.g., +// input = [[2, 4, 0], +// [3, 1, 10]] +// full_size = [2, 12] +// output = [ 2 * 12 + 3, 4 * 12 + 1, 0 * 12 + 10 ] = [27, 49, 10] +// +// In other words, assuming that each `indices[i, :]` is a valid index to a +// tensor `t` of shape `full_size`. This returns the corresponding indices to +// the flattened tensor `t.reshape( prod(full_size[:indices.size(0)]), -1 )`. +// if forceClone is true, the result will forced to be a clone of self. +// if force_clone is true, the result will forced to be a clone of self. +inline LongTensor flatten_indices(const Tensor& indices, IntArrayRef full_size, bool force_clone = false) { + int64_t sparse_dim = indices.size(0); + if (sparse_dim == 1) { + if (force_clone) { + return indices.squeeze(0).clone(at::MemoryFormat::Contiguous); + } else { + return indices.squeeze(0); + } + } else { + std::vector indices_mult_cpu_vec; + indices_mult_cpu_vec.reserve(sparse_dim); + int64_t mult = 1; + for (int64_t i = sparse_dim - 1; i >= 0; i--) { + indices_mult_cpu_vec[i] = mult; + mult *= full_size[i]; + } + auto indices_mult_cpu = at::from_blob( + indices_mult_cpu_vec.data(), + /*size=*/{sparse_dim, 1}, + indices.options().device(kCPU)); + // NB: must be blocking because this blob may be freed after this closure, + // and non_blocking copy will see garbage. + auto indices_mult = indices_mult_cpu.to(indices.device(), /*non_blocking=*/false); + // Ideally we want matmul but matmul is slow on CPU Long and not implemented + // on CUDA Long. So mul is faster. + return indices.mul(indices_mult).sum(0); + } +} + +// Flatten sparse tensor's indices from nD to 1D, similar to NOTE [ Flatten Sparse Indices ], +// except this one allows partial flatten: only flatten on specified dims. Note that +// the flatten indices might be uncoalesced if dims_to_flatten.size() < sparse_dim. +// Also if input indices is already coalesced, the flattened indices will also be sorted. +// +// args: +// indices: sparse tensor indices +// sizes: sparse tensor sizes +// dims_to_flatten: a list of dim index to flatten +// +// Ex1: +// indices = [[2, 4, 0], +// [3, 1, 3]] +// sizes = [2, 12] +// dims_to_flatten = [0, 1] +// new_indices = [ 2 * 12 + 3, 4 * 12 + 1, 0 * 12 + 3 ] = [27, 49, 3] +// +// Ex2: +// dims_to_flatten = [1] +// new_indices = [ 3, 1, 3 ] # uncoalesced +inline LongTensor flatten_indices_by_dims(const LongTensor& indices, const IntArrayRef& sizes, const IntArrayRef& dims_to_flatten){ + LongTensor new_indices = at::zeros({indices.size(1)}, indices.options()); + for (auto d : dims_to_flatten) { + new_indices.mul_(sizes[d]); + new_indices.add_(indices.select(0, d)); + } + return new_indices; +} + +}} // namespace at::sparse diff --git a/thirdparty/libtorch/include/ATen/Storage.h b/thirdparty/libtorch/include/ATen/Storage.h new file mode 100644 index 0000000000..5d6285281f --- /dev/null +++ b/thirdparty/libtorch/include/ATen/Storage.h @@ -0,0 +1,2 @@ +#pragma once +#include diff --git a/thirdparty/libtorch/include/ATen/Tensor.h b/thirdparty/libtorch/include/ATen/Tensor.h new file mode 100644 index 0000000000..2dc92d43d4 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/Tensor.h @@ -0,0 +1,12 @@ +#pragma once + +/* + * We split Tensor.h into TensorBody.h and TensorMethods.h because we want + * all TensorMethods to be inlined, but they depend on the Dispatcher, + * which in turn depends on many other things, which then depend back on Tensor. + * + * We can break this dependency chain by having the dispatcher only depend on + * TensorBody.h and not TensorMethods.h. + */ +#include +#include diff --git a/thirdparty/libtorch/include/ATen/TensorAccessor.h b/thirdparty/libtorch/include/ATen/TensorAccessor.h new file mode 100644 index 0000000000..528ed7b876 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/TensorAccessor.h @@ -0,0 +1,2 @@ +#pragma once +#include diff --git a/thirdparty/libtorch/include/ATen/TensorGeometry.h b/thirdparty/libtorch/include/ATen/TensorGeometry.h new file mode 100644 index 0000000000..291892a14d --- /dev/null +++ b/thirdparty/libtorch/include/ATen/TensorGeometry.h @@ -0,0 +1,62 @@ +#pragma once + +#include +#include + +namespace at { + +struct CAFFE2_API TensorGeometry { + TensorGeometry() : storage_offset_(0) {} + + explicit TensorGeometry(IntArrayRef sizes) + : sizes_(sizes.vec()) + , strides_(sizes.size()) + , storage_offset_(0) { + int64_t dim = sizes.size(); + int64_t expected_stride = 1; + for (int64_t i = dim - 1; i >= 0; i--) { + strides_[i] = expected_stride; + expected_stride *= sizes_[i]; + } + numel_ = expected_stride; + } + + explicit TensorGeometry(const Tensor& t) + : sizes_(t.sizes().vec()) + , strides_(t.strides().vec()) + , storage_offset_(t.storage_offset()) + , numel_(t.numel()) {} + + // true if the tensor is contiguous + bool is_contiguous() const; + + int64_t dim() const { return sizes_.size(); } + int64_t size(int64_t dim) const { + dim = maybe_wrap_dim(dim, this->dim()); + return sizes_.at(static_cast(dim)); + } + IntArrayRef sizes() const { return IntArrayRef{ sizes_ }; } + int64_t stride(int64_t dim) const { + dim = maybe_wrap_dim(dim, this->dim()); + return strides_.at(static_cast(dim)); + } + IntArrayRef strides() const { return IntArrayRef{ strides_ }; } + int64_t storage_offset() const { return storage_offset_; } + int64_t numel() const { return numel_; } + + TensorGeometry transpose(int64_t dim0, int64_t dim1) { + TensorGeometry r = *this; // copy + TORCH_CHECK(dim0 < dim(), "transpose: dim0=", dim0, " out of range (dim=", dim(), ")") + TORCH_CHECK(dim1 < dim(), "transpose: dim1=", dim1, " out of range (dim=", dim(), ")") + std::swap(r.sizes_[dim0], r.sizes_[dim1]); + std::swap(r.strides_[dim0], r.strides_[dim1]); + return r; + } + + std::vector sizes_; + std::vector strides_; + int64_t storage_offset_; + int64_t numel_; +}; + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/TensorNames.h b/thirdparty/libtorch/include/ATen/TensorNames.h new file mode 100644 index 0000000000..797051795b --- /dev/null +++ b/thirdparty/libtorch/include/ATen/TensorNames.h @@ -0,0 +1,77 @@ +#pragma once + +#include +#include + +namespace at { namespace namedinference { + +#ifdef BUILD_NAMEDTENSOR + +// TensorName and TensorNames are wrappers around Dimname and DimnameList +// that contain helper functions to make writing name inference rules easier. +// +// A TensorName represents a Dimname associated with some DimnameList (from a Tensor). +// This encapsulates all the information that is needed to check if names *match* +// and to *unify* names. +// +// Definition: Two names in two tensors *match* if they are equal, or if at +// least one of them is a wildcard that can be *refined* to the other name. +// +// Definition: unify(name, other) fails if the names do not match. Otherwise, +// it returns the most refined of name and other. +// +// Here is an example of checking if two names match. +// tensor: Tensor[A, None] +// other: Tensor[A] +// +// Let's say we wish to check if tensor.names[-1] matches other.names[-1]. +// None (in tensor) cannot match A (in other) because if the None were refined +// to A, `tensor` would have duplicate names [A, A]. Therefore we need to check +// tensor.names [A, None] for the existence of A. +struct CAFFE2_API TensorName { + explicit TensorName(ArrayRef origin, int origin_idx) + : origin_(origin), + name_(origin[maybe_wrap_dim(origin_idx, origin.size())]), + origin_idx_(origin_idx) {} + + // op_name is only used for error reporting. + const TensorName& unify(const TensorName& other, const char* op_name) const; + Dimname toDimname() const; + + private: + ArrayRef origin_; + Dimname name_; + int origin_idx_; // A named tensor can have at most 64 dims. + + CAFFE2_API friend std::ostream& operator<<( + std::ostream& out, + const TensorName& tensorname); +}; + +using TensorNameVec = SmallVector; + +struct CAFFE2_API TensorNames { + explicit TensorNames(ArrayRef names); + + // Create TensorNames from names[start:end]. Each individual TensorName stores + // `names`, NOT names[start:end], because the original tensor's names are `names`. + explicit TensorNames(ArrayRef names, int64_t start, int64_t end); + + // op_name is only used for error reporting. + TensorNames& unifyFromRightInplace( + const TensorNames& other, + const char* op_name = "unify"); + void checkUnique(const char* op_name) const; + + void append(TensorName&& name); + std::vector toDimnameVec() const; + + private: + explicit TensorNames(TensorNameVec&& names) : names_(names) {}; + + TensorNameVec names_; +}; + +#endif + +}} // namespace at::namedinference diff --git a/thirdparty/libtorch/include/ATen/TensorOperators.h b/thirdparty/libtorch/include/ATen/TensorOperators.h new file mode 100644 index 0000000000..1756ee1cfc --- /dev/null +++ b/thirdparty/libtorch/include/ATen/TensorOperators.h @@ -0,0 +1,99 @@ +#pragma once + +#include +#include + +#include +#include + +namespace at { + +inline Tensor & Tensor::operator=(Tensor const & rhs) && { + return copy_(rhs); +} +inline Tensor & Tensor::operator=(Tensor && rhs) && { + return copy_(rhs); +} +inline Tensor & Tensor::operator=(Scalar v) && { + return fill_(v); +} +inline Tensor Tensor::operator-() const { + return neg(); +} +inline Tensor& Tensor::operator+=(const Tensor & other) { + return add_(other); +} +inline Tensor& Tensor::operator+=(Scalar other) { + return add_(other); +} +inline Tensor& Tensor::operator-=(const Tensor & other) { + return sub_(other); +} +inline Tensor& Tensor::operator-=(Scalar other) { + return sub_(other); +} +inline Tensor& Tensor::operator*=(const Tensor & other) { + return mul_(other); +} +inline Tensor& Tensor::operator*=(Scalar other) { + return mul_(other); +} +inline Tensor& Tensor::operator/=(const Tensor & other) { + return div_(other); +} +inline Tensor& Tensor::operator/=(Scalar other) { + return div_(other); +} +inline Tensor Tensor::operator[](Scalar index) const { + if (!index.isIntegral(false)) { + AT_INDEX_ERROR("Can only index tensors with integral scalars"); + } + return select(0, index.toLong()); +} +inline Tensor Tensor::operator[](Tensor index) const { + // These properties are checked in the Scalar constructor, but we already + // check them here to provide more useful diagnostics for the user. + if (!index.defined()) { + AT_INDEX_ERROR("Can only index with tensors that are defined"); + } + if (index.dim() != 0) { + AT_INDEX_ERROR( + "Can only index with tensors that are scalars (zero-dim)"); + } + // The Scalar(Tensor) constructor is explicit, so we need to call it. + return this->operator[](index.item()); +} +inline Tensor Tensor::operator[](int64_t index) const { + return select(0, index); +} + +#define AT_FORALL_BINARY_OPS(_) \ +_(+,x.add(y), y.add(x)) \ +_(*,x.mul(y), y.mul(x)) \ +_(-,x.sub(y), ::at::empty_like(y, at::MemoryFormat::Preserve).fill_(x).sub_(y)) \ +_(/,x.div(y), ::at::empty_like(y, at::MemoryFormat::Preserve).fill_(x).div_(y)) \ +_(%,x.remainder(y), ::at::empty_like(y, at::MemoryFormat::Preserve).fill_(x).remainder_(y)) \ +_(<,x.lt(y), y.gt(x)) \ +_(<=,x.le(y), y.ge(x)) \ +_(>,x.gt(y),y.lt(x)) \ +_(>=,x.ge(y), y.le(x)) \ +_(==,x.eq(y), y.eq(x)) \ +_(!=,x.ne(y), y.ne(x)) + +#define DEFINE_OPERATOR(op,body,reverse_scalar_body) \ +static inline Tensor operator op(const Tensor & x, const Tensor & y) { \ + return body; \ +} \ +static inline Tensor operator op(const Tensor & x, Scalar y) { \ + return body; \ +} \ +static inline Tensor operator op(Scalar x, const Tensor & y) { \ + return reverse_scalar_body; \ +} + + +AT_FORALL_BINARY_OPS(DEFINE_OPERATOR) +#undef DEFINE_OPERATOR +#undef AT_FORALL_BINARY_OPS + +} diff --git a/thirdparty/libtorch/include/ATen/TensorOptions.h b/thirdparty/libtorch/include/ATen/TensorOptions.h new file mode 100644 index 0000000000..b3edba8efd --- /dev/null +++ b/thirdparty/libtorch/include/ATen/TensorOptions.h @@ -0,0 +1,2 @@ +#pragma once +#include diff --git a/thirdparty/libtorch/include/ATen/TensorUtils.h b/thirdparty/libtorch/include/ATen/TensorUtils.h new file mode 100644 index 0000000000..a210a13d1f --- /dev/null +++ b/thirdparty/libtorch/include/ATen/TensorUtils.h @@ -0,0 +1,153 @@ +#pragma once + +#include +#include +#include + +// These functions are NOT in Utils.h, because this file has a dep on Tensor.h + +namespace at { + +// The following are utility functions for checking that arguments +// make sense. These are particularly useful for native functions, +// which do NO argument checking by default. + +struct CAFFE2_API TensorArg { + Tensor tensor; + const char* name; + int pos; // 1-indexed + TensorArg(Tensor tensor, const char* name, int pos) + : tensor(std::move(tensor)), name(name), pos(pos) {} + const Tensor* operator->() const { return &tensor; } + const Tensor& operator*() const { return tensor; } +}; + +struct CAFFE2_API TensorGeometryArg { + TensorGeometry tensor; + const char* name; + int pos; // 1-indexed + /* implicit */ TensorGeometryArg(TensorArg arg) + : tensor(TensorGeometry{arg.tensor}), name(arg.name), pos(arg.pos) {} + TensorGeometryArg(TensorGeometry tensor, const char* name, int pos) + : tensor(tensor), name(name), pos(pos) {} + const TensorGeometry* operator->() const { return &tensor; } + const TensorGeometry& operator*() const { return tensor; } +}; + +// A string describing which function did checks on its input +// arguments. +// TODO: Consider generalizing this into a call stack. +using CheckedFrom = const char*; + +// The undefined convention: singular operators assume their arguments +// are defined, but functions which take multiple tensors will +// implicitly filter out undefined tensors (to make it easier to perform +// tests which should apply if the tensor is defined, and should not +// otherwise.) +// +// NB: This means that the n-ary operators take lists of TensorArg, +// not TensorGeometryArg, because the Tensor to TensorGeometry +// conversion will blow up if you have undefined tensors. + +CAFFE2_API std::ostream& operator<<(std::ostream& out, TensorGeometryArg t); +CAFFE2_API void checkDim( + CheckedFrom c, + const TensorGeometryArg& t, + int64_t dim); +// NB: this is an inclusive-exclusive range +CAFFE2_API void checkDimRange( + CheckedFrom c, + const TensorGeometryArg& t, + int64_t dim_start, + int64_t dim_end); +CAFFE2_API void checkSameDim( + CheckedFrom c, + const TensorGeometryArg& t1, + const TensorGeometryArg& t2); +CAFFE2_API void checkContiguous(CheckedFrom c, const TensorGeometryArg& t); +CAFFE2_API void checkAllContiguous(CheckedFrom c, at::ArrayRef ts); +CAFFE2_API void checkSize( + CheckedFrom c, + const TensorGeometryArg& t, + IntArrayRef sizes); +CAFFE2_API void checkSize( + CheckedFrom c, + const TensorGeometryArg& t, + int64_t dim, + int64_t size); +CAFFE2_API void checkNumel( + CheckedFrom c, + const TensorGeometryArg& t, + int64_t numel); +CAFFE2_API void checkSameNumel( + CheckedFrom c, + const TensorGeometryArg& t1, + const TensorGeometryArg& t2); +CAFFE2_API void checkAllSameNumel(CheckedFrom c, ArrayRef tensors); +CAFFE2_API void checkScalarType( + CheckedFrom c, + const TensorArg& t, + ScalarType s); +CAFFE2_API void checkScalarTypes( + CheckedFrom c, + const TensorArg& t, + at::ArrayRef l); +CAFFE2_API void checkSameGPU( + CheckedFrom c, + const TensorArg& t1, + const TensorArg& t2); +CAFFE2_API void checkAllSameGPU(CheckedFrom c, ArrayRef tensors); +CAFFE2_API void checkSameType( + CheckedFrom c, + const TensorArg& t1, + const TensorArg& t2); +CAFFE2_API void checkAllSameType(CheckedFrom c, ArrayRef tensors); +CAFFE2_API void checkSameSize( + CheckedFrom c, + const TensorArg& t1, + const TensorArg& t2); +CAFFE2_API void checkDefined(CheckedFrom c, const TensorArg& t); +CAFFE2_API void checkAllDefined(CheckedFrom c, at::ArrayRef t); + +// FixMe: does TensorArg slow things down? +CAFFE2_API void checkBackend( + CheckedFrom c, + at::ArrayRef t, + at::Backend backend); + +CAFFE2_API void checkDeviceType( + CheckedFrom c, + at::ArrayRef tensors, + at::DeviceType device_type); + +CAFFE2_API void checkLayout(CheckedFrom c, const Tensor& t, Layout layout); + +CAFFE2_API void checkLayout(CheckedFrom c, at::ArrayRef tensors, at::Layout layout); + +// Methods for getting data_ptr if tensor is defined +CAFFE2_API void* maybe_data_ptr(const Tensor& tensor); +CAFFE2_API void* maybe_data_ptr(const TensorArg& tensor); + +// Return if the tensor geometry represented by `sizes` and `strides` is contiguous +// Although we cache is_contiguous in tensor now, this is till useful because it +// allows checking if a particular geometry is contiguous without explicitly +// constructing a tensor, e.g., when you want to choose a kernel strategy based +// on whether a subgeometry is contiguous. +CAFFE2_API bool geometry_is_contiguous(IntArrayRef sizes, IntArrayRef strides); + +// Correspond to THCUNN_check_dim_size/THNN_check_dim_size +CAFFE2_API void check_dim_size( + const Tensor& tensor, + int64_t dim, + int64_t dim_size, + int64_t size); + +namespace detail { +CAFFE2_API std::vector defaultStrides(IntArrayRef sizes); +CAFFE2_API int64_t computeStorageSize(IntArrayRef sizes, IntArrayRef strides); +CAFFE2_API c10::optional> computeStride( + IntArrayRef oldshape, + IntArrayRef oldstride, + IntArrayRef newshape); +} // namespace detail +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/ThreadLocalDebugInfo.h b/thirdparty/libtorch/include/ATen/ThreadLocalDebugInfo.h new file mode 100644 index 0000000000..f464e4a4c6 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/ThreadLocalDebugInfo.h @@ -0,0 +1,46 @@ +#pragma once + +#include + +#include +#include + +namespace at { + +// Thread local debug information is propagated across the forward +// (including async fork tasks) and backward passes and is supposed +// to be utilized by the user's code to pass extra information from +// the higher layers (e.g. model id) down to the operator callbacks +// (e.g. used for logging) + +class CAFFE2_API ThreadLocalDebugInfoBase { + public: + ThreadLocalDebugInfoBase() {} + virtual ~ThreadLocalDebugInfoBase() {} +}; + +CAFFE2_API std::shared_ptr +getThreadLocalDebugInfo() noexcept; + +// Sets thread local debug information, returns the previously set +// debug information +CAFFE2_API std::shared_ptr +setThreadLocalDebugInfo( + std::shared_ptr info) noexcept; + +class CAFFE2_API DebugInfoGuard { + public: + explicit DebugInfoGuard( + std::shared_ptr info) { + prev_info_ = setThreadLocalDebugInfo(std::move(info)); + } + + ~DebugInfoGuard() { + setThreadLocalDebugInfo(std::move(prev_info_)); + } + + private: + std::shared_ptr prev_info_; +}; + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/TypeDefault.h b/thirdparty/libtorch/include/ATen/TypeDefault.h new file mode 100644 index 0000000000..905293401b --- /dev/null +++ b/thirdparty/libtorch/include/ATen/TypeDefault.h @@ -0,0 +1,867 @@ +#pragma once + +// @generated by aten/src/ATen/gen.py + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace c10 { +struct Storage; +} + +namespace at { + +class Tensor; +using TensorList = ArrayRef; + +class Context; +struct Generator; + +struct Quantizer; +// This is temporary typedef to enable Quantizer in aten native function API +// we'll remove them when we are actually exposing Quantizer class +// to frontend +using ConstQuantizerPtr = const c10::intrusive_ptr&; + +namespace TypeDefault { + Tensor _cast_Byte(const Tensor & self, bool non_blocking); + Tensor _cast_Char(const Tensor & self, bool non_blocking); + Tensor _cast_Double(const Tensor & self, bool non_blocking); + Tensor _cast_Float(const Tensor & self, bool non_blocking); + Tensor _cast_Int(const Tensor & self, bool non_blocking); + Tensor _cast_Long(const Tensor & self, bool non_blocking); + Tensor _cast_Short(const Tensor & self, bool non_blocking); + Tensor _cast_Half(const Tensor & self, bool non_blocking); + void backward(const Tensor & self, const Tensor & gradient, bool keep_graph, bool create_graph); + void set_data(const Tensor & self, const Tensor & new_data); + Tensor data(const Tensor & self); + bool is_leaf(const Tensor & self); + int64_t output_nr(const Tensor & self); + int64_t _version(const Tensor & self); + Tensor & requires_grad_(Tensor & self, bool _requires_grad); + #ifdef BUILD_NAMEDTENSOR + Tensor & rename_(Tensor & self, c10::optional names); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor rename(const Tensor & self, c10::optional names); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor align_to(const Tensor & self, DimnameList names); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor align_to(const Tensor & self, DimnameList order, int64_t ellipsis_idx); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor align_as(const Tensor & self, const Tensor & other); + #endif + #ifdef BUILD_NAMEDTENSOR + std::vector align_tensors(TensorList tensors); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor refine_names(const Tensor & self, DimnameList names); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor unflatten(const Tensor & self, Dimname dim, IntArrayRef sizes, DimnameList names); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor unflatten(const Tensor & self, int64_t dim, IntArrayRef sizes, DimnameList names); + #endif + int64_t _debug_has_internal_overlap(const Tensor & self); + std::tuple _sobol_engine_draw(const Tensor & quasi, int64_t n, const Tensor & sobolstate, int64_t dimension, int64_t num_generated, c10::optional dtype); + Tensor & _sobol_engine_ff_(Tensor & self, int64_t n, const Tensor & sobolstate, int64_t dimension, int64_t num_generated); + Tensor & _sobol_engine_scramble_(Tensor & self, const Tensor & ltm, int64_t dimension); + Tensor & _sobol_engine_initialize_state_(Tensor & self, int64_t dimension); + Tensor _reshape_from_tensor(const Tensor & self, const Tensor & shape); + Tensor _shape_as_tensor(const Tensor & self); + Tensor dropout(const Tensor & input, double p, bool train); + Tensor & dropout_(Tensor & self, double p, bool train); + Tensor feature_dropout(const Tensor & input, double p, bool train); + Tensor & feature_dropout_(Tensor & self, double p, bool train); + Tensor alpha_dropout(const Tensor & input, double p, bool train); + Tensor & alpha_dropout_(Tensor & self, double p, bool train); + Tensor feature_alpha_dropout(const Tensor & input, double p, bool train); + Tensor & feature_alpha_dropout_(Tensor & self, double p, bool train); + Tensor abs(const Tensor & self); + Tensor & abs_(Tensor & self); + Tensor & abs_out(Tensor & out, const Tensor & self); + Tensor angle(const Tensor & self); + Tensor real(const Tensor & self); + Tensor imag(const Tensor & self); + Tensor conj(const Tensor & self); + Tensor acos(const Tensor & self); + Tensor & acos_(Tensor & self); + Tensor & acos_out(Tensor & out, const Tensor & self); + Tensor avg_pool1d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad); + Tensor adaptive_avg_pool1d(const Tensor & self, IntArrayRef output_size); + std::tuple adaptive_max_pool1d(const Tensor & self, IntArrayRef output_size); + Tensor add(const Tensor & self, Scalar other, Scalar alpha); + Tensor & add_(Tensor & self, Scalar other, Scalar alpha); + Tensor addr(const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha); + Tensor & addr_(Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha); + Tensor & addr_out(Tensor & out, const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha); + Tensor affine_grid_generator(const Tensor & theta, IntArrayRef size, bool align_corners); + Tensor affine_grid_generator_backward(const Tensor & grad, IntArrayRef size, bool align_corners); + Tensor all(const Tensor & self, int64_t dim, bool keepdim); + Tensor & all_out(Tensor & out, const Tensor & self, int64_t dim, bool keepdim); + #ifdef BUILD_NAMEDTENSOR + Tensor all(const Tensor & self, Dimname dim, bool keepdim); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor & all_out(Tensor & out, const Tensor & self, Dimname dim, bool keepdim); + #endif + bool allclose(const Tensor & self, const Tensor & other, double rtol, double atol, bool equal_nan); + Tensor any(const Tensor & self, int64_t dim, bool keepdim); + Tensor & any_out(Tensor & out, const Tensor & self, int64_t dim, bool keepdim); + #ifdef BUILD_NAMEDTENSOR + Tensor any(const Tensor & self, Dimname dim, bool keepdim); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor & any_out(Tensor & out, const Tensor & self, Dimname dim, bool keepdim); + #endif + Tensor arange(Scalar end, const TensorOptions & options); + Tensor arange(Scalar start, Scalar end, const TensorOptions & options); + Tensor arange(Scalar start, Scalar end, Scalar step, const TensorOptions & options); + Tensor & arange_out(Tensor & out, Scalar end); + Tensor _dim_arange(const Tensor & like, int64_t dim); + Tensor argmax(const Tensor & self, c10::optional dim, bool keepdim); + Tensor argmin(const Tensor & self, c10::optional dim, bool keepdim); + Tensor & as_strided_(Tensor & self, IntArrayRef size, IntArrayRef stride, c10::optional storage_offset); + Tensor asin(const Tensor & self); + Tensor & asin_(Tensor & self); + Tensor & asin_out(Tensor & out, const Tensor & self); + Tensor atan(const Tensor & self); + Tensor & _baddbmm_mkl_(Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha); + Tensor bartlett_window(int64_t window_length, const TensorOptions & options); + Tensor bartlett_window(int64_t window_length, bool periodic, const TensorOptions & options); + Tensor batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps, bool cudnn_enabled); + std::tuple _batch_norm_impl_index(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps, bool cudnn_enabled); + std::tuple _batch_norm_impl_index_backward(int64_t impl_index, const Tensor & input, const Tensor & grad_output, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_var_transform, bool train, double eps, std::array output_mask, const Tensor & reservedSpace); + Tensor bernoulli(const Tensor & self, Generator * generator); + Tensor & bernoulli_out(Tensor & out, const Tensor & self, Generator * generator); + Tensor bernoulli(const Tensor & self, double p, Generator * generator); + Tensor bilinear(const Tensor & input1, const Tensor & input2, const Tensor & weight, const Tensor & bias); + Tensor binary_cross_entropy_with_logits(const Tensor & self, const Tensor & target, const Tensor & weight, const Tensor & pos_weight, int64_t reduction); + Tensor binary_cross_entropy_with_logits_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, const Tensor & pos_weight, int64_t reduction); + Tensor bitwise_not(const Tensor & self); + Tensor & bitwise_not_(Tensor & self); + Tensor logical_not(const Tensor & self); + Tensor & logical_not_(Tensor & self); + Tensor logical_xor(const Tensor & self, const Tensor & other); + Tensor & logical_xor_(Tensor & self, const Tensor & other); + Tensor blackman_window(int64_t window_length, const TensorOptions & options); + Tensor blackman_window(int64_t window_length, bool periodic, const TensorOptions & options); + std::vector broadcast_tensors(TensorList tensors); + Tensor cat(TensorList tensors, int64_t dim); + Tensor & cat_out(Tensor & out, TensorList tensors, int64_t dim); + #ifdef BUILD_NAMEDTENSOR + Tensor cat(TensorList tensors, Dimname dim); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor & cat_out(Tensor & out, TensorList tensors, Dimname dim); + #endif + Tensor ceil(const Tensor & self); + Tensor & ceil_(Tensor & self); + Tensor chain_matmul(TensorList matrices); + std::vector chunk(const Tensor & self, int64_t chunks, int64_t dim); + Tensor clamp(const Tensor & self, c10::optional min, c10::optional max); + Tensor clamp_max(const Tensor & self, Scalar max); + Tensor clamp_min(const Tensor & self, Scalar min); + bool cudnn_is_acceptable(const Tensor & self); + Tensor constant_pad_nd(const Tensor & self, IntArrayRef pad, Scalar value); + Tensor contiguous(const Tensor & self, MemoryFormat memory_format); + Tensor convolution(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups); + Tensor convolution_overrideable(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups); + std::tuple convolution_backward_overrideable(const Tensor & grad_output, const Tensor & input, const Tensor & weight, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups, std::array output_mask); + Tensor _convolution(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled); + Tensor _convolution_nogroup(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding); + std::tuple _convolution_double_backward(const Tensor & ggI, const Tensor & ggW, const Tensor & ggb, const Tensor & gO, const Tensor & weight, const Tensor & self, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled, std::array output_mask); + Tensor conv1d(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, int64_t groups); + Tensor conv2d(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, int64_t groups); + Tensor conv3d(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, int64_t groups); + Tensor conv_tbc(const Tensor & self, const Tensor & weight, const Tensor & bias, int64_t pad); + std::tuple conv_tbc_backward(const Tensor & self, const Tensor & input, const Tensor & weight, const Tensor & bias, int64_t pad); + Tensor conv_transpose1d(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, int64_t groups, IntArrayRef dilation); + Tensor conv_transpose2d(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, int64_t groups, IntArrayRef dilation); + Tensor conv_transpose3d(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, int64_t groups, IntArrayRef dilation); + Tensor & copy_(Tensor & self, const Tensor & src, bool non_blocking); + Tensor cos(const Tensor & self); + Tensor cosh(const Tensor & self); + Tensor cosine_embedding_loss(const Tensor & input1, const Tensor & input2, const Tensor & target, double margin, int64_t reduction); + Tensor cumsum(const Tensor & self, int64_t dim, c10::optional dtype); + Tensor & cumsum_out(Tensor & out, const Tensor & self, int64_t dim, c10::optional dtype); + #ifdef BUILD_NAMEDTENSOR + Tensor cumsum(const Tensor & self, Dimname dim, c10::optional dtype); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor & cumsum_out(Tensor & out, const Tensor & self, Dimname dim, c10::optional dtype); + #endif + Tensor cumprod(const Tensor & self, int64_t dim, c10::optional dtype); + Tensor & cumprod_out(Tensor & out, const Tensor & self, int64_t dim, c10::optional dtype); + #ifdef BUILD_NAMEDTENSOR + Tensor cumprod(const Tensor & self, Dimname dim, c10::optional dtype); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor & cumprod_out(Tensor & out, const Tensor & self, Dimname dim, c10::optional dtype); + #endif + Tensor ctc_loss(const Tensor & log_probs, const Tensor & targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank, int64_t reduction, bool zero_infinity); + Tensor ctc_loss(const Tensor & log_probs, const Tensor & targets, const Tensor & input_lengths, const Tensor & target_lengths, int64_t blank, int64_t reduction, bool zero_infinity); + Tensor det(const Tensor & self); + Tensor diag_embed(const Tensor & self, int64_t offset, int64_t dim1, int64_t dim2); + Tensor diagflat(const Tensor & self, int64_t offset); + Tensor diagonal(const Tensor & self, int64_t offset, int64_t dim1, int64_t dim2); + Tensor & fill_diagonal_(Tensor & self, Scalar fill_value, bool wrap); + Tensor div(const Tensor & self, Scalar other); + Tensor & div_(Tensor & self, Scalar other); + Tensor & dot_out(Tensor & out, const Tensor & self, const Tensor & tensor); + Tensor einsum(std::string equation, TensorList tensors); + Tensor embedding(const Tensor & weight, const Tensor & indices, int64_t padding_idx, bool scale_grad_by_freq, bool sparse); + Tensor embedding_backward(const Tensor & grad, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq, bool sparse); + Tensor embedding_sparse_backward(const Tensor & grad, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq); + std::tuple embedding_bag(const Tensor & weight, const Tensor & indices, const Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const Tensor & per_sample_weights); + Tensor _embedding_bag_backward(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, const Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse, const Tensor & per_sample_weights); + Tensor _embedding_bag_sparse_backward(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const Tensor & per_sample_weights); + #ifdef BUILD_NAMEDTENSOR + Tensor empty(IntArrayRef size, c10::optional names, const TensorOptions & options, c10::optional memory_format); + #endif + Tensor new_empty(const Tensor & self, IntArrayRef size, const TensorOptions & options); + Tensor new_full(const Tensor & self, IntArrayRef size, Scalar fill_value, const TensorOptions & options); + Tensor new_zeros(const Tensor & self, IntArrayRef size, const TensorOptions & options); + Tensor & empty_out(Tensor & out, IntArrayRef size, c10::optional memory_format); + Tensor empty_like(const Tensor & self, c10::optional memory_format); + Tensor empty_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format); + Tensor erf(const Tensor & self); + Tensor erfc(const Tensor & self); + Tensor exp(const Tensor & self); + Tensor expm1(const Tensor & self); + Tensor & expm1_(Tensor & self); + Tensor expand(const Tensor & self, IntArrayRef size, bool implicit); + Tensor expand_as(const Tensor & self, const Tensor & other); + Tensor eye(int64_t n, const TensorOptions & options); + Tensor eye(int64_t n, int64_t m, const TensorOptions & options); + Tensor flatten(const Tensor & self, int64_t start_dim, int64_t end_dim); + #ifdef BUILD_NAMEDTENSOR + Tensor flatten(const Tensor & self, int64_t start_dim, int64_t end_dim, Dimname out_dim); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor flatten(const Tensor & self, Dimname start_dim, Dimname end_dim, Dimname out_dim); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor flatten(const Tensor & self, DimnameList dims, Dimname out_dim); + #endif + Tensor & fill_(Tensor & self, Scalar value); + Tensor & fill_(Tensor & self, const Tensor & value); + Tensor floor(const Tensor & self); + Tensor & floor_(Tensor & self); + Tensor frac(const Tensor & self); + Tensor & frac_(Tensor & self); + Tensor & frac_out(Tensor & out, const Tensor & self); + #ifdef BUILD_NAMEDTENSOR + Tensor full(IntArrayRef size, Scalar fill_value, c10::optional names, const TensorOptions & options); + #endif + Tensor full(IntArrayRef size, Scalar fill_value, const TensorOptions & options); + Tensor & full_out(Tensor & out, IntArrayRef size, Scalar fill_value); + Tensor full_like(const Tensor & self, Scalar fill_value, c10::optional memory_format); + Tensor full_like(const Tensor & self, Scalar fill_value, const TensorOptions & options, c10::optional memory_format); + Tensor grid_sampler(const Tensor & input, const Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); + Tensor hann_window(int64_t window_length, const TensorOptions & options); + Tensor hann_window(int64_t window_length, bool periodic, const TensorOptions & options); + Tensor hamming_window(int64_t window_length, const TensorOptions & options); + Tensor hamming_window(int64_t window_length, bool periodic, const TensorOptions & options); + Tensor hamming_window(int64_t window_length, bool periodic, double alpha, const TensorOptions & options); + Tensor hamming_window(int64_t window_length, bool periodic, double alpha, double beta, const TensorOptions & options); + Tensor hinge_embedding_loss(const Tensor & self, const Tensor & target, double margin, int64_t reduction); + Tensor group_norm(const Tensor & input, int64_t num_groups, const Tensor & weight, const Tensor & bias, double eps, bool cudnn_enabled); + Tensor fft(const Tensor & self, int64_t signal_ndim, bool normalized); + Tensor ifft(const Tensor & self, int64_t signal_ndim, bool normalized); + Tensor rfft(const Tensor & self, int64_t signal_ndim, bool normalized, bool onesided); + Tensor irfft(const Tensor & self, int64_t signal_ndim, bool normalized, bool onesided, IntArrayRef signal_sizes); + int64_t _cufft_get_plan_cache_size(int64_t device_index); + int64_t _cufft_get_plan_cache_max_size(int64_t device_index); + void _cufft_set_plan_cache_max_size(int64_t device_index, int64_t max_size); + void _cufft_clear_plan_cache(int64_t device_index); + Tensor index(const Tensor & self, TensorList indices); + Tensor & index_copy_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & source); + Tensor index_copy(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & source); + #ifdef BUILD_NAMEDTENSOR + Tensor & index_copy_(Tensor & self, Dimname dim, const Tensor & index, const Tensor & source); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor index_copy(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & source); + #endif + Tensor & index_put_(Tensor & self, TensorList indices, const Tensor & values, bool accumulate); + Tensor index_put(const Tensor & self, TensorList indices, const Tensor & values, bool accumulate); + Tensor & _index_put_impl_(Tensor & self, TensorList indices, const Tensor & values, bool accumulate, bool unsafe); + Tensor instance_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool use_input_stats, double momentum, double eps, bool cudnn_enabled); + Tensor inverse(const Tensor & self); + Tensor & inverse_out(Tensor & out, const Tensor & self); + Tensor isclose(const Tensor & self, const Tensor & other, double rtol, double atol, bool equal_nan); + Tensor isnan(const Tensor & self); + bool is_distributed(const Tensor & self); + bool is_floating_point(const Tensor & self); + bool is_complex(const Tensor & self); + bool is_nonzero(const Tensor & self); + bool is_same_size(const Tensor & self, const Tensor & other); + bool is_signed(const Tensor & self); + Tensor kl_div(const Tensor & self, const Tensor & target, int64_t reduction); + std::tuple kthvalue(const Tensor & self, int64_t k, int64_t dim, bool keepdim); + #ifdef BUILD_NAMEDTENSOR + std::tuple kthvalue(const Tensor & self, int64_t k, Dimname dim, bool keepdim); + #endif + #ifdef BUILD_NAMEDTENSOR + std::tuple kthvalue_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, Dimname dim, bool keepdim); + #endif + Tensor layer_norm(const Tensor & input, IntArrayRef normalized_shape, const Tensor & weight, const Tensor & bias, double eps, bool cudnn_enable); + Tensor linear(const Tensor & input, const Tensor & weight, const Tensor & bias); + Tensor fbgemm_linear_int8_weight_fp32_activation(const Tensor & input, const Tensor & weight, const Tensor & packed, const Tensor & col_offsets, Scalar weight_scale, Scalar weight_zero_point, const Tensor & bias); + Tensor fbgemm_linear_int8_weight(const Tensor & input, const Tensor & weight, const Tensor & packed, const Tensor & col_offsets, Scalar weight_scale, Scalar weight_zero_point, const Tensor & bias); + std::tuple fbgemm_linear_quantize_weight(const Tensor & input); + Tensor fbgemm_pack_gemm_matrix_fp16(const Tensor & input); + Tensor fbgemm_linear_fp16_weight_fp32_activation(const Tensor & input, const Tensor & packed_weight, const Tensor & bias); + Tensor fbgemm_linear_fp16_weight(const Tensor & input, const Tensor & packed_weight, const Tensor & bias); + Tensor fbgemm_pack_quantized_matrix(const Tensor & input); + Tensor fbgemm_pack_quantized_matrix(const Tensor & input, int64_t K, int64_t N); + Tensor linspace(Scalar start, Scalar end, int64_t steps, const TensorOptions & options); + Tensor log(const Tensor & self); + Tensor & log_(Tensor & self); + Tensor log10(const Tensor & self); + Tensor & log10_(Tensor & self); + Tensor log1p(const Tensor & self); + Tensor log2(const Tensor & self); + Tensor & log2_(Tensor & self); + Tensor logdet(const Tensor & self); + Tensor logspace(Scalar start, Scalar end, int64_t steps, double base, const TensorOptions & options); + Tensor log_softmax(const Tensor & self, int64_t dim, c10::optional dtype); + #ifdef BUILD_NAMEDTENSOR + Tensor log_softmax(const Tensor & self, Dimname dim, c10::optional dtype); + #endif + Tensor logsumexp(const Tensor & self, IntArrayRef dim, bool keepdim); + Tensor & logsumexp_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim); + #ifdef BUILD_NAMEDTENSOR + Tensor logsumexp(const Tensor & self, DimnameList dim, bool keepdim); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor & logsumexp_out(Tensor & out, const Tensor & self, DimnameList dim, bool keepdim); + #endif + Tensor margin_ranking_loss(const Tensor & input1, const Tensor & input2, const Tensor & target, double margin, int64_t reduction); + Tensor matmul(const Tensor & self, const Tensor & other); + Tensor & matmul_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor matrix_rank(const Tensor & self, double tol, bool symmetric); + Tensor matrix_rank(const Tensor & self, bool symmetric); + Tensor matrix_power(const Tensor & self, int64_t n); + std::tuple max(const Tensor & self, int64_t dim, bool keepdim); + std::tuple max_out(Tensor & max, Tensor & max_values, const Tensor & self, int64_t dim, bool keepdim); + Tensor max_values(const Tensor & self, IntArrayRef dim, bool keepdim); + #ifdef BUILD_NAMEDTENSOR + std::tuple max(const Tensor & self, Dimname dim, bool keepdim); + #endif + #ifdef BUILD_NAMEDTENSOR + std::tuple max_out(Tensor & max, Tensor & max_values, const Tensor & self, Dimname dim, bool keepdim); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor max_values(const Tensor & self, DimnameList dim, bool keepdim); + #endif + std::tuple max_pool1d_with_indices(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode); + Tensor max_pool1d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode); + Tensor max_pool2d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode); + Tensor max_pool3d(const Tensor & self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode); + #ifdef BUILD_NAMEDTENSOR + Tensor mean(const Tensor & self, DimnameList dim, bool keepdim, c10::optional dtype); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor & mean_out(Tensor & out, const Tensor & self, DimnameList dim, bool keepdim, c10::optional dtype); + #endif + std::tuple median(const Tensor & self, int64_t dim, bool keepdim); + std::tuple median_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool keepdim); + #ifdef BUILD_NAMEDTENSOR + std::tuple median(const Tensor & self, Dimname dim, bool keepdim); + #endif + #ifdef BUILD_NAMEDTENSOR + std::tuple median_out(Tensor & values, Tensor & indices, const Tensor & self, Dimname dim, bool keepdim); + #endif + std::tuple min(const Tensor & self, int64_t dim, bool keepdim); + std::tuple min_out(Tensor & min, Tensor & min_indices, const Tensor & self, int64_t dim, bool keepdim); + Tensor min_values(const Tensor & self, IntArrayRef dim, bool keepdim); + #ifdef BUILD_NAMEDTENSOR + std::tuple min(const Tensor & self, Dimname dim, bool keepdim); + #endif + #ifdef BUILD_NAMEDTENSOR + std::tuple min_out(Tensor & min, Tensor & min_indices, const Tensor & self, Dimname dim, bool keepdim); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor min_values(const Tensor & self, DimnameList dim, bool keepdim); + #endif + Tensor mkldnn_convolution(const Tensor & self, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups); + Tensor mkldnn_convolution_backward_input(IntArrayRef self_size, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool bias_defined); + std::tuple mkldnn_convolution_backward_weights(IntArrayRef weight_size, const Tensor & grad_output, const Tensor & self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool bias_defined); + std::tuple mkldnn_convolution_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, std::array output_mask); + Tensor _sparse_mm(const Tensor & sparse, const Tensor & dense); + std::tuple mode(const Tensor & self, int64_t dim, bool keepdim); + std::tuple mode_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool keepdim); + #ifdef BUILD_NAMEDTENSOR + std::tuple mode(const Tensor & self, Dimname dim, bool keepdim); + #endif + #ifdef BUILD_NAMEDTENSOR + std::tuple mode_out(Tensor & values, Tensor & indices, const Tensor & self, Dimname dim, bool keepdim); + #endif + Tensor mul(const Tensor & self, Scalar other); + Tensor & mul_(Tensor & self, Scalar other); + Tensor mvlgamma(const Tensor & self, int64_t p); + Tensor & mvlgamma_(Tensor & self, int64_t p); + Tensor narrow(const Tensor & self, int64_t dim, int64_t start, int64_t length); + bool _nnpack_available(); + Tensor _nnpack_spatial_convolution(const Tensor & input, const Tensor & weight, const Tensor & bias, IntArrayRef padding, IntArrayRef stride); + std::tuple _nnpack_spatial_convolution_backward(const Tensor & input, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding, std::array output_mask); + Tensor _nnpack_spatial_convolution_backward_input(const Tensor & input, const Tensor & grad_output, const Tensor & weight, IntArrayRef padding); + Tensor _nnpack_spatial_convolution_backward_weight(const Tensor & input, IntArrayRef weightsize, const Tensor & grad_output, IntArrayRef padding); + #ifdef BUILD_NAMEDTENSOR + Tensor ones(IntArrayRef size, c10::optional names, const TensorOptions & options); + #endif + Tensor ones(IntArrayRef size, const TensorOptions & options); + Tensor & ones_out(Tensor & out, IntArrayRef size); + Tensor ones_like(const Tensor & self, c10::optional memory_format); + Tensor ones_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format); + Tensor pairwise_distance(const Tensor & x1, const Tensor & x2, double p, double eps, bool keepdim); + Tensor cdist(const Tensor & x1, const Tensor & x2, double p, c10::optional compute_mode); + Tensor _cdist_backward(const Tensor & grad, const Tensor & x1, const Tensor & x2, double p, const Tensor & cdist); + Tensor pdist(const Tensor & self, double p); + Tensor _pdist_forward(const Tensor & self, double p); + Tensor _pdist_backward(const Tensor & grad, const Tensor & self, double p, const Tensor & pdist); + Tensor cosine_similarity(const Tensor & x1, const Tensor & x2, int64_t dim, double eps); + Tensor permute(const Tensor & self, IntArrayRef dims); + Tensor numpy_T(const Tensor & self); + Tensor pixel_shuffle(const Tensor & self, int64_t upscale_factor); + bool is_pinned(const Tensor & self); + Tensor pin_memory(const Tensor & self); + Tensor pinverse(const Tensor & self, double rcond); + Tensor poisson_nll_loss(const Tensor & input, const Tensor & target, bool log_input, bool full, double eps, int64_t reduction); + Tensor scalar_tensor(Scalar s, const TensorOptions & options); + #ifdef BUILD_NAMEDTENSOR + Tensor rand(IntArrayRef size, c10::optional names, const TensorOptions & options); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor rand(IntArrayRef size, Generator * generator, c10::optional names, const TensorOptions & options); + #endif + Tensor rand(IntArrayRef size, const TensorOptions & options); + Tensor rand(IntArrayRef size, Generator * generator, const TensorOptions & options); + Tensor & rand_out(Tensor & out, IntArrayRef size); + Tensor & rand_out(Tensor & out, IntArrayRef size, Generator * generator); + Tensor rand_like(const Tensor & self, c10::optional memory_format); + Tensor rand_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format); + Tensor randint(int64_t high, IntArrayRef size, const TensorOptions & options); + Tensor randint(int64_t high, IntArrayRef size, Generator * generator, const TensorOptions & options); + Tensor randint(int64_t low, int64_t high, IntArrayRef size, const TensorOptions & options); + Tensor randint(int64_t low, int64_t high, IntArrayRef size, Generator * generator, const TensorOptions & options); + Tensor & randint_out(Tensor & out, int64_t high, IntArrayRef size); + Tensor & randint_out(Tensor & out, int64_t high, IntArrayRef size, Generator * generator); + Tensor & randint_out(Tensor & out, int64_t low, int64_t high, IntArrayRef size); + Tensor & randint_out(Tensor & out, int64_t low, int64_t high, IntArrayRef size, Generator * generator); + Tensor randint_like(const Tensor & self, int64_t high, c10::optional memory_format); + Tensor randint_like(const Tensor & self, int64_t low, int64_t high, c10::optional memory_format); + Tensor randint_like(const Tensor & self, int64_t high, const TensorOptions & options, c10::optional memory_format); + Tensor randint_like(const Tensor & self, int64_t low, int64_t high, const TensorOptions & options, c10::optional memory_format); + Tensor randn(IntArrayRef size, const TensorOptions & options); + Tensor randn(IntArrayRef size, Generator * generator, const TensorOptions & options); + #ifdef BUILD_NAMEDTENSOR + Tensor randn(IntArrayRef size, c10::optional names, const TensorOptions & options); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor randn(IntArrayRef size, Generator * generator, c10::optional names, const TensorOptions & options); + #endif + Tensor & randn_out(Tensor & out, IntArrayRef size); + Tensor & randn_out(Tensor & out, IntArrayRef size, Generator * generator); + Tensor randn_like(const Tensor & self, c10::optional memory_format); + Tensor randn_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format); + Tensor randperm(int64_t n, const TensorOptions & options); + Tensor randperm(int64_t n, Generator * generator, const TensorOptions & options); + Tensor & randperm_out(Tensor & out, int64_t n); + Tensor range(Scalar start, Scalar end, Scalar step, const TensorOptions & options); + Tensor range(Scalar start, Scalar end, const TensorOptions & options); + Tensor reciprocal(const Tensor & self); + Tensor neg(const Tensor & self); + Tensor & neg_(Tensor & self); + Tensor repeat(const Tensor & self, IntArrayRef repeats); + Tensor repeat_interleave(const Tensor & self, const Tensor & repeats, c10::optional dim); + Tensor repeat_interleave(const Tensor & self, int64_t repeats, c10::optional dim); + Tensor reshape(const Tensor & self, IntArrayRef shape); + Tensor reshape_as(const Tensor & self, const Tensor & other); + Tensor round(const Tensor & self); + Tensor & round_(Tensor & self); + Tensor rrelu(const Tensor & self, Scalar lower, Scalar upper, bool training, Generator * generator); + Tensor & rrelu_(Tensor & self, Scalar lower, Scalar upper, bool training, Generator * generator); + Tensor rsqrt(const Tensor & self); + Tensor & rsqrt_(Tensor & self); + #ifdef BUILD_NAMEDTENSOR + Tensor select(const Tensor & self, Dimname dim, int64_t index); + #endif + Tensor select(const Tensor & self, int64_t dim, int64_t index); + Tensor selu(const Tensor & self); + Tensor & selu_(Tensor & self); + Tensor celu(const Tensor & self, Scalar alpha); + Tensor & celu_(Tensor & self, Scalar alpha); + Tensor & sigmoid_out(Tensor & out, const Tensor & self); + Tensor sin(const Tensor & self); + Tensor & sin_(Tensor & self); + Tensor sinh(const Tensor & self); + Tensor & sinh_(Tensor & self); + Tensor & sinh_out(Tensor & out, const Tensor & self); + Tensor detach(const Tensor & self); + Tensor & detach_(Tensor & self); + int64_t size(const Tensor & self, int64_t dim); + #ifdef BUILD_NAMEDTENSOR + int64_t size(const Tensor & self, Dimname dim); + #endif + Tensor slice(const Tensor & self, int64_t dim, int64_t start, int64_t end, int64_t step); + std::tuple slogdet(const Tensor & self); + Tensor smm(const Tensor & self, const Tensor & mat2); + Tensor softmax(const Tensor & self, int64_t dim, c10::optional dtype); + #ifdef BUILD_NAMEDTENSOR + Tensor softmax(const Tensor & self, Dimname dim, c10::optional dtype); + #endif + std::vector split(const Tensor & self, int64_t split_size, int64_t dim); + std::vector split_with_sizes(const Tensor & self, IntArrayRef split_sizes, int64_t dim); + Tensor squeeze(const Tensor & self); + Tensor squeeze(const Tensor & self, int64_t dim); + #ifdef BUILD_NAMEDTENSOR + Tensor squeeze(const Tensor & self, Dimname dim); + #endif + Tensor & squeeze_(Tensor & self); + Tensor & squeeze_(Tensor & self, int64_t dim); + #ifdef BUILD_NAMEDTENSOR + Tensor & squeeze_(Tensor & self, Dimname dim); + #endif + Tensor sspaddmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha); + Tensor stack(TensorList tensors, int64_t dim); + Tensor & stack_out(Tensor & out, TensorList tensors, int64_t dim); + Tensor stft(const Tensor & self, int64_t n_fft, c10::optional hop_length, c10::optional win_length, const Tensor & window, bool normalized, bool onesided); + int64_t stride(const Tensor & self, int64_t dim); + #ifdef BUILD_NAMEDTENSOR + int64_t stride(const Tensor & self, Dimname dim); + #endif + Tensor sum(const Tensor & self, c10::optional dtype); + Tensor sum(const Tensor & self, IntArrayRef dim, bool keepdim, c10::optional dtype); + #ifdef BUILD_NAMEDTENSOR + Tensor sum(const Tensor & self, DimnameList dim, bool keepdim, c10::optional dtype); + #endif + Tensor & sum_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim, c10::optional dtype); + #ifdef BUILD_NAMEDTENSOR + Tensor & sum_out(Tensor & out, const Tensor & self, DimnameList dim, bool keepdim, c10::optional dtype); + #endif + Tensor sum_to_size(const Tensor & self, IntArrayRef size); + Tensor sqrt(const Tensor & self); + Tensor & sqrt_(Tensor & self); + Tensor & sqrt_out(Tensor & out, const Tensor & self); + Tensor std(const Tensor & self, bool unbiased); + Tensor std(const Tensor & self, IntArrayRef dim, bool unbiased, bool keepdim); + std::tuple std_mean(const Tensor & self, bool unbiased); + std::tuple std_mean(const Tensor & self, IntArrayRef dim, bool unbiased, bool keepdim); + #ifdef BUILD_NAMEDTENSOR + std::tuple std_mean(const Tensor & self, DimnameList dim, bool unbiased, bool keepdim); + #endif + Tensor & std_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool unbiased, bool keepdim); + #ifdef BUILD_NAMEDTENSOR + Tensor std(const Tensor & self, DimnameList dim, bool unbiased, bool keepdim); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor & std_out(Tensor & out, const Tensor & self, DimnameList dim, bool unbiased, bool keepdim); + #endif + Tensor prod(const Tensor & self, c10::optional dtype); + Tensor prod(const Tensor & self, int64_t dim, bool keepdim, c10::optional dtype); + Tensor & prod_out(Tensor & out, const Tensor & self, int64_t dim, bool keepdim, c10::optional dtype); + #ifdef BUILD_NAMEDTENSOR + Tensor prod(const Tensor & self, Dimname dim, bool keepdim, c10::optional dtype); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor & prod_out(Tensor & out, const Tensor & self, Dimname dim, bool keepdim, c10::optional dtype); + #endif + Tensor t(const Tensor & self); + Tensor & t_(Tensor & self); + Tensor tan(const Tensor & self); + Tensor tanh(const Tensor & self); + Tensor tensordot(const Tensor & self, const Tensor & other, IntArrayRef dims_self, IntArrayRef dims_other); + Tensor threshold(const Tensor & self, Scalar threshold, Scalar value); + Tensor & threshold_(Tensor & self, Scalar threshold, Scalar value); + Tensor & threshold_out(Tensor & out, const Tensor & self, Scalar threshold, Scalar value); + Tensor threshold_backward(const Tensor & grad_output, const Tensor & self, Scalar threshold); + Tensor transpose(const Tensor & self, int64_t dim0, int64_t dim1); + #ifdef BUILD_NAMEDTENSOR + Tensor transpose(const Tensor & self, Dimname dim0, Dimname dim1); + #endif + Tensor & transpose_(Tensor & self, int64_t dim0, int64_t dim1); + Tensor one_hot(const Tensor & self, int64_t num_classes); + Tensor rot90(const Tensor & self, int64_t k, IntArrayRef dims); + Tensor trapz(const Tensor & y, const Tensor & x, int64_t dim); + Tensor trapz(const Tensor & y, double dx, int64_t dim); + Tensor _trilinear(const Tensor & i1, const Tensor & i2, const Tensor & i3, IntArrayRef expand1, IntArrayRef expand2, IntArrayRef expand3, IntArrayRef sumdim, int64_t unroll_dim); + Tensor triplet_margin_loss(const Tensor & anchor, const Tensor & positive, const Tensor & negative, double margin, double p, double eps, bool swap, int64_t reduction); + Tensor trunc(const Tensor & self); + Tensor & trunc_(Tensor & self); + Tensor type_as(const Tensor & self, const Tensor & other); + bool _has_compatible_shallow_copy_type(const Tensor & self, const Tensor & from); + Tensor _unsafe_view(const Tensor & self, IntArrayRef size); + Tensor unsqueeze(const Tensor & self, int64_t dim); + Tensor & unsqueeze_(Tensor & self, int64_t dim); + Tensor var(const Tensor & self, bool unbiased); + Tensor var(const Tensor & self, IntArrayRef dim, bool unbiased, bool keepdim); + Tensor & var_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool unbiased, bool keepdim); + #ifdef BUILD_NAMEDTENSOR + Tensor var(const Tensor & self, DimnameList dim, bool unbiased, bool keepdim); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor & var_out(Tensor & out, const Tensor & self, DimnameList dim, bool unbiased, bool keepdim); + #endif + std::tuple var_mean(const Tensor & self, bool unbiased); + std::tuple var_mean(const Tensor & self, IntArrayRef dim, bool unbiased, bool keepdim); + #ifdef BUILD_NAMEDTENSOR + std::tuple var_mean(const Tensor & self, DimnameList dim, bool unbiased, bool keepdim); + #endif + Tensor view_as(const Tensor & self, const Tensor & other); + Tensor where(const Tensor & condition, const Tensor & self, const Tensor & other); + std::vector where(const Tensor & condition); + Tensor norm_except_dim(const Tensor & v, int64_t pow, int64_t dim); + Tensor _weight_norm(const Tensor & v, const Tensor & g, int64_t dim); + std::tuple _weight_norm_differentiable_backward(const Tensor & grad_w, const Tensor & saved_v, const Tensor & saved_g, const Tensor & saved_norms, int64_t dim); + #ifdef BUILD_NAMEDTENSOR + Tensor zeros(IntArrayRef size, c10::optional names, const TensorOptions & options); + #endif + Tensor zeros(IntArrayRef size, const TensorOptions & options); + Tensor & zeros_out(Tensor & out, IntArrayRef size); + Tensor zeros_like(const Tensor & self, c10::optional memory_format); + Tensor zeros_like(const Tensor & self, const TensorOptions & options, c10::optional memory_format); + Tensor _sparse_sum(const Tensor & self); + Tensor _sparse_sum(const Tensor & self, ScalarType dtype); + Tensor _sparse_sum(const Tensor & self, IntArrayRef dim); + Tensor _sparse_sum(const Tensor & self, IntArrayRef dim, ScalarType dtype); + Tensor norm(const Tensor & self, c10::optional p, ScalarType dtype); + Tensor norm(const Tensor & self, Scalar p); + Tensor norm(const Tensor & self, c10::optional p, IntArrayRef dim, bool keepdim, ScalarType dtype); + Tensor norm(const Tensor & self, c10::optional p, IntArrayRef dim, bool keepdim); + Tensor & norm_out(Tensor & out, const Tensor & self, c10::optional p, IntArrayRef dim, bool keepdim, ScalarType dtype); + Tensor & norm_out(Tensor & out, const Tensor & self, c10::optional p, IntArrayRef dim, bool keepdim); + #ifdef BUILD_NAMEDTENSOR + Tensor norm(const Tensor & self, c10::optional p, DimnameList dim, bool keepdim, ScalarType dtype); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor norm(const Tensor & self, c10::optional p, DimnameList dim, bool keepdim); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor & norm_out(Tensor & out, const Tensor & self, c10::optional p, DimnameList dim, bool keepdim, ScalarType dtype); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor & norm_out(Tensor & out, const Tensor & self, c10::optional p, DimnameList dim, bool keepdim); + #endif + Tensor frobenius_norm(const Tensor & self); + Tensor frobenius_norm(const Tensor & self, IntArrayRef dim, bool keepdim); + Tensor & frobenius_norm_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim); + Tensor nuclear_norm(const Tensor & self, bool keepdim); + Tensor & nuclear_norm_out(Tensor & out, const Tensor & self, bool keepdim); + Tensor nuclear_norm(const Tensor & self, IntArrayRef dim, bool keepdim); + Tensor & nuclear_norm_out(Tensor & out, const Tensor & self, IntArrayRef dim, bool keepdim); + Tensor & resize_as_(Tensor & self, const Tensor & the_template, c10::optional memory_format); + Tensor sub(const Tensor & self, Scalar other, Scalar alpha); + Tensor & sub_(Tensor & self, Scalar other, Scalar alpha); + Tensor rsub(const Tensor & self, const Tensor & other, Scalar alpha); + Tensor rsub(const Tensor & self, Scalar other, Scalar alpha); + Tensor _sparse_addmm(const Tensor & self, const Tensor & sparse, const Tensor & dense, Scalar beta, Scalar alpha); + Tensor sparse_coo_tensor(IntArrayRef size, const TensorOptions & options); + Tensor sparse_coo_tensor(const Tensor & indices, const Tensor & values, const TensorOptions & options); + Tensor sparse_coo_tensor(const Tensor & indices, const Tensor & values, IntArrayRef size, const TensorOptions & options); + Tensor _sparse_coo_tensor_unsafe(const Tensor & indices, const Tensor & values, IntArrayRef size, const TensorOptions & options); + Tensor to_dense_backward(const Tensor & grad, const Tensor & input); + std::vector unbind(const Tensor & self, int64_t dim); + #ifdef BUILD_NAMEDTENSOR + std::vector unbind(const Tensor & self, Dimname dim); + #endif + Tensor to_mkldnn_backward(const Tensor & grad, const Tensor & input); + Tensor to(const Tensor & self, const TensorOptions & options, bool non_blocking, bool copy, c10::optional memory_format); + Tensor to(const Tensor & self, Device device, ScalarType dtype, bool non_blocking, bool copy, c10::optional memory_format); + Tensor to(const Tensor & self, ScalarType dtype, bool non_blocking, bool copy, c10::optional memory_format); + Tensor to(const Tensor & self, const Tensor & other, bool non_blocking, bool copy, c10::optional memory_format); + std::vector meshgrid(TensorList tensors); + Tensor cartesian_prod(TensorList tensors); + Tensor combinations(const Tensor & self, int64_t r, bool with_replacement); + Scalar item(const Tensor & self); + ScalarType result_type(const Tensor & tensor, const Tensor & other); + ScalarType result_type(const Tensor & tensor, Scalar other); + ScalarType result_type(Scalar scalar, const Tensor & tensor); + ScalarType result_type(Scalar scalar1, Scalar scalar2); + bool can_cast(ScalarType from, ScalarType to); + ScalarType promote_types(ScalarType type1, ScalarType type2); + std::tuple _thnn_differentiable_lstm_cell_backward(const Tensor & grad_hy, const Tensor & grad_cy, const Tensor & input_gates, const Tensor & hidden_gates, const Tensor & input_bias, const Tensor & hidden_bias, const Tensor & cx, const Tensor & cy); + std::tuple _thnn_differentiable_gru_cell_backward(const Tensor & grad_hy, const Tensor & input_gates, const Tensor & hidden_gates, const Tensor & hx, const Tensor & input_bias, const Tensor & hidden_bias); + std::tuple lstm(const Tensor & input, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); + std::tuple lstm(const Tensor & data, const Tensor & batch_sizes, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); + std::tuple gru(const Tensor & input, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); + std::tuple gru(const Tensor & data, const Tensor & batch_sizes, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); + std::tuple rnn_tanh(const Tensor & input, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); + std::tuple rnn_tanh(const Tensor & data, const Tensor & batch_sizes, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); + std::tuple rnn_relu(const Tensor & input, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); + std::tuple rnn_relu(const Tensor & data, const Tensor & batch_sizes, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); + std::tuple lstm_cell(const Tensor & input, TensorList hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh); + Tensor gru_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh); + Tensor rnn_tanh_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh); + Tensor rnn_relu_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh); + std::tuple quantized_lstm(const Tensor & input, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first, c10::optional dtype, bool use_dynamic); + std::tuple quantized_lstm(const Tensor & data, const Tensor & batch_sizes, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, c10::optional dtype, bool use_dynamic); + std::tuple quantized_gru(const Tensor & input, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); + std::tuple quantized_gru(const Tensor & data, const Tensor & batch_sizes, const Tensor & hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); + std::tuple quantized_lstm_cell(const Tensor & input, TensorList hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh, const Tensor & packed_ih, const Tensor & packed_hh, const Tensor & col_offsets_ih, const Tensor & col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh); + Tensor quantized_gru_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh, const Tensor & packed_ih, const Tensor & packed_hh, const Tensor & col_offsets_ih, const Tensor & col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh); + Tensor quantized_rnn_relu_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh, const Tensor & packed_ih, const Tensor & packed_hh, const Tensor & col_offsets_ih, const Tensor & col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh); + Tensor quantized_rnn_tanh_cell(const Tensor & input, const Tensor & hx, const Tensor & w_ih, const Tensor & w_hh, const Tensor & b_ih, const Tensor & b_hh, const Tensor & packed_ih, const Tensor & packed_hh, const Tensor & col_offsets_ih, const Tensor & col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh); + std::tuple _pack_padded_sequence(const Tensor & input, const Tensor & lengths, bool batch_first); + Tensor _pack_padded_sequence_backward(const Tensor & grad, IntArrayRef input_size, const Tensor & batch_sizes, bool batch_first); + std::tuple _pad_packed_sequence(const Tensor & data, const Tensor & batch_sizes, bool batch_first, Scalar padding_value, int64_t total_length); + Tensor masked_fill(const Tensor & self, const Tensor & mask, Scalar value); + Tensor masked_fill(const Tensor & self, const Tensor & mask, const Tensor & value); + Tensor masked_scatter(const Tensor & self, const Tensor & mask, const Tensor & source); + Tensor index_add(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & source); + #ifdef BUILD_NAMEDTENSOR + Tensor index_add(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & source); + #endif + Tensor index_fill(const Tensor & self, int64_t dim, const Tensor & index, Scalar value); + Tensor index_fill(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & value); + #ifdef BUILD_NAMEDTENSOR + Tensor & index_fill_(Tensor & self, Dimname dim, const Tensor & index, Scalar value); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor & index_fill_(Tensor & self, Dimname dim, const Tensor & index, const Tensor & value); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor index_fill(const Tensor & self, Dimname dim, const Tensor & index, Scalar value); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor index_fill(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & value); + #endif + Tensor scatter(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & src); + Tensor scatter(const Tensor & self, int64_t dim, const Tensor & index, Scalar value); + #ifdef BUILD_NAMEDTENSOR + Tensor scatter(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & src); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor scatter(const Tensor & self, Dimname dim, const Tensor & index, Scalar value); + #endif + Tensor scatter_add(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & src); + #ifdef BUILD_NAMEDTENSOR + Tensor scatter_add(const Tensor & self, Dimname dim, const Tensor & index, const Tensor & src); + #endif + Tensor & lt_(Tensor & self, Scalar other); + Tensor & lt_(Tensor & self, const Tensor & other); + Tensor & gt_(Tensor & self, Scalar other); + Tensor & gt_(Tensor & self, const Tensor & other); + Tensor & le_(Tensor & self, Scalar other); + Tensor & le_(Tensor & self, const Tensor & other); + Tensor & ge_(Tensor & self, Scalar other); + Tensor & ge_(Tensor & self, const Tensor & other); + Tensor & eq_(Tensor & self, Scalar other); + Tensor & eq_(Tensor & self, const Tensor & other); + Tensor & ne_(Tensor & self, Scalar other); + Tensor & ne_(Tensor & self, const Tensor & other); + Tensor bitwise_xor(const Tensor & self, Scalar other); + Tensor bitwise_xor(const Tensor & self, const Tensor & other); + Tensor & bitwise_xor_(Tensor & self, Scalar other); + Tensor & bitwise_xor_(Tensor & self, const Tensor & other); + Tensor __xor__(const Tensor & self, Scalar other); + Tensor __xor__(const Tensor & self, const Tensor & other); + Tensor & __ixor__(Tensor & self, Scalar other); + Tensor & __ixor__(Tensor & self, const Tensor & other); + Tensor & atan2_(Tensor & self, const Tensor & other); + Tensor & digamma_(Tensor & self); + Tensor & polygamma_(Tensor & self, int64_t n); + Tensor & addcdiv_(Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value); + Tensor & cross_out(Tensor & out, const Tensor & self, const Tensor & other, c10::optional dim); + Tensor cross(const Tensor & self, const Tensor & other, c10::optional dim); + Tensor triu(const Tensor & self, int64_t diagonal); + Tensor tril(const Tensor & self, int64_t diagonal); + #ifdef BUILD_NAMEDTENSOR + Tensor & index_select_out(Tensor & out, const Tensor & self, Dimname dim, const Tensor & index); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor index_select(const Tensor & self, Dimname dim, const Tensor & index); + #endif + std::vector nonzero_numpy(const Tensor & self); + #ifdef BUILD_NAMEDTENSOR + Tensor & gather_out(Tensor & out, const Tensor & self, Dimname dim, const Tensor & index, bool sparse_grad); + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor gather(const Tensor & self, Dimname dim, const Tensor & index, bool sparse_grad); + #endif + Tensor _gather_sparse_backward(const Tensor & self, int64_t dim, const Tensor & index, const Tensor & grad); + Tensor & addcmul_out(Tensor & out, const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value); + Tensor addcmul(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value); + Tensor & addcmul_(Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value); + Tensor & addcdiv_out(Tensor & out, const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value); + Tensor addcdiv(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value); + std::tuple triangular_solve_out(Tensor & X, Tensor & M, const Tensor & self, const Tensor & A, bool upper, bool transpose, bool unitriangular); + std::tuple triangular_solve(const Tensor & self, const Tensor & A, bool upper, bool transpose, bool unitriangular); + std::tuple symeig_out(Tensor & e, Tensor & V, const Tensor & self, bool eigenvectors, bool upper); + std::tuple symeig(const Tensor & self, bool eigenvectors, bool upper); + std::tuple svd_out(Tensor & U, Tensor & S, Tensor & V, const Tensor & self, bool some, bool compute_uv); + std::tuple svd(const Tensor & self, bool some, bool compute_uv); + Tensor & cholesky_out(Tensor & out, const Tensor & self, bool upper); + Tensor cholesky(const Tensor & self, bool upper); + Tensor & cholesky_solve_out(Tensor & out, const Tensor & self, const Tensor & input2, bool upper); + Tensor cholesky_solve(const Tensor & self, const Tensor & input2, bool upper); + std::tuple solve(const Tensor & self, const Tensor & A); + std::tuple solve_out(Tensor & solution, Tensor & lu, const Tensor & self, const Tensor & A); + std::tuple qr_out(Tensor & Q, Tensor & R, const Tensor & self, bool some); + std::tuple qr(const Tensor & self, bool some); + Tensor & lu_solve_out(Tensor & out, const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots); + Tensor lu_solve(const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots); + Tensor & digamma_out(Tensor & out, const Tensor & self); + Tensor digamma(const Tensor & self); + Tensor & polygamma_out(Tensor & out, int64_t n, const Tensor & self); + Tensor polygamma(int64_t n, const Tensor & self); + Tensor sign(const Tensor & self); + Tensor & sign_(Tensor & self); + Tensor & atan2_out(Tensor & out, const Tensor & self, const Tensor & other); + Tensor atan2(const Tensor & self, const Tensor & other); + #ifdef BUILD_NAMEDTENSOR + std::tuple sort_out(Tensor & values, Tensor & indices, const Tensor & self, Dimname dim, bool descending); + #endif + #ifdef BUILD_NAMEDTENSOR + std::tuple sort(const Tensor & self, Dimname dim, bool descending); + #endif + Tensor argsort(const Tensor & self, int64_t dim, bool descending); + #ifdef BUILD_NAMEDTENSOR + Tensor argsort(const Tensor & self, Dimname dim, bool descending); + #endif + Tensor all(const Tensor & self); + Tensor any(const Tensor & self); + Tensor normal(double mean, double std, IntArrayRef size, Generator * generator, const TensorOptions & options); + Tensor & normal_out(Tensor & out, double mean, double std, IntArrayRef size, Generator * generator); + Tensor alias(const Tensor & self); + Tensor & mse_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor mse_loss(const Tensor & self, const Tensor & target, int64_t reduction); + Tensor & l1_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor l1_loss(const Tensor & self, const Tensor & target, int64_t reduction); + Tensor l1_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor & multilabel_margin_loss_out(Tensor & out, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor multilabel_margin_loss(const Tensor & self, const Tensor & target, int64_t reduction); + Tensor & nll_loss_out(Tensor & out, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); + Tensor nll_loss(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); + Tensor & nll_loss2d_out(Tensor & out, const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); + Tensor nll_loss2d(const Tensor & self, const Tensor & target, const Tensor & weight, int64_t reduction, int64_t ignore_index); + Tensor smooth_l1_loss(const Tensor & self, const Tensor & target, int64_t reduction); + Tensor smooth_l1_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction); + Tensor & log_sigmoid_out(Tensor & out, const Tensor & self); + Tensor log_sigmoid(const Tensor & self); + Tensor adaptive_avg_pool2d(const Tensor & self, IntArrayRef output_size); + Tensor _test_optional_float(const Tensor & self, c10::optional scale); + Tensor sigmoid_backward(const Tensor & grad_output, const Tensor & output); + Tensor & thnn_conv2d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); + Tensor thnn_conv2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); + Tensor & thnn_conv_depthwise2d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation); + Tensor thnn_conv_depthwise2d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation); + Tensor & slow_conv3d_out(Tensor & out, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); + Tensor slow_conv3d(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding); + Tensor isfinite(const Tensor & self); +} // namespace TypeDefault + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/Utils.h b/thirdparty/libtorch/include/ATen/Utils.h new file mode 100644 index 0000000000..d7cf4cdee3 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/Utils.h @@ -0,0 +1,164 @@ +#pragma once + +#include +#include +#include +#include + +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +#if defined(__clang__) +#define __ubsan_ignore_float_divide_by_zero__ __attribute__((no_sanitize("float-divide-by-zero"))) +#define __ubsan_ignore_vptr__ __attribute__((no_sanitize("vptr"))) +#else +#define __ubsan_ignore_float_divide_by_zero__ +#define __ubsan_ignore_vptr__ +#endif + +#define AT_DISALLOW_COPY_AND_ASSIGN(TypeName) \ + TypeName(const TypeName&) = delete; \ + void operator=(const TypeName&) = delete + +namespace at { + +CAFFE2_API int _crash_if_asan(int); + +static inline const Storage& checked_storage( + const Storage& expr, + const char* name, + int pos, + DeviceType device_type, + caffe2::TypeMeta dtype) { + if (expr.device_type() != device_type) { + AT_ERROR( + "Expected object of device type ", + device_type, + " but got device type ", + expr.data_ptr().device().type(), + " for argument #", + pos, + " '", + name, + "'"); + } + if (expr.dtype() != dtype) { + AT_ERROR( + "Expected object of data type ", + dtype, + " but got data type ", + expr.dtype().id(), + " for argument #", + pos, + " '", + name, + "'"); + } + return expr; +} + +// TODO: This unwrapping code is ONLY used for TH bindings; once TH goes +// away, we can delete this function +static inline TensorImpl* checked_dense_tensor_unwrap(const Tensor& expr, const char * name, int pos, const char * api, bool allowNull, DeviceType device_type, ScalarType scalar_type) { + if(allowNull && !expr.defined()) { + return nullptr; + } + if (expr.layout() != Layout::Strided) { + AT_ERROR("Expected dense tensor but got ", expr.layout(), + " for argument #", pos, " '", name, "' in call to ", api); + } + if (expr.device().type() != device_type) { + AT_ERROR("Expected object of device type ", device_type, " but got device type ", expr.device().type(), + " for argument #", pos, " '", name, "' in call to ", api); + } + if (expr.scalar_type() != scalar_type) { + AT_ERROR("Expected object of scalar type ", scalar_type, " but got scalar type ", expr.scalar_type(), + " for argument #", pos, " '", name, "' in call to ", api); + } + return expr.unsafeGetTensorImpl(); +} + +// Converts a TensorList (i.e. ArrayRef to vector of TensorImpl*) +static inline std::vector checked_tensor_list_unwrap(ArrayRef tensors, const char * name, int pos, Backend backend, ScalarType scalar_type) { + std::vector unwrapped; + unwrapped.reserve(tensors.size()); + for (unsigned int i = 0; i < tensors.size(); ++i) { + const auto& expr = tensors[i]; + if (tensorTypeIdToBackend(c10::impl::dispatchTypeId(expr.type_set())) != backend) { + AT_ERROR("Expected object of backend ", backend, " but got backend ", tensorTypeIdToBackend(c10::impl::dispatchTypeId(expr.type_set())), + " for sequence element ", i, " in sequence argument at position #", pos, " '", name, "'"); + } + if (expr.scalar_type() != scalar_type) { + AT_ERROR("Expected object of scalar type ", scalar_type, " but got scalar type ", expr.scalar_type(), + " for sequence element ", i , " in sequence argument at position #", pos, " '", name, "'"); + } + unwrapped.emplace_back(expr.unsafeGetTensorImpl()); + } + return unwrapped; +} + +template +std::array check_intlist(ArrayRef list, const char * name, int pos) { + if (list.empty()) { + // TODO: is this necessary? We used to treat nullptr-vs-not in IntList differently + // with strides as a way of faking optional. + list = {}; + } + auto res = std::array(); + if (list.size() == 1 && N > 1) { + res.fill(list[0]); + return res; + } + if (list.size() != N) { + AT_ERROR("Expected a list of ", N, " ints but got ", list.size(), " for argument #", pos, " '", name, "'"); + } + std::copy_n(list.begin(), N, res.begin()); + return res; +} + +inline int64_t sum_intlist(ArrayRef list) { + return std::accumulate(list.begin(), list.end(), 0ll); +} + +inline int64_t prod_intlist(ArrayRef list) { + return std::accumulate(list.begin(), list.end(), 1ll, std::multiplies()); +} + +/** + * Utility function used in tensor implementations, which + * supplies the default generator to tensors, if an input generator + * is not supplied. The input Generator* is also static casted to + * the backend generator type (CPU/CUDAGenerator etc.) + */ +template +static inline T * get_generator_or_default(Generator * expr, Generator * defaultValue) { + if (!expr) { + expr = defaultValue; + } + if (T::device_type() == expr->device().type()) { + return static_cast(expr); + } + AT_ERROR("Expected a '", T::device_type(), "' device type for generator but found '", expr->device().type(), "'"); +} + +/** + * Utility function to static cast input Generator* to + * the backend generator type (CPU/CUDAGenerator etc.) + */ +template +static inline T * check_generator(Generator * expr) { + if (T::device_type() == expr->device().type()) { + return static_cast(expr); + } + AT_ERROR("Expected a '", T::device_type(), "' device type for generator but found '", expr->device().type(), "'"); +} + +} // at diff --git a/thirdparty/libtorch/include/ATen/Version.h b/thirdparty/libtorch/include/ATen/Version.h new file mode 100644 index 0000000000..18fd31d3ed --- /dev/null +++ b/thirdparty/libtorch/include/ATen/Version.h @@ -0,0 +1,14 @@ +#include + +namespace at { + +/// Returns a detailed string describing the configuration PyTorch. +CAFFE2_API std::string show_config(); + +CAFFE2_API std::string get_mkl_version(); + +CAFFE2_API std::string get_mkldnn_version(); + +CAFFE2_API std::string get_openmp_version(); + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/WrapDimUtils.h b/thirdparty/libtorch/include/ATen/WrapDimUtils.h new file mode 100644 index 0000000000..1cdbe150c1 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/WrapDimUtils.h @@ -0,0 +1,82 @@ +#pragma once + +#include +#include +#include + +namespace at { + +static inline int64_t maybe_wrap_dim(int64_t dim, int64_t dim_post_expr, bool wrap_scalar=true) { + return c10::maybe_wrap_dim(dim, dim_post_expr, wrap_scalar); +} + +static inline int64_t maybe_wrap_dim(int64_t dim, TensorImpl *tensor) { + return maybe_wrap_dim(dim, tensor->dim()); +} + +static inline int64_t maybe_wrap_dim(int64_t dim, TensorList tensors) { + if (tensors.size() == 0) { + // can't wrap empty TensorList; rely on underlying implementation to throw error if necessary. + return dim; + } + return maybe_wrap_dim(dim, tensors[0].dim()); +} + +static inline int64_t maybe_wrap_dim(int64_t dim, const std::vector> & tensor_sizes) { + if (tensor_sizes.size() == 0) { + // can't wrap empty list; rely on underlying implementation to throw error if necessary + return dim; + } + return maybe_wrap_dim(dim, tensor_sizes[0].size()); +} + +// wrap each of dims basing on dim_post_expr +static inline void maybe_wrap_dims(std::vector& dims, int64_t dim_post_expr) { + if (dim_post_expr <= 0) { + dim_post_expr = 1; // this will make range [-1, 0] + } + int64_t min = -dim_post_expr; + int64_t max = dim_post_expr - 1; + for (auto& dim : dims) { + if (dim < min || dim > max) { + AT_INDEX_ERROR( + "Dimension out of range (expected to be in range of [", + min, ", ", max, "], but got ", dim, ")"); + } + if (dim < 0) dim += dim_post_expr; + } +} + +// previously, size [0] tensors were the only possible empty tensors; thus, it wasn't possible +// to cat empty tensors unless all the other tensors were 1-dimensional, so we allowed these tensors +// to be "skipped" (both for wrap dimension behavior and dimension size checking). +// We maintain this behavior for backwards compatibility, but only for this specific size +// (i.e. other empty sizes are not skipped). +static inline int64_t legacy_cat_wrap_dim(int64_t dim, const std::vector>& tensor_sizes) { + for (auto& sizes : tensor_sizes) { + if (sizes == std::vector({0})) { + continue; + } + return maybe_wrap_dim(dim, sizes.size()); + } + return dim; +} + +static inline int64_t legacy_cat_wrap_dim(int64_t dim, TensorList tensors) { + for (auto& tensor : tensors) { + if (tensor.dim() == 1 && tensor.sizes()[0] == 0) { + continue; + } + return maybe_wrap_dim(dim, tensor.dim()); + } + return dim; +} + +// wrap negative dims in a vector +static inline void wrap_all_dims(std::vector& dims_to_wrap, int64_t tensor_total_dims) { + for (size_t i = 0; i < dims_to_wrap.size(); i++) { + dims_to_wrap[i] = maybe_wrap_dim(dims_to_wrap[i], tensor_total_dims); + } +} + +} diff --git a/thirdparty/libtorch/include/ATen/WrapDimUtilsMulti.h b/thirdparty/libtorch/include/ATen/WrapDimUtilsMulti.h new file mode 100644 index 0000000000..a2af1b0dcd --- /dev/null +++ b/thirdparty/libtorch/include/ATen/WrapDimUtilsMulti.h @@ -0,0 +1,26 @@ +#pragma once + +#include +#include +#include +#include + +namespace at { + +// This is in an extra file to work around strange interaction of +// bitset on Windows with operator overloading + +constexpr size_t dim_bitset_size = 64; + +static inline std::bitset dim_list_to_bitset(IntArrayRef dims, int64_t ndims) { + TORCH_CHECK(ndims <= (int64_t) dim_bitset_size, "only tensors with up to ", dim_bitset_size, " dims are supported"); + std::bitset seen; + for (size_t i = 0; i < dims.size(); i++) { + size_t dim = maybe_wrap_dim(dims[i], ndims); + TORCH_CHECK(!seen[dim], "dim ", dim, " appears multiple times in the list of dims"); + seen[dim] = true; + } + return seen; +} + +} diff --git a/thirdparty/libtorch/include/ATen/core/ATenGeneral.h b/thirdparty/libtorch/include/ATen/core/ATenGeneral.h new file mode 100644 index 0000000000..9b787a2163 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/ATenGeneral.h @@ -0,0 +1,3 @@ +#pragma once + +#include diff --git a/thirdparty/libtorch/include/ATen/core/Array.h b/thirdparty/libtorch/include/ATen/core/Array.h new file mode 100644 index 0000000000..043d132cdf --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/Array.h @@ -0,0 +1,38 @@ +#pragma once + +// A fixed-size array type usable from both host and +// device code. + +#include + +namespace at { namespace detail { + +template +struct alignas(16) Array { + T data[size]; + + C10_HOST_DEVICE T operator[](int i) const { + return data[i]; + } + C10_HOST_DEVICE T& operator[](int i) { + return data[i]; + } +#ifdef __HIP_PLATFORM_HCC__ + C10_HOST_DEVICE Array() = default; + C10_HOST_DEVICE Array(const Array&) = default; + C10_HOST_DEVICE Array& operator=(const Array&) = default; +#else + Array() = default; + Array(const Array&) = default; + Array& operator=(const Array&) = default; +#endif + + // Fill the array with x. + C10_HOST_DEVICE Array(T x) { + for (int i = 0; i < size; i++) { + data[i] = x; + } + } +}; + +}} diff --git a/thirdparty/libtorch/include/ATen/core/Backtrace.h b/thirdparty/libtorch/include/ATen/core/Backtrace.h new file mode 100644 index 0000000000..ac72896875 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/Backtrace.h @@ -0,0 +1,2 @@ +#include +#include diff --git a/thirdparty/libtorch/include/ATen/core/DeprecatedTypeProperties.h b/thirdparty/libtorch/include/ATen/core/DeprecatedTypeProperties.h new file mode 100644 index 0000000000..719cd9a186 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/DeprecatedTypeProperties.h @@ -0,0 +1,131 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include + + +namespace at { + +class Tensor; + +// This class specifies a Backend and a ScalarType. Currently, it primarily +// serves as a replacement return value for Tensor::type(). Previously, +// Tensor::type() returned Type&, but we are changing Type to not be +// dtype-specific. +class CAFFE2_API DeprecatedTypeProperties { + public: + DeprecatedTypeProperties(Backend backend, ScalarType scalar_type) + : backend_(backend), scalar_type_(scalar_type) {} + + Backend backend() const { + return backend_; + } + + Layout layout() const { + return layout_from_backend(backend_); + } + + bool is_sparse() const { + return layout_from_backend(backend()) == kSparse; + } + + DeviceType device_type() const { + return backendToDeviceType(backend_); + } + + bool is_cuda() const { + return backendToDeviceType(backend_) == kCUDA; + } + + ScalarType scalarType() const { + return scalar_type_; + } + + caffe2::TypeMeta typeMeta() const { + return scalarTypeToTypeMeta(scalar_type_); + } + + bool operator==(const DeprecatedTypeProperties& other) const { + return backend_ == other.backend() && scalar_type_ == other.scalarType(); + } + + bool operator!=(const DeprecatedTypeProperties& other) const { + return !(*this == other); + } + + std::string toString() const { + std::string base_str; + if (backend_ == Backend::Undefined || scalar_type_ == ScalarType::Undefined) { + base_str = "UndefinedType"; + } else { + base_str = std::string(at::toString(backend_)) + at::toString(scalar_type_) + "Type"; + } + return base_str; + } + + DeprecatedTypeProperties & toBackend(Backend b) const { + return globalDeprecatedTypePropertiesRegistry().getDeprecatedTypeProperties( + b, scalar_type_); + } + + DeprecatedTypeProperties & toScalarType(ScalarType s) const { + return globalDeprecatedTypePropertiesRegistry().getDeprecatedTypeProperties( + backend_, s); + } + + DeprecatedTypeProperties & cpu() const { + return toBackend(Backend::CPU); + } + + DeprecatedTypeProperties & cuda() const { + return toBackend(Backend::CUDA); + } + + DeprecatedTypeProperties & hip() const { + return toBackend(Backend::HIP); + } + + /// Constructs the `TensorOptions` from a type and a `device_index`. + TensorOptions options(int16_t device_index = -1) const { + return TensorOptions().dtype(typeMeta()) + .device(device_type(), device_index) + .layout(layout()); + } + + /// Constructs the `TensorOptions` from a type and a Device. Asserts that + /// the device type matches the device type of the type. + TensorOptions options(c10::optional device_opt) const { + if (!device_opt.has_value()) { + return options(-1); + } else { + Device device = device_opt.value(); + AT_ASSERT(device.type() == device_type()); + return options(device.index()); + } + } + + operator TensorOptions() const { + return options(); + } + + int64_t id() const { + return static_cast(backend()) * + static_cast(ScalarType::NumOptions) + + static_cast(scalarType()); + } + + Tensor unsafeTensorFromTH(void * th_pointer, bool retain) const; + Storage unsafeStorageFromTH(void * th_pointer, bool retain) const; + Tensor copy(const Tensor & src, bool non_blocking=false, c10::optional to_device={}) const; + + private: + Backend backend_; + ScalarType scalar_type_; +}; + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/core/DeprecatedTypePropertiesRegistry.h b/thirdparty/libtorch/include/ATen/core/DeprecatedTypePropertiesRegistry.h new file mode 100644 index 0000000000..d9b29a35b3 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/DeprecatedTypePropertiesRegistry.h @@ -0,0 +1,31 @@ +#pragma once + +// In order to preserve bc, we make DeprecatedTypeProperties instances unique +// just like they are for Type. + +#include +#include + +namespace at { + +class DeprecatedTypeProperties; + +struct CAFFE2_API DeprecatedTypePropertiesDeleter { + void operator()(DeprecatedTypeProperties * ptr); +}; + +class CAFFE2_API DeprecatedTypePropertiesRegistry { + public: + DeprecatedTypePropertiesRegistry(); + + DeprecatedTypeProperties& getDeprecatedTypeProperties(Backend p, ScalarType s) const; + +private: + std::unique_ptr registry + [static_cast(Backend::NumOptions)] + [static_cast(ScalarType::NumOptions)]; +}; + +CAFFE2_API DeprecatedTypePropertiesRegistry& globalDeprecatedTypePropertiesRegistry(); + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/core/Dict.h b/thirdparty/libtorch/include/ATen/core/Dict.h new file mode 100644 index 0000000000..304afbdfde --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/Dict.h @@ -0,0 +1,373 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +namespace c10 { +struct IValue; +template class Dict; +struct Type; +using TypePtr = std::shared_ptr; + +namespace impl { +bool shallowEquals(const IValue& lhs, const IValue& rhs); + +using valid_dict_key_types = guts::typelist::typelist< + int64_t, + std::string, + double, + bool, + at::Tensor +>; +} + +namespace detail { + +struct DictKeyHash { + size_t operator()(const IValue& ivalue) const; +}; + +struct DictKeyEqualTo { + bool operator()(const IValue& lhs, const IValue& rhs) const { + return impl::shallowEquals(lhs, rhs); + } +}; + +struct DictImpl final : public c10::intrusive_ptr_target { + using dict_map_type = ska_ordered::order_preserving_flat_hash_map; + struct DictElementTypes final { + TypePtr keyType; + TypePtr valueType; + }; + + explicit DictImpl(dict_map_type dict_, DictElementTypes elementTypes_) + : dict(std::move(dict_)) + , elementTypes(std::move(elementTypes_)) {} + dict_map_type dict; + + DictElementTypes elementTypes; + + intrusive_ptr copy() const; +}; + +} + +namespace impl { +template class DictIterator; + +/** + * A reference to an entry in the Dict. + * Use the `key()` and `value()` methods to read the element. + */ +template +class DictEntryRef final { +public: + explicit DictEntryRef(Iterator iterator) + : iterator_(std::move(iterator)) {} + + Key key() const { + return iterator_->first.template to(); + } + + Value value() const { + return iterator_->second.template to(); + } + + template + void setValue(Value_&& value) const { + static_assert(std::is_constructible::value, "Wrong type for the value argument of setValue()"); + iterator_->second = Value(std::forward(value)); + } + +private: + // allow copying and moving, but only our friends (i.e. the Dict class) can do + // it. Copying/moving this reference wrapper would be too ambiguous to allow it + // in the public API. + DictEntryRef(const DictEntryRef&) = default; + DictEntryRef& operator=(const DictEntryRef&) = default; + DictEntryRef(DictEntryRef&&) noexcept = default; + DictEntryRef& operator=(DictEntryRef&& rhs) & noexcept = default; + + Iterator iterator_; + friend class DictIterator; + friend class Dict; +}; + +// this wraps map_type::iterator to make sure user code can't rely +// on it being the type of the underlying map. +template +class DictIterator final : public std::iterator> { +public: + explicit DictIterator() = default; + ~DictIterator() = default; + + DictIterator(const DictIterator& rhs): entryRef_(rhs.entryRef_) {} + DictIterator(DictIterator&& rhs) noexcept: entryRef_(std::move(rhs.entryRef_)) {} + DictIterator& operator=(const DictIterator& rhs) { + entryRef_ = rhs.entryRef_; + return *this; + } + DictIterator& operator=(DictIterator&& rhs) noexcept { + entryRef_ = std::move(rhs.entryRef_); + return *this; + } + + DictIterator& operator++() { + ++entryRef_.iterator_; + return *this; + } + + DictIterator operator++(int) { + DictIterator copy(*this); + ++*this; + return copy; + } + + const DictEntryRef& operator*() const { + return entryRef_; + } + + const DictEntryRef* operator->() const { + return &entryRef_; + } + + friend typename std::iterator>::difference_type operator-(const DictIterator& lhs, const DictIterator& rhs) { + return lhs.entryRef_.iterator_ - rhs.entryRef_.iterator_; + } + +private: + explicit DictIterator(Iterator iterator): entryRef_(std::move(iterator)) {} + + const Iterator& get_iterator_() const { + return entryRef_.iterator_; + } + + friend bool operator==(const DictIterator& lhs, const DictIterator& rhs) { + return lhs.get_iterator_() == rhs.get_iterator_(); + } + + friend bool operator!=(const DictIterator& lhs, const DictIterator& rhs) { + return lhs.get_iterator_() != rhs.get_iterator_(); + } + + friend bool operator<(const DictIterator& lhs, const DictIterator& rhs) { + return lhs.get_iterator_() < rhs.get_iterator_(); + } + + friend bool operator<=(const DictIterator& lhs, const DictIterator& rhs) { + return lhs.get_iterator_() <= rhs.get_iterator_(); + } + + friend bool operator>(const DictIterator& lhs, const DictIterator& rhs) { + return lhs.get_iterator_() > rhs.get_iterator_(); + } + + friend bool operator>=(const DictIterator& lhs, const DictIterator& rhs) { + return lhs.get_iterator_() >= rhs.get_iterator_(); + } + + DictEntryRef entryRef_; + + friend class DictIterator; + friend class Dict; +}; + +template Dict toTypedDict(Dict dict); +template Dict toGenericDict(Dict dict); +} + +/** + * An object of this class stores a map from Key to Value. + * + * This is a pointer type. After a copy, both Dicts + * will share the same storage: + * + * > Dict a; + * > Dict b = a; + * > b.insert(3, "three"); + * > ASSERT("three" == a.at(3)); + * + * We use this class in the PyTorch kernel API because that + * allows us to do optimizations and switch out the underlying + * map implementation without breaking backwards compatibility + * for the kernel API. + */ +template +class Dict final { +private: + static_assert((std::is_same::value && std::is_same::value) || guts::typelist::contains::value, "Invalid Key type for Dict. We only support int64_t, double, bool, and string."); + + // impl_ stores the underlying map as a ska_ordered::order_preserving_flat_hash_map. + // We intentionally don't offer conversion from/to + // order_preserving_flat_hash_map, return references to it or something like that, + // because such operations would get expensive if we switch out + // the actual map implementation. + // This is an intrusive_ptr because Dict is a pointer type. + // Invariant: This will never be a nullptr, there will always be a valid + // DictImpl. + c10::intrusive_ptr impl_; + + explicit Dict(c10::intrusive_ptr&& impl); + friend struct IValue; + template friend Dict impl::toTypedDict(Dict); + template friend Dict impl::toGenericDict(Dict); + +public: + using key_type = Key; + using mapped_type = Value; + using size_type = typename detail::DictImpl::dict_map_type::size_type; + using iterator = impl::DictIterator; + + /** + * Creates an empty dict. + */ + explicit Dict(); + + /** + * Create a generic dict with runtime type information. + * This only works for c10::impl::GenericDict and is not part of the public API + * but only supposed to be used internally by PyTorch. + */ + explicit Dict(TypePtr keyType, TypePtr valueType); + + ~Dict() = default; + + Dict(const Dict&) = default; + Dict& operator=(const Dict&) = default; + Dict(Dict&&) noexcept; + Dict& operator=(Dict&&) noexcept; + + /** + * Create a new Dict pointing to a deep copy of the same data. + * The Dict returned is a new dict with separate storage. + * Changes in it are not reflected in the original dict or vice versa. + */ + Dict copy() const; + + /** + * Returns an iterator to the first element of the container. + * If the container is empty, the returned iterator will be equal to end(). + */ + iterator begin() const; + + /** + * Returns an iterator to the element following the last element of the container. + * This element acts as a placeholder; attempting to access it results in undefined behavior. + */ + iterator end() const; + + /** + * Checks if the container has no elements. + */ + bool empty() const; + + /** + * Returns the number of elements in the container. + */ + size_type size() const; + + /** + * Erases all elements from the container. After this call, size() returns zero. + * Invalidates any references, pointers, or iterators referring to contained elements. May also invalidate past-the-end iterators. + */ + void clear() const; + + /** + * Inserts element(s) into the container, if the container doesn't already contain an element with an equivalent key. + * May invalidate any references, pointers, or iterators referring to contained elements. + * + * @return A pair consisting of an iterator to the inserted element (or to the element that prevented the insertion) and a bool denoting whether the insertion took place. + */ + template + std::pair insert(Key_&& key, Value_&& value) const; + + /** + * If an element with the given key already exists, it is overwritten with the given value. + * Otherwise, a new element with the given key and value are inserted. + * May invalidate any references, pointers, or iterators referring to contained elements. + * + * @return The bool component is true if the insertion took place and false if the assignment took place. The iterator component is pointing at the element that was inserted or updated. + */ + template + std::pair insert_or_assign(Key_&& key, Value_&& value) const; + + /** + * Removes the element pointed to by iter. + * May invalidate any references, pointers, or iterators referring to contained elements. + * The iterator iter must be valid and dereferenceable. Thus the end() iterator (which is valid, but is not dereferenceable) cannot be used as a value for iter. + */ + void erase(iterator iter) const; + + /** + * Removes the element with the given key, if it exists. + * May invalidate any references, pointers, or iterators referring to contained elements. + * + * @return The number of elements removed. This is either '1' if an element with the key existed, or '0' if it didn't. + */ + C10_NODISCARD size_t erase(const Key& key) const; + + /** + * Returns the mapped value of the element with key equivalent to key. + * If no such element exists, an exception of type std::out_of_range is thrown. + */ + Value at(const Key& key) const; + + /** + * Finds an element with key equivalent to key. + * + * @return Iterator to an element with key equivalent to key. + * If no such element is found, past-the-end (see end()) iterator is returned. + */ + iterator find(const Key& key) const; + + /** + * Checks if there is an element with key equivalent to key in the container. + * + * @return true if there is such an element, otherwise false. + */ + bool contains(const Key& key) const; + + /** + * Increase the capacity so that at least count elements can be stored without + * having to reallocate or rehash. + */ + void reserve(size_type count) const; + + + // private API for now because the return type will change to TypePtr + // instead of optional once types are mandatory. + TypePtr keyType() const; + TypePtr valueType() const; + + // [unsafe set type] + // These functions mutate the tagged type of this dictionary in place. + // There is no checking that the members of the dictionary are instances + // of the new types, nor is there a check that other IValues which + // hold references to this dictionary have the right static type. + // This functionality is used only in the unpickler, where at + // creation type the real type of the dictionary is unknown, but + // then later recovered from the static type information of the + // unpickled object. + void unsafeSetKeyType(TypePtr t); + void unsafeSetValueType(TypePtr t); +}; + +namespace impl { +// GenericDict is how IValue stores dicts. It is, however, not part of the +// public API. Kernels should use Dicts with concrete Key, Value types instead +// (maybe except for some internal prim ops). +using GenericDict = Dict; + +} +} + +namespace torch { + template using Dict = c10::Dict; +} + +#include diff --git a/thirdparty/libtorch/include/ATen/core/Dict_inl.h b/thirdparty/libtorch/include/ATen/core/Dict_inl.h new file mode 100644 index 0000000000..a0e57fb182 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/Dict_inl.h @@ -0,0 +1,204 @@ +#pragma once + +#include +#include + +namespace c10 { + +template TypePtr getTypePtr(); +std::string toString(TypePtr typePtr); + +namespace impl { +inline bool shallowEquals(const IValue& lhs, const IValue& rhs) { + if (lhs.isNone()) { + return rhs.isNone(); + } else if (lhs.isInt()) { + return rhs.isInt() && lhs.toInt() == rhs.toInt(); + } else if (lhs.isString()) { + return rhs.isString() && lhs.toStringRef() == rhs.toStringRef(); + } else if (lhs.isDouble()) { + return rhs.isDouble() && lhs.toDouble() == rhs.toDouble(); + } else if (lhs.isBool()) { + return rhs.isBool() && lhs.toBool() == rhs.toBool(); + } else if (lhs.isIntList()) { + return rhs.isIntList() && lhs.toIntListRef() == rhs.toIntListRef(); + } else if (lhs.isTensor()) { + return lhs.toTensor().is_same(rhs.toTensor()); + } else { + AT_ERROR("shallowEquals(IValue, IValue) not implemented for type ", lhs.tagKind()); + } +} + +template +Dict toTypedDict(GenericDict dict) { + TORCH_INTERNAL_ASSERT(*getTypePtr() == *dict.impl_->elementTypes.keyType, "Tried to cast a Dict<", toString(dict.impl_->elementTypes.keyType), ", ", toString(dict.impl_->elementTypes.valueType) ,"> to a Dict<", toString(getTypePtr()), ", ", toString(getTypePtr()), ">. Key types mismatch."); + TORCH_INTERNAL_ASSERT(*getTypePtr() == *dict.impl_->elementTypes.valueType, "Tried to cast a Dict<", toString(dict.impl_->elementTypes.keyType), ", ", toString(dict.impl_->elementTypes.valueType) ,"> to a Dict<", toString(getTypePtr()), ", ", toString(getTypePtr()), ">. Value types mismatch."); + + return Dict(std::move(dict.impl_)); +} + +template +GenericDict toGenericDict(Dict dict) { + return GenericDict(std::move(dict.impl_)); +} +} + +namespace detail { + +inline size_t DictKeyHash::operator()(const IValue& ivalue) const { + if (ivalue.isInt()) { + return std::hash()(ivalue.toInt()); + } else if (ivalue.isString()) { + return std::hash()(ivalue.toStringRef()); + } else if (ivalue.isDouble()) { + return std::hash()(ivalue.toDouble()); + } else if (ivalue.isBool()) { + return std::hash()(ivalue.toBool()); + } else if (ivalue.isTensor()) { + return std::hash()(ivalue.toTensor().unsafeGetTensorImpl()); + } else { + throw std::runtime_error("Can't hash IValues with this tag"); + } +} + +inline intrusive_ptr DictImpl::copy() const { + return make_intrusive(dict, elementTypes); +} + +} + +template +Dict::Dict() + :Dict(make_intrusive( + detail::DictImpl::dict_map_type(), + detail::DictImpl::DictElementTypes{getTypePtr(), getTypePtr()})) { + static_assert(!std::is_same::value, "This constructor is not valid for Dict. Please use c10::impl::GenericDict(keyType, valueType) instead, or if you absolutely have to, use c10::impl::GenericDict(c10::impl::deprecatedUntypedDict())."); + static_assert(!std::is_same::value, "This constructor is not valid for Dict<_, IValue>. Please use c10::impl::GenericDict(keyType, valueType) instead, or if you absolutely have to, use c10::impl::GenericDict(c10::impl::deprecatedUntypedDict())."); +} + +template +Dict::Dict(TypePtr keyType, TypePtr valueType) +: Dict(make_intrusive( + detail::DictImpl::dict_map_type(), + detail::DictImpl::DictElementTypes {std::move(keyType), std::move(valueType)})) { + static_assert(std::is_same::value, "This constructor is only valid for c10::impl::GenericDict."); + static_assert(std::is_same::value, "This constructor is only valid for c10::impl::GenericDict."); +} + +template +Dict::Dict(Dict&& rhs) noexcept: impl_(std::move(rhs.impl_)) { + rhs.impl_ = make_intrusive(detail::DictImpl::dict_map_type(), impl_->elementTypes); +} + +template +Dict::Dict(c10::intrusive_ptr&& impl): impl_(std::move(impl)) {} + +template +Dict& Dict::operator=(Dict&& rhs) noexcept { + impl_ = std::move(rhs.impl_); + rhs.impl_ = make_intrusive(detail::DictImpl::dict_map_type(), impl_->elementTypes); + return *this; +} + +template +Dict Dict::copy() const { + return Dict(impl_->copy()); +} + +template +typename Dict::iterator Dict::begin() const { + return iterator{impl_->dict.begin()}; +} + +template +typename Dict::iterator Dict::end() const { + return iterator{impl_->dict.end()}; +} + +template +bool Dict::empty() const { + return impl_->dict.empty(); +} + +template +typename Dict::size_type Dict::size() const { + return impl_->dict.size(); +} + +template +void Dict::clear() const { + impl_->dict.clear(); +} + +template +template +std::pair::iterator, bool> Dict::insert(Key_&& key, Value_&& value) const { + static_assert(std::is_constructible::value, "Wrong type for the key argument of Dict::insert"); + static_assert(std::is_constructible::value, "Wrong type for the value argument of Dict::insert"); + auto inserted = impl_->dict.insert(std::pair{ + Key(std::forward(key)), + Value(std::forward(value))}); + return {iterator{inserted.first}, inserted.second}; +} + +template +template +std::pair::iterator, bool> Dict::insert_or_assign(Key_&& key, Value_&& value) const { + static_assert(std::is_constructible::value, "Wrong type for the key argument of Dict::insert_or_assign"); + static_assert(std::is_constructible::value, "Wrong type for the value argument of Dict::insert_or_assign"); + auto inserted = impl_->dict.insert_or_assign( + Key(std::forward(key)), + Value(std::forward(value))); + return {iterator{inserted.first}, inserted.second}; +} + +template +void Dict::erase(iterator iter) const { + impl_->dict.erase(iter.entryRef_.iterator_); +} + +template +C10_NODISCARD size_t Dict::erase(const Key& key) const { + return impl_->dict.erase(key); +} + +template +Value Dict::at(const Key& key) const { + return impl_->dict.at(key).template to(); +} + +template +typename Dict::iterator Dict::find(const Key& key) const { + return iterator{impl_->dict.find(key)}; +} + +template +bool Dict::contains(const Key& key) const { + return end() != find(key); +} + +template +void Dict::reserve(size_type count) const { + impl_->dict.reserve(count); +} + +template +TypePtr Dict::keyType() const { + return impl_->elementTypes.keyType; +} + +template +TypePtr Dict::valueType() const { + return impl_->elementTypes.valueType; +} +template +void Dict::unsafeSetKeyType(TypePtr t) { + impl_->elementTypes.keyType = std::move(t); +} + +template +void Dict::unsafeSetValueType(TypePtr t) { + impl_->elementTypes.valueType = std::move(t); +} + +} diff --git a/thirdparty/libtorch/include/ATen/core/DimVector.h b/thirdparty/libtorch/include/ATen/core/DimVector.h new file mode 100644 index 0000000000..6e9e2c037a --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/DimVector.h @@ -0,0 +1,13 @@ +#pragma once + +#include +#include + +namespace at { + +constexpr size_t kDimVectorStaticSize = 5; + +/// A container for sizes or strides +using DimVector = SmallVector; + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/core/Dimname.h b/thirdparty/libtorch/include/ATen/core/Dimname.h new file mode 100644 index 0000000000..499e7f52a1 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/Dimname.h @@ -0,0 +1,51 @@ +#pragma once +#include + +#ifdef BUILD_NAMEDTENSOR +#include +#include +#include +#include + +namespace at { + +enum class NameType: uint8_t { BASIC, WILDCARD }; + +struct CAFFE2_API Dimname { + static Dimname fromSymbol(Symbol name); + static Dimname wildcard(); + static bool isValidName(const std::string& name); + + NameType type() const { return type_; } + Symbol symbol() const { return name_; } + + bool isBasic() const { return type_ == NameType::BASIC; } + bool isWildcard() const { return type_ == NameType::WILDCARD; } + + bool matches(Dimname other) const; + optional unify(Dimname other) const; + + private: + Dimname(Symbol name) + : name_(name), type_(NameType::BASIC) {} + Dimname(Symbol name, NameType type) + : name_(name), type_(type) {} + + Symbol name_; + NameType type_; +}; + +using DimnameList = c10::ArrayRef; + +CAFFE2_API std::ostream& operator<<(std::ostream& out, const Dimname& dimname); + +inline bool operator==(const Dimname& lhs, const Dimname& rhs) { + return lhs.symbol() == rhs.symbol(); +} + +inline bool operator!=(const Dimname& lhs, const Dimname& rhs) { + return !(lhs == rhs); +} + +} // namespace at +#endif diff --git a/thirdparty/libtorch/include/ATen/core/DistributionsHelper.h b/thirdparty/libtorch/include/ATen/core/DistributionsHelper.h new file mode 100644 index 0000000000..03252d9200 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/DistributionsHelper.h @@ -0,0 +1,264 @@ +#pragma once + +// define constants like M_PI and C keywords for MSVC +#ifdef _MSC_VER +#define _USE_MATH_DEFINES +#include +#endif + +#include +#include +#include +#include +#include + +/** + * Distributions kernel adapted from THRandom.cpp + * The kernels try to follow std::random distributions signature + * For instance: in ATen + * auto gen = at::detail::createCPUGenerator(); + * at::uniform_real_distribution uniform(0, 1); + * auto sample = uniform(gen.get()); + * + * vs std::random + * + * std::mt19937 gen; + * std::uniform_real_distribution uniform(0, 1); + * auto sample = uniform(gen); + */ + + +namespace at { + +// Using VectorType in Box-muller derived distributions to avoid +// code duplication +template +struct VectorType { }; + +#if defined(__CUDACC__) || defined(__HIPCC__) +template <> struct VectorType { using type = at::detail::Array; }; +#endif +template <> struct VectorType { using type = at::detail::Array; }; +template <> struct VectorType { using type = at::detail::Array; }; +template <> struct VectorType { using type = at::detail::Array; }; + +template +using vect_type = typename VectorType::type; + +// Using DistAccumType in accumulate types for distributions. +// Note: Ideally we'd be using ATen/AccumulateType.h but looks +// like the there is some inconsistency in how accumulate types +// are mapped currently, e.g. for the cpu side, float is mapped +// to double. +template +struct DistAccumType { }; + +#if defined(__CUDACC__) || defined(__HIPCC__) +template <> struct DistAccumType { using type = float; }; +#endif +template <> struct DistAccumType { using type = float; }; +template <> struct DistAccumType { using type = float; }; +template <> struct DistAccumType { using type = double; }; + +template +using dist_acctype = typename DistAccumType::type; + +// Constants for uniform distribution +// doubles have 52 bits of mantissa (fractional part) +constexpr uint64_t DOUBLE_MASK = (1ULL << 53) - 1; +constexpr double DOUBLE_DIVISOR = 1.0 / (1ULL << 53); + +// floats have 23 bits of mantissa (fractional part) +constexpr uint32_t FLOAT_MASK = (1 << 24) - 1; +constexpr float FLOAT_DIVISOR = 1.0f / (1 << 24); + +/** + * Samples a uniform distribution in the range [0,1) of type T + */ +template +struct uniform_real_distribution { + + inline uniform_real_distribution(T a_in, T b_in) { + TORCH_CHECK(a_in <= b_in); + TORCH_CHECK(b_in-a_in <= std::numeric_limits::max()); + a = a_in; + b = b_in; + } + + inline dist_acctype operator()(at::CPUGenerator* generator){ + dist_acctype x; + if(std::is_same::value) { + x = (generator->random64() & DOUBLE_MASK) * DOUBLE_DIVISOR; + } else { + x = (generator->random() & FLOAT_MASK) * FLOAT_DIVISOR; + } + return (x * (b - a) + a); + } + + private: + T a; + T b; +}; + +/** + * Samples a normal distribution using the Box-Muller method + * Takes mean and standard deviation as inputs + * Note that Box-muller method returns two samples at a time. + * Hence, we cache the "next" sample in the CPUGenerator class. + */ +template +struct normal_distribution { + + inline normal_distribution(T mean_in, T stdv_in) { + TORCH_CHECK(stdv_in > 0); + mean = mean_in; + stdv = stdv_in; + } + + inline dist_acctype operator()(at::CPUGenerator* generator){ + dist_acctype ret; + // return cached values if available + if (std::is_same::value) { + if (generator->next_double_normal_sample()) { + ret = *(generator->next_double_normal_sample()) * stdv + mean; + // reset c10::optional to null + generator->set_next_double_normal_sample(c10::optional()); + return ret; + } + } else { + if (generator->next_float_normal_sample()) { + ret = *(generator->next_float_normal_sample()) * stdv + mean; + // reset c10::optional to null + generator->set_next_float_normal_sample(c10::optional()); + return ret; + } + } + // otherwise generate new normal values + uniform_real_distribution uniform(0.0, 1.0); + const dist_acctype u1 = uniform(generator); + const dist_acctype u2 = uniform(generator); + const dist_acctype r = ::sqrt(static_cast(-2.0) * ::log(static_cast(1.0)-u2)); + const dist_acctype theta = static_cast(2.0) * static_cast(M_PI) * u1; + if (std::is_same::value) { + dist_acctype cache = r * ::sin(theta); + generator->set_next_double_normal_sample(c10::optional(cache)); + } else { + dist_acctype cache = r * ::sin(theta); + generator->set_next_float_normal_sample(c10::optional(cache)); + } + ret = r * ::cos(theta) * stdv + mean; + return ret; + } + + private: + T mean; + T stdv; +}; + +/** + * Samples a bernoulli distribution given a probability input + */ +template +struct bernoulli_distribution { + + inline bernoulli_distribution(T p_in) { + TORCH_CHECK(p_in >= 0 && p_in <= 1); + p = p_in; + } + + inline int operator()(at::CPUGenerator* generator) { + uniform_real_distribution uniform(0.0, 1.0); + return uniform(generator) < p; + } + + private: + T p; +}; + +/** + * Samples a geometric distribution given a probability input + */ +template +struct geometric_distribution { + + inline geometric_distribution(T p_in) { + TORCH_CHECK(p_in > 0 && p_in < 1); + p = p_in; + } + + inline int operator()(at::CPUGenerator* generator) { + uniform_real_distribution uniform(0.0, 1.0); + dist_acctype sample = uniform(generator); + return static_cast(::log(static_cast(1.0)-sample) / ::log(p)) + 1; + } + + private: + T p; +}; + +/** + * Samples an exponential distribution given a lambda input + */ +template +struct exponential_distribution { + + inline exponential_distribution(T lambda_in) { + lambda = lambda_in; + } + + inline T operator()(at::CPUGenerator* generator) { + uniform_real_distribution uniform(0.0, 1.0); + dist_acctype sample = uniform(generator); + return static_cast(-1.0) / lambda * ::log(static_cast(1.0)-sample); + } + + private: + T lambda; +}; + +/** + * Samples a cauchy distribution given median and sigma as inputs + */ +template +struct cauchy_distribution { + + inline cauchy_distribution(T median_in, T sigma_in) { + median = median_in; + sigma = sigma_in; + } + + inline T operator()(at::CPUGenerator* generator) { + uniform_real_distribution uniform(0.0, 1.0); + return median + sigma * ::tan(static_cast(M_PI) * (uniform(generator)-static_cast(0.5))); + } + + private: + T median; + T sigma; +}; + +/** + * Samples a lognormal distribution + * Takes mean and standard deviation as inputs + * Outputs two samples at a time + */ +template +struct lognormal_distribution { + + inline lognormal_distribution(T mean_in, T stdv_in) { + TORCH_CHECK(stdv_in > 0); + mean = mean_in; + stdv = stdv_in; + } + + inline T operator()(at::CPUGenerator* generator){ + normal_distribution normal(mean, stdv); + return ::exp(normal(generator)); + } + + private: + T mean; + T stdv; +}; + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/core/EnableNamedTensor.h b/thirdparty/libtorch/include/ATen/core/EnableNamedTensor.h new file mode 100644 index 0000000000..f304ca3e88 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/EnableNamedTensor.h @@ -0,0 +1,11 @@ +#pragma once + +#include + +// We are working on removing the BUILD_NAMEDTENSOR flag from the codebase. +// +// PyTorch's codegen also uses a similar flag. You can find it in +// - aten/src/ATen/env.py +#ifndef BUILD_NAMEDTENSOR +#define BUILD_NAMEDTENSOR +#endif diff --git a/thirdparty/libtorch/include/ATen/core/Formatting.h b/thirdparty/libtorch/include/ATen/core/Formatting.h new file mode 100644 index 0000000000..63c5e12e96 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/Formatting.h @@ -0,0 +1,29 @@ +#pragma once + +#include +#include +#include + + +namespace c10 { +CAFFE2_API std::ostream& operator<<(std::ostream& out, Backend b); +} +namespace at { + +CAFFE2_API std::ostream& operator<<(std::ostream& out, const DeprecatedTypeProperties& t); +CAFFE2_API std::ostream& print( + std::ostream& stream, + const Tensor& tensor, + int64_t linesize); +static inline std::ostream& operator<<(std::ostream & out, const Tensor & t) { + return print(out,t,80); +} +static inline void print(const Tensor & t, int64_t linesize=80) { + print(std::cout,t,linesize); +} + +static inline std::ostream& operator<<(std::ostream & out, Scalar s) { + return out << (s.isFloatingPoint() ? s.toDouble() : s.toLong()); +} + +} diff --git a/thirdparty/libtorch/include/ATen/core/Generator.h b/thirdparty/libtorch/include/ATen/core/Generator.h new file mode 100644 index 0000000000..161824a11f --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/Generator.h @@ -0,0 +1,89 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include + +/** + * Note [Generator] + * ~~~~~~~~~~~~~~~~ + * A Pseudo Random Number Generator (PRNG) is an engine that uses an algorithm to + * generate a seemingly random sequence of numbers, that may be later be used in creating + * a random distribution. Such an engine almost always maintains a state and requires a + * seed to start off the creation of random numbers. Often times, users have + * found it beneficial to be able to explicitly create, retain, and destroy + * PRNG states and also be able to have control over the seed value. + * + * A Generator in ATen gives users the ability to read, write and modify a PRNG engine. + * For instance, it does so by letting users seed a PRNG engine, fork the state of the + * engine, etc. + * + * By default, there is one generator per device, and a device's generator is + * lazily created. A user can use the torch.Generator() api to create their own generator. + * Currently torch.Generator() can only create a CPUGenerator. + */ + +/** + * Note [Acquire lock when using random generators] + * ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + * Generator and its derived classes are NOT thread-safe. Please note that most of the + * places where we have inserted locking for generators are historically based, and we + * haven't actually checked that everything is truly thread safe (and it probably isn't). + * Please use the public mutex_ when using any methods from these classes, except for the + * read-only methods. You can learn about the usage by looking into the unittests + * (aten/src/ATen/cpu_generator_test.cpp) and other places where we have used lock_guard. + * + * TODO: Look into changing the threading semantics of Generators in ATen (e.g., making + * them non-thread safe and instead making the generator state splittable, to accommodate + * forks into other threads). + */ + +namespace at { + +// The default seed is selected to be a large number +// with good distribution of 0s and 1s in bit representation +constexpr uint64_t default_rng_seed_val = 67280421310721; + +struct CAFFE2_API Generator { + // Constructors + Generator(Device device_in); + + // Delete all copy and move assignment in favor of clone() + // method + Generator(const Generator& other) = delete; + Generator(Generator&& other) = delete; + Generator& operator=(const Generator& other) = delete; + + virtual ~Generator() = default; + std::shared_ptr clone() const; + + // Common methods for all generators + virtual void set_current_seed(uint64_t seed) = 0; + virtual uint64_t current_seed() const = 0; + virtual uint64_t seed() = 0; + Device device() const; + + // See Note [Acquire lock when using random generators] + std::mutex mutex_; + + private: + Device device_; + virtual Generator* clone_impl() const = 0; +}; + +namespace detail { + +CAFFE2_API uint64_t getNonDeterministicRandom(bool is_cuda = false); + +} // namespace detail + +} // namespace at + diff --git a/thirdparty/libtorch/include/ATen/core/LegacyDeviceTypeInit.h b/thirdparty/libtorch/include/ATen/core/LegacyDeviceTypeInit.h new file mode 100644 index 0000000000..dd3a5529a4 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/LegacyDeviceTypeInit.h @@ -0,0 +1,37 @@ +#pragma once + +// The legacy mechanism for initializing device types; this is used by +// LegacyTypeDispatch. + +#include +#include +#include +#include + +namespace at { + +struct CAFFE2_API LegacyDeviceTypeInitInterface { + virtual ~LegacyDeviceTypeInitInterface() {} + virtual void initCPU() const { + AT_ERROR("cannot use CPU without ATen library"); + } + virtual void initCUDA() const { + AT_ERROR("cannot use CUDA without ATen CUDA library"); + } + virtual void initHIP() const { + AT_ERROR("cannot use HIP without ATen HIP library"); + } +}; + +struct CAFFE2_API LegacyDeviceTypeInitArgs {}; + +C10_DECLARE_REGISTRY( + LegacyDeviceTypeInitRegistry, + LegacyDeviceTypeInitInterface, + LegacyDeviceTypeInitArgs); +#define REGISTER_LEGACY_TYPE_INIT(clsname) \ + C10_REGISTER_CLASS(LegacyDeviceTypeInitRegistry, clsname, clsname) + +CAFFE2_API const LegacyDeviceTypeInitInterface& getLegacyDeviceTypeInit(); + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/core/LegacyTypeDispatch.h b/thirdparty/libtorch/include/ATen/core/LegacyTypeDispatch.h new file mode 100644 index 0000000000..e6ed3ecd51 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/LegacyTypeDispatch.h @@ -0,0 +1,92 @@ +#pragma once + +// The legacy mechanism for dispatching operators in ATen is a Type +// object, which is essentially a giant virtual dispatch table +// for every operation we support dynamically dispatching over. +// +// This has been deprecated in favor of ATenDispatch, and in the future, +// c10 dispatcher. +// TODO: Clean up what remains here + +#include +#include +#include +#include +#include +#include +#include + +namespace at { + +class CAFFE2_API LegacyTypeDispatch { + public: + void initForTensorTypeSet(TensorTypeSet ts) { + // TODO: Avoid use of legacyExtractTypeId here. The key + // problem is that you may get a TensorTypeSet with + // VariableTensorId set; should you initialize the "underlying" + // type in that case? Hard to say. + auto b = tensorTypeIdToBackend(legacyExtractTypeId(ts)); + auto p = backendToDeviceType(b); + static std::once_flag cpu_once; + static std::once_flag cuda_once; + if (p == DeviceType::CPU) { + std::call_once(cpu_once, [] { + getLegacyDeviceTypeInit().initCPU(); + }); + } else if (p == DeviceType::CUDA) { + std::call_once(cuda_once, [] { + getLegacyDeviceTypeInit().initCUDA(); + }); + } else if (p == DeviceType::HIP) { + std::call_once(cuda_once, [] { + getLegacyDeviceTypeInit().initHIP(); + }); + } + } +}; + +CAFFE2_API LegacyTypeDispatch& globalLegacyTypeDispatch(); + +// A RAII, thread local (!) guard that will disable dispatch to variable +// handler. +// +// NOTE [ Treating Variables as non-Variables in type dispatch ] +// +// What exactly does AutoNonVariableType do? The short answer is, it causes +// dispatches on ATen functions to go to the non-variable implementation, +// bypassing autograd handling (and also profiling and tracing). +// +// To understand why this guard exists, it's helpful to understand the history +// behind how Variable was implemented. Previously, Variables were implemented +// as a wrapper on Tensors; so the act of processing a Variable involved +// unwrapping the underlying Tensor, and then calling the underlying base +// operation on /that/ operation +// +// However, after the Variable/Tensor merge, there is no concept of unwrapping +// a tensor anymore. If you just call the operation on the same variable +// again inside your VariableType handler, you'll dispatch back to +// VariableType, which is not what we want. +// +// The solution to the above problem is to add `at::NonVariableTypeMode`, which +// when enabled will cause `legacyTensorType()` and `getType()` to always return +// non-Variable type, even if the tensor being called on is a variable. +// +// TODO: Since `torch::NoGradGuard` serves almost the same purpose in libtorch, +// we should merge these two thread-local guards. However, NoGradGuard does +// something subtly different: it turns off gradient recording, but DOES NOT +// skip VariableType implementation (as we still might need to profile or +// trace). To unify the two, we would first have to move profiling and tracing +// out of VariableType. + +struct CAFFE2_API AutoNonVariableTypeMode { + // NB: The enabled parameter must ALWAYS be black, as Henry Ford used to say. + // TODO: Eliminate this parameter entirely + AutoNonVariableTypeMode(bool enabled = true) : + guard_(TensorTypeId::VariableTensorId) { + + TORCH_INTERNAL_ASSERT(enabled); + } + c10::impl::ExcludeTensorTypeIdGuard guard_; +}; + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/core/List.h b/thirdparty/libtorch/include/ATen/core/List.h new file mode 100644 index 0000000000..3126da8fb7 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/List.h @@ -0,0 +1,473 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +namespace at { +class Tensor; +} +namespace c10 { +struct IValue; +template class List; +struct Type; +using TypePtr = std::shared_ptr; + +namespace detail { + +template +struct ListImpl final : public c10::intrusive_ptr_target { + using list_type = std::vector; + + explicit ListImpl(list_type list_, TypePtr elementType_) + : list(std::move(list_)) + , elementType(std::move(elementType_)) {} + + list_type list; + + TypePtr elementType; + + intrusive_ptr copy() const { + return make_intrusive(list, elementType); + } +}; +} + +namespace impl { + +template class ListIterator; + +template class ListElementReference; + +template +void swap(ListElementReference&& lhs, ListElementReference&& rhs); + +template +class ListElementReference final { +public: + operator T() const; + + ListElementReference& operator=(T&& new_value) &&; + + ListElementReference& operator=(const T& new_value) &&; + + // assigning another ref to this assigns the underlying value + ListElementReference& operator=(ListElementReference&& rhs) &&; + + friend void swap(ListElementReference&& lhs, ListElementReference&& rhs); + +private: + ListElementReference(Iterator iter) + : iterator_(iter) {} + + ListElementReference(const ListElementReference&) = delete; + ListElementReference& operator=(const ListElementReference&) = delete; + + // allow moving, but only our friends (i.e. the List class) can move us + ListElementReference(ListElementReference&&) noexcept = default; + ListElementReference& operator=(ListElementReference&& rhs) & noexcept { + iterator_ = std::move(rhs.iterator_); + return *this; + } + + friend class List; + friend class ListIterator; + + Iterator iterator_; +}; + +// this wraps vector::iterator to make sure user code can't rely +// on it being the type of the underlying vector. +template +class ListIterator final : public std::iterator { +public: + explicit ListIterator() = default; + ~ListIterator() = default; + + ListIterator(const ListIterator&) = default; + ListIterator(ListIterator&&) noexcept = default; + ListIterator& operator=(const ListIterator&) = default; + ListIterator& operator=(ListIterator&&) = default; + + ListIterator& operator++() { + ++iterator_; + return *this; + } + + ListIterator operator++(int) { + ListIterator copy(*this); + ++*this; + return copy; + } + + ListIterator& operator--() { + --iterator_; + return *this; + } + + ListIterator operator--(int) { + ListIterator copy(*this); + --*this; + return copy; + } + + ListIterator& operator+=(typename List::size_type offset) { + iterator_ += offset; + return *this; + } + + ListIterator& operator-=(typename List::size_type offset) { + iterator_ -= offset; + return *this; + } + + ListIterator operator+(typename List::size_type offset) const { + return ListIterator{iterator_ + offset}; + } + + ListIterator operator-(typename List::size_type offset) const { + return ListIterator{iterator_ - offset}; + } + + friend typename std::iterator::difference_type operator-(const ListIterator& lhs, const ListIterator& rhs) { + return lhs.iterator_ - rhs.iterator_; + } + + ListElementReference operator*() const { + return {iterator_}; + } + +private: + explicit ListIterator(Iterator iterator): iterator_(std::move(iterator)) {} + + Iterator iterator_; + + friend bool operator==(const ListIterator& lhs, const ListIterator& rhs) { + return lhs.iterator_ == rhs.iterator_; + } + + friend bool operator!=(const ListIterator& lhs, const ListIterator& rhs) { + return !(lhs == rhs); + } + + friend bool operator<(const ListIterator& lhs, const ListIterator& rhs) { + return lhs.iterator_ < rhs.iterator_; + } + + friend bool operator<=(const ListIterator& lhs, const ListIterator& rhs) { + return lhs.iterator_ <= rhs.iterator_; + } + + friend bool operator>(const ListIterator& lhs, const ListIterator& rhs) { + return lhs.iterator_ > rhs.iterator_; + } + + friend bool operator>=(const ListIterator& lhs, const ListIterator& rhs) { + return lhs.iterator_ >= rhs.iterator_; + } + + friend class ListIterator::list_type::iterator, StorageT>; + friend class List; +}; + +template List toTypedList(List list); +template List toGenericList(List list); +const IValue* ptr_to_first_element(const List& list); +template List toList(std::vector list); +template const std::vector& toVector(const List& list); +} +template bool list_is_equal(const List& lhs, const List& rhs); + +/** + * An object of this class stores a list of values of type T. + * + * This is a pointer type. After a copy, both Lists + * will share the same storage: + * + * > List a; + * > List b = a; + * > b.push_back("three"); + * > ASSERT("three" == a.get(0)); + * + * We use this class in the PyTorch kernel API instead of + * std::vector, because that allows us to do optimizations + * and switch out the underlying list implementation without + * breaking backwards compatibility for the kernel API. + */ +template +class List final { +private: + // List of types that don't use IValue based lists + using types_with_direct_list_implementation = guts::typelist::typelist< + int64_t, + double, + bool, + at::Tensor + >; + + using StorageT = guts::conditional_t< + guts::typelist::contains::value, + T, // The types listed in types_with_direct_list_implementation store the list as std::vector + IValue // All other types store the list as std::vector + >; + + // This is an intrusive_ptr because List is a pointer type. + // Invariant: This will never be a nullptr, there will always be a valid + // ListImpl. + c10::intrusive_ptr> impl_; + + using internal_reference_type = impl::ListElementReference::StorageT>::list_type::iterator, typename List::StorageT>; + +public: + using value_type = T; + using size_type = typename detail::ListImpl::list_type::size_type; + using iterator = impl::ListIterator::list_type::iterator, StorageT>; + using reverse_iterator = impl::ListIterator::list_type::reverse_iterator, StorageT>; + using internal_value_type_test_only = StorageT; + + /** + * Constructs an empty list. + */ + explicit List(); + + /** + * Constructs a list with some initial values. + * Example: + * List a({2, 3, 4}); + */ + explicit List(std::initializer_list initial_values); + explicit List(ArrayRef initial_values); + + /** + * Create a generic list with runtime type information. + * This only works for c10::impl::GenericList and is not part of the public API + * but only supposed to be used internally by PyTorch. + */ + explicit List(TypePtr elementType); + + List(const List&) = default; + List& operator=(const List&) = default; + List(List&&) noexcept; + List& operator=(List&&) noexcept; + + /** + * Create a new List pointing to a deep copy of the same data. + * The List returned is a new list with separate storage. + * Changes in it are not reflected in the original list or vice versa. + */ + List copy() const; + + /** + * Returns the element at specified location pos, with bounds checking. + * If pos is not within the range of the container, an exception of type std::out_of_range is thrown. + */ + value_type get(size_type pos) const; + + /** + * Moves out the element at the specified location pos and returns it, with bounds checking. + * If pos is not within the range of the container, an exception of type std::out_of_range is thrown. + * The list contains an invalid element at position pos afterwards. Any operations + * on it before re-setting it are invalid. + */ + value_type extract(size_type pos) const; + + /** + * Returns a reference to the element at specified location pos, with bounds checking. + * If pos is not within the range of the container, an exception of type std::out_of_range is thrown. + * + * You cannot store the reference, but you can read it and assign new values to it: + * + * List list = ...; + * list[2] = 5; + * int64_t v = list[1]; + */ + internal_reference_type operator[](size_type pos) const; + + /** + * Assigns a new value to the element at location pos. + */ + void set(size_type pos, const value_type& value) const; + + /** + * Assigns a new value to the element at location pos. + */ + void set(size_type pos, value_type&& value) const; + + /** + * Returns an iterator to the first element of the container. + * If the container is empty, the returned iterator will be equal to end(). + */ + iterator begin() const; + + /** + * Returns an iterator to the element following the last element of the container. + * This element acts as a placeholder; attempting to access it results in undefined behavior. + */ + iterator end() const; + + /** + * Checks if the container has no elements. + */ + bool empty() const; + + /** + * Returns the number of elements in the container + */ + size_type size() const; + + /** + * Increase the capacity of the vector to a value that's greater or equal to new_cap. + */ + void reserve(size_type new_cap) const; + + /** + * Erases all elements from the container. After this call, size() returns zero. + * Invalidates any references, pointers, or iterators referring to contained elements. Any past-the-end iterators are also invalidated. + */ + void clear() const; + + /** + * Inserts value before pos. + * May invalidate any references, pointers, or iterators referring to contained elements. Any past-the-end iterators may also be invalidated. + */ + iterator insert(iterator pos, const T& value) const; + + /** + * Inserts value before pos. + * May invalidate any references, pointers, or iterators referring to contained elements. Any past-the-end iterators may also be invalidated. + */ + iterator insert(iterator pos, T&& value) const; + + /** + * Inserts a new element into the container directly before pos. + * The new element is constructed with the given arguments. + * May invalidate any references, pointers, or iterators referring to contained elements. Any past-the-end iterators may also be invalidated. + */ + template + iterator emplace(iterator pos, Args&&... value) const; + + /** + * Appends the given element value to the end of the container. + * May invalidate any references, pointers, or iterators referring to contained elements. Any past-the-end iterators may also be invalidated. + */ + void push_back(const T& value) const; + + /** + * Appends the given element value to the end of the container. + * May invalidate any references, pointers, or iterators referring to contained elements. Any past-the-end iterators may also be invalidated. + */ + void push_back(T&& value) const; + + /** + * Appends the given list to the end of the container. Uses at most one memory allocation. + * May invalidate any references, pointers, or iterators referring to contained elements. Any past-the-end iterators may also be invalidated. + */ + void append(List lst) const; + + /** + * Appends the given element value to the end of the container. + * The new element is constructed with the given arguments. + * May invalidate any references, pointers, or iterators referring to contained elements. Any past-the-end iterators may also be invalidated. + */ + template + void emplace_back(Args&&... args) const; + + /** + * Removes the element at pos. + * May invalidate any references, pointers, or iterators referring to contained elements. Any past-the-end iterators may also be invalidated. + */ + iterator erase(iterator pos) const; + + /** + * Removes the elements in the range [first, last). + * May invalidate any references, pointers, or iterators referring to contained elements. Any past-the-end iterators may also be invalidated. + */ + iterator erase(iterator first, iterator last) const; + + /** + * Removes the last element of the container. + * Calling pop_back on an empty container is undefined. + * May invalidate any references, pointers, or iterators referring to contained elements. Any past-the-end iterators may also be invalidated. + */ + void pop_back() const; + + /** + * Resizes the container to contain count elements. + * If the current size is less than count, additional default-inserted elements are appended. + * May invalidate any references, pointers, or iterators referring to contained elements. Any past-the-end iterators may also be invalidated. + */ + void resize(size_type count) const; + + /** + * Resizes the container to contain count elements. + * If the current size is less than count, additional copies of value are appended. + * May invalidate any references, pointers, or iterators referring to contained elements. Any past-the-end iterators may also be invalidated. + */ + void resize(size_type count, const T& value) const; + + /** + * Compares two lists for equality. Two lists are equal if they have the + * same number of elements and for each list position the elements at + * that position are equal. + */ + friend bool list_is_equal(const List& lhs, const List& rhs); + + /** + * Returns the number of Lists currently pointing to this same list. + * If this is the only instance pointing to this list, returns 1. + */ + // TODO Test use_count + size_t use_count() const; + + TypePtr elementType() const; + + // See [unsafe set type] for why this exists. + void unsafeSetElementType(TypePtr t); + +private: + explicit List(c10::intrusive_ptr>&& elements); + friend struct IValue; + template friend List impl::toTypedList(List); + template friend List impl::toGenericList(List); + friend const IValue* impl::ptr_to_first_element(const List& list); + template friend List impl::toList(std::vector list); + template friend const std::vector& impl::toVector(const List& list); +}; + +namespace impl { +// GenericList is how IValue stores lists. It is, however, not part of the +// public API. Kernels should use Lists with concrete types instead +// (maybe except for some internal prim ops). +using GenericList = List; + +inline const IValue* ptr_to_first_element(const GenericList& list) { + return &list.impl_->list[0]; +} + +template +const std::vector& toVector(const List& list) { + static_assert(std::is_same::value || std::is_same::StorageT>::value, "toVector only works for lists that store their elements as std::vector. You tried to call it for a list that stores its elements as std::vector."); + + return list.impl_->list; +} + +template +List toList(std::vector list) { + static_assert(std::is_same::value || std::is_same::StorageT>::value, "toList only works for lists that store their elements as std::vector. You tried to call it for a list that stores its elements as std::vector."); + List result; + result.impl_->list = std::move(list); + return result; +} + +} +} + +namespace torch { + template using List = c10::List; +} + +#include diff --git a/thirdparty/libtorch/include/ATen/core/List_inl.h b/thirdparty/libtorch/include/ATen/core/List_inl.h new file mode 100644 index 0000000000..d954d4d5cc --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/List_inl.h @@ -0,0 +1,292 @@ +#pragma once + +#include +#include + +namespace c10 { + +template TypePtr getTypePtr(); +std::string toString(TypePtr typePtr); + +template +List::List(c10::intrusive_ptr>&& elements) +: impl_(std::move(elements)) {} + +template +List::List() +: List(make_intrusive::StorageT>>( + typename detail::ListImpl::StorageT>::list_type(), + getTypePtr())) { + static_assert(!std::is_same::value, "This constructor is not valid for List. Please use c10::impl::GenericList(elementType) instead."); +} + +template +List::List(ArrayRef values) +: List(make_intrusive::StorageT>>( + typename detail::ListImpl::StorageT>::list_type(), + getTypePtr())) { + static_assert(!std::is_same::value, "This constructor is not valid for List. Please use c10::impl::GenericList(elementType)."); + impl_->list.reserve(values.size()); + for (const T& element : values) { + impl_->list.push_back(element); + } +} + +template +List::List(std::initializer_list initial_values) +: List(ArrayRef(initial_values)) { + static_assert(!std::is_same::value, "This constructor is not valid for List. Please use c10::impl::GenericList(elementType)."); +} + +template +List::List(TypePtr elementType) +: List(make_intrusive>( + typename detail::ListImpl::list_type(), + std::move(elementType))) { + static_assert(std::is_same::value, "This constructor is only valid for c10::impl::GenericList."); +} + +namespace impl { +template +List toTypedList(impl::GenericList list) { + static_assert(std::is_same::StorageT>::value, "Can only call toTypedList with lists that store their elements as IValues."); + TORCH_INTERNAL_ASSERT(*getTypePtr() == *list.impl_->elementType, "Tried to cast a List<", toString(list.impl_->elementType), "> to a List<", toString(getTypePtr()), ">. Types mismatch."); + return List(std::move(list.impl_)); +} + +template +impl::GenericList toGenericList(List list) { + static_assert(std::is_same::StorageT>::value, "Can only call toGenericList with lists that store their elements as IValues."); + return GenericList(std::move(list.impl_)); +} +} + +template +List::List(List&& rhs) noexcept: impl_(std::move(rhs.impl_)) { + rhs.impl_ = make_intrusive>(std::vector{}, impl_->elementType); +} + +template +List& List::operator=(List&& rhs) noexcept { + impl_ = std::move(rhs.impl_); + rhs.impl_ = make_intrusive>(std::vector{}, impl_->elementType); + return *this; +} + +template +List List::copy() const { + return List(impl_->copy()); +} + +namespace detail { + template + T list_element_to(T element) { + return element; + } + template + T list_element_to(const IValue& element) { + return element.template to(); + } + template + T list_element_to(IValue&& element) { + return std::move(element).template to(); + } + template + StorageT list_element_from(const T& element) { + return element; + } + template + StorageT list_element_from(T&& element) { + return std::move(element); + } +} + +namespace impl { + +template +ListElementReference::operator T() const { + return detail::list_element_to(*iterator_); +} + +template +ListElementReference& ListElementReference::operator=(T&& new_value) && { + *iterator_ = detail::list_element_from(std::move(new_value)); + return *this; +} + +template +ListElementReference& ListElementReference::operator=(const T& new_value) && { + *iterator_ = detail::list_element_from(std::move(new_value)); + return *this; +} + +template +ListElementReference& ListElementReference::operator=(ListElementReference&& rhs) && { + *iterator_ = *rhs.iterator_; + return *this; +} + +template +void swap(ListElementReference&& lhs, ListElementReference&& rhs) { + std::swap(*lhs.iterator_, *rhs.iterator_); +} +} + +template +void List::set(size_type pos, const value_type& value) const { + impl_->list.at(pos) = detail::list_element_from(value); +} + +template +void List::set(size_type pos, value_type&& value) const { + impl_->list.at(pos) = detail::list_element_from(std::move(value)); +} + +template +typename List::value_type List::get(size_type pos) const { + return detail::list_element_to(impl_->list.at(pos)); +} + +template +typename List::internal_reference_type List::operator[](size_type pos) const { + static_cast(impl_->list.at(pos)); // Throw the exception if it is out of range. + return {impl_->list.begin() + pos}; +} + +template +typename List::value_type List::extract(size_type pos) const { + auto& elem = impl_->list.at(pos); + auto result = detail::list_element_to(std::move(elem)); + if (std::is_same::value) { + // Reset the list element to a T() instead of None to keep it correctly typed + elem = detail::list_element_from(T{}); + } + return result; +} + +template +typename List::iterator List::begin() const { + return iterator(impl_->list.begin()); +} + +template +typename List::iterator List::end() const { + return iterator(impl_->list.end()); +} + +template +bool List::empty() const { + return impl_->list.empty(); +} + +template +typename List::size_type List::size() const { + return impl_->list.size(); +} + +template +void List::reserve(size_type new_cap) const { + impl_->list.reserve(new_cap); +} + +template +void List::clear() const { + impl_->list.clear(); +} + +template +typename List::iterator List::insert(iterator pos, const T& value) const { + return iterator { impl_->list.insert(pos.iterator_, detail::list_element_from(value)) }; +} + +template +typename List::iterator List::insert(iterator pos, T&& value) const { + return iterator { impl_->list.insert(pos.iterator_, detail::list_element_from(std::move(value))) }; +} + +template +template +typename List::iterator List::emplace(iterator pos, Args&&... value) const { + // TODO Use list_element_from? + return iterator { impl_->list.emplace(pos.iterator_, std::forward(value)...) }; +} + +template +void List::push_back(const T& value) const { + impl_->list.push_back(detail::list_element_from(value)); +} + +template +void List::push_back(T&& value) const { + impl_->list.push_back(detail::list_element_from(std::move(value))); +} + +template +void List::append(List b) const { + if (b.use_count() == 1) { + impl_->list.insert(impl_->list.end(), make_move_iterator(b.impl_->list.begin()), make_move_iterator(b.impl_->list.end())); + } else { + impl_->list.insert(impl_->list.end(), b.impl_->list.begin(), b.impl_->list.end()); + } +} + +template +template +void List::emplace_back(Args&&... args) const { + // TODO Use list_element_from? + impl_->list.emplace_back(std::forward(args)...); +} + +template +typename List::iterator List::erase(iterator pos) const { + return iterator { impl_->list.erase(pos.iterator_) }; +} + +template +typename List::iterator List::erase(iterator first, iterator last) const { + return iterator { impl_->list.erase(first.iterator_, last.iterator_) }; +} + +template +void List::pop_back() const { + impl_->list.pop_back(); +} + +template +void List::resize(size_type count) const { + impl_->list.resize(count, T{}); +} + +template +void List::resize(size_type count, const T& value) const { + impl_->list.resize(count, value); +} + +template +bool list_is_equal(const List& lhs, const List& rhs) { + if (lhs.size() != rhs.size()) { + return false; + } + for (size_t i = 0; i < lhs.size(); ++i) { + if (lhs.get(i) != rhs.get(i)) { + return false; + } + } + return true; +} + +template +size_t List::use_count() const { + return impl_.use_count(); +} + +template +TypePtr List::elementType() const { + return impl_->elementType; +} + +template +void List::unsafeSetElementType(TypePtr t) { + impl_->elementType = std::move(t); +} +} diff --git a/thirdparty/libtorch/include/ATen/core/MT19937RNGEngine.h b/thirdparty/libtorch/include/ATen/core/MT19937RNGEngine.h new file mode 100644 index 0000000000..ac3b685731 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/MT19937RNGEngine.h @@ -0,0 +1,192 @@ +#pragma once + +// define constants like M_PI and C keywords for MSVC +#ifdef _MSC_VER +#define _USE_MATH_DEFINES +#include +#endif + +#include +#include +#include + +namespace at { + +constexpr int MERSENNE_STATE_N = 624; +constexpr int MERSENNE_STATE_M = 397; +constexpr uint32_t MATRIX_A = 0x9908b0df; +constexpr uint32_t UMASK = 0x80000000; +constexpr uint32_t LMASK = 0x7fffffff; + +/** + * Note [Mt19937 Engine implementation] + * ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + * Originally implemented in: + * http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/MTARCOK/mt19937ar-cok.c + * and modified with C++ constructs. Moreover the state array of the engine + * has been modified to hold 32 bit uints instead of 64 bits. + * + * Note that we reimplemented mt19937 instead of using std::mt19937 because, + * at::mt19937 turns out to be faster in the pytorch codebase. PyTorch builds with -O2 + * by default and following are the benchmark numbers (benchmark code can be found at + * https://github.com/syed-ahmed/benchmark-rngs): + * + * with -O2 + * Time to get 100000000 philox randoms with at::uniform_real_distribution = 0.462759s + * Time to get 100000000 at::mt19937 randoms with at::uniform_real_distribution = 0.39628s + * Time to get 100000000 std::mt19937 randoms with std::uniform_real_distribution = 0.352087s + * Time to get 100000000 std::mt19937 randoms with at::uniform_real_distribution = 0.419454s + * + * std::mt19937 is faster when used in conjuction with std::uniform_real_distribution, + * however we can't use std::uniform_real_distribution because of this bug: + * http://open-std.org/JTC1/SC22/WG21/docs/lwg-active.html#2524. Plus, even if we used + * std::uniform_real_distribution and filtered out the 1's, it is a different algorithm + * than what's in pytorch currently and that messes up the tests in tests_distributions.py. + * The other option, using std::mt19937 with at::uniform_real_distribution is a tad bit slower + * than at::mt19937 with at::uniform_real_distribution and hence, we went with the latter. + * + * Copyright notice: + * A C-program for MT19937, with initialization improved 2002/2/10. + * Coded by Takuji Nishimura and Makoto Matsumoto. + * This is a faster version by taking Shawn Cokus's optimization, + * Matthe Bellew's simplification, Isaku Wada's real version. + * + * Before using, initialize the state by using init_genrand(seed) + * or init_by_array(init_key, key_length). + * + * Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura, + * All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * 3. The names of its contributors may not be used to endorse or promote + * products derived from this software without specific prior written + * permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR + * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + * LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * + * Any feedback is very welcome. + * http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html + * email: m-mat @ math.sci.hiroshima-u.ac.jp (remove space) + */ + +/** + * mt19937_data_pod is used to get POD data in and out + * of mt19937_engine. Used in torch.get_rng_state and + * torch.set_rng_state functions. + */ +struct mt19937_data_pod { + uint64_t seed_; + int left_; + bool seeded_; + uint32_t next_; + std::array state_; +}; + +class mt19937_engine { +public: + + inline explicit mt19937_engine(uint64_t seed = 5489) { + init_with_uint32(seed); + } + + inline mt19937_data_pod data() const { + return data_; + } + + inline void set_data(mt19937_data_pod data) { + data_ = data; + } + + inline uint64_t seed() const { + return data_.seed_; + } + + inline bool is_valid() { + if ((data_.seeded_ == true) + && (data_.left_ > 0 && data_.left_ <= MERSENNE_STATE_N) + && (data_.next_ <= MERSENNE_STATE_N)) { + return true; + } + return false; + } + + inline uint32_t operator()() { + uint32_t y; + + if (--(data_.left_) == 0) { + next_state(); + } + y = *(data_.state_.data() + data_.next_++); + y ^= (y >> 11); + y ^= (y << 7) & 0x9d2c5680; + y ^= (y << 15) & 0xefc60000; + y ^= (y >> 18); + + return y; + } + +private: + mt19937_data_pod data_; + + inline void init_with_uint32(uint64_t seed) { + data_.seed_ = seed; + data_.seeded_ = true; + data_.state_[0] = seed & 0xffffffff; + for(int j = 1; j < MERSENNE_STATE_N; j++) { + data_.state_[j] = (1812433253 * (data_.state_[j-1] ^ (data_.state_[j-1] >> 30)) + j); + data_.state_[j] &= 0xffffffff; + } + data_.left_ = 1; + data_.next_ = 0; + } + + inline uint32_t mix_bits(uint32_t u, uint32_t v) { + return (u & UMASK) | (v & LMASK); + } + + inline uint32_t twist(uint32_t u, uint32_t v) { + return (mix_bits(u,v) >> 1) ^ (v & 1 ? MATRIX_A : 0); + } + + inline void next_state() { + uint32_t* p = data_.state_.data(); + data_.left_ = MERSENNE_STATE_N; + data_.next_ = 0; + + for(int j = MERSENNE_STATE_N - MERSENNE_STATE_M + 1; --j; p++) { + *p = p[MERSENNE_STATE_M] ^ twist(p[0], p[1]); + } + + for(int j = MERSENNE_STATE_M; --j; p++) { + *p = p[MERSENNE_STATE_M - MERSENNE_STATE_N] ^ twist(p[0], p[1]); + } + + *p = p[MERSENNE_STATE_M - MERSENNE_STATE_N] ^ twist(p[0], data_.state_[0]); + } + +}; + +typedef mt19937_engine mt19937; + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/core/Macros.h b/thirdparty/libtorch/include/ATen/core/Macros.h new file mode 100644 index 0000000000..f8643d1ace --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/Macros.h @@ -0,0 +1,2 @@ +#pragma once +#include diff --git a/thirdparty/libtorch/include/ATen/core/NamedTensor.h b/thirdparty/libtorch/include/ATen/core/NamedTensor.h new file mode 100644 index 0000000000..61a8b3ce98 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/NamedTensor.h @@ -0,0 +1,119 @@ +#pragma once + +#include +#include +#include +#include + +#ifdef BUILD_NAMEDTENSOR +namespace at { + +// XXX: This file exists because TensorImpl is in c10, but Dimname is in ATen. +// Due to the c10/ATen library split, TensorImpl cannot depend on Dimname, +// so we have a couple of workarounds. +// +// In the long term, we'll move Dimname to c10 and everything in this file +// can be refactored out. The main blocker for that is that "c10::Symbol" +// actually exists outside of c10 and needs to be moved in. + +// TensorImpl has a unique_ptr field. +// XXX: Ideally we would just put optional> into TensorImpl. +struct CAFFE2_API NamedTensorMeta : public c10::NamedTensorMetaInterface { + explicit NamedTensorMeta(int64_t num_names) + : names_(std::vector(num_names, Dimname::wildcard())) {} + + explicit NamedTensorMeta(DimnameList names) + : names_(names.vec()) {} + explicit NamedTensorMeta(std::vector&& names) + : names_(std::move(names)) {} + + std::unique_ptr clone() const override { + return c10::guts::make_unique(names_); + } + + bool has_names() const; + DimnameList names() const { return names_; } + + // Used for an assertion in TensorImpl.h + int64_t slow_dim() const override { + return names_.size(); + } + + void set_names(DimnameList new_names) { + TORCH_INTERNAL_ASSERT(new_names.size() == names_.size()); + std::copy(new_names.begin(), new_names.end(), names_.begin()); + } + + void set_names(std::vector&& new_names) { + TORCH_INTERNAL_ASSERT(new_names.size() == names_.size()); + names_ = std::move(new_names); + } + + private: + std::vector names_; +}; + +// When NamesMode is disabled, then all operations ignore tensors' names fields. +// Concretely speaking, all tensors are treated as having nullopt names. +struct CAFFE2_API NamesMode { + static bool is_enabled(); + static void set_enabled(bool enabled); +}; + + +// A RAII, thread local (!) guard that enables or disables names upon +// construction, and sets it back to the original value upon destruction. +struct CAFFE2_API NoNamesGuard { + NoNamesGuard() : prev_mode(NamesMode::is_enabled()) { + NamesMode::set_enabled(false); + } + ~NoNamesGuard() { + NamesMode::set_enabled(prev_mode); + } + private: + bool prev_mode; +}; + +void check_names_valid_for(const Tensor& tensor, DimnameList names); +void check_names_valid_for(int64_t tensor_dim, DimnameList names); + +// Sets the names of `tensor` to be `names`. +CAFFE2_API Tensor& internal_set_names_inplace(Tensor& tensor, optional names); +CAFFE2_API Tensor& internal_set_names_inplace(Tensor& tensor, std::vector&& names, bool validate_names); + +constexpr size_t kMaxNamedTensorDim = 64; + +DimnameList default_names(size_t len); + +namespace impl { + +// Some helper functions on TensorImpl. Useful for working with names in TH. +// XXX: Ideally these would exist as methods on TensorImpl +CAFFE2_API void internal_set_names_inplace(TensorImpl* impl, optional names, bool validate_names); +CAFFE2_API void internal_set_names_inplace(TensorImpl* impl, std::vector&& names, bool validate_names); + +void check_names_valid_for(TensorImpl* impl, DimnameList names); + +// Returns true if the tensor's names exist and are not all 'None'. +// Returns false if the tensor's names don't exist (were not allocated), +// or if all names are 'None'. +// We treat not-allocated-names the same as allocated names that are all 'None'. +CAFFE2_API bool has_names(const TensorImpl* impl); + +// Returns the names of the tensor's dimensions. +// Unnamed tensors are treated as having 'None' in all dimension; this method +// would return a DimnameList of all 'None's for an unnamed tensor. +CAFFE2_API DimnameList get_names(const TensorImpl* impl); + +// This is more of an implementation detail; one should use impl::get_names / +// Tensor::names() whenever possible because it provides a cleaner API. +// Returns the names of the tensor if they have been allocated; returns nullopt +// instead if the haven't been. The names of a tensor are not allocated if a +// tensor is constructed with names=None. +CAFFE2_API optional get_opt_names(const TensorImpl* impl); + + +} // namespace impl + +} // namespace at +#endif diff --git a/thirdparty/libtorch/include/ATen/core/OpsAlreadyMovedToC10.h b/thirdparty/libtorch/include/ATen/core/OpsAlreadyMovedToC10.h new file mode 100644 index 0000000000..ab68c0d4fd --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/OpsAlreadyMovedToC10.h @@ -0,0 +1,14 @@ +#pragma once + +#include + +namespace c10 { +struct OperatorName; +} + +namespace at { + +// list of ATen ops that come from native_functions.yaml +CAFFE2_API bool is_aten_op(const c10::OperatorName& opName); + +} diff --git a/thirdparty/libtorch/include/ATen/core/PhiloxRNGEngine.h b/thirdparty/libtorch/include/ATen/core/PhiloxRNGEngine.h new file mode 100644 index 0000000000..1e597e8fc1 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/PhiloxRNGEngine.h @@ -0,0 +1,203 @@ +#pragma once + +// define constants like M_PI and C keywords for MSVC +#ifdef _MSC_VER +#define _USE_MATH_DEFINES +#include +#endif + +#include + +#ifdef __CUDACC__ +#include +#endif + +#include +#include +#include +#include +#include + +namespace at { + +// typedefs for holding vector data +namespace detail { + +typedef at::detail::Array UINT4; +typedef at::detail::Array UINT2; +typedef at::detail::Array DOUBLE2; +typedef at::detail::Array FLOAT2; + +} // namespace detail + +/** + * Note [Philox Engine implementation] + * ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + * Originally implemented in PyTorch's fusion compiler + * Refer to: http://www.thesalmons.org/john/random123/papers/random123sc11.pdf + * for details regarding the engine. + * + * Note that currently this implementation of the philox engine is not used + * anywhere except for tests in cpu_generator_test.cpp. However, this engine + * will replace curandStatePhilox4_32_10_t in the future. + * + * The philox engine takes a seed value, a subsequeunce + * for starting the generation and an offset for the subsequence. + * Think of this engine as an algorithm producing a huge array. We are + * parallelizing this array by partitioning the huge array and assigning + * a thread index to each partition. In other words, each seed value + * (there are 2^64 possible seed values) gives a sub array of size + * 2^128 (each element in that array is a 128 bit number). Reasoning + * behind the array being of size 2^128 is, there are 2^64 possible + * thread index value and there is an array of size 2^64 for each of + * those thread index. Hence 2^64 * 2^64 = 2^128 for each seed value. + * + * In short, this generator can produce 2^64 (seed values) * 2^128 (number + * of elements in an array given by a seed value) = 2^192 values. + * + * Arguments: + * seed: Seed values could be any number from 0 to 2^64-1. + * subsequence: Subsequence is just the cuda thread indexing with: + * - blockIdx.x * blockDim.x + threadIdx.x + * offset: The offset variable in PhiloxEngine decides how many 128-bit + * random numbers to skip (i.e. how many groups of 4, 32-bit numbers to skip) + * and hence really decides the total number of randoms that can be achieved + * for the given subsequence. + */ + +class philox_engine { +public: + + C10_HOST_DEVICE inline explicit philox_engine(uint64_t seed = 67280421310721, + uint64_t subsequence = 0, + uint64_t offset = 0) { + key[0] = static_cast(seed); + key[1] = static_cast(seed >> 32); + counter = detail::UINT4(0); + counter[2] = static_cast(subsequence); + counter[3] = static_cast(subsequence >> 32); + STATE = 0; + incr_n(offset); + } + + /** + * Produces a unique 32-bit pseudo random number on every invocation + */ + C10_HOST_DEVICE inline uint32_t operator()() { + if(STATE == 0) { + detail::UINT4 counter_ = counter; + detail::UINT2 key_ = key; + + counter_ = single_round(counter_, key_); + key_[0] += (kPhilox10A); key_[1] += (kPhilox10B); + counter_ = single_round(counter_, key_); + key_[0] += (kPhilox10A); key_[1] += (kPhilox10B); + counter_ = single_round(counter_, key_); + key_[0] += (kPhilox10A); key_[1] += (kPhilox10B); + counter_ = single_round(counter_, key_); + key_[0] += (kPhilox10A); key_[1] += (kPhilox10B); + counter_ = single_round(counter_, key_); + key_[0] += (kPhilox10A); key_[1] += (kPhilox10B); + counter_ = single_round(counter_, key_); + key_[0] += (kPhilox10A); key_[1] += (kPhilox10B); + counter_ = single_round(counter_, key_); + key_[0] += (kPhilox10A); key_[1] += (kPhilox10B); + counter_ = single_round(counter_, key_); + key_[0] += (kPhilox10A); key_[1] += (kPhilox10B); + counter_ = single_round(counter_, key_); + key_[0] += (kPhilox10A); key_[1] += (kPhilox10B); + + output = single_round(counter_, key_); + incr(); + } + uint32_t ret = output[STATE]; + STATE = (STATE + 1) & 3; + return ret; + } + + /** + * Function that Skips N 128 bit numbers in a subsequence + */ + C10_HOST_DEVICE inline void incr_n(uint64_t n) { + uint32_t nlo = static_cast(n); + uint32_t nhi = static_cast(n >> 32); + counter[0] += nlo; + // if overflow in x has occured, carry over to nhi + if (counter[0] < nlo) { + nhi++; + // if overflow in nhi has occured during carry over, + // propagate that overflow to y and exit to increment z + // otherwise return + counter[1] += nhi; + if(nhi != 0) { + if (nhi <= counter[1]) { + return; + } + } + } else { + // if overflow in y has occured during addition, + // exit to increment z + // otherwise return + counter[1] += nhi; + if (nhi <= counter[1]) { + return; + } + } + if (++counter[2]) + return; + ++counter[3]; + } + + /** + * Function that Skips one 128 bit number in a subsequence + */ + C10_HOST_DEVICE inline void incr() { + if (++counter[0]) + return; + if (++counter[1]) + return; + if (++counter[2]) { + return; + } + ++counter[3]; + } + +private: + detail::UINT4 counter; + detail::UINT4 output; + detail::UINT2 key; + uint32_t STATE; + + C10_HOST_DEVICE inline uint32_t mulhilo32(uint32_t a, uint32_t b, + uint32_t *result_high) { + #ifdef __CUDA_ARCH__ + *result_high = __umulhi(a, b); + return a*b; + #else + const uint64_t product = static_cast(a) * b; + *result_high = static_cast(product >> 32); + return static_cast(product); + #endif + } + + C10_HOST_DEVICE inline detail::UINT4 single_round(detail::UINT4 ctr, detail::UINT2 in_key) { + uint32_t hi0; + uint32_t hi1; + uint32_t lo0 = mulhilo32(kPhiloxSA, ctr[0], &hi0); + uint32_t lo1 = mulhilo32(kPhiloxSB, ctr[2], &hi1); + detail::UINT4 ret; + ret[0] = hi1 ^ ctr[1] ^ in_key[0]; + ret[1] = lo1; + ret[2] = hi0 ^ ctr[3] ^ in_key[1]; + ret[3] = lo0; + return ret; + } + static const uint32_t kPhilox10A = 0x9E3779B9; + static const uint32_t kPhilox10B = 0xBB67AE85; + static const uint32_t kPhiloxSA = 0xD2511F53; + static const uint32_t kPhiloxSB = 0xCD9E8D57; +}; + +typedef philox_engine Philox4_32_10; + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/core/Range.h b/thirdparty/libtorch/include/ATen/core/Range.h new file mode 100644 index 0000000000..2bf6b2b73a --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/Range.h @@ -0,0 +1,25 @@ +#pragma once + +#include +#include + +namespace at { + +struct Range { + Range(int64_t begin, int64_t end) + : begin(begin) + , end(end) {} + + int64_t size() const { return end - begin; } + + Range operator/(int64_t divisor) { + return Range(begin / divisor, end / divisor); + } + + int64_t begin; + int64_t end; +}; + +std::ostream& operator<<(std::ostream& out, const Range& range); + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/core/Reduction.h b/thirdparty/libtorch/include/ATen/core/Reduction.h new file mode 100644 index 0000000000..23c6ea3cab --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/Reduction.h @@ -0,0 +1,16 @@ +#pragma once + +namespace at { +namespace Reduction { + +// NB: Keep this in sync with Reduction class in torch/nn/_reduction.py +// These constants control the reduction behavior of loss functions. +// Ideally, this would be a scoped enum, but jit doesn't support that +enum Reduction { + None, // Do not reduce + Mean, // (Possibly weighted) mean of losses + Sum, // Sum losses + END +}; +} // namespace Reduction +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/core/Scalar.h b/thirdparty/libtorch/include/ATen/core/Scalar.h new file mode 100644 index 0000000000..a14b48f012 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/Scalar.h @@ -0,0 +1 @@ +#include diff --git a/thirdparty/libtorch/include/ATen/core/ScalarType.h b/thirdparty/libtorch/include/ATen/core/ScalarType.h new file mode 100644 index 0000000000..eb30ee86f7 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/ScalarType.h @@ -0,0 +1 @@ +#include diff --git a/thirdparty/libtorch/include/ATen/core/Tensor.h b/thirdparty/libtorch/include/ATen/core/Tensor.h new file mode 100644 index 0000000000..2dc92d43d4 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/Tensor.h @@ -0,0 +1,12 @@ +#pragma once + +/* + * We split Tensor.h into TensorBody.h and TensorMethods.h because we want + * all TensorMethods to be inlined, but they depend on the Dispatcher, + * which in turn depends on many other things, which then depend back on Tensor. + * + * We can break this dependency chain by having the dispatcher only depend on + * TensorBody.h and not TensorMethods.h. + */ +#include +#include diff --git a/thirdparty/libtorch/include/ATen/core/TensorAccessor.h b/thirdparty/libtorch/include/ATen/core/TensorAccessor.h new file mode 100644 index 0000000000..95f37fcb09 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/TensorAccessor.h @@ -0,0 +1,232 @@ +#pragma once + +#include +#include +#include +#include + +namespace at { + +// The PtrTraits argument to the TensorAccessor/GenericPackedTensorAccessor +// is used to enable the __restrict__ keyword/modifier for the data +// passed to cuda. +template +struct DefaultPtrTraits { + typedef T* PtrType; +}; + +#if defined(__CUDACC__) || defined(__HIPCC__) +template +struct RestrictPtrTraits { + typedef T* __restrict__ PtrType; +}; +#endif + +// TensorAccessorBase and TensorAccessor are used for both CPU and CUDA tensors. +// For CUDA tensors it is used in device code (only). This means that we restrict ourselves +// to functions and types available there (e.g. IntArrayRef isn't). + +// The PtrTraits argument is only relevant to cuda to support `__restrict__` pointers. +template class PtrTraits = DefaultPtrTraits, typename index_t = int64_t> +class TensorAccessorBase { +public: + typedef typename PtrTraits::PtrType PtrType; + + C10_HOST_DEVICE TensorAccessorBase( + PtrType data_, + const index_t* sizes_, + const index_t* strides_) + : data_(data_), sizes_(sizes_), strides_(strides_) {} + C10_HOST IntArrayRef sizes() const { + return IntArrayRef(sizes_,N); + } + C10_HOST IntArrayRef strides() const { + return IntArrayRef(strides_,N); + } + C10_HOST_DEVICE index_t stride(index_t i) const { + return strides_[i]; + } + C10_HOST_DEVICE index_t size(index_t i) const { + return sizes_[i]; + } + C10_HOST_DEVICE PtrType data() { + return data_; + } + C10_HOST_DEVICE const PtrType data() const { + return data_; + } +protected: + PtrType data_; + const index_t* sizes_; + const index_t* strides_; +}; + +// The `TensorAccessor` is typically instantiated for CPU `Tensor`s using +// `Tensor.accessor()`. +// For CUDA `Tensor`s, `GenericPackedTensorAccessor` is used on the host and only +// indexing on the device uses `TensorAccessor`s. +template class PtrTraits = DefaultPtrTraits, typename index_t = int64_t> +class TensorAccessor : public TensorAccessorBase { +public: + typedef typename PtrTraits::PtrType PtrType; + + C10_HOST_DEVICE TensorAccessor( + PtrType data_, + const index_t* sizes_, + const index_t* strides_) + : TensorAccessorBase(data_,sizes_,strides_) {} + + C10_HOST_DEVICE TensorAccessor operator[](index_t i) { + return TensorAccessor(this->data_ + this->strides_[0]*i,this->sizes_+1,this->strides_+1); + } + + C10_HOST_DEVICE const TensorAccessor operator[](index_t i) const { + return TensorAccessor(this->data_ + this->strides_[0]*i,this->sizes_+1,this->strides_+1); + } +}; + +template class PtrTraits, typename index_t> +class TensorAccessor : public TensorAccessorBase { +public: + typedef typename PtrTraits::PtrType PtrType; + + C10_HOST_DEVICE TensorAccessor( + PtrType data_, + const index_t* sizes_, + const index_t* strides_) + : TensorAccessorBase(data_,sizes_,strides_) {} + C10_HOST_DEVICE T & operator[](index_t i) { + return this->data_[this->strides_[0]*i]; + } + C10_HOST_DEVICE const T & operator[](index_t i) const { + return this->data_[this->strides_[0]*i]; + } +}; + + +// GenericPackedTensorAccessorBase and GenericPackedTensorAccessor are used on for CUDA `Tensor`s on the host +// and as +// In contrast to `TensorAccessor`s, they copy the strides and sizes on instantiation (on the host) +// in order to transfer them on the device when calling kernels. +// On the device, indexing of multidimensional tensors gives to `TensorAccessor`s. +// Use RestrictPtrTraits as PtrTraits if you want the tensor's data pointer to be marked as __restrict__. +// Instantiation from data, sizes, strides is only needed on the host and std::copy isn't available +// on the device, so those functions are host only. +template class PtrTraits = DefaultPtrTraits, typename index_t = int64_t> +class GenericPackedTensorAccessorBase { +public: + typedef typename PtrTraits::PtrType PtrType; + C10_HOST GenericPackedTensorAccessorBase( + PtrType data_, + const index_t* sizes_, + const index_t* strides_) + : data_(data_) { + std::copy(sizes_, sizes_ + N, std::begin(this->sizes_)); + std::copy(strides_, strides_ + N, std::begin(this->strides_)); + } + + // if index_t is not int64_t, we want to have an int64_t constructor + template ::value>::type> + C10_HOST GenericPackedTensorAccessorBase( + PtrType data_, + const source_index_t* sizes_, + const source_index_t* strides_) + : data_(data_) { + for (int i = 0; i < N; i++) { + this->sizes_[i] = sizes_[i]; + this->strides_[i] = strides_[i]; + } + } + + C10_HOST_DEVICE index_t stride(index_t i) const { + return strides_[i]; + } + C10_HOST_DEVICE index_t size(index_t i) const { + return sizes_[i]; + } + C10_HOST_DEVICE PtrType data() { + return data_; + } + C10_HOST_DEVICE const PtrType data() const { + return data_; + } +protected: + PtrType data_; + index_t sizes_[N]; + index_t strides_[N]; +}; + +template class PtrTraits = DefaultPtrTraits, typename index_t = int64_t> +class GenericPackedTensorAccessor : public GenericPackedTensorAccessorBase { +public: + typedef typename PtrTraits::PtrType PtrType; + + C10_HOST GenericPackedTensorAccessor( + PtrType data_, + const index_t* sizes_, + const index_t* strides_) + : GenericPackedTensorAccessorBase(data_, sizes_, strides_) {} + + // if index_t is not int64_t, we want to have an int64_t constructor + template ::value>::type> + C10_HOST GenericPackedTensorAccessor( + PtrType data_, + const source_index_t* sizes_, + const source_index_t* strides_) + : GenericPackedTensorAccessorBase(data_, sizes_, strides_) {} + + C10_DEVICE TensorAccessor operator[](index_t i) { + index_t* new_sizes = this->sizes_ + 1; + index_t* new_strides = this->strides_ + 1; + return TensorAccessor(this->data_ + this->strides_[0]*i, new_sizes, new_strides); + } + + C10_DEVICE const TensorAccessor operator[](index_t i) const { + const index_t* new_sizes = this->sizes_ + 1; + const index_t* new_strides = this->strides_ + 1; + return TensorAccessor(this->data_ + this->strides_[0]*i, new_sizes, new_strides); + } +}; + +template class PtrTraits, typename index_t> +class GenericPackedTensorAccessor : public GenericPackedTensorAccessorBase { +public: + typedef typename PtrTraits::PtrType PtrType; + C10_HOST GenericPackedTensorAccessor( + PtrType data_, + const index_t* sizes_, + const index_t* strides_) + : GenericPackedTensorAccessorBase(data_, sizes_, strides_) {} + + // if index_t is not int64_t, we want to have an int64_t constructor + template ::value>::type> + C10_HOST GenericPackedTensorAccessor( + PtrType data_, + const source_index_t* sizes_, + const source_index_t* strides_) + : GenericPackedTensorAccessorBase(data_, sizes_, strides_) {} + + C10_DEVICE T & operator[](index_t i) { + return this->data_[this->strides_[0] * i]; + } + C10_DEVICE const T& operator[](index_t i) const { + return this->data_[this->strides_[0]*i]; + } +}; + + +// Can't put this directly into the macro function args because of commas +#define AT_X GenericPackedTensorAccessor + +// Old name for `GenericPackedTensorAccessor` +template class PtrTraits = DefaultPtrTraits, typename index_t = int64_t> +C10_DEFINE_DEPRECATED_USING(PackedTensorAccessor, AT_X) + +#undef AT_X + +template class PtrTraits = DefaultPtrTraits> +using PackedTensorAccessor32 = GenericPackedTensorAccessor; + +template class PtrTraits = DefaultPtrTraits> +using PackedTensorAccessor64 = GenericPackedTensorAccessor; +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/core/TensorBody.h b/thirdparty/libtorch/include/ATen/core/TensorBody.h new file mode 100644 index 0000000000..256a3c1288 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/TensorBody.h @@ -0,0 +1,1122 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace caffe2 { +class Tensor; +} +namespace c10{ +struct TensorOptions; +} +namespace at { +struct Generator; +struct Type; +class DeprecatedTypeProperties; +class Tensor; +} // namespace at + +namespace torch { namespace autograd { + +struct Node; + +}} // namespace torch::autograd + +namespace at { + +class Tensor; +using TensorList = ArrayRef; + +struct Quantizer; +// This is temporary typedef to enable Quantizer in aten native function API +// we'll remove them when we are actually exposing Quantizer class +// to frontend +using QuantizerPtr = c10::intrusive_ptr; +using ConstQuantizerPtr = const c10::intrusive_ptr&; + +namespace impl { +inline bool variable_excluded_from_dispatch() { + return c10::impl::tls_local_tensor_type_set().excluded_.has(TensorTypeId::VariableTensorId); +} +} + +// Tensor is a "generic" object holding a pointer to the underlying TensorImpl object, which +// has an embedded reference count. In this way, Tensor is similar to boost::intrusive_ptr. +// +// For example: +// +// void func(Tensor a) { +// Tensor b = a; +// ... +// } +// +// In this example, when we say Tensor b = a, we are creating a new object that points to the +// same underlying TensorImpl, and bumps its reference count. When b goes out of scope, the +// destructor decrements the reference count by calling release() on the TensorImpl it points to. +// The existing constructors, operator overloads, etc. take care to implement the correct semantics. +// +// Note that Tensor can also be NULL, i.e. it is not associated with any underlying TensorImpl, and +// special care must be taken to handle this. +class CAFFE2_API Tensor { + public: + Tensor(){}; + // This constructor should not be used by end users and is an implementation + // detail invoked by autogenerated code. + explicit Tensor( + c10::intrusive_ptr tensor_impl) + : impl_(std::move(tensor_impl)) { + if (impl_.get() == nullptr) { + throw std::runtime_error("TensorImpl with nullptr is not supported"); + } + } + Tensor(const Tensor&) = default; + Tensor(Tensor&&) = default; + + + public: + // Creates a new wrapper from TensorImpl. Intentionally a free method because + // it should be used with care. Checks necessary invariants + static Tensor wrap_tensor_impl( + c10::intrusive_ptr tensor_impl) { + Tensor r(std::move(tensor_impl)); + r.enforce_invariants(); + return r; + } + + int64_t dim() const { + return impl_->dim(); + } + int64_t storage_offset() const { + return impl_->storage_offset(); + } + + TensorImpl * unsafeGetTensorImpl() const { + return impl_.get(); + } + TensorImpl * unsafeReleaseTensorImpl() { + return impl_.release(); + } + const c10::intrusive_ptr& getIntrusivePtr() const { + return impl_; + } + + bool defined() const { + return impl_; + } + + void reset() { + impl_.reset(); + } + + // The following overloads are very intruiging. Consider the following + // program: + // + // x[1] = 3; + // + // We would expect that the first entry of x is written to 3. But how can we + // actually achieve this? x[1] evaluates to a tensor... + // + // The answer is, using a ref-qualifier. x[1] is an rvalue, which cannot be + // (profitably) assigned to in the traditional sense, so we overload + // assignment to mean, "Actually, copy 3 into the tensor data." This is done + // with an rvalue-reference ref-qualified overload (the methods with && at the + // end of their type.) + // + // There's one more fly in the ointment: We also want + // + // Tensor x = y; + // + // to work, and we want it NOT to copy. So we need a traditional operator= + // overload. But we MUST specify a mutable lvalue ref-qualifier, to + // disambiguate the traditional overload from the rvalue-reference + // ref-qualified overload. Otherwise, it will be ambiguous, because + // a non ref-qualified method is eligible for all situations. + + // Unfortunately, we have to write these constructors out manually + // to work around an MSVC bug: + // error C2580: 'at::Tensor &at::Tensor::operator =(const at::Tensor &) &': + // multiple versions of a defaulted special member functions are not allowed + // Tensor& operator=(const Tensor&) & = default; + // Tensor& operator=(Tensor&&) & = default; + Tensor& operator=(const Tensor& x) & { + impl_ = x.impl_; + return *this; + } + Tensor& operator=(Tensor&& x) & { + impl_ = std::move(x.impl_); + return *this; + } + + Tensor& operator=(Scalar v) &&; + Tensor& operator=(const Tensor&) &&; + Tensor& operator=(Tensor&&) &&; + + bool is_same(const Tensor& other) const noexcept { + return impl_ == other.impl_; + } + size_t use_count() const noexcept { + return impl_.use_count(); + } + size_t weak_use_count() const noexcept { + return impl_.weak_use_count(); + } + + std::string toString() const; + + IntArrayRef sizes() const { + return impl_->sizes(); + } + IntArrayRef strides() const { + return impl_->strides(); + } +#ifdef BUILD_NAMEDTENSOR + // See impl::get_opt_names in ATen/NamedTensor.h for docs. + optional opt_names() const { + return impl::get_opt_names(unsafeGetTensorImpl()); + } + // See impl::get_names in ATen/NamedTensor.h for docs. + DimnameList names() const { + return impl::get_names(unsafeGetTensorImpl()); + } +#endif + int64_t ndimension() const { + return dim(); + } + + bool is_contiguous(at::MemoryFormat memory_format=at::MemoryFormat::Contiguous) const { + return impl_->is_contiguous(memory_format); + } + + bool is_non_overlapping_and_dense() const { + return impl_->is_non_overlapping_and_dense(); + } + + at::MemoryFormat suggest_memory_format() const { + if (!is_mkldnn() && !is_sparse() && !impl_->is_contiguous() && impl_->is_strides_like_channels_last()) { + return at::MemoryFormat::ChannelsLast; + } + return at::MemoryFormat::Contiguous; + } + + // Total bytes consumed by the "view" of elements of the array. Does not + // include size of metadata. The number reported here does not necessarily + // correspond to the true physical memory consumed by a tensor; instead, + // it reports the memory the tensor would take *if* it were contiguous. + // Defined to be numel() * itemsize() + size_t nbytes() const { + return impl_->numel() * impl_->itemsize(); + } + + int64_t numel() const { + return impl_->numel(); + } + + // Length of one array element in bytes. This is the traditional + // Numpy naming. + size_t itemsize() const { + return impl_->itemsize(); + } + + // Same as itemsize(). This is the PyTorch naming. + size_t element_size() const { + return impl_->itemsize(); + } + + DeprecatedTypeProperties & type() const { + return globalDeprecatedTypePropertiesRegistry().getDeprecatedTypeProperties( + tensorTypeIdToBackend(legacyExtractTypeId(type_set())), + scalar_type()); + } + TensorTypeSet type_set() const { + return impl_->type_set(); + } + ScalarType scalar_type() const { + return typeMetaToScalarType(impl_->dtype()); + } + bool has_storage() const { + return defined() && impl_->has_storage(); + } + const Storage& storage() const { + return impl_->storage(); + } + bool is_alias_of(const at::Tensor& other) const{ + return impl_->storage().is_alias_of(other.storage()); + } + Tensor toType(ScalarType t) const; + Tensor toBackend(Backend b) const; + + C10_DEPRECATED_MESSAGE("Tensor.is_variable() is deprecated; everything is a variable now. (If you want to assert that variable has been appropriately handled already, use at::impl::variable_excluded_from_dispatch())") + bool is_variable() const noexcept { + return !at::impl::variable_excluded_from_dispatch(); + } + + /// Returns a `Tensor`'s layout. Defined in Type.h + Layout layout() const noexcept; + + /// Returns a `Tensor`'s dtype (`TypeMeta`). Defined in TensorMethods.h + caffe2::TypeMeta dtype() const noexcept; + + /// Returns a `Tensor`'s device. + Device device() const; + + /// Returns a `Tensor`'s device index. + int64_t get_device() const; + + /// Returns if a `Tensor` has CUDA backend. + bool is_cuda() const; + + /// Returns if a `Tensor` has HIP backend. + bool is_hip() const; + + /// Returns if a `Tensor` has sparse backend. + bool is_sparse() const; + + /// Returns if a `Tensor` is mkldnn tensor. + bool is_mkldnn() const; + + /// Returns if a `Tensor` has quantized backend. + bool is_quantized() const; + + /// If a tensor is a quantized tensor, returns its quantizer + /// TODO: it's not in native_functions.yaml yet as it's not exposed to python + QuantizerPtr quantizer() const; + +#ifdef BUILD_NAMEDTENSOR + /// Returns if a `Tensor` has any dimension names + bool has_names() const; + + /// Returns a `Tensor`'s dimension names data structure + const NamedTensorMeta* get_named_tensor_meta() const; + NamedTensorMeta* get_named_tensor_meta(); +#endif + + /// Returns the `TensorOptions` corresponding to this `Tensor`. Defined in + /// TensorOptions.h. + TensorOptions options() const; + + void* data_ptr() const { + return this->unsafeGetTensorImpl()->data(); + } + + template + T * data_ptr() const; + + template + C10_DEPRECATED_MESSAGE("Tensor.data() is deprecated. Please use Tensor.data_ptr() instead.") + T * data() const { + return data_ptr(); + } + + template + T item() const; + + // Purposely not defined here to avoid inlining + void print() const; + + // Return a `TensorAccessor` for CPU `Tensor`s. You have to specify scalar type and + // dimension. + template + TensorAccessor accessor() const& { + static_assert(N > 0, "accessor is used for indexing tensor, for scalars use *data_ptr()"); + TORCH_CHECK(dim() == N, "expected ", N, " dims but tensor has ", dim()); + return TensorAccessor(data_ptr(),sizes().data(),strides().data()); + } + template + TensorAccessor accessor() && = delete; + + // Return a `GenericPackedTensorAccessor` for CUDA `Tensor`s. You have to specify scalar type and + // dimension. You can optionally specify RestrictPtrTraits as a template parameter to + // cast the data pointer to a __restrict__ pointer. + // In order to use this, your CUDA kernel has to take a corresponding GenericPackedTensorAccessor + // as an argument. + template class PtrTraits = DefaultPtrTraits, typename index_t = int64_t> + GenericPackedTensorAccessor generic_packed_accessor() const& { + static_assert(N > 0, "accessor is used for indexing tensor, for scalars use *data_ptr()"); + TORCH_CHECK(dim() == N, "expected ", N, " dims but tensor has ", dim()); + return GenericPackedTensorAccessor(static_cast::PtrType>(data_ptr()),sizes().data(),strides().data()); + } + template class PtrTraits = DefaultPtrTraits, typename index_t = int64_t> + GenericPackedTensorAccessor generic_packed_accessor() && = delete; + + template class PtrTraits = DefaultPtrTraits> + PackedTensorAccessor32 packed_accessor32() const& { + return generic_packed_accessor(); + } + template class PtrTraits = DefaultPtrTraits> + PackedTensorAccessor32 packed_accessor32() && = delete; + + template class PtrTraits = DefaultPtrTraits> + PackedTensorAccessor64 packed_accessor64() const& { + return generic_packed_accessor(); + } + template class PtrTraits = DefaultPtrTraits> + PackedTensorAccessor64 packed_accessor64() && = delete; + + template class PtrTraits = DefaultPtrTraits, typename index_t = int64_t> + C10_DEPRECATED_MESSAGE("packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead") + GenericPackedTensorAccessor packed_accessor() const & { + return generic_packed_accessor(); + } + template class PtrTraits = DefaultPtrTraits, typename index_t = int64_t> + C10_DEPRECATED_MESSAGE("packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead") + GenericPackedTensorAccessor packed_accessor() && = delete; + + Tensor operator-() const; + Tensor& operator+=(const Tensor & other); + Tensor& operator+=(Scalar other); + Tensor& operator-=(const Tensor & other); + Tensor& operator-=(Scalar other); + Tensor& operator*=(const Tensor & other); + Tensor& operator*=(Scalar other); + Tensor& operator/=(const Tensor & other); + Tensor& operator/=(Scalar other); + Tensor operator[](Scalar index) const; + Tensor operator[](Tensor index) const; + Tensor operator[](int64_t index) const; + + Tensor cpu() const; + Tensor cuda() const; + Tensor hip() const; + + // ~~~~~ Autograd API ~~~~~ + + Tensor& set_requires_grad(bool requires_grad) { + impl_->set_requires_grad(requires_grad); + return *this; + } + bool requires_grad() const { + return impl_->requires_grad(); + } + + Tensor& grad() { + return impl_->grad(); + } + const Tensor& grad() const { + return impl_->grad(); + } + + // STOP. Thinking of adding a method here, which only makes use + // of other ATen methods? Define it in native_functions.yaml. + + //example + //Tensor * add(Tensor & b); + void backward(const Tensor & gradient={}, bool keep_graph=false, bool create_graph=false) const; + void set_data(const Tensor & new_data) const; + Tensor data() const; + bool is_leaf() const; + int64_t output_nr() const; + int64_t _version() const; + Tensor & requires_grad_(bool _requires_grad=true) const; + #ifdef BUILD_NAMEDTENSOR + Tensor & rename_(c10::optional names) const; + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor rename(c10::optional names) const; + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor align_to(DimnameList names) const; + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor align_to(DimnameList order, int64_t ellipsis_idx) const; + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor align_as(const Tensor & other) const; + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor refine_names(DimnameList names) const; + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor unflatten(Dimname dim, IntArrayRef sizes, DimnameList names) const; + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor unflatten(int64_t dim, IntArrayRef sizes, DimnameList names) const; + #endif + Tensor abs() const; + Tensor & abs_() const; + Tensor angle() const; + Tensor real() const; + Tensor imag() const; + Tensor conj() const; + Tensor acos() const; + Tensor & acos_() const; + Tensor add(const Tensor & other, Scalar alpha=1) const; + Tensor & add_(const Tensor & other, Scalar alpha=1) const; + Tensor add(Scalar other, Scalar alpha=1) const; + Tensor & add_(Scalar other, Scalar alpha=1) const; + Tensor addmv(const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1) const; + Tensor & addmv_(const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1) const; + Tensor addr(const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1) const; + Tensor & addr_(const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1) const; + Tensor all(int64_t dim, bool keepdim=false) const; + #ifdef BUILD_NAMEDTENSOR + Tensor all(Dimname dim, bool keepdim=false) const; + #endif + bool allclose(const Tensor & other, double rtol=1e-05, double atol=1e-08, bool equal_nan=false) const; + Tensor any(int64_t dim, bool keepdim=false) const; + #ifdef BUILD_NAMEDTENSOR + Tensor any(Dimname dim, bool keepdim=false) const; + #endif + Tensor argmax(c10::optional dim=c10::nullopt, bool keepdim=false) const; + Tensor argmin(c10::optional dim=c10::nullopt, bool keepdim=false) const; + Tensor as_strided(IntArrayRef size, IntArrayRef stride, c10::optional storage_offset=c10::nullopt) const; + Tensor & as_strided_(IntArrayRef size, IntArrayRef stride, c10::optional storage_offset=c10::nullopt) const; + Tensor asin() const; + Tensor & asin_() const; + Tensor atan() const; + Tensor & atan_() const; + Tensor baddbmm(const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1) const; + Tensor & baddbmm_(const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1) const; + Tensor bernoulli(Generator * generator=nullptr) const; + Tensor & bernoulli_(const Tensor & p, Generator * generator=nullptr) const; + Tensor & bernoulli_(double p=0.5, Generator * generator=nullptr) const; + Tensor bernoulli(double p, Generator * generator=nullptr) const; + Tensor bincount(const Tensor & weights={}, int64_t minlength=0) const; + Tensor bitwise_not() const; + Tensor & bitwise_not_() const; + Tensor logical_not() const; + Tensor & logical_not_() const; + Tensor logical_xor(const Tensor & other) const; + Tensor & logical_xor_(const Tensor & other) const; + Tensor bmm(const Tensor & mat2) const; + Tensor ceil() const; + Tensor & ceil_() const; + std::vector chunk(int64_t chunks, int64_t dim=0) const; + Tensor clamp(c10::optional min=c10::nullopt, c10::optional max=c10::nullopt) const; + Tensor & clamp_(c10::optional min=c10::nullopt, c10::optional max=c10::nullopt) const; + Tensor clamp_max(Scalar max) const; + Tensor & clamp_max_(Scalar max) const; + Tensor clamp_min(Scalar min) const; + Tensor & clamp_min_(Scalar min) const; + Tensor contiguous(MemoryFormat memory_format=MemoryFormat::Contiguous) const; + Tensor & copy_(const Tensor & src, bool non_blocking=false) const; + Tensor cos() const; + Tensor & cos_() const; + Tensor cosh() const; + Tensor & cosh_() const; + Tensor cumsum(int64_t dim, c10::optional dtype=c10::nullopt) const; + #ifdef BUILD_NAMEDTENSOR + Tensor cumsum(Dimname dim, c10::optional dtype=c10::nullopt) const; + #endif + Tensor cumprod(int64_t dim, c10::optional dtype=c10::nullopt) const; + #ifdef BUILD_NAMEDTENSOR + Tensor cumprod(Dimname dim, c10::optional dtype=c10::nullopt) const; + #endif + Tensor det() const; + Tensor diag_embed(int64_t offset=0, int64_t dim1=-2, int64_t dim2=-1) const; + Tensor diagflat(int64_t offset=0) const; + Tensor diagonal(int64_t offset=0, int64_t dim1=0, int64_t dim2=1) const; + Tensor & fill_diagonal_(Scalar fill_value, bool wrap=false) const; + Tensor div(const Tensor & other) const; + Tensor & div_(const Tensor & other) const; + Tensor div(Scalar other) const; + Tensor & div_(Scalar other) const; + Tensor dot(const Tensor & tensor) const; + Tensor new_empty(IntArrayRef size, const TensorOptions & options={}) const; + Tensor new_full(IntArrayRef size, Scalar fill_value, const TensorOptions & options={}) const; + Tensor new_zeros(IntArrayRef size, const TensorOptions & options={}) const; + Tensor & resize_(IntArrayRef size, c10::optional memory_format=c10::nullopt) const; + Tensor erf() const; + Tensor & erf_() const; + Tensor erfc() const; + Tensor & erfc_() const; + Tensor exp() const; + Tensor & exp_() const; + Tensor expm1() const; + Tensor & expm1_() const; + Tensor expand(IntArrayRef size, bool implicit=false) const; + Tensor expand_as(const Tensor & other) const; + Tensor flatten(int64_t start_dim=0, int64_t end_dim=-1) const; + #ifdef BUILD_NAMEDTENSOR + Tensor flatten(int64_t start_dim, int64_t end_dim, Dimname out_dim) const; + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor flatten(Dimname start_dim, Dimname end_dim, Dimname out_dim) const; + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor flatten(DimnameList dims, Dimname out_dim) const; + #endif + Tensor & fill_(Scalar value) const; + Tensor & fill_(const Tensor & value) const; + Tensor floor() const; + Tensor & floor_() const; + Tensor frac() const; + Tensor & frac_() const; + Tensor ger(const Tensor & vec2) const; + Tensor fft(int64_t signal_ndim, bool normalized=false) const; + Tensor ifft(int64_t signal_ndim, bool normalized=false) const; + Tensor rfft(int64_t signal_ndim, bool normalized=false, bool onesided=true) const; + Tensor irfft(int64_t signal_ndim, bool normalized=false, bool onesided=true, IntArrayRef signal_sizes={}) const; + Tensor index(TensorList indices) const; + Tensor & index_copy_(int64_t dim, const Tensor & index, const Tensor & source) const; + Tensor index_copy(int64_t dim, const Tensor & index, const Tensor & source) const; + #ifdef BUILD_NAMEDTENSOR + Tensor & index_copy_(Dimname dim, const Tensor & index, const Tensor & source) const; + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor index_copy(Dimname dim, const Tensor & index, const Tensor & source) const; + #endif + Tensor & index_put_(TensorList indices, const Tensor & values, bool accumulate=false) const; + Tensor index_put(TensorList indices, const Tensor & values, bool accumulate=false) const; + Tensor inverse() const; + Tensor isclose(const Tensor & other, double rtol=1e-05, double atol=1e-08, bool equal_nan=false) const; + bool is_distributed() const; + bool is_floating_point() const; + bool is_complex() const; + bool is_nonzero() const; + bool is_same_size(const Tensor & other) const; + bool is_signed() const; + std::tuple kthvalue(int64_t k, int64_t dim=-1, bool keepdim=false) const; + #ifdef BUILD_NAMEDTENSOR + std::tuple kthvalue(int64_t k, Dimname dim, bool keepdim=false) const; + #endif + Tensor log() const; + Tensor & log_() const; + Tensor log10() const; + Tensor & log10_() const; + Tensor log1p() const; + Tensor & log1p_() const; + Tensor log2() const; + Tensor & log2_() const; + Tensor logdet() const; + Tensor log_softmax(int64_t dim, c10::optional dtype=c10::nullopt) const; + #ifdef BUILD_NAMEDTENSOR + Tensor log_softmax(Dimname dim, c10::optional dtype=c10::nullopt) const; + #endif + Tensor logsumexp(IntArrayRef dim, bool keepdim=false) const; + #ifdef BUILD_NAMEDTENSOR + Tensor logsumexp(DimnameList dim, bool keepdim=false) const; + #endif + Tensor matmul(const Tensor & other) const; + Tensor matrix_power(int64_t n) const; + std::tuple max(int64_t dim, bool keepdim=false) const; + Tensor max_values(IntArrayRef dim, bool keepdim=false) const; + #ifdef BUILD_NAMEDTENSOR + std::tuple max(Dimname dim, bool keepdim=false) const; + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor max_values(DimnameList dim, bool keepdim=false) const; + #endif + Tensor mean(c10::optional dtype=c10::nullopt) const; + Tensor mean(IntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt) const; + #ifdef BUILD_NAMEDTENSOR + Tensor mean(DimnameList dim, bool keepdim=false, c10::optional dtype=c10::nullopt) const; + #endif + std::tuple median(int64_t dim, bool keepdim=false) const; + #ifdef BUILD_NAMEDTENSOR + std::tuple median(Dimname dim, bool keepdim=false) const; + #endif + std::tuple min(int64_t dim, bool keepdim=false) const; + Tensor min_values(IntArrayRef dim, bool keepdim=false) const; + #ifdef BUILD_NAMEDTENSOR + std::tuple min(Dimname dim, bool keepdim=false) const; + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor min_values(DimnameList dim, bool keepdim=false) const; + #endif + Tensor mm(const Tensor & mat2) const; + std::tuple mode(int64_t dim=-1, bool keepdim=false) const; + #ifdef BUILD_NAMEDTENSOR + std::tuple mode(Dimname dim, bool keepdim=false) const; + #endif + Tensor mul(const Tensor & other) const; + Tensor & mul_(const Tensor & other) const; + Tensor mul(Scalar other) const; + Tensor & mul_(Scalar other) const; + Tensor mv(const Tensor & vec) const; + Tensor mvlgamma(int64_t p) const; + Tensor & mvlgamma_(int64_t p) const; + Tensor narrow_copy(int64_t dim, int64_t start, int64_t length) const; + Tensor narrow(int64_t dim, int64_t start, int64_t length) const; + Tensor permute(IntArrayRef dims) const; + Tensor numpy_T() const; + bool is_pinned() const; + Tensor pin_memory() const; + Tensor pinverse(double rcond=1e-15) const; + Tensor reciprocal() const; + Tensor & reciprocal_() const; + Tensor neg() const; + Tensor & neg_() const; + Tensor repeat(IntArrayRef repeats) const; + Tensor repeat_interleave(const Tensor & repeats, c10::optional dim=c10::nullopt) const; + Tensor repeat_interleave(int64_t repeats, c10::optional dim=c10::nullopt) const; + Tensor reshape(IntArrayRef shape) const; + Tensor reshape_as(const Tensor & other) const; + Tensor round() const; + Tensor & round_() const; + Tensor relu() const; + Tensor & relu_() const; + Tensor prelu(const Tensor & weight) const; + std::tuple prelu_backward(const Tensor & grad_output, const Tensor & weight) const; + Tensor hardshrink(Scalar lambd=0.5) const; + Tensor hardshrink_backward(const Tensor & grad_out, Scalar lambd) const; + Tensor rsqrt() const; + Tensor & rsqrt_() const; + #ifdef BUILD_NAMEDTENSOR + Tensor select(Dimname dim, int64_t index) const; + #endif + Tensor select(int64_t dim, int64_t index) const; + Tensor sigmoid() const; + Tensor & sigmoid_() const; + Tensor sin() const; + Tensor & sin_() const; + Tensor sinh() const; + Tensor & sinh_() const; + Tensor detach() const; + Tensor & detach_() const; + int64_t size(int64_t dim) const; + #ifdef BUILD_NAMEDTENSOR + int64_t size(Dimname dim) const; + #endif + Tensor slice(int64_t dim=0, int64_t start=0, int64_t end=9223372036854775807, int64_t step=1) const; + std::tuple slogdet() const; + Tensor smm(const Tensor & mat2) const; + Tensor softmax(int64_t dim, c10::optional dtype=c10::nullopt) const; + #ifdef BUILD_NAMEDTENSOR + Tensor softmax(Dimname dim, c10::optional dtype=c10::nullopt) const; + #endif + std::vector split(int64_t split_size, int64_t dim=0) const; + std::vector split_with_sizes(IntArrayRef split_sizes, int64_t dim=0) const; + Tensor squeeze() const; + Tensor squeeze(int64_t dim) const; + #ifdef BUILD_NAMEDTENSOR + Tensor squeeze(Dimname dim) const; + #endif + Tensor & squeeze_() const; + Tensor & squeeze_(int64_t dim) const; + #ifdef BUILD_NAMEDTENSOR + Tensor & squeeze_(Dimname dim) const; + #endif + Tensor sspaddmm(const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1) const; + Tensor stft(int64_t n_fft, c10::optional hop_length=c10::nullopt, c10::optional win_length=c10::nullopt, const Tensor & window={}, bool normalized=false, bool onesided=true) const; + int64_t stride(int64_t dim) const; + #ifdef BUILD_NAMEDTENSOR + int64_t stride(Dimname dim) const; + #endif + Tensor sum(c10::optional dtype=c10::nullopt) const; + Tensor sum(IntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt) const; + #ifdef BUILD_NAMEDTENSOR + Tensor sum(DimnameList dim, bool keepdim=false, c10::optional dtype=c10::nullopt) const; + #endif + Tensor sum_to_size(IntArrayRef size) const; + Tensor sqrt() const; + Tensor & sqrt_() const; + Tensor std(bool unbiased=true) const; + Tensor std(IntArrayRef dim, bool unbiased=true, bool keepdim=false) const; + #ifdef BUILD_NAMEDTENSOR + Tensor std(DimnameList dim, bool unbiased=true, bool keepdim=false) const; + #endif + Tensor prod(c10::optional dtype=c10::nullopt) const; + Tensor prod(int64_t dim, bool keepdim=false, c10::optional dtype=c10::nullopt) const; + #ifdef BUILD_NAMEDTENSOR + Tensor prod(Dimname dim, bool keepdim=false, c10::optional dtype=c10::nullopt) const; + #endif + Tensor t() const; + Tensor & t_() const; + Tensor tan() const; + Tensor & tan_() const; + Tensor tanh() const; + Tensor & tanh_() const; + Tensor transpose(int64_t dim0, int64_t dim1) const; + #ifdef BUILD_NAMEDTENSOR + Tensor transpose(Dimname dim0, Dimname dim1) const; + #endif + Tensor & transpose_(int64_t dim0, int64_t dim1) const; + Tensor flip(IntArrayRef dims) const; + Tensor roll(IntArrayRef shifts, IntArrayRef dims={}) const; + Tensor rot90(int64_t k=1, IntArrayRef dims={0,1}) const; + Tensor trunc() const; + Tensor & trunc_() const; + Tensor type_as(const Tensor & other) const; + Tensor unsqueeze(int64_t dim) const; + Tensor & unsqueeze_(int64_t dim) const; + Tensor var(bool unbiased=true) const; + Tensor var(IntArrayRef dim, bool unbiased=true, bool keepdim=false) const; + #ifdef BUILD_NAMEDTENSOR + Tensor var(DimnameList dim, bool unbiased=true, bool keepdim=false) const; + #endif + Tensor view_as(const Tensor & other) const; + Tensor where(const Tensor & condition, const Tensor & other) const; + Tensor norm(c10::optional p, ScalarType dtype) const; + Tensor norm(Scalar p=2) const; + Tensor norm(c10::optional p, IntArrayRef dim, bool keepdim, ScalarType dtype) const; + Tensor norm(c10::optional p, IntArrayRef dim, bool keepdim=false) const; + #ifdef BUILD_NAMEDTENSOR + Tensor norm(c10::optional p, DimnameList dim, bool keepdim, ScalarType dtype) const; + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor norm(c10::optional p, DimnameList dim, bool keepdim=false) const; + #endif + Tensor clone(c10::optional memory_format=c10::nullopt) const; + Tensor & resize_as_(const Tensor & the_template, c10::optional memory_format=c10::nullopt) const; + Tensor pow(Scalar exponent) const; + Tensor & zero_() const; + Tensor sub(const Tensor & other, Scalar alpha=1) const; + Tensor & sub_(const Tensor & other, Scalar alpha=1) const; + Tensor sub(Scalar other, Scalar alpha=1) const; + Tensor & sub_(Scalar other, Scalar alpha=1) const; + Tensor addmm(const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1) const; + Tensor & addmm_(const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1) const; + Tensor & sparse_resize_(IntArrayRef size, int64_t sparse_dim, int64_t dense_dim) const; + Tensor & sparse_resize_and_clear_(IntArrayRef size, int64_t sparse_dim, int64_t dense_dim) const; + Tensor sparse_mask(const Tensor & mask) const; + Tensor to_dense() const; + int64_t sparse_dim() const; + int64_t _dimI() const; + int64_t dense_dim() const; + int64_t _dimV() const; + int64_t _nnz() const; + Tensor coalesce() const; + bool is_coalesced() const; + Tensor _indices() const; + Tensor _values() const; + Tensor & _coalesced_(bool coalesced) const; + Tensor indices() const; + Tensor values() const; + std::vector unbind(int64_t dim=0) const; + #ifdef BUILD_NAMEDTENSOR + std::vector unbind(Dimname dim) const; + #endif + Tensor to_sparse(int64_t sparse_dim) const; + Tensor to_sparse() const; + Tensor to_mkldnn() const; + Tensor dequantize() const; + double q_scale() const; + int64_t q_zero_point() const; + Tensor q_per_channel_scales() const; + Tensor q_per_channel_zero_points() const; + int64_t q_per_channel_axis() const; + Tensor int_repr() const; + QScheme qscheme() const; + Tensor to(const TensorOptions & options, bool non_blocking=false, bool copy=false, c10::optional memory_format=c10::nullopt) const; + Tensor to(Device device, ScalarType dtype, bool non_blocking=false, bool copy=false, c10::optional memory_format=c10::nullopt) const; + Tensor to(ScalarType dtype, bool non_blocking=false, bool copy=false, c10::optional memory_format=c10::nullopt) const; + Tensor to(const Tensor & other, bool non_blocking=false, bool copy=false, c10::optional memory_format=c10::nullopt) const; + Scalar item() const; + Tensor & set_(Storage source) const; + Tensor & set_(Storage source, int64_t storage_offset, IntArrayRef size, IntArrayRef stride={}) const; + Tensor & set_(const Tensor & source) const; + Tensor & set_() const; + Tensor & set_quantizer_(ConstQuantizerPtr quantizer) const; + bool is_set_to(const Tensor & tensor) const; + Tensor & masked_fill_(const Tensor & mask, Scalar value) const; + Tensor masked_fill(const Tensor & mask, Scalar value) const; + Tensor & masked_fill_(const Tensor & mask, const Tensor & value) const; + Tensor masked_fill(const Tensor & mask, const Tensor & value) const; + Tensor & masked_scatter_(const Tensor & mask, const Tensor & source) const; + Tensor masked_scatter(const Tensor & mask, const Tensor & source) const; + Tensor view(IntArrayRef size) const; + Tensor & put_(const Tensor & index, const Tensor & source, bool accumulate=false) const; + Tensor & index_add_(int64_t dim, const Tensor & index, const Tensor & source) const; + Tensor index_add(int64_t dim, const Tensor & index, const Tensor & source) const; + #ifdef BUILD_NAMEDTENSOR + Tensor index_add(Dimname dim, const Tensor & index, const Tensor & source) const; + #endif + Tensor & index_fill_(int64_t dim, const Tensor & index, Scalar value) const; + Tensor index_fill(int64_t dim, const Tensor & index, Scalar value) const; + Tensor & index_fill_(int64_t dim, const Tensor & index, const Tensor & value) const; + Tensor index_fill(int64_t dim, const Tensor & index, const Tensor & value) const; + #ifdef BUILD_NAMEDTENSOR + Tensor & index_fill_(Dimname dim, const Tensor & index, Scalar value) const; + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor & index_fill_(Dimname dim, const Tensor & index, const Tensor & value) const; + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor index_fill(Dimname dim, const Tensor & index, Scalar value) const; + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor index_fill(Dimname dim, const Tensor & index, const Tensor & value) const; + #endif + Tensor & scatter_(int64_t dim, const Tensor & index, const Tensor & src) const; + Tensor scatter(int64_t dim, const Tensor & index, const Tensor & src) const; + Tensor & scatter_(int64_t dim, const Tensor & index, Scalar value) const; + Tensor scatter(int64_t dim, const Tensor & index, Scalar value) const; + #ifdef BUILD_NAMEDTENSOR + Tensor scatter(Dimname dim, const Tensor & index, const Tensor & src) const; + #endif + #ifdef BUILD_NAMEDTENSOR + Tensor scatter(Dimname dim, const Tensor & index, Scalar value) const; + #endif + Tensor & scatter_add_(int64_t dim, const Tensor & index, const Tensor & src) const; + Tensor scatter_add(int64_t dim, const Tensor & index, const Tensor & src) const; + #ifdef BUILD_NAMEDTENSOR + Tensor scatter_add(Dimname dim, const Tensor & index, const Tensor & src) const; + #endif + Tensor & lt_(Scalar other) const; + Tensor & lt_(const Tensor & other) const; + Tensor & gt_(Scalar other) const; + Tensor & gt_(const Tensor & other) const; + Tensor & le_(Scalar other) const; + Tensor & le_(const Tensor & other) const; + Tensor & ge_(Scalar other) const; + Tensor & ge_(const Tensor & other) const; + Tensor & eq_(Scalar other) const; + Tensor & eq_(const Tensor & other) const; + Tensor & ne_(Scalar other) const; + Tensor & ne_(const Tensor & other) const; + Tensor __and__(Scalar other) const; + Tensor __and__(const Tensor & other) const; + Tensor & __iand__(Scalar other) const; + Tensor & __iand__(const Tensor & other) const; + Tensor __or__(Scalar other) const; + Tensor __or__(const Tensor & other) const; + Tensor & __ior__(Scalar other) const; + Tensor & __ior__(const Tensor & other) const; + Tensor bitwise_xor(Scalar other) const; + Tensor bitwise_xor(const Tensor & other) const; + Tensor & bitwise_xor_(Scalar other) const; + Tensor & bitwise_xor_(const Tensor & other) const; + Tensor __xor__(Scalar other) const; + Tensor __xor__(const Tensor & other) const; + Tensor & __ixor__(Scalar other) const; + Tensor & __ixor__(const Tensor & other) const; + Tensor __lshift__(Scalar other) const; + Tensor __lshift__(const Tensor & other) const; + Tensor & __ilshift__(Scalar other) const; + Tensor & __ilshift__(const Tensor & other) const; + Tensor __rshift__(Scalar other) const; + Tensor __rshift__(const Tensor & other) const; + Tensor & __irshift__(Scalar other) const; + Tensor & __irshift__(const Tensor & other) const; + Tensor & lgamma_() const; + Tensor & atan2_(const Tensor & other) const; + Tensor & tril_(int64_t diagonal=0) const; + Tensor & triu_(int64_t diagonal=0) const; + Tensor & digamma_() const; + Tensor & polygamma_(int64_t n) const; + Tensor & renorm_(Scalar p, int64_t dim, Scalar maxnorm) const; + Tensor & pow_(Scalar exponent) const; + Tensor & pow_(const Tensor & exponent) const; + Tensor & lerp_(const Tensor & end, Scalar weight) const; + Tensor & lerp_(const Tensor & end, const Tensor & weight) const; + Tensor & fmod_(Scalar other) const; + Tensor & fmod_(const Tensor & other) const; + Tensor & remainder_(Scalar other) const; + Tensor & remainder_(const Tensor & other) const; + Tensor & addbmm_(const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1) const; + Tensor addbmm(const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1) const; + Tensor & addcdiv_(const Tensor & tensor1, const Tensor & tensor2, Scalar value=1) const; + Tensor & random_(int64_t from, int64_t to, Generator * generator=nullptr) const; + Tensor & random_(int64_t to, Generator * generator=nullptr) const; + Tensor & random_(Generator * generator=nullptr) const; + Tensor & uniform_(double from=0, double to=1, Generator * generator=nullptr) const; + Tensor & normal_(double mean=0, double std=1, Generator * generator=nullptr) const; + Tensor & cauchy_(double median=0, double sigma=1, Generator * generator=nullptr) const; + Tensor & log_normal_(double mean=1, double std=2, Generator * generator=nullptr) const; + Tensor & exponential_(double lambd=1, Generator * generator=nullptr) const; + Tensor & geometric_(double p, Generator * generator=nullptr) const; + Tensor diag(int64_t diagonal=0) const; + Tensor cross(const Tensor & other, c10::optional dim=c10::nullopt) const; + Tensor triu(int64_t diagonal=0) const; + Tensor tril(int64_t diagonal=0) const; + Tensor trace() const; + Tensor ne(Scalar other) const; + Tensor ne(const Tensor & other) const; + Tensor eq(Scalar other) const; + Tensor eq(const Tensor & other) const; + Tensor ge(Scalar other) const; + Tensor ge(const Tensor & other) const; + Tensor le(Scalar other) const; + Tensor le(const Tensor & other) const; + Tensor gt(Scalar other) const; + Tensor gt(const Tensor & other) const; + Tensor lt(Scalar other) const; + Tensor lt(const Tensor & other) const; + Tensor take(const Tensor & index) const; + Tensor index_select(int64_t dim, const Tensor & index) const; + #ifdef BUILD_NAMEDTENSOR + Tensor index_select(Dimname dim, const Tensor & index) const; + #endif + Tensor masked_select(const Tensor & mask) const; + Tensor nonzero() const; + std::vector nonzero_numpy() const; + Tensor gather(int64_t dim, const Tensor & index, bool sparse_grad=false) const; + #ifdef BUILD_NAMEDTENSOR + Tensor gather(Dimname dim, const Tensor & index, bool sparse_grad=false) const; + #endif + Tensor addcmul(const Tensor & tensor1, const Tensor & tensor2, Scalar value=1) const; + Tensor & addcmul_(const Tensor & tensor1, const Tensor & tensor2, Scalar value=1) const; + Tensor addcdiv(const Tensor & tensor1, const Tensor & tensor2, Scalar value=1) const; + std::tuple lstsq(const Tensor & A) const; + std::tuple triangular_solve(const Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false) const; + std::tuple symeig(bool eigenvectors=false, bool upper=true) const; + std::tuple eig(bool eigenvectors=false) const; + std::tuple svd(bool some=true, bool compute_uv=true) const; + Tensor cholesky(bool upper=false) const; + Tensor cholesky_solve(const Tensor & input2, bool upper=false) const; + std::tuple solve(const Tensor & A) const; + Tensor cholesky_inverse(bool upper=false) const; + std::tuple qr(bool some=true) const; + std::tuple geqrf() const; + Tensor orgqr(const Tensor & input2) const; + Tensor ormqr(const Tensor & input2, const Tensor & input3, bool left=true, bool transpose=false) const; + Tensor lu_solve(const Tensor & LU_data, const Tensor & LU_pivots) const; + Tensor multinomial(int64_t num_samples, bool replacement=false, Generator * generator=nullptr) const; + Tensor lgamma() const; + Tensor digamma() const; + Tensor polygamma(int64_t n) const; + Tensor erfinv() const; + Tensor & erfinv_() const; + Tensor sign() const; + Tensor & sign_() const; + Tensor dist(const Tensor & other, Scalar p=2) const; + Tensor atan2(const Tensor & other) const; + Tensor lerp(const Tensor & end, Scalar weight) const; + Tensor lerp(const Tensor & end, const Tensor & weight) const; + Tensor histc(int64_t bins=100, Scalar min=0, Scalar max=0) const; + Tensor fmod(Scalar other) const; + Tensor fmod(const Tensor & other) const; + Tensor remainder(Scalar other) const; + Tensor remainder(const Tensor & other) const; + Tensor min(const Tensor & other) const; + Tensor min() const; + Tensor max(const Tensor & other) const; + Tensor max() const; + Tensor median() const; + std::tuple sort(int64_t dim=-1, bool descending=false) const; + #ifdef BUILD_NAMEDTENSOR + std::tuple sort(Dimname dim, bool descending=false) const; + #endif + Tensor argsort(int64_t dim=-1, bool descending=false) const; + #ifdef BUILD_NAMEDTENSOR + Tensor argsort(Dimname dim, bool descending=false) const; + #endif + std::tuple topk(int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true) const; + Tensor all() const; + Tensor any() const; + Tensor renorm(Scalar p, int64_t dim, Scalar maxnorm) const; + Tensor unfold(int64_t dimension, int64_t size, int64_t step) const; + bool equal(const Tensor & other) const; + Tensor pow(const Tensor & exponent) const; + Tensor alias() const; + + // We changed .dtype() to return a TypeMeta in #12766. Ideally, we want the + // at::kDouble and its friends to be TypeMeta's, but that hasn't happened yet. + // Before that change, we make this method to maintain BC for C++ usage like + // `x.to(y.dtype)`. + // TODO: remove following two after at::kDouble and its friends are TypeMeta's. + inline Tensor to(caffe2::TypeMeta type_meta, bool non_blocking=false, bool copy=false) const { + return this->to(/*scalar_type=*/typeMetaToScalarType(type_meta), non_blocking, copy); + } + inline Tensor to(Device device, caffe2::TypeMeta type_meta, bool non_blocking=false, bool copy=false) const { + return this->to(device, /*scalar_type=*/typeMetaToScalarType(type_meta), non_blocking, copy); + } + + template + auto m(F func, Args&&... params) const -> decltype(func(*this, std::forward(params)...)) { + return func(*this, std::forward(params)...); + } + + /// NOTE: This is similar to the legacy `.data()` function on `Variable`, and is intended + /// to be used from functions that need to access the `Variable`'s equivalent `Tensor` + /// (i.e. `Tensor` that shares the same storage and tensor metadata with the `Variable`). + /// + /// One notable difference with the legacy `.data()` function is that changes to the + /// returned `Tensor`'s tensor metadata (e.g. sizes / strides / storage / storage_offset) + /// will not update the original `Variable`, due to the fact that this function + /// shallow-copies the `Variable`'s underlying TensorImpl. + at::Tensor tensor_data() const; + + /// NOTE: `var.variable_data()` in C++ has the same semantics as `tensor.data` + /// in Python, which create a new `Variable` that shares the same storage and + /// tensor metadata with the original `Variable`, but with a completely new + /// autograd history. + /// + /// NOTE: If we change the tensor metadata (e.g. sizes / strides / + /// storage / storage_offset) of a variable created from `var.variable_data()`, those + /// changes will not update the original variable `var`. In `.variable_data()`, we set + /// `allow_tensor_metadata_change_` to false to make such changes explicitly illegal, + /// in order to prevent users from changing metadata of `var.variable_data()` + /// and expecting the original variable `var` to also be updated. + at::Tensor variable_data() const; + + // Gradient Node and Edges + //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + /// Gets the gradient function of the `Variable`. If this is a leaf variable, + /// the pointer returned will be null. + /// + /// For View Variables: + /// Gets the up-to-date grad_fn. If the shared data or base was modified, we + /// re-create the grad_fn to express the up-to-date view relationship between + /// this and the base Variable. + const std::shared_ptr& grad_fn() const; + + // Hooks + //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + template + using hook_return_void_t = c10::guts::enable_if_t::type>::value, unsigned>; + template + using hook_return_var_t = c10::guts::enable_if_t::type, Tensor>::value, unsigned>; + + // Returns the index of the hook in the list which can be used to remove hook + // Register a hook with no return value + template + hook_return_void_t register_hook(T&& hook) const; + // Register a hook with variable return value + template + hook_return_var_t register_hook(T&& hook) const; + +private: + unsigned _register_hook(std::function hook) const; + +public: + + // Remove hook at given position + void remove_hook(unsigned pos) const; + + // View Variables + //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + /// Returns true if this `Variable` is a view of another `Variable`. + bool is_view() const; + + /// Returns the `Variable` that this `Variable` is a view of. If this + /// `Variable` is not a view, throw a `std::runtime_error`. + const Tensor& base() const; + + // Miscellaneous + //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + const std::string& name() const; + +protected: + friend class ::caffe2::Tensor; + + void enforce_invariants(); + c10::intrusive_ptr impl_; +}; + +namespace detail { +// Helper creator for Tensor class which doesn't requires the users to pass +// in an intrusive_ptr instead it just converts the argument passed to +// requested intrusive_ptr type. +template +Tensor make_tensor(Args&&... args) { + return Tensor(c10::make_intrusive(std::forward(args)...)); +} + +} // namespace detail + +static inline TensorTypeId legacyExtractTypeId(const Tensor& t) { + return legacyExtractTypeId(t.type_set()); +} + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/core/TensorMethods.h b/thirdparty/libtorch/include/ATen/core/TensorMethods.h new file mode 100644 index 0000000000..0ac8ffe365 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/TensorMethods.h @@ -0,0 +1,6354 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#ifdef USE_STATIC_DISPATCH +#include +#include +#include +#include +#endif + +namespace at { + +struct Quantizer; +// This is temporary typedef to enable Quantizer in aten native function API +// we'll remove them when we are actually exposing Quantizer class +// to frontend +using ConstQuantizerPtr = const c10::intrusive_ptr&; + +inline Tensor Tensor::cpu() const { + return to(options().device(DeviceType::CPU), /*non_blocking*/ false, /*copy*/ false); +} + +// TODO: The Python version also accepts arguments +inline Tensor Tensor::cuda() const { + return to(options().device(DeviceType::CUDA), /*non_blocking*/ false, /*copy*/ false); +} + +inline Tensor Tensor::hip() const { + return to(options().device(DeviceType::HIP), /*non_blocking*/ false, /*copy*/ false); +} + +inline Tensor Tensor::toType(ScalarType t) const { + return to(options().dtype(t), /*non_blocking*/ false, /*copy*/ false); +} + +// TODO: Deprecate me +inline Tensor Tensor::toBackend(Backend b) const { + return to(options().device(backendToDeviceType(b)).layout(layout_from_backend(b)), /*non_blocking*/ false, /*copy*/ false); +} + +inline TensorOptions Tensor::options() const { + return TensorOptions().dtype(dtype()) + .device(device()) + .layout(layout()); +} + +// all static inline to allow for inlining of the non-dynamic part of dispatch +inline void Tensor::backward(const Tensor & gradient, bool keep_graph, bool create_graph) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + TypeDefault::backward(const_cast(*this), gradient, keep_graph, create_graph); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), gradient, keep_graph, create_graph); +#endif +} +inline void Tensor::set_data(const Tensor & new_data) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + TypeDefault::set_data(const_cast(*this), new_data); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::set_data", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), new_data); +#endif +} +inline Tensor Tensor::data() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::data(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::data", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline bool Tensor::is_leaf() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::is_leaf(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::is_leaf", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline int64_t Tensor::output_nr() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::output_nr(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::output_nr", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline int64_t Tensor::_version() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::_version(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::_version", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::requires_grad_(bool _requires_grad) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::requires_grad_(const_cast(*this), _requires_grad); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::requires_grad_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), _requires_grad); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor & Tensor::rename_(c10::optional names) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rename_(const_cast(*this), names); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::rename_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), names); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::rename(c10::optional names) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rename(const_cast(*this), names); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::rename", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), names); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::align_to(DimnameList names) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::align_to(const_cast(*this), names); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::align_to", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), names); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::align_to(DimnameList order, int64_t ellipsis_idx) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::align_to(const_cast(*this), order, ellipsis_idx); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::align_to", "ellipsis_idx"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), order, ellipsis_idx); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::align_as(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::align_as(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::align_as", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::refine_names(DimnameList names) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::refine_names(const_cast(*this), names); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::refine_names", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), names); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::unflatten(Dimname dim, IntArrayRef sizes, DimnameList names) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::unflatten(const_cast(*this), dim, sizes, names); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::unflatten", "Dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, sizes, names); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::unflatten(int64_t dim, IntArrayRef sizes, DimnameList names) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::unflatten(const_cast(*this), dim, sizes, names); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::unflatten", "int"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, sizes, names); +#endif +} +#endif +inline Tensor Tensor::abs() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::abs(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::abs", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::abs_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::abs_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::abs_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::angle() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::angle(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::angle", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::real() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::real(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::real", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::imag() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::imag(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::imag", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::conj() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::conj(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::conj", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::acos() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::acos(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::acos", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::acos_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::acos_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::acos_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::add(const Tensor & other, Scalar alpha) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::add(const_cast(*this), other, alpha); + break; + case Backend::SparseCPU: + return SparseCPUType::add(const_cast(*this), other, alpha); + break; + default: + AT_ERROR("add not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::add", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other, alpha); +#endif +} +inline Tensor & Tensor::add_(const Tensor & other, Scalar alpha) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::add_(const_cast(*this), other, alpha); + break; + case Backend::SparseCPU: + return SparseCPUType::add_(const_cast(*this), other, alpha); + break; + default: + AT_ERROR("add_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::add_", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other, alpha); +#endif +} +inline Tensor Tensor::add(Scalar other, Scalar alpha) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::add(const_cast(*this), other, alpha); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::add", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other, alpha); +#endif +} +inline Tensor & Tensor::add_(Scalar other, Scalar alpha) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::add_(const_cast(*this), other, alpha); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::add_", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other, alpha); +#endif +} +inline Tensor Tensor::addmv(const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::addmv(const_cast(*this), mat, vec, beta, alpha); + break; + default: + AT_ERROR("addmv not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::addmv", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), mat, vec, beta, alpha); +#endif +} +inline Tensor & Tensor::addmv_(const Tensor & mat, const Tensor & vec, Scalar beta, Scalar alpha) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::addmv_(const_cast(*this), mat, vec, beta, alpha); + break; + default: + AT_ERROR("addmv_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::addmv_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), mat, vec, beta, alpha); +#endif +} +inline Tensor Tensor::addr(const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::addr(const_cast(*this), vec1, vec2, beta, alpha); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::addr", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), vec1, vec2, beta, alpha); +#endif +} +inline Tensor & Tensor::addr_(const Tensor & vec1, const Tensor & vec2, Scalar beta, Scalar alpha) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::addr_(const_cast(*this), vec1, vec2, beta, alpha); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::addr_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), vec1, vec2, beta, alpha); +#endif +} +inline Tensor Tensor::all(int64_t dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::all(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::all", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), dim, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::all(Dimname dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::all(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::all", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, keepdim); +#endif +} +#endif +inline bool Tensor::allclose(const Tensor & other, double rtol, double atol, bool equal_nan) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::allclose(const_cast(*this), other, rtol, atol, equal_nan); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::allclose", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other, rtol, atol, equal_nan); +#endif +} +inline Tensor Tensor::any(int64_t dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::any(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::any", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), dim, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::any(Dimname dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::any(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::any", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, keepdim); +#endif +} +#endif +inline Tensor Tensor::argmax(c10::optional dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::argmax(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::argmax", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed, bool>( + op, const_cast(*this), dim, keepdim); +#endif +} +inline Tensor Tensor::argmin(c10::optional dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::argmin(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::argmin", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed, bool>( + op, const_cast(*this), dim, keepdim); +#endif +} +inline Tensor Tensor::as_strided(IntArrayRef size, IntArrayRef stride, c10::optional storage_offset) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::as_strided(const_cast(*this), size, stride, storage_offset); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::as_strided(const_cast(*this), size, stride, storage_offset); + break; + default: + AT_ERROR("as_strided not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::as_strided", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), size, stride, storage_offset); +#endif +} +inline Tensor & Tensor::as_strided_(IntArrayRef size, IntArrayRef stride, c10::optional storage_offset) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::as_strided_(const_cast(*this), size, stride, storage_offset); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::as_strided_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), size, stride, storage_offset); +#endif +} +inline Tensor Tensor::asin() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::asin(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::asin", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::asin_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::asin_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::asin_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::atan() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::atan(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::atan", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::atan_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::atan_(const_cast(*this)); + break; + default: + AT_ERROR("atan_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::atan_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::baddbmm(const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::baddbmm(const_cast(*this), batch1, batch2, beta, alpha); + break; + default: + AT_ERROR("baddbmm not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::baddbmm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), batch1, batch2, beta, alpha); +#endif +} +inline Tensor & Tensor::baddbmm_(const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::baddbmm_(const_cast(*this), batch1, batch2, beta, alpha); + break; + default: + AT_ERROR("baddbmm_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::baddbmm_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), batch1, batch2, beta, alpha); +#endif +} +inline Tensor Tensor::bernoulli(Generator * generator) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::bernoulli(const_cast(*this), generator); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::bernoulli", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), generator); +#endif +} +inline Tensor & Tensor::bernoulli_(const Tensor & p, Generator * generator) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::bernoulli_(const_cast(*this), p, generator); + break; + default: + AT_ERROR("bernoulli_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::bernoulli_", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), p, generator); +#endif +} +inline Tensor & Tensor::bernoulli_(double p, Generator * generator) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::bernoulli_(const_cast(*this), p, generator); + break; + default: + AT_ERROR("bernoulli_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::bernoulli_", "float"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), p, generator); +#endif +} +inline Tensor Tensor::bernoulli(double p, Generator * generator) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::bernoulli(const_cast(*this), p, generator); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::bernoulli", "p"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), p, generator); +#endif +} +inline Tensor Tensor::bincount(const Tensor & weights, int64_t minlength) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::bincount(const_cast(*this), weights, minlength); + break; + default: + AT_ERROR("bincount not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::bincount", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), weights, minlength); +#endif +} +inline Tensor Tensor::bitwise_not() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::bitwise_not(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::bitwise_not", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::bitwise_not_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::bitwise_not_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::bitwise_not_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::logical_not() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::logical_not(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::logical_not", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::logical_not_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::logical_not_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::logical_not_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::logical_xor(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::logical_xor(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::logical_xor", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::logical_xor_(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::logical_xor_(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::logical_xor_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::bmm(const Tensor & mat2) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::bmm(const_cast(*this), mat2); + break; + default: + AT_ERROR("bmm not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::bmm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), mat2); +#endif +} +inline Tensor Tensor::ceil() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::ceil(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::ceil", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::ceil_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::ceil_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::ceil_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline std::vector Tensor::chunk(int64_t chunks, int64_t dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::chunk(const_cast(*this), chunks, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::chunk", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, int64_t>( + op, const_cast(*this), chunks, dim); +#endif +} +inline Tensor Tensor::clamp(c10::optional min, c10::optional max) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::clamp(const_cast(*this), min, max); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::clamp", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed, c10::optional>( + op, const_cast(*this), min, max); +#endif +} +inline Tensor & Tensor::clamp_(c10::optional min, c10::optional max) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::clamp_(const_cast(*this), min, max); + break; + default: + AT_ERROR("clamp_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::clamp_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, c10::optional>( + op, const_cast(*this), min, max); +#endif +} +inline Tensor Tensor::clamp_max(Scalar max) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::clamp_max(const_cast(*this), max); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::clamp_max", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), max); +#endif +} +inline Tensor & Tensor::clamp_max_(Scalar max) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::clamp_max_(const_cast(*this), max); + break; + default: + AT_ERROR("clamp_max_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::clamp_max_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), max); +#endif +} +inline Tensor Tensor::clamp_min(Scalar min) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::clamp_min(const_cast(*this), min); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::clamp_min", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), min); +#endif +} +inline Tensor & Tensor::clamp_min_(Scalar min) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::clamp_min_(const_cast(*this), min); + break; + default: + AT_ERROR("clamp_min_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::clamp_min_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), min); +#endif +} +inline Tensor Tensor::contiguous(MemoryFormat memory_format) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::contiguous(const_cast(*this), memory_format); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::contiguous", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), memory_format); +#endif +} +inline Tensor & Tensor::copy_(const Tensor & src, bool non_blocking) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::copy_(const_cast(*this), src, non_blocking); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::copy_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), src, non_blocking); +#endif +} +inline Tensor Tensor::cos() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cos(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::cos", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::cos_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::cos_(const_cast(*this)); + break; + default: + AT_ERROR("cos_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::cos_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::cosh() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cosh(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::cosh", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::cosh_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::cosh_(const_cast(*this)); + break; + default: + AT_ERROR("cosh_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::cosh_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::cumsum(int64_t dim, c10::optional dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cumsum(const_cast(*this), dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::cumsum", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), dim, dtype); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::cumsum(Dimname dim, c10::optional dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cumsum(const_cast(*this), dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::cumsum", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), dim, dtype); +#endif +} +#endif +inline Tensor Tensor::cumprod(int64_t dim, c10::optional dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cumprod(const_cast(*this), dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::cumprod", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), dim, dtype); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::cumprod(Dimname dim, c10::optional dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cumprod(const_cast(*this), dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::cumprod", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), dim, dtype); +#endif +} +#endif +inline Tensor Tensor::det() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::det(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::det", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::diag_embed(int64_t offset, int64_t dim1, int64_t dim2) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::diag_embed(const_cast(*this), offset, dim1, dim2); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::diag_embed", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), offset, dim1, dim2); +#endif +} +inline Tensor Tensor::diagflat(int64_t offset) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::diagflat(const_cast(*this), offset); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::diagflat", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), offset); +#endif +} +inline Tensor Tensor::diagonal(int64_t offset, int64_t dim1, int64_t dim2) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::diagonal(const_cast(*this), offset, dim1, dim2); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::diagonal", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), offset, dim1, dim2); +#endif +} +inline Tensor & Tensor::fill_diagonal_(Scalar fill_value, bool wrap) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::fill_diagonal_(const_cast(*this), fill_value, wrap); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::fill_diagonal_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), fill_value, wrap); +#endif +} +inline Tensor Tensor::div(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::div(const_cast(*this), other); + break; + case Backend::SparseCPU: + return SparseCPUType::div(const_cast(*this), other); + break; + default: + AT_ERROR("div not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::div", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::div_(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::div_(const_cast(*this), other); + break; + case Backend::SparseCPU: + return SparseCPUType::div_(const_cast(*this), other); + break; + default: + AT_ERROR("div_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::div_", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::div(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::div(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::div", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::div_(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::div_(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::div_", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::dot(const Tensor & tensor) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::dot(const_cast(*this), tensor); + break; + default: + AT_ERROR("dot not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::dot", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), tensor); +#endif +} +inline Tensor Tensor::new_empty(IntArrayRef size, const TensorOptions & options) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::new_empty(const_cast(*this), size, options); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::new_empty", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), size, options); +#endif +} +inline Tensor Tensor::new_full(IntArrayRef size, Scalar fill_value, const TensorOptions & options) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::new_full(const_cast(*this), size, fill_value, options); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::new_full", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), size, fill_value, options); +#endif +} +inline Tensor Tensor::new_zeros(IntArrayRef size, const TensorOptions & options) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::new_zeros(const_cast(*this), size, options); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::new_zeros", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), size, options); +#endif +} +inline Tensor & Tensor::resize_(IntArrayRef size, c10::optional memory_format) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::resize_(const_cast(*this), size, memory_format); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::resize_(const_cast(*this), size, memory_format); + break; + default: + AT_ERROR("resize_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::resize_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), size, memory_format); +#endif +} +inline Tensor Tensor::erf() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::erf(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::erf", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::erf_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::erf_(const_cast(*this)); + break; + default: + AT_ERROR("erf_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::erf_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::erfc() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::erfc(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::erfc", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::erfc_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::erfc_(const_cast(*this)); + break; + default: + AT_ERROR("erfc_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::erfc_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::exp() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::exp(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::exp", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::exp_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::exp_(const_cast(*this)); + break; + default: + AT_ERROR("exp_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::exp_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::expm1() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::expm1(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::expm1", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::expm1_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::expm1_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::expm1_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::expand(IntArrayRef size, bool implicit) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::expand(const_cast(*this), size, implicit); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::expand", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), size, implicit); +#endif +} +inline Tensor Tensor::expand_as(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::expand_as(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::expand_as", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::flatten(int64_t start_dim, int64_t end_dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::flatten(const_cast(*this), start_dim, end_dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::flatten", "using_ints"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), start_dim, end_dim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::flatten(int64_t start_dim, int64_t end_dim, Dimname out_dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::flatten(const_cast(*this), start_dim, end_dim, out_dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::flatten", "named_out_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), start_dim, end_dim, out_dim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::flatten(Dimname start_dim, Dimname end_dim, Dimname out_dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::flatten(const_cast(*this), start_dim, end_dim, out_dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::flatten", "using_names"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), start_dim, end_dim, out_dim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::flatten(DimnameList dims, Dimname out_dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::flatten(const_cast(*this), dims, out_dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::flatten", "DimnameList"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dims, out_dim); +#endif +} +#endif +inline Tensor & Tensor::fill_(Scalar value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::fill_(const_cast(*this), value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::fill_", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), value); +#endif +} +inline Tensor & Tensor::fill_(const Tensor & value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::fill_(const_cast(*this), value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::fill_", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), value); +#endif +} +inline Tensor Tensor::floor() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::floor(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::floor", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::floor_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::floor_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::floor_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::frac() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::frac(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::frac", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::frac_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::frac_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::frac_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::ger(const Tensor & vec2) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::ger(const_cast(*this), vec2); + break; + default: + AT_ERROR("ger not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::ger", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), vec2); +#endif +} +inline Tensor Tensor::fft(int64_t signal_ndim, bool normalized) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::fft(const_cast(*this), signal_ndim, normalized); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::fft", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), signal_ndim, normalized); +#endif +} +inline Tensor Tensor::ifft(int64_t signal_ndim, bool normalized) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::ifft(const_cast(*this), signal_ndim, normalized); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::ifft", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), signal_ndim, normalized); +#endif +} +inline Tensor Tensor::rfft(int64_t signal_ndim, bool normalized, bool onesided) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rfft(const_cast(*this), signal_ndim, normalized, onesided); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::rfft", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), signal_ndim, normalized, onesided); +#endif +} +inline Tensor Tensor::irfft(int64_t signal_ndim, bool normalized, bool onesided, IntArrayRef signal_sizes) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::irfft(const_cast(*this), signal_ndim, normalized, onesided, signal_sizes); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::irfft", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), signal_ndim, normalized, onesided, signal_sizes); +#endif +} +inline Tensor Tensor::index(TensorList indices) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index(const_cast(*this), indices); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), indices); +#endif +} +inline Tensor & Tensor::index_copy_(int64_t dim, const Tensor & index, const Tensor & source) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_copy_(const_cast(*this), dim, index, source); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index_copy_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index, source); +#endif +} +inline Tensor Tensor::index_copy(int64_t dim, const Tensor & index, const Tensor & source) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_copy(const_cast(*this), dim, index, source); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index_copy", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), dim, index, source); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor & Tensor::index_copy_(Dimname dim, const Tensor & index, const Tensor & source) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_copy_(const_cast(*this), dim, index, source); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index_copy_", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index, source); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::index_copy(Dimname dim, const Tensor & index, const Tensor & source) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_copy(const_cast(*this), dim, index, source); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index_copy", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index, source); +#endif +} +#endif +inline Tensor & Tensor::index_put_(TensorList indices, const Tensor & values, bool accumulate) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_put_(const_cast(*this), indices, values, accumulate); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index_put_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), indices, values, accumulate); +#endif +} +inline Tensor Tensor::index_put(TensorList indices, const Tensor & values, bool accumulate) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_put(const_cast(*this), indices, values, accumulate); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index_put", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), indices, values, accumulate); +#endif +} +inline Tensor Tensor::inverse() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::inverse(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::inverse", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::isclose(const Tensor & other, double rtol, double atol, bool equal_nan) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::isclose(const_cast(*this), other, rtol, atol, equal_nan); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::isclose", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other, rtol, atol, equal_nan); +#endif +} +inline bool Tensor::is_distributed() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::is_distributed(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::is_distributed", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline bool Tensor::is_floating_point() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::is_floating_point(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::is_floating_point", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline bool Tensor::is_complex() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::is_complex(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::is_complex", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline bool Tensor::is_nonzero() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::is_nonzero(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::is_nonzero", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline bool Tensor::is_same_size(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::is_same_size(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::is_same_size", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline bool Tensor::is_signed() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::is_signed(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::is_signed", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline std::tuple Tensor::kthvalue(int64_t k, int64_t dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::kthvalue(const_cast(*this), k, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::kthvalue", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, int64_t, bool>( + op, const_cast(*this), k, dim, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline std::tuple Tensor::kthvalue(int64_t k, Dimname dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::kthvalue(const_cast(*this), k, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::kthvalue", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, Dimname, bool>( + op, const_cast(*this), k, dim, keepdim); +#endif +} +#endif +inline Tensor Tensor::log() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::log", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::log_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::log_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::log10() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log10(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::log10", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::log10_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log10_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::log10_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::log1p() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log1p(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::log1p", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::log1p_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::log1p_(const_cast(*this)); + break; + case Backend::SparseCPU: + return SparseCPUType::log1p_(const_cast(*this)); + break; + default: + AT_ERROR("log1p_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::log1p_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::log2() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log2(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::log2", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::log2_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log2_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::log2_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::logdet() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::logdet(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::logdet", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::log_softmax(int64_t dim, c10::optional dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log_softmax(const_cast(*this), dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::log_softmax", "int"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), dim, dtype); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::log_softmax(Dimname dim, c10::optional dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::log_softmax(const_cast(*this), dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::log_softmax", "Dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), dim, dtype); +#endif +} +#endif +inline Tensor Tensor::logsumexp(IntArrayRef dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::logsumexp(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::logsumexp", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::logsumexp(DimnameList dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::logsumexp(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::logsumexp", "names"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, keepdim); +#endif +} +#endif +inline Tensor Tensor::matmul(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::matmul(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::matmul", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::matrix_power(int64_t n) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::matrix_power(const_cast(*this), n); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::matrix_power", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), n); +#endif +} +inline std::tuple Tensor::max(int64_t dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::max(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::max", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, bool>( + op, const_cast(*this), dim, keepdim); +#endif +} +inline Tensor Tensor::max_values(IntArrayRef dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::max_values(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::max_values", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline std::tuple Tensor::max(Dimname dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::max(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::max", "names_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, Dimname, bool>( + op, const_cast(*this), dim, keepdim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::max_values(DimnameList dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::max_values(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::max_values", "names"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, keepdim); +#endif +} +#endif +inline Tensor Tensor::mean(c10::optional dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::mean(const_cast(*this), dtype); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::mean(const_cast(*this), dtype); + break; + default: + AT_ERROR("mean not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::mean", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), dtype); +#endif +} +inline Tensor Tensor::mean(IntArrayRef dim, bool keepdim, c10::optional dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::mean(const_cast(*this), dim, keepdim, dtype); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::mean(const_cast(*this), dim, keepdim, dtype); + break; + default: + AT_ERROR("mean not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::mean", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), dim, keepdim, dtype); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::mean(DimnameList dim, bool keepdim, c10::optional dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mean(const_cast(*this), dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::mean", "names_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), dim, keepdim, dtype); +#endif +} +#endif +inline std::tuple Tensor::median(int64_t dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::median(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::median", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, bool>( + op, const_cast(*this), dim, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline std::tuple Tensor::median(Dimname dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::median(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::median", "names_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, Dimname, bool>( + op, const_cast(*this), dim, keepdim); +#endif +} +#endif +inline std::tuple Tensor::min(int64_t dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::min(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::min", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, bool>( + op, const_cast(*this), dim, keepdim); +#endif +} +inline Tensor Tensor::min_values(IntArrayRef dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::min_values(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::min_values", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline std::tuple Tensor::min(Dimname dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::min(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::min", "names_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, Dimname, bool>( + op, const_cast(*this), dim, keepdim); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::min_values(DimnameList dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::min_values(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::min_values", "names"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, keepdim); +#endif +} +#endif +inline Tensor Tensor::mm(const Tensor & mat2) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::mm(const_cast(*this), mat2); + break; + case Backend::SparseCPU: + return SparseCPUType::mm(const_cast(*this), mat2); + break; + default: + AT_ERROR("mm not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::mm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), mat2); +#endif +} +inline std::tuple Tensor::mode(int64_t dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mode(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::mode", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, bool>( + op, const_cast(*this), dim, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline std::tuple Tensor::mode(Dimname dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mode(const_cast(*this), dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::mode", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, Dimname, bool>( + op, const_cast(*this), dim, keepdim); +#endif +} +#endif +inline Tensor Tensor::mul(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::mul(const_cast(*this), other); + break; + case Backend::SparseCPU: + return SparseCPUType::mul(const_cast(*this), other); + break; + default: + AT_ERROR("mul not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::mul", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::mul_(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::mul_(const_cast(*this), other); + break; + case Backend::SparseCPU: + return SparseCPUType::mul_(const_cast(*this), other); + break; + default: + AT_ERROR("mul_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::mul_", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::mul(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mul(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::mul", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::mul_(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mul_(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::mul_", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::mv(const Tensor & vec) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::mv(const_cast(*this), vec); + break; + default: + AT_ERROR("mv not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::mv", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), vec); +#endif +} +inline Tensor Tensor::mvlgamma(int64_t p) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mvlgamma(const_cast(*this), p); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::mvlgamma", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), p); +#endif +} +inline Tensor & Tensor::mvlgamma_(int64_t p) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::mvlgamma_(const_cast(*this), p); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::mvlgamma_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), p); +#endif +} +inline Tensor Tensor::narrow_copy(int64_t dim, int64_t start, int64_t length) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::narrow_copy(const_cast(*this), dim, start, length); + break; + case Backend::SparseCPU: + return SparseCPUType::narrow_copy(const_cast(*this), dim, start, length); + break; + default: + AT_ERROR("narrow_copy not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::narrow_copy", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), dim, start, length); +#endif +} +inline Tensor Tensor::narrow(int64_t dim, int64_t start, int64_t length) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::narrow(const_cast(*this), dim, start, length); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::narrow", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, start, length); +#endif +} +inline Tensor Tensor::permute(IntArrayRef dims) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::permute(const_cast(*this), dims); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::permute", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dims); +#endif +} +inline Tensor Tensor::numpy_T() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::numpy_T(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::numpy_T", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline bool Tensor::is_pinned() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::is_pinned(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::is_pinned", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::pin_memory() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::pin_memory(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::pin_memory", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::pinverse(double rcond) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::pinverse(const_cast(*this), rcond); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::pinverse", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), rcond); +#endif +} +inline Tensor Tensor::reciprocal() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::reciprocal(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::reciprocal", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::reciprocal_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::reciprocal_(const_cast(*this)); + break; + default: + AT_ERROR("reciprocal_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::reciprocal_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::neg() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::neg(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::neg", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::neg_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::neg_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::neg_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::repeat(IntArrayRef repeats) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::repeat(const_cast(*this), repeats); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::repeat", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), repeats); +#endif +} +inline Tensor Tensor::repeat_interleave(const Tensor & repeats, c10::optional dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::repeat_interleave(const_cast(*this), repeats, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::repeat_interleave", "self_Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed>( + op, const_cast(*this), repeats, dim); +#endif +} +inline Tensor Tensor::repeat_interleave(int64_t repeats, c10::optional dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::repeat_interleave(const_cast(*this), repeats, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::repeat_interleave", "self_int"}).value(); + return c10::Dispatcher::singleton().callUnboxed>( + op, const_cast(*this), repeats, dim); +#endif +} +inline Tensor Tensor::reshape(IntArrayRef shape) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::reshape(const_cast(*this), shape); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::reshape", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), shape); +#endif +} +inline Tensor Tensor::reshape_as(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::reshape_as(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::reshape_as", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::round() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::round(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::round", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::round_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::round_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::round_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::relu() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::relu(const_cast(*this)); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::relu(const_cast(*this)); + break; + default: + AT_ERROR("relu not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::relu", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::relu_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::relu_(const_cast(*this)); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::relu_(const_cast(*this)); + break; + default: + AT_ERROR("relu_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::relu_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::prelu(const Tensor & weight) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::prelu(const_cast(*this), weight); + break; + default: + AT_ERROR("prelu not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::prelu", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), weight); +#endif +} +inline std::tuple Tensor::prelu_backward(const Tensor & grad_output, const Tensor & weight) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::prelu_backward(grad_output, const_cast(*this), weight); + break; + default: + AT_ERROR("prelu_backward not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::prelu_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, const Tensor &>( + op, grad_output, const_cast(*this), weight); +#endif +} +inline Tensor Tensor::hardshrink(Scalar lambd) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::hardshrink(const_cast(*this), lambd); + break; + default: + AT_ERROR("hardshrink not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::hardshrink", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), lambd); +#endif +} +inline Tensor Tensor::hardshrink_backward(const Tensor & grad_out, Scalar lambd) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::hardshrink_backward(grad_out, const_cast(*this), lambd); + break; + default: + AT_ERROR("hardshrink_backward not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::hardshrink_backward", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, grad_out, const_cast(*this), lambd); +#endif +} +inline Tensor Tensor::rsqrt() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rsqrt(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::rsqrt", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::rsqrt_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rsqrt_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::rsqrt_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::select(Dimname dim, int64_t index) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::select(const_cast(*this), dim, index); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::select", "Dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index); +#endif +} +#endif +inline Tensor Tensor::select(int64_t dim, int64_t index) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::select(const_cast(*this), dim, index); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::select", "int"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index); +#endif +} +inline Tensor Tensor::sigmoid() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::sigmoid(const_cast(*this)); + break; + default: + AT_ERROR("sigmoid not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sigmoid", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::sigmoid_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::sigmoid_(const_cast(*this)); + break; + default: + AT_ERROR("sigmoid_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sigmoid_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::sin() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sin(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sin", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::sin_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sin_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sin_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::sinh() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sinh(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sinh", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::sinh_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sinh_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sinh_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::detach() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::detach(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::detach", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::detach_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::detach_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::detach_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline int64_t Tensor::size(int64_t dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::size(const_cast(*this), dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::size", "int"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), dim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline int64_t Tensor::size(Dimname dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::size(const_cast(*this), dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::size", "Dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim); +#endif +} +#endif +inline Tensor Tensor::slice(int64_t dim, int64_t start, int64_t end, int64_t step) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::slice(const_cast(*this), dim, start, end, step); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::slice", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, start, end, step); +#endif +} +inline std::tuple Tensor::slogdet() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::slogdet(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::slogdet", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &>( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::smm(const Tensor & mat2) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::smm(const_cast(*this), mat2); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::smm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), mat2); +#endif +} +inline Tensor Tensor::softmax(int64_t dim, c10::optional dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::softmax(const_cast(*this), dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::softmax", "int"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), dim, dtype); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::softmax(Dimname dim, c10::optional dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::softmax(const_cast(*this), dim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::softmax", "Dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), dim, dtype); +#endif +} +#endif +inline std::vector Tensor::split(int64_t split_size, int64_t dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::split(const_cast(*this), split_size, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::split", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, int64_t>( + op, const_cast(*this), split_size, dim); +#endif +} +inline std::vector Tensor::split_with_sizes(IntArrayRef split_sizes, int64_t dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::split_with_sizes(const_cast(*this), split_sizes, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::split_with_sizes", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, IntArrayRef, int64_t>( + op, const_cast(*this), split_sizes, dim); +#endif +} +inline Tensor Tensor::squeeze() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::squeeze(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::squeeze", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::squeeze(int64_t dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::squeeze(const_cast(*this), dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::squeeze", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::squeeze(Dimname dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::squeeze(const_cast(*this), dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::squeeze", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim); +#endif +} +#endif +inline Tensor & Tensor::squeeze_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::squeeze_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::squeeze_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::squeeze_(int64_t dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::squeeze_(const_cast(*this), dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::squeeze_", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor & Tensor::squeeze_(Dimname dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::squeeze_(const_cast(*this), dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::squeeze_", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim); +#endif +} +#endif +inline Tensor Tensor::sspaddmm(const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sspaddmm(const_cast(*this), mat1, mat2, beta, alpha); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sspaddmm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), mat1, mat2, beta, alpha); +#endif +} +inline Tensor Tensor::stft(int64_t n_fft, c10::optional hop_length, c10::optional win_length, const Tensor & window, bool normalized, bool onesided) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::stft(const_cast(*this), n_fft, hop_length, win_length, window, normalized, onesided); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::stft", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, c10::optional, const Tensor &, bool, bool>( + op, const_cast(*this), n_fft, hop_length, win_length, window, normalized, onesided); +#endif +} +inline int64_t Tensor::stride(int64_t dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::stride(const_cast(*this), dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::stride", "int"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), dim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline int64_t Tensor::stride(Dimname dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::stride(const_cast(*this), dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::stride", "Dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim); +#endif +} +#endif +inline Tensor Tensor::sum(c10::optional dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sum(const_cast(*this), dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sum", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), dtype); +#endif +} +inline Tensor Tensor::sum(IntArrayRef dim, bool keepdim, c10::optional dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sum(const_cast(*this), dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sum", "dim_IntList"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), dim, keepdim, dtype); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::sum(DimnameList dim, bool keepdim, c10::optional dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sum(const_cast(*this), dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sum", "dim_DimnameList"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), dim, keepdim, dtype); +#endif +} +#endif +inline Tensor Tensor::sum_to_size(IntArrayRef size) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sum_to_size(const_cast(*this), size); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sum_to_size", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), size); +#endif +} +inline Tensor Tensor::sqrt() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sqrt(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sqrt", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::sqrt_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sqrt_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sqrt_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::std(bool unbiased) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::std(const_cast(*this), unbiased); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::std", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), unbiased); +#endif +} +inline Tensor Tensor::std(IntArrayRef dim, bool unbiased, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::std(const_cast(*this), dim, unbiased, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::std", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, unbiased, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::std(DimnameList dim, bool unbiased, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::std(const_cast(*this), dim, unbiased, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::std", "names_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, unbiased, keepdim); +#endif +} +#endif +inline Tensor Tensor::prod(c10::optional dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::prod(const_cast(*this), dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::prod", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), dtype); +#endif +} +inline Tensor Tensor::prod(int64_t dim, bool keepdim, c10::optional dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::prod(const_cast(*this), dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::prod", "dim_int"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), dim, keepdim, dtype); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::prod(Dimname dim, bool keepdim, c10::optional dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::prod(const_cast(*this), dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::prod", "dim_Dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), dim, keepdim, dtype); +#endif +} +#endif +inline Tensor Tensor::t() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::t(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::t", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::t_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::t_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::t_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::tan() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::tan(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::tan", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::tan_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::tan_(const_cast(*this)); + break; + default: + AT_ERROR("tan_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::tan_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::tanh() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::tanh(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::tanh", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::tanh_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::tanh_(const_cast(*this)); + break; + default: + AT_ERROR("tanh_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::tanh_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::transpose(int64_t dim0, int64_t dim1) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::transpose(const_cast(*this), dim0, dim1); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::transpose", "int"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim0, dim1); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::transpose(Dimname dim0, Dimname dim1) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::transpose(const_cast(*this), dim0, dim1); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::transpose", "Dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim0, dim1); +#endif +} +#endif +inline Tensor & Tensor::transpose_(int64_t dim0, int64_t dim1) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::transpose_(const_cast(*this), dim0, dim1); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::transpose_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim0, dim1); +#endif +} +inline Tensor Tensor::flip(IntArrayRef dims) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::flip(const_cast(*this), dims); + break; + default: + AT_ERROR("flip not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::flip", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dims); +#endif +} +inline Tensor Tensor::roll(IntArrayRef shifts, IntArrayRef dims) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::roll(const_cast(*this), shifts, dims); + break; + default: + AT_ERROR("roll not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::roll", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), shifts, dims); +#endif +} +inline Tensor Tensor::rot90(int64_t k, IntArrayRef dims) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::rot90(const_cast(*this), k, dims); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::rot90", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), k, dims); +#endif +} +inline Tensor Tensor::trunc() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::trunc(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::trunc", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::trunc_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::trunc_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::trunc_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::type_as(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::type_as(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::type_as", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::unsqueeze(int64_t dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::unsqueeze(const_cast(*this), dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::unsqueeze", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim); +#endif +} +inline Tensor & Tensor::unsqueeze_(int64_t dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::unsqueeze_(const_cast(*this), dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::unsqueeze_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim); +#endif +} +inline Tensor Tensor::var(bool unbiased) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::var(const_cast(*this), unbiased); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::var", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), unbiased); +#endif +} +inline Tensor Tensor::var(IntArrayRef dim, bool unbiased, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::var(const_cast(*this), dim, unbiased, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::var", "dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, unbiased, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::var(DimnameList dim, bool unbiased, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::var(const_cast(*this), dim, unbiased, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::var", "names_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, unbiased, keepdim); +#endif +} +#endif +inline Tensor Tensor::view_as(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::view_as(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::view_as", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::where(const Tensor & condition, const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::where(condition, const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::where", "self"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, condition, const_cast(*this), other); +#endif +} +inline Tensor Tensor::norm(c10::optional p, ScalarType dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::norm(const_cast(*this), p, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::norm", "ScalarOpt_dtype"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, ScalarType>( + op, const_cast(*this), p, dtype); +#endif +} +inline Tensor Tensor::norm(Scalar p) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::norm(const_cast(*this), p); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::norm", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), p); +#endif +} +inline Tensor Tensor::norm(c10::optional p, IntArrayRef dim, bool keepdim, ScalarType dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::norm(const_cast(*this), p, dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::norm", "ScalarOpt_dim_dtype"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, IntArrayRef, bool, ScalarType>( + op, const_cast(*this), p, dim, keepdim, dtype); +#endif +} +inline Tensor Tensor::norm(c10::optional p, IntArrayRef dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::norm(const_cast(*this), p, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::norm", "ScalarOpt_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, IntArrayRef, bool>( + op, const_cast(*this), p, dim, keepdim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::norm(c10::optional p, DimnameList dim, bool keepdim, ScalarType dtype) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::norm(const_cast(*this), p, dim, keepdim, dtype); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::norm", "names_ScalarOpt_dim_dtype"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, DimnameList, bool, ScalarType>( + op, const_cast(*this), p, dim, keepdim, dtype); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::norm(c10::optional p, DimnameList dim, bool keepdim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::norm(const_cast(*this), p, dim, keepdim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::norm", "names_ScalarOpt_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, DimnameList, bool>( + op, const_cast(*this), p, dim, keepdim); +#endif +} +#endif +inline Tensor Tensor::clone(c10::optional memory_format) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::clone(const_cast(*this), memory_format); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::clone(const_cast(*this), memory_format); + break; + case Backend::SparseCPU: + return SparseCPUType::clone(const_cast(*this), memory_format); + break; + default: + AT_ERROR("clone not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::clone", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), memory_format); +#endif +} +inline Tensor & Tensor::resize_as_(const Tensor & the_template, c10::optional memory_format) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::resize_as_(const_cast(*this), the_template, memory_format); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::resize_as_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), the_template, memory_format); +#endif +} +inline Tensor Tensor::pow(Scalar exponent) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::pow(const_cast(*this), exponent); + break; + case Backend::SparseCPU: + return SparseCPUType::pow(const_cast(*this), exponent); + break; + default: + AT_ERROR("pow not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::pow", "Tensor_Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), exponent); +#endif +} +inline Tensor & Tensor::zero_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::zero_(const_cast(*this)); + break; + case Backend::SparseCPU: + return SparseCPUType::zero_(const_cast(*this)); + break; + default: + AT_ERROR("zero_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::zero_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::sub(const Tensor & other, Scalar alpha) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::sub(const_cast(*this), other, alpha); + break; + case Backend::SparseCPU: + return SparseCPUType::sub(const_cast(*this), other, alpha); + break; + default: + AT_ERROR("sub not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sub", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other, alpha); +#endif +} +inline Tensor & Tensor::sub_(const Tensor & other, Scalar alpha) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::sub_(const_cast(*this), other, alpha); + break; + case Backend::SparseCPU: + return SparseCPUType::sub_(const_cast(*this), other, alpha); + break; + default: + AT_ERROR("sub_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sub_", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other, alpha); +#endif +} +inline Tensor Tensor::sub(Scalar other, Scalar alpha) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sub(const_cast(*this), other, alpha); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sub", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other, alpha); +#endif +} +inline Tensor & Tensor::sub_(Scalar other, Scalar alpha) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sub_(const_cast(*this), other, alpha); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sub_", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other, alpha); +#endif +} +inline Tensor Tensor::addmm(const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::addmm(const_cast(*this), mat1, mat2, beta, alpha); + break; + case Backend::SparseCPU: + return SparseCPUType::addmm(const_cast(*this), mat1, mat2, beta, alpha); + break; + default: + AT_ERROR("addmm not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::addmm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), mat1, mat2, beta, alpha); +#endif +} +inline Tensor & Tensor::addmm_(const Tensor & mat1, const Tensor & mat2, Scalar beta, Scalar alpha) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::addmm_(const_cast(*this), mat1, mat2, beta, alpha); + break; + case Backend::SparseCPU: + return SparseCPUType::addmm_(const_cast(*this), mat1, mat2, beta, alpha); + break; + default: + AT_ERROR("addmm_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::addmm_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), mat1, mat2, beta, alpha); +#endif +} +inline Tensor & Tensor::sparse_resize_(IntArrayRef size, int64_t sparse_dim, int64_t dense_dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::SparseCPU: + return SparseCPUType::sparse_resize_(const_cast(*this), size, sparse_dim, dense_dim); + break; + default: + AT_ERROR("sparse_resize_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sparse_resize_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), size, sparse_dim, dense_dim); +#endif +} +inline Tensor & Tensor::sparse_resize_and_clear_(IntArrayRef size, int64_t sparse_dim, int64_t dense_dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::SparseCPU: + return SparseCPUType::sparse_resize_and_clear_(const_cast(*this), size, sparse_dim, dense_dim); + break; + default: + AT_ERROR("sparse_resize_and_clear_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sparse_resize_and_clear_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), size, sparse_dim, dense_dim); +#endif +} +inline Tensor Tensor::sparse_mask(const Tensor & mask) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::SparseCPU: + return SparseCPUType::sparse_mask(const_cast(*this), mask); + break; + default: + AT_ERROR("sparse_mask not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sparse_mask", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), mask); +#endif +} +inline Tensor Tensor::to_dense() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::SparseCPU: + return SparseCPUType::to_dense(const_cast(*this)); + break; + default: + AT_ERROR("to_dense not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::to_dense", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline int64_t Tensor::sparse_dim() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::SparseCPU: + return SparseCPUType::sparse_dim(const_cast(*this)); + break; + default: + AT_ERROR("sparse_dim not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sparse_dim", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline int64_t Tensor::_dimI() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::SparseCPU: + return SparseCPUType::_dimI(const_cast(*this)); + break; + default: + AT_ERROR("_dimI not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::_dimI", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline int64_t Tensor::dense_dim() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::SparseCPU: + return SparseCPUType::dense_dim(const_cast(*this)); + break; + default: + AT_ERROR("dense_dim not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::dense_dim", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline int64_t Tensor::_dimV() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::SparseCPU: + return SparseCPUType::_dimV(const_cast(*this)); + break; + default: + AT_ERROR("_dimV not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::_dimV", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline int64_t Tensor::_nnz() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::SparseCPU: + return SparseCPUType::_nnz(const_cast(*this)); + break; + default: + AT_ERROR("_nnz not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::_nnz", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::coalesce() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::SparseCPU: + return SparseCPUType::coalesce(const_cast(*this)); + break; + default: + AT_ERROR("coalesce not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::coalesce", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline bool Tensor::is_coalesced() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::SparseCPU: + return SparseCPUType::is_coalesced(const_cast(*this)); + break; + default: + AT_ERROR("is_coalesced not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::is_coalesced", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::_indices() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::SparseCPU: + return SparseCPUType::_indices(const_cast(*this)); + break; + default: + AT_ERROR("_indices not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::_indices", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::_values() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::SparseCPU: + return SparseCPUType::_values(const_cast(*this)); + break; + default: + AT_ERROR("_values not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::_values", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::_coalesced_(bool coalesced) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::SparseCPU: + return SparseCPUType::_coalesced_(const_cast(*this), coalesced); + break; + default: + AT_ERROR("_coalesced_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::_coalesced_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), coalesced); +#endif +} +inline Tensor Tensor::indices() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::SparseCPU: + return SparseCPUType::indices(const_cast(*this)); + break; + default: + AT_ERROR("indices not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::indices", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::values() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::SparseCPU: + return SparseCPUType::values(const_cast(*this)); + break; + default: + AT_ERROR("values not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::values", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline std::vector Tensor::unbind(int64_t dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::unbind(const_cast(*this), dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::unbind", "int"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t>( + op, const_cast(*this), dim); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline std::vector Tensor::unbind(Dimname dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::unbind(const_cast(*this), dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::unbind", "Dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, Dimname>( + op, const_cast(*this), dim); +#endif +} +#endif +inline Tensor Tensor::to_sparse(int64_t sparse_dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::to_sparse(const_cast(*this), sparse_dim); + break; + default: + AT_ERROR("to_sparse not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::to_sparse", "sparse_dim"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), sparse_dim); +#endif +} +inline Tensor Tensor::to_sparse() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::to_sparse(const_cast(*this)); + break; + default: + AT_ERROR("to_sparse not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::to_sparse", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::to_mkldnn() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::to_mkldnn(const_cast(*this)); + break; + default: + AT_ERROR("to_mkldnn not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::to_mkldnn", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::dequantize() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::QuantizedCPU: + return QuantizedCPUType::dequantize(const_cast(*this)); + break; + default: + AT_ERROR("dequantize not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::dequantize", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline double Tensor::q_scale() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::QuantizedCPU: + return QuantizedCPUType::q_scale(const_cast(*this)); + break; + default: + AT_ERROR("q_scale not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::q_scale", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline int64_t Tensor::q_zero_point() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::QuantizedCPU: + return QuantizedCPUType::q_zero_point(const_cast(*this)); + break; + default: + AT_ERROR("q_zero_point not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::q_zero_point", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::q_per_channel_scales() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::QuantizedCPU: + return QuantizedCPUType::q_per_channel_scales(const_cast(*this)); + break; + default: + AT_ERROR("q_per_channel_scales not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::q_per_channel_scales", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::q_per_channel_zero_points() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::QuantizedCPU: + return QuantizedCPUType::q_per_channel_zero_points(const_cast(*this)); + break; + default: + AT_ERROR("q_per_channel_zero_points not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::q_per_channel_zero_points", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline int64_t Tensor::q_per_channel_axis() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::QuantizedCPU: + return QuantizedCPUType::q_per_channel_axis(const_cast(*this)); + break; + default: + AT_ERROR("q_per_channel_axis not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::q_per_channel_axis", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::int_repr() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::QuantizedCPU: + return QuantizedCPUType::int_repr(const_cast(*this)); + break; + default: + AT_ERROR("int_repr not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::int_repr", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline QScheme Tensor::qscheme() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::QuantizedCPU: + return QuantizedCPUType::qscheme(const_cast(*this)); + break; + default: + AT_ERROR("qscheme not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::qscheme", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::to(const TensorOptions & options, bool non_blocking, bool copy, c10::optional memory_format) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::to(const_cast(*this), options, non_blocking, copy, memory_format); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::to", "dtype_layout"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), options, non_blocking, copy, memory_format); +#endif +} +inline Tensor Tensor::to(Device device, ScalarType dtype, bool non_blocking, bool copy, c10::optional memory_format) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::to(const_cast(*this), device, dtype, non_blocking, copy, memory_format); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::to", "device"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), device, dtype, non_blocking, copy, memory_format); +#endif +} +inline Tensor Tensor::to(ScalarType dtype, bool non_blocking, bool copy, c10::optional memory_format) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::to(const_cast(*this), dtype, non_blocking, copy, memory_format); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::to", "dtype"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), dtype, non_blocking, copy, memory_format); +#endif +} +inline Tensor Tensor::to(const Tensor & other, bool non_blocking, bool copy, c10::optional memory_format) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::to(const_cast(*this), other, non_blocking, copy, memory_format); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::to", "other"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly>( + op, const_cast(*this), other, non_blocking, copy, memory_format); +#endif +} +inline Scalar Tensor::item() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::item(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::item", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::set_(Storage source) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::set_(const_cast(*this), source); + break; + default: + AT_ERROR("set_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::set_", "source_Storage"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), source); +#endif +} +inline Tensor & Tensor::set_(Storage source, int64_t storage_offset, IntArrayRef size, IntArrayRef stride) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::set_(const_cast(*this), source, storage_offset, size, stride); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::set_(const_cast(*this), source, storage_offset, size, stride); + break; + default: + AT_ERROR("set_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::set_", "source_Storage_storage_offset"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), source, storage_offset, size, stride); +#endif +} +inline Tensor & Tensor::set_(const Tensor & source) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::set_(const_cast(*this), source); + break; + default: + AT_ERROR("set_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::set_", "source_Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), source); +#endif +} +inline Tensor & Tensor::set_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::set_(const_cast(*this)); + break; + default: + AT_ERROR("set_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::set_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::set_quantizer_(ConstQuantizerPtr quantizer) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::QuantizedCPU: + return QuantizedCPUType::set_quantizer_(const_cast(*this), quantizer); + break; + default: + AT_ERROR("set_quantizer_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::set_quantizer_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), quantizer); +#endif +} +inline bool Tensor::is_set_to(const Tensor & tensor) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::is_set_to(const_cast(*this), tensor); + break; + default: + AT_ERROR("is_set_to not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::is_set_to", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), tensor); +#endif +} +inline Tensor & Tensor::masked_fill_(const Tensor & mask, Scalar value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::masked_fill_(const_cast(*this), mask, value); + break; + default: + AT_ERROR("masked_fill_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::masked_fill_", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), mask, value); +#endif +} +inline Tensor Tensor::masked_fill(const Tensor & mask, Scalar value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::masked_fill(const_cast(*this), mask, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::masked_fill", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), mask, value); +#endif +} +inline Tensor & Tensor::masked_fill_(const Tensor & mask, const Tensor & value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::masked_fill_(const_cast(*this), mask, value); + break; + default: + AT_ERROR("masked_fill_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::masked_fill_", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), mask, value); +#endif +} +inline Tensor Tensor::masked_fill(const Tensor & mask, const Tensor & value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::masked_fill(const_cast(*this), mask, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::masked_fill", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), mask, value); +#endif +} +inline Tensor & Tensor::masked_scatter_(const Tensor & mask, const Tensor & source) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::masked_scatter_(const_cast(*this), mask, source); + break; + default: + AT_ERROR("masked_scatter_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::masked_scatter_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), mask, source); +#endif +} +inline Tensor Tensor::masked_scatter(const Tensor & mask, const Tensor & source) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::masked_scatter(const_cast(*this), mask, source); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::masked_scatter", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), mask, source); +#endif +} +inline Tensor Tensor::view(IntArrayRef size) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::view(const_cast(*this), size); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::view(const_cast(*this), size); + break; + default: + AT_ERROR("view not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::view", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), size); +#endif +} +inline Tensor & Tensor::put_(const Tensor & index, const Tensor & source, bool accumulate) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::put_(const_cast(*this), index, source, accumulate); + break; + default: + AT_ERROR("put_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::put_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), index, source, accumulate); +#endif +} +inline Tensor & Tensor::index_add_(int64_t dim, const Tensor & index, const Tensor & source) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::index_add_(const_cast(*this), dim, index, source); + break; + default: + AT_ERROR("index_add_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index_add_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index, source); +#endif +} +inline Tensor Tensor::index_add(int64_t dim, const Tensor & index, const Tensor & source) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_add(const_cast(*this), dim, index, source); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index_add", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), dim, index, source); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::index_add(Dimname dim, const Tensor & index, const Tensor & source) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_add(const_cast(*this), dim, index, source); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index_add", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index, source); +#endif +} +#endif +inline Tensor & Tensor::index_fill_(int64_t dim, const Tensor & index, Scalar value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::index_fill_(const_cast(*this), dim, index, value); + break; + default: + AT_ERROR("index_fill_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index_fill_", "int_Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index, value); +#endif +} +inline Tensor Tensor::index_fill(int64_t dim, const Tensor & index, Scalar value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_fill(const_cast(*this), dim, index, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index_fill", "int_Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), dim, index, value); +#endif +} +inline Tensor & Tensor::index_fill_(int64_t dim, const Tensor & index, const Tensor & value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::index_fill_(const_cast(*this), dim, index, value); + break; + default: + AT_ERROR("index_fill_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index_fill_", "int_Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index, value); +#endif +} +inline Tensor Tensor::index_fill(int64_t dim, const Tensor & index, const Tensor & value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_fill(const_cast(*this), dim, index, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index_fill", "int_Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), dim, index, value); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor & Tensor::index_fill_(Dimname dim, const Tensor & index, Scalar value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_fill_(const_cast(*this), dim, index, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index_fill_", "Dimname_Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index, value); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +inline Tensor & Tensor::index_fill_(Dimname dim, const Tensor & index, const Tensor & value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_fill_(const_cast(*this), dim, index, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index_fill_", "Dimname_Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index, value); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::index_fill(Dimname dim, const Tensor & index, Scalar value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_fill(const_cast(*this), dim, index, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index_fill", "Dimname_Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index, value); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::index_fill(Dimname dim, const Tensor & index, const Tensor & value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_fill(const_cast(*this), dim, index, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index_fill", "Dimname_Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index, value); +#endif +} +#endif +inline Tensor & Tensor::scatter_(int64_t dim, const Tensor & index, const Tensor & src) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::scatter_(const_cast(*this), dim, index, src); + break; + default: + AT_ERROR("scatter_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::scatter_", "src"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index, src); +#endif +} +inline Tensor Tensor::scatter(int64_t dim, const Tensor & index, const Tensor & src) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::scatter(const_cast(*this), dim, index, src); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::scatter", "src"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), dim, index, src); +#endif +} +inline Tensor & Tensor::scatter_(int64_t dim, const Tensor & index, Scalar value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::scatter_(const_cast(*this), dim, index, value); + break; + default: + AT_ERROR("scatter_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::scatter_", "value"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index, value); +#endif +} +inline Tensor Tensor::scatter(int64_t dim, const Tensor & index, Scalar value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::scatter(const_cast(*this), dim, index, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::scatter", "value"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), dim, index, value); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::scatter(Dimname dim, const Tensor & index, const Tensor & src) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::scatter(const_cast(*this), dim, index, src); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::scatter", "dimname_src"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index, src); +#endif +} +#endif +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::scatter(Dimname dim, const Tensor & index, Scalar value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::scatter(const_cast(*this), dim, index, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::scatter", "dimname_value"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index, value); +#endif +} +#endif +inline Tensor & Tensor::scatter_add_(int64_t dim, const Tensor & index, const Tensor & src) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::scatter_add_(const_cast(*this), dim, index, src); + break; + default: + AT_ERROR("scatter_add_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::scatter_add_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index, src); +#endif +} +inline Tensor Tensor::scatter_add(int64_t dim, const Tensor & index, const Tensor & src) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::scatter_add(const_cast(*this), dim, index, src); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::scatter_add", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), dim, index, src); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::scatter_add(Dimname dim, const Tensor & index, const Tensor & src) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::scatter_add(const_cast(*this), dim, index, src); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::scatter_add", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index, src); +#endif +} +#endif +inline Tensor & Tensor::lt_(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::lt_(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::lt_", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::lt_(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::lt_(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::lt_", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::gt_(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::gt_(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::gt_", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::gt_(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::gt_(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::gt_", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::le_(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::le_(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::le_", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::le_(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::le_(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::le_", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::ge_(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::ge_(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::ge_", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::ge_(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::ge_(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::ge_", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::eq_(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::eq_(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::eq_", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::eq_(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::eq_(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::eq_", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::ne_(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::ne_(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::ne_", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::ne_(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::ne_(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::ne_", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::__and__(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::__and__(const_cast(*this), other); + break; + default: + AT_ERROR("__and__ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__and__", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::__and__(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::__and__(const_cast(*this), other); + break; + default: + AT_ERROR("__and__ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__and__", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::__iand__(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::__iand__(const_cast(*this), other); + break; + default: + AT_ERROR("__iand__ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__iand__", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::__iand__(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::__iand__(const_cast(*this), other); + break; + default: + AT_ERROR("__iand__ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__iand__", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::__or__(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::__or__(const_cast(*this), other); + break; + default: + AT_ERROR("__or__ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__or__", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::__or__(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::__or__(const_cast(*this), other); + break; + default: + AT_ERROR("__or__ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__or__", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::__ior__(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::__ior__(const_cast(*this), other); + break; + default: + AT_ERROR("__ior__ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__ior__", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::__ior__(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::__ior__(const_cast(*this), other); + break; + default: + AT_ERROR("__ior__ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__ior__", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::bitwise_xor(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::bitwise_xor(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::bitwise_xor", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::bitwise_xor(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::bitwise_xor(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::bitwise_xor", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::bitwise_xor_(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::bitwise_xor_(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::bitwise_xor_", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::bitwise_xor_(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::bitwise_xor_(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::bitwise_xor_", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::__xor__(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::__xor__(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__xor__", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::__xor__(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::__xor__(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__xor__", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::__ixor__(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::__ixor__(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__ixor__", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::__ixor__(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::__ixor__(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__ixor__", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::__lshift__(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::__lshift__(const_cast(*this), other); + break; + default: + AT_ERROR("__lshift__ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__lshift__", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::__lshift__(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::__lshift__(const_cast(*this), other); + break; + default: + AT_ERROR("__lshift__ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__lshift__", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::__ilshift__(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::__ilshift__(const_cast(*this), other); + break; + default: + AT_ERROR("__ilshift__ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__ilshift__", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::__ilshift__(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::__ilshift__(const_cast(*this), other); + break; + default: + AT_ERROR("__ilshift__ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__ilshift__", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::__rshift__(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::__rshift__(const_cast(*this), other); + break; + default: + AT_ERROR("__rshift__ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__rshift__", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::__rshift__(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::__rshift__(const_cast(*this), other); + break; + default: + AT_ERROR("__rshift__ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__rshift__", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::__irshift__(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::__irshift__(const_cast(*this), other); + break; + default: + AT_ERROR("__irshift__ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__irshift__", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::__irshift__(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::__irshift__(const_cast(*this), other); + break; + default: + AT_ERROR("__irshift__ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::__irshift__", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::lgamma_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::lgamma_(const_cast(*this)); + break; + default: + AT_ERROR("lgamma_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::lgamma_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::atan2_(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::atan2_(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::atan2_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::tril_(int64_t diagonal) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::tril_(const_cast(*this), diagonal); + break; + default: + AT_ERROR("tril_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::tril_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), diagonal); +#endif +} +inline Tensor & Tensor::triu_(int64_t diagonal) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::triu_(const_cast(*this), diagonal); + break; + default: + AT_ERROR("triu_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::triu_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), diagonal); +#endif +} +inline Tensor & Tensor::digamma_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::digamma_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::digamma_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::polygamma_(int64_t n) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::polygamma_(const_cast(*this), n); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::polygamma_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), n); +#endif +} +inline Tensor & Tensor::renorm_(Scalar p, int64_t dim, Scalar maxnorm) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::renorm_(const_cast(*this), p, dim, maxnorm); + break; + default: + AT_ERROR("renorm_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::renorm_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), p, dim, maxnorm); +#endif +} +inline Tensor & Tensor::pow_(Scalar exponent) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::pow_(const_cast(*this), exponent); + break; + default: + AT_ERROR("pow_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::pow_", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), exponent); +#endif +} +inline Tensor & Tensor::pow_(const Tensor & exponent) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::pow_(const_cast(*this), exponent); + break; + default: + AT_ERROR("pow_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::pow_", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), exponent); +#endif +} +inline Tensor & Tensor::lerp_(const Tensor & end, Scalar weight) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::lerp_(const_cast(*this), end, weight); + break; + default: + AT_ERROR("lerp_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::lerp_", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), end, weight); +#endif +} +inline Tensor & Tensor::lerp_(const Tensor & end, const Tensor & weight) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::lerp_(const_cast(*this), end, weight); + break; + default: + AT_ERROR("lerp_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::lerp_", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), end, weight); +#endif +} +inline Tensor & Tensor::fmod_(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::fmod_(const_cast(*this), other); + break; + default: + AT_ERROR("fmod_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::fmod_", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::fmod_(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::fmod_(const_cast(*this), other); + break; + default: + AT_ERROR("fmod_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::fmod_", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::remainder_(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::remainder_(const_cast(*this), other); + break; + default: + AT_ERROR("remainder_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::remainder_", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::remainder_(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::remainder_(const_cast(*this), other); + break; + default: + AT_ERROR("remainder_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::remainder_", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), other); +#endif +} +inline Tensor & Tensor::addbmm_(const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::addbmm_(const_cast(*this), batch1, batch2, beta, alpha); + break; + default: + AT_ERROR("addbmm_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::addbmm_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), batch1, batch2, beta, alpha); +#endif +} +inline Tensor Tensor::addbmm(const Tensor & batch1, const Tensor & batch2, Scalar beta, Scalar alpha) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::addbmm(const_cast(*this), batch1, batch2, beta, alpha); + break; + default: + AT_ERROR("addbmm not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::addbmm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), batch1, batch2, beta, alpha); +#endif +} +inline Tensor & Tensor::addcdiv_(const Tensor & tensor1, const Tensor & tensor2, Scalar value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::addcdiv_(const_cast(*this), tensor1, tensor2, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::addcdiv_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), tensor1, tensor2, value); +#endif +} +inline Tensor & Tensor::random_(int64_t from, int64_t to, Generator * generator) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::random_(const_cast(*this), from, to, generator); + break; + default: + AT_ERROR("random_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::random_", "from"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), from, to, generator); +#endif +} +inline Tensor & Tensor::random_(int64_t to, Generator * generator) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::random_(const_cast(*this), to, generator); + break; + default: + AT_ERROR("random_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::random_", "to"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), to, generator); +#endif +} +inline Tensor & Tensor::random_(Generator * generator) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::random_(const_cast(*this), generator); + break; + default: + AT_ERROR("random_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::random_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), generator); +#endif +} +inline Tensor & Tensor::uniform_(double from, double to, Generator * generator) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::uniform_(const_cast(*this), from, to, generator); + break; + default: + AT_ERROR("uniform_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::uniform_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), from, to, generator); +#endif +} +inline Tensor & Tensor::normal_(double mean, double std, Generator * generator) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::normal_(const_cast(*this), mean, std, generator); + break; + default: + AT_ERROR("normal_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::normal_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), mean, std, generator); +#endif +} +inline Tensor & Tensor::cauchy_(double median, double sigma, Generator * generator) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::cauchy_(const_cast(*this), median, sigma, generator); + break; + default: + AT_ERROR("cauchy_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::cauchy_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), median, sigma, generator); +#endif +} +inline Tensor & Tensor::log_normal_(double mean, double std, Generator * generator) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::log_normal_(const_cast(*this), mean, std, generator); + break; + default: + AT_ERROR("log_normal_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::log_normal_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), mean, std, generator); +#endif +} +inline Tensor & Tensor::exponential_(double lambd, Generator * generator) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::exponential_(const_cast(*this), lambd, generator); + break; + default: + AT_ERROR("exponential_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::exponential_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), lambd, generator); +#endif +} +inline Tensor & Tensor::geometric_(double p, Generator * generator) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::geometric_(const_cast(*this), p, generator); + break; + default: + AT_ERROR("geometric_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::geometric_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), p, generator); +#endif +} +inline Tensor Tensor::diag(int64_t diagonal) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::diag(const_cast(*this), diagonal); + break; + default: + AT_ERROR("diag not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::diag", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), diagonal); +#endif +} +inline Tensor Tensor::cross(const Tensor & other, c10::optional dim) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cross(const_cast(*this), other, dim); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::cross", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed>( + op, const_cast(*this), other, dim); +#endif +} +inline Tensor Tensor::triu(int64_t diagonal) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::triu(const_cast(*this), diagonal); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::triu", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), diagonal); +#endif +} +inline Tensor Tensor::tril(int64_t diagonal) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::tril(const_cast(*this), diagonal); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::tril", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), diagonal); +#endif +} +inline Tensor Tensor::trace() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::trace(const_cast(*this)); + break; + default: + AT_ERROR("trace not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::trace", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::ne(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::ne(const_cast(*this), other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::ne(const_cast(*this), other); + break; + default: + AT_ERROR("ne not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::ne", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::ne(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::ne(const_cast(*this), other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::ne(const_cast(*this), other); + break; + default: + AT_ERROR("ne not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::ne", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::eq(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::eq(const_cast(*this), other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::eq(const_cast(*this), other); + break; + default: + AT_ERROR("eq not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::eq", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::eq(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::eq(const_cast(*this), other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::eq(const_cast(*this), other); + break; + default: + AT_ERROR("eq not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::eq", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::ge(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::ge(const_cast(*this), other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::ge(const_cast(*this), other); + break; + default: + AT_ERROR("ge not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::ge", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::ge(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::ge(const_cast(*this), other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::ge(const_cast(*this), other); + break; + default: + AT_ERROR("ge not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::ge", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::le(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::le(const_cast(*this), other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::le(const_cast(*this), other); + break; + default: + AT_ERROR("le not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::le", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::le(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::le(const_cast(*this), other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::le(const_cast(*this), other); + break; + default: + AT_ERROR("le not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::le", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::gt(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::gt(const_cast(*this), other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::gt(const_cast(*this), other); + break; + default: + AT_ERROR("gt not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::gt", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::gt(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::gt(const_cast(*this), other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::gt(const_cast(*this), other); + break; + default: + AT_ERROR("gt not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::gt", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::lt(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::lt(const_cast(*this), other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::lt(const_cast(*this), other); + break; + default: + AT_ERROR("lt not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::lt", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::lt(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::lt(const_cast(*this), other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::lt(const_cast(*this), other); + break; + default: + AT_ERROR("lt not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::lt", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::take(const Tensor & index) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::take(const_cast(*this), index); + break; + default: + AT_ERROR("take not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::take", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), index); +#endif +} +inline Tensor Tensor::index_select(int64_t dim, const Tensor & index) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::index_select(const_cast(*this), dim, index); + break; + case Backend::SparseCPU: + return SparseCPUType::index_select(const_cast(*this), dim, index); + break; + default: + AT_ERROR("index_select not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index_select", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), dim, index); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::index_select(Dimname dim, const Tensor & index) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::index_select(const_cast(*this), dim, index); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::index_select", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index); +#endif +} +#endif +inline Tensor Tensor::masked_select(const Tensor & mask) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::masked_select(const_cast(*this), mask); + break; + default: + AT_ERROR("masked_select not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::masked_select", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), mask); +#endif +} +inline Tensor Tensor::nonzero() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::nonzero(const_cast(*this)); + break; + default: + AT_ERROR("nonzero not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::nonzero", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline std::vector Tensor::nonzero_numpy() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::nonzero_numpy(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::nonzero_numpy", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &>( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::gather(int64_t dim, const Tensor & index, bool sparse_grad) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::gather(const_cast(*this), dim, index, sparse_grad); + break; + default: + AT_ERROR("gather not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::gather", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), dim, index, sparse_grad); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::gather(Dimname dim, const Tensor & index, bool sparse_grad) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::gather(const_cast(*this), dim, index, sparse_grad); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::gather", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, index, sparse_grad); +#endif +} +#endif +inline Tensor Tensor::addcmul(const Tensor & tensor1, const Tensor & tensor2, Scalar value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::addcmul(const_cast(*this), tensor1, tensor2, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::addcmul", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), tensor1, tensor2, value); +#endif +} +inline Tensor & Tensor::addcmul_(const Tensor & tensor1, const Tensor & tensor2, Scalar value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::addcmul_(const_cast(*this), tensor1, tensor2, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::addcmul_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), tensor1, tensor2, value); +#endif +} +inline Tensor Tensor::addcdiv(const Tensor & tensor1, const Tensor & tensor2, Scalar value) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::addcdiv(const_cast(*this), tensor1, tensor2, value); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::addcdiv", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), tensor1, tensor2, value); +#endif +} +inline std::tuple Tensor::lstsq(const Tensor & A) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::lstsq(const_cast(*this), A); + break; + default: + AT_ERROR("lstsq not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::lstsq", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &>( + op, const_cast(*this), A); +#endif +} +inline std::tuple Tensor::triangular_solve(const Tensor & A, bool upper, bool transpose, bool unitriangular) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::triangular_solve(const_cast(*this), A, upper, transpose, unitriangular); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::triangular_solve", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &, bool, bool, bool>( + op, const_cast(*this), A, upper, transpose, unitriangular); +#endif +} +inline std::tuple Tensor::symeig(bool eigenvectors, bool upper) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::symeig(const_cast(*this), eigenvectors, upper); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::symeig", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, bool, bool>( + op, const_cast(*this), eigenvectors, upper); +#endif +} +inline std::tuple Tensor::eig(bool eigenvectors) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::eig(const_cast(*this), eigenvectors); + break; + default: + AT_ERROR("eig not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::eig", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, bool>( + op, const_cast(*this), eigenvectors); +#endif +} +inline std::tuple Tensor::svd(bool some, bool compute_uv) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::svd(const_cast(*this), some, compute_uv); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::svd", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, bool, bool>( + op, const_cast(*this), some, compute_uv); +#endif +} +inline Tensor Tensor::cholesky(bool upper) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cholesky(const_cast(*this), upper); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::cholesky", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), upper); +#endif +} +inline Tensor Tensor::cholesky_solve(const Tensor & input2, bool upper) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::cholesky_solve(const_cast(*this), input2, upper); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::cholesky_solve", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), input2, upper); +#endif +} +inline std::tuple Tensor::solve(const Tensor & A) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::solve(const_cast(*this), A); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::solve", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, const Tensor &>( + op, const_cast(*this), A); +#endif +} +inline Tensor Tensor::cholesky_inverse(bool upper) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::cholesky_inverse(const_cast(*this), upper); + break; + default: + AT_ERROR("cholesky_inverse not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::cholesky_inverse", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), upper); +#endif +} +inline std::tuple Tensor::qr(bool some) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::qr(const_cast(*this), some); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::qr", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, bool>( + op, const_cast(*this), some); +#endif +} +inline std::tuple Tensor::geqrf() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::geqrf(const_cast(*this)); + break; + default: + AT_ERROR("geqrf not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::geqrf", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &>( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::orgqr(const Tensor & input2) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::orgqr(const_cast(*this), input2); + break; + default: + AT_ERROR("orgqr not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::orgqr", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), input2); +#endif +} +inline Tensor Tensor::ormqr(const Tensor & input2, const Tensor & input3, bool left, bool transpose) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::ormqr(const_cast(*this), input2, input3, left, transpose); + break; + default: + AT_ERROR("ormqr not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::ormqr", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), input2, input3, left, transpose); +#endif +} +inline Tensor Tensor::lu_solve(const Tensor & LU_data, const Tensor & LU_pivots) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::lu_solve(const_cast(*this), LU_data, LU_pivots); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::lu_solve", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), LU_data, LU_pivots); +#endif +} +inline Tensor Tensor::multinomial(int64_t num_samples, bool replacement, Generator * generator) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::multinomial(const_cast(*this), num_samples, replacement, generator); + break; + default: + AT_ERROR("multinomial not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::multinomial", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), num_samples, replacement, generator); +#endif +} +inline Tensor Tensor::lgamma() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::lgamma(const_cast(*this)); + break; + default: + AT_ERROR("lgamma not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::lgamma", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::digamma() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::digamma(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::digamma", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::polygamma(int64_t n) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::polygamma(n, const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::polygamma", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, n, const_cast(*this)); +#endif +} +inline Tensor Tensor::erfinv() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::erfinv(const_cast(*this)); + break; + default: + AT_ERROR("erfinv not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::erfinv", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::erfinv_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::erfinv_(const_cast(*this)); + break; + default: + AT_ERROR("erfinv_ not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::erfinv_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::sign() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sign(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sign", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor & Tensor::sign_() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sign_(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sign_", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::dist(const Tensor & other, Scalar p) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::dist(const_cast(*this), other, p); + break; + default: + AT_ERROR("dist not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::dist", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other, p); +#endif +} +inline Tensor Tensor::atan2(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::atan2(const_cast(*this), other); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::atan2", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::lerp(const Tensor & end, Scalar weight) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::lerp(const_cast(*this), end, weight); + break; + default: + AT_ERROR("lerp not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::lerp", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), end, weight); +#endif +} +inline Tensor Tensor::lerp(const Tensor & end, const Tensor & weight) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::lerp(const_cast(*this), end, weight); + break; + default: + AT_ERROR("lerp not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::lerp", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), end, weight); +#endif +} +inline Tensor Tensor::histc(int64_t bins, Scalar min, Scalar max) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::histc(const_cast(*this), bins, min, max); + break; + default: + AT_ERROR("histc not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::histc", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), bins, min, max); +#endif +} +inline Tensor Tensor::fmod(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::fmod(const_cast(*this), other); + break; + default: + AT_ERROR("fmod not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::fmod", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::fmod(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::fmod(const_cast(*this), other); + break; + default: + AT_ERROR("fmod not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::fmod", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::remainder(Scalar other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::remainder(const_cast(*this), other); + break; + default: + AT_ERROR("remainder not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::remainder", "Scalar"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::remainder(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::remainder(const_cast(*this), other); + break; + default: + AT_ERROR("remainder not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::remainder", "Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::min(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::min(const_cast(*this), other); + break; + default: + AT_ERROR("min not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::min", "other"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::min() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::min(const_cast(*this)); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::min(const_cast(*this)); + break; + default: + AT_ERROR("min not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::min", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::max(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::max(const_cast(*this), other); + break; + default: + AT_ERROR("max not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::max", "other"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::max() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::max(const_cast(*this)); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::max(const_cast(*this)); + break; + default: + AT_ERROR("max not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::max", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::median() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::median(const_cast(*this)); + break; + default: + AT_ERROR("median not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::median", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline std::tuple Tensor::sort(int64_t dim, bool descending) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::sort(const_cast(*this), dim, descending); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::sort(const_cast(*this), dim, descending); + break; + default: + AT_ERROR("sort not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sort", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, bool>( + op, const_cast(*this), dim, descending); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline std::tuple Tensor::sort(Dimname dim, bool descending) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::sort(const_cast(*this), dim, descending); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::sort", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, Dimname, bool>( + op, const_cast(*this), dim, descending); +#endif +} +#endif +inline Tensor Tensor::argsort(int64_t dim, bool descending) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::argsort(const_cast(*this), dim, descending); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::argsort", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), dim, descending); +#endif +} +#ifdef BUILD_NAMEDTENSOR +inline Tensor Tensor::argsort(Dimname dim, bool descending) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::argsort(const_cast(*this), dim, descending); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::argsort", "dimname"}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dim, descending); +#endif +} +#endif +inline std::tuple Tensor::topk(int64_t k, int64_t dim, bool largest, bool sorted) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::topk(const_cast(*this), k, dim, largest, sorted); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::topk(const_cast(*this), k, dim, largest, sorted); + break; + default: + AT_ERROR("topk not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::topk", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly, const Tensor &, int64_t, int64_t, bool, bool>( + op, const_cast(*this), k, dim, largest, sorted); +#endif +} +inline Tensor Tensor::all() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::all(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::all", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::any() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::any(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::any", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this)); +#endif +} +inline Tensor Tensor::renorm(Scalar p, int64_t dim, Scalar maxnorm) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::renorm(const_cast(*this), p, dim, maxnorm); + break; + default: + AT_ERROR("renorm not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::renorm", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), p, dim, maxnorm); +#endif +} +inline Tensor Tensor::unfold(int64_t dimension, int64_t size, int64_t step) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::unfold(const_cast(*this), dimension, size, step); + break; + default: + AT_ERROR("unfold not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::unfold", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this), dimension, size, step); +#endif +} +inline bool Tensor::equal(const Tensor & other) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::equal(const_cast(*this), other); + break; + case Backend::QuantizedCPU: + return QuantizedCPUType::equal(const_cast(*this), other); + break; + default: + AT_ERROR("equal not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::equal", ""}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), other); +#endif +} +inline Tensor Tensor::pow(const Tensor & exponent) const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + switch(tensorTypeIdToBackend(c10::impl::dispatchTypeId(type_set()))) { + case Backend::CPU: + return CPUType::pow(const_cast(*this), exponent); + break; + default: + AT_ERROR("pow not implemented for ", at::toString(type_set())); + } +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::pow", "Tensor_Tensor"}).value(); + return c10::Dispatcher::singleton().callUnboxed( + op, const_cast(*this), exponent); +#endif +} +inline Tensor Tensor::alias() const { +#ifdef USE_STATIC_DISPATCH + at::AutoNonVariableTypeMode _var_guard(true); + return TypeDefault::alias(const_cast(*this)); +#else + static c10::OperatorHandle op = c10::Dispatcher::singleton().findSchema({"aten::alias", ""}).value(); + return c10::Dispatcher::singleton().callUnboxedOnly( + op, const_cast(*this)); +#endif +} + +inline caffe2::TypeMeta Tensor::dtype() const noexcept { + return impl_->dtype(); +} + +inline Layout Tensor::layout() const noexcept { + return impl_->layout(); +} + +inline Device Tensor::device() const { + return impl_->device(); +} + +inline int64_t Tensor::get_device() const { + // NB: this is not a native function to avoid dispatching overhead. + return impl_->get_device(); +} + +inline int64_t get_device(Tensor self) { + return self.get_device(); +} + +inline bool Tensor::is_cuda() const { + // NB: this is not a native function to avoid dispatching overhead. + return impl_->is_cuda(); +} + +#ifdef BUILD_NAMEDTENSOR +inline NamedTensorMeta* Tensor::get_named_tensor_meta() { + return static_cast(impl_->named_tensor_meta()); +} + +inline const NamedTensorMeta* Tensor::get_named_tensor_meta() const { + return static_cast(impl_->named_tensor_meta()); +} + +inline bool Tensor::has_names() const { + return impl::has_names(unsafeGetTensorImpl()); +} +#endif + +inline bool is_cuda(Tensor self) { + return self.is_cuda(); +} + +inline bool Tensor::is_hip() const { + // NB: this is not a native function to avoid dispatching overhead. + return impl_->is_hip(); +} + +inline bool is_hip(Tensor self) { + return self.is_hip(); +} + +inline bool Tensor::is_sparse() const { + // NB: this is not a native function to avoid dispatching overhead. + return impl_->is_sparse(); +} + +inline bool is_sparse(Tensor self) { + return self.is_sparse(); +} + +inline bool Tensor::is_mkldnn() const { + // NB: this is not a native function to avoid dispatching overhead. + return impl_->is_mkldnn(); +} + +inline bool is_mkldnn(Tensor self) { + return self.is_mkldnn(); +} + +inline bool Tensor::is_quantized() const { + // NB: this is not a native function to avoid dispatching overhead. + return impl_->is_quantized(); +} + +inline bool is_quantized(Tensor self) { + return self.is_quantized(); +} + +#define DEFINE_CAST(T, name) \ + template <> \ + inline T* Tensor::data_ptr() const { \ + TORCH_CHECK( \ + scalar_type() == ScalarType::name, \ + "expected scalar type ", \ + #name, \ + " but found ", \ + c10::toString(scalar_type())); \ + return static_cast(this->unsafeGetTensorImpl()->data()); \ + } + +AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_EXCEPT_COMPLEX_HALF(DEFINE_CAST) +AT_FORALL_QINT_TYPES(DEFINE_CAST) +#undef DEFINE_CAST + +#define DEFINE_ITEM(T, name) \ + template <> \ + inline T Tensor::item() const { \ + return item().to##name(); \ + } + +AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_EXCEPT_COMPLEX_HALF(DEFINE_ITEM) +#undef DEFINE_ITEM + +// Gradient Node and Edges +//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +template +auto Tensor::register_hook(T&& hook) const -> Tensor::hook_return_void_t { + // Return the grad argument in case of a hook with void return type to have an + // std::function with Tensor return type + std::function fn(hook); + return _register_hook([fn](const Tensor& grad) { + fn(grad); + return Tensor(); + }); +} + +template +auto Tensor::register_hook(T&& hook) const -> Tensor::hook_return_var_t { + return _register_hook(hook); +} + + +} //namespace at diff --git a/thirdparty/libtorch/include/ATen/core/UndefinedTensorImpl.h b/thirdparty/libtorch/include/ATen/core/UndefinedTensorImpl.h new file mode 100644 index 0000000000..885f6e195f --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/UndefinedTensorImpl.h @@ -0,0 +1 @@ +#include diff --git a/thirdparty/libtorch/include/ATen/core/UnsafeFromTH.h b/thirdparty/libtorch/include/ATen/core/UnsafeFromTH.h new file mode 100644 index 0000000000..4abf66aaf4 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/UnsafeFromTH.h @@ -0,0 +1,20 @@ +#include + +namespace at { + +inline Tensor unsafeTensorFromTH(void * th_pointer, bool retain) { + auto tensor_impl = c10::intrusive_ptr::reclaim(static_cast(th_pointer)); + if (retain && tensor_impl.get() != UndefinedTensorImpl::singleton()) { + c10::raw::intrusive_ptr::incref(tensor_impl.get()); + } + return Tensor(std::move(tensor_impl)); +} + +inline Storage unsafeStorageFromTH(void * th_pointer, bool retain) { + if (retain && th_pointer) { + c10::raw::intrusive_ptr::incref(static_cast(th_pointer)); + } + return Storage(c10::intrusive_ptr::reclaim(static_cast(th_pointer))); +} + +} diff --git a/thirdparty/libtorch/include/ATen/core/VariableHooksInterface.h b/thirdparty/libtorch/include/ATen/core/VariableHooksInterface.h new file mode 100644 index 0000000000..b6ac67df2b --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/VariableHooksInterface.h @@ -0,0 +1,62 @@ +#pragma once + +#include +#include + +// A little explanation about why this file exists at all. We have +// a few methods on Tensor class which require access to reified access to +// AutogradMeta. In open source, this isn't a big deal: we just access +// torch/csrc/autograd/variable.h from aten/src/ATen/core/Tensor.cpp and +// we can put the definitions inline. This is because everything gets balled +// into a single dynamic library in the end. +// +// However, inside our Facebook internal version of our build system, we +// have a split between aten and torch/csrc. So we cannot simply just +// cross this boundary. "Now wait," you might say, "Why don't we just +// merge the libraries inside Facebook". Well, the problem is that there +// are some downstream applications which are at binary size limit, and +// incorporating all of the extra code from libtorch would push them +// over (admarket/adreview/service:adreviewservice, see also +// https://github.com/pytorch/pytorch/pull/29299) So if you want to do that, +// we have to fix all of the services like this. +// +// I didn't want to block eliminating Tensor-Variable on this work, so I +// had to introduce another dynamic dispatch to get to the variable +// implementations (which live in torch/csrc/autograd/variable.cpp, FYI). +// +// I also considered using our existing dynamic dispatch mechanism, c10 +// dispatcher, to do this. However, (1) some of the functions on Tensor +// have weird signatures that are not supported by autograd, and (2) +// see this bug https://github.com/pytorch/pytorch/issues/30102 + +namespace torch { namespace autograd { + +struct Node; + +}} // namespace torch::autograd + +namespace at { +namespace impl { + +struct CAFFE2_API VariableHooksInterface { + virtual ~VariableHooksInterface() = default; + virtual Tensor tensor_data(const Tensor&) const = 0; + virtual Tensor variable_data(const Tensor&) const = 0; + virtual const std::shared_ptr& grad_fn(const Tensor&) const = 0; + virtual unsigned _register_hook(const Tensor&, std::function hook) const = 0; + virtual void remove_hook(const Tensor&, unsigned pos) const = 0; + virtual bool is_view(const Tensor&) const = 0; + virtual const Tensor& base(const Tensor&) const = 0; + virtual const std::string& name(const Tensor&) const = 0; +}; + +CAFFE2_API void SetVariableHooks(VariableHooksInterface* hooks); +CAFFE2_API VariableHooksInterface* GetVariableHooks(); + +struct CAFFE2_API VariableHooksRegisterer { + explicit VariableHooksRegisterer(VariableHooksInterface* hooks) { + SetVariableHooks(hooks); + } +}; + +}} // namespace at::impl diff --git a/thirdparty/libtorch/include/ATen/core/Variadic.h b/thirdparty/libtorch/include/ATen/core/Variadic.h new file mode 100644 index 0000000000..b49d94bba1 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/Variadic.h @@ -0,0 +1,76 @@ +#pragma once + +#include +#include +#include +#include + +#include + +namespace at { + +// This class allows you to write variadic functions which +// call a (possibly overloaded) function on each argument, +// in order. This is most commonly used in autogenerated code, +// where it is convenient to have a function that can uniformly +// take arguments of different types. If your arguments +// are homogenous consider using a std::initializer_list instead. +// +// For examples of this in use, see torch/csrc/utils/variadic.h +template +struct IterArgs { + template + inline F& apply() { + return self(); + } + + // NB: Use perfect forwarding here, otherwise we'll make value + // copies of all arguments! + template + inline F& apply(T&& arg, Args&&... args) { + self()(std::forward(arg)); + if (self().short_circuit()) { + return self(); + } else { + return apply(std::forward(args)...); + } + } + + // Here are some handy overloads which provide sensible + // defaults for container-like structures that one might + // be interested in recursing into. You can enable them + // by adding: + // + // using IterArgs::operator() + // + // to your struct. These are not enabled by default because + // you may be able to process these structures more efficiently + // than handling them one-by-one. + + template + void operator()(at::ArrayRef args) { + for (const auto& arg : args) { + self()(arg); + if (self().short_circuit()) + return; + } + } + + // NB: we need to specify std::vector manually as C++ won't + // do an implicit conversion to make a template deduction go through. + template + void operator()(const std::vector& args) { + self()(at::ArrayRef{args}); + } + + constexpr bool short_circuit() const { + return false; + } + + private: + inline F& self() { + return *static_cast(this); + } +}; + +} // namespace torch diff --git a/thirdparty/libtorch/include/ATen/core/alias_info.h b/thirdparty/libtorch/include/ATen/core/alias_info.h new file mode 100644 index 0000000000..96c4d0a4e3 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/alias_info.h @@ -0,0 +1,119 @@ +#pragma once +#include +#include +#include +#include + +namespace c10 { +/** + * class AliasInfo + * + * Data structure to hold aliasing information for an `Argument`. They can be + * nested to represent aliasing information on contained types. + * + * There is a `beforeSet` which describes the aliasing information before the + * operator executes, and an `afterSet` that describes aliasing info + * after execution. + */ +class AliasInfo { + public: + // Symbol for the set that can alias anything + static Symbol wildcardSet() { + static const Symbol wc = Symbol::fromQualString("alias::*"); + return wc; + } + + void setIsWrite(bool isWrite) { + isWrite_ = isWrite; + } + + bool isWrite() const { + return isWrite_; + } + + void addBeforeSet(Symbol aliasSet) { + beforeSets_.insert(aliasSet); + } + + void addAfterSet(Symbol aliasSet) { + afterSets_.insert(aliasSet); + } + + const std::unordered_set& beforeSets() const { + return beforeSets_; + } + + const std::unordered_set& afterSets() const { + return afterSets_; + } + + Symbol beforeSet() const { + AT_ASSERT(beforeSets_.size() == 1); + return *beforeSets_.begin(); + } + + bool isWildcardBefore() const { + return beforeSets_.count(wildcardSet()) != 0; + } + + bool isWildcardAfter() const { + return afterSets_.count(wildcardSet()) != 0; + } + + // the alias info for the contained types of the type + // e.g. if this is an annotation on List[T], `sets` refers to + // the alias sets that the list may be in + // while containedTypes()[0] refers to the sets that members of the list + // may be in + void addContainedType(AliasInfo aliasInfo) { + containedTypes_.push_back(std::move(aliasInfo)); + } + const std::vector& containedTypes() const { + return containedTypes_; + } + + private: + std::unordered_set beforeSets_; + std::unordered_set afterSets_; + std::vector containedTypes_; + bool isWrite_ = false; +}; + +inline bool operator==(const AliasInfo& lhs, const AliasInfo& rhs) { + return lhs.isWrite() == rhs.isWrite() + && lhs.beforeSets() == rhs.beforeSets() + && lhs.afterSets() == rhs.afterSets() + && lhs.containedTypes() == rhs.containedTypes(); +} + +// this does match the way things are represented in the schema +inline std::ostream& operator<<(std::ostream& out, const AliasInfo& aliasInfo) { + out << "("; + bool first = true; + for (const auto& set : aliasInfo.beforeSets()) { + if (first) { + first = false; + } else { + out << "|"; + } + out << set.toUnqualString(); + } + if (aliasInfo.isWrite()) { + out << "!"; + } + if (aliasInfo.beforeSets() != aliasInfo.afterSets()) { + out << " -> "; + first = true; + for (const auto& set : aliasInfo.afterSets()) { + if (first) { + first = false; + } else { + out << "|"; + } + out << set.toUnqualString(); + } + } + out << ")"; + return out; +} +} // namespace c10 diff --git a/thirdparty/libtorch/include/ATen/core/aten_interned_strings.h b/thirdparty/libtorch/include/ATen/core/aten_interned_strings.h new file mode 100644 index 0000000000..ecb0a74e95 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/aten_interned_strings.h @@ -0,0 +1,1020 @@ +#pragma once + +// ATen symbols correspond exactly to operators defined in ATen. 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a/thirdparty/libtorch/include/ATen/core/blob.h b/thirdparty/libtorch/include/ATen/core/blob.h new file mode 100644 index 0000000000..60694160c7 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/blob.h @@ -0,0 +1,210 @@ +#pragma once + +#include +#include +#include +#include +#include + +#include +#include +#include + +namespace caffe2 { + +class Tensor; + +/** + * @brief Blob is a general container that hosts a typed pointer. + * + * A Blob hosts a pointer as well as its type, and takes charge of deleting it + * properly when the blob is deallocated or re-allocated with a new type. A blob + * could contain anything, although the most common case is to contain a Tensor. + */ +class CAFFE2_API Blob final : public c10::intrusive_ptr_target { + public: + /** + * Initializes an empty Blob. + */ + Blob() noexcept : meta_(), pointer_(nullptr), has_ownership_(false) {} + ~Blob() { + Reset(); + } + + Blob(Blob&& other) noexcept : Blob() { + swap(other); + } + + Blob& operator=(Blob&& other) noexcept { + Blob(std::move(other)).swap(*this); + return *this; + } + + /** + * Checks if the content stored in the blob is of type T. + */ + template + bool IsType() const noexcept { + return meta_.Match(); + } + + /** + * Returns the meta info of the blob. + */ + const TypeMeta& meta() const noexcept { + return meta_; + } + + /** + * Returns a printable typename of the blob. + */ + const char* TypeName() const noexcept { + return meta_.name(); + } + + /** + * @brief Gets the const reference of the stored object. The code checks if + * the stored object is of the desired type. + */ + // TODO(jerryzh): add a Get(DeviceType) function? + template + const T& Get() const { + AT_ASSERTM( + IsType(), + "wrong type for the Blob instance. Blob contains ", + meta_.name(), + " while caller expects ", + TypeMeta::TypeName()); + // TODO: after we add Get(DeviceType) + // and changed all the callsites, we can add + // a static assert here to enforce T != Tensor + return *static_cast(pointer_); + } + + const void* GetRaw() const noexcept { + return pointer_; + } + void* GetRaw() noexcept { + return pointer_; + } + + /** + * @brief Gets a mutable pointer to the stored object. + * + * If the current object is not of the right type, a new object is created + * and the old object is freed. Note that type T should have a default + * constructor. Otherwise, create the object yourself first, and use + * Reset(). + */ + template + T* GetMutable() { + static_assert( + std::is_default_constructible::value, + "GetMutable can't be called with non-default-constructible types. " + "Try using specialized methods"); + if (IsType()) { + return static_cast(pointer_); + } else { + // TODO Re-enable logging + // VLOG(1) << "Create new mutable object " << TypeMeta::TypeName(); + return Reset(new T()); + } + } + + template + T* GetMutableOrNull() { + if (IsType()) { + return static_cast(pointer_); + } else { + return nullptr; + } + } + + /** + * Sets the underlying object to the allocated one. The Blob then takes over + * the ownership of the passed in pointer. If there is already an object in + * the Blob, the old object is freed. + * + * This is used when the underlying class T does not have a default ctor, or + * complex initializations needs to be done outside the blob. + */ + template + T* Reset(T* allocated) { + free_(); + meta_ = TypeMeta::Make(); + pointer_ = static_cast(allocated); + has_ownership_ = true; + return allocated; + } + + /** + * Sets the underlying object to the allocated one, but does not take over + * the ownership of the passed in pointer. If there is already an object in + * the Blob, the old object is freed. + * + * Unlike Reset, this does not take over the ownership of the pointer and the + * caller is responsible for making sure that the lifetime of the allocated + * blob outlasts the lifetime of any access to this blob, until another Reset + * call is made or the blob is destructed. + */ + template + typename std::remove_const::type* ShareExternal( + typename std::remove_const::type* allocated) { + return static_cast(ShareExternal( + static_cast(allocated), + TypeMeta::Make::type>())); + } + + // TODO Remove ShareExternal() and have Blob always own its content + void* ShareExternal(void* allocated, const TypeMeta& meta) { + free_(); + meta_ = meta; + pointer_ = static_cast(allocated); + has_ownership_ = false; + return allocated; + } + + /** + * Resets the Blob to an empty one. + */ + void Reset() { + free_(); + pointer_ = nullptr; + meta_ = TypeMeta(); + has_ownership_ = false; + } + + /** + * @brief Swaps the underlying storage of two blobs. + */ + void swap(Blob& rhs) { + using std::swap; + swap(meta_, rhs.meta_); + swap(pointer_, rhs.pointer_); + swap(has_ownership_, rhs.has_ownership_); + } + + private: + void free_() { + if (has_ownership_) { + AT_ASSERTM(pointer_ != nullptr, "Can't have ownership of nullptr"); + (*meta_.deleteFn())(pointer_); + } + } + + TypeMeta meta_; + void* pointer_ = nullptr; + bool has_ownership_ = false; + + C10_DISABLE_COPY_AND_ASSIGN(Blob); +}; + +inline void swap(Blob& lhs, Blob& rhs) { + lhs.swap(rhs); +} + +inline std::ostream& operator<<(std::ostream& out, const Blob& v) { + return out << "Blob[" << v.TypeName() << "]"; +} + +} // namespace caffe2 diff --git a/thirdparty/libtorch/include/ATen/core/boxing/KernelFunction.h b/thirdparty/libtorch/include/ATen/core/boxing/KernelFunction.h new file mode 100644 index 0000000000..90c9ebdfe2 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/boxing/KernelFunction.h @@ -0,0 +1,408 @@ +#pragma once + +#include +#include +#include +#include +#include + +namespace c10 { + +namespace detail { +template struct boxAndCallBoxedFunc; +} + +/** + * KernelFunction is similar to std::function but stores a kernel function. + * You can create a KernelFunction from a boxed or unboxed function/functor/lambda + * and call it in a boxed or unboxed way. If the way it was created doesn't + * match the way it was called, it will do boxing or unboxing as necessary. + */ +class CAFFE2_API KernelFunction final { +public: + using BoxedKernelFunction = void(OperatorKernel*, Stack*); + + KernelFunction() + : functorFactory_() + , functor_(nullptr) + , boxed_kernel_func_(nullptr) + , unboxed_kernel_func_(nullptr) + {} + + bool isValid() const { + // TODO We want to introduce the invariant that all kernels must be callable in a boxed way, then this should only check boxed_kernel_func_. + return boxed_kernel_func_ != nullptr || unboxed_kernel_func_ != nullptr; + } + + /** + * Call the function in a boxed way. + * If the kernel function was created with an unboxed function, + * this will call an unboxing wrapper which then calls into that + * unboxed function. + * + * Example: + * + * > void boxed_func(OperatorKernel*, Stack* stack) {...} + * > KernelFunction func = KernelFunction::makeFromBoxedFunction(&boxed_func); + * > Tensor result = func.callBoxed(stack); + * + * Or, with an unboxed implementation: + * + * > KernelFunction func = KernelFunction::makeFromUnboxedLambda( + * > [] (Tensor a, bool b) -> Tensor {...}); + * > Tensor result = func.callBoxed(stack); + */ + void callBoxed(Stack* stack) const { + if (C10_UNLIKELY(boxed_kernel_func_ == nullptr)) { + if (unboxed_kernel_func_ == nullptr) { + TORCH_INTERNAL_ASSERT(false, "Tried to call KernelFunction::callBoxed() on an uninitialized KernelFunction."); + } else { + // TODO We want to introduce the invariant that all kernels must be callable in a boxed way, then this case should be impossible. + TORCH_INTERNAL_ASSERT(false, "Tried to call KernelFunction::callBoxed() on a KernelFunction that can only be called with KernelFunction::callUnboxed()."); + } + } + + (*boxed_kernel_func_)(getFunctor_(), stack); + } + + /** + * Call the function in an unboxed way. + * As the "Only" in the name suggests, this only works for KernelFunctions + * that are backed by an unboxed kernel. If the KernelFunction was created + * in a boxed way, this will fail (also see KernelFunction::callUnboxed()). + * + * KernelFunction::callUnboxed() is generally better, since it will allow + * calling KernelFunctions that are backed by either boxed or unboxed + * kernels, but that one will not work for all types. + * + * Example: + * + * > KernelFunction func = KernelFunction::makeFromUnboxedLambda( + * > [] (Tensor a, bool b) -> Tensor {...}); + * > Tensor result = func.callUnboxedOnly(tensor1, true); + */ + template + Return callUnboxedOnly(Args... args) const { + // note: Args above is intentionally not Args&&. We don't want perfect + // forwarding, which would require Args to be deduced, but instead we + // want callers to explicitly specify the Args. + + // TODO Remove this function once all kernels support a boxed variant + + if (C10_LIKELY(unboxed_kernel_func_ != nullptr)) { + using ActualSignature = Return (OperatorKernel*, Args...); + ActualSignature* func = reinterpret_cast(unboxed_kernel_func_); + return (*func)(getFunctor_(), std::forward(args)...); + } + + TORCH_INTERNAL_ASSERT(false, "Tried to call KernelFunction::callUnboxedOnly() for a kernel that doesn't have an unboxed version."); + } + + /** + * Call the function in an unboxed way. + * If the kernel function was created with a boxed function, + * this will box all inputs and then call into that boxed function. + * + * Note that this doesn't work for all types yet. + * + * Example: + * + * > KernelFunction func = KernelFunction::makeFromUnboxedLambda( + * > [] (Tensor a, bool b) -> Tensor {...}); + * > Tensor result = func.callUnboxed(tensor1, true); + * + * Or, with a boxed implementation: + * + * > void boxed_func(OperatorKernel*, Stack* stack) {...} + * > KernelFunction func = KernelFunction::makeFromBoxedFunction(&boxed_func); + * > Tensor result = func.callUnboxed(tensor1, true); + */ + template + Return callUnboxed(Args... args) const { + // note: Args above is intentionally not Args&&. We don't want perfect + // forwarding, which would require Args to be deduced, but instead we + // want callers to explicitly specify the Args. + + if (C10_LIKELY(unboxed_kernel_func_ != nullptr)) { + using ActualSignature = Return (OperatorKernel*, Args...); + ActualSignature* func = reinterpret_cast(unboxed_kernel_func_); + return (*func)(getFunctor_(), std::forward(args)...); + } + + TORCH_INTERNAL_ASSERT(boxed_kernel_func_ != nullptr, "Tried to call KernelFunction::callUnboxed() on an uninitialized KernelFunction."); + return detail::boxAndCallBoxedFunc::call(boxed_kernel_func_, getFunctor_(), std::forward(args)...); + } + + /** + * Create a KernelFunction from a boxed function. + * + * Example: + * + * > void boxed_func(OperatorKernel*, Stack* stack) {...} + * > KernelFunction func = KernelFunction::makeFromBoxedFunction(&boxed_func); + */ + static KernelFunction makeFromBoxedFunction(BoxedKernelFunction* func) { + return KernelFunction( + nullptr, // no functorFactory_, this can only be called in a boxed way. + nullptr, // no functor_ object either + func, + nullptr // no unboxed function pointer + ); + } + + /** + * Create a KernelFunction from an unboxed functor. + * + * Example: + * + * > class MyFunctor final { + * > public: + * > Tensor operator()(Tensor a, Tensor b) {...} + * > }; + * > KernelFunction func = KernelFunction::makeFromUnboxedFunctor(guts::make_unique()); + */ + template + static KernelFunction makeFromUnboxedFunctor(std::unique_ptr kernelFunctor) { + static_assert(guts::is_functor::value, "Tried to call KernelFunction::makeFromUnboxedFunctor but the argument is not a functor."); + static_assert(std::is_base_of::value, "Tried to call KernelFunction::makeFromUnboxedFunctor, but the functor doesn't inherit from c10::OperatorKernel. Please have the functor inherit from it."); + + return KernelFunction( + nullptr, // no functorFactory_ because we already have the functor_ + std::move(kernelFunctor), + &detail::wrap_kernel_functor_boxed::call, + reinterpret_cast(&detail::wrap_kernel_functor_unboxed::call) + ); + } + + /** + * Create a KernelFunction from an unboxed functor and delay functor creation + * until the first call to the KernelFunction. This is useful for functors + * that are registered at static initialization time but can't be created + * there yet. For example, we want to allow functors to store Tensor members + * (we can't create Tensor objects at static initialization time because of SIOF) + * but these functors are registered as kernels at static initialization time. + * Using this method, we can delay functor instantiation until the operator + * is called for the first time. + * + * Example: + * + * > class MyFunctor final { + * > public: + * > Tensor operator()(Tensor a, Tensor b) {...} + * > }; + * > KernelFunction func = KernelFunction::makeFromUnboxedFunctor([] { + * > return guts::make_unique(); + * > }); + */ + template + static KernelFunction makeFromUnboxedFunctorFactory(std::function()> kernelFunctorFactory) { + static_assert(guts::is_functor::value, "Tried to call KernelFunction::makeFromUnboxedFunctor but the argument is not a functor."); + static_assert(std::is_base_of::value, "Tried to call KernelFunction::makeFromUnboxedFunctor, but the functor doesn't inherit from c10::OperatorKernel. Please have the functor inherit from it."); + + return KernelFunction( + std::move(kernelFunctorFactory), + nullptr, // delay creation of functor_ (it will be created by calling functorFactory_ later) + &detail::wrap_kernel_functor_boxed::call, + reinterpret_cast(&detail::wrap_kernel_functor_unboxed::call) + ); + } + + /** + * Create a KernelFunction from an unboxed functor and prevent creation of an + * unboxing-wrapper. This means that you can only call this KernelFunction + * using KernelFunction::callUnboxedOnly(), not using KernelFunction::callBoxed() + * or KernelFunction::callUnboxed(). + * + * This is necessary because our unboxing wrappers don't work for all types + * yet, so if you want to use one of these types as function arguments, + * you need to use makeFromUnboxedOnlyFunctor. + * + * Example: + * + * > class MyFunctor final { + * > public: + * > Tensor operator()(Tensor a, Tensor b) {...} + * > }; + * > KernelFunction func = KernelFunction::makeFromUnboxedOnlyFunctor(guts::make_unique()); + */ + template + static KernelFunction makeFromUnboxedOnlyFunctor(std::unique_ptr kernelFunctor) { + // TODO We want to get rid of kernels that have only an unboxed function pointer. + // All kernels should have a boxed pointer. + + static_assert(guts::is_functor::value, "Tried to call KernelFunction::makeFromUnboxedFunctor but the argument is not a functor."); + static_assert(std::is_base_of::value, "Tried to call KernelFunction::makeFromUnboxedFunctor, but the functor doesn't inherit from c10::OperatorKernel. Please have the functor inherit from it."); + + return KernelFunction( + nullptr, // no functorFactory_ because we already have the functor_ + std::move(kernelFunctor), + nullptr, // Don't create a boxed kernel for this + reinterpret_cast(&detail::wrap_kernel_functor_unboxed::call) + ); + } + + /** + * Create a KernelFunction from an unboxed function. + * This is usually better than KernelFunction::makeFromUnboxedRuntimeFunction + * because knowing the function pointer as a template argument (i.e. at + * compile time) allows the compiler to inline the function into its + * unboxing wrapper and yields better performance when calling the function. + * + * Example: + * + * > Tensor unboxed_func(Tensor a, Tensor b) {...} + * > KernelFunction func = KernelFunction::makeFromUnboxedFunction(); + */ + template + static KernelFunction makeFromUnboxedFunction() { + static_assert(guts::is_function_type::value, "Tried to call KernelFunction::makeFromUnboxedFunction with invalid template parameters. They must be ."); + static_assert(!std::is_same::value, "Tried to call KernelFunction::makeFromUnboxedFunction with a boxed function pointer. Please use KernelFunction::makeFromBoxedFunction instead."); + static_assert(func != nullptr, "Kernel function cannot be nullptr"); + + return makeFromUnboxedFunctor::type>( + guts::make_unique_base::type>() + ); + } + + /** + * Create a KernelFunction from an unboxed function and prevent creation of an + * unboxing-wrapper. This means that you can only call this KernelFunction + * using KernelFunction::callUnboxedOnly(), not using KernelFunction::callBoxed() + * or KernelFunction::callUnboxed(). + * + * This is necessary because our unboxing wrappers don't work for all types + * yet, so if you want to use one of these types as function arguments, + * you need to use makeFromUnboxedOnlyFunctor. + * + * Example: + * + * > Tensor unboxed_func(Tensor a, Tensor b) {...} + * > KernelFunction func = KernelFunction::makeFromUnboxedOnlyFunction(); + */ + template + static KernelFunction makeFromUnboxedOnlyFunction() { + // TODO We want to get rid of kernels that have only an unboxed function pointer. + // All kernels should have a boxed pointer. + + static_assert(guts::is_function_type::value, "Tried to call KernelFunction::makeFromUnboxedOnlyFunction with invalid template parameters. They must be ."); + static_assert(!std::is_same::value, "Tried to call KernelFunction::makeFromUnboxedOnlyFunction with a boxed function pointer. Please use KernelFunction::makeFromBoxedFunction instead."); + static_assert(func != nullptr, "Kernel function cannot be nullptr"); + + return makeFromUnboxedOnlyFunctor::type> ( + guts::make_unique_base::type>() + ); + } + + /** + * Create a KernelFunction from an unboxed function. + * KernelFunction::makeFromUnboxedFunction is usually a better choice than + * this if you know the function pointer at compile time, see doc comment + * there for an explanation. + * + * Example: + * + * > Tensor unboxed_func(Tensor a, Tensor b) {...} + * > KernelFunction func = KernelFunction::makeFromUnboxedRuntimeFunction(&unboxed_func); + */ + template + static KernelFunction makeFromUnboxedRuntimeFunction(FuncType* func) { + static_assert(guts::is_function_type::value, "Tried to call KernelFunction::makeFromUnboxedRuntimeFunction with a non-function type."); + static_assert(!std::is_same::value, "Tried to call KernelFunction::makeFromUnboxedRuntimeFunction with a boxed function pointer. Please use KernelFunction::makeFromBoxedFunction instead."); + TORCH_INTERNAL_ASSERT(func != nullptr, "Kernel function cannot be nullptr"); + + return makeFromUnboxedFunctor>>( + guts::make_unique_base>>(func) + ); + } + + template + static KernelFunction makeFromUnboxedOnlyRuntimeFunction(FuncType* func) { + static_assert(guts::is_function_type::value, "Tried to call KernelFunction::makeFromUnboxedRuntimeFunction with a non-function type."); + static_assert(!std::is_same::value, "Tried to call KernelFunction::makeFromUnboxedRuntimeFunction with a boxed function pointer. Please use KernelFunction::makeFromBoxedFunction instead."); + TORCH_INTERNAL_ASSERT(func != nullptr, "Kernel function cannot be nullptr"); + + return makeFromUnboxedOnlyFunctor>>( + guts::make_unique_base>>(func) + ); + } + + /** + * Create a KernelFunction from an unboxed lambda. + * + * Example: + * + * > KernelFunction func = KernelFunction::makeFromUnboxedLambda( + * > [] (Tensor a, bool b) -> Tensor {...}); + */ + template + static KernelFunction makeFromUnboxedLambda(Lambda&& lambda) { + static_assert(guts::is_functor>::value, "Tried to call KernelFunction::makeFromUnboxedLambda with a non-lambda type."); + + return makeFromUnboxedFunctor>>( + guts::make_unique_base>>(std::forward(lambda)) + ); + } + +private: + + explicit KernelFunction(std::function()> functorFactory, std::unique_ptr functor, BoxedKernelFunction* boxed_kernel_func, void* unboxed_kernel_func) + : functorFactory_(std::move(functorFactory)) + , functor_(std::move(functor)) + , boxed_kernel_func_(boxed_kernel_func) + , unboxed_kernel_func_(unboxed_kernel_func) + {} + + OperatorKernel* getFunctor_() const { + if (functor_.get() == nullptr) { + if (!functorFactory_) { + return nullptr; + } + functor_ = functorFactory_(); + } + return functor_.get(); + } + + // If the operator has an unboxed_kernel_func, then either + // functorFactory_ or functor_ must be set, possibly both. + // If functor_ is not set but functorFactory_ is, we will create + // functor_ by calling functorFactory_ the first time it is needed. + // We use this indirection because many KernelFunctions are created + // at static initialization time but are created with functors that + // store Tensor and we can't call the Tensor() constructor at static + // initialization time yet (SIOF). So these register with a + // functorFactory_ instead of a functor_ and will be initialized + // on the first call to the KernelFunction. + std::function()> functorFactory_; + mutable std::shared_ptr functor_; + + BoxedKernelFunction* boxed_kernel_func_; + void* unboxed_kernel_func_; +}; + +namespace detail { +template +struct boxAndCallBoxedFunc final { + static Return call(KernelFunction::BoxedKernelFunction* boxed_kernel_func, OperatorKernel* functor, Args... args) { + // TODO Reuse stack vector instead of allocating? + std::vector stack {std::forward(args)...}; + + (*boxed_kernel_func)(functor, &stack); + + TORCH_INTERNAL_ASSERT(stack.size() == 1, "A boxed kernel should only push one return to the stack"); + return std::move(stack[0]).to(); + } +}; +template +struct boxAndCallBoxedFunc final { + static void call(KernelFunction::BoxedKernelFunction* boxed_kernel_func, OperatorKernel* functor, Args... args) { + // TODO Reuse stack vector instead of allocating? + std::vector stack {std::forward(args)...}; + + (*boxed_kernel_func)(functor, &stack); + + TORCH_INTERNAL_ASSERT(stack.size() == 0, "A boxed kernel returned a value but when we called it with KernelFunction::callUnboxed, we expected it to return void."); + } +}; +} + +} diff --git a/thirdparty/libtorch/include/ATen/core/boxing/kernel_function.h b/thirdparty/libtorch/include/ATen/core/boxing/kernel_function.h new file mode 100644 index 0000000000..ebd82fc540 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/boxing/kernel_function.h @@ -0,0 +1,29 @@ +#pragma once + +#include + +namespace c10 { +namespace detail { + // WrapKernelFunction: Wraps a compile time function pointer into a kernel functor. + // Since it is a compile time function pointer, many compilers can inline it + // into the wrapper and you don't get any performance overhead for wrapping. + template class WrapKernelFunction_ {}; + template + class WrapKernelFunction_> final : public c10::OperatorKernel { + public: + auto operator()(Parameters... args) -> decltype((*kernel_func)(std::forward(args)...)) { + return (*kernel_func)(std::forward(args)...); + } + }; + template::value>> + struct WrapKernelFunction final { + using type = WrapKernelFunction_< + FuncType, + kernel_func, + typename guts::function_traits::return_type, + typename guts::function_traits::parameter_types + >; + }; +} + +} diff --git a/thirdparty/libtorch/include/ATen/core/boxing/kernel_functor.h b/thirdparty/libtorch/include/ATen/core/boxing/kernel_functor.h new file mode 100644 index 0000000000..3b7296fdfa --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/boxing/kernel_functor.h @@ -0,0 +1,302 @@ +#pragma once + +#include +#include + +namespace c10 { + +using Stack = torch::jit::Stack; // TODO Instead of this, move torch::jit::Stack to the c10 namespace. + +/** + * Inherit from OperatorKernel to implement a c10 kernel. + * + * Example: + * > namespace { + * > class my_kernel_cpu final : public c10::OperatorKernel { + * > public: + * > Tensor operator()(Tensor a, Tensor b) {...} + * > }; + * > } + * + * The kernel class is allowed to have members but these are equivalent + * to global variables. The kernel implementation is responsible for + * preventing race conditions on them. + * + * See below for how to register this kernel with PyTorch. + */ +struct CAFFE2_API OperatorKernel { + virtual ~OperatorKernel() = default; +}; + +namespace detail { + // supported_primitive_arg_types defines which primitive types we allow in + // kernel functions as arguments or returns. + // Additionally, we support lists, dicts and optionals containing these types. + using supported_primitive_arg_types = guts::typelist::typelist< + int64_t, + double, + bool, + std::string, + at::Tensor, + at::Scalar + >; + + template struct assert_is_valid_input_type { + assert_is_valid_input_type() { + auto tmap = c10::getCustomClassTypeMap(); + TORCH_CHECK(c10::isCustomClassRegistered(), "Tried to use undefined class as input argument"); + } + }; + + template + struct assert_is_valid_input_type::value>> { + // everything is ok, this is a primitive type + }; + + template + struct assert_is_valid_input_type, AllowDeprecatedTypes> + : assert_is_valid_input_type {}; + + template + struct assert_is_valid_input_type, AllowDeprecatedTypes> + : assert_is_valid_input_type { + static_assert(guts::typelist::contains::value, "You tried to register a kernel with an unsupported input type: Dict where Key is invalid. We only support int64_t, double, bool, and string."); + }; + + template + struct assert_is_valid_input_type, AllowDeprecatedTypes> + : assert_is_valid_input_type { + static_assert(AllowDeprecatedTypes, "You tried to register a kernel with an unsupported input type: std::unordered_map. Please use Dict instead."); + static_assert(guts::typelist::contains::value, "You tried to register a kernel with an unsupported input type: std::unordered_map where Key is invalid. We only support int64_t, double, bool, and string."); + }; + + template + struct assert_is_valid_input_type, AllowDeprecatedTypes> + : assert_is_valid_input_type { + static_assert(!std::is_same::value, "You tried to register a kernel with an unsupported input type: List. Please use List, List or Tensor instead."); + }; + + template + struct assert_is_valid_input_type, AllowDeprecatedTypes> + : assert_is_valid_input_type { + static_assert(!std::is_same::value, "You tried to register a kernel with an unsupported input type: std::vector. Please use List, List or Tensor instead."); + // TODO static_assert(AllowDeprecatedTypes, "You tried to register a kernel with an unsupported input type: std::vector. Please use List instead."); + }; + + // The following specialisations of assert_is_valid_input_type are technically not + // necessary since we would hit the base case and show an error message + // there if they didn't exist, but we can show a better error message + // in some common error scenarios. + template + struct assert_is_valid_input_type::value>> { + // There is no reason to support float when we have double. Keep the API lean. + static_assert(guts::false_t::value, "You tried to register a kernel with an unsupported input type: float. Please use double instead."); + }; + template + struct assert_is_valid_input_type::value>> { + static_assert(guts::false_t::value, "You tried to register a kernel with an unsupported input type: const char*. Please use std::string instead."); + }; + template + struct assert_is_valid_input_type, T>::value>> { + static_assert(guts::false_t::value, "You tried to register a kernel with an unsupported input type: vector. Please use List instead."); + }; + template + struct assert_is_valid_input_type::value && !guts::typelist::contains::value>> { + static_assert(guts::false_t::value, "You tried to register a kernel with an unsupported integral input type. Please use int64_t instead."); + }; + + template struct assert_is_valid_output_type { + assert_is_valid_output_type() { + auto tmap = getCustomClassTypeMap(); + TORCH_CHECK(c10::isCustomClassRegistered(), "Tried to use undefined class as output"); + } + }; + + template + struct assert_is_valid_output_type::value>> { + // everything is ok, this is a primitive type + }; + + template + struct assert_is_valid_output_type, AllowDeprecatedTypes> + : assert_is_valid_output_type {}; + + template + struct assert_is_valid_output_type, AllowDeprecatedTypes> + : assert_is_valid_output_type { + static_assert(guts::typelist::contains::value, "You tried to register a kernel with an unsupported output type: Dict where Key is invalid. We only support int64_t, double, bool, and string."); + static_assert(!std::is_same::value, "You tried to register a kernel with an unsupported output type: Dict. Please use Dict or Dict."); + }; + + template + struct assert_is_valid_output_type, AllowDeprecatedTypes> + : assert_is_valid_output_type { + static_assert(AllowDeprecatedTypes, "You tried to register a kernel with an unsupported output type: std::unordered_map. Please use Dict instead."); + static_assert(guts::typelist::contains::value, "You tried to register a kernel with an unsupported output type: std::unordered_map where Key is invalid. We only support int64_t, double, bool, and string."); + static_assert(!std::is_same::value, "You tried to register a kernel with an unsupported output type: std::unordered_map. Please use Dict or Dict."); + }; + + template + struct assert_is_valid_output_type, AllowDeprecatedTypes> + : assert_is_valid_output_type { + static_assert(!std::is_same::value, "You tried to register a kernel with an unsupported output type: List. Please use List, List or Tensor instead."); + }; + + template + struct assert_is_valid_output_type, AllowDeprecatedTypes> + : assert_is_valid_output_type { + static_assert(!std::is_same::value, "You tried to register a kernel with an unsupported output type: std::vector. Please use List, List or Tensor instead."); + // TODO static_assert(AllowDeprecatedTypes, "You tried to register a kernel with an unsupported output type: std::vector. Please use List instead."); + }; + + // The following specialisations of assert_is_valid_output_type are technically not + // necessary since we would hit the base case and show an error message + // there if they didn't exist, but we can show a better error message + // in some common error scenarios. + template + struct assert_is_valid_output_type::value>> { + // There is no reason to support float when we have double. Keep the API lean. + static_assert(guts::false_t::value, "You tried to register a kernel with an unsupported output type: float. Please use double instead."); + }; + template + struct assert_is_valid_output_type::value>> { + static_assert(guts::false_t::value, "You tried to register a kernel with an unsupported output type: const char*. Please use std::string instead."); + }; + template + struct assert_is_valid_output_type, T>::value>> { + static_assert(guts::false_t::value, "You tried to register a kernel with an unsupported output type: vector. Please use List instead."); + }; + template + struct assert_is_valid_output_type::value && !guts::typelist::contains::value>> { + static_assert(guts::false_t::value, "You tried to register a kernel with an unsupported integral output type. Please use int64_t instead."); + }; + + + template + T ivalue_to_arg(IValue&& v) { + assert_is_valid_input_type(); + return std::move(v).to(); + } + + template + IValue return_to_ivalue(T&& v) { + assert_is_valid_output_type(); + return c10::ivalue::from(v); + } + + template + typename guts::infer_function_traits_t::return_type call_functor_with_args_from_stack_(Functor* functor, Stack* stack, guts::index_sequence) { + (void)(stack); // when sizeof...(ivalue_arg_indices) == 0, this argument would be unused and we have to silence the compiler warning. + + constexpr size_t num_ivalue_args = sizeof...(ivalue_arg_indices); + + using IValueArgTypes = typename guts::infer_function_traits_t::parameter_types; + return (*functor)(ivalue_to_arg>>, AllowDeprecatedTypes>( + std::move(torch::jit::peek(*stack, ivalue_arg_indices, num_ivalue_args)) + )...); + } + + template + typename guts::infer_function_traits_t::return_type call_functor_with_args_from_stack(Functor* functor, Stack* stack) { + constexpr size_t num_ivalue_args = guts::infer_function_traits_t::number_of_parameters; + return call_functor_with_args_from_stack_(functor, stack, guts::make_index_sequence()); + } + + template + struct push_outputs final { + static void call(OutputType&& output, Stack* stack) { + torch::jit::push(*stack, return_to_ivalue(std::move(output))); + } + }; + template + struct push_outputs, AllowDeprecatedTypes> final { + static void call(std::tuple&& output, Stack* stack) { + call_(std::move(output), stack, guts::make_index_sequence()); + } + + private: + template + static void call_(std::tuple&& output, Stack* stack, guts::index_sequence) { + torch::jit::push(*stack, return_to_ivalue(std::move(std::get(output)))...); + } + }; + + template struct wrap_kernel_functor_boxed final {}; + + // SFINAE version for kernels that return an output + template + struct wrap_kernel_functor_boxed::return_type>::value>> final { + static_assert(std::is_base_of::value, "Tried to register a kernel functor using the kernel() API, but it doesn't inherit from c10::OperatorKernel. Please have the functor inherit from it."); + + static void call(OperatorKernel* functor, Stack* stack) { + constexpr size_t num_inputs = guts::infer_function_traits_t::number_of_parameters; + KernelFunctor* functor_ = static_cast(functor); + auto output = call_functor_with_args_from_stack(functor_, stack); + torch::jit::drop(*stack, num_inputs); + push_outputs::return_type, AllowDeprecatedTypes>::call(std::move(output), stack); + } + }; + + // SFINAE version for kernels that don't return an output + template + struct wrap_kernel_functor_boxed::return_type>::value>> final { + static_assert(std::is_base_of::value, "Tried to register a kernel functor using the kernel() API, but it doesn't inherit from c10::OperatorKernel. Please have the functor inherit from it."); + + static void call(OperatorKernel* functor, Stack* stack) { + constexpr size_t num_inputs = guts::infer_function_traits_t::number_of_parameters; + KernelFunctor* functor_ = static_cast(functor); + call_functor_with_args_from_stack(functor_, stack); + torch::jit::pop(*stack, num_inputs); + } + }; + + template struct wrap_kernel_functor_unboxed_ final {}; + template struct wrap_kernel_functor_unboxed_ final { + static_assert(std::is_same::return_type>::value, "Return type mismatch"); + static_assert(std::is_same, typename guts::infer_function_traits_t::parameter_types>::value, "Parameter types mismatch"); + + static ReturnType call(OperatorKernel* functor, ParameterTypes... args) { + KernelFunctor* functor_ = static_cast(functor); + return (*functor_)(std::forward(args)...); + } + }; + template using wrap_kernel_functor_unboxed = wrap_kernel_functor_unboxed_::func_type>; + + template + class KernelFactory final { + static_assert(std::is_constructible::value, "Wrong argument types for constructor of kernel functor."); + + public: + explicit constexpr KernelFactory(Args... args) + : constructor_parameters_(std::move(args)...) {} + + std::unique_ptr operator()() const { + return guts::apply( + [] (const Args&... params) -> std::unique_ptr {return guts::make_unique_base(params...); }, + constructor_parameters_); + } + + private: + std::tuple constructor_parameters_; + }; + + template + std::unique_ptr inferFunctionSchema_() { + return guts::make_unique(inferFunctionSchema("", "")); + } + + template + class FunctionSchemaInferer final { + public: + using func_type = typename c10::guts::infer_function_traits_t::func_type; + std::unique_ptr operator()() const { + return inferFunctionSchema_(); + } + }; +} + +} + +namespace torch { + using OperatorKernel = c10::OperatorKernel; +} diff --git a/thirdparty/libtorch/include/ATen/core/boxing/kernel_lambda.h b/thirdparty/libtorch/include/ATen/core/boxing/kernel_lambda.h new file mode 100644 index 0000000000..6210535840 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/boxing/kernel_lambda.h @@ -0,0 +1,37 @@ +#pragma once + +#include +#include + +namespace c10 { + +namespace detail { + // WrapRuntimeKernelFunctor: Wraps any runtime functor into a functor that + // inherits from c10::OperatorKernel, so it can be used as a c10 kernel. + // This can, for example, be used for lamdas, functors or even function pointers. + // In the case of function pointers, since it is a runtime function pointer, + // there is an overhead for calling it whenever the kernel is invoked. + template class WrapRuntimeKernelFunctor_ {}; + template + class WrapRuntimeKernelFunctor_> final : public c10::OperatorKernel { + public: + template + explicit WrapRuntimeKernelFunctor_(FuncType_&& kernel_func) + : kernel_func_(std::forward(kernel_func)) {} + + auto operator()(Parameters... args) -> decltype(std::declval()(std::forward(args)...)) { + return kernel_func_(std::forward(args)...); + } + + private: + FuncType kernel_func_; + }; + template + using WrapRuntimeKernelFunctor = WrapRuntimeKernelFunctor_< + FuncType, + typename guts::infer_function_traits_t::return_type, + typename guts::infer_function_traits_t::parameter_types + >; +} + +} diff --git a/thirdparty/libtorch/include/ATen/core/boxing/test_helpers.h b/thirdparty/libtorch/include/ATen/core/boxing/test_helpers.h new file mode 100644 index 0000000000..4957cbf661 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/boxing/test_helpers.h @@ -0,0 +1,86 @@ +#pragma once + +#include +#include + +#include +#include +#include +#include + +template +inline std::vector makeStack(Inputs&&... inputs) { + return {std::forward(inputs)...}; +} + +inline at::Tensor dummyTensor(c10::TensorTypeId dispatch_key) { + auto* allocator = c10::GetCPUAllocator(); + int64_t nelements = 1; + auto dtype = caffe2::TypeMeta::Make(); + auto storage_impl = c10::make_intrusive( + dtype, + nelements, + allocator->allocate(nelements * dtype.itemsize()), + allocator, + /*resizable=*/true); + return at::detail::make_tensor(storage_impl, dispatch_key); +} + +template +inline std::vector callOp(const c10::OperatorHandle& op, Args... args) { + auto stack = makeStack(std::forward(args)...); + c10::Dispatcher::singleton().callBoxed(op, &stack); + return stack; +} + +template +inline Result callOpUnboxed(const c10::OperatorHandle& op, Args... args) { + return c10::Dispatcher::singleton() + .template callUnboxed(op, std::forward(args)...); +} + +inline void expectDoesntFindKernel(const char* op_name, c10::TensorTypeId dispatch_key) { + auto op = c10::Dispatcher::singleton().findSchema({op_name, ""}); + EXPECT_ANY_THROW( + callOp(*op, dummyTensor(dispatch_key), 5); + ); +} + +inline void expectDoesntFindOperator(const char* op_name) { + auto op = c10::Dispatcher::singleton().findSchema({op_name, ""}); + EXPECT_FALSE(op.has_value()); +} + +template +inline void expectThrows(Functor&& functor, const char* expectMessageContains) { + try { + std::forward(functor)(); + } catch (const Exception& e) { + EXPECT_THAT(e.what(), testing::HasSubstr(expectMessageContains)); + return; + } + ADD_FAILURE() << "Expected to throw exception containing \"" + << expectMessageContains << "\" but didn't throw"; +} + +template +void expectListEquals(c10::ArrayRef expected, c10::List actual) { + EXPECT_EQ(expected.size(), actual.size()); + for (size_t i = 0; i < expected.size(); ++i) { + EXPECT_EQ(expected[i], actual.get(i)); + } +} + +template +void expectListEquals(c10::ArrayRef expected, std::vector actual) { + EXPECT_EQ(expected.size(), actual.size()); + for (size_t i = 0; i < expected.size(); ++i) { + EXPECT_EQ(expected[i], actual[i]); + } +} + +// NB: This is not really sound, but all of the type sets constructed here +// are singletons so it's fine +static inline c10::TensorTypeId extractTypeId(const at::Tensor& t) { + return legacyExtractTypeId(t.type_set()); +} diff --git a/thirdparty/libtorch/include/ATen/core/dispatch/DispatchKeyExtractor.h b/thirdparty/libtorch/include/ATen/core/dispatch/DispatchKeyExtractor.h new file mode 100644 index 0000000000..79995cdee1 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/dispatch/DispatchKeyExtractor.h @@ -0,0 +1,121 @@ +#pragma once + +#include +#include +#include +#include +#include + +namespace c10 { + +namespace impl { + +// Take a TensorTypeSet for a Tensor, and combine it with the current thread +// local valid (implemented) and enabled (not implemented) TensorTypeSets +// to determine what the actual dispatch TensorTypeId should be. Unlike +// Tensor::type_set(), the value of this on a tensor can change depending +// on TLS. +// +// NB: I didn't make this take a Tensor to avoid header include shenanigans. +// +// TODO: I'm not sure if this should live in this header or not; the operant +// question is whether or not we have access to all the relevant TLS at this +// point. +static inline TensorTypeId dispatchTypeId(TensorTypeSet ts) { + c10::impl::LocalTensorTypeSet local = c10::impl::tls_local_tensor_type_set(); + return ((ts | local.included_) - local.excluded_).highestPriorityTypeId(); +} + +} + +namespace detail { + struct MultiDispatchTensorTypeSet : at::IterArgs { + TensorTypeSet ts; + void operator()(const at::Tensor& x) { + ts = ts | x.type_set(); + } + void operator()(const TensorOptions& x) { + ts = ts | x.type_set(); + } + void operator()(at::ArrayRef xs) { + for (const auto& x : xs) { + ts = ts | x.type_set(); + } + } + template + void operator()(const T& x) { + // do nothing + } + }; + + // NB: take by const reference (Don't do universal forwarding here! You + // don't want to move into this function!) + template + TensorTypeSet multi_dispatch_tensor_type_set(const Args&... args) { + return MultiDispatchTensorTypeSet().apply(args...).ts; + } +} + +/** + * An instance of DispatchKeyExtractor knows how to get a dispatch key given + * a list of arguments for an operator call. The instance is specific for + * a certain operator as different operators have different ways to extract + * the dispatch key (e.g. different numbers of arguments). + */ +struct DispatchKeyExtractor final { +public: + static DispatchKeyExtractor make(const FunctionSchema& schema) { + return DispatchKeyExtractor(schema.arguments().size()); + } + + c10::optional getDispatchKeyBoxed(const Stack* stack) const { + // TODO Unboxed dispatch supports TensorOptions (i.e. ScalarType/Device/Layout) arguments + // but boxed doesn't yet. These should be aligned and do the same thing. + + TensorTypeSet ts; + for (const auto& ivalue : torch::jit::last(*stack, num_args_)) { + if (C10_LIKELY(ivalue.isTensor())) { + // NB: Take care not to introduce a refcount bump (there's + // no safe toTensorRef method, alas) + ts = ts | ivalue.unsafeToTensorImpl()->type_set(); + } else if (C10_UNLIKELY(ivalue.isTensorList())) { + for (const auto& tensor : ivalue.toTensorListRef()) { + ts = ts | tensor.type_set(); + } + } + } + if (C10_UNLIKELY(ts.empty())) { + return c10::nullopt; + } + + // TODO: Don't use legacy extractor; blocked on c10 understanding variable + return c10::legacyExtractTypeId(ts); + } + + template + c10::optional getDispatchKeyUnboxed(const Args&... args) const { + auto type_set = detail::multi_dispatch_tensor_type_set(args...); + return typeSetToDispatchKey_(type_set); + } + +private: + static c10::optional typeSetToDispatchKey_(const TensorTypeSet& typeSet) { + if (C10_UNLIKELY(typeSet.empty())) { + return c10::nullopt; + } + + return impl::dispatchTypeId(typeSet); + } + + explicit DispatchKeyExtractor(size_t num_args) + : num_args_(num_args) {} + + // this is caching the index so we don't have to parse the schema inputs + // again and again for each dispatcher lookup. + // num_args_ is allowed to be zero; that just means you must do the + // fallthrough + // TODO: a potential optimization is to store a bitfield of arg locations, + size_t num_args_; +}; + +} diff --git a/thirdparty/libtorch/include/ATen/core/dispatch/DispatchTable.h b/thirdparty/libtorch/include/ATen/core/dispatch/DispatchTable.h new file mode 100644 index 0000000000..65848a11ef --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/dispatch/DispatchTable.h @@ -0,0 +1,149 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +namespace c10 { + +/** + * Per-operator dispatch table. + * + * Given an operator specified by a FunctionSchema, this class records a dispatch + * table for various kernels provided for this operator. For example, if we + * consider the operator add(Tensor, Tensor), the dispatch table for this + * operator may contain implementations for various dynamic tensor types, such + * as CPUTensorId, CUDATensorId, etc. + */ +class DispatchTable final { + public: + DispatchTable(const FunctionSchema& schema) + : kernels_() + , catchallKernel_(c10::nullopt) + , dispatchKeyExtractor_(DispatchKeyExtractor::make(schema)) + , operatorName_(toString(schema.operator_name())) {} + + /** + * Register a kernel in the table at some dispatch key. + * @param dispatch_key Dispatch key to define when this kernel is selected. + * @param kernel Concrete kernel function implementation to register + */ + void setKernel(TensorTypeId dispatchKey, const KernelFunction& kernel) { + TORCH_INTERNAL_ASSERT(dispatchKey != TensorTypeId::UndefinedTensorId); + // The following assertion is disabled because we're codegenerating + // autograd kernels for operators without tensor arguments even though + // they are never called. These, however, register kernels for + // VariableTensorId. + // TODO Stop generating those kernels and re-enable this assertion here. + auto emplaced = kernels_.emplace(dispatchKey, kernel); + if (!emplaced.second) { + // Element already existed. Overwrite it. + emplaced.first->second = kernel; + TORCH_WARN("Registered a kernel for operator ", operatorName_," with dispatch key ", toString(dispatchKey), " that overwrote a previously registered kernel with the same dispatch key for the same operator."); + } + } + + /** + * Deregister the kernel for some dispatch key. + * + * @param dispatch_key Dispatch key to unregister. + */ + void removeKernelIfExists(TensorTypeId dispatchKey) { + auto num_removed = kernels_.erase(dispatchKey); + TORCH_INTERNAL_ASSERT(num_removed <= 1); // This is not a multi-map + } + + /** + * Register a catch-all kernel that is called for this operator + * independent of the inputs. An operator can have either + * a catch-all kernel or a set of kernels with concrete + * dispatch keys, not both. + */ + void setCatchallKernel(const KernelFunction& kernel) { + if (catchallKernel_.has_value()) { + TORCH_WARN("Registered a catch-all kernel for operator ", operatorName_," that overwrote a previously registered catch-all kernel for the same operator."); + } + catchallKernel_ = kernel; + } + + /** + * Remove the catch-all kernel. + */ + void removeCatchallKernel() { + TORCH_INTERNAL_ASSERT(catchallKernel_.has_value(), "Tried to remove the catch-all kernel for operator ", operatorName_," but there is no catch-all kernel registered."); + catchallKernel_ = c10::nullopt; + } + + bool isEmpty() const { + return !catchallKernel_.has_value() && kernels_.size() == 0; + } + + std::string listAllDispatchKeys() const { + std::ostringstream str; + str << "["; + + if (kernels_.size() != 0) { + str << toString(kernels_.begin()->first); + for (auto iter = ++kernels_.begin(); iter != kernels_.end(); ++iter) { + str << ", " << toString(iter->first); + } + } + if (catchallKernel_.has_value()) { + if (kernels_.size() != 0) { + str << ", "; + } + str << "CATCH-ALL"; + } + str << "]"; + return str.str(); + } + + const KernelFunction* lookup(TensorTypeId dispatchKey) const { + auto found = kernels_.find(dispatchKey); + if (found != kernels_.end()) { + return &found->second; + } else { + return nullptr; + } + } + + const KernelFunction* lookupCatchallKernel() const { + if (!catchallKernel_.has_value()) { + return nullptr; + } + + return &*catchallKernel_; + } + + const DispatchKeyExtractor& dispatchKeyExtractor() const { + return dispatchKeyExtractor_; + } + + const std::string& operatorName() const { + return operatorName_; + } + +private: + + ska::flat_hash_map kernels_; + c10::optional catchallKernel_; + DispatchKeyExtractor dispatchKeyExtractor_; + std::string operatorName_; +}; + +} // namespace c10 diff --git a/thirdparty/libtorch/include/ATen/core/dispatch/Dispatcher.h b/thirdparty/libtorch/include/ATen/core/dispatch/Dispatcher.h new file mode 100644 index 0000000000..7fd4bc4263 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/dispatch/Dispatcher.h @@ -0,0 +1,260 @@ +#pragma once + +#include +#include +#include +#include +#include + +namespace c10 { + +class CAFFE2_API OperatorHandle; + +/** + * Implement this interface and register your instance with the dispatcher + * to get notified when operators are registered or deregistered with + * the dispatcher. + */ +class CAFFE2_API OpRegistrationListener { +public: + virtual ~OpRegistrationListener(); + + virtual void onOperatorRegistered(const OperatorHandle& op) = 0; + virtual void onOperatorDeregistered(const OperatorHandle& op) = 0; +}; + +namespace detail { +class RegistrationListenerList; +} +class SchemaRegistrationHandleRAII; + +/** + * Top-level dispatch interface for dispatching via the dynamic dispatcher. + */ +class CAFFE2_API Dispatcher final { +private: + struct OperatorDef final { + explicit OperatorDef(FunctionSchema&& schema, OperatorOptions&& options) + : op(std::move(schema), std::move(options)), refcount(0) {} + + impl::OperatorEntry op; + size_t refcount; + }; + friend class OperatorHandle; + +public: + ~Dispatcher(); + + // Implementation note: this class abstracts over the fact that we have per-operator + // dispatch tables. This could be easily adjusted to have a single global hash + // table. + + static Dispatcher& singleton(); + + /** + * Register a new operator schema. + * + * If a schema with the same operator name and overload name already exists, + * this function will check that both schemas are exactly identical. + * + * @return An OperatorHandle for the registered schema which can be used to + * register kernels for the operator and a RegistrationHandleRAII RAII + * object that manages the lifetime of the registration. Once that + * object is destructed, the kernel will be deregistered. + */ + std::pair registerSchema(FunctionSchema schema, OperatorOptions options); + + /** + * Looks for an operator schema with the given name and overload name + * and returns it if it is registered. + * Returns nullopt otherwise. + */ + c10::optional findSchema(const OperatorName& operator_name); + + /** + * Register a kernel to the dispatch table for an operator. + * If dispatch_key is nullopt, then this registers a fallback kernel. + * + * @return A RAII object that manages the lifetime of the registration. + * Once that object is destructed, the kernel will be deregistered. + */ + RegistrationHandleRAII registerKernel(const OperatorHandle& op, TensorTypeId dispatch_key, KernelFunction kernel); + + /** + * Register a fallback kernel for an operator. + * After this, when trying to lookup a kernel for an unknown dispatch key, + * it will not fail anymore, but return the fallback kernel instead. + * + * @return A RAII object that manages the lifetime of the registration. + * Once that object is destructed, the kernel will be deregistered. + */ + RegistrationHandleRAII registerCatchallKernel(const OperatorHandle& op, KernelFunction kernel); + + /** + * Register a fallback kernel for a backend. + * If an operator is called but there is no concrete kernel for the dispatch + * key of the given operator arguments, it will check if there is such a + * fallback kernel for the given dispatch key and, if yes, call that one. + */ + RegistrationHandleRAII registerBackendFallbackKernel(TensorTypeId dispatch_key, KernelFunction kernel); + + template + Return callUnboxed(const OperatorHandle& op, Args... args) const; + + template + Return callUnboxedOnly(const OperatorHandle& op, Args... args) const; + + void callBoxed(const OperatorHandle& op, Stack* stack) const; + + /** + * Add a listener that gets called whenever a new op is registered or an existing + * op is deregistered. Immediately after registering, this listener gets called + * for all previously registered ops, so it can be used to keep track of ops + * registered with this dispatcher. + */ + void addRegistrationListener(std::unique_ptr listener); + +private: + Dispatcher(); + + OperatorHandle findOrRegisterSchema_(FunctionSchema&& schema, OperatorOptions&& options); + + void deregisterSchema_(const OperatorHandle& op, const OperatorName& op_name); + void deregisterBackendFallbackKernel_(TensorTypeId dispatchKey); + + static const KernelFunction& dispatch_(const DispatchTable& dispatchTable, const ska::flat_hash_map& backendFallbackKernels, c10::optional dispatch_key); + + template + Return doCallUnboxed(const DispatchTable& dispatchTable, const LeftRight>& backendFallbackKernels_, Args... args) const; + template + Return doCallUnboxedOnly(const DispatchTable& dispatchTable, const LeftRight>& backendFallbackKernels_, Args... args) const; + + std::list operators_; + LeftRight> operatorLookupTable_; + LeftRight> backendFallbackKernels_; + std::unique_ptr listeners_; + std::mutex mutex_; +}; + +/** + * This is a handle to an operator schema registered with the dispatcher. + * This handle can be used to register kernels with the dispatcher or + * to lookup a kernel for a certain set of arguments. + */ +class CAFFE2_API OperatorHandle final { +public: + OperatorHandle(OperatorHandle&&) noexcept = default; + OperatorHandle& operator=(OperatorHandle&&) noexcept = default; + OperatorHandle(const OperatorHandle&) = default; + OperatorHandle& operator=(const OperatorHandle&) = default; + + const FunctionSchema& schema() const { + return operatorIterator_->op.schema(); + } + + const OperatorOptions& options() const { + return operatorIterator_->op.options(); + } + +private: + explicit OperatorHandle(std::list::iterator operatorIterator) + : operatorIterator_(std::move(operatorIterator)) {} + friend class Dispatcher; + + std::list::iterator operatorIterator_; +}; + +namespace detail { +template inline void unused_arg_(const Args&...) {} +} + +template +inline Return Dispatcher::callUnboxed(const OperatorHandle& op, Args... args) const { + detail::unused_arg_(args...); // workaround for a false-positive warning about unused parameters in gcc 5 + + // note: this doesn't need the mutex because write operations on the list keep iterators intact. + return op.operatorIterator_->op.readDispatchTable([&] (const DispatchTable& dispatchTable) -> Return { + // TODO This should be a nested lambda instead of a separate function call, but that triggers an internal + // compiler error on GCC5. Change this once we don't need gcc 5 anymore. + return doCallUnboxed(dispatchTable, backendFallbackKernels_, std::forward(args)...); + }); +} + +template +inline Return Dispatcher::doCallUnboxed(const DispatchTable& dispatchTable, const LeftRight>& backendFallbackKernels, Args... args) const { + detail::unused_arg_(args...); // workaround for a false-positive warning about unused parameters in gcc 5 + return backendFallbackKernels.read([&] (const ska::flat_hash_map& backendFallbackKernels) -> Return { + c10::optional dispatchKey = dispatchTable.dispatchKeyExtractor().getDispatchKeyUnboxed(args...); + const KernelFunction& kernel = dispatch_(dispatchTable, backendFallbackKernels, dispatchKey); + return kernel.template callUnboxed(std::forward(args)...); + }); +} + +template +inline Return Dispatcher::callUnboxedOnly(const OperatorHandle& op, Args... args) const { + detail::unused_arg_(args...); // workaround for a false-positive warning about unused parameters in gcc 5 + + // note: this doesn't need the mutex because write operations on the list keep iterators intact. + return op.operatorIterator_->op.readDispatchTable([&] (const DispatchTable& dispatchTable) -> Return { + // TODO This should be a nested lambda instead of a separate function call, but that triggers an internal + // compiler error on GCC5. Change this once we don't need gcc 5 anymore. + return doCallUnboxedOnly(dispatchTable, backendFallbackKernels_, std::forward(args)...); + }); +} + +template +inline Return Dispatcher::doCallUnboxedOnly(const DispatchTable& dispatchTable, const LeftRight>& backendFallbackKernels, Args... args) const { + detail::unused_arg_(args...); // workaround for a false-positive warning about unused parameters in gcc 5 + return backendFallbackKernels.read([&] (const ska::flat_hash_map& backendFallbackKernels) -> Return { + c10::optional dispatchKey = dispatchTable.dispatchKeyExtractor().getDispatchKeyUnboxed(args...); + const KernelFunction& kernel = dispatch_(dispatchTable, backendFallbackKernels, dispatchKey); + return kernel.template callUnboxedOnly(std::forward(args)...); + }); +} + +inline void Dispatcher::callBoxed(const OperatorHandle& op, Stack* stack) const { + // note: this doesn't need the mutex because write operations on the list keep iterators intact. + return op.operatorIterator_->op.readDispatchTable([&] (const DispatchTable& dispatchTable) { + return backendFallbackKernels_.read([&] (const ska::flat_hash_map& backendFallbackKernels) { + c10::optional dispatchKey = dispatchTable.dispatchKeyExtractor().getDispatchKeyBoxed(stack); + const KernelFunction& kernel = dispatch_(dispatchTable, backendFallbackKernels, dispatchKey); + kernel.callBoxed(stack); + }); + }); +} + +inline const KernelFunction& Dispatcher::dispatch_(const DispatchTable& dispatchTable, const ska::flat_hash_map& backendFallbackKernels, c10::optional dispatchKey) { + if (C10_LIKELY(dispatchKey.has_value())) { + const KernelFunction* backendKernel = dispatchTable.lookup(*dispatchKey); + + if (nullptr != backendKernel) { + return *backendKernel; + } + + auto backendFallbackKernel = backendFallbackKernels.find(*dispatchKey); + if (backendFallbackKernel != backendFallbackKernels.end()) { + return backendFallbackKernel->second; + } + } + + const KernelFunction* catchallKernel = dispatchTable.lookupCatchallKernel(); + if (C10_LIKELY(nullptr != catchallKernel)) { + return *catchallKernel; + } + + if (!dispatchKey.has_value() || *dispatchKey == TensorTypeId::UndefinedTensorId) { + TORCH_CHECK(false, + "There were no tensor arguments to this function (e.g., you passed an " + "empty list of Tensors), but no fallback function is registered for schema ", dispatchTable.operatorName(), + ". This usually means that this function requires a non-empty list of Tensors. " + "Available functions are ", dispatchTable.listAllDispatchKeys()) + } + + const std::string dispatchKeyStr = toString(*dispatchKey); + TORCH_CHECK(false, "Could not run '", dispatchTable.operatorName(), "' with arguments", + " from the '", dispatchKeyStr, "' backend. '", + dispatchTable.operatorName(), "' is only available for these backends: ", + dispatchTable.listAllDispatchKeys(), "."); +} + +} // namespace c10 diff --git a/thirdparty/libtorch/include/ATen/core/dispatch/OperatorEntry.h b/thirdparty/libtorch/include/ATen/core/dispatch/OperatorEntry.h new file mode 100644 index 0000000000..47d1f0e3a9 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/dispatch/OperatorEntry.h @@ -0,0 +1,104 @@ +#pragma once + +#include +#include +#include +#include + +namespace c10 { +namespace impl { + class OperatorEntry; +} + +namespace impl { + +// This is a private class used inside the Dispatcher to represent an operator +// and its dispatch table. This is not part of the public API. +class OperatorEntry final { +public: + explicit OperatorEntry(FunctionSchema&& schema, OperatorOptions&& options); + + OperatorEntry(const OperatorEntry&) = delete; + OperatorEntry(OperatorEntry&&) noexcept = delete; + OperatorEntry& operator=(const OperatorEntry&) = delete; + OperatorEntry& operator=(OperatorEntry&&) noexcept = delete; + + const FunctionSchema& schema() const { + return schema_; + } + + template + typename guts::infer_function_traits_t::return_type readDispatchTable(Functor&& functor) const { + return dispatchTable_.read(std::forward(functor)); + } + + void prepareForDeregistration(); + + RegistrationHandleRAII registerKernel(TensorTypeId dispatch_key, KernelFunction kernel); + RegistrationHandleRAII registerCatchallKernel(KernelFunction kernel); + + const OperatorOptions& options() { + return options_; + } + + void updateOptionsAliasAnalysis(AliasAnalysisKind a) { + options_.setAliasAnalysis(a); + } + +private: + void deregisterKernel_(TensorTypeId dispatch_key, std::list::iterator kernel); + void deregisterCatchallKernel_(std::list::iterator kernel); + + FunctionSchema schema_; + + // The dispatchTable stores the current kernel for each dispatch key + LeftRight dispatchTable_; + + // kernels_ stores all registered kernels for the corresponding dispatch key + // and catchAllKernels_ stores the catch-all kernels. + // If an operator library gets loaded that overwrites an already existing kernel, + // both kernels will be in that list but only the newer one will be in + // dispatchTable. If any of the kernels go away (say the library gets + // unloaded), we remove the kernel from this list and update the + // dispatchTable if necessary. + // Kernels in the list are ordered by registration time descendingly, + // newer registrations are before older registrations. + // We do not combine dispatchTable and kernels into one hash map because + // kernels is a larger data structure and accessed quite infrequently + // while dispatchTable is accessed often and should be kept small to fit + // into CPU caches. + // Invariants: + // - dispatchTable[dispatch_key] == kernels_[dispatch_key].front() + // - dispatchTable[dispatch_key] does not exist if and only if + // kernels_[dispatch_key] does not exist + // - If kernels_[dispatch_key] exists, then it has elements. + // It is never an empty list. + // Analogous invariants for catchAllKernels_. + // + // Why do we do that? + // ----- + // We mostly do this to enable Jupyter notebooks where a cell registering + // a kernel could be executed multiple times and the later execution + // should overwrite the earlier one. Note that this still fails when the + // function schema changed between the executions, but it works as long + // as the function schema didn't change. A better solution would be to + // unload the old extension library from the Jupyter cell when the cell is + // re-executed and then only allow one kernel here, i.e. error if a kernel + // is already registered, but that's a lot of effort to implement and + // currently not high-pri. + ska::flat_hash_map> kernels_; + std::list catchAllKernels_; + + // Some metadata about the operator + OperatorOptions options_; + + std::mutex kernelsMutex_; // protects kernels_ + + // This function re-establishes the invariant that dispatchTable + // contains the front element from the kernels list for a given dispatch key. + void updateDispatchTable_(TensorTypeId dispatch_key); + void updateCatchallDispatchTable_(); +}; + +} +} diff --git a/thirdparty/libtorch/include/ATen/core/dispatch/OperatorOptions.h b/thirdparty/libtorch/include/ATen/core/dispatch/OperatorOptions.h new file mode 100644 index 0000000000..0fe5eeafae --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/dispatch/OperatorOptions.h @@ -0,0 +1,61 @@ +#pragma once + +#include + +namespace c10 { +namespace impl { +class OperatorEntry; +} + +enum class AliasAnalysisKind : uint8_t { + INTERNAL_SPECIAL_CASE, + CONSERVATIVE, // The most conservative alias analysis type, assumes + // side-effects. This is the default analysis. + FROM_SCHEMA, + PURE_FUNCTION +}; + +#if !defined(_MSC_VER) +constexpr // Our current MSVC version has a bug that doesn't allow this to be constexpr. +#endif +inline const char* toString(AliasAnalysisKind aliasAnalysisKind) { + return (aliasAnalysisKind == AliasAnalysisKind::CONSERVATIVE) + ? "CONSERVATIVE" + : (aliasAnalysisKind == AliasAnalysisKind::FROM_SCHEMA) + ? "FROM_SCHEMA" + : (aliasAnalysisKind == AliasAnalysisKind::PURE_FUNCTION) + ? "PURE_FUNCTION" + : (aliasAnalysisKind == AliasAnalysisKind::INTERNAL_SPECIAL_CASE) + ? "INTERNAL_SPECIAL_CASE" + : "UNKNOWN"; +} + +struct OperatorOptions final { +public: + bool isDefaultAliasAnalysisKind() const { + return aliasAnalysisKind_ == c10::nullopt; + } + + AliasAnalysisKind aliasAnalysis() const { + return !isDefaultAliasAnalysisKind() + ? *aliasAnalysisKind_ + : AliasAnalysisKind::CONSERVATIVE; + } + + void setAliasAnalysis(AliasAnalysisKind v) { + aliasAnalysisKind_ = v; + } + + friend bool operator==(const OperatorOptions& lhs, const OperatorOptions& rhs) { + return lhs.aliasAnalysisKind_ == rhs.aliasAnalysisKind_; + } + + friend bool operator!=(const OperatorOptions& lhs, const OperatorOptions& rhs) { + return !(lhs == rhs); + } + +private: + c10::optional aliasAnalysisKind_; +}; + +} // namespace c10 diff --git a/thirdparty/libtorch/include/ATen/core/dispatch/RegistrationHandleRAII.h b/thirdparty/libtorch/include/ATen/core/dispatch/RegistrationHandleRAII.h new file mode 100644 index 0000000000..e6ef2128fd --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/dispatch/RegistrationHandleRAII.h @@ -0,0 +1,36 @@ +#pragma once + +#include + +namespace c10 { + +class RegistrationHandleRAII final { +public: + explicit RegistrationHandleRAII(std::function onDestruction) + : onDestruction_(std::move(onDestruction)) {} + + ~RegistrationHandleRAII() { + if (onDestruction_) { + onDestruction_(); + } + } + + RegistrationHandleRAII(const RegistrationHandleRAII&) = delete; + RegistrationHandleRAII& operator=(const RegistrationHandleRAII&) = delete; + + RegistrationHandleRAII(RegistrationHandleRAII&& rhs) noexcept + : onDestruction_(std::move(rhs.onDestruction_)) { + rhs.onDestruction_ = nullptr; + } + + RegistrationHandleRAII& operator=(RegistrationHandleRAII&& rhs) noexcept { + onDestruction_ = std::move(rhs.onDestruction_); + rhs.onDestruction_ = nullptr; + return *this; + } + +private: + std::function onDestruction_; +}; + +} diff --git a/thirdparty/libtorch/include/ATen/core/function_schema.h b/thirdparty/libtorch/include/ATen/core/function_schema.h new file mode 100644 index 0000000000..d8bdbf24b3 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/function_schema.h @@ -0,0 +1,340 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +namespace c10 { + +// schema as used in the compiler for resolving function calls and reporting +// errors. These objects should be constructed from C10 schema once those +// are available. + +struct Argument; +struct FunctionSchema; + +namespace detail { +inline bool defaultValueEquals_( + const c10::optional& lhs, + const c10::optional& rhs) { + if (lhs.has_value()) { + return rhs.has_value() && impl::shallowEquals(*lhs, *rhs); + } else { + return !rhs.has_value(); + } +} +} // namespace detail + +bool operator==(const Argument& lhs, const Argument& rhs); + +struct Argument { + Argument( + std::string name = "", + TypePtr type = nullptr, + c10::optional N = c10::nullopt, + c10::optional default_value = c10::nullopt, + bool kwarg_only = false, + c10::optional alias_info = c10::nullopt, + bool is_inferred_type = false) + : name_(std::move(name)), + type_(type ? type : TensorType::get()), + N_(std::move(N)), + default_value_(std::move(default_value)), + kwarg_only_(kwarg_only), + alias_info_(std::move(alias_info)), + is_inferred_type_(is_inferred_type) { + } + const std::string& name() const { + return name_; + } + TypePtr type() const { + return type_; + } + c10::optional N() const { + return N_; + } + const c10::optional& default_value() const { + return default_value_; + } + bool kwarg_only() const { + return kwarg_only_; + } + const c10::optional& alias_info() const { + return alias_info_; + } + bool is_inferred_type() const { + return is_inferred_type_; + } + std::string formatTypeMismatchMsg(const std::string& actual_type) const { + std::string inferred_type_hint; + if (is_inferred_type()) { + inferred_type_hint = c10::str( + "Inferred '", + name(), + "' to be of type 'Tensor' ", + "because it was not annotated with an explicit type.\n"); + } + return c10::str( + "Expected a value of type '", + type()->python_str(), + "' for argument '", + name(), + "' but instead found type '", + actual_type, + "'.\n", + inferred_type_hint); + } + + Argument cloneWithType(TypePtr new_type) const { + return Argument(name_, new_type, N_, default_value_, kwarg_only_, alias_info_); + } + + // this function check whether this Argument is backward compatible with + // the old one. we consider the following cases are backward compatible: + // 1) two arguments are equal + // 2) this arg's type should be subtype of old + // 3) this arg must provide the same default value if old arg has one, + bool isBackwardCompatibleWith( + const Argument& old, + std::ostream* why_not=nullptr) const; + +private: + std::string name_; + TypePtr type_; + // for list types, an optional statically known length for the list + // e.g. for int[3]: type = ListType::ofInts(), N = 3 + // If present, this will allow scalars to be broadcast to this length to + // become a list. + c10::optional N_; + + c10::optional default_value_; + // is this only specifyable as a keyword argument? + bool kwarg_only_; + c10::optional alias_info_; + bool is_inferred_type_; +}; + +inline bool operator==(const Argument& lhs, const Argument& rhs) { + return lhs.name() == rhs.name() + && *lhs.type() == *rhs.type() + && lhs.N() == rhs.N() + && detail::defaultValueEquals_(lhs.default_value(), rhs.default_value()) + && lhs.kwarg_only() == rhs.kwarg_only() + && lhs.alias_info() == rhs.alias_info(); +} + +bool operator==(const FunctionSchema& lhs, const FunctionSchema& rhs); + +struct FunctionSchema { + FunctionSchema( + std::string name, + std::string overload_name, + std::vector arguments, + std::vector returns, + bool is_vararg = false, + bool is_varret = false) + : name_({std::move(name), std::move(overload_name)}), + arguments_(std::move(arguments)), + returns_(std::move(returns)), + is_vararg_(is_vararg), + is_varret_(is_varret) {} + + FunctionSchema( + Symbol name, + std::string overload_name, + std::vector arguments, + std::vector returns, + bool is_vararg = false, + bool is_varret = false) + : FunctionSchema( + name.toQualString(), + std::move(overload_name), + std::move(arguments), + std::move(returns), + is_vararg, + is_varret) {} + + // check whether this schema is backward compatible with the old one. + // the following conditions are considered as this schema is backward + // compatible with old: + // 1) two schemas are equal + // 2) this schema has the same or more positional args than old, + // and any positional arg in this schema is backward compatible + // with the corresponding one in old schema, which could be an arg + // or a kwarg, if it has, or it must provide a default value + // 3) this schema has the same or more kwargs than old, and all the kwargs + // in old schema can find the corresponding kwarg in this schema which + // is backward compatible with the old kwarg, and the extra kwargs in + // this schema must provide default values. + bool isBackwardCompatibleWith( + const FunctionSchema& old, + std::ostream* why_not=nullptr) const; + +private: + OperatorName name_; + std::vector arguments_; + std::vector returns_; + // if true then this schema takes an arbitrary number of additional arguments + // after the argument specified in arguments + // currently this is used primarily to represent 'primtive' operators whose + // arguments are not checked by schema + bool is_vararg_; + bool is_varret_; + void checkArg(const IValue& value, const Argument& argument, optional pos) const; + +public: + const OperatorName& operator_name() const { + return name_; + } + const std::string& name() const { + return name_.name; + } + const std::string& overload_name() const { + return name_.overload_name; + } + const std::vector& arguments() const { + return arguments_; + } + const std::vector& returns() const { + return returns_; + } + bool is_vararg() const { + return is_vararg_; + } + bool is_varret() const { + return is_varret_; + } + bool is_mutable() const { + return std::any_of( + arguments_.cbegin(), arguments_.cend(), [](const Argument& arg) { + const auto& aliasInfo = arg.alias_info(); + return aliasInfo && aliasInfo.value().isWrite(); + }); + } + + c10::optional argumentIndexWithName(const std::string& name) const { + for(size_t i = 0; i < arguments().size(); ++i) { + if(name == arguments()[i].name()) + return i; + } + return c10::nullopt; + } + FunctionSchema cloneWithArguments(std::vector new_arguments) const { + return FunctionSchema( + name(), + overload_name(), + std::move(new_arguments), + returns(), + is_vararg(), + is_varret()); + } + + std::string formatTypeMismatchMsg( + const Argument& expected, + const std::string& actual_type, + c10::optional position = c10::nullopt, + c10::optional value = c10::nullopt) const; + + FunctionSchema cloneWithRemappedTypes( + const std::function type_map) const; + + // Check that inputs have the correct types and appends any missing default + // values. + void checkAndNormalizeInputs( + std::vector& inputs, + const std::unordered_map& kwargs) const; + + void findErrorInKwargs(const std::vector& kwargs) const; + + bool hasAnyAliasInfo() const { + for (const auto& arg : arguments_) { + if (arg.alias_info().has_value()) { + return true; + } + } + for (const auto& ret : returns_) { + if (ret.alias_info().has_value()) { + return true; + } + } + return false; + } + + // can a function with this schema be substituted for a function of rhs's + // schema and have the program typecheck? + // as_method - if true, treat this schema as a method and ignore + // the first argument, which will be the object in both cases + bool isSubtypeOf(const FunctionSchema& rhs, bool as_method, std::ostream* why_not=nullptr) const; +}; + +inline bool operator==(const FunctionSchema& lhs, const FunctionSchema& rhs) { + return lhs.name() == rhs.name() + && lhs.overload_name() == rhs.overload_name() + && lhs.arguments() == rhs.arguments() + && lhs.returns() == rhs.returns() + && lhs.is_vararg() == rhs.is_vararg() + && lhs.is_varret() == rhs.is_varret(); +} + +inline bool operator!=(const FunctionSchema& lhs, const FunctionSchema& rhs) { + return !(lhs == rhs); +} + +// print out Argument, which is compatible with FunctionSchema parser +// full format: Type(alias)? name=default_value +inline std::ostream& operator<<(std::ostream& out, const Argument& arg) { + bool optional_type = arg.type()->kind() == OptionalType::Kind; + // for adjusting the ? position. + // in schema, we have Tensor?(a!) input, and t(a!)?. + // however, t?(a!) doesn't work with schema parser. + // so we always use Type(alias)? format + std::stringstream oss; + if (auto list = arg.type()->cast()) { + oss << list->getElementType()->str(); + oss << "["; + if (arg.N()) { + oss << *arg.N(); + } + oss << "]"; + } else { + oss << arg.type()->str(); + } + if (optional_type) { + oss.seekp(oss.str().size() - 1); + } + if (arg.alias_info()) { + oss << arg.alias_info().value(); + } + if (optional_type) { + oss << "?"; + } + out << oss.str(); + if (!arg.name().empty()) { + out << " " << arg.name(); + } + if (arg.default_value()) { + out << "="; + if (arg.type()->kind() == c10::TypeKind::StringType) { + printQuotedString(out, arg.default_value().value().toStringRef()); + } else { + out << arg.default_value().value(); + } + } + return out; +} + +inline std::ostream& operator<<(std::ostream& out, const FunctionSchema& schema); + +inline std::string toString(const FunctionSchema& schema) { + std::ostringstream str; + str << schema; + return str.str(); +} + +} // namespace c10 + +#include diff --git a/thirdparty/libtorch/include/ATen/core/function_schema_inl.h b/thirdparty/libtorch/include/ATen/core/function_schema_inl.h new file mode 100644 index 0000000000..18855f6ab9 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/function_schema_inl.h @@ -0,0 +1,334 @@ +#pragma once + +// note: windows build doesn't find symbols in operator files unless +// this is a header file + +namespace c10 { + +inline std::ostream& operator<<(std::ostream& out, const FunctionSchema& schema) { + // eventually this should look almost identical to python arg parser, but + // it is simpler for now to work directly on this schema + + out << schema.name(); + if (schema.overload_name() != "") { + out << "." << schema.overload_name(); + } + out << "("; + + bool seen_kwarg_only = false; + for(size_t i = 0; i < schema.arguments().size(); ++i) { + if (i > 0) out << ", "; + if (schema.arguments()[i].kwarg_only() && !seen_kwarg_only) { + out << "*, "; + seen_kwarg_only = true; + } + out << schema.arguments()[i]; + } + + if(schema.is_vararg()) { + if(schema.arguments().size() > 0) + out << ", "; + out << "..."; + } + + out << ") -> "; + + const auto& returns = schema.returns(); + out << "("; + for(size_t i = 0; i < returns.size(); ++i) { + if (i > 0) { + out << ", "; + } + out << returns.at(i); + } + if (schema.is_varret()) { + if (returns.size() != 0) { + out << ", "; + } + out << "..."; + } + out << ")"; + return out; +} + +inline bool Argument::isBackwardCompatibleWith( + const Argument& old, + std::ostream* why_not) const { + const Argument* lhs = this; + const Argument* rhs = &old; + if (!(lhs->name() == rhs->name() + && lhs->N() == rhs->N() + && lhs->alias_info() == rhs->alias_info())) { + return false; + } + if (lhs->kwarg_only() && !rhs->kwarg_only()) { + return false; + } + if (!rhs->type()->isSubtypeOfExt(lhs->type(), why_not)) { + return false; + } + if (rhs->default_value().has_value() && + !detail::defaultValueEquals_(lhs->default_value(), + rhs->default_value())) { + return false; + } + return true; +} + +inline std::string FunctionSchema::formatTypeMismatchMsg( + const Argument& expected, + const std::string& actual_type, + c10::optional position, + c10::optional value) const { + std::string position_str; + if (position) { + position_str = c10::str("Position: ", *position, "\n"); + } + std::string value_str; + if (value) { + value_str = c10::str("Value: ", *value, "\n"); + } + return c10::str( + name(), + "() ", + expected.formatTypeMismatchMsg(actual_type), + position_str, + value_str, + "Declaration: ", + *this); +} + +inline bool FunctionSchema::isBackwardCompatibleWith( + const FunctionSchema& old, + std::ostream* why_not) const { + if (!(name() == old.name() + && overload_name() == old.overload_name() + // we are conservative on is_vararg and is_varret, + // since they are only used by internal operators + && is_vararg() == old.is_vararg() + && is_varret() == old.is_varret() + && returns().size() == old.returns().size() + && arguments().size() >= old.arguments().size())) { + return false; + } + for (size_t i = 0; i < returns().size(); ++i) { + // functions are covariant in arguments but contravariant in returns + if (!old.returns().at(i).isBackwardCompatibleWith( + returns().at(i), + why_not)) { + return false; + } + } + std::vector args, old_args; + std::map kwargs, old_kwargs; + auto split_func = [](const std::vector& arguments, + std::vector* positionals, + std::map* nameds) { + for (const Argument& arg : arguments) { + if (!arg.kwarg_only()) { + positionals->emplace_back(&arg); + } + nameds->emplace(arg.name(), &arg); + } + }; + // we split args into positional and keyward parts, + split_func(arguments(), &args, &kwargs); + split_func(old.arguments(), &old_args, &old_kwargs); + if (old_args.size() > args.size()) { + return false; + } + // make sure that all the old positional args have their corresponding + // backward compatible positional args in this schema + for (size_t i = 0; i < old_args.size(); ++i) { + if (!args.at(i)->isBackwardCompatibleWith( + *old_args.at(i), + why_not)) { + return false; + } + } + // check the extra positional args in this schema either has corresponding + // backward compatible keyward args since positional args also can be used as + // a keyward arg, or provided default values + for (size_t i = old_args.size(); i < args.size(); ++i) { + if (!args.at(i)->default_value()) { + auto it = old_kwargs.find(args.at(i)->name()); + if (it == old_kwargs.end() || + !args.at(i)->isBackwardCompatibleWith( + *it->second, + why_not)) { + return false; + } + } + } + // make sure that all the keyword args in the old schema have their + // corresponding backward compatible keyward args in this schema + for (auto& kv : old_kwargs) { + auto it = kwargs.find(kv.first); + if (it == kwargs.end() || + !it->second->isBackwardCompatibleWith( + *kv.second, + why_not)) { + return false; + } + kwargs.erase(it); + } + // check all the extra keyword args in this schema provide default values + for (auto& kv : kwargs) { + if (!kv.second->default_value()) { + return false; + } + } + + return true; +} + +inline void FunctionSchema::checkArg( + const IValue& value, + const Argument& argument, + optional pos) const { + if (!value.type()->isSubtypeOf(argument.type())) { + std::string position = pos ? ::c10::str(" in position ", *pos) : ""; + TORCH_CHECK( + false, + formatTypeMismatchMsg( + argument, value.type()->python_str(), pos)); + } +} + +inline void FunctionSchema::findErrorInKwargs(const std::vector& kwargs) const { + // First check if any of the kwargs are unknown, i.e. don't match the name of + // any argument in the schema. + for (const auto& kwarg : kwargs) { + if (!std::count_if( + arguments().begin(), + arguments().end(), + [&kwarg](const Argument& argument) { + return argument.name() == kwarg; + })) { + throw std::runtime_error(c10::str( + "Unknown keyword argument '", + kwarg, + "' for operator '", + name(), + "'. Schema: ", + *this)); + } + } + // If there are unconsumed kwargs but none of them were unknown, the first + // positional argument present in the kwargs is duplicated. + for (const auto& argument : arguments()) { + if (std::find(kwargs.begin(), kwargs.end(), argument.name()) != kwargs.end()) { + AT_ASSERT(!argument.default_value()); + throw std::runtime_error(c10::str( + "Argument '", + argument.name(), + "' specified both as positional and ", + "keyword argument. Schema: ", + *this)); + } + } +} + +inline void FunctionSchema::checkAndNormalizeInputs( + std::vector& inputs, + const std::unordered_map& kwargs) const { + // Do we have more inputs than the schema accepts? + TORCH_CHECK( + inputs.size() <= arguments().size(), + "Expected at most ", + arguments().size(), + " argument(s) for operator '", + name(), + "', but received ", + inputs.size(), + " argument(s). Declaration: ", + *this); + + size_t consumed_kwargs = 0; + for (size_t pos = 0; pos < arguments().size(); ++pos) { + const auto& argument = arguments()[pos]; + if (pos < inputs.size()) { + checkArg(inputs[pos], argument, pos); + continue; + } + auto it = kwargs.find(argument.name()); + if (it != kwargs.end()) { + checkArg(it->second, argument, nullopt); + inputs.push_back(it->second); + consumed_kwargs++; + continue; + } + if (argument.default_value()) { + inputs.push_back(*argument.default_value()); + continue; + } + AT_ERROR( + name(), + "() is missing value for argument '", + argument.name(), + "'. Declaration: ", + *this); + } + if (consumed_kwargs != kwargs.size()) { + std::vector names; + for(const auto& k : kwargs) { + names.emplace_back(k.first); + } + findErrorInKwargs(names); + } +} + +inline FunctionSchema FunctionSchema::cloneWithRemappedTypes( + const std::function type_map) const { + auto update_args = [&](const std::vector& args) { + std::vector new_args; + new_args.reserve(args.size()); + for(const Argument& arg : args) { + new_args.emplace_back(arg.cloneWithType(type_map(arg.type()))); + } + return new_args; + }; + return FunctionSchema( + name(), + overload_name(), + update_args(arguments()), + update_args(returns()), + is_vararg(), + is_varret()); +} + +// covariant subtyping of list of Arguments +inline bool isSubtypeOfList( + ArrayRef child, + ArrayRef parent, + std::ostream* why_not) { + if (child.size() != parent.size()) { + return false; + } + for (size_t i = 0; i < child.size(); ++i) { + const Argument& c = child[i]; + const Argument& p = parent[i]; + if (c.name() != p.name()) { + return false; + } + if (!c.type()->isSubtypeOfExt(p.type(), why_not)) { + return false; + } + } + return true; +} + +inline bool FunctionSchema::isSubtypeOf( + const FunctionSchema& rhs, + bool as_method, + std::ostream* why_not) const { + size_t start = as_method ? 1 : 0; + // functions are covariant in arguments but contravariant in returns + return isSubtypeOfList( + ArrayRef(arguments()).slice(start), + ArrayRef(rhs.arguments()).slice(start), + why_not) && + isSubtypeOfList(rhs.returns(), returns(), why_not); +} + +} // namespace c10 diff --git a/thirdparty/libtorch/include/ATen/core/functional.h b/thirdparty/libtorch/include/ATen/core/functional.h new file mode 100644 index 0000000000..6b4f3447f5 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/functional.h @@ -0,0 +1,54 @@ +#pragma once + +#include +#include + +namespace c10 { + +// The passed in function must take T by value (T), or by +// const reference (const T&); taking T by non-const reference +// will result in an error like: +// +// error: no type named 'type' in 'class std::result_of' +// +// No explicit template parameters are required. + +// Overload for explicit function and ArrayRef +template +inline auto fmap(const T& inputs, const F& fn) -> std::vector { + std::vector r; + r.reserve(inputs.size()); + for(const auto & input : inputs) + r.push_back(fn(input)); + return r; +} + +// C++ forbids taking an address of a constructor, so here's a workaround... +// Overload for constructor (R) application +template +inline std::vector fmap(const T& inputs) { + std::vector r; + r.reserve(inputs.size()); + for(auto & input : inputs) + r.push_back(R(input)); + return r; +} + +template +inline std::vector filter(at::ArrayRef inputs, const F& fn) { + std::vector r; + r.reserve(inputs.size()); + for(auto & input : inputs) { + if (fn(input)) { + r.push_back(input); + } + } + return r; +} + +template +inline std::vector filter(const std::vector& inputs, const F& fn) { + return filter(static_cast>(inputs), fn); +} + +} // namespace c10 diff --git a/thirdparty/libtorch/include/ATen/core/grad_mode.h b/thirdparty/libtorch/include/ATen/core/grad_mode.h new file mode 100644 index 0000000000..acd5fd09e5 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/grad_mode.h @@ -0,0 +1,30 @@ +#pragma once + +#include + +namespace at { + +struct CAFFE2_API GradMode { + static bool is_enabled(); + static void set_enabled(bool enabled); +}; + +// A RAII, thread local (!) guard that enables or disables grad mode upon +// construction, and sets it back to the original value upon destruction. +struct CAFFE2_API AutoGradMode { + AutoGradMode(bool enabled) : prev_mode(GradMode::is_enabled()) { + GradMode::set_enabled(enabled); + } + ~AutoGradMode() { + GradMode::set_enabled(prev_mode); + } + bool prev_mode; +}; + +// A RAII, thread local (!) guard that stops future operations from building +// gradients. +struct CAFFE2_API NoGradGuard : public AutoGradMode { + NoGradGuard() : AutoGradMode(/*enabled=*/false) {} +}; + +} diff --git a/thirdparty/libtorch/include/ATen/core/interned_strings.h b/thirdparty/libtorch/include/ATen/core/interned_strings.h new file mode 100644 index 0000000000..4426136ee2 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/interned_strings.h @@ -0,0 +1,403 @@ +#pragma once +#include +#include +#include +#include +#include + +#include + +#if !defined(C10_MOBILE) || defined(FEATURE_TORCH_MOBILE) +#include +#endif + +namespace c10 { + +#if !defined(C10_MOBILE) || defined(FEATURE_TORCH_MOBILE) +#define FORALL_NS_SYMBOLS(_) \ + _(namespaces, prim) \ + _(namespaces, aten) \ + _(namespaces, onnx) \ + _(namespaces, attr) \ + _(namespaces, scope) \ + _(namespaces, user) \ + _(namespaces, _caffe2) \ + _(namespaces, dimname) \ + _(namespaces, namespaces) \ + _(prim, Assign) \ + _(prim, BroadcastingChunk) \ + _(prim, BroadcastSizes) \ + _(prim, Constant) \ + _(prim, ChunkSizes) \ + _(prim, Drop) \ + _(prim, Eval) \ + _(prim, Expand) /* onnx */ \ + _(prim, FusionGroup) \ + _(prim, DifferentiableGraph) \ + _(prim, If) \ + _(prim, Jump) /* debug */ \ + _(prim, JumpNZ) /* debug */ \ + _(prim, JumpZ) /* debug */ \ + _(prim, Load) \ + _(prim, Loop) \ + _(prim, Param) \ + _(prim, PackPadded) /* onnx */ \ + _(prim, PadPacked) /* onnx */ \ + _(prim, Placeholder) /* debug */ \ + _(prim, Print) \ + _(prim, PythonOp) \ + _(prim, IgnoredPythonOp) \ + _(prim, Reverse) \ + _(prim, Return) \ + _(prim, ReturnStmt) \ + _(prim, BreakStmt) \ + _(prim, ContinueStmt) \ + _(prim, Store) \ + _(prim, AutogradZero) \ + _(prim, AutogradAnyNonZero) \ + _(prim, Starred) \ + _(prim, TupleConstruct) \ + _(prim, TupleUnpack) \ + _(prim, TupleIndex) \ + _(prim, TupleSlice) \ + _(prim, ListConstruct) \ + _(prim, ListUnpack) \ + _(prim, DictConstruct) \ + _(prim, StringIndex) \ + _(prim, NumToTensor) \ + _(prim, Uninitialized) \ + _(prim, ImplicitTensorToNum) \ + _(aten, Bool) \ + _(aten, Int) \ + _(aten, Float) \ + _(aten, str) \ + _(prim, device) \ + _(prim, dtype) \ + _(prim, shape) \ + _(prim, requires_grad) \ + _(prim, AutogradAdd) \ + _(prim, GradOf) \ + _(aten, grad) \ + _(aten, backward) \ + _(prim, Guard) \ + _(prim, BailOut) \ + _(prim, FusedConcat) \ + _(prim, ConstantChunk) \ + _(prim, MMTreeReduce) \ + _(prim, MMBatchSide) \ + _(prim, min) \ + _(prim, max) \ + _(prim, abs) \ + _(aten, divmod) \ + _(prim, zip) \ + _(prim, enumerate) \ + _(prim, range) \ + _(prim, rangelist) \ + _(prim, isinstance) \ + _(prim, unchecked_cast) \ + _(aten, _grad_sum_to_size) \ + _(aten, _size_if_not_equal) \ + _(aten, _ncf_unsqueeze) \ + _(aten, warn) \ + _(aten, sorted) \ + _(aten, floordiv) \ + _(aten, __range_length) \ + _(aten, __derive_index) \ + _(aten, __round_to_zero_floordiv) \ + _(aten, _unwrap_optional) \ + _(prim, fork) \ + _(prim, forkClosure) \ + _(prim, RaiseException) \ + _(prim, Function) \ + _(prim, CreateObject) \ + _(prim, SetAttr) \ + _(prim, GetAttr) \ + _(prim, HasAttr) \ + _(prim, profile) \ + _(prim, AddStatValue) \ + _(prim, TimePoint) \ + _(prim, CallFunction) \ + _(prim, CallMethod) \ + _(prim, LoopContinuation) \ + _(prim, annotate) \ + _(prim, TracedModuleForward) \ + _(prim, TracedFork) \ + _(prim, TracedAttr) \ + _(aten, append) \ + _(aten, item) \ + _(aten, format) \ + _(aten, __not__) \ + _(aten, __is__) \ + _(aten, __isnot__) \ + _(aten, copy) \ + _(aten, copy_) \ + _(aten, t_) \ + _(aten, addbmm_) \ + _(aten, addcdiv_) \ + _(aten, addcmul_) \ + _(aten, addmv_) \ + _(aten, addr_) \ + _(aten, baddbmm_) \ + _(aten, ge_) \ + _(aten, gt_) \ + _(aten, le_) \ + _(aten, lerp_) \ + _(aten, lt_) \ + _(aten, ne_) \ + _(aten, transpose_) \ + _(aten, unsqueeze_) \ + _(aten, __getitem__) \ + _(aten, _set_item) \ + _(aten, manual_seed) \ + _(aten, set_) \ + _(aten, index_put_) \ + _(aten, device) \ + _(aten, hash) \ + _(aten, len) \ + _(aten, list) \ + _(aten, wait) \ + _(aten, save) \ + _(aten, keys) \ + _(aten, ord) \ + _(aten, chr) \ + _(aten, hex) \ + _(aten, oct) \ + _(aten, clear) \ + _(aten, setdefault) \ + _(aten, bin) \ + _(prim, unchecked_unwrap_optional) \ + _(aten, __contains__) \ + _(prim, BailoutTemplate) \ + FORALL_ATEN_BASE_SYMBOLS(_) \ + _(onnx, Add) \ + _(onnx, Concat) \ + _(onnx, Constant) \ + _(onnx, ConstantFill) \ + _(onnx, Div) \ + _(onnx, GRU) \ + _(onnx, Gather) \ + _(onnx, Gemm) \ + _(onnx, LSTM) \ + _(onnx, Mul) \ + _(onnx, Pow) \ + _(onnx, RNN) \ + _(onnx, Shape) \ + _(onnx, Size) \ + _(onnx, Slice) \ + _(onnx, Squeeze) \ + _(onnx, Sub) \ + _(onnx, Transpose) \ + _(onnx, Unsqueeze) \ + _(onnx, Loop) \ + _(onnx, If) \ + _(onnx, Reshape) \ + _(onnx, Expand) \ + _(onnx, Equal) \ + _(onnx, Greater) \ + _(onnx, Less) \ + _(onnx, Not) \ + _(onnx, ATen) \ + _(onnx, Split) \ + _(onnx, ConstantOfShape) \ + _(onnx, Cast) \ + _(onnx, Mod) \ + FORALL_ATTR_BASE_SYMBOLS(_) \ + _(attr, Subgraph) \ + _(attr, ReverseSubgraph) \ + _(attr, f_real_outputs) \ + _(attr, df_input_vjps) \ + _(attr, df_input_captured_inputs) \ + _(attr, df_input_captured_outputs) \ + _(attr, df_output_vjps) \ + _(attr, axes) \ + _(attr, axis) \ + _(attr, broadcast) \ + _(attr, direction) \ + _(attr, ends) \ + _(attr, inplace) \ + _(attr, input_as_shape) \ + _(attr, is_zero) \ + _(attr, perm) \ + _(attr, sizes) \ + _(attr, starts) \ + _(attr, transA) \ + _(attr, transB) \ + _(attr, name) \ + _(attr, a) \ + _(attr, b) \ + _(attr, beg) \ + _(attr, idx) \ + _(attr, split) \ + _(attr, slot) \ + _(attr, kinds) \ + _(attr, types) \ + _(attr, scope) +#else +#define FORALL_NS_SYMBOLS(_) \ + _(namespaces, prim) \ + _(namespaces, aten) \ + _(namespaces, onnx) \ + _(namespaces, attr) \ + _(namespaces, scope) \ + _(namespaces, user) \ + _(namespaces, _caffe2) \ + _(namespaces, dimname) \ + _(namespaces, namespaces) +#endif + +// 'prim' symbols are synthetic operators that occur only in the IR +// and don't have corresponding implementations in ATen. + +// 'onnx' symbols correspond to ONNX operators. Their semantics +// are defined in https://github.com/onnx/onnx/blob/master/docs/Operators.md +// The particular version we are targeting is specified by '_onnx_opset_version' +// in torch.onnx.symbolic_helper +// +// In general, most ONNX operators won't get an entry here, because they +// are handled from the Python end. However, you may occasionally need +// to intern an ONNX symbol here so that you can conveniently write an +// optimization on ONNX operations. + +// 'attr' symbols are attribute keys. They are shared between both ONNX and ATen +// operators (you disambiguate their meaning by looking at the operator itself). +// In general, you only need to define attribute keys that are used by +// onnx or prim; ATen attributes are automatically generated in FORALL_ATTR_BASE_SYMBOLS. + +// Note [Symbol allocation] +// ~~~~~~~~~~~~~~~~~~~~~~~~ +// +// 1. Symbol namespace is split up into namespaces. +// +// 2. The intended access pattern for built-in symbols is onnx::MatMul +// in the c10 namespace (this is a Symbol). +// + +// Built-in constant definition strategy: +// - Enum is the most convenient way to generate a contiguous sequence +// of numbers for an identifier. +// - However, an enum gives you a fresh type. We want onnx::MatMul to +// be type Symbol, not some random enum type! +// - Therefore, after using enums to generate the sequence of integers, +// we then declare constexpr Symbols to get everything the actual Symbol +// type we want. Symbols must be constexpr to be valid to be "case"ed on. + +using unique_t = uint32_t; + +const std::string& domain_prefix(); + +// A Symbol is like an interned string, but with a little extra +// structure; it is namespaced via SymbolNamespace and the resulting +// intern pointers support efficient namespace testing. +struct CAFFE2_API Symbol { + explicit constexpr Symbol() : value(0) {}; + explicit constexpr Symbol(unique_t uniq) + : value(uniq) {} + + // Get a Symbol for a qualified string like "attr::bar" + static Symbol fromQualString(const std::string & s); + + // Get a Symbol from a domain and an unqualified string like "org.pytorch.attr" and "bar" + static Symbol fromDomainAndUnqualString(const std::string & d, const std::string & s); + + // Constructors for our various namespaced strings. This will construct + // the appropriate namespaced string, e.g., "attr::foo" for the + // argument "foo", and then attempt to intern it. DO NOT USE THIS + // with a string literal; attr::foo should be available in that case + // (and if it's not, you should add it to the built-ins list above.) + static Symbol attr(const std::string & s); + static Symbol aten(const std::string & s); + static Symbol onnx(const std::string & s); + static Symbol prim(const std::string & s); + static Symbol user(const std::string & s); + static Symbol caffe2(const std::string & s); + static Symbol dimname(const std::string & s); + // TODO: eliminate me + static Symbol scope(const std::string & s); + + bool is_attr() const; + bool is_aten() const; + bool is_prim() const; + bool is_onnx() const; + bool is_user() const; + bool is_caffe2() const; + bool is_dimname() const; + + // So we can switch on this + constexpr operator unique_t() const { + return value; + } + + Symbol ns() const; + + // Give a string corresponding to the unqualified version of this name, e.g., + // "mm". Use this in a context where the intended namespace of the string is + // obvious; this is a *lossy* conversion. + const char * toUnqualString() const; + + // Give a string corresponding to the qualified version of this name, + // e.g., "aten::mm". This string format is made available to Python bindings + // (so we know how to parse it.) + const char * toQualString() const; + + // This describes a symbol in a case where humans read it. At the moment it's + // the same as toQualString. This has to be a const char* returned because + // a lot of printf style macros use it. + const char * toDisplayString() const; + + // Give a string corresponding to the domain name for the symbol, + // e.g., "org.pytorch.aten". + std::string domainString() const; + +private: + explicit Symbol(Symbol ns, const std::string & s); + unique_t value; +}; + +static inline bool operator==(Symbol lhs, Symbol rhs) { + return static_cast(lhs) == static_cast(rhs); +} + +enum class _keys : unique_t { + #define DEFINE_KEY(ns, s) ns##_##s, + FORALL_NS_SYMBOLS(DEFINE_KEY) + #undef DEFINE_KEY + num_symbols +}; + +#define DEFINE_SYMBOL(s) \ + constexpr Symbol s(static_cast(_keys::s)); + +#undef DEFINE_SYMBOL + +#define DEFINE_SYMBOL(ns, s) \ + namespace ns { constexpr Symbol s(static_cast(_keys::ns##_##s)); } +FORALL_NS_SYMBOLS(DEFINE_SYMBOL) +#undef DEFINE_SYMBOL + +inline Symbol Symbol::attr(const std::string & s) { return Symbol::fromQualString("attr::" + s); } +inline Symbol Symbol::aten(const std::string & s) { return Symbol::fromQualString("aten::" + s); } +inline Symbol Symbol::onnx(const std::string & s) { return Symbol::fromQualString("onnx::" + s); } +inline Symbol Symbol::prim(const std::string & s) { return Symbol::fromQualString("prim::" + s); } +inline Symbol Symbol::scope(const std::string & s) { return Symbol::fromQualString("scope::" + s); } +inline Symbol Symbol::user(const std::string & s) { return Symbol::fromQualString("user::" + s); } +inline Symbol Symbol::caffe2(const std::string & s) { return Symbol::fromQualString("_caffe2::" + s); } +inline Symbol Symbol::dimname(const std::string & s) { return Symbol::fromQualString("dimname::" + s); } +inline bool Symbol::is_attr() const { return ns() == namespaces::attr; } +inline bool Symbol::is_aten() const { return ns() == namespaces::aten; } +inline bool Symbol::is_prim() const { return ns() == namespaces::prim; } +inline bool Symbol::is_onnx() const { return ns() == namespaces::onnx; } +inline bool Symbol::is_user() const { return ns() == namespaces::user; } +inline bool Symbol::is_caffe2() const { return ns() == namespaces::_caffe2; } +inline bool Symbol::is_dimname() const { return ns() == namespaces::dimname; } + +} // namespace c10 + +// make symbol behave like an integer in hash tables +namespace std { +template <> +struct hash { + size_t operator()(c10::Symbol s) const { + return std::hash()(static_cast(s)); + } +}; +} diff --git a/thirdparty/libtorch/include/ATen/core/interned_strings_class.h b/thirdparty/libtorch/include/ATen/core/interned_strings_class.h new file mode 100644 index 0000000000..b13e3f18eb --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/interned_strings_class.h @@ -0,0 +1,36 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace c10 { + +struct CAFFE2_API InternedStrings { + InternedStrings(); + Symbol symbol(const std::string& s); + std::pair string(Symbol sym); + Symbol ns(Symbol sym); + + private: + // prereq - holding mutex_ + Symbol _symbol(const std::string& s); + std::pair customString(Symbol sym); + std::unordered_map string_to_sym_; + + struct SymbolInfo { + Symbol ns; + std::string qual_name; + std::string unqual_name; + }; + std::vector sym_to_info_; + + std::mutex mutex_; +}; + +} // namespace c10 diff --git a/thirdparty/libtorch/include/ATen/core/ivalue.h b/thirdparty/libtorch/include/ATen/core/ivalue.h new file mode 100644 index 0000000000..9faa9a7629 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/ivalue.h @@ -0,0 +1,651 @@ +#pragma once + +#include +#include +#include +#include +#include + +namespace torch { +namespace jit { +class CustomClassHolder : public c10::intrusive_ptr_target {}; +struct Function; +namespace script { +struct CompilationUnit; +struct Module; +} +} // namespace jit +} // namespace torch +namespace c10 { +template class Dict; +template class List; +struct IValue; +struct ClassType; +struct Type; +using TypePtr = std::shared_ptr; +namespace ivalue { +struct Tuple; +struct Future; +struct ConstantString; +struct GenericDict; +struct Object; +} + +// IValue is the generic tagged union used by the interpreter to hold +// all value types. +// It is a 16-byte object with an 8-byte payload and an 8-byte tag. +// The tag is currently 4 bytes to determine the type, and 1 byte +// to mark whether that type is a subtype of c10::intrusive_ptr_target and needs +// retain/release calls. + +#define TORCH_FORALL_TAGS(_) \ + _(None) \ + _(Tensor) \ + _(Double) \ + _(Int) \ + _(Bool) \ + _(Tuple) \ + _(IntList) \ + _(DoubleList) \ + _(BoolList) \ + _(String) \ + _(TensorList) \ + _(Blob) \ + _(GenericList) \ + _(GenericDict) \ + _(Future) \ + _(Device) \ + _(Object) \ + _(Uninitialized) \ + _(Capsule) + +struct CAFFE2_API IValue final { + IValue() : payload{0}, tag(Tag::None), is_intrusive_ptr(false) {} + IValue(const IValue& rhs) + : IValue(rhs.payload, rhs.tag, rhs.is_intrusive_ptr) { + if (is_intrusive_ptr) { + c10::raw::intrusive_ptr::incref(payload.as_intrusive_ptr); + } + } + IValue(IValue&& rhs) noexcept : IValue() { + swap(rhs); + } + ~IValue() { + if (is_intrusive_ptr) { + c10::raw::intrusive_ptr::decref(payload.as_intrusive_ptr); + } + } + IValue & operator=(IValue && rhs) & noexcept { + IValue(std::move(rhs)).swap(*this); // this also sets rhs to None + return *this; + } + IValue & operator=(IValue const & rhs) & { + IValue(rhs).swap(*this); + return *this; + } + + void dump() const; + + bool isAliasOf(const IValue& rhs) const { + if (this->tag != rhs.tag) { + // Trivially don't alias if the type is different + return false; + } + + if (!this->is_intrusive_ptr) { + // Primitive types don't alias anything + return false; + } + + AT_ASSERT(rhs.is_intrusive_ptr); + + // Tensors should be compared based on internal storage + if (this->isTensor()) { + const auto thisTensor = this->toTensor(); + const auto rhsTensor = rhs.toTensor(); + return thisTensor.is_alias_of(rhsTensor); + } + + // Other types can be compared by their ptr value + return this->payload.as_intrusive_ptr == rhs.payload.as_intrusive_ptr; + } + + size_t use_count() const noexcept { + if (!is_intrusive_ptr) { + return 1; + } + + return c10::raw::intrusive_ptr::use_count(payload.as_intrusive_ptr); + } + + void swap(IValue & rhs) noexcept { + std::swap(payload, rhs.payload); + std::swap(is_intrusive_ptr, rhs.is_intrusive_ptr); + std::swap(tag, rhs.tag); + } + + // Accessors for subtypes are arranged together below + // While some of these accessors could be generated through templates, + // we prefer to write them manually for clarity + + // Tensor + IValue(at::Tensor t) + : tag(Tag::Tensor), is_intrusive_ptr(t.defined()) { + // Note: the undefined tensor is not refcounted, so while it + // is tagged as a tensor, is_intrusive_ptr is set to false. + // This is not an optional optimization: our incref call + // *will not* do the right thing when called on an + // undefined tensor. + payload.as_intrusive_ptr = t.unsafeReleaseTensorImpl(); + } + bool isTensor() const { return Tag::Tensor == tag; } + at::Tensor toTensor() &&; + at::Tensor toTensor() const &; + at::TensorImpl* unsafeToTensorImpl() const { + return static_cast(payload.as_intrusive_ptr); + } + + const IValue& toIValue() const { + return *this; + } + IValue& toIValue() { + return *this; + } + + IValue(intrusive_ptr blob) + : tag(Tag::Blob), is_intrusive_ptr(true) { + // TODO (after Tensor merge) If we pass in a Blob holding a Tensor, extract + // and store it as a Tensor instead. + payload.as_intrusive_ptr = blob.release(); + } + bool isBlob() const { + return Tag::Blob == tag; + } + c10::intrusive_ptr toBlob() &&; + c10::intrusive_ptr toBlob() const &; + + // Capsule + IValue(intrusive_ptr blob); + bool isCapsule() const { + return Tag::Capsule == tag; + } + c10::intrusive_ptr toCapsule() &&; + c10::intrusive_ptr toCapsule() const &; + + // Tuple + IValue(c10::intrusive_ptr v); + + template < + typename... Args, + c10::guts::enable_if_t< + !c10::guts::disjunction< + std::is_lvalue_reference..., + c10::guts::negation>...>:: + value, + std::nullptr_t> = nullptr> + IValue(const std::tuple& t); + bool isTuple() const { return Tag::Tuple == tag; } + c10::intrusive_ptr toTuple() &&; + c10::intrusive_ptr toTuple() const &; + + // Double + IValue(double d) + : tag(Tag::Double), is_intrusive_ptr(false) { + payload.as_double = d; + } + bool isDouble() const { return Tag::Double == tag; } + double toDouble() const { + AT_ASSERT(isDouble()); + return payload.as_double; + } + + // Future + IValue(c10::intrusive_ptr v); + bool isFuture() const { return Tag::Future == tag; } + c10::intrusive_ptr toFuture() &&; + c10::intrusive_ptr toFuture() const &; + + // Int + IValue(int64_t i) + : tag(Tag::Int), is_intrusive_ptr(false) { + payload.as_int = i; + } + + // allow you to pass literals (3, 4) without ambiguity + IValue(int32_t i) + : IValue(static_cast(i)) {} + + bool isInt() const { return Tag::Int == tag; } + + int64_t toInt() const { + AT_ASSERT(isInt()); + return payload.as_int; + } + + // Bool + IValue(bool b) + : tag(Tag::Bool), is_intrusive_ptr(false) { + payload.as_bool = b; + } + bool isBool() const { return Tag::Bool == tag; } + bool toBool() const { + AT_ASSERT(isBool()); + return payload.as_bool; + } + + // IntList + IValue(c10::List v); + IValue(c10::ArrayRef v); + /// \cond DOXYGEN_CANNOT_HANDLE_CONSTRUCTORS_WITH_MACROS_SO_EXCLUDE_THIS_LINE_FROM_DOXYGEN + C10_DEPRECATED_MESSAGE("IValues based on std::vector are potentially slow and deprecated. Please use c10::List instead.") + /// \endcond + IValue(std::vector v); + bool isIntList() const { return Tag::IntList == tag; } + c10::List toIntList() &&; + c10::List toIntList() const &; + c10::ArrayRef toIntListRef() const; + + // ConstantString + IValue(c10::intrusive_ptr v); + IValue(std::string v); + IValue(const char* v): IValue(std::string(v)) {} + bool isString() const { return Tag::String == tag; } + c10::intrusive_ptr toString() &&; + c10::intrusive_ptr toString() const &; + const std::string& toStringRef() const; + + // DoubleList + IValue(c10::List v); + /// \cond DOXYGEN_CANNOT_HANDLE_CONSTRUCTORS_WITH_MACROS_SO_EXCLUDE_THIS_LINE_FROM_DOXYGEN + C10_DEPRECATED_MESSAGE("IValues based on std::vector are potentially slow and deprecated. Please use c10::List instead.") + /// \endcond + IValue(std::vector v); + bool isDoubleList() const { return Tag::DoubleList == tag; } + c10::List toDoubleList() &&; + c10::List toDoubleList() const &; + c10::ArrayRef toDoubleListRef() const; + + // BoolList + IValue(c10::List v); + /// \cond DOXYGEN_CANNOT_HANDLE_CONSTRUCTORS_WITH_MACROS_SO_EXCLUDE_THIS_LINE_FROM_DOXYGEN + C10_DEPRECATED_MESSAGE("IValues based on std::vector are potentially slow and deprecated. Please use c10::List instead.") + /// \endcond + IValue(std::vector v); + bool isBoolList() const { return Tag::BoolList == tag; } + c10::List toBoolList() &&; + c10::List toBoolList() const &; + + //TensorList + IValue(c10::List v); + /// \cond DOXYGEN_CANNOT_HANDLE_CONSTRUCTORS_WITH_MACROS_SO_EXCLUDE_THIS_LINE_FROM_DOXYGEN + C10_DEPRECATED_MESSAGE("IValues based on std::vector are potentially slow and deprecated. Please use c10::List instead.") + /// \endcond + IValue(std::vector v); + bool isTensorList() const { return Tag::TensorList == tag; } + c10::List toTensorList() &&; + c10::List toTensorList() const &; + c10::ArrayRef toTensorListRef() const; + + //GenericList + IValue(c10::List v); + bool isGenericList() const { return Tag::GenericList == tag; } + c10::List toGenericList() &&; + c10::List toGenericList() const &; + c10::ArrayRef toGenericListRef() const; + + template + IValue(c10::List v); + template + /// \cond DOXYGEN_CANNOT_HANDLE_CONSTRUCTORS_WITH_MACROS_SO_EXCLUDE_THIS_LINE_FROM_DOXYGEN + C10_DEPRECATED_MESSAGE("IValues based on std::vector are potentially slow and deprecated. Please use c10::List instead.") + /// \endcond + IValue(std::vector v); + + // GenericDict + IValue(c10::Dict v); + bool isGenericDict() const { return Tag::GenericDict == tag; } + c10::Dict toGenericDict() &&; + c10::Dict toGenericDict() const &; + + template + IValue(c10::Dict v); + + template + /// \cond DOXYGEN_CANNOT_HANDLE_CONSTRUCTORS_WITH_MACROS_SO_EXCLUDE_THIS_LINE_FROM_DOXYGEN + C10_DEPRECATED_MESSAGE("IValues based on std::unordered_map are slow and deprecated. Please use c10::Dict instead.") + /// \endcond + IValue(std::unordered_map v); + + template + IValue(c10::optional v); + IValue(c10::nullopt_t); + + // ClassType + IValue(c10::intrusive_ptr v); + bool isObject() const { return tag == Tag::Object; } + c10::intrusive_ptr toObject() &&; + c10::intrusive_ptr toObject() const & ; + const ivalue::Object& toObjectRef() const; + + torch::jit::script::Module toModule() const; + bool isModule() const; + + // None + bool isNone() const { + return Tag::None == tag; + } + std::string toNone() const { + AT_ASSERT(isNone()); + return "None"; + } + + static IValue uninitialized() { + auto i = IValue(); + i.tag = Tag::Uninitialized; + return i; + } + + // Scalar, which gets encoded as either an Int or a Double + IValue(at::Scalar s) + : IValue() { + if(s.isFloatingPoint()) { + *this = s.toDouble(); + } else { + *this = s.toLong(); + } + } + bool isScalar() const { + return isDouble() || isInt(); + } + at::Scalar toScalar() const { + if(isDouble()) + return toDouble(); + else if(isInt()) + return toInt(); + throw std::runtime_error("IValue is not a Scalar"); + } + + // Device + IValue(c10::Device d) + : tag(Tag::Device), is_intrusive_ptr(false) { + payload.as_device.type = d.type(); + payload.as_device.index = d.index(); + } + bool isDevice() const { return Tag::Device == tag; } + c10::Device toDevice() const { + AT_ASSERT(isDevice()); + return c10::Device(payload.as_device.type, payload.as_device.index); + } + + // ScalarType + IValue(ScalarType t) + : IValue(static_cast::type>(t)) {} + at::ScalarType toScalarType() const { + return static_cast(toInt()); + } + + // Layout + IValue(Layout l) + : IValue(static_cast::type>(l)) {} + at::Layout toLayout() const { + return static_cast(toInt()); + } + + // MemoryFormat + IValue(MemoryFormat m) + : IValue(static_cast::type>(m)) {} + at::MemoryFormat toMemoryFormat() const { + return static_cast(toInt()); + } + + // QScheme + IValue(at::QScheme qscheme) + : tag(Tag::Int), is_intrusive_ptr(false) { + payload.as_int = static_cast(qscheme); + } + + at::QScheme toQScheme() const { + return static_cast(toInt()); + } + + + // for debugging + std::string tagKind() const { + switch(tag) { + #define DEFINE_CASE(x) case Tag::x: return #x; + TORCH_FORALL_TAGS(DEFINE_CASE) + #undef DEFINE_CASE + } + return "InvalidTag(" + c10::guts::to_string(static_cast(tag)) + ")"; + } + + // generic v.to() implementations + // that can be used in special functions like pop/push + // that use template meta-programming. + // prefer the directly named methods when you can, + // since they are simpler to understand + + // Note: if you get linker errors saying one of these is missing, + // change it to ... && = delete; and you will see better error messages for why + // However, we cannot commit this because some compiler versions barf on it. + template + T to() &&; + template + T to() const &; + + // ToOptional: convert a IValue to the Optional obj that accepts both T and None + template + optional toOptional(); + + // this is a shallow comparison of two IValues to test the object identity + bool isSameIdentity(const IValue& rhs) const; + + CAFFE2_API friend std::ostream& operator<<( + std::ostream& out, + const IValue& v); + + bool isPtrType() const { + return is_intrusive_ptr; + } + + const void* internalToPointer() const { + TORCH_INTERNAL_ASSERT(isPtrType(), "Can only call internalToPointer() for pointer types"); + return payload.as_intrusive_ptr; + } + + TypePtr type() const; + + private: + // NOTE: IValue tags are intentionally private. In the future we may encode + // this value different (e.g. using NaN boxing), and this would make it more + // costly to determine the tag for all types vs just determining if something + // is a particular type. Instead we want clients to use the `isX` methods when + // possible. If for perf. reasons you really, absolutely, must have a jump + // table, then we can revisit this. + enum class Tag : uint32_t { +#define DEFINE_TAG(x) x, + TORCH_FORALL_TAGS(DEFINE_TAG) +#undef DEFINE_TAG + }; + + template> + c10::intrusive_ptr moveToIntrusivePtr(); + template> + c10::intrusive_ptr toIntrusivePtr() const; + + void clearToNone() { + payload.as_int = 0; + tag = Tag::None; + is_intrusive_ptr = false; + } + + union Payload { + int64_t as_int; + double as_double; + bool as_bool; + c10::intrusive_ptr_target* as_intrusive_ptr; + struct { + DeviceType type; + DeviceIndex index; + } as_device; + }; + + IValue(Payload p, Tag t, bool i) + : payload(p), tag(t), is_intrusive_ptr(i) {} + + Payload payload; + Tag tag; + bool is_intrusive_ptr; + friend struct WeakIValue; +}; + +struct CAFFE2_API WeakIValue final { + WeakIValue() + : payload{0} + , tag(IValue::Tag::None) + , is_intrusive_ptr(false) {} + + WeakIValue(const WeakIValue& rhs) + : payload(rhs.payload), + tag(rhs.tag), + is_intrusive_ptr(rhs.is_intrusive_ptr) { + if (is_intrusive_ptr) { + c10::raw::weak_intrusive_ptr::incref(payload.as_intrusive_ptr); + } + } + WeakIValue(const IValue& rhs) + : payload(rhs.payload), + tag(rhs.tag), + is_intrusive_ptr(rhs.is_intrusive_ptr) { + if (is_intrusive_ptr) { + c10::raw::weak_intrusive_ptr::incref(payload.as_intrusive_ptr); + } + } + WeakIValue(WeakIValue&& rhs) noexcept : WeakIValue() { + swap(rhs); + } + ~WeakIValue() { + if (is_intrusive_ptr) { + c10::raw::weak_intrusive_ptr::decref(payload.as_intrusive_ptr); + } + } + WeakIValue & operator=(WeakIValue && rhs) & noexcept { + WeakIValue(std::move(rhs)).swap(*this); // this also sets rhs to None + return *this; + } + WeakIValue & operator=(WeakIValue const & rhs) & { + WeakIValue(rhs).swap(*this); + return *this; + } + void swap(WeakIValue & rhs) noexcept { + std::swap(payload, rhs.payload); + std::swap(is_intrusive_ptr, rhs.is_intrusive_ptr); + std::swap(tag, rhs.tag); + } + + bool isSameIdentity(const WeakIValue& rhs) const { + return payload.as_int == rhs.payload.as_int && tag == rhs.tag && + is_intrusive_ptr == rhs.is_intrusive_ptr; + } + + IValue lock() const { + if (!is_intrusive_ptr) { + return IValue(payload, tag, false); + } + auto temp = c10::weak_intrusive_ptr::reclaim( + payload.as_intrusive_ptr); + IValue::Payload pl; + pl.as_intrusive_ptr = temp.lock().release(); + temp.release(); + if (!pl.as_intrusive_ptr) { + return IValue(); + } else { + return IValue(pl, tag, true); + } + } + + size_t use_count() const noexcept { + if (!is_intrusive_ptr) { + return 1; + } + auto temp = c10::weak_intrusive_ptr::reclaim( + payload.as_intrusive_ptr); + size_t result = temp.use_count(); + temp.release(); + return result; + } + + size_t weak_use_count() const noexcept { + if (!is_intrusive_ptr) { + return 1; + } + auto temp = c10::weak_intrusive_ptr::reclaim( + payload.as_intrusive_ptr); + size_t result = temp.weak_use_count(); + temp.release(); + return result; + } + size_t hash() const { + return payload.as_int; + } + +private: + IValue::Payload payload; + IValue::Tag tag; + bool is_intrusive_ptr; +}; + +// An owning pointer to a Class. Just a pair of shared_ptrs to the class type +// and its owning CU, so that the class type is guaranteed to stay alive as long +// as we hold this object. +struct StrongTypePtr { + StrongTypePtr( + std::shared_ptr cu, + std::shared_ptr type) + : cu_(std::move(cu)), type_(type) { + TORCH_INTERNAL_ASSERT(cu_); + TORCH_INTERNAL_ASSERT(type_); + } + std::shared_ptr cu_; + std::shared_ptr type_; +}; + +TORCH_API std::unordered_map& getCustomClassTypeMap(); + +#ifndef C10_MOBILE + +template +c10::StrongTypePtr getCustomClassType() { + auto tmap = c10::getCustomClassTypeMap(); + auto res = tmap.find(typeid(T).name()); + if (res == tmap.end()) { + throw c10::Error("Can't find class id in custom class type map", ""); + } + return res->second; +} + +template +inline bool isCustomClassRegistered() { + auto tmap = c10::getCustomClassTypeMap(); + return tmap.find(typeid(T).name()) != tmap.end(); +} + +#else // C10_MOBILE + +template +c10::StrongTypePtr getCustomClassType() { + throw c10::Error("Custom class is not supported on mobile.", ""); +} + +template +inline bool isCustomClassRegistered() { + return false; +} + +#endif // C10_MOBILE + +TORCH_API std::unordered_map>& +getClassConverter(); +} + +#include diff --git a/thirdparty/libtorch/include/ATen/core/ivalue_inl.h b/thirdparty/libtorch/include/ATen/core/ivalue_inl.h new file mode 100644 index 0000000000..bece57fe77 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/ivalue_inl.h @@ -0,0 +1,848 @@ +#pragma once + +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +namespace torch { +namespace jit { +struct Function; +namespace script { +struct CompilationUnit; +} +} // namespace jit +} // namespace torch +namespace c10 { +struct IValue; +struct ClassType; +struct TupleType; + +// For custom class __init__ registration, we need to pass in a function +// that looks like this: [](IValue x, args...) + +// However, kernel_functor.h automatically sets the input types of the function +// by introspecting the types of the functor (which is IValue in this case). +// However, we need the type it binds to be Foo. + +// Instead, we pass in a lambda [](ivalue_holder x, args...) from +// which getTypePtr can recover the original class pointer. + +template +struct tagged_capsule { + IValue ivalue; +}; + +template +c10::intrusive_ptr IValue::moveToIntrusivePtr() { + auto t = c10::intrusive_ptr::reclaim(static_cast(payload.as_intrusive_ptr)); + clearToNone(); + return t; +} +template +c10::intrusive_ptr IValue::toIntrusivePtr() const { + auto r = c10::intrusive_ptr::reclaim(static_cast(payload.as_intrusive_ptr)); + auto p = r; + r.release(); + return p; +} + +template +intrusive_ptr static_intrusive_pointer_cast(intrusive_ptr r) { + return intrusive_ptr::reclaim(static_cast(r.release())); +} + +inline c10::intrusive_ptr IValue::toFuture() && { + AT_ASSERT(isFuture(), "Expected Future but got ", tagKind()); + return moveToIntrusivePtr(); +} +inline c10::intrusive_ptr IValue::toFuture() const & { + AT_ASSERT(isFuture(), "Expected Future but got ", tagKind()); + return toIntrusivePtr(); +} +inline c10::intrusive_ptr IValue::toString() && { + AT_ASSERT(isString(), "Expected String but got ", tagKind()); + return moveToIntrusivePtr(); +} +inline c10::intrusive_ptr IValue::toString() const & { + AT_ASSERT(isString(), "Expected String but got ", tagKind()); + return toIntrusivePtr(); +} +inline c10::intrusive_ptr IValue::toObject() && { + AT_ASSERT(isObject(), "Expected Object but got ", tagKind()); + return toIntrusivePtr(); +} +inline c10::intrusive_ptr IValue::toObject() const & { + AT_ASSERT(isObject(), "Expected Object but got ", tagKind()); + return toIntrusivePtr(); +} +inline at::Tensor IValue::toTensor() && { + AT_ASSERT(isTensor(), "Expected Tensor but got ", tagKind()); + return at::Tensor(moveToIntrusivePtr()); +} +inline at::Tensor IValue::toTensor() const & { + AT_ASSERT(isTensor(), "Expected Tensor but got ", tagKind()); + return at::Tensor(toIntrusivePtr()); +} +inline c10::intrusive_ptr IValue::toBlob() && { + AT_ASSERT(isBlob(), "Expected Blob but got ", tagKind()); + return moveToIntrusivePtr(); +} +inline c10::intrusive_ptr IValue::toBlob() const & { + AT_ASSERT(isBlob(), "Expected Blob but got ", tagKind()); + return toIntrusivePtr();; +} +inline c10::intrusive_ptr IValue::toCapsule() && { + TORCH_INTERNAL_ASSERT(isCapsule()); + return moveToIntrusivePtr(); +} +inline c10::intrusive_ptr IValue::toCapsule() const & { + TORCH_INTERNAL_ASSERT(isCapsule()); + return toIntrusivePtr(); +} + +namespace ivalue { + +template +using Shared = c10::intrusive_ptr; + +// string +struct CAFFE2_API ConstantString final : c10::intrusive_ptr_target { + private: + const std::string str_; + public: + ConstantString(std::string str) + : str_(std::move(str)) {} + static c10::intrusive_ptr create(std::string str_); + const std::string & string() const { + return str_; + } + operator const std::string & () const { + return string(); + } + CAFFE2_API friend std::ostream& operator<<( + std::ostream& out, + const ConstantString& v); +}; + +struct Future; + +struct CAFFE2_API Tuple : c10::intrusive_ptr_target { + private: + std::vector elements_; + mutable std::shared_ptr type_; // lazily computed for unnamed tuples + + public: + // named tuples have additional type information, so we + // directly create them tagged + static c10::intrusive_ptr createNamed( + std::vector elements_, + std::shared_ptr type_) { + return c10::make_intrusive(std::move(elements_), type_); + } + static c10::intrusive_ptr create(std::vector elements_) { + return c10::make_intrusive(std::move(elements_)); + } + + template + static c10::intrusive_ptr create(Args... elements_) { + return c10::make_intrusive(std::vector{IValue(elements_)...}); + } + + const std::vector& elements() const & { + return elements_; + } + operator const std::vector&() const { + return elements(); + } + + std::vector& elements() & { + return elements_; + } + operator std::vector&() { + return elements(); + } + + std::vector&& elements() && { + return std::move(elements_); + } + std::shared_ptr type() const; + + private: + Tuple(std::vector elements, std::shared_ptr type = nullptr) + : elements_(std::move(elements)), type_(std::move(type)) {} + + friend class c10::intrusive_ptr; +}; + +struct Object; +} + +// Future +struct C10_EXPORT ivalue::Future final : c10::intrusive_ptr_target { + private: + c10::intrusive_ptr intrusive_from_this() { + c10::raw::intrusive_ptr::incref(this); // we are creating a new pointer + // from a raw `this` pointer + // so we need to bump the refcount + // to account for this ownership + return c10::intrusive_ptr::reclaim(this); + } + + public: + Future(TypePtr type) : type_(type) {} + struct CAFFE2_API FutureError final : public std::exception { + FutureError(std::string&& error_msg_) + : error_msg(std::move(error_msg_)) {} + + FutureError() = default; + + const char* what() const noexcept override { + return error_msg.c_str(); + } + + std::string error_msg; + }; + + /** + * Wait on the future until it completes. + */ + void wait() { + std::unique_lock lock(mutex_); + while (!completed_) { + finished_cv_.wait(lock); + } + } + + /** + * Explicitly mark the future as completed with the output value. + */ + void markCompleted(IValue value) { + std::unique_lock lock(mutex_); + AT_ASSERT(!completed()); + completed_ = true; + value_ = std::move(value); + + fireCallbacks(); + finished_cv_.notify_all(); + } + + void markCompleted() { + markCompleted(IValue {}); + } + + void markCompleted(FutureError&& error_) { + std::unique_lock lock(mutex_); + AT_ASSERT(!completed()); + completed_ = true; + has_error = true; + error = std::move(error_); + + fireCallbacks(); + finished_cv_.notify_all(); + } + + // Get the result of the current future. + IValue value() { + std::unique_lock lock(mutex_); + AT_ASSERT(completed()); + if (has_error) { + throw error; + } + return value_; + } + + /** + * Add a callback to the future. + * The callbacks will be executed once the future completes. + * If the future has already completed, + * this function will execute the callback immediately. + */ + void addCallback(std::function callback) { + std::unique_lock lock(mutex_); + if (completed()) { + lock.unlock(); + callback(); + return; + } + callbacks.push_back(callback); + } + + // Check if the current future has completed + bool completed() const{ + return completed_; + } + + CAFFE2_API friend std::ostream& operator<<( + std::ostream& out, + const Future& v); + + TypePtr type() const { + return type_; + } + + private: + void fireCallbacks() { + AT_ASSERT(completed()); + // There is no need to protect callbacks with the lock. + // Once completed_ is set to true, no one can add new callback to the list. + for (auto& callback : callbacks) { + callback(); + } + callbacks.clear(); + } + + std::mutex mutex_; + std::atomic_bool completed_ = {false}; // is this future complete + std::condition_variable finished_cv_; + + IValue value_; // when finished the value + TypePtr type_; + std::vector> callbacks; + bool has_error = false; + FutureError error; +}; + +// User-defined object. +struct C10_EXPORT ivalue::Object final : c10::intrusive_ptr_target { + public: + Object(StrongTypePtr type, size_t numSlots) : type_(std::move(type)) { + slots_.resize(numSlots); + } + + static c10::intrusive_ptr create( + StrongTypePtr type, + size_t numSlots) { + return c10::make_intrusive(std::move(type), numSlots); + } + + /** + * Slot API. + * + * Attributes are stored as a simple vector so that lookups are fast at + * runtime. A "slot" is just an index into that vector, which can be computed + * statically if you have access to the class type. Use this API if you are + * writing compiler stuff. + */ + void setSlot(size_t slot, IValue v) { + if (slot >= slots_.size()) { + // for module types, it is possible that the members of the class have + // expanded after the object was created. In this case, we expand + // the slots to the right size + resizeObject(slot); + } + slots_[slot] = v; + } + + const IValue& getSlot(size_t slot) const { + return slots_.at(slot); + } + + void unsafeRemoveSlot(size_t slot) { + TORCH_CHECK(slot < slots_.size()); + slots_.erase(slots_.begin() + slot); + } + + /** + * Attribute API. + * + * Wrappers around the slot stuff so that users can access attributes + * directly. Use this API if you are a user. + * + * Note: Unlike in Python, TorchScript must make a distinction between + * attributes (which are IValues) and methods (which are Methods). If you + * want a method, use `obj.type()->getMethod()` + */ + IValue getAttr(const std::string& name) const; + void setAttr(const std::string& name, IValue v); + // Remove attribute by name, caller is responsible for + // the safety of this operation + // We didn't remove the attribute in the type because the type + // might be shared by multiple objects. + // Therefore after removing attribute, the object is in an inconsistent + // state where it has more attribute types in its Type than + // the attribute slots it has, user needs to make sure the object + // has consistent by removing the attribute in type as well + void unsafeRemoveAttr(const std::string& name); + + std::string name() const; + + const std::vector& slots() const { + return slots_; + } + std::shared_ptr type() const { + return type_.type_; + } + + std::shared_ptr compilation_unit() { + return type_.cu_; + } + + private: + void resizeObject(size_t slot); + StrongTypePtr type_; + std::vector slots_; +}; + +std::vector> iterationOrder(const c10::Dict& dict); + +#undef TORCH_FORALL_TAGS + +namespace detail { + +struct _guarded_unsigned_long_unique_dummy final { + _guarded_unsigned_long_unique_dummy(int64_t){}; +}; +using _guarded_unsigned_long = c10::guts::conditional_t< + std::is_same::value || + std::is_same::value, + _guarded_unsigned_long_unique_dummy, + unsigned long>; + +} // namespace detail + +inline const ivalue::Object& IValue::toObjectRef() const { + AT_ASSERT(isObject(), "Expected Object but got ", tagKind()); + return *static_cast(payload.as_intrusive_ptr); +} + +// note: when adding a DEFINE_TO case here you should also add a +// toX method to IValue. These named methods are much more discoverable +// than the to templated function. + +#define DEFINE_TO(type, method_name) \ +template<> \ +inline type IValue::to() && { \ + return std::move(*this).method_name(); \ +} \ +template<> \ +inline type IValue::to() const & { \ + return this->method_name(); \ +} +DEFINE_TO(at::Tensor, toTensor) +DEFINE_TO(float, toDouble) +DEFINE_TO(double, toDouble) +DEFINE_TO(unsigned char, toInt) +DEFINE_TO(signed char, toInt) +DEFINE_TO(unsigned short, toInt) +DEFINE_TO(short, toInt) +DEFINE_TO(int, toInt) +DEFINE_TO(uint32_t, toInt) +DEFINE_TO(uint64_t, toInt) +DEFINE_TO(detail::_guarded_unsigned_long, toInt) +DEFINE_TO(int64_t, toInt) +DEFINE_TO(bool, toBool) +DEFINE_TO(c10::intrusive_ptr, toBlob); +DEFINE_TO(c10::intrusive_ptr, toString) +DEFINE_TO(c10::intrusive_ptr, toObject) +DEFINE_TO(at::Scalar, toScalar) +DEFINE_TO(c10::List, toIntList) +DEFINE_TO(c10::List, toDoubleList) +DEFINE_TO(c10::List, toBoolList) +DEFINE_TO(c10::List, toTensorList) +DEFINE_TO(c10::impl::GenericList, toGenericList) +DEFINE_TO(c10::impl::GenericDict, toGenericDict) +DEFINE_TO(c10::intrusive_ptr, toTuple) +DEFINE_TO(std::string, toStringRef) +DEFINE_TO(c10::intrusive_ptr, toFuture) +DEFINE_TO(IValue, toIValue) +DEFINE_TO(c10::Device, toDevice) +DEFINE_TO(at::ScalarType, toScalarType) +DEFINE_TO(at::Layout, toLayout) +DEFINE_TO(at::MemoryFormat, toMemoryFormat) +DEFINE_TO(at::QScheme, toQScheme) + +template +struct _fake_type {}; + +// generic_to converts an IValue from a generic list or generic dict +// to a concrete list/dict type likelike List, Dict<...> or optional. +// Note that in the case of lists, this only works for IValue-based lists, +// i.e. not for int64_t, double, ... +// generic_to is an implementation detail of IValue::to and not +// supposed to be called directly. +// The _fake_type parameter allows us to overload +// based on the return type. +template +C10_DEPRECATED_MESSAGE("IValues based on std::vector are potentially slow and deprecated. Please use c10::List instead.") +std::vector generic_to( + IValue ivalue, + _fake_type>) { + // We need to do a deep copy of the vector because there might be other + // references to this same IValue that also use the list. We can't just + // move the elements out. + auto list = std::move(ivalue).to>(); + std::vector result; + result.reserve(list.size()); + for (Elem v : list) { + result.push_back(std::move(v)); + } + return result; +} + +template +T generic_to( + IValue ivalue, + _fake_type) { + using ElemType = typename std::remove_pointer::type::element_type; + auto obj = ivalue.toObject(); + auto capsule = obj->getSlot(0); + return c10::static_intrusive_pointer_cast(capsule.toCapsule()); +} + +template +tagged_capsule generic_to( + IValue ivalue, + _fake_type>) { + return tagged_capsule{ivalue}; +} + +template +c10::List generic_to( + IValue ivalue, + _fake_type>) { + return impl::toTypedList(std::move(ivalue).toGenericList()); +} + +template +c10::Dict generic_to( + IValue ivalue, + _fake_type>) { + return impl::toTypedDict(std::move(ivalue).toGenericDict()); +} + +template +C10_DEPRECATED_MESSAGE("IValues based on std::unordered_map are slow and deprecated. Please use c10::Dict instead.") +std::unordered_map generic_to( + IValue ivalue, + _fake_type>) { + std::unordered_map specialized_dict; + + for (const auto& item : std::move(ivalue).toGenericDict()) { + specialized_dict[item.key().to()] = item.value().to(); + } + + return specialized_dict; +} + +template +c10::optional generic_to( + IValue ivalue, + _fake_type>) { + if (ivalue.isNone()) { + return c10::nullopt; + } + return std::move(ivalue).to(); +} + +namespace detail { +template +Tuple generic_to_tuple_impl( + const std::vector& t, + c10::guts::index_sequence) { + return std::make_tuple( + t[INDEX].to::type>()...); +} +} + +template < + typename... Args, + typename Indices = c10::guts::make_index_sequence, + c10::guts::enable_if_t< + !c10::guts::disjunction< + std::is_lvalue_reference..., + c10::guts::negation>...>::value, + std::nullptr_t> = nullptr> +std::tuple generic_to(IValue ivalue, _fake_type>) { + auto vals = ivalue.toTuple()->elements(); + TORCH_CHECK(vals.size() == sizeof...(Args)); + return detail::generic_to_tuple_impl>(vals, Indices{}); +} + +template +inline T IValue::to() && { + return generic_to(std::move(*this), _fake_type{}); +} + +template +inline T IValue::to() const& { + return generic_to(*this, _fake_type{}); +} + +inline c10::List IValue::toIntList() && { + AT_ASSERT(isIntList(), "Expected IntList but got ", tagKind()); + return c10::List(moveToIntrusivePtr>()); +} +inline c10::List IValue::toIntList() const & { + AT_ASSERT(isIntList(), "Expected IntList but got ", tagKind()); + return c10::List(toIntrusivePtr>()); +} +inline c10::ArrayRef IValue::toIntListRef() const { + AT_ASSERT(isIntList(), "Expected IntList but got ", tagKind()); + return static_cast*>(payload.as_intrusive_ptr)->list; +} +inline c10::List IValue::toDoubleList() && { + AT_ASSERT(isDoubleList(), "Expected DoubleList but got ", tagKind()); + return c10::List(moveToIntrusivePtr>()); +} +inline c10::List IValue::toDoubleList() const & { + AT_ASSERT(isDoubleList(), "Expected DoubleList but got ", tagKind()); + return c10::List(toIntrusivePtr>()); +} +inline c10::ArrayRef IValue::toDoubleListRef() const { + AT_ASSERT(isDoubleList(), "Expected DoubleList but got ", tagKind()); + return static_cast*>(payload.as_intrusive_ptr)->list; +} +inline c10::List IValue::toBoolList() && { + AT_ASSERT(isBoolList(), "Expected BoolList but got ", tagKind()); + return c10::List(moveToIntrusivePtr>()); +} +inline c10::List IValue::toBoolList() const & { + AT_ASSERT(isBoolList(), "Expected BoolList but got ", tagKind()); + return c10::List(toIntrusivePtr>()); +} +inline c10::List IValue::toTensorList() && { + AT_ASSERT(isTensorList(), "Expected TensorList but got ", tagKind()); + return c10::List(moveToIntrusivePtr>()); +} +inline c10::List IValue::toTensorList() const & { + AT_ASSERT(isTensorList(), "Expected TensorList but got ", tagKind()); + return c10::List(toIntrusivePtr>()); +} +inline c10::ArrayRef IValue::toTensorListRef() const { + AT_ASSERT(isTensorList(), "Expected TensorList but got ", tagKind()); + return static_cast*>(payload.as_intrusive_ptr)->list; +} +inline c10::List IValue::toGenericList() && { + AT_ASSERT(isGenericList(), "Expected GenericList but got ", tagKind()); + return c10::List(moveToIntrusivePtr>()); +} +inline c10::List IValue::toGenericList() const & { + AT_ASSERT(isGenericList(), "Expected GenericList but got ", tagKind()); + return c10::List(toIntrusivePtr>()); +} +inline c10::ArrayRef IValue::toGenericListRef() const { + AT_ASSERT(isGenericList(), "Expected GenericList but got ", tagKind()); + return static_cast*>(payload.as_intrusive_ptr)->list; +} +inline c10::Dict IValue::toGenericDict() && { + AT_ASSERT(isGenericDict(), "Expected GenericDict but got ", tagKind()); + return c10::Dict(moveToIntrusivePtr()); +} +inline c10::Dict IValue::toGenericDict() const & { + AT_ASSERT(isGenericDict(), "Expected GenericDict but got ", tagKind()); + return c10::Dict(toIntrusivePtr()); +} +inline c10::intrusive_ptr IValue::toTuple() && { + AT_ASSERT(isTuple(), "Expected Tuple but got ", tagKind()); + return moveToIntrusivePtr(); +} +inline c10::intrusive_ptr IValue::toTuple() const & { + AT_ASSERT(isTuple(), "Expected Tuple but got ", tagKind()); + return toIntrusivePtr(); +} + +inline IValue::IValue(c10::intrusive_ptr v) +: tag(Tag::Tuple), is_intrusive_ptr(true) { + payload.as_intrusive_ptr = v.release(); +} +template < + typename... Args, + c10::guts::enable_if_t< + !c10::guts::disjunction< + std::is_lvalue_reference..., + c10::guts::negation>...>::value, + std::nullptr_t>> +inline IValue::IValue(const std::tuple& t) + : IValue( + std::move(c10::guts::apply(c10::ivalue::Tuple::create, t))) { +} +inline IValue::IValue(c10::List v) +: tag(Tag::IntList), is_intrusive_ptr(true) { + payload.as_intrusive_ptr = v.impl_.release(); +} +inline IValue::IValue(std::vector v) +: IValue(c10::impl::toList(v)) {} +inline IValue::IValue(c10::ArrayRef v) +: IValue(c10::List(v)) {} + +inline IValue::IValue(c10::intrusive_ptr v) +: tag(Tag::String), is_intrusive_ptr(true) { + payload.as_intrusive_ptr = v.release(); +} +inline IValue::IValue(std::string v) +: IValue(ivalue::ConstantString::create(std::move(v))) {} + +inline IValue::IValue(c10::List v) +: tag(Tag::DoubleList), is_intrusive_ptr(true) { + payload.as_intrusive_ptr = v.impl_.release(); +} +inline IValue::IValue(std::vector v) +: IValue(c10::impl::toList(std::move(v))) {} + +inline IValue::IValue(c10::List v) +: tag(Tag::BoolList), is_intrusive_ptr(true) { + payload.as_intrusive_ptr = v.impl_.release(); +} +inline IValue::IValue(std::vector v) +: IValue(c10::impl::toList(std::move(v))) {} + +inline IValue::IValue(c10::List v) +: tag(Tag::TensorList), is_intrusive_ptr(true) { + payload.as_intrusive_ptr = v.impl_.release(); +} +inline IValue::IValue(std::vector v) +: IValue(c10::impl::toList(std::move(v))) {} + +inline IValue::IValue(c10::impl::GenericList v) +: tag(Tag::GenericList), is_intrusive_ptr(true) { + payload.as_intrusive_ptr = v.impl_.release(); +} + +template inline IValue::IValue(c10::List v) +: IValue(impl::toGenericList(std::move(v))) { + static_assert(std::is_same::StorageT>::value, "Can only use this constructor for generic list types"); +} +template inline IValue::IValue(std::vector v) +: IValue(c10::List()) { + static_assert(std::is_same::StorageT>::value, "Can only use this constructor for generic list types"); + auto list = to>(); + list.reserve(v.size()); + for (auto& e : v) { + list.push_back(std::move(e)); + } +} + +inline IValue::IValue(c10::impl::GenericDict v) +: tag(Tag::GenericDict), is_intrusive_ptr(true) { + payload.as_intrusive_ptr = v.impl_.release(); +} +template +inline IValue::IValue(c10::Dict v) +: IValue(impl::toGenericDict(std::move(v))) {} + +template inline IValue::IValue(std::unordered_map v) +: IValue(Dict()) { + auto dict = to>(); + dict.reserve(v.size()); + for (auto& e : v) { + dict.insert(std::move(e.first), std::move(e.second)); + } +} + +template inline IValue::IValue(c10::optional v): IValue() { + if (v.has_value()) { + *this = IValue(std::move(*v)); + } +} + +inline IValue::IValue(c10::nullopt_t): IValue() {} + +inline IValue::IValue(c10::intrusive_ptr v) +: tag(Tag::Object), is_intrusive_ptr(true) { + payload.as_intrusive_ptr = v.release(); +} +inline IValue::IValue(c10::intrusive_ptr v) +: tag(Tag::Capsule), is_intrusive_ptr(true) { + payload.as_intrusive_ptr = v.release(); +} +inline IValue::IValue(c10::intrusive_ptr v) +: tag(Tag::Future), is_intrusive_ptr(true) { + payload.as_intrusive_ptr = v.release(); +} + +inline const std::string& IValue::toStringRef() const { + return toString()->string(); +} + +template +inline optional IValue::toOptional() { + if (this->isNone()) { + return nullopt; + } + return this->to(); +} + +inline bool IValue::isSameIdentity(const IValue& rhs) const { + // We choose to not use memcmp for payload check due to potential random padding characters on union type + + // Semantics: + // 1. None is None, False is False, and True is True are all true + // 2. If it is a tensor type, we need to take undefined tensor into account + // 3. Undefined_tensor is None and vice versa should be true + // 4. If it is a reference type (i.e. is_intrusive_ptr), then is is True when the pointed-to object is the same. + // 5. False for all other comparisons. + if (this->isNone() && rhs.isNone()) { + return true; + } else if (this->isBool() && rhs.isBool()) { + // for bool type, do equality check + return this->toBool() == rhs.toBool(); + } else if (this->isTensor() && rhs.isTensor()) { + // for tensor type, just check the as_intrusive_ptr since is_intrusive_ptr is false for undefined tensor + return this->payload.as_intrusive_ptr == rhs.payload.as_intrusive_ptr; + } else if (this->isTensor() && rhs.isNone()) { + // special case: undefined tensor and None are the same identity + return !this->is_intrusive_ptr; + } else if (this->isNone() && rhs.isTensor()) { + // special case: undefined tensor and None are the same identity + return !rhs.is_intrusive_ptr; + } else { + // for objects holding in IValue, do shallow compare on pointer address to testify the identity + return this->is_intrusive_ptr && rhs.is_intrusive_ptr + && this->payload.as_intrusive_ptr == rhs.payload.as_intrusive_ptr; + } +} + +namespace ivalue { +namespace detail { +// This code allows us to template on a function based on whether IValue has a +// constructor for it. Specifically, has_constructor{} inherits from std::true_type if +// IValue(T) compiles, and inherits from std::false_type if IValue(T) doesn't. +// We use it for calling the IValue constructor for `from` if it exists, and otherwise +// attempt to use our custom class code. +template struct type_sink { typedef void type; }; +template using type_sink_t = typename type_sink::type; +template struct has_constructor : std::false_type {}; \ +template struct has_constructor< + T, + type_sink_t< decltype( IValue(std::declval())) > +>: std::true_type {}; + +template +IValue from_(T x, std::true_type) { + return IValue(x); +} +template +IValue from_(c10::intrusive_ptr x, std::false_type) { + using inputType = c10::intrusive_ptr; + if (!isCustomClassRegistered()) { + throw c10::Error("Trying to return a class that we don't support and isn't a registered custom class.", ""); + } + auto res = getCustomClassType(); + auto retObject = ivalue::Object::create(res->second, 1); + auto objPtr = c10::static_intrusive_pointer_cast(x); + + retObject->setSlot(0, IValue(objPtr)); + auto resIVal = IValue(std::move(retObject)); + return resIVal; +} +template +IValue from_(T x, std::false_type) { + static_assert(guts::false_t::value, "You are calling from with a type that it doesn't support, and isn't a potential custom class (ie: is an intrusive_ptr)"); + return IValue(); +} +} + +template +IValue from(T x) { + return detail::from_(x, detail::has_constructor{}); +} + +} +} // namespace c10 diff --git a/thirdparty/libtorch/include/ATen/core/jit_type.h b/thirdparty/libtorch/include/ATen/core/jit_type.h new file mode 100644 index 0000000000..7b877ba5ca --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/jit_type.h @@ -0,0 +1,1644 @@ +#pragma once + +#include +#include +#include +#include +#include +#include + +#include + +#include +#include +#include +#include + +struct ClassType; +namespace torch { +namespace jit { +struct Function; +namespace script { +struct CompilationUnit; +} +} // namespace jit +} // namespace torch + +namespace c10 { + +struct FunctionSchema; +using OptNameList = c10::optional>; + +#define C10_FORALL_TYPES(_) \ + _(AnyType) \ + _(TensorType) \ + _(TupleType) \ + _(ListType) \ + _(DictType) \ + _(NumberType) \ + _(FloatType) \ + _(FutureType) \ + _(IntType) \ + _(NoneType) \ + _(StringType) \ + _(GeneratorType) \ + _(BoolType) \ + _(OptionalType) \ + _(VarType) \ + _(DeviceObjType) \ + _(FunctionType) \ + _(ClassType) \ + _(CapsuleType) \ + _(InterfaceType) + +enum class TypeKind { +#define DEFINE_TYPE(T) T, + C10_FORALL_TYPES(DEFINE_TYPE) +#undef DEFINE_TYPE +}; + +CAFFE2_API const char* typeKindToString(TypeKind kind); + +struct Type; +using TypePtr = std::shared_ptr; + +struct CAFFE2_API Type : std::enable_shared_from_this { + private: + TypeKind kind_; + + protected: + Type(TypeKind kind) : kind_(kind) {} + + public: + virtual bool operator==(const Type& rhs) const = 0; + + // subtyping relation. By default, we return true for the case + // when the type is exactly equal or if this <: T where rhs = Optional[T] + + // if this returns false and the why_not stream is non-null, it contains + // additional details that describe why this is not a subtype of 'rhs'. + // This additional information should only contain details that are not obvious + // from the python_str() that describes the type. For instance it is clear that `int <: str` is false + // but not clear why `Foo <: InterfaceBar` might be false. + virtual bool isSubtypeOfExt(const TypePtr rhs, std::ostream* why_not) const; + virtual bool is_module() const; + bool isSubtypeOf(const TypePtr rhs) const { + return isSubtypeOfExt(rhs, nullptr); + } + + // How this type will appear in FunctionSchema declarations + virtual std::string str() const = 0; + + // How this type will appear as if it were a type annotation in Python + // which is sometimes different than how it appears in declarations (e.g. + // int[] vs List[int]) + virtual std::string python_str() const { + return str(); + } + + TypeKind kind() const { + return kind_; + } + + virtual bool requires_grad() const { + for (const auto& ct : containedTypes()) { + if (ct->requires_grad()) { + return true; + } + } + return false; + } + + // Dynamically cast this object to the subclass indicated by the + // template variable, returning nullptr if the cast is invalid. + template + std::shared_ptr cast() { + if (T::Kind == kind()) { + return std::static_pointer_cast(shared_from_this()); + } + return nullptr; + } + template + std::shared_ptr cast() const { + if (T::Kind == kind()) { + return std::static_pointer_cast(shared_from_this()); + } + return nullptr; + } + template + std::shared_ptr expect() { + auto r = cast(); + AT_ASSERT(r); + return r; + } + template + std::shared_ptr expect() const { + auto r = cast(); + AT_ASSERT(r); + return r; + } + virtual ~Type() = default; + virtual bool hasFreeVariables() const { + return false; + } + // list of types this type contains, e.g. for a List then element type of a + // list for a tuple, the types of the tuple elements + virtual at::ArrayRef containedTypes() const { + return {}; + } + // create a new version of this type, replacing its contained types with + // contained_types + TypePtr withContained(std::vector contained_types) { + auto current_contained = containedTypes(); + AT_ASSERT(current_contained.size() == contained_types.size()); + if (current_contained.equals(contained_types)) { + return shared_from_this(); + } + return createWithContained(std::move(contained_types)); + } + // per-type constructor, you only need to override this if the + // containedTypes() is not empty + virtual TypePtr createWithContained( + std::vector contained_types) const { + AT_ERROR( + "type with contained types did not overload createWithContained: ", + str()); + } +}; + +struct AnyType; +using AnyTypePtr = std::shared_ptr; +// Any is the top of the type hierarchy, all other types are subtypes +// T <: Any, forall T +struct CAFFE2_API AnyType : public Type { + static AnyTypePtr create() { + return AnyTypePtr( + new AnyType()); // NOLINT(modernize-make-shared) + } + bool operator==(const Type& rhs) const override { + return rhs.kind() == kind(); + } + std::string str() const override { + return "Any"; + } + static const TypeKind Kind = TypeKind::AnyType; + // global singleton + static AnyTypePtr get(); + + private: + AnyType() : Type(TypeKind::AnyType) {} +}; + +inline std::string toString(TypePtr typePtr) { + return typePtr->str(); +} + +inline bool operator!=(const Type& lhs, const Type& rhs) { + return !(lhs == rhs); +} + +// common base for all types that have a single sub element +// e.g. Future[T], Option[T], List[T] +template +struct SingleElementType : public Type { + static const TypeKind Kind = K; + + TypePtr getElementType() const { + return elem; + } + + bool hasFreeVariables() const override { + return getElementType()->hasFreeVariables(); + } + + at::ArrayRef containedTypes() const override { + return elem; + } + + bool operator==(const Type& rhs) const override { + if (auto rhs_ = rhs.cast()) { + return *getElementType() == *rhs_->getElementType(); + } + return false; + } + + protected: + SingleElementType(TypePtr elem) : Type(Kind), elem(std::move(elem)) {} + + private: + TypePtr elem; +}; + +struct OptionalType; +using OptionalTypePtr = std::shared_ptr; +// This type represents an optional type, for each element type. +// Optional[T] can accept both T and None(nullopt in C++) +// Subtype hierarchy for Optional: +// 1. Optional[T] <: Optional[R] iff T <: R +// 2. T <: Optional[R] if T <: R +// 3. None <: Optional[T] for all T +struct CAFFE2_API OptionalType + : public SingleElementType { + static OptionalTypePtr create(TypePtr element) { + // Optional is a union of [None, T], so Optional[[Optional[T]]] -> + // Optional[T] + if (auto opt_ptr = element->cast()) { + return opt_ptr; + } + return OptionalTypePtr( + new OptionalType(std::move(element))); // NOLINT(modernize-make-shared) + } + + std::string str() const override { + std::stringstream ss; + ss << getElementType()->str() << "?"; + return ss.str(); + } + std::string python_str() const override { + std::stringstream ss; + ss << "Optional[" << getElementType()->python_str() << "]"; + return ss.str(); + } + + TypePtr createWithContained( + std::vector contained_types) const override { + AT_ASSERT(contained_types.size() == 1); + return create(contained_types[0]); + } + + bool isSubtypeOfExt(const TypePtr rhs, std::ostream* why_not) const override { + if (Type::isSubtypeOfExt(rhs, why_not)) { + return true; + } + if (auto rhs_ = rhs->cast()) { + return getElementType()->isSubtypeOfExt(rhs_->getElementType(), why_not); + } + return false; + } + // common cast Optional[Tensor] for undefined tensor type + static OptionalTypePtr ofTensor(); + + private: + OptionalType(TypePtr elem) : SingleElementType(elem) {} +}; + +template +inline c10::optional merge_primitive( + const c10::optional& a, + const c10::optional& b) { + if (a.has_value() && b.has_value() && a.value() == b.value()) { + return a; + } + return c10::optional{}; +} + +// `VaryingShape` tracks if individual dimensions or a rank vary across +// profiled runs. A *varying* or *dynamic* dimension is expressed as +// an empty c10::optional in `sizes_`. If a rank is dynamic, the entire +// `sizes_` becomes the empty optional. +struct CAFFE2_API VaryingShape { + using ListOfOptionalInts = std::vector>; + VaryingShape(const std::vector& vec) + : VaryingShape(ListOfOptionalInts(vec.begin(), vec.end())) {} + + VaryingShape(c10::ArrayRef vec) + : VaryingShape(ListOfOptionalInts(vec.begin(), vec.end())){} + + VaryingShape(c10::optional size = c10::nullopt) : dims_(c10::nullopt) { + if (size) { + dims_ = ListOfOptionalInts(*size); + } + } + + VaryingShape(ListOfOptionalInts dims) + : dims_(std::move(dims)) {} + + VaryingShape(size_t size) : VaryingShape(c10::optional(size)) {} + + bool operator==(const VaryingShape& other) const { + return dims_ == other.dims_; + } + + const c10::optional& operator[](int i) const { + if (!dims_) { + throw std::runtime_error("Rank isn't fixed"); + } + return (*dims_).at(i); + } + + c10::optional size() const { + if (!dims_) { + return c10::nullopt; + } + const auto& dims = dims_.value(); + return dims.size(); + } + + const c10::optional& sizes() const { + return dims_; + } + + VaryingShape merge(const VaryingShape& other) const; + + c10::optional> concrete_sizes() const { + if (!dims_) { + return c10::nullopt; + } + std::vector sizes; + for (auto d : *dims_) { + if (!d) { + return c10::nullopt; + } + sizes.push_back(d.value()); + } + return sizes; + } + + bool isComplete() const { + if (!dims_) { + return false; + } + for (auto d : *dims_) { + if(!d) { + return false; + } + } + return true; + } + + private: + c10::optional dims_; +}; + +using VaryingStrides = VaryingShape; + +struct TensorType; +using TensorTypePtr = std::shared_ptr; +// This type represents a single Tensor with a specific size +struct CAFFE2_API TensorType : public Type { + static TensorTypePtr create(const at::Tensor& t) { + return TensorTypePtr(new TensorType(t)); + } + + static TensorTypePtr create(c10::optional scalar_type, + c10::optional device, + const VaryingShape &sizes, + const VaryingStrides &strides, + c10::optional requires_grad, + c10::optional undefined = false) { + return TensorTypePtr(new TensorType(scalar_type, device, sizes, strides, + requires_grad, undefined)); + } + + static TensorTypePtr create( + c10::optional scalar_type, + c10::optional device, + c10::optional dim, + c10::optional requires_grad) { + return TensorType::create( + scalar_type, + device, + VaryingShape(dim), + VaryingShape(dim), + requires_grad); + } + + // overloaded create variadic template argument as it could not distinguish + // initializer list + static TensorTypePtr createContiguous( + at::ScalarType scalar_type, + at::Device device, + at::IntArrayRef sizes) { + return create( + scalar_type, + device, + VaryingShape(sizes), + VaryingShape(contiguousStridesOf(sizes)), + c10::nullopt); + } + static TensorTypePtr create( + at::ScalarType scalar_type, + at::Device device, + at::IntArrayRef sizes, + at::IntArrayRef strides) { + return create( + scalar_type, + device, + VaryingShape(sizes), + c10::VaryingShape(strides), + c10::nullopt); + } + static TypePtr fromNumberType(TypePtr typ); + static TypePtr fromBoolType(); + + c10::optional dim() const { + return sizes().size(); + } + + const VaryingShape& sizes() const { + return sizes_; + } + const VaryingStrides& strides() const { + return strides_; + } + c10::optional device() const { + return device_; + } + c10::optional scalarType() const { + return scalar_type_; + } + c10::optional requiresGrad() const { + return requires_grad_; + } + bool requires_grad() const override { + return requires_grad_ ? *requires_grad_ : true; + } + + + bool operator==(const Type& rhs) const override { + if (rhs.kind() != kind()) { + return false; + } + + auto rt = rhs.expect(); + return scalar_type_ == rt->scalarType() && sizes() == rt->sizes() && + strides() == rt->strides() && device() == rt->device() && + requiresGrad() == rt->requiresGrad() && + undefined() == rt->undefined(); + } + bool isSubtypeOfExt(const TypePtr rhs, std::ostream* why_not) const override; + + std::string str() const override; + + c10::optional numel() const { + size_t prod = 1; + const auto& shape = sizes(); + + for (size_t i = 0; i < shape.size(); i++) { + if (!shape[i]) { + return c10::optional{}; + } + prod *= shape[i].value(); + } + return prod; + } + + TensorTypePtr withRequiresGrad(c10::optional s) { + auto copy = clone(); + copy->requires_grad_ = s; + return copy; + } + + TensorTypePtr withScalarType(c10::optional st) { + auto copy = clone(); + copy->scalar_type_ = st; + return copy; + } + + + TensorTypePtr withDim(c10::optional d) { + auto copy = clone(); + copy->sizes_ = VaryingShape(d); + copy->strides_ = VaryingShape(d); + return copy; + } + + TensorTypePtr withSizesStrides( + at::IntArrayRef sizes, + at::IntArrayRef strides) const { + auto cloned = clone(); + cloned->sizes_ = VaryingShape(sizes); + cloned->strides_ = VaryingStrides(strides); + return cloned; + } + + TensorTypePtr withSizes(at::IntArrayRef sizes) const { + return withSizesStrides( + sizes, contiguousStridesOf(sizes)); + } + + TensorTypePtr dimensionedOnly() const { + auto copy = clone(); + copy->sizes_ = VaryingShape(sizes().size()); + copy->strides_ = VaryingShape(sizes().size()); + return copy; + } + + TensorTypePtr contiguous() const { + auto cloned = clone(); + if (auto concrete_sizes = sizes().concrete_sizes()) { + cloned->strides_ = VaryingShape(contiguousStridesOf(*concrete_sizes)); + } else { + cloned->strides_ = VaryingShape(sizes().size()); + } + return cloned; + } + + TensorTypePtr merge(TensorTypePtr other) const; + + // is all information about the type specified except for autograd? + // This replaces the notion of a 'CompleteTensorType' that used to exist + // in the type-hierarchy. Excluding require_grad and undefined allows + // this to match the old behavior. + bool isComplete() const { + return scalar_type_ && device_ && sizes_.isComplete() && strides_.isComplete(); + } + + // this property is used by GuardElimination + // please see `checkInputs` for more details + bool isSummarized() const { + return !(isComplete() && requiresGrad().has_value() && + undefined().has_value()); + } + + TensorTypePtr withUndefined() { + auto r = clone(); + r->undefined_ = true; + return r; + } + + TensorTypePtr withPossiblyUndefined() { + auto r = clone(); + r->undefined_ = c10::nullopt; + return r; + } + + c10::optional undefined() const { return undefined_; } + + static TensorTypePtr get(); + + static const TypeKind Kind = TypeKind::TensorType; + + private: + TensorType(const at::Tensor& tensor) + : Type(TypeKind::TensorType), + scalar_type_(tensor.scalar_type()), + device_(tensor.device()), + sizes_(tensor.sizes().size()), + strides_(tensor.sizes().size()), + requires_grad_(tensor.requires_grad()), + undefined_(!tensor.defined()) { + if (!tensor.is_mkldnn() && !tensor.is_sparse()) { + sizes_ = tensor.sizes().vec(); + strides_ = tensor.strides().vec(); + } + } + TensorType( + c10::optional scalar_type, + c10::optional device, + const VaryingShape& sizes, + const VaryingStrides& strides, + c10::optional requires_grad, + c10::optional undefined = false) + : Type(TypeKind::TensorType), + scalar_type_(scalar_type), + device_(device), + sizes_(sizes), + strides_(strides), + requires_grad_(requires_grad), + undefined_(undefined) {} + + TensorTypePtr clone() const { + return TensorTypePtr(new TensorType( + scalar_type_, device_, sizes_, strides_, requires_grad_, undefined_)); + } + + static std::vector contiguousStridesOf(at::IntArrayRef sizes) { + std::vector strides(sizes.size()); + if (sizes.empty()) // zero-dim case + return strides; + strides.back() = 1; + for (size_t i = strides.size() - 1; i > 0; i--) { + strides[i - 1] = strides[i] * sizes[i]; + } + return strides; + } + + c10::optional scalar_type_; + c10::optional device_; + VaryingShape sizes_; + VaryingStrides strides_; + c10::optional requires_grad_; + // we exploit the fact certain tensors must be zero in the autograd to + // optimize gradient computation. Such zero tensors are currently implemented + // with `UndefinedTensorImpl.` They can be handled only by special operators + // (e.g. `AutogradAdd`) and their `Tensor::defined()` property returns false. + // Normally, `undefined_` is set to false, unless a type was created + // with `withUndefined` + // This will also mean that `undefined` tensors will fail + // `subtypeOf(TensorType::get())` check + // undefined_ may become `c10::nullopt` if the tensor was observed to be both + // defined and undefined. However, no tensor type starts out with + // `undefined_` set to `c10::nullopt` + c10::optional undefined_; +}; + +struct ListType; +using ListTypePtr = std::shared_ptr; +struct CAFFE2_API ListType + : public SingleElementType { + // It's not exactly a singleton, but there should be exactly one instance of + // List[T] for every T + friend struct Type; + template + static ListTypePtr create(T&&... all) { + return ListTypePtr( + new ListType(std::forward(all)...)); // NOLINT(modernize-make-shared) + } + + std::string str() const override { + std::stringstream ss; + ss << getElementType()->str() << "[]"; + return ss.str(); + } + std::string python_str() const override { + std::stringstream ss; + ss << "List[" << getElementType()->python_str() << "]"; + return ss.str(); + } + TypePtr createWithContained( + std::vector contained_types) const override { + return create(contained_types.at(0)); + } + // common cast List[Tensor] + static ListTypePtr ofTensors(); + static ListTypePtr ofInts(); + static ListTypePtr ofFloats(); + static ListTypePtr ofBools(); + + private: + ListType(TypePtr elem) : SingleElementType(elem) {} +}; + +struct DictType; +using DictTypePtr = std::shared_ptr; +struct CAFFE2_API DictType : public Type { + friend struct Type; + static const TypeKind Kind = TypeKind::DictType; + + static DictTypePtr create(TypePtr key, TypePtr value) { + switch (key->kind()) { + case TypeKind::AnyType: + case TypeKind::IntType: + case TypeKind::FloatType: + case TypeKind::StringType: + case TypeKind::TensorType: + return DictTypePtr(new DictType(key, value)); + default: + AT_ERROR( + "Cannot create dict for key type '", + key->str(), + "', only int, float, Tensor and string keys are supported"); + } + } + + // aligned with the format in FunctionSchema + std::string str() const override { + std::stringstream ss; + ss << "Dict(" << getKeyType()->str() << ", " << getValueType()->str() + << ")"; + return ss.str(); + } + + std::string python_str() const override { + std::stringstream ss; + ss << "Dict[" << getKeyType()->python_str() << ", " + << getValueType()->python_str() << "]"; + return ss.str(); + } + + TypePtr createWithContained( + std::vector contained_types) const override { + if (contained_types.size() != 2) { + throw std::runtime_error("Expected 2 contained types"); + } + return create(contained_types.at(0), contained_types.at(1)); + } + + TypePtr getKeyType() const { + return types.at(0); + } + + TypePtr getValueType() const { + return types.at(1); + } + + bool hasFreeVariables() const override { + return has_free_variables; + } + + at::ArrayRef containedTypes() const override { + return types; + } + + bool operator==(const Type& rhs) const override { + if (auto dict_rhs = rhs.cast()) { + return *getKeyType() == *(dict_rhs->getKeyType()) && + *getValueType() == *(dict_rhs->getValueType()); + } + return false; + } + + private: + DictType(TypePtr key, TypePtr value) + : Type(TypeKind::DictType), + types({key, value}), + has_free_variables( + key->hasFreeVariables() || value->hasFreeVariables()) {} + std::vector types; + bool has_free_variables; +}; + +struct FutureType; +using FutureTypePtr = std::shared_ptr; + +struct CAFFE2_API FutureType + : public SingleElementType { + friend struct Type; + template + static FutureTypePtr create(TypePtr elem) { + return FutureTypePtr( + new FutureType(std::move(elem))); // NOLINT(modernize-make-shared) + } + + std::string str() const override { + std::stringstream ss; + ss << "Future(" << getElementType()->str() << ")"; + return ss.str(); + } + std::string python_str() const override { + std::stringstream ss; + ss << "Future[" << getElementType()->python_str() << "]"; + return ss.str(); + } + TypePtr createWithContained( + std::vector contained_types) const override { + return create(contained_types.at(0)); + } + + private: + FutureType(TypePtr elem) : SingleElementType(elem) {} +}; + +using ::torch::jit::Function; +struct NamedType; +using NamedTypePtr = std::shared_ptr; + +struct CAFFE2_API NamedType : public Type { + NamedType(TypeKind tk, c10::optional name) + : Type(tk), name_(std::move(name)) {} + + // Fully qualified name of type + // Looks like: "foo.bar.Baz". + const c10::optional& name() const { + return name_; + } +private: + c10::optional name_; +}; + +// Any should never appear in a named type like a class, namedtuple or +// interface. If it does, then dynamic type information will be lost in the +// Pickler, leading to hard-to-track-down bugs that will only occur +// after saving or loading a model. This is because we rely on the +// static types in named types to reconstruct type tags of loaded +// values. Lifting this restriction requires solving the serialization +// problem first. +CAFFE2_API void checkNoAny( + const Type& base, + const char* what, + const std::string& attrname, + const TypePtr& attrtype); + +struct TupleType; +using TupleTypePtr = std::shared_ptr; +using NameList = std::vector; +// This type represents a Tuple +struct CAFFE2_API TupleType : public NamedType { + static TupleTypePtr createNamed(const c10::optional& name, + const std::vector& field_names, + const std::vector& types); + static TupleTypePtr create( + std::vector types) { + return TupleTypePtr(new TupleType( + std::move(types), + c10::nullopt, + nullptr)); // NOLINT(modernize-make-shared) + } + + at::ArrayRef elements() const { + return elements_; + } + + bool operator==(const Type& rhs) const override; + bool isSubtypeOfExt(const TypePtr rhs_, std::ostream* why_not) const override; + + std::string str() const override; + std::string python_str() const override; + bool hasFreeVariables() const override { + return has_free_variables_; + } + at::ArrayRef containedTypes() const override { + return elements_; + } + TypePtr createWithContained( + std::vector contained_types) const override { + return std::shared_ptr( + new TupleType(std::move(contained_types), name(), schema())); + } + const std::shared_ptr& schema() const { + return schema_; + } + + static const TypeKind Kind = TypeKind::TupleType; + + private: + TupleType( + std::vector elements_, + c10::optional name, + std::shared_ptr schema); + + bool compare( + const Type& rhs, + std::function fn) const { + if (rhs.kind() != kind()) { + return false; + } + + const auto& l_elements = elements(); + const auto& r_elements = rhs.cast()->elements(); + if (l_elements.size() != r_elements.size()) + return false; + for (size_t i = 0; i < l_elements.size(); ++i) { + if (!fn(l_elements[i], r_elements[i])) + return false; + } + return true; + } + + std::vector elements_; + bool has_free_variables_; + std::shared_ptr schema_; +}; + +struct NumberType; +using NumberTypePtr = std::shared_ptr; +// This type represents a Python number +// Subtype hierarchy for Number Types (NumberType as the base type): +// IntType <: NumberType +// FloatType <: NumberType +struct CAFFE2_API NumberType : public Type { + static NumberTypePtr create() { + return NumberTypePtr(new NumberType()); // NOLINT(modernize-make-shared) + } + bool operator==(const Type& rhs) const override { + return rhs.kind() == kind(); + } + std::string str() const override { + return "Scalar"; // match what PythonArgParser says for clarity + } + std::string python_str() const override { + return "number"; // technically not a valid python type, but + // we need to use it when parsing back in annotations + // for implicit conversions + } + static const TypeKind Kind = TypeKind::NumberType; + // global singleton + static NumberTypePtr get(); + + protected: + NumberType(TypeKind kind = TypeKind::NumberType) : Type(kind) {} +}; + +struct FloatType; +using FloatTypePtr = std::shared_ptr; +// This type represents a Python float number +struct CAFFE2_API FloatType : public NumberType { + static FloatTypePtr create() { + return FloatTypePtr(new FloatType()); // NOLINT(modernize-make-shared) + } + bool operator==(const Type& rhs) const override { + return rhs.kind() == kind(); + } + std::string str() const override { + return "float"; + } + std::string python_str() const override { + return "float"; + } + bool isSubtypeOfExt(const TypePtr rhs, std::ostream* why_not) const override { + return rhs->kind() == TypeKind::NumberType || NumberType::isSubtypeOfExt(rhs, why_not); + } + static const TypeKind Kind = TypeKind::FloatType; + // global singleton + static FloatTypePtr get(); + + private: + FloatType() : NumberType(TypeKind::FloatType) {} +}; + +struct IntType; +using IntTypePtr = std::shared_ptr; +// This type represents a Python int number +struct CAFFE2_API IntType : public NumberType { + static IntTypePtr create() { + return IntTypePtr(new IntType()); // NOLINT(modernize-make-shared) + } + bool operator==(const Type& rhs) const override { + return rhs.kind() == kind(); + } + std::string str() const override { + return "int"; + } + std::string python_str() const override { + return "int"; + } + bool isSubtypeOfExt(const TypePtr rhs, std::ostream* why_not) const override { + return rhs->kind() == TypeKind::NumberType || NumberType::isSubtypeOfExt(rhs, why_not); + } + static const TypeKind Kind = TypeKind::IntType; + // global singleton + static IntTypePtr get(); + + private: + IntType() : NumberType(TypeKind::IntType) {} +}; + +struct BoolType; +using BoolTypePtr = std::shared_ptr; +// This node represents a Python bool value +struct CAFFE2_API BoolType : public Type { + static BoolTypePtr create() { + return BoolTypePtr(new BoolType()); + } + bool operator==(const Type& rhs) const override { + return rhs.kind() == kind(); + } + std::string str() const override { + return "bool"; + } + static const TypeKind Kind = TypeKind::BoolType; + // global singleton + static BoolTypePtr get(); + + private: + BoolType() : Type(TypeKind::BoolType) {} +}; + +struct StringType; +using StringTypePtr = std::shared_ptr; +// This type represents a Python string +struct CAFFE2_API StringType : public Type { + static StringTypePtr create() { + return StringTypePtr(new StringType()); // NOLINT(modernize-make-shared) + } + bool operator==(const Type& rhs) const override { + return rhs.kind() == kind(); + } + std::string str() const override { + // we only use "str" (not "string") in both FunctionSchema and script + return python_str(); + } + std::string python_str() const override { + return "str"; + } + static const TypeKind Kind = TypeKind::StringType; + // global singleton + static StringTypePtr get(); + + private: + StringType() : Type(TypeKind::StringType) {} +}; + +struct FunctionType; +using FunctionTypePtr = std::shared_ptr; +using ::torch::jit::Function; +struct CAFFE2_API FunctionType : public NamedType { + static FunctionTypePtr create(Function* function) { + return FunctionTypePtr( + new FunctionType(function)); // NOLINT(modernize-make-shared) + } + bool operator==(const Type& rhs) const override { + if (auto func_type = rhs.cast()) { + return func_type->function_ == function_; + } + + return false; + } + std::string str() const override { + return "Function"; + } + std::string python_str() const override { + throw "Function"; + } + Function* function() const { + return function_; + } + static const TypeKind Kind = TypeKind::FunctionType; + + private: + FunctionType(Function* function); + Function* function_; +}; + +struct NoneType; +using NoneTypePtr = std::shared_ptr; +// This type represents a Python None +struct CAFFE2_API NoneType : public Type { + static NoneTypePtr create() { + return NoneTypePtr(new NoneType()); // NOLINT(modernize-make-shared) + } + bool operator==(const Type& rhs) const override { + return rhs.kind() == kind(); + } + std::string str() const override { + return "None"; + } + bool isSubtypeOfExt(const TypePtr rhs, std::ostream *why_not) const override { + if (rhs->kind() == OptionalType::Kind) { + return true; + } + return Type::isSubtypeOfExt(rhs, why_not); + } + static const TypeKind Kind = TypeKind::NoneType; + // global singleton + static NoneTypePtr get(); + + private: + NoneType() : Type(TypeKind::NoneType) {} +}; + +struct GeneratorType; +using GeneratorTypePtr = std::shared_ptr; +// This type represents a Generator +struct CAFFE2_API GeneratorType : public Type { + static GeneratorTypePtr create() { + return GeneratorTypePtr( + new GeneratorType()); // NOLINT(modernize-make-shared) + } + bool operator==(const Type& rhs) const override { + return rhs.kind() == kind(); + } + std::string str() const override { + return "Generator"; + } + static const TypeKind Kind = TypeKind::GeneratorType; + // global singleton + static GeneratorTypePtr get(); + + private: + GeneratorType() : Type(TypeKind::GeneratorType) {} +}; + +struct DeviceObjType; +using DeviceObjTypePtr = std::shared_ptr; +// This type represents a Generator +struct CAFFE2_API DeviceObjType : public Type { + static DeviceObjTypePtr create() { + return DeviceObjTypePtr( + new DeviceObjType()); // NOLINT(modernize-make-shared) + } + bool operator==(const Type& rhs) const override { + return rhs.kind() == kind(); + } + std::string str() const override { + return "Device"; + } + static const TypeKind Kind = TypeKind::DeviceObjType; + // global singleton + static DeviceObjTypePtr get(); + + private: + DeviceObjType() : Type(TypeKind::DeviceObjType) {} +}; + +struct VarType; +using VarTypePtr = std::shared_ptr; +// This type represents a type variable, used in FunctionSchema +struct VarType : public Type { + static VarTypePtr create(std::string name_) { + return VarTypePtr(new VarType(std::move(name_))); + } + bool operator==(const Type& rhs) const override { + return rhs.kind() == kind(); + } + std::string str() const override { + return name(); + } + const std::string& name() const { + return name_; + } + bool hasFreeVariables() const override { + return true; + } + static const TypeKind Kind = TypeKind::VarType; + + private: + VarType(std::string name_) + : Type(TypeKind::VarType), name_(std::move(name_)) {} + std::string name_; +}; + +struct CapsuleType; +using CapsuleTypePtr = std::shared_ptr; +// This type represents a Python Capsule +struct CAFFE2_API CapsuleType : public Type { + static CapsuleTypePtr create() { + return CapsuleTypePtr(new CapsuleType()); // NOLINT(modernize-make-shared) + } + bool operator==(const Type& rhs) const override { + return rhs.kind() == kind(); + } + std::string str() const override { + return "Capsule"; + } + static const TypeKind Kind = TypeKind::CapsuleType; + // global singleton + static CapsuleTypePtr get(); +private: + CapsuleType() + : Type(TypeKind::CapsuleType) {} +}; + +CAFFE2_API std::ostream& operator<<(std::ostream& out, const Type& t); +CAFFE2_API std::ostream& operator<<(std::ostream& out, const VaryingShape& t); +// what is the type, ignoring extra size/shape information? +// e.g. Tensor(2x3) -> Dynamic, and Tuple(Tensor(2x3),...) -> Tuple(Dynamic,...) + +inline TypePtr unshapedType(const TypePtr& type) { + if (type->isSubtypeOf(TensorType::get())) { + return TensorType::get(); + } + return type->withContained(fmap(type->containedTypes(), unshapedType)); +} + +inline TypePtr TensorType::fromNumberType(TypePtr typ) { + if (typ->isSubtypeOf(IntType::get())) { + return TensorType::createContiguous(at::kLong, at::kCPU, {}); + } else if (typ->isSubtypeOf(FloatType::get())) { + return TensorType::createContiguous(at::kFloat, at::kCPU, {}); + } else if (typ->isSubtypeOf(BoolType::get())) { + return TensorType::createContiguous(at::kLong, at::kCPU, {}); + } + TORCH_CHECK(false, "Unknown number type: ", typ->str()); +} +inline TypePtr TensorType::fromBoolType() { + return TensorType::createContiguous(at::kLong, at::kCPU, {}); +} + +inline c10::optional tryScalarTypeFromJitType(const c10::TypePtr & type) { + if (type == FloatType::get()) { + return at::ScalarType::Double; + } else if (type == IntType::get()) { + return at::ScalarType::Long; + } else if (type == BoolType::get()) { + return at::ScalarType::Bool; + } + return c10::nullopt; +} + +inline at::ScalarType scalarTypeFromJitType(const c10::TypePtr& type) { + auto result = tryScalarTypeFromJitType(type); + AT_ASSERTM( + result, + "Add new condition, expected Float, Int, or Bool but got", + type->str()); + return *result; +} + +// Attempt to find the correct supertype of t1 and t2. If none is found then +// nullopt will be returned. If t1 == t2, or t1 is a type refinement of t2, +// then t2 will be returned (and vice versa). +// Two different tensortypes will return dynamic. +// Currently we chose not to support returning a NumberType for a float & int +// input because of a lack of operator support for NumberType +CAFFE2_API c10::optional unifyTypes( + const TypePtr& t1, + const TypePtr& t2); + +CAFFE2_API c10::optional unifyTypeList(at::ArrayRef elements); + +namespace detail { +template +struct getTypePtr_ final { + static TypePtr call() { + if (!isCustomClassRegistered()) { + throw c10::Error("Type could not be converted to any of the known types.", ""); + } + auto res = getCustomClassType(); + return std::dynamic_pointer_cast(res.type_); + } +}; + +template <> +struct getTypePtr_ final { + static TypePtr call() { + return TensorType::get(); + } +}; +template <> +struct getTypePtr_ final { + static TypePtr call() { + return FloatType::get(); + } +}; +template <> +struct getTypePtr_ final { + static TypePtr call() { + return IntType::get(); + } +}; +template <> +struct getTypePtr_ final { + static TypePtr call() { + return BoolType::get(); + } +}; +template <> +struct getTypePtr_ final { + static TypePtr call() { + return NumberType::get(); + } +}; +template <> +struct getTypePtr_ final { + static TypePtr call() { + return OptionalType::create(GeneratorType::get()); + } +}; +template <> +struct getTypePtr_ final { + static TypePtr call() { + return StringType::get(); + } +}; +template +struct getTypePtr_> final { + static TypePtr call() { + static auto type = ListType::create(getTypePtr_::call()); + return type; + } +}; +template +struct getTypePtr_> final { + static TypePtr call() { + static auto type = ListType::create(getTypePtr_::call()); + return type; + } +}; +template +struct getTypePtr_> final { + static TypePtr call() { + static auto type = ListType::create(getTypePtr_::call()); + return type; + } +}; +template +struct getTypePtr_> final { + static TypePtr call() { + static auto type = ListType::create(getTypePtr_::call()); + return type; + } +}; +template +struct getTypePtr_> final { + static TypePtr call() { + static auto type = + DictType::create(getTypePtr_::call(), getTypePtr_::call()); + return type; + } +}; +template +struct getTypePtr_> final { + static TypePtr call() { + static auto type = + DictType::create(getTypePtr_::call(), getTypePtr_::call()); + return type; + } +}; +template +struct getTypePtr_> final { + static TypePtr call() { + static auto type = OptionalType::create(getTypePtr_::call()); + return type; + } +}; +} // namespace detail +template +inline TypePtr getTypePtr() { + // TODO: static_assert that a templated function exists, and throw a friendy + // error message if not + return detail::getTypePtr_::call(); +} + +using TypeEnv = std::unordered_map; +struct MatchTypeReturn { + MatchTypeReturn(std::string reason) : reason_(std::move(reason)) {} + static MatchTypeReturn Success() { + return MatchTypeReturn(); + } + bool success() const { + return !reason_.has_value(); + } + const std::string& reason() const { + return reason_.value(); + } + + private: + MatchTypeReturn() + : reason_(c10::nullopt) {} + c10::optional reason_; // is there is no match, this contains the reason +}; + +// attempt to match the type variables in formal to actual, adding them to type_env. +// If no match is possible this returns a MatchTypeReturn with r.success() == false +// and a r.reason() that describes why it could not match. +// note: It is possible to successfully match a formal, but for type variables +// in the formal to still not be defined. In particular, None matches Optional[T] +// but does not define the value of T. +CAFFE2_API MatchTypeReturn +matchTypeVariables(TypePtr formal, TypePtr actual, TypeEnv& type_env); + +// replace type variables appearing in `type` with the values in +// `type_env`. Returns nullptr if a variable used in `type` +// does not appear in `type_env` +CAFFE2_API TypePtr tryEvalTypeVariables(TypePtr type, TypeEnv& type_env); + + +/** + * User Defined Types + */ + +struct ClassType; +using ClassTypePtr = std::shared_ptr; +using ::torch::jit::script::CompilationUnit; + +// This represents a class in TorchScript. +struct CAFFE2_API ClassType : public NamedType { + // Create a class type with name `name` and its methods stored in `cu`. + static ClassTypePtr create( + c10::optional qualifiedName, + std::weak_ptr cu, + bool is_module = false); + + bool operator==(const Type& rhs) const override { + if (auto user_rhs = rhs.cast()) { + const auto& lhs_name = name().value(); + const auto& rhs_name = user_rhs->name().value(); + + return lhs_name == rhs_name; + } + return false; + } + + std::string str() const override { + return python_str(); + } + + std::string python_str() const override { + const auto& n = name().value(); + return n.qualifiedName(); + } + + const std::vector& methods() const; + + TypePtr getAttribute(const std::string& name) const { + AT_ASSERT(attributeNames_.size() == attributeTypes_.size()); + size_t pos = 0; + for (const auto& attr : attributeNames_) { + if (name == attr) { + break; + } + ++pos; + } + + if (pos >= attributeNames_.size()) { + return nullptr; + } + return attributeTypes_[pos]; + } + + size_t numAttributes() const { + AT_ASSERT(attributeNames_.size() == attributeTypes_.size()); + return attributeNames_.size(); + } + + const TypePtr& getAttribute(size_t slot) const { + AT_ASSERT(attributeNames_.size() == attributeTypes_.size()); + AT_ASSERT(slot < attributeTypes_.size()); + return attributeTypes_[slot]; + } + + const std::string& getAttributeName(size_t slot) const { + AT_ASSERT(attributeNames_.size() == attributeTypes_.size()); + AT_ASSERT(slot < attributeTypes_.size()); + return attributeNames_[slot]; + } + + // Attributes are stored in a specific slot at runtime for effiency. + // When emitting instructions we specify the slot so that attribute access is + // a constant lookup + c10::optional findAttributeSlot(const std::string& name) const { + AT_ASSERT(attributeNames_.size() == attributeTypes_.size()); + size_t slot = 0; + for (const auto& attr : attributeNames_) { + if (name == attr) { + return slot; + } + slot++; + } + return c10::nullopt; + } + size_t getAttributeSlot(const std::string& name) const { + if (auto r = findAttributeSlot(name)) { + return *r; + } + TORCH_CHECK( + false, + python_str(), + " does not have a field with the name '", + name, + "'"); + } + + bool hasAttribute(const std::string& name) const { + return std::find_if( + attributeNames_.cbegin(), + attributeNames_.cend(), + [&](const std::string& attr) { return attr == name; }) != + attributeNames_.cend(); + } + + size_t addAttribute( + const std::string& name, + TypePtr type, + bool is_parameter = false); + + // [Internal Only] Remove attribute from the ClassType, + // caller is responsible to make sure the modification is safe: + // it is unsafe to having existing allocations + // of this object around anymore, and any code that works on + // the attribute is now invalid. Only newly created code is + // valid again. + void unsafeRemoveAttribute(const std::string& name); + + // Add attribute \p NAME if it doesn't exist or verify that it has a + // compatible type otherwise. + size_t addOrCheckAttribute( + const std::string& name, + TypePtr ty, + bool is_parameter = false) { + auto slot_idx = findAttributeSlot(name); + if (!slot_idx) { + return addAttribute(name, ty, is_parameter); + } + + TORCH_CHECK( + is_parameter == this->is_parameter(*slot_idx), + "Parameter field mismatch for the field '", + name, + "'"); + TypePtr atype = getAttribute(*slot_idx); + TORCH_CHECK( + ty->isSubtypeOf(atype), + ty->python_str(), + " is not compatible with the type ", + atype->python_str(), + " for the field '", + name, + "'"); + return *slot_idx; + } + + at::ArrayRef attributeNames() const { + return attributeNames_; + } + + at::ArrayRef containedTypes() const override { + return attributeTypes_; + } + + TypePtr createWithContained(std::vector contained_types) const override { + auto ptr = ClassType::create(name(), compilation_unit_); + AT_ASSERT(numAttributes() == contained_types.size()); + for(size_t i = 0; i < attributeNames_.size(); ++i) { + AT_ASSERT(attributeTypes_[i]->isSubtypeOf(contained_types[i])); + ptr->addAttribute(attributeNames_[i], contained_types[i]); + } + // Copy methods over + for (const auto& method : methods()) { + ptr->addMethod(method); + } + return ptr; + } + + bool is_module() const override { + return bool(parameterSlots_); + } + bool is_parameter(size_t slot) const { + TORCH_INTERNAL_ASSERT( + is_module(), "asking for parameterSlots of non-Module"); + return parameterSlots_->at(slot); + } + + void addMethod(Function* method); + Function* getMethod(const std::string& name) const; + + std::shared_ptr compilation_unit(); + std::shared_ptr compilation_unit() const; + + // generate a refined version of this class. + // It has the same name but the slot Types are subtypes of + // the original slots. It is only valid to refine a class type in a context + // where it is know that there are not assignments to the objects slots + // that would invalidate the refinement. + // These variants are not registered in the global class table. + ClassTypePtr refine(at::ArrayRef refined_slots) const; + + bool isSubtypeOfExt(const TypePtr rhs, std::ostream* why_not) const override; + + static const TypeKind Kind = TypeKind::ClassType; + + private: + ClassType( + c10::optional name, + std::weak_ptr cu, + bool is_module); + + // Mapping of attribute names -> their type. + // NOTE: this does not contain methods, which are stored in the module + // TODO: once modules support arbitrary ivalue attributes, we don't need this + // anymore. + // TODO: This is better represented as an OrderedDict, but alas it is not yet + // available from c10 + std::vector attributeNames_; + std::vector attributeTypes_; + // Holds method attributes + std::weak_ptr compilation_unit_; + + // if present, this class inherits from torch.nn.Module + // and these are the indices of the attributes which are parameters + std::shared_ptr> parameterSlots_; + + // List of methods associated with this class. + std::vector methods_; + +}; + +struct InterfaceType; +using InterfaceTypePtr = std::shared_ptr; +using ::torch::jit::script::CompilationUnit; +using ::torch::jit::Function; + +// Interfaces are a list of abstract methods that a class might meet. +// If a class provides those methods, it implicitly meets the interface. + +// Subtype relations for Interface with ClassType: +// lhs (ClassType or InterfaceType) is a subtype of rhs if: +// 1. lhs methods are a superset of rhs methods +// 2. if rhs is module interface, the lhs must be module interface or module itself +struct CAFFE2_API InterfaceType : public NamedType { + static InterfaceTypePtr create( + QualifiedName qualifiedName, bool is_module=false); + + bool operator==(const Type& rhs) const override { + if (auto user_rhs = rhs.cast()) { + return name() == user_rhs->name(); + } + return false; + } + + std::string str() const override { + return std::string("InterfaceType<") + name()->name() + ">"; + } + + std::string python_str() const override { + return name()->qualifiedName(); + } + + bool isSubtypeOfExt(const TypePtr rhs, std::ostream* why_not) const override; + + // try to find a method of this interface, + // returns nullptr if not found. + const FunctionSchema* getMethod(const std::string& name) const; + void addMethod(FunctionSchema schema); + const std::vector& methods() { + return *methods_; + } + + bool is_module() const override{ + return is_module_; + } + static const TypeKind Kind = TypeKind::InterfaceType; + ~InterfaceType() override; + private: + InterfaceType(QualifiedName name, bool is_module); + + // shared_ptr so that this header does not have to depend on + // FunctionSchema.h + std::shared_ptr> methods_; + // flag to distinguish if it's an interface type from a module or not + bool is_module_; +}; + +} // namespace c10 diff --git a/thirdparty/libtorch/include/ATen/core/op_registration/infer_schema.h b/thirdparty/libtorch/include/ATen/core/op_registration/infer_schema.h new file mode 100644 index 0000000000..9971435cf7 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/op_registration/infer_schema.h @@ -0,0 +1,144 @@ +#pragma once + +/** + * This file contains functionality to take a C++ function and infer its + * c10::FunctionSchema. + */ + +#include +#include +#include + +namespace c10 { +namespace detail { + +namespace infer_schema { + +/// The templated inference code creates `ArgumentDef` instead of `Argument`, +/// because that can be constructed at compile time and has a much smaller +/// binary size than having calls to `Argument` constructors in the template. +/// Creating `Argument` objects from `ArgumentDef` can then be done at +/// runtime in a non-templated way. +struct ArgumentDef final { + using GetTypeFn = TypePtr(); + GetTypeFn* getTypeFn; +}; + +template +struct bool_t {}; +template<> struct bool_t : std::true_type {}; +template<> struct bool_t : std::false_type {}; + +/// Checks the static C++ types `Types` for correctness to catch common error cases. +template +constexpr int checkStaticTypes() { + // Give nice error messages for some of the common error cases. + // Use a LOUD ERROR MESSAGE SO USERS SEE THE STATIC_ASSERT + static_assert(guts::conjunction< + bool_t::value || std::is_same::value || std::is_same::value>... + >::value, "INVALID TYPE: Only int64_t and bool are supported as an integral argument type"); + static_assert(guts::conjunction< + bool_t::value>... + >::value, "INVALID TYPE: float is not supported as an argument type, use double instead"); + return 0; +} + +template +constexpr std::array createArgumentVectorFromTypes(guts::index_sequence) { + return ( + // Check types for common errors + checkStaticTypes(), + + // Create the return value + std::array{{ArgumentDef{&getTypePtr_>::call}...}} + ); +} + +/// Creates a vector of `ArgumentDef` from a list of C++ types that are specified +/// as template arguments. +template struct createArguments final {}; +template +struct createArguments> final { + static constexpr std::array call() { + return createArgumentVectorFromTypes( + guts::make_index_sequence() + ); + } +}; + +/// Creates a vector of `ArgumentDef` from a list of C++ types that are specified +/// as a tuple (i.e. in the way c10 kernels return values). +/// It can be a tuple if there's three output arguments with types A, B, C. +/// It can be an empty tuple<>, or void for kernels that don't return anything. +/// It can be a single type A (i.e. no tuple) for the case where a kernel just +/// returns one value. +template struct createReturns final {}; + +template +struct createReturns, void> final { + static constexpr std::array call() { + return createArgumentVectorFromTypes( + guts::make_index_sequence() + ); + } +}; + +template +struct createReturns::value && !guts::is_instantiation_of::value>> final { + static constexpr std::array call() { + return createReturns>::call(); + } +}; + +template<> +struct createReturns final { + static constexpr std::array call() { + return createReturns>::call(); + } +}; + +template +std::vector createArgumentVector(const std::array& args) { + std::vector result; + result.reserve(NumArgs); + for (size_t i = 0; i < args.size(); ++i) { + // Arguments are named "_" + result.push_back(Argument("_" + c10::guts::to_string(i), (*args[i].getTypeFn)())); + } + return result; +} + +// This is intentionally a separate function +// because then the template is smaller and that benefits binary size +inline FunctionSchema make_function_schema(std::string&& name, std::string&& overload_name, std::vector&& arguments, std::vector&& returns) { + return FunctionSchema(std::move(name), std::move(overload_name), std::move(arguments), std::move(returns)); +} + +template +inline FunctionSchema make_function_schema(std::string&& name, std::string&& overload_name, const std::array& arguments, const std::array& returns) { + return make_function_schema(std::move(name), std::move(overload_name), createArgumentVector(arguments), createArgumentVector(returns)); +} + +/// Creates a `FunctionSchema` object from a `FunctionTraits` type for a +/// function. +template +FunctionSchema createFunctionSchemaFromTraits(std::string&& name, std::string&& overload_name) { + using ReturnType = typename FunctionTraits::return_type; + using ParameterTypes = typename FunctionTraits::parameter_types; + + constexpr auto arguments = createArguments::call(); + constexpr auto returns = createReturns::call(); + + return make_function_schema(std::move(name), std::move(overload_name), arguments, returns); +} +} +} + +template +FunctionSchema inferFunctionSchema(std::string&& name, std::string&& overload_name) { + return detail::infer_schema::createFunctionSchemaFromTraits>(std::move(name), std::move(overload_name)); +} + +CAFFE2_API c10::optional findSchemaDifferences(const FunctionSchema& inferred, const FunctionSchema& specified); + +} diff --git a/thirdparty/libtorch/include/ATen/core/op_registration/op_registration.h b/thirdparty/libtorch/include/ATen/core/op_registration/op_registration.h new file mode 100644 index 0000000000..a87768485f --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/op_registration/op_registration.h @@ -0,0 +1,591 @@ +#pragma once + +/** + * Include this file if you want to register operators. It includes all + * functionality needed to do so for you. + */ + +#include +#include +#if !defined(CAFFE2_IS_XPLAT_BUILD) +#include +#endif +#include + +namespace c10 { + +/** + * An instance of this class handles the registration for one or more operators. + * Make sure you keep the RegisterOperators instance around since it will + * deregister the operator it's responsible for in its destructor. + * + * Example: + * + * > namespace { + * > class my_kernel_cpu final : public c10::OperatorKernel { + * > public: + * > Tensor operator()(Tensor a, Tensor b) {...} + * > }; + * > } + * > + * > static auto registry = c10::RegisterOperators() + * > .op(c10::RegisterOperators::options() + * > .schema("my_op") + * > .kernel(TensorTypeId::CPUTensorId)); + */ +class CAFFE2_API RegisterOperators final { +public: + RegisterOperators(); + ~RegisterOperators(); + + RegisterOperators(const RegisterOperators&) = delete; + RegisterOperators& operator=(const RegisterOperators&) = delete; + RegisterOperators(RegisterOperators&&) noexcept; + RegisterOperators& operator=(RegisterOperators&&) noexcept; + + class CAFFE2_API Options final { + public: + Options(const Options&) = delete; + Options(Options&&) noexcept = delete; + Options& operator=(const Options&) = delete; + Options& operator=(Options&&) noexcept = delete; + + // internal-only for registering stack based kernels + Options&& kernel(TensorTypeId dispatch_key, KernelFunction::BoxedKernelFunction* kernel_func) && { + return std::move(*this).kernel(dispatch_key, KernelFunction::makeFromBoxedFunction(kernel_func), nullptr); + } + + // internal-only for registering stack based catch-all kernels + Options&& catchAllKernel(KernelFunction::BoxedKernelFunction* kernel_func) && { + return std::move(*this).kernel(c10::nullopt, KernelFunction::makeFromBoxedFunction(kernel_func), nullptr); + } + + // internal only for registering caffe2 ops + Options&& schema(FunctionSchema&& schema) { + TORCH_CHECK(!schemaOrName_.has_value(), "You can only specify the schema once per operator registration."); + schemaOrName_ = c10::make_right(std::move(schema)); + return std::move(*this); + } + + /** + * Use this to specify the schema for an operator. You can also specify + * the operator name only to have the function signature part of the + * schema be inferred from the kernel function. + * + * Example: + * + * > // Infer function signature from my_kernel_cpu + * > static auto registry = c10::RegisterOperators() + * > .op(c10::RegisterOperators::options() + * > .schema("my_op") + * > .kernel(TensorTypeId::CPUTensorId)); + * > + * > + * > // Explicitly specify full schema + * > static auto registry = c10::RegisterOperators() + * > .op(c10::RegisterOperators::options() + * > .schema("my_op(Tensor a) -> Tensor") + * > .kernel(TensorTypeId::CPUTensorId)); + */ + Options&& schema(const std::string& schemaOrName) { + TORCH_CHECK(!schemaOrName_.has_value(), "Tried to register operator ", schemaOrName," but specified schema multiple times. You can only specify the schema once per operator registration."); + + #if defined(CAFFE2_IS_XPLAT_BUILD) + throw std::logic_error("Tried to register operator " + schemaOrName + ". We don't support registering c10 ops on mobile yet because the function schema parser isn't present in the mobile build."); + #else + schemaOrName_ = torch::jit::parseSchemaOrName(schemaOrName); + #endif + + return std::move(*this); + } + + /** + * Use this to register an operator whose kernel is implemented as a functor. + * The kernel is only called for inputs matching the given dispatch key. + * You can register multiple kernels for different dispatch keys. + * + * Example: + * + * > namespace { + * > class my_kernel_cpu final : public c10::OperatorKernel { + * > public: + * > Tensor operator()(Tensor a, Tensor b) {...} + * > }; + * > } + * > + * > static auto registry = c10::RegisterOperators() + * > .op(c10::RegisterOperators::options() + * > .schema("my_op") + * > .kernel(TensorTypeId::CPUTensorId)); + * + * The functor constructor can take arguments to configure the kernel. + * The arguments are defined in the kernel registration. + * Example: + * + * > namespace { + * > class my_kernel_cpu final : public c10::OperatorKernel { + * > public: + * > explicit my_kernel_cpu(std::string some_configuration, int a, bool b) + * > : ... {...} + * > + * > Tensor operator()(Tensor a, Tensor b) {...} + * > }; + * > } + * > + * > static auto registry = c10::RegisterOperators() + * > .op(c10::RegisterOperators::options() + * > .schema("my_op") + * > .kernel(TensorTypeId::CPUTensorId, "some_configuration", 3, true)); + */ + template + // enable_if: only enable it if KernelFunctor is actually a functor + guts::enable_if_t::value, Options&&> kernel(TensorTypeId dispatch_key, ConstructorParameters&&... constructorParameters) && { + static_assert(std::is_base_of::value, "Tried to register a kernel functor using the kernel() API, but it doesn't inherit from c10::OperatorKernel. Please have the functor inherit from it."); + static_assert(std::is_constructible::value, "Wrong argument list for constructor of kernel functor. The arguments to kernel(arguments...) must match one of the constructors of Functor."); + + return std::move(*this).kernel( + std::move(dispatch_key), + KernelFunction::makeFromUnboxedFunctorFactory(detail::KernelFactory...>(std::forward(constructorParameters)...)), + detail::FunctionSchemaInferer()() + ); + } + + /** + * Use this to register an operator whose kernel is implemented as a functor. + * The kernel is a catch-all kernel, meaning it's called independent from + * the input. Dispatch is disabled for this operator. + * + * Example: + * + * > namespace { + * > class my_kernel_cpu final : public c10::OperatorKernel { + * > public: + * > Tensor operator()(Tensor a, Tensor b) {...} + * > }; + * > } + * > + * > static auto registry = c10::RegisterOperators() + * > .op(c10::RegisterOperators::options() + * > .schema("my_op") + * > .catchAllKernel()); + * + * The functor constructor can take arguments to configure the kernel. + * The arguments are defined in the kernel registration. + * Example: + * + * > namespace { + * > class my_kernel_cpu final : public c10::OperatorKernel { + * > public: + * > explicit my_kernel_cpu(std::string some_configuration, int a, bool b) + * > : ... {...} + * > + * > Tensor operator()(Tensor a, Tensor b) {...} + * > }; + * > } + * > + * > static auto registry = c10::RegisterOperators() + * > .op(c10::RegisterOperators::options() + * > .schema("my_op") + * > .catchAllKernel("some_configuration", 3, true)); + */ + template + // enable_if: only enable it if KernelFunctor is actually a functor + guts::enable_if_t::value, Options&&> catchAllKernel(ConstructorParameters&&... constructorParameters) && { + static_assert(std::is_base_of::value, "Tried to register a kernel functor using the kernel() API, but it doesn't inherit from c10::OperatorKernel. Please have the functor inherit from it."); + static_assert(std::is_constructible::value, "Wrong argument list for constructor of kernel functor. The arguments to kernel(arguments...) must match one of the constructors of Functor."); + + return std::move(*this).kernel( + c10::nullopt, + KernelFunction::makeFromUnboxedFunctorFactory(detail::KernelFactory...>(std::forward(constructorParameters)...)), + detail::FunctionSchemaInferer()() + ); + } + + /** + * Use this to register an operator whose kernel is implemented by a function. + * The kernel is only called for inputs matching the given dispatch key. + * You can register multiple kernels for different dispatch keys. + * + * Example: + * + * > namespace { Tensor my_kernel_cpu(Tensor a, Tensor b) {...} } + * > + * > static auto registry = c10::RegisterOperators() + * > .op(c10::RegisterOperators::options() + * > .schema("my_op") + * > .kernel(TensorTypeId::CPUTensorId)); + */ + template + // enable_if: only enable it if FuncType is actually a function + guts::enable_if_t::value, Options&&> kernel(TensorTypeId dispatch_key) && { + static_assert(!std::is_same::value, "Tried to register a stackbased (i.e. internal) kernel function using the public kernel<...>() API. Please either use the internal kernel(...) API or also implement the kernel function as defined by the public API."); + static_assert(kernel_func != nullptr, "Kernel function cannot be nullptr"); + + return std::move(*this).kernel( + std::move(dispatch_key), + KernelFunction::makeFromUnboxedFunction(), + // TODO Do schema inference without relying on WrapKernelFunction + detail::FunctionSchemaInferer::type>()() + ); + } + + /** + * Use this to register an operator whose kernel is implemented by a function. + * The kernel is a catch-all kernel, meaning it's called independent from + * the input. Dispatch is disabled for this operator. + * + * Example: + * + * > namespace { Tensor my_kernel_cpu(Tensor a, Tensor b) {...} } + * > + * > static auto registry = c10::RegisterOperators() + * > .op(c10::RegisterOperators::options() + * > .schema("my_op") + * > .catchAllKernel()); + */ + template + // enable_if: only enable it if FuncType is actually a function + guts::enable_if_t::value, Options&&> catchAllKernel() && { + static_assert(!std::is_same::value, "Tried to register a stackbased (i.e. internal) kernel function using the public kernel<...>() API. Please either use the internal kernel(...) API or also implement the kernel function as defined by the public API."); + static_assert(kernel_func != nullptr, "Kernel function cannot be nullptr"); + + return std::move(*this).kernel( + c10::nullopt, + KernelFunction::makeFromUnboxedFunction(), + // TODO Do schema inference without relying on WrapKernelFunction + detail::FunctionSchemaInferer::type>()() + ); + } + + template + // enable_if: only enable it if FuncType is actually a function + guts::enable_if_t::value, Options&&> kernel(TensorTypeId dispatch_key, FuncType* kernel_func) && { + static_assert(!std::is_same::value, "Tried to register a stackbased (i.e. internal) kernel function using the public kernel<...>() API. Please either use the internal kernel(...) API or also implement the kernel function as defined by the public API."); + TORCH_INTERNAL_ASSERT(kernel_func != nullptr, "Kernel function cannot be nullptr"); + + return std::move(*this).kernel( + std::move(dispatch_key), + KernelFunction::makeFromUnboxedRuntimeFunction(kernel_func), + // TODO Do schema inference without relying on WrapKernelFunction + detail::FunctionSchemaInferer>>()() + ); + } + + template + // enable_if: only enable it if FuncType is actually a function + guts::enable_if_t::value, Options&&> catchAllKernel(FuncType* kernel_func) && { + static_assert(!std::is_same::value, "Tried to register a stackbased (i.e. internal) kernel function using the public kernel<...>() API. Please either use the internal kernel(...) API or also implement the kernel function as defined by the public API."); + TORCH_INTERNAL_ASSERT(kernel_func != nullptr, "Kernel function cannot be nullptr"); + + return std::move(*this).kernel( + c10::nullopt, + KernelFunction::makeFromUnboxedRuntimeFunction(kernel_func), + // TODO Do schema inference without relying on WrapKernelFunction + detail::FunctionSchemaInferer>>()() + ); + } + + // TODO Remove impl_unboxedOnlyKernel once all of aten can generate boxed kernels + template + // enable_if: only enable it if FuncType is actually a function + guts::enable_if_t::value, Options&&> impl_unboxedOnlyKernel(TensorTypeId dispatch_key) && { + static_assert(!std::is_same::value, "Tried to register a stackbased (i.e. internal) kernel function using the public kernel<...>() API. Please either use the internal kernel(...) API or also implement the kernel function as defined by the public API."); + static_assert(kernel_func != nullptr, "Kernel function cannot be nullptr"); + + return std::move(*this).kernel( + std::move(dispatch_key), + KernelFunction::makeFromUnboxedOnlyRuntimeFunction(kernel_func), + nullptr // disable function schema inference because some ops from native_functions.yaml don't support it yet + ); + } + + // TODO Remove impl_unboxedOnlyCatchAllKernel once all of aten can generate boxed kernels + template + // enable_if: only enable it if FuncType is actually a function + guts::enable_if_t::value, Options&&> impl_unboxedOnlyCatchAllKernel() && { + static_assert(!std::is_same::value, "Tried to register a stackbased (i.e. internal) kernel function using the public kernel<...>() API. Please either use the internal kernel(...) API or also implement the kernel function as defined by the public API."); + static_assert(kernel_func != nullptr, "Kernel function cannot be nullptr"); + + return std::move(*this).kernel( + c10::nullopt, + KernelFunction::makeFromUnboxedOnlyRuntimeFunction(kernel_func), + nullptr // disable function schema inference because some ops from native_functions.yaml don't support it yet + ); + } + + /** + * Use this to register an operator whose kernel is implemented as a lambda. + * The kernel is only called for inputs matching the given dispatch key. + * You can register multiple kernels for different dispatch keys. + * + * The lambda must be stateless, i.e. not have a capture. If your kernel + * needs to store some configuration parameters, write the kernel as a + * functor instead. + * + * Example: + * + * > static auto registry = c10::RegisterOperators() + * > .op(c10::RegisterOperators::options() + * > .schema("my_op") + * > .kernel(TensorTypeId::CPUTensorId, [] (Tensor a) -> Tensor {...})); + */ + template + // enable_if: only enable it if Lambda is a functor (note: lambdas are functors) + guts::enable_if_t< + guts::is_functor>::value + && !std::is_same>::func_type, KernelFunction::BoxedKernelFunction>::value, + Options&&> kernel(TensorTypeId dispatch_key, Lambda&& functor) && { + static_assert(!std::is_base_of>::value, "The kernel(x) API for registering a kernel is only meant to be used with lambdas. Your kernel is a functor. Please use the kernel() API instead."); + + // We don't support stateful lambdas (i.e. lambdas with a capture), because their + // behavior would be nonobvious. A functor kernel with cache gets a new instance of + // its cache each time the kernel is looked up from the dispatch table. + // A lambda with a capture would be global and share its capture between all kernel lookups. + // So, instead of making users having to think about it (including the thread-safety + // issues this causes), let's just forbid stateful lambdas alltogether. + static_assert(guts::is_stateless_lambda>::value, "The kernel(x) API for registering a kernel only works for stateless lambdas (i.e. lambdas without captures). If you need a cache, please use the functor based API kernel() instead."); + + return std::move(*this).kernel( + std::move(dispatch_key), + KernelFunction::makeFromUnboxedLambda(std::forward(functor)), + // TODO Do schema inference without relying on WrapRuntimeKernelFunctor + detail::FunctionSchemaInferer>>()() + ); + } + + /** + * Use this to register an operator whose kernel is implemented as a lambda. + * The kernel is a catch-all kernel, meaning it's called independent from + * the input. Dispatch is disabled for this operator. + * + * The lambda must be stateless, i.e. not have a capture. If your kernel + * needs to store some configuration parameters, write the kernel as a + * functor instead. + * + * Example: + * + * > static auto registry = c10::RegisterOperators() + * > .op(c10::RegisterOperators::options() + * > .schema("my_op") + * > .catchAllKernel([] (Tensor a) -> Tensor {...})); + */ + template + // enable_if: only enable it if Lambda is a functor (note: lambdas are functors) + guts::enable_if_t< + guts::is_functor>::value + && !std::is_same>::func_type, KernelFunction::BoxedKernelFunction>::value, + Options&&> catchAllKernel(Lambda&& lambda) && { + static_assert(!std::is_base_of>::value, "The kernel(x) API for registering a kernel is only meant to be used with lambdas. Your kernel is a functor. Please use the kernel() API instead."); + + // We don't support stateful lambdas (i.e. lambdas with a capture), because their + // behavior would be nonobvious. + // A lambda with a capture would be global and share its capture between all kernel lookups. + // This would be a likely source for unexpected race conditions, so we forbid it. + // If a kernel really needs global state, they can just have regular global state + // in their .cpp file next to the kernel lambda. + static_assert(guts::is_stateless_lambda>::value, "The kernel(x) API for registering a kernel only works for stateless lambdas (i.e. lambdas without captures). If you need a cache, please use the functor based API kernel() instead."); + + return std::move(*this).kernel( + c10::nullopt, + KernelFunction::makeFromUnboxedLambda(std::forward(lambda)), + // TODO Do schema inference without relying on WrapRuntimeKernelFunctor + detail::FunctionSchemaInferer>>()() + ); + } + + Options&& aliasAnalysis(AliasAnalysisKind aliasAnalysisKind) && { + TORCH_CHECK(!aliasAnalysisKind_.has_value(), "You can only call aliasAnalysis() once per operator registration."); + aliasAnalysisKind_ = aliasAnalysisKind; + return std::move(*this); + } + + private: + Options&& kernel(c10::optional&& dispatch_key, KernelFunction&& func, std::unique_ptr&& inferred_function_schema) && { + KernelRegistrationConfig config; + config.dispatch_key = dispatch_key; + config.func = std::move(func); + config.inferred_function_schema = std::move(inferred_function_schema); + kernels.push_back(std::move(config)); + return std::move(*this); + } + + Options() + : schemaOrName_(c10::nullopt) + , kernels() + , aliasAnalysisKind_(c10::nullopt) + {} + + // KernelRegistrationConfig accumulates all information from the config + // parameters passed to a RegisterOperators::op() call into one object. + struct KernelRegistrationConfig final { + KernelRegistrationConfig() + : dispatch_key(c10::nullopt) + , func() + , inferred_function_schema(nullptr) + {} + + c10::optional dispatch_key; + KernelFunction func; + std::unique_ptr inferred_function_schema; + }; + + c10::optional> schemaOrName_; + + std::vector kernels; + optional aliasAnalysisKind_; + friend class RegisterOperators; + }; + + /** + * Call this to get an instance of registration options, which + * can be passed to a call to RegisterOperators::op() to specify + * these options for the operator registration. + * See class doc comment for examples. + */ + static Options options() { + return {}; + } + + /** + * Call this to register an operator. See class doc comment for examples. + */ + RegisterOperators&& op(Options&& options) && { + checkSchemaAndRegisterOp_(std::move(options)); + return std::move(*this); + } + + /** + * This is a shorthand for RegisterOperators::op(Options) where you can + * specify the operator schema outside of the options parameter. + * See class doc comment for examples. + */ + RegisterOperators&& op(const std::string& schemaOrName, Options&& options = RegisterOperators::options()) && { + return std::move(*this).op(std::move(options).schema(schemaOrName)); + } + + // internal only for registering caffe2 ops + RegisterOperators&& op(FunctionSchema schema, Options&& options) && { + return std::move(*this).op(std::move(options).schema(std::move(schema))); + } + + template + explicit RegisterOperators(const std::string& schemaOrName, FuncType&& func, Options&& options = RegisterOperators::options()) + : RegisterOperators() { + std::move(*this).op(schemaOrName, std::forward(func), std::move(options)); + } + + /** + * This API registers an operator based on a kernel function pointer. + * + * Given a kernel + * + * > namespace { Tensor my_kernel_cpu(Tensor a, Tensor b) {...} } + * + * This API looks like: + * + * > static auto registry = c10::RegisterOperators() + * > .op("my_op", &my_kernel_cpu); + * + * If your kernel is small and the overhead of calling it matters, + * then this API might be the wrong choice since the following API + * has a slightly lower overhead for calling into the kernel: + * + * > static auto registry = c10::RegisterOperators() + * > .op("my_op", c10::RegisterOperators::options() + * > .kernel()); + * + * Or, alternatively, write your kernel as a functor: + * + * > namespace { + * > class my_kernel_cpu final : public c10::OperatorKernel { + * > public: + * > Tensor operator()(Tensor a, Tensor b) {...} + * > }; + * > } + * > + * > static auto registry = c10::RegisterOperators() + * > .op("my_op", c10::RegisterOperators::options() + * > .kernel()); + */ + template + // enable_if: only enable it if FuncType is actually a function, but not a stack based BoxedKernelFunction. + guts::enable_if_t::value && !std::is_same::value, RegisterOperators&&> + op(const std::string& schemaOrName, FuncType* func, Options&& options = RegisterOperators::options()) && { + constexpr bool AllowLegacyTypes = true; + return std::move(*this).op(std::move(options).schema(schemaOrName).kernel( + c10::nullopt, + KernelFunction::makeFromUnboxedRuntimeFunction(func), + // TODO Do schema inference without relying on WrapRuntimeKernelFunctor + detail::FunctionSchemaInferer>>()() + )); + } + + /** + * This API registers an operator based on a kernel lambda. + * + * This API looks like: + * + * > static auto registry = c10::RegisterOperators() + * > .op("my_op", [] (Tensor a, Tensor b) {...}); + * + * This is equivalent to: + * + * > static auto registry = c10::RegisterOperators() + * > .op("my_op", c10::RegisterOperators::options() + * > .catchAllKernel([] (Tensor a, Tensor b) {...})); + * + */ + template + // enable_if: only enable it if Lambda is actually a stateless lambda + guts::enable_if_t::value && guts::is_stateless_lambda>::value, RegisterOperators&&> + op(const std::string& schemaOrName, Lambda&& lambda, Options&& options = RegisterOperators::options()) && { + static_assert(!std::is_base_of::value, "c10::OperatorKernel is part of the new kernel registration API and shouldn't be used together with the deprecated registration API. Please use the new RegisterOperators::options().kernel() based API instead."); + + constexpr bool AllowLegacyTypes = true; + return std::move(*this).op(std::move(options).schema(schemaOrName).kernel( + c10::nullopt, + KernelFunction::makeFromUnboxedLambda(std::forward(lambda)), + // TODO Do schema inference without relying on WrapRuntimeKernelFunctor + detail::FunctionSchemaInferer>>()() + )); + } + + template + C10_DEPRECATED_MESSAGE("Registering operator kernels with stateful lambdas (i.e. lambdas with a capture) has non-obvious behavior. This is deprecated. Please use a lambda without a capture or a functor class instead.") + // enable_if: only enable it if Lambda is actually a functor but not a stateless lambda + guts::enable_if_t::value && !guts::is_stateless_lambda>::value, RegisterOperators&&> + op(const std::string& schemaOrName, Lambda&& lambda, Options&& options = RegisterOperators::options()) && { + static_assert(!std::is_base_of::value, "c10::OperatorKernel is part of the new kernel registration API and shouldn't be used together with the deprecated registration API. Please use the new RegisterOperators::options().kernel() based API instead."); + + constexpr bool AllowLegacyTypes = true; + return std::move(*this).op(std::move(options).schema(schemaOrName).kernel( + c10::nullopt, + KernelFunction::makeFromUnboxedLambda(std::forward(lambda)), + // TODO Do schema inference without relying on WrapRuntimeKernelFunctor + detail::FunctionSchemaInferer>>()() + )); + } + +private: + void checkSchemaAndRegisterOp_(Options&& config); + + static c10::FunctionSchema inferSchemaFromKernels_(const OperatorName& opNameStr, const Options& options); + void checkNoDuplicateKernels_(const Options& options); + void registerOp_(Options&& options); + void registerSchemaAndKernel_(FunctionSchema schema, Options::KernelRegistrationConfig&& config, OperatorOptions&& options); + void registerSchemaOnly_(FunctionSchema&& schema, OperatorOptions&& options); + static OperatorOptions makeOperatorOptions_(const Options& options); + + class OperatorRegistrar; + + std::vector registrars_; + + static_assert(std::is_nothrow_move_constructible>::value, ""); + static_assert(std::is_nothrow_move_assignable>::value, ""); +}; + +} + +namespace torch { + using RegisterOperators = c10::RegisterOperators; +} diff --git a/thirdparty/libtorch/include/ATen/core/operator_name.h b/thirdparty/libtorch/include/ATen/core/operator_name.h new file mode 100644 index 0000000000..323108871c --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/operator_name.h @@ -0,0 +1,40 @@ +#pragma once + +#include +#include + +namespace c10 { + +struct OperatorName final { + std::string name; + std::string overload_name; + OperatorName(std::string name, const std::string& overload_name) + : name(std::move(name)), overload_name(overload_name) {} +}; + +inline bool operator==(const OperatorName& lhs, const OperatorName& rhs) { + return lhs.name == rhs.name && lhs.overload_name == rhs.overload_name; +} + +inline bool operator!=(const OperatorName& lhs, const OperatorName& rhs) { + return !operator==(lhs, rhs); +} + +inline std::string toString(const OperatorName& opName) { + std::string result = opName.name; + if (opName.overload_name.size() != 0) { + result += "." + opName.overload_name; + } + return result; +} + +} + +namespace std { + template <> + struct hash<::c10::OperatorName> { + size_t operator()(const ::c10::OperatorName& x) const { + return std::hash()(x.name) ^ (~ std::hash()(x.overload_name)); + } + }; +} diff --git a/thirdparty/libtorch/include/ATen/core/qualified_name.h b/thirdparty/libtorch/include/ATen/core/qualified_name.h new file mode 100644 index 0000000000..629d05f73e --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/qualified_name.h @@ -0,0 +1,160 @@ +#pragma once + +#include +#include +#include +#include + +namespace c10 { + +// Represents a name of the form "foo.bar.baz" +struct QualifiedName { + QualifiedName() {} + + // `name` can be a dotted string, like "foo.bar.baz", or just a bare name. + /* implicit */ QualifiedName(const std::string& name) { + TORCH_CHECK(!name.empty()); + // split the string into its atoms. + size_t startSearchFrom = 0; + size_t pos = name.find(delimiter_, startSearchFrom); + + while (pos != std::string::npos) { + auto atom = name.substr(startSearchFrom, pos - startSearchFrom); + AT_ASSERTM( + atom.size() > 0, "Invalid name for qualified name: '", name, "'"); + atoms_.push_back(std::move(atom)); + startSearchFrom = pos + 1; + pos = name.find(delimiter_, startSearchFrom); + } + + auto finalAtom = name.substr(startSearchFrom, pos - startSearchFrom); + AT_ASSERTM( + finalAtom.size() > 0, "Invalid name for qualified name: '", name, "'"); + atoms_.push_back(std::move(finalAtom)); + + cacheAccessors(); + } + + explicit QualifiedName(std::vector atoms) { + for (const auto& atom : atoms) { + TORCH_CHECK(!atom.empty(), "Atom cannot be empty"); + TORCH_CHECK( + atom.find(delimiter_) == std::string::npos, + "Delimiter not allowed in atom"); + } + atoms_ = atoms; + cacheAccessors(); + } + // Unnecessary copy. Ideally we'd use somoething like std::string_view. + /* implicit */ QualifiedName(const char* name) + : QualifiedName(std::string(name)) {} + + // `name` must be a bare name (no dots!) + explicit QualifiedName(const QualifiedName& prefix, std::string name) { + TORCH_INTERNAL_ASSERT(!name.empty()); + TORCH_INTERNAL_ASSERT(name.find(delimiter_) == std::string::npos); + atoms_.insert(atoms_.begin(), prefix.atoms_.begin(), prefix.atoms_.end()); + atoms_.push_back(std::move(name)); + + cacheAccessors(); + } + + // Is `this` a prefix of `other`? + // For example, "foo.bar" is a prefix of "foo.bar.baz" + bool isPrefixOf(const QualifiedName& other) const { + const auto& thisAtoms = atoms_; + const auto& otherAtoms = other.atoms_; + + if (thisAtoms.size() > otherAtoms.size()) { + // Can't be a prefix if it's bigger + return false; + } + for (size_t i = 0; i < thisAtoms.size(); i++) { + if (thisAtoms[i] != otherAtoms[i]) { + return false; + } + } + return true; + } + + // The fully qualified name, like "foo.bar.baz" + const std::string& qualifiedName() const { + return qualifiedName_; + } + + // The leading qualifier, like "foo.bar" + const std::string& prefix() const { + return prefix_; + } + + // The base name, like "baz" + const std::string& name() const { + return name_; + } + + const std::vector& atoms() const { + return atoms_; + } + + bool operator==(const QualifiedName& other) const { + return this->qualifiedName_ == other.qualifiedName_; + } + + bool operator!=(const QualifiedName& other) const { + return !(*this == other); + } + + private: + static constexpr char delimiter_ = '.'; + + // Helper for cacheAccessors() below. + template + std::string join(char delimiter, const T& v) { + std::string out; + size_t reserve = 0; + for (const auto& e : v) { + reserve += e.size() + 1; + } + out.reserve(reserve); + for (size_t i = 0; i < v.size(); ++i) { + if (i != 0) { + out.push_back(delimiter); + } + out.append(v[i]); + } + return out; + } + + void cacheAccessors() { + qualifiedName_ = join(delimiter_, atoms_); + if (atoms_.size() > 1) { + ArrayRef view(atoms_); + const auto prefixView = view.slice(0, view.size() - 1); + prefix_ = join(delimiter_, prefixView); + } + + if (atoms_.size() >= 1) { + name_ = atoms_.back(); + } + } + + // The actual list of names, like "{foo, bar, baz}" + std::vector atoms_; + + /* + * Cached accessors, derived from `atoms_`. + */ + std::string qualifiedName_; + std::string prefix_; + std::string name_; +}; +} // namespace c10 + +namespace std { +template <> +struct hash { + size_t operator()(const c10::QualifiedName& n) const noexcept { + return std::hash()(n.qualifiedName()); + } +}; +} // namespace std diff --git a/thirdparty/libtorch/include/ATen/core/stack.h b/thirdparty/libtorch/include/ATen/core/stack.h new file mode 100644 index 0000000000..3bf8c2101b --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/stack.h @@ -0,0 +1,135 @@ +#pragma once + +#include + +// TODO move this to c10 namespace + +namespace torch { +namespace jit { + +using c10::IValue; +using Stack = std::vector; +using Operation = std::function; + +// An operation with N inputs and M outputs pops the last N inputs off +// the stack and pushes its M inputs onto the stack +// before: I0, I1, ... IN <- stack.back() +// after: O0, O1, ... OM +// operations are defined this way so that ownership of inputs can be +// transferred to the operation and it can incrementally drop ownership of +// tensors when they become unneeded. For large operations, like 'run an entire +// subgraph', this functionality is very important for minimizing gpu memory +// usage return value is the relative 'offset' to jump to for the next +// operation: +// pc += 1 + offset +// so a return value of 0 goes to the next instruction + +// treat the last N elements of the stack as a list, looking up +// element i +static inline IValue& peek(Stack& stack, size_t i, size_t N) { + return *(stack.end() - N + i); +} +static inline const IValue& peek(const Stack& stack, size_t i, size_t N) { + return *(stack.end() - N + i); +} +// treat the last N elements of the stack as a list, looking up the +// slice starting at index i and having length len +static inline at::ArrayRef peekSlice( + const Stack& stack, + size_t i, + size_t len, + size_t N) { + return at::ArrayRef(stack).slice(stack.size() - N + i, len); +} +static inline at::ArrayRef last(const Stack& stack, size_t N) { + return peekSlice(stack, 0, N, N); +} +static inline void drop(Stack& stack, size_t n) { + stack.erase(stack.end() - n, stack.end()); +} +static inline IValue pop(Stack& stack) { + auto r = std::move(stack.back()); + stack.pop_back(); + return r; +} +static inline std::vector pop(Stack& stack, size_t n) { + std::vector result; + result.reserve(n); + for (size_t i = 0; i < n; ++i) { + result.push_back(std::move(peek(stack, i, n))); + } + drop(stack, n); + return result; +} + +// variadic pop: +// int64_t a; at::Tensor b; +// pop(stack, a, b); +// equivalent to: +// b = pop(stack).toTensor(); +// a = pop(stack).toInt(); +template +static inline void pop(Stack& stack, Types&... args) { + size_t i = 0; + constexpr size_t N = sizeof...(args); + int result[N] = { + (args = std::move(peek(stack, i++, N)).template to(), 0)...}; + (void)result; + drop(stack, N); +} +template +static inline void push_one(Stack& stack, Type&& arg) { + stack.emplace_back(std::forward(arg)); +} + +static inline void push_one(Stack& stack, c10::TensorOptions options) { + stack.emplace_back(c10::typeMetaToScalarType(options.dtype())); + stack.emplace_back(options.layout()); + stack.emplace_back(options.device()); + stack.emplace_back(options.pinned_memory()); +} + +template +static inline void push(Stack& stack, Types&&... args) { + (void)std::initializer_list{(push_one(stack, args), 0)...}; +} +template +static inline void push_list_elements(Stack& stack, const c10::List& elements) { + for (T elem : elements) { + stack.push_back(std::move(elem)); + } +} + +// The packer here is carefully written not to make any unnecessary +// copies. + +// pack takes the return values of aten functions pushes them onto the stack +template +inline void pack(Stack& stack, T&& v) { + stack.emplace_back(std::forward(v)); +} + +template +struct TuplePacker { + // NB: *Not* a universal reference. + static void execute(Stack& stack, std::tuple&& t) { + // NB: The move here does not "destroy" the entire tuple, that is + // not what std::move does; only the particular tuple index + // processed here gets stolen. + pack(stack, std::get(std::move(t))); + TuplePacker::execute(stack, std::move(t)); + } +}; + +template +struct TuplePacker<0, Args...> { + static void execute(Stack& stack, std::tuple&& t){}; +}; + +template +inline void pack(Stack& stack, std::tuple&& t) { + TuplePacker::execute(stack, std::move(t)); +} + +} // namespace jit +} // namespace torch diff --git a/thirdparty/libtorch/include/ATen/core/typeid.h b/thirdparty/libtorch/include/ATen/core/typeid.h new file mode 100644 index 0000000000..5967c0a165 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/core/typeid.h @@ -0,0 +1 @@ +#include diff --git a/thirdparty/libtorch/include/ATen/cpp_custom_type_hack.h b/thirdparty/libtorch/include/ATen/cpp_custom_type_hack.h new file mode 100644 index 0000000000..e9d7b69973 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cpp_custom_type_hack.h @@ -0,0 +1,47 @@ +// WARNING! WARNING! WARNING! +// This file is a temporary hack to enable development of pytorch quantization +// +// It's a stub for wrapping arbitrary cpp types in TorchScript. Proper +// implementation (under development) is to use TorchScript custom types. +// In the meantime, we abuse ByteTensor with custom deleter for this purpose. +// +// Template argument has to be registered with CAFFE_KNOWN_TYPE mechanism. + +#include + +namespace at { +namespace cpp_custom_type_hack { + +template +T& cast(const Tensor& packed) { + TORCH_CHECK( + packed.scalar_type() == kByte, "Expected temporary cpp type wrapper"); + TORCH_CHECK( + packed.storage().data_ptr().get_deleter() == + caffe2::TypeMeta::Make().deleteFn(), + "Expected temporary cpp type wrapper of type ", + caffe2::TypeMeta::TypeName()); + return *reinterpret_cast(packed.storage().data_ptr().get()); +} + +template +Tensor create(std::unique_ptr ptr, TensorOptions options) { + // None of this should trace, so turn off Variable handling + at::AutoNonVariableTypeMode guard; + + // We store this instance away in a Tensor and register a deleter function + // so that we do not leak memory. On the other side, we pull out the storage's + // data_ptr and get the right typed pointer. + void* raw_ptr = ptr.release(); + at::DataPtr at_ptr( + raw_ptr, raw_ptr, caffe2::TypeMeta::Make().deleteFn(), at::kCPU); + + // size doesn't really matter, but we can align it to the actual size + // returning variables because one likely want to use this hack from python + auto retval = at::empty({sizeof(T)}, options.device(kCPU).dtype(at::kByte)); + retval.storage().set_data_ptr(std::move(at_ptr)); + return retval; +} + +} // namespace cpp_custom_type_hack +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/cpu/FlushDenormal.h b/thirdparty/libtorch/include/ATen/cpu/FlushDenormal.h new file mode 100644 index 0000000000..ca6820fc63 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cpu/FlushDenormal.h @@ -0,0 +1,14 @@ +/// Flush-To-Zero and Denormals-Are-Zero mode +/// +/// Flush-To-Zero (FTZ) and Denormals-Are-Zero (DAZ) are modes that bypass +/// IEEE 754 methods of dealing with denormal floating-point numbers on x86-64 +/// and some x86 CPUs. They result in reduced precision for values near zero, +/// but increased performance. +/// +/// See https://software.intel.com/en-us/articles/x87-and-sse-floating-point-assists-in-ia-32-flush-to-zero-ftz-and-denormals-are-zero-daz + +namespace at { namespace cpu { + +bool set_flush_denormal(bool on); + +}} // namespace at::cpu diff --git a/thirdparty/libtorch/include/ATen/cpu/vml.h b/thirdparty/libtorch/include/ATen/cpu/vml.h new file mode 100644 index 0000000000..45523166b9 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cpu/vml.h @@ -0,0 +1,200 @@ +#pragma once + +#include +#include +#include +#include + +// This header implements various unary operations using a MKL VML style +// interface. + +// It implements various functions with a simple interface +// For example it enables the user to call vsin(float* out, const float* in, +// size) This functions takes a pointer to a contious output array of floats and +// a constant input array. It will then apply sin to each value in in the input +// array and write the result into the output array. out and in may point to the +// same memory, i.e. this fully supports in-place operations. These functions +// also implement their own parallelization, so take precautions when calling +// these from threaded functions. + +// When MKL is available it will call into MKL's VML library similar to NumPy +// If MKL is not available it will use SLEEF. + +// This file might be compiled under AVX or AVX2 when called from e.g. +// UnaryOpsKernel.cpp + +#include +#include +#include +#include +#include +#include + +#if AT_MKL_ENABLED() && !defined(__APPLE__) +#include +#endif + +// [Note SSE-AVX transitions] +// There is a bug in Glibc2.23 +// https://bugs.launchpad.net/ubuntu/+source/glibc/+bug/1663280. Calling zeroall +// when using AVX/AVX2 code resolves this. +#if defined(__AVX__) && defined(__GLIBC__) && __GLIBC_MINOR__ == 23 +#define DL_RUNTIME_BUG(op, type) \ + using value_t = typename at::native::ztype::value_t; \ + volatile value_t x = (value_t)(1); \ + x = std::op(x); \ + _mm256_zeroall(); +#else +#define DL_RUNTIME_BUG(op, type) +#endif + +namespace at { +namespace vml { +namespace { + +using namespace vec256; + +template +inline void vrsqrt(scalar_t* out, scalar_t* in, int64_t size) { + parallel_for(0, size, 2048, [out, in](int64_t begin, int64_t end) { + map( + [](const Vec256& x) { + return Vec256((scalar_t)(1)) / x.sqrt(); + }, + out + begin, + in + begin, + end - begin); + }); +} + +// NB: We ignore numerical errors by convention and leave them to the user + +// We unfortunately need to duplicate code here to deal with the SSE-AVX +// transition bug (see [Note SSE-AVX transitions]). As soon as we can expect +// users to use a version of glibc newer than 2.23 we will be able to ditch +// this. This duplication is also necessary since not all functions (e.g. rsqrt) +// might be part of cmath. + +#define IMPLEMENT_VML_BUG(op) \ + template \ + inline void v##op(scalar_t* out, const scalar_t* in, int64_t size) { \ + DL_RUNTIME_BUG(op, scalar_t) \ + parallel_for(0, size, 2048, [out, in](int64_t begin, int64_t end) { \ + map([](const Vec256& x) { return x.op(); }, \ + out + begin, \ + in + begin, \ + end - begin); \ + }); \ + } + +#define IMPLEMENT_VML(op) \ + template \ + inline void v##op(scalar_t* out, const scalar_t* in, int64_t size) { \ + parallel_for(0, size, 2048, [out, in](int64_t begin, int64_t end) { \ + map([](const Vec256& x) { return x.op(); }, \ + out + begin, \ + in + begin, \ + end - begin); \ + }); \ + } + +IMPLEMENT_VML_BUG(abs) +IMPLEMENT_VML_BUG(acos) +IMPLEMENT_VML_BUG(asin) +IMPLEMENT_VML_BUG(atan) +IMPLEMENT_VML_BUG(ceil) +IMPLEMENT_VML_BUG(cos) +// IMPLEMENT_VML_BUG(cosh) +IMPLEMENT_VML_BUG(erf) +IMPLEMENT_VML_BUG(erfc) +IMPLEMENT_VML(erfinv) +IMPLEMENT_VML_BUG(exp) +IMPLEMENT_VML_BUG(expm1) +IMPLEMENT_VML_BUG(floor) +IMPLEMENT_VML(reciprocal) +IMPLEMENT_VML_BUG(log) +IMPLEMENT_VML_BUG(log10) +IMPLEMENT_VML_BUG(log1p) +IMPLEMENT_VML_BUG(log2) +IMPLEMENT_VML(neg) +IMPLEMENT_VML_BUG(sin) +// IMPLEMENT_VML_BUG(sinh) +IMPLEMENT_VML_BUG(sqrt) +IMPLEMENT_VML_BUG(round) +IMPLEMENT_VML(rsqrt) +IMPLEMENT_VML_BUG(tan) +IMPLEMENT_VML_BUG(tanh) +IMPLEMENT_VML_BUG(trunc) +IMPLEMENT_VML_BUG(lgamma) + + +#if AT_MKL_ENABLED() && !defined(__APPLE__) + +// NB: LP64 MKL is the most commonly used and thus we assume it here. That means +// we need to expect MKL_INT to be of type int, which implies int32_t in most +// cases. +static_assert( + std::is_same::value, + "MKL_INT is assumed to be int32_t"); +#define IMPLEMENT_VML_MKL_STUB(op, mklop, type, mkltype) \ + template <> \ + inline void v##op(type * out, const type * in, int64_t size) { \ + int64_t max_mkl_ind = std::numeric_limits::max(); \ + if (size <= static_cast(max_mkl_ind)) { \ + vm##mkltype##mklop( \ + size, in, out, VML_HA | VML_FTZDAZ_OFF | VML_ERRMODE_IGNORE); \ + } else { \ + MKL_INT ind = 0; \ + int64_t chunks = size / max_mkl_ind; \ + int64_t rest = size % max_mkl_ind; \ + for (; ind < chunks; ind++) { \ + vm##mkltype##mklop( \ + max_mkl_ind, \ + in + ind * max_mkl_ind, \ + out + ind * max_mkl_ind, \ + VML_HA | VML_FTZDAZ_OFF | VML_ERRMODE_IGNORE); \ + } \ + vm##mkltype##mklop( \ + rest, \ + in + ind * max_mkl_ind, \ + out + ind * max_mkl_ind, \ + VML_HA | VML_FTZDAZ_OFF | VML_ERRMODE_IGNORE); \ + } \ + } + +#define IMPLEMENT_VML_MKL(op, mklop) \ + IMPLEMENT_VML_MKL_STUB(op, mklop, float, s) \ + IMPLEMENT_VML_MKL_STUB(op, mklop, double, d) + +// NB: abs, cosh and sinh were temporarily disabled due to issues with Apple clang + +IMPLEMENT_VML_MKL(abs, Abs) +IMPLEMENT_VML_MKL(acos, Acos) +IMPLEMENT_VML_MKL(asin, Asin) +IMPLEMENT_VML_MKL(atan, Atan) +IMPLEMENT_VML_MKL(cos, Cos) +// IMPLEMENT_VML_MKL(cosh, Cosh) +IMPLEMENT_VML_MKL(erf, Erf) +IMPLEMENT_VML_MKL(erfc, Erfc) +IMPLEMENT_VML_MKL(erfinv, ErfInv) +IMPLEMENT_VML_MKL(exp, Exp) +IMPLEMENT_VML_MKL(expm1, Expm1) +IMPLEMENT_VML_MKL(log, Ln) +IMPLEMENT_VML_MKL(log10, Log10) +IMPLEMENT_VML_MKL(log1p, Log1p) +IMPLEMENT_VML_MKL(sin, Sin) +// IMPLEMENT_VML_MKL(sinh, Sinh) +IMPLEMENT_VML_MKL(sqrt, Sqrt) +IMPLEMENT_VML_MKL(tan, Tan) +IMPLEMENT_VML_MKL(tanh, Tanh) +IMPLEMENT_VML_MKL(trunc, Trunc) + +#if INTEL_MKL_VERSION >= 20180406 +IMPLEMENT_VML_MKL(log2, Log2) +#endif + +#endif + +} // namespace +} // namespace vml +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/cuda/ATenCUDAGeneral.h b/thirdparty/libtorch/include/ATen/cuda/ATenCUDAGeneral.h new file mode 100644 index 0000000000..cb45756847 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cuda/ATenCUDAGeneral.h @@ -0,0 +1,9 @@ +#pragma once + +#include +#include +#include + +#include + +// Use TORCH_CUDA_API for exports from this folder diff --git a/thirdparty/libtorch/include/ATen/cuda/CUDAApplyUtils.cuh b/thirdparty/libtorch/include/ATen/cuda/CUDAApplyUtils.cuh new file mode 100644 index 0000000000..ffe61b2128 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cuda/CUDAApplyUtils.cuh @@ -0,0 +1,1241 @@ +#pragma once + +#include +#include +#include +#include +#include +#include + +#include + +// +// This file contains pointwise operation functions and kernels that +// work on both contiguous and non-contiguous tensor arguments of +// arbitrary (up to MAX_CUTORCH_DIMS) dimensioned arguments without +// copying or temporary storage. +// + +/* + NOTE [ CUDA_tensor_applyN helpers ] + + The following CUDA_tensor_applyN (where N currently can be 1, 2, 3, or 4) + functions apply a pointwise operator to N tensor(s). + + The calling convention is + + 1. The template arguments should be, sequentially, + - First N typename args specify the scalar types of each of the N tensors. + - (Optional) `int step` arg specifies the number of elements processed + together at the same time. + Default is 1. + - A usually omitted (i.e., inferred) typename arg specifies the type of the + function/functor applied on `N * step` values in each iteration of each + CUDA thread. + 2. The arguments should be, sequentially, + - N tensors + - op: a function/functor that processes `N * step` values at the same time. + - If `step == 1`, it must have signature + `void(*)(scalar1_t&, scalar2_t&, ..., scalarN_t&)`, where + `scalar*_t`s are the first N typename template args, and the inputs + are the `N` values from the `N` tensors retrieved at a common index. + - Otherwise, it must must have signature + void(*)(int n, scalar1_t&, scalar1_t&, ..., scalar1_t&, // repeat `step` times + scalar2_t&, scalar2_t&, ..., scalar2_t&, // repeat `step` times + ..., + scalarN_t&, scalarN_t&, ..., scalarN_t&) // repeat `step` times + Different from `step == 1` case, it processes `N * step` values taken + from `step` common indices. Moreover, the first input `n` represents the + number of valid indices (it will always have `0 < n <= step`). It will + almost always be `step`, but at the boundary we may not have full `step` + elements and `n` can be a lesser value. + + E.g., if `step == 4` and `N == 2`, `op` could be + + [](int n, scalar1_t &u1, scalar1_t &u2, scalar1_t &u3, scalar1_t &u4, + scalar2_t &v1, scalar2_t &v2, scalar2_t &v3, scalar2_t &v4) { + // Only process u1, ..., un and v1, ..., vn. + // So if `n == 3`, `u4` and `v4` need not to be considered. + } + + In both cases, the references can actually be const, but at least one of + them should be non-const in order to write the output. + - (Optional, but recommended) N TensorArgType args that specify for each + tensor whether `op` reads AND writes ] (i.e., TensorArgType::ReadWrite), + or only reads (i.e., TensorArgType::ReadOnly). + Default is TensorArgType::ReadWrite for first Tensor, and + TensorArgType::ReadOnly for the rest. + + E.g., + + to compute a = b^2 for a and b of same dtype, we can call + + CUDA_tensor_apply2( + a, b, + [] __device__ (scalar &a_val, const scalar &b_val) { a_val = b_val * b_val; } + ); + + to work on 2 values at the same time, we can call + + CUDA_tensor_apply2( + a, b, + [] __device__ (int n, scalar1 &a_val1, scalar1 &a_val2, + const scalar2 &b_val1, const scalar2 &b_val2) { + // call special vectorized op here, or just do elementwise and enjoy unrolling... + // if n == 1, only process a_val1 and b_val1 + } + ); +*/ + +namespace at { +namespace cuda { + +// TODO: combine with TensorArg? So far that's been for debugging, and this is functional... +enum class TensorArgType { ReadWrite, ReadOnly }; + +namespace { + +// Rearrange dimensions for pointwise operations so that strides are in +// decreasing order as much as possible, so that kernels have better memory +// access patterns. +// +// For example, consider a binary operation on two "transposed" 2-dim tensors: +// sizes: 256 512 +// aInfo->strides: 1 256 +// bInfo->strides: 1 256 +// +// Given this, each concurrent memory access inside kernelPointwiseApply2() is +// exactly 256 elements apart, resulting in poor performance. +// +// This function exchanges dimensions so that memory access is contiguous: +// sizes: 512 256 +// aInfo->strides: 256 1 +// bInfo->strides: 256 1 +// +// (Actually, it becomes even better because now collapseDims() can turn each +// input into one contiguous array.) +// +// In general, given M (<=4) TensorInfo's with N dimensions, we can view each +// strides[i] (0 <= i < N) as an M-tuple. Given each pair i < j, we exchange +// strides[i] and [j] if +// (1) strides[i][k] < strides[j][k] for some k (0 <= k < M) +// (exchanging them will benefit input #k), and +// (2) strides[i][k] <= strieds[j][k] for all k +// (exchanging them will not make any input worse). +template +inline void rearrangeDims(detail::TensorInfo* aInfo, + detail::TensorInfo* bInfo = nullptr, + detail::TensorInfo* cInfo = nullptr, + detail::TensorInfo* dInfo = nullptr) { + int numInfos = 1; + int dims = aInfo->dims; + IndexType *sizes[4] = { aInfo->sizes, }; + IndexType *strides[4] = { aInfo->strides, }; + + if (bInfo != nullptr) { + ++numInfos; + if (bInfo->dims != dims) return; + sizes[1] = bInfo->sizes; + strides[1] = bInfo->strides; + } + + if (cInfo != nullptr) { + ++numInfos; + if (cInfo->dims != dims) return; + sizes[2] = cInfo->sizes; + strides[2] = cInfo->strides; + } + + if (dInfo != nullptr) { + ++numInfos; + if (dInfo->dims != dims) return; + sizes[3] = dInfo->sizes; + strides[3] = dInfo->strides; + } + + // Bail out if sizes do not match: we are using "deprecated pointwise + // behavior" among tensors of different shapes but same number of elements. + for (int i = 1; i < numInfos; ++i) { + for (int j = 0; j < dims; ++j) { + if (sizes[i][j] != sizes[0][j]) return; + } + } + + for (int i = 0; i < dims - 1; ++i) { + // No need to consider dimensions of size 1. + if (sizes[0][i] == 1) continue; + + for (int j = i + 1; j < dims; ++j) { + if (sizes[0][j] == 1) continue; + + // Compare the relative sizes of strides between dim #i and dim #j. + bool hasIncreasingStrides = false; + bool hasDecreasingStrides = false; + + for (int k = 0; k < numInfos; k++) { + IndexType stride_i = strides[k][i]; + IndexType stride_j = strides[k][j]; + if (stride_i < stride_j) { + hasIncreasingStrides = true; + } else if (stride_i > stride_j) { + hasDecreasingStrides = true; + } + } + + if (hasIncreasingStrides && !hasDecreasingStrides) { + for (int k = 0; k < numInfos; k++) { + IndexType size = sizes[k][i]; + sizes[k][i] = sizes[k][j]; + sizes[k][j] = size; + + IndexType stride = strides[k][i]; + strides[k][i] = strides[k][j]; + strides[k][j] = stride; + } + } + } + } +} + +// Threads per block for our apply kernel +// FIXME: use occupancy calculator instead +constexpr uint32_t AT_APPLY_THREADS_PER_BLOCK = 512; +constexpr uint32_t AT_APPLY_BLOCKS_PER_SM = 4; + +// The `remaining_steps` argument is used to support Op that operates on +// multiple elements at the same time. Generally, the strategy of ApplyOpN is to +// 1. Initialize `remaining_steps = step`, where `step` is the template arg of +// CUDA_tensor_applyN helpers. The input arg `n` to `apply()` represents the +// number of elements in bound for this call. It will almost always equal to +// `step` except at boundaries. +// 2. If `remaining_steps > 0` convert the current linearIndex to offset (if in +// bound), and recursively call `ApplyOpN` with `remaining_steps - 1`. +// 3. At `remaining_steps = 0`, +// if `step = 1`, call `op(tensor1_val, tensor2_val, ...)`; +// if `step > 1`, call `op(n, tensor1_val1, tensor1_val2, ..., tesor1_valstep, +// tensor2_val1, tensor2_val2, ..., tesor2_valstep, +// ... +// tensorN_val1, tensorN_val2, ..., tesorN_valstep);` +// +// See NOTE [ CUDA_tensor_applyN helpers ] above for how Op may look like. + +template +struct ApplyOp1 { +__device__ __forceinline__ +static void apply(detail::TensorInfo &a, const Op &op, int n, + IndexType linearIndex, Offsets... aOffsets) { + // Convert `linearIndex` into an offset of `a` + const IndexType aOffset = sizeof...(Offsets) < n ? + detail::IndexToOffset::get(linearIndex, a) : 0; + + ApplyOp1::apply( + a, op, n, linearIndex + 1, aOffsets..., aOffset + ); +} +}; + +// Specialize `step=1` case (i.e., `remaining_steps=0` and `len(Offsets)=1`). +// We don't need to pass in how many elements need to processed in this case. +template +struct ApplyOp1 { +__device__ __forceinline__ +static void apply(detail::TensorInfo &a, const Op &op, + int n, IndexType linearIndex, Offset offset) { + op(a.data[offset]); +} +}; + +template +struct ApplyOp1 { +__device__ __forceinline__ +static void apply(detail::TensorInfo &a, const Op &op, int n, + IndexType linearIndex, Offsets... offsets) { + op(n, a.data[offsets]...); +} +}; + +template +#if __CUDA_ARCH__ >= 350 || defined __HIP_PLATFORM_HCC__ +C10_LAUNCH_BOUNDS_2(AT_APPLY_THREADS_PER_BLOCK, AT_APPLY_BLOCKS_PER_SM) +#endif +__global__ void kernelPointwiseApply1(detail::TensorInfo a, + IndexType totalElements, const Op op) { + for (IndexType linearIndex = (blockIdx.x * blockDim.x + threadIdx.x) * step; + linearIndex < totalElements; + linearIndex += gridDim.x * blockDim.x * step) { + ApplyOp1::apply( + a, op, ::min(step, static_cast(totalElements - linearIndex)), linearIndex); + } +} + + +template +struct ApplyOp2 { +__device__ __forceinline__ +static void apply(detail::TensorInfo &a, + detail::TensorInfo &b, + const Op &op, int n, IndexType linearIndex, + Offsets... aOffsets, Offsets... bOffsets) { + // Convert `linearIndex` into an offset of `a` + const IndexType aOffset = sizeof...(Offsets) < n ? + detail::IndexToOffset::get(linearIndex, a) : 0; + + // Convert `linearIndex` into an offset of `b` + const IndexType bOffset = sizeof...(Offsets) < n ? + detail::IndexToOffset::get(linearIndex, b) : 0; + + ApplyOp2::apply( + a, b, op, n, linearIndex + 1, aOffsets..., aOffset, bOffsets..., bOffset + ); +} +}; + +// Specialize `step=1` case (i.e., `remaining_steps=0` and `len(Offsets)=1`). +// We don't need to pass in how many elements need to processed in this case. +template +struct ApplyOp2 { +__device__ __forceinline__ +static void apply(detail::TensorInfo &a, + detail::TensorInfo &b, + const Op &op, int n, IndexType linearIndex, + Offset aOffset, Offset bOffset) { + op(a.data[aOffset], b.data[bOffset]); +} +}; + +template +struct ApplyOp2 { +__device__ __forceinline__ +static void apply(detail::TensorInfo &a, + detail::TensorInfo &b, + const Op &op, int n, IndexType linearIndex, + Offsets... aOffsets, Offsets... bOffsets) { + op(n, a.data[aOffsets]..., b.data[bOffsets]...); +} +}; + +template +#if __CUDA_ARCH__ >= 350 || defined __HIP_PLATFORM_HCC__ +C10_LAUNCH_BOUNDS_2(AT_APPLY_THREADS_PER_BLOCK, AT_APPLY_BLOCKS_PER_SM) +#endif +__global__ void +kernelPointwiseApply2(detail::TensorInfo a, + detail::TensorInfo b, + IndexType totalElements, + const Op op) { + for (IndexType linearIndex = (blockIdx.x * blockDim.x + threadIdx.x) * step; + linearIndex < totalElements; + linearIndex += gridDim.x * blockDim.x * step) { + ApplyOp2::apply( + a, b, op, ::min(step, static_cast(totalElements - linearIndex)), + linearIndex); + } +} + + +template +struct ApplyOp3 { +__device__ __forceinline__ +static void apply(detail::TensorInfo &a, + detail::TensorInfo &b, + detail::TensorInfo &c, + const Op &op, int n, IndexType linearIndex, + Offsets... aOffsets, Offsets... bOffsets, + Offsets... cOffsets) { + // Convert `linearIndex` into an offset of `a` + const IndexType aOffset = sizeof...(Offsets) < n ? + detail::IndexToOffset::get(linearIndex, a) : 0; + + // Convert `linearIndex` into an offset of `b` + const IndexType bOffset = sizeof...(Offsets) < n ? + detail::IndexToOffset::get(linearIndex, b) : 0; + + // Convert `linearIndex` into an offset of `c` + const IndexType cOffset = sizeof...(Offsets) < n ? + detail::IndexToOffset::get(linearIndex, c) : 0; + + ApplyOp3::apply( + a, b, c, op, n, linearIndex + 1, aOffsets..., aOffset, bOffsets..., bOffset, + cOffsets..., cOffset + ); +} +}; + +// Specialize `step=1` case (i.e., `remaining_steps=0` and `len(Offsets)=1`). +// We don't need to pass in how many elements need to processed in this case. +template +struct ApplyOp3 { +__device__ __forceinline__ +static void apply(detail::TensorInfo &a, + detail::TensorInfo &b, + detail::TensorInfo &c, + const Op &op, int n, IndexType linearIndex, + Offset aOffset, Offset bOffset, Offset cOffset) { + op(a.data[aOffset], b.data[bOffset], c.data[cOffset]); +} +}; + +template +struct ApplyOp3 { +__device__ __forceinline__ +static void apply(detail::TensorInfo &a, + detail::TensorInfo &b, + detail::TensorInfo &c, + const Op &op, int n, IndexType linearIndex, + Offsets... aOffsets, Offsets... bOffsets, + Offsets... cOffsets) { + op(n, a.data[aOffsets]..., b.data[bOffsets]..., c.data[cOffsets]...); +} +}; + + +template +#if __CUDA_ARCH__ >= 350 || defined __HIP_PLATFORM_HCC__ +C10_LAUNCH_BOUNDS_2(AT_APPLY_THREADS_PER_BLOCK, AT_APPLY_BLOCKS_PER_SM) +#endif +__global__ void +kernelPointwiseApply3(detail::TensorInfo a, + detail::TensorInfo b, + detail::TensorInfo c, + IndexType totalElements, + const Op op) { + for (IndexType linearIndex = (blockIdx.x * blockDim.x + threadIdx.x) * step; + linearIndex < totalElements; + linearIndex += gridDim.x * blockDim.x * step) { + ApplyOp3::apply( + a, b, c, op, ::min(step, static_cast(totalElements - linearIndex)), linearIndex); + } +} + + +template +struct ApplyOp4 { +__device__ __forceinline__ +static void apply(detail::TensorInfo &a, + detail::TensorInfo &b, + detail::TensorInfo &c, + detail::TensorInfo &d, + const Op &op, int n, IndexType linearIndex, + Offsets... aOffsets, Offsets... bOffsets, + Offsets... cOffsets, Offsets... dOffsets) { + // Convert `linearIndex` into an offset of `a` + const IndexType aOffset = sizeof...(Offsets) < n ? + detail::IndexToOffset::get(linearIndex, a) : 0; + + // Convert `linearIndex` into an offset of `b` + const IndexType bOffset = sizeof...(Offsets) < n ? + detail::IndexToOffset::get(linearIndex, b) : 0; + + // Convert `linearIndex` into an offset of `c` + const IndexType cOffset = sizeof...(Offsets) < n ? + detail::IndexToOffset::get(linearIndex, c) : 0; + + // Convert `linearIndex` into an offset of `d` + const IndexType dOffset = sizeof...(Offsets) < n ? + detail::IndexToOffset::get(linearIndex, d) : 0; + + ApplyOp4::apply( + a, b, c, d, op, n, linearIndex + 1, aOffsets..., aOffset, bOffsets..., bOffset, + cOffsets..., cOffset, dOffsets..., dOffset + ); +} +}; + +// Specialize `step=1` case (i.e., `remaining_steps=0` and `len(Offsets)=1`). +// We don't need to pass in how many elements need to processed in this case. +template +struct ApplyOp4 { +__device__ __forceinline__ +static void apply(detail::TensorInfo &a, + detail::TensorInfo &b, + detail::TensorInfo &c, + detail::TensorInfo &d, + const Op &op, int n, IndexType linearIndex, + Offset aOffset, Offset bOffset, + Offset cOffset, Offset dOffset) { + op(a.data[aOffset], b.data[bOffset], c.data[cOffset], d.data[dOffset]); +} +}; + +template +struct ApplyOp4 { +__device__ __forceinline__ +static void apply(detail::TensorInfo &a, + detail::TensorInfo &b, + detail::TensorInfo &c, + detail::TensorInfo &d, + const Op &op, int n, IndexType linearIndex, + Offsets... aOffsets, Offsets... bOffsets, + Offsets... cOffsets, Offsets... dOffsets) { + op(n, a.data[aOffsets]..., b.data[bOffsets]..., c.data[cOffsets]..., d.data[dOffsets]...); +} +}; + +template +#if __CUDA_ARCH__ >= 350 || defined __HIP_PLATFORM_HCC__ +C10_LAUNCH_BOUNDS_2(AT_APPLY_THREADS_PER_BLOCK, AT_APPLY_BLOCKS_PER_SM) +#endif +__global__ void +kernelPointwiseApply4(detail::TensorInfo a, + detail::TensorInfo b, + detail::TensorInfo c, + detail::TensorInfo d, + IndexType totalElements, + const Op op) { + for (IndexType linearIndex = (blockIdx.x * blockDim.x + threadIdx.x) * step; + linearIndex < totalElements; + linearIndex += gridDim.x * blockDim.x * step) { + ApplyOp4::apply( + a, b, c, d, op, ::min(step, static_cast(totalElements - linearIndex)), linearIndex); + } +} + +} // namespace + +/** + Computes ceil(a / b) +*/ +template +__host__ __device__ __forceinline__ T ATenCeilDiv(T a, T b) { + return (a + b - 1) / b; +} + +template +inline bool getApplyGrid(uint64_t totalElements, dim3& grid, int64_t curDevice) { + if (curDevice == -1) return false; + uint64_t numel_per_thread = static_cast(AT_APPLY_THREADS_PER_BLOCK) * static_cast(step); + uint64_t numBlocks = ATenCeilDiv(totalElements, numel_per_thread); + uint64_t maxGridX = at::cuda::getDeviceProperties(curDevice)->maxGridSize[0]; + if (numBlocks > maxGridX) + numBlocks = maxGridX; + grid = dim3(numBlocks); + return true; +} + +inline dim3 getApplyBlock() { + return dim3(AT_APPLY_THREADS_PER_BLOCK); +} + +template +inline bool CUDA_tensor_apply2(at::Tensor a, + at::Tensor b, + const Op op, + TensorArgType aType = TensorArgType::ReadWrite, + TensorArgType bType = TensorArgType::ReadOnly) { + checkBackend("CUDA_tensor_apply2", {a, b}, Backend::CUDA); + int64_t totalElements = a.numel(); + + if (totalElements != b.numel()) { + return false; + } + + if (a.dim() > MAX_TENSORINFO_DIMS || + b.dim() > MAX_TENSORINFO_DIMS) { + return false; + } + + if (a.numel() == 0) { + // Empty tensor; do nothing + return true; + } + const dim3 block = getApplyBlock(); + + dim3 grid; + int64_t curDevice = current_device(); + if (curDevice == -1) return false; + if (!getApplyGrid(totalElements, grid, curDevice)) { + return false; + } + + /* + Expands readable/writable tensors whose indices may be "overlapped." + This ensures that each element of the tensor is operated on once and only + once. + */ + Tensor oldA; + Tensor oldB; + + if (aType == TensorArgType::ReadWrite && detail::maybeOverlappingIndices(a)) { + // Must perform in contiguous space + oldA = a; + a = a.contiguous(); + } + if (bType == TensorArgType::ReadWrite && detail::maybeOverlappingIndices(b)) { + // Must perform in contiguous space + oldB = b; + b = b.contiguous(); + } + + // It is possible that the tensor dimensions are able to be collapsed, + // and thus we can reduce the actual code complexity of the copy by + // exploiting this knowledge statically, since the div/mod is the + // most expensive part of the operation, more so than memory accesses. + // For instance, when copying a non-contiguous to a contiguous tensor + // (or vice versa), the contiguous tensor can be collapsed to one + // dimension, and the loop to translate the linear index to the array + // index can be similarly collapsed. That is what this unrolling is for. + +#define HANDLE_CASE(TYPE, A, B) \ + kernelPointwiseApply2 \ + <<>>( \ + aInfo, bInfo, static_cast(totalElements), op); + +#define HANDLE_B_CASE(TYPE, A, B) { \ + switch (B) { \ + case 1: \ + HANDLE_CASE(TYPE, A, 1); \ + break; \ + case 2: \ + HANDLE_CASE(TYPE, A, 2); \ + break; \ + default: \ + HANDLE_CASE(TYPE, A, -1); \ + break; \ + } \ +} + +#define HANDLE_A_CASE(TYPE, A, B) { \ + switch (A) { \ + case 1: \ + HANDLE_B_CASE(TYPE, 1, B); \ + break; \ + case 2: \ + HANDLE_B_CASE(TYPE, 2, B); \ + break; \ + default: \ + HANDLE_B_CASE(TYPE, -1, B); \ + break; \ + } \ +} + + if (detail::canUse32BitIndexMath(a) && + detail::canUse32BitIndexMath(b)) { + detail::TensorInfo aInfo = + detail::getTensorInfo(a); + + detail::TensorInfo bInfo = + detail::getTensorInfo(b); + rearrangeDims(&aInfo, &bInfo); + aInfo.collapseDims(); + bInfo.collapseDims(); + + HANDLE_A_CASE(unsigned int, aInfo.dims, bInfo.dims); + } else { + detail::TensorInfo aInfo = + detail::getTensorInfo(a); + + detail::TensorInfo bInfo = + detail::getTensorInfo(b); + rearrangeDims(&aInfo, &bInfo); + aInfo.collapseDims(); + bInfo.collapseDims(); + + /* + Only instantiates the all 1D special case and the fallback all nD case for + large (64-bit indexed) tensors to reduce compilation time. + */ + if (aInfo.dims == 1 && bInfo.dims == 1) { + HANDLE_CASE(uint64_t, 1, 1); + } else { + HANDLE_CASE(uint64_t, -1, -1); + } + } +#undef HANDLE_CASE +#undef HANDLE_B_CASE +#undef HANDLE_A_CASE + + if (oldA.defined()) { + // Ignore overlaps when copying back; if we use copy + // instead, it will recursively try and invoke ourselves to make + // oldA contiguous. + at::native::legacy::cuda::_th_copy_ignoring_overlaps_(oldA, a); + } + + if (oldB.defined()) { + // Ignore overlaps when copying back; if we use copy + // instead, it will recursively try and invoke ourselves to make + // oldB contiguous. + at::native::legacy::cuda::_th_copy_ignoring_overlaps_(oldB, b); + } + + return true; +} + +/* Provides default step = 1 to CUDA_tensor_apply2. */ +template +inline bool CUDA_tensor_apply2(at::Tensor a, + at::Tensor b, + const Op op, + TensorArgType aType = TensorArgType::ReadWrite, + TensorArgType bType = TensorArgType::ReadOnly) { + return CUDA_tensor_apply2(a, b, op, aType, bType); +} + + +template +inline bool CUDA_tensor_apply3(at::Tensor a, + at::Tensor b, + at::Tensor c, + const Op op, + TensorArgType aType = TensorArgType::ReadWrite, + TensorArgType bType = TensorArgType::ReadOnly, + TensorArgType cType = TensorArgType::ReadOnly) { + checkBackend("CUDA_tensor_apply3", {a, b, c}, Backend::CUDA); + int64_t totalElements = a.numel(); + + if (totalElements != b.numel() || + totalElements != c.numel()) { + return false; + } + + if (a.dim() > MAX_TENSORINFO_DIMS || + b.dim() > MAX_TENSORINFO_DIMS || + c.dim() > MAX_TENSORINFO_DIMS) { + return false; + } + + if (a.numel() == 0) { + // Empty tensor; do nothing + return true; + } + + const dim3 block = getApplyBlock(); + + dim3 grid; + int64_t curDevice = current_device(); + if (curDevice == -1) return false; + if (!getApplyGrid(totalElements, grid, curDevice)) { + return false; + } + + /* + Expands readable/writable tensors whose indices may be "overlapped." + This ensures that each element of the tensor is operated on once and only + once. + */ + Tensor oldA; + Tensor oldB; + Tensor oldC; + + if (aType == TensorArgType::ReadWrite && detail::maybeOverlappingIndices(a)) { + // Must perform in contiguous space + oldA = a; + a = a.contiguous(); + } + if (bType == TensorArgType::ReadWrite && detail::maybeOverlappingIndices(b)) { + // Must perform in contiguous space + oldB = b; + b = b.contiguous(); + } + if (cType == TensorArgType::ReadWrite && detail::maybeOverlappingIndices(c)) { + // Must perform in contiguous space + oldC = c; + c = c.contiguous(); + } + +#define HANDLE_CASE(TYPE, A, B, C) \ + kernelPointwiseApply3 \ + <<>>( \ + aInfo, bInfo, cInfo, static_cast(totalElements), op); + +#define HANDLE_C_CASE(TYPE, A, B, C) { \ + switch (C) { \ + case 1: \ + HANDLE_CASE(TYPE, A, B, 1); \ + break; \ + case 2: \ + HANDLE_CASE(TYPE, A, B, 2); \ + break; \ + default: \ + HANDLE_CASE(TYPE, A, B, -1); \ + break; \ + } \ +} + +#define HANDLE_B_CASE(TYPE, A, B, C) { \ + switch (B) { \ + case 1: \ + HANDLE_C_CASE(TYPE, A, 1, C); \ + break; \ + case 2: \ + HANDLE_C_CASE(TYPE, A, 2, C); \ + break; \ + default: \ + HANDLE_C_CASE(TYPE, A, -1, C); \ + break; \ + } \ +} + +#define HANDLE_A_CASE(TYPE, A, B, C) { \ + switch (A) { \ + case 1: \ + HANDLE_B_CASE(TYPE, 1, B, C); \ + break; \ + case 2: \ + HANDLE_B_CASE(TYPE, 2, B, C); \ + break; \ + default: \ + HANDLE_B_CASE(TYPE, -1, B, C); \ + break; \ + } \ +} + + if (detail::canUse32BitIndexMath(a) && + detail::canUse32BitIndexMath(b) && + detail::canUse32BitIndexMath(c)) { + detail::TensorInfo aInfo = + detail::getTensorInfo(a); + + detail::TensorInfo bInfo = + detail::getTensorInfo(b); + + detail::TensorInfo cInfo = + detail::getTensorInfo(c); + + rearrangeDims(&aInfo, &bInfo, &cInfo); + aInfo.collapseDims(); + bInfo.collapseDims(); + cInfo.collapseDims(); + + HANDLE_A_CASE(unsigned int, aInfo.dims, bInfo.dims, cInfo.dims); + } else { + detail::TensorInfo aInfo = + detail::getTensorInfo(a); + + detail::TensorInfo bInfo = + detail::getTensorInfo(b); + + detail::TensorInfo cInfo = + detail::getTensorInfo(c); + + rearrangeDims(&aInfo, &bInfo, &cInfo); + aInfo.collapseDims(); + bInfo.collapseDims(); + cInfo.collapseDims(); + + /* + Only instantiates the all 1D special case and the fallback all nD case for + large (64-bit indexed) tensors to reduce compilation time. + */ + if (aInfo.dims == 1 && bInfo.dims == 1 && cInfo.dims == 1) { + HANDLE_CASE(uint64_t, 1, 1, 1); + } else { + HANDLE_CASE(uint64_t, -1, -1, -1); + } + } +#undef HANDLE_CASE +#undef HANDLE_C_CASE +#undef HANDLE_B_CASE +#undef HANDLE_A_CASE + + if (oldA.defined()) { + // Ignore overlaps when copying back; if we use THCTensor_copy + // instead, it will recursively try and invoke ourselves to make + // oldA contiguous. + at::native::legacy::cuda::_th_copy_ignoring_overlaps_(oldA, a); + a = oldA; + } + + if (oldB.defined()) { + // Ignore overlaps when copying back; if we use THCTensor_copy + // instead, it will recursively try and invoke ourselves to make + // oldB contiguous. + at::native::legacy::cuda::_th_copy_ignoring_overlaps_(oldB, b); + b = oldB; + } + + if (oldC.defined()) { + // Ignore overlaps when copying back; if we use THCTensor_copy + // instead, it will recursively try and invoke ourselves to make + // oldC contiguous. + at::native::legacy::cuda::_th_copy_ignoring_overlaps_(oldC, c); + c = oldC; + } + + return true; +} + +/* Provides default step = 1 to CUDA_tensor_apply3. */ +template +inline bool CUDA_tensor_apply3(at::Tensor a, + at::Tensor b, + at::Tensor c, + const Op op, + TensorArgType aType = TensorArgType::ReadWrite, + TensorArgType bType = TensorArgType::ReadOnly, + TensorArgType cType = TensorArgType::ReadOnly) { + return CUDA_tensor_apply3( + a, b, c, op, aType, bType, cType); +} + + +template +inline bool CUDA_tensor_apply4(at::Tensor a, + at::Tensor b, + at::Tensor c, + at::Tensor d, + const Op op, + TensorArgType aType = TensorArgType::ReadWrite, + TensorArgType bType = TensorArgType::ReadOnly, + TensorArgType cType = TensorArgType::ReadOnly, + TensorArgType dType = TensorArgType::ReadOnly) { + checkBackend("CUDA_tensor_apply4", {a, b, c, d}, Backend::CUDA); + int64_t totalElements = a.numel(); + + if (totalElements != b.numel() || + totalElements != c.numel() || + totalElements != d.numel()) { + return false; + } + + if (a.dim() > MAX_TENSORINFO_DIMS || + b.dim() > MAX_TENSORINFO_DIMS || + c.dim() > MAX_TENSORINFO_DIMS || + d.dim() > MAX_TENSORINFO_DIMS) { + return false; + } + + if (a.numel() == 0) { + // Empty tensor; do nothing + return true; + } + + const dim3 block = getApplyBlock(); + + dim3 grid; + int64_t curDevice = current_device(); + if (curDevice == -1) return false; + if (!getApplyGrid(totalElements, grid, curDevice)) { + return false; + } + + /* + Expands readable/writable tensors whose indices may be "overlapped." + This ensures that each element of the tensor is operated on once and only + once. + */ + Tensor oldA; + Tensor oldB; + Tensor oldC; + Tensor oldD; + + if (aType == TensorArgType::ReadWrite && detail::maybeOverlappingIndices(a)) { + // Must perform in contiguous space + oldA = a; + a = a.contiguous(); + } + if (bType == TensorArgType::ReadWrite && detail::maybeOverlappingIndices(b)) { + // Must perform in contiguous space + oldB = b; + b = b.contiguous(); + } + if (cType == TensorArgType::ReadWrite && detail::maybeOverlappingIndices(c)) { + // Must perform in contiguous space + oldC = c; + c = c.contiguous(); + } + if (dType == TensorArgType::ReadWrite && detail::maybeOverlappingIndices(c)) { + // Must perform in contiguous space + oldD = d; + d = d.contiguous(); + } + +#define HANDLE_CASE(TYPE, A, B, C, D) \ + kernelPointwiseApply4 \ + <<>>( \ + aInfo, bInfo, cInfo, dInfo, static_cast(totalElements), op); + +#define HANDLE_D_CASE(TYPE, A, B, C, D) { \ + switch (D) { \ + case 1: \ + HANDLE_CASE(TYPE, A, B, C, 1); \ + break; \ + case 2: \ + HANDLE_CASE(TYPE, A, B, C, 2); \ + break; \ + default: \ + HANDLE_CASE(TYPE, A, B, C, -1); \ + break; \ + } \ +} + +#define HANDLE_C_CASE(TYPE, A, B, C, D) { \ + switch (C) { \ + case 1: \ + HANDLE_D_CASE(TYPE, A, B, 1, D); \ + break; \ + case 2: \ + HANDLE_D_CASE(TYPE, A, B, 2, D); \ + break; \ + default: \ + HANDLE_D_CASE(TYPE, A, B, -1, D); \ + break; \ + } \ +} + +#define HANDLE_B_CASE(TYPE, A, B, C, D) { \ + switch (B) { \ + case 1: \ + HANDLE_C_CASE(TYPE, A, 1, C, D); \ + break; \ + case 2: \ + HANDLE_C_CASE(TYPE, A, 2, C, D); \ + break; \ + default: \ + HANDLE_C_CASE(TYPE, A, -1, C, D); \ + break; \ + } \ +} + +#define HANDLE_A_CASE(TYPE, A, B, C, D) { \ + switch (A) { \ + case 1: \ + HANDLE_B_CASE(TYPE, 1, B, C, D); \ + break; \ + case 2: \ + HANDLE_B_CASE(TYPE, 2, B, C, D); \ + break; \ + default: \ + HANDLE_B_CASE(TYPE, -1, B, C, D); \ + break; \ + } \ +} + + if (detail::canUse32BitIndexMath(a) && + detail::canUse32BitIndexMath(b) && + detail::canUse32BitIndexMath(c) && + detail::canUse32BitIndexMath(d)) { + detail::TensorInfo aInfo = + detail::getTensorInfo(a); + + detail::TensorInfo bInfo = + detail::getTensorInfo(b); + + detail::TensorInfo cInfo = + detail::getTensorInfo(c); + + detail::TensorInfo dInfo = + detail::getTensorInfo(d); + + rearrangeDims(&aInfo, &bInfo, &cInfo, &dInfo); + aInfo.collapseDims(); + bInfo.collapseDims(); + cInfo.collapseDims(); + dInfo.collapseDims(); + + HANDLE_A_CASE(unsigned int, aInfo.dims, bInfo.dims, cInfo.dims, dInfo.dims); + } else { + detail::TensorInfo aInfo = + detail::getTensorInfo(a); + + detail::TensorInfo bInfo = + detail::getTensorInfo(b); + + detail::TensorInfo cInfo = + detail::getTensorInfo(c); + + detail::TensorInfo dInfo = + detail::getTensorInfo(d); + + rearrangeDims(&aInfo, &bInfo, &cInfo, &dInfo); + aInfo.collapseDims(); + bInfo.collapseDims(); + cInfo.collapseDims(); + dInfo.collapseDims(); + + /* + Only instantiates the all 1D special case and the fallback all nD case for + large (64-bit indexed) tensors to reduce compilation time. + */ + if (aInfo.dims == 1 && bInfo.dims == 1 && cInfo.dims == 1 && dInfo.dims == 1) { + HANDLE_CASE(uint64_t, 1, 1, 1, 1); + } else { + HANDLE_CASE(uint64_t, -1, -1, -1, -1); + } + } +#undef HANDLE_CASE +#undef HANDLE_D_CASE +#undef HANDLE_C_CASE +#undef HANDLE_B_CASE +#undef HANDLE_A_CASE + + if (oldA.defined()) { + // Ignore overlaps when copying back; if we use THCTensor_copy + // instead, it will recursively try and invoke ourselves to make + // oldA contiguous. + at::native::legacy::cuda::_th_copy_ignoring_overlaps_(oldA, a); + } + + if (oldB.defined()) { + // Ignore overlaps when copying back; if we use THCTensor_copy + // instead, it will recursively try and invoke ourselves to make + // oldB contiguous. + at::native::legacy::cuda::_th_copy_ignoring_overlaps_(oldB, b); + } + + if (oldC.defined()) { + // Ignore overlaps when copying back; if we use THCTensor_copy + // instead, it will recursively try and invoke ourselves to make + // oldC contiguous. + at::native::legacy::cuda::_th_copy_ignoring_overlaps_(oldC, c); + } + + if (oldD.defined()) { + // Ignore overlaps when copying back; if we use THCTensor_copy + // instead, it will recursively try and invoke ourselves to make + // oldC contiguous. + at::native::legacy::cuda::_th_copy_ignoring_overlaps_(oldD, c); + } + + return true; +} + +/* Provides default step = 1 to CUDA_tensor_apply4. */ +template +inline bool CUDA_tensor_apply4(at::Tensor a, + at::Tensor b, + at::Tensor c, + at::Tensor d, + const Op op, + TensorArgType aType = TensorArgType::ReadWrite, + TensorArgType bType = TensorArgType::ReadOnly, + TensorArgType cType = TensorArgType::ReadOnly, + TensorArgType dType = TensorArgType::ReadOnly) { + return CUDA_tensor_apply4( + a, b, c, d, op, aType, bType, cType); +} + +} // cuda +} // at diff --git a/thirdparty/libtorch/include/ATen/cuda/CUDABlas.h b/thirdparty/libtorch/include/ATen/cuda/CUDABlas.h new file mode 100644 index 0000000000..ae9f3ff93b --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cuda/CUDABlas.h @@ -0,0 +1,68 @@ +#pragma once +/* + Provides a subset of CUDA BLAS functions as templates: + + gemm(stream, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, + ldc) + + gemv(stream, transa, m, n, alpha, a, lda, x, incx, beta, y, incy) + + where Dtype is double, float, at::Half or at::BFloat16(ROCm). The functions are + available in at::cuda::blas namespace. + */ + +#include + +namespace at { +namespace cuda { +namespace blas { + +/* LEVEL 3 BLAS FUNCTIONS */ + +#define CUDABLAS_GEMM_ARGTYPES(Dtype) \ + cudaStream_t stream, char transa, char transb, int64_t m, int64_t n, \ + int64_t k, Dtype alpha, const Dtype *a, int64_t lda, const Dtype *b, \ + int64_t ldb, Dtype beta, Dtype *c, int64_t ldc + +template +inline void gemm(CUDABLAS_GEMM_ARGTYPES(Dtype)) { + AT_ERROR("at::cuda::blas::gemm: not implemented for ", typeid(Dtype).name()); +} + +template <> +void gemm(CUDABLAS_GEMM_ARGTYPES(double)); +template <> +void gemm(CUDABLAS_GEMM_ARGTYPES(float)); +template <> +void gemm(CUDABLAS_GEMM_ARGTYPES(at::Half)); +#ifdef __HIP_PLATFORM_HCC__ +template <> +void gemm(CUDABLAS_GEMM_ARGTYPES(at::BFloat16)); +#endif + +/* LEVEL 2 BLAS FUNCTIONS */ + +#define CUDABLAS_GEMV_ARGTYPES(Dtype) \ + cudaStream_t stream, char trans, int64_t m, int64_t n, Dtype alpha, \ + const Dtype *a, int64_t lda, const Dtype *x, int64_t incx, Dtype beta, \ + Dtype *y, int64_t incy + +template +inline void gemv(CUDABLAS_GEMV_ARGTYPES(Dtype)) { + AT_ERROR("at::cuda::blas::gemv: not implemented for ", typeid(Dtype).name()); +} + +template <> +void gemv(CUDABLAS_GEMV_ARGTYPES(double)); +template <> +void gemv(CUDABLAS_GEMV_ARGTYPES(float)); +template <> +void gemv(CUDABLAS_GEMV_ARGTYPES(at::Half)); +#ifdef __HIP_PLATFORM_HCC__ +template <> +void gemv(CUDABLAS_GEMV_ARGTYPES(at::BFloat16)); +#endif + +} // namespace blas +} // namespace cuda +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/cuda/CUDAConfig.h b/thirdparty/libtorch/include/ATen/cuda/CUDAConfig.h new file mode 100644 index 0000000000..ca23913603 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cuda/CUDAConfig.h @@ -0,0 +1,13 @@ +#pragma once + +// Test these using #if AT_CUDNN_ENABLED(), not #ifdef, so that it's +// obvious if you forgot to include Config.h +// c.f. https://stackoverflow.com/questions/33759787/generating-an-error-if-checked-boolean-macro-is-not-defined +// +// NB: This header MUST NOT be included from other headers; it should +// only be included from C++ files. + +#define AT_CUDNN_ENABLED() 0 +#define AT_ROCM_ENABLED() 0 + +#define NVCC_FLAGS_EXTRA "" diff --git a/thirdparty/libtorch/include/ATen/cuda/CUDAContext.h b/thirdparty/libtorch/include/ATen/cuda/CUDAContext.h new file mode 100644 index 0000000000..fea822d0d1 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cuda/CUDAContext.h @@ -0,0 +1,69 @@ +#pragma once + +#include + +#include +#include +#include + +#include +#include +#include +#include +#include + +namespace at { +namespace cuda { + +/* +A common CUDA interface for ATen. + +This interface is distinct from CUDAHooks, which defines an interface that links +to both CPU-only and CUDA builds. That interface is intended for runtime +dispatch and should be used from files that are included in both CPU-only and +CUDA builds. + +CUDAContext, on the other hand, should be preferred by files only included in +CUDA builds. It is intended to expose CUDA functionality in a consistent +manner. + +This means there is some overlap between the CUDAContext and CUDAHooks, but +the choice of which to use is simple: use CUDAContext when in a CUDA-only file, +use CUDAHooks otherwise. + +Note that CUDAContext simply defines an interface with no associated class. +It is expected that the modules whose functions compose this interface will +manage their own state. There is only a single CUDA context/state. +*/ + +/** + * DEPRECATED: use device_count() instead + */ +inline int64_t getNumGPUs() { + return c10::cuda::device_count(); +} + +/** + * CUDA is available if we compiled with CUDA, and there are one or more + * devices. If we compiled with CUDA but there is a driver problem, etc., + * this function will report CUDA is not available (rather than raise an error.) + */ +inline bool is_available() { + return c10::cuda::device_count() > 0; +} + +TORCH_CUDA_API cudaDeviceProp* getCurrentDeviceProperties(); + +TORCH_CUDA_API int warp_size(); + +TORCH_CUDA_API cudaDeviceProp* getDeviceProperties(int64_t device); + +TORCH_CUDA_API Allocator* getCUDADeviceAllocator(); + +/* Handles */ +TORCH_CUDA_API cusparseHandle_t getCurrentCUDASparseHandle(); +TORCH_CUDA_API cublasHandle_t getCurrentCUDABlasHandle(); + + +} // namespace cuda +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/cuda/CUDADevice.h b/thirdparty/libtorch/include/ATen/cuda/CUDADevice.h new file mode 100644 index 0000000000..9d14ab1627 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cuda/CUDADevice.h @@ -0,0 +1,17 @@ +#pragma once + +#include + +#include +#include + +namespace at { +namespace cuda { + +inline Device getDeviceFromPtr(void* ptr) { + cudaPointerAttributes attr; + AT_CUDA_CHECK(cudaPointerGetAttributes(&attr, ptr)); + return {DeviceType::CUDA, static_cast(attr.device)}; +} + +}} // namespace at::cuda diff --git a/thirdparty/libtorch/include/ATen/cuda/CUDAEvent.h b/thirdparty/libtorch/include/ATen/cuda/CUDAEvent.h new file mode 100644 index 0000000000..bd1e645b27 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cuda/CUDAEvent.h @@ -0,0 +1,185 @@ +#pragma once + +#include +#include +#include +#include +#include +#include + +#include + +#include +#include + +namespace at { namespace cuda { + +/* +* CUDAEvents are movable not copyable wrappers around CUDA's events. +* +* CUDAEvents are constructed lazily when first recorded unless it is +* reconstructed from a cudaIpcEventHandle_t. The event has a device, and this +* device is acquired from the first recording stream. However, if reconstructed +* from a handle, the device should be explicitly specified; or if ipc_handle() is +* called before the event is ever recorded, it will use the current device. +* Later streams that record the event must match this device. +*/ +struct TORCH_CUDA_API CUDAEvent { + // Constructors + // Default value for `flags` is specified below - it's cudaEventDisableTiming + CUDAEvent() {} + CUDAEvent(unsigned int flags) : flags_{flags} {} + + CUDAEvent( + DeviceIndex device_index, const cudaIpcEventHandle_t* handle) { + #ifndef __HIP_PLATFORM_HCC__ + device_index_ = device_index; + CUDAGuard guard(device_index_); + + AT_CUDA_CHECK(cudaIpcOpenEventHandle(&event_, *handle)); + is_created_ = true; + #else + AT_ERROR("cuIpcOpenEventHandle with HIP is not supported"); + #endif + } + + // Note: event destruction done on creating device to avoid creating a + // CUDA context on other devices. + ~CUDAEvent() { + try { + if (is_created_) { + CUDAGuard guard(device_index_); + cudaEventDestroy(event_); + } + } catch (...) { /* No throw */ } + } + + CUDAEvent(const CUDAEvent&) = delete; + CUDAEvent& operator=(const CUDAEvent&) = delete; + + CUDAEvent(CUDAEvent&& other) { moveHelper(std::move(other)); } + CUDAEvent& operator=(CUDAEvent&& other) { + moveHelper(std::move(other)); + return *this; + } + + operator cudaEvent_t() const { return event(); } + + // Less than operator (to allow use in sets) + friend bool operator<(const CUDAEvent& left, const CUDAEvent& right) { + return left.event_ < right.event_; + } + + optional device() const { + if (is_created_) { + return at::Device(at::kCUDA, device_index_); + } else { + return {}; + } + } + + bool isCreated() const { return is_created_; } + DeviceIndex device_index() const {return device_index_;} + cudaEvent_t event() const { return event_; } + + // Note: cudaEventQuery can be safely called from any device + bool query() const { + if (!is_created_) { + return true; + } + + cudaError_t err = cudaEventQuery(event_); + if (err == cudaSuccess) { + return true; + } else if (err != cudaErrorNotReady) { + C10_CUDA_CHECK(err); + } + + return false; + } + + void record() { record(getCurrentCUDAStream()); } + + void recordOnce(const CUDAStream& stream) { + if (!was_recorded_) record(stream); + } + + // Note: cudaEventRecord must be called on the same device as the event. + void record(const CUDAStream& stream) { + if (!is_created_) { + createEvent(stream.device_index()); + } + + TORCH_CHECK(device_index_ == stream.device_index(), "Event device ", device_index_, + " does not match recording stream's device ", stream.device_index(), "."); + CUDAGuard guard(device_index_); + AT_CUDA_CHECK(cudaEventRecord(event_, stream)); + was_recorded_ = true; + } + + // Note: cudaStreamWaitEvent must be called on the same device as the stream. + // The event has no actual GPU resources associated with it. + void block(const CUDAStream& stream) { + if (is_created_) { + CUDAGuard guard(stream.device_index()); + AT_CUDA_CHECK(cudaStreamWaitEvent(stream, event_, 0)); + } + } + + // Note: cudaEventElapsedTime can be safely called from any device + float elapsed_time(const CUDAEvent& other) const { + TORCH_CHECK(is_created_ && other.isCreated(), + "Both events must be recorded before calculating elapsed time."); + float time_ms = 0; + // raise cudaErrorNotReady if either event is recorded but not yet completed + AT_CUDA_CHECK(cudaEventElapsedTime(&time_ms, event_, other.event_)); + return time_ms; + } + + // Note: cudaEventSynchronize can be safely called from any device + void synchronize() const { + if (is_created_) { + AT_CUDA_CHECK(cudaEventSynchronize(event_)); + } + } + + // Note: cudaIpcGetEventHandle must be called on the same device as the event + void ipc_handle(cudaIpcEventHandle_t * handle) { + #ifndef __HIP_PLATFORM_HCC__ + if (!is_created_) { + // this CUDAEvent object was initially constructed from flags but event_ + // is not created yet. + createEvent(getCurrentCUDAStream().device_index()); + } + CUDAGuard guard(device_index_); + AT_CUDA_CHECK(cudaIpcGetEventHandle(handle, event_)); + #else + AT_ERROR("cuIpcGetEventHandle with HIP is not supported"); + #endif + } + +private: + unsigned int flags_ = cudaEventDisableTiming; + bool is_created_ = false; + bool was_recorded_ = false; + DeviceIndex device_index_ = -1; + cudaEvent_t event_; + + void createEvent(DeviceIndex device_index) { + device_index_ = device_index; + CUDAGuard guard(device_index_); + AT_CUDA_CHECK(cudaEventCreateWithFlags(&event_, flags_)); + is_created_ = true; + } + + void moveHelper(CUDAEvent&& other) { + std::swap(flags_, other.flags_); + std::swap(is_created_, other.is_created_); + std::swap(was_recorded_, other.was_recorded_); + std::swap(device_index_, other.device_index_); + std::swap(event_, other.event_); + } +}; + +} // namespace cuda +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/cuda/CUDAMultiStreamGuard.h b/thirdparty/libtorch/include/ATen/cuda/CUDAMultiStreamGuard.h new file mode 100644 index 0000000000..c03b1550a3 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cuda/CUDAMultiStreamGuard.h @@ -0,0 +1,59 @@ +#pragma once + +#include +#include +#include +#include + +#include + +namespace at { namespace cuda { + +// TODO: Implement this generically in c10. You'll need some way to get +// the number of GPUs from the GuardImpl, in that case. +class CUDAMultiStreamGuard final { +public: + /// Calls `set_stream` on each of the streams in the list. + /// This may be useful if you need to set different streams + /// for different devices. + explicit CUDAMultiStreamGuard(ArrayRef streams) : CUDAMultiStreamGuard() { + for (const auto& s : streams) { + setCurrentCUDAStream(s); + } + } + + CUDAMultiStreamGuard() { + const size_t device_count = getNumGPUs(); + original_streams_.reserve(device_count); + for (size_t device = 0; device < device_count; ++device) { + original_streams_.push_back(getCurrentCUDAStream(device)); + } + } + + CUDAMultiStreamGuard(const CUDAGuard&) = delete; + CUDAMultiStreamGuard& operator=(const CUDAGuard&) = delete; + + // See Note [Move construction for RAII guards is tricky] + CUDAMultiStreamGuard(CUDAGuard&& other) = delete; + + // See Note [Move assignment for RAII guards is tricky] + CUDAMultiStreamGuard& operator=(CUDAGuard&& other) = delete; + + ArrayRef original_streams() const { + return original_streams_; + } + + /// Resets the CUDA stream on each device to the one that was active upon + /// construction. + ~CUDAMultiStreamGuard() { + for (const auto& s : original_streams_) { + setCurrentCUDAStream(s); + } + } + +private: + /// The original streams that were active on all devices. + std::vector original_streams_; +}; + +}} // namespace at::cuda diff --git a/thirdparty/libtorch/include/ATen/cuda/CUDATensorMethods.cuh b/thirdparty/libtorch/include/ATen/cuda/CUDATensorMethods.cuh new file mode 100644 index 0000000000..e4e89ea1cd --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cuda/CUDATensorMethods.cuh @@ -0,0 +1,15 @@ +#pragma once + +#include +#include + +#include +#include +#include + +namespace at { +template <> +inline __half* Tensor::data() const { + return reinterpret_cast<__half*>(data()); +} +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/cuda/CUDAUtils.h b/thirdparty/libtorch/include/ATen/cuda/CUDAUtils.h new file mode 100644 index 0000000000..15b5e02664 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cuda/CUDAUtils.h @@ -0,0 +1,20 @@ +#pragma once + +#include + +namespace at { namespace cuda { + +// Check if every tensor in a list of tensors matches the current +// device. +inline bool check_device(ArrayRef ts) { + if (ts.empty()) { + return true; + } + Device curDevice = Device(kCUDA, current_device()); + for (const Tensor& t : ts) { + if (t.device() != curDevice) return false; + } + return true; +} + +}} // namespace at::cuda diff --git a/thirdparty/libtorch/include/ATen/cuda/Exceptions.h b/thirdparty/libtorch/include/ATen/cuda/Exceptions.h new file mode 100644 index 0000000000..8ffa6ffce2 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cuda/Exceptions.h @@ -0,0 +1,103 @@ +#pragma once + +#include +#include +#include +#include +#include + +// See Note [CHECK macro] +#define AT_CUDNN_CHECK(EXPR) \ + do { \ + cudnnStatus_t status = EXPR; \ + if (status != CUDNN_STATUS_SUCCESS) { \ + if (status == CUDNN_STATUS_NOT_SUPPORTED) { \ + AT_ERROR( \ + "cuDNN error: ", \ + cudnnGetErrorString(status), \ + ". This error may appear if you passed in a non-contiguous input."); \ + } else { \ + AT_ERROR("cuDNN error: ", cudnnGetErrorString(status)); \ + } \ + } \ + } while (0) + +namespace at { namespace cuda { namespace blas { +const char* _cublasGetErrorEnum(cublasStatus_t error); +}}} // namespace at::cuda::blas + +#define TORCH_CUDABLAS_CHECK(EXPR) \ + do { \ + cublasStatus_t __err = EXPR; \ + TORCH_CHECK(__err == CUBLAS_STATUS_SUCCESS, \ + "CUDA error: ", \ + at::cuda::blas::_cublasGetErrorEnum(__err), \ + " when calling `" #EXPR "`"); \ + } while (0) + +const char *cusparseGetErrorString(cusparseStatus_t status); + +#define TORCH_CUDASPARSE_CHECK(EXPR) \ + do { \ + cusparseStatus_t __err = EXPR; \ + TORCH_CHECK(__err == CUSPARSE_STATUS_SUCCESS, \ + "CUDA error: ", \ + cusparseGetErrorString(__err), \ + " when calling `" #EXPR "`"); \ + } while (0) + +#define AT_CUDA_CHECK(EXPR) C10_CUDA_CHECK(EXPR) + +// For CUDA Driver API +// +// This is here instead of in c10 because NVRTC is loaded dynamically via a stub +// in ATen, and we need to use its nvrtcGetErrorString. +// See NOTE [ USE OF NVRTC AND DRIVER API ]. +#ifndef __HIP_PLATFORM_HCC__ + +#define AT_CUDA_DRIVER_CHECK(EXPR) \ + do { \ + CUresult __err = EXPR; \ + if (__err != CUDA_SUCCESS) { \ + const char* err_str; \ + CUresult get_error_str_err C10_UNUSED = at::globalContext().getNVRTC().cuGetErrorString(__err, &err_str); \ + if (get_error_str_err != CUDA_SUCCESS) { \ + AT_ERROR("CUDA driver error: unknown error"); \ + } else { \ + AT_ERROR("CUDA driver error: ", err_str); \ + } \ + } \ + } while (0) + +#else + +#define AT_CUDA_DRIVER_CHECK(EXPR) \ + do { \ + CUresult __err = EXPR; \ + if (__err != CUDA_SUCCESS) { \ + AT_ERROR("CUDA driver error: ", static_cast(__err)); \ + } \ + } while (0) + +#endif + +// For CUDA NVRTC +// +// Note: As of CUDA 10, nvrtc error code 7, NVRTC_ERROR_BUILTIN_OPERATION_FAILURE, +// incorrectly produces the error string "NVRTC unknown error." +// The following maps it correctly. +// +// This is here instead of in c10 because NVRTC is loaded dynamically via a stub +// in ATen, and we need to use its nvrtcGetErrorString. +// See NOTE [ USE OF NVRTC AND DRIVER API ]. +#define AT_CUDA_NVRTC_CHECK(EXPR) \ + do { \ + nvrtcResult __err = EXPR; \ + if (__err != NVRTC_SUCCESS) { \ + if (static_cast(__err) != 7) { \ + AT_ERROR("CUDA NVRTC error: ", at::globalContext().getNVRTC().nvrtcGetErrorString(__err)); \ + } else { \ + AT_ERROR("CUDA NVRTC error: NVRTC_ERROR_BUILTIN_OPERATION_FAILURE"); \ + } \ + } \ + } while (0) diff --git a/thirdparty/libtorch/include/ATen/cuda/NumericLimits.cuh b/thirdparty/libtorch/include/ATen/cuda/NumericLimits.cuh new file mode 100644 index 0000000000..7081e94837 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cuda/NumericLimits.cuh @@ -0,0 +1,121 @@ +#pragma once + +#include +#include +#include +#include + +// NumericLimits.cuh is a holder for numeric limits definitions of commonly used +// types. This header is very specific to ROCm HIP and may be removed in the future. +// This header is derived from the legacy THCNumerics.cuh. + +// The lower_bound and upper_bound constants are same as lowest and max for +// integral types, but are -inf and +inf for floating point types. They are +// useful in implementing min, max, etc. + +namespace at { + +template +struct numeric_limits { +}; + +// WARNING: the following at::numeric_limits definitions are there only to support +// HIP compilation for the moment. Use std::numeric_limits if you are not +// compiling for ROCm. +// from @colesbury: "The functions on numeric_limits aren't marked with +// __device__ which is why they don't work with ROCm. CUDA allows them +// because they're constexpr." + +namespace { + // ROCm doesn't like INFINITY too. + constexpr double inf = INFINITY; +} + +template <> +struct numeric_limits { + static inline __host__ __device__ bool lowest() { return false; } + static inline __host__ __device__ bool max() { return true; } + static inline __host__ __device__ bool lower_bound() { return false; } + static inline __host__ __device__ bool upper_bound() { return true; } +}; + +template <> +struct numeric_limits { + static inline __host__ __device__ uint8_t lowest() { return 0; } + static inline __host__ __device__ uint8_t max() { return UINT8_MAX; } + static inline __host__ __device__ uint8_t lower_bound() { return 0; } + static inline __host__ __device__ uint8_t upper_bound() { return UINT8_MAX; } +}; + +template <> +struct numeric_limits { + static inline __host__ __device__ int8_t lowest() { return INT8_MIN; } + static inline __host__ __device__ int8_t max() { return INT8_MAX; } + static inline __host__ __device__ int8_t lower_bound() { return INT8_MIN; } + static inline __host__ __device__ int8_t upper_bound() { return INT8_MAX; } +}; + +template <> +struct numeric_limits { + static inline __host__ __device__ int16_t lowest() { return INT16_MIN; } + static inline __host__ __device__ int16_t max() { return INT16_MAX; } + static inline __host__ __device__ int16_t lower_bound() { return INT16_MIN; } + static inline __host__ __device__ int16_t upper_bound() { return INT16_MAX; } +}; + +template <> +struct numeric_limits { + static inline __host__ __device__ int32_t lowest() { return INT32_MIN; } + static inline __host__ __device__ int32_t max() { return INT32_MAX; } + static inline __host__ __device__ int32_t lower_bound() { return INT32_MIN; } + static inline __host__ __device__ int32_t upper_bound() { return INT32_MAX; } +}; + +template <> +struct numeric_limits { +#ifdef _MSC_VER + static inline __host__ __device__ int64_t lowest() { return _I64_MIN; } + static inline __host__ __device__ int64_t max() { return _I64_MAX; } + static inline __host__ __device__ int64_t lower_bound() { return _I64_MIN; } + static inline __host__ __device__ int64_t upper_bound() { return _I64_MAX; } +#else + static inline __host__ __device__ int64_t lowest() { return INT64_MIN; } + static inline __host__ __device__ int64_t max() { return INT64_MAX; } + static inline __host__ __device__ int64_t lower_bound() { return INT64_MIN; } + static inline __host__ __device__ int64_t upper_bound() { return INT64_MAX; } +#endif +}; + +template <> +struct numeric_limits { + static inline __host__ __device__ at::Half lowest() { return at::Half(0xFBFF, at::Half::from_bits()); } + static inline __host__ __device__ at::Half max() { return at::Half(0x7BFF, at::Half::from_bits()); } + static inline __host__ __device__ at::Half lower_bound() { return at::Half(0xFC00, at::Half::from_bits()); } + static inline __host__ __device__ at::Half upper_bound() { return at::Half(0x7C00, at::Half::from_bits()); } +}; + +template <> +struct numeric_limits { + static inline __host__ __device__ at::BFloat16 lowest() { return at::BFloat16(0xFF7F, at::BFloat16::from_bits()); } + static inline __host__ __device__ at::BFloat16 max() { return at::BFloat16(0x7F7F, at::BFloat16::from_bits()); } + static inline __host__ __device__ at::BFloat16 lower_bound() { return at::BFloat16(0xFF80, at::BFloat16::from_bits()); } + static inline __host__ __device__ at::BFloat16 upper_bound() { return at::BFloat16(0x7F80, at::BFloat16::from_bits()); } +}; + +template <> +struct numeric_limits { + static inline __host__ __device__ float lowest() { return -FLT_MAX; } + static inline __host__ __device__ float max() { return FLT_MAX; } + static inline __host__ __device__ float lower_bound() { return -static_cast(inf); } + static inline __host__ __device__ float upper_bound() { return static_cast(inf); } +}; + +template <> +struct numeric_limits { + static inline __host__ __device__ double lowest() { return -DBL_MAX; } + static inline __host__ __device__ double max() { return DBL_MAX; } + static inline __host__ __device__ double lower_bound() { return -inf; } + static inline __host__ __device__ double upper_bound() { return inf; } +}; + +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/cuda/PinnedMemoryAllocator.h b/thirdparty/libtorch/include/ATen/cuda/PinnedMemoryAllocator.h new file mode 100644 index 0000000000..e980908857 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cuda/PinnedMemoryAllocator.h @@ -0,0 +1,9 @@ +#pragma once + +#include + +namespace at { namespace cuda { + +TORCH_CUDA_API at::Allocator* getPinnedMemoryAllocator(); + +}} // namespace at::cuda diff --git a/thirdparty/libtorch/include/ATen/cuda/detail/CUDAHooks.h b/thirdparty/libtorch/include/ATen/cuda/detail/CUDAHooks.h new file mode 100644 index 0000000000..afd2394f16 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cuda/detail/CUDAHooks.h @@ -0,0 +1,40 @@ +#include + +#include +#include + +// TODO: No need to have this whole header, we can just put it all in +// the cpp file + +namespace at { namespace cuda { namespace detail { + +// The real implementation of CUDAHooksInterface +struct CUDAHooks : public at::CUDAHooksInterface { + CUDAHooks(at::CUDAHooksArgs) {} + std::unique_ptr initCUDA() const override; + Device getDeviceFromPtr(void* data) const override; + bool isPinnedPtr(void* data) const override; + Generator* getDefaultCUDAGenerator(DeviceIndex device_index = -1) const override; + bool hasCUDA() const override; + bool hasMAGMA() const override; + bool hasCuDNN() const override; + const at::cuda::NVRTC& nvrtc() const override; + int64_t current_device() const override; + bool hasPrimaryContext(int64_t device_index) const override; + c10::optional getDevceIndexWithPrimaryContext() const override; + Allocator* getPinnedMemoryAllocator() const override; + bool compiledWithCuDNN() const override; + bool compiledWithMIOpen() const override; + bool supportsDilatedConvolutionWithCuDNN() const override; + bool supportsDepthwiseConvolutionWithCuDNN() const override; + long versionCuDNN() const override; + std::string showConfig() const override; + double batchnormMinEpsilonCuDNN() const override; + int64_t cuFFTGetPlanCacheMaxSize(int64_t device_index) const override; + void cuFFTSetPlanCacheMaxSize(int64_t device_index, int64_t max_size) const override; + int64_t cuFFTGetPlanCacheSize(int64_t device_index) const override; + void cuFFTClearPlanCache(int64_t device_index) const override; + int getNumGPUs() const override; +}; + +}}} // at::cuda::detail diff --git a/thirdparty/libtorch/include/ATen/cuda/detail/DeviceThreadHandles.h b/thirdparty/libtorch/include/ATen/cuda/detail/DeviceThreadHandles.h new file mode 100644 index 0000000000..8ef1112533 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cuda/detail/DeviceThreadHandles.h @@ -0,0 +1,141 @@ +// Some stateful GPU libraries, such as cuDNN, cuBLAS, use handles to store states. +// These handles are tied to device, and these libraries requires/recommends not to +// share handles across host threads. +// +// These libraries recommend using one handle per host thread. We may not want to do +// this because threads are relatively light-weight, but creating and destroying +// handles is expensive (destroying the handle causes synchronizations). DataParallel, +// for example, creates new threads for each forward pass. +// +// This file implements a handle pool mechanism. The handle pool returns handles on +// demand as threads request them. If all existing handles in the pool are in use, +// it creates a new one. As threads terminate, they release handles back into the pool. +// In this way, the handle pool never creates more handles than the high-water mark of +// active threads, so it's efficient with DataParallel. + +#pragma once + +#include +#include +#include +#include +#include + +#include + +namespace at { namespace cuda { namespace { + +template +struct DeviceThreadHandlePool { + + struct Handle { + Handle_t handle; + Handle(bool create = false) : handle(nullptr) + { + if(create) Create(&handle); + } + // std::vector.emplace() and push_back() may route through temporaries and call + // copy/move constructors along the way. If this is the case, we don't want + // the destructors of temporaries to call cudnnDestroy on the handle. + // We can achieve safety (for the narrow case of stashing within std::vectors) + // by making Handle moveable but not copyable, and transferring handle ownership + // to the latest constructed object. This is not a substitute for full-blown + // reference counting, but reference counting may be overkill here. + // Another alternative is to wrap the saved Handles in unique_ptrs, i.e., + // unordered_map>> created_handles; + Handle(const Handle& rhs) = delete; + // Following https://stackoverflow.com/questions/3279543/what-is-the-copy-and-swap-idiom + Handle(Handle&& rhs) : Handle() { std::swap(handle, rhs.handle); } + // operator= takes argument by value + Handle& operator=(Handle rhs) { std::swap(handle, rhs.handle); return *this; } + ~Handle() { + if(handle) Destroy(handle); + } + }; + + std::mutex mutex; + + // Handles are lazily created as different threads request them, + // but are never destroyed until the end of the process. + // The maximum number of handles this process will create for each device is equal + // to the high-water mark of the number of concurrently active threads that request + // handles for that device. + // When threads terminate, they release their handles back into the pool for reuse. + // Otherwise, new handles would be created every time new threads were spawned, + // resulting in poor performance for Python modules that repeatedly or frequently + // spawned new sets of threads (like DataParallel, which creates a new set of threads + // for each forward pass). + // + // To prevent potential deadlocks, we explicitly choose not to cap the number + // of handles that are created per device. + // Example of danger: If we cap the max handles at 4, and 5 threads are sharing a device, + // only 4 can make forward progress at any time. The other 4 will not release their + // handles until they exit, so the fifth cannot make progress until then. This is + // not a problem...UNLESS all 5 threads attempt some sort of synchronization at an + // intermediate point (ie, before any of them have exited). We have no way to anticipate + // or enforce that user threads will not attempt such intermediate synchronization. + // The only way to ensure safety is to avoid imposing a cap on the number of handles. + std::unordered_map> created_handles; + std::unordered_map> available_handles; + + // PoolWindow lazily creates and caches the handles that a particular thread is using, + // so in the common case handle access doesn't incur either handle creation or a mutex lock. + class PoolWindow + { + DeviceThreadHandlePool &parent; + public: + PoolWindow(DeviceThreadHandlePool &parent): parent(parent) {} + ~PoolWindow(){ release(); } + + Handle_t reserve(int device) + { + // If this thread already has a handle for this device, return it + if(my_handles.find(device) != my_handles.end()) + return my_handles[device]; + + // otherwise, either grab a handle from the pool if one is available, + // or if not, create a new one. + std::lock_guard guard(parent.mutex); + + if(parent.available_handles[device].size() > 0) + { + my_handles[device] = parent.available_handles[device].back(); + parent.available_handles[device].pop_back(); + } + else + { + // In local testing, I do observe that emplace_back sometimes routes through temporaries + // that incur move-constructor and destructor calls. See comments in Handle above. + parent.created_handles[device].emplace_back(true /*create*/); + my_handles[device] = parent.created_handles[device].back().handle; + } + + return my_handles[device]; + } + + private: + // Stores the per-device handles currently owned by this thread + std::unordered_map my_handles; + + // Called by the destructor. Releases this thread's handles back into the pool. + void release() { + if(my_handles.size() > 0) { + std::lock_guard guard(parent.mutex); + for(auto d_h : my_handles) + parent.available_handles[d_h.first].push_back(d_h.second); + } + } + }; + + // Warning: + // If you want to change this function, be aware that this function will be called + // by multiple threads and there is no mutex guarding the call of this function, so + // make sure your implementation is thread-safe. + PoolWindow *newPoolWindow() { + // The returned pointer will be owned by a thread local variable + // so that different threads does not share the same PoolWindow. + return new PoolWindow(*this); + } +}; + +}}} // namespace at::cuda::detail:: diff --git a/thirdparty/libtorch/include/ATen/cuda/detail/IndexUtils.cuh b/thirdparty/libtorch/include/ATen/cuda/detail/IndexUtils.cuh new file mode 100644 index 0000000000..315897ae65 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cuda/detail/IndexUtils.cuh @@ -0,0 +1,32 @@ +#pragma once + +#include +#include +#include + +namespace at { +namespace cuda { +namespace detail { + +TORCH_CUDA_API bool maybeOverlappingIndices(const at::Tensor& t); +TORCH_CUDA_API bool canUse32BitIndexMath(const at::Tensor &t, int64_t max_elem=std::numeric_limits::max()); + +template +TensorInfo +getTensorInfo(const at::Tensor& t) { + IndexType sz[MAX_TENSORINFO_DIMS]; + IndexType st[MAX_TENSORINFO_DIMS]; + + int dims = t.dim(); + for (int i = 0; i < dims; ++i) { + sz[i] = t.size(i); + st[i] = t.stride(i); + } + + return TensorInfo( + t.data_ptr(), dims, sz, st); +} + +} // detail +} // cuda +} // at diff --git a/thirdparty/libtorch/include/ATen/cuda/detail/KernelUtils.h b/thirdparty/libtorch/include/ATen/cuda/detail/KernelUtils.h new file mode 100644 index 0000000000..af788ff8f8 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cuda/detail/KernelUtils.h @@ -0,0 +1,31 @@ +#pragma once + +#include + +// Contents of this file are copied from THCUNN/common.h for the ease of porting +// THCUNN functions into ATen. + +namespace at { namespace cuda { namespace detail { + +// CUDA: grid stride looping +// +// int64_t _i_n_d_e_x specifically prevents overflow in the loop increment. +// If input.numel() < INT_MAX, _i_n_d_e_x < INT_MAX, except after the final +// iteration of the loop where _i_n_d_e_x += blockDim.x * gridDim.x can be +// greater than INT_MAX. But in that case _i_n_d_e_x >= n, so there are no +// further iterations and the overflowed value in i=_i_n_d_e_x is not used. +#define CUDA_KERNEL_LOOP(i, n) \ + int64_t _i_n_d_e_x = blockIdx.x * blockDim.x + threadIdx.x; \ + for (int i=_i_n_d_e_x; _i_n_d_e_x < (n); _i_n_d_e_x+=blockDim.x * gridDim.x, i=_i_n_d_e_x) + +// Use 1024 threads per block, which requires cuda sm_2x or above +constexpr int CUDA_NUM_THREADS = 1024; + +// CUDA: number of blocks for threads. +inline int GET_BLOCKS(const int N) +{ + AT_ASSERTM(N > 0, "CUDA kernel launch blocks must be positive, but got N=", N); + return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; +} + +}}} // namespace at::cuda::detail diff --git a/thirdparty/libtorch/include/ATen/cuda/detail/OffsetCalculator.cuh b/thirdparty/libtorch/include/ATen/cuda/detail/OffsetCalculator.cuh new file mode 100644 index 0000000000..e43967db4d --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cuda/detail/OffsetCalculator.cuh @@ -0,0 +1,64 @@ +#pragma once + +#include +#include +#include +#include +#include + +/// OffsetCalculator calculates the offset in bytes of a linear index for NARGS +/// operands that share the same shape, but may have different strides. + +#ifdef __HIP_PLATFORM_HCC__ +constexpr int MAX_DIMS = 16; +#else +constexpr int MAX_DIMS = 25; +#endif + +template +struct OffsetCalculator { + // The offset for each argument (in bytes). Wrapper around fixed-size array. + using offset_type = at::detail::Array; + + OffsetCalculator(int dims, const int64_t* sizes, const int64_t* const* strides) : dims(dims) { + TORCH_CHECK(dims <= MAX_DIMS, "tensor has too many (>", MAX_DIMS, ") dims"); + for (int i = 0; i < MAX_DIMS; ++i) { + if (i < dims) { + sizes_[i] = IntDivider(sizes[i]); + } else { + sizes_[i] = IntDivider(1); + } + for (int arg = 0; arg < NARGS; arg++) { + strides_[i][arg] = i < dims ? strides[arg][i] : 0; + } + } + } + + C10_HOST_DEVICE offset_type get(index_t linear_idx) const { + offset_type offsets; + #pragma unroll + for (int arg = 0; arg < NARGS; arg++) { + offsets[arg] = 0; + } + + #pragma unroll + for (int dim = 0; dim < MAX_DIMS; ++dim) { + if (dim == dims) { + break; + } + auto divmod = sizes_[dim].divmod(linear_idx); + linear_idx = divmod.div; + + #pragma unroll + for (int arg = 0; arg < NARGS; arg++) { + offsets[arg] += divmod.mod * strides_[dim][arg]; + } + + } + return offsets; + } + + int dims; + IntDivider sizes_[MAX_DIMS]; + index_t strides_[MAX_DIMS][NARGS]; +}; diff --git a/thirdparty/libtorch/include/ATen/cuda/detail/TensorInfo.cuh b/thirdparty/libtorch/include/ATen/cuda/detail/TensorInfo.cuh new file mode 100644 index 0000000000..b5fcbe2223 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cuda/detail/TensorInfo.cuh @@ -0,0 +1,121 @@ +#pragma once + +#include +#include + +namespace at { +namespace cuda { +namespace detail { + +#define MAX_TENSORINFO_DIMS 25 + +// CUDA kernel argument that defines tensor layout +template +struct TensorInfo { + TensorInfo(); + TensorInfo(T* p, + int dim, + IndexType sz[MAX_TENSORINFO_DIMS], + IndexType st[MAX_TENSORINFO_DIMS]); + + // Set the size of the given dimension to 1, as if it were a + // reduction dim (allows you to calculate offsets of the reduction + // slice) + void reduceDim(int dim); + + // See note on [collapse dims]. + int collapseDims(const int excludeDim = -1); + + // Contiguous tensors of more than one dimension are collapsed down + // to one tensor + __host__ __device__ inline bool isContiguous() const { + return (dims == 1 && strides[0] == 1); + } + + T* data; + IndexType sizes[MAX_TENSORINFO_DIMS]; + IndexType strides[MAX_TENSORINFO_DIMS]; + int dims; +}; + +template +TensorInfo::TensorInfo() { + data = nullptr; + dims = 0; +} + +template +TensorInfo::TensorInfo(T* p, + int dim, + IndexType sz[MAX_TENSORINFO_DIMS], + IndexType st[MAX_TENSORINFO_DIMS]) { + data = p; + dims = dim; + AT_ASSERT(dims < MAX_TENSORINFO_DIMS); + + for (int i = 0; i < dim; ++i) { + sizes[i] = sz[i]; + strides[i] = st[i]; + } +} + +template +void +TensorInfo::reduceDim(int dim) { + TORCH_CHECK(dim < dims && dim >= 0, "expected dim between 0 and dims - 1"); + sizes[dim] = 1; +} + +template +int +TensorInfo::collapseDims(const int excludeDim) { + auto result = at::collapse_dims(sizes, strides, dims, excludeDim); + dims = std::get<1>(result); + return std::get<0>(result); +} + +// Translate a linear index for the apply to a T* offset; +// specialized on `Dims` to reduce nvcc compilation time +template +struct IndexToOffset { + static __host__ __device__ IndexType get( + IndexType linearId, + const TensorInfo& info) { + + IndexType offset = 0; + + // Uses static dims + for (int i = Dims - 1; i > 0; --i) { + IndexType curDimIndex = linearId % info.sizes[i]; + IndexType curDimOffset = curDimIndex * info.strides[i]; + offset += curDimOffset; + linearId /= info.sizes[i]; + } + + return offset + linearId * info.strides[0]; + } +}; + +// Uses dynamic (runtime) instead of static (compiletime) dims +template +struct IndexToOffset { + static inline __host__ __device__ IndexType get( + IndexType linearId, + const TensorInfo& info) { + + IndexType offset = 0; + + for (int i = info.dims - 1; i > 0; --i) { + IndexType curDimIndex = linearId % info.sizes[i]; + IndexType curDimOffset = curDimIndex * info.strides[i]; + offset += curDimOffset; + linearId /= info.sizes[i]; + } + + return offset + linearId * info.strides[0]; + } +}; + +} // detail +} // cuda +} // at diff --git a/thirdparty/libtorch/include/ATen/cudnn/Descriptors.h b/thirdparty/libtorch/include/ATen/cudnn/Descriptors.h new file mode 100644 index 0000000000..6853017cde --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cudnn/Descriptors.h @@ -0,0 +1,287 @@ +#pragma once + +#include +#include + +#include +#include +#include +#include +#include +#include + +namespace at { namespace native { + +// TODO: Add constructors for all of the descriptors + +inline int dataSize(cudnnDataType_t dataType) +{ + switch (dataType) { + case CUDNN_DATA_HALF: return 2; + case CUDNN_DATA_FLOAT: return 4; + default: return 8; + } +} + +// The stride for a size-1 dimensions is not uniquely determined; in +// fact, it can be anything you want, because the fact that the +// tensor is size 1 at this dimension means that you will never actually +// try advancing your pointer by this stride. +// +// However, CuDNN has a much more stringent requirement on strides: +// if you are passing a contiguous input, it better be the case +// that the stride for dim i is the product of the sizes of dims +// i+1 to the end. This stride is indeed uniquely determined. This +// function modifies 'stride' in place so this invariant holds. +static inline void fixSizeOneDimStride(int dim, const int *size, int *stride) { + int64_t z = 1; + for(int d = dim-1; d >= 0; d--) + { + if (size[d] == 1) { + stride[d] = z; + } else { + z *= size[d]; + } + } +} + +template +struct DescriptorDeleter { + void operator()(T* x) { + if (x != nullptr) { + AT_CUDNN_CHECK(dtor(x)); + } + } +}; + +// A generic class for wrapping cuDNN descriptor types. All you need +// is to give the underlying type the Descriptor_t points to (usually, +// if it's cudnnTensorDescriptor_t it points to cudnnTensorStruct), +// the constructor and the destructor. Subclasses are responsible +// for defining a set() function to actually set the descriptor. +// +// Descriptors default construct to a nullptr, and have a descriptor +// initialized the first time you call set() or any other initializing +// function. +template +class TORCH_CUDA_API Descriptor +{ +public: + // TODO: Figure out why const-correctness doesn't work here + + // Use desc() to access the underlying descriptor pointer in + // a read-only fashion. Most client code should use this. + // If the descriptor was never initialized, this will return + // nullptr. + T* desc() const { return desc_.get(); } + T* desc() { return desc_.get(); } + + // Use mut_desc() to access the underlying desciptor pointer + // if you intend to modify what it points to (e.g., using + // cudnnSetFooDescriptor). This will ensure that the descriptor + // is initialized. Code in this file will use this function. + T* mut_desc() { init(); return desc_.get(); } +protected: + void init() { + if (desc_ == nullptr) { + T* raw_desc; + AT_CUDNN_CHECK(ctor(&raw_desc)); + desc_.reset(raw_desc); + } + } +private: + std::unique_ptr> desc_; +}; + +class TORCH_CUDA_API TensorDescriptor + : public Descriptor +{ +public: + TensorDescriptor() {} + explicit TensorDescriptor(const at::Tensor &t, size_t pad = 0) { + set(t, pad); + } + + // Note [CuDNN broadcast padding] + // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + // pad specifies the minimum dimensionality of the tensor descriptor + // we produce (it doesn't have anything to do with, e.g., convolution + // padding). If 't' is lower-dimensional than 'pad', the remaining + // dimensions (on the right) are padded with ones. This doesn't + // affect the underlying data layout. This is particularly useful for + // dealing with a pecularity of the CuDNN API, which is that broadcasting in CuDNN is + // done in two steps: first, the client code is expected to pad out + // (the dimensions) input tensors to be the same dimension as the + // target broadcast, and then second, CuDNN takes of actually + // broadcasting size 1 dimensions. + + void set(const at::Tensor &t, size_t pad = 0); + void set(cudnnDataType_t dataType, IntArrayRef sizes, IntArrayRef strides, size_t pad = 0); + + void print(); + +private: + void set(cudnnDataType_t dataType, int dim, int* size, int* stride) { + fixSizeOneDimStride(dim, size, stride); + AT_CUDNN_CHECK(cudnnSetTensorNdDescriptor(mut_desc(), dataType, dim, size, stride)); + } +}; + +std::ostream& operator<<(std::ostream & out, const TensorDescriptor& d); + +class FilterDescriptor + : public Descriptor +{ +public: + void set(const at::Tensor &t, int64_t pad = 0); + +private: + void set(cudnnDataType_t dataType, int dim, int* size, cudnnTensorFormat_t filter_format) { + AT_CUDNN_CHECK(cudnnSetFilterNdDescriptor(mut_desc(), dataType, filter_format, dim, size)); + } +}; + +struct TORCH_CUDA_API ConvolutionDescriptor + : public Descriptor +{ + void set(cudnnDataType_t dataType, int dim, int* pad, int* stride, int * upscale /* aka dilation */, int groups) { + cudnnDataType_t mathType = dataType; + if (dataType == CUDNN_DATA_HALF) mathType = CUDNN_DATA_FLOAT; + AT_CUDNN_CHECK(cudnnSetConvolutionNdDescriptor(mut_desc(), dim, pad, stride, upscale, + CUDNN_CROSS_CORRELATION, mathType)); + AT_CUDNN_CHECK(cudnnSetConvolutionGroupCount(mut_desc(), groups)); + // See Note [behavior of cudnnFind and cudnnGet] + AT_CUDNN_CHECK(cudnnSetConvolutionMathType(mut_desc(), CUDNN_DEFAULT_MATH)); + if(dataType == CUDNN_DATA_HALF) + AT_CUDNN_CHECK(cudnnSetConvolutionMathType(mut_desc(), CUDNN_TENSOR_OP_MATH)); + + } +}; + +struct TORCH_CUDA_API SpatialTransformerDescriptor + : public Descriptor +{ + void set(cudnnDataType_t dataType, int dim, int* size) { + AT_CUDNN_CHECK(cudnnSetSpatialTransformerNdDescriptor(mut_desc(), CUDNN_SAMPLER_BILINEAR, dataType, dim, size)); + } +}; + +struct TORCH_CUDA_API DropoutDescriptor + : public Descriptor +{ + at::Tensor state; + + // Initialize a dropout descriptor's RNG state. + // WARNING: This function is very expensive, avoid calling this function! + // NB: it takes a Type so that we can generate a Variable if necessary. + void initialize_rng(cudnnHandle_t handle, float dropout, long long int seed, const TensorOptions& options) { + AT_ASSERTM(dropout > 0, "dropout must be nonzero; otherwise call set_no_dropout"); + size_t state_size; + AT_CUDNN_CHECK(cudnnDropoutGetStatesSize(handle, &state_size)); + AT_ASSERT(options.device().type() == kCUDA); + AT_ASSERT(options.dtype() == kByte); + state = at::empty({static_cast(state_size)}, options); + setCuDNNStreamToCurrent(); + AT_CUDNN_CHECK(cudnnSetDropoutDescriptor(mut_desc(), handle, dropout, state.data_ptr(), state_size, seed)); + } + + // Restore a dropout descriptor given a dropout probability and existing RNG state. + void set(cudnnHandle_t handle, float dropout, at::Tensor state_) { + AT_ASSERTM(dropout > 0, "dropout must be nonzero; otherwise call set_no_dropout"); + state = state_; + void *state_ptr = state.data_ptr(); + size_t state_size = state.size(0); + // NB: The seed doesn't actually matter, so we give a dummy value + setCuDNNStreamToCurrent(); + AT_CUDNN_CHECK(cudnnRestoreDropoutDescriptor(mut_desc(), handle, dropout, state_ptr, state_size, 0 /* seed */)); + } + + // Restore a dropout descriptor corresponding to no dropout + void set_no_dropout(cudnnHandle_t handle) { + // NB: seed doesn't matter when dropout = 0, because no random number + // initialization actually takes place when there is no dropout. + // NB: Empirically, cudnnSetDropoutDescriptor is cheap when + // dropoot == 0 + AT_CUDNN_CHECK(cudnnSetDropoutDescriptor(mut_desc(), handle, 0 /* dropout */, nullptr, 0 /* state_size */, 0 /* seed */)); + } +}; + +struct TORCH_CUDA_API RNNDescriptor + : public Descriptor +{ + DropoutDescriptor dropout_desc_; + void set(cudnnHandle_t handle, int hidden_size, int num_layers, DropoutDescriptor&& dropout_desc, + cudnnRNNInputMode_t input_mode, cudnnDirectionMode_t bidirectional, + cudnnRNNMode_t mode, cudnnDataType_t datatype, cudnnDataType_t input_type, cudnnRNNAlgo_t algo) { + dropout_desc_ = std::move(dropout_desc); + AT_CUDNN_CHECK(cudnnSetRNNDescriptor_v6( + handle, + mut_desc(), + hidden_size, + num_layers, + dropout_desc_.desc(), + input_mode, + bidirectional, + mode, + algo, + datatype)); +#if CUDA_VERSION >= 9000 + cudaDeviceProp* prop = at::cuda::getCurrentDeviceProperties(); + if (prop->major >= 7) { + if (input_type == CUDNN_DATA_HALF) { + cudnnSetRNNMatrixMathType(mut_desc(), CUDNN_TENSOR_OP_MATH); + } else { + // Technically, as the default it's not necessary to explicitly + // set this. + cudnnSetRNNMatrixMathType(mut_desc(), CUDNN_DEFAULT_MATH); + } + } +#endif + } +}; + +struct TORCH_CUDA_API CTCLossDescriptor + : public Descriptor +{ + void set(cudnnDataType_t datatype) { + AT_CUDNN_CHECK(cudnnSetCTCLossDescriptor(mut_desc(), datatype)); + } +#if CUDNN_VERSION >= 7600 + void setEx( + cudnnDataType_t datatype, + cudnnLossNormalizationMode_t normMode, + cudnnNanPropagation_t gradMode) { + AT_CUDNN_CHECK( + cudnnSetCTCLossDescriptorEx(mut_desc(), datatype, normMode, gradMode)); + } +#endif +}; + +union Constant +{ + float f; + double d; + Constant(cudnnDataType_t dataType, double value) { + if (dataType == CUDNN_DATA_HALF || dataType == CUDNN_DATA_FLOAT) { + f = static_cast(value); + } else { + d = value; + } + } +}; + +}} // namespace diff --git a/pyrevolve/examples/__init__.py b/thirdparty/libtorch/include/ATen/cudnn/Exceptions.h similarity index 100% rename from pyrevolve/examples/__init__.py rename to thirdparty/libtorch/include/ATen/cudnn/Exceptions.h diff --git a/thirdparty/libtorch/include/ATen/cudnn/Handle.h b/thirdparty/libtorch/include/ATen/cudnn/Handle.h new file mode 100644 index 0000000000..408d2edf99 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cudnn/Handle.h @@ -0,0 +1,10 @@ +#pragma once + +#include +#include + +namespace at { namespace native { + +TORCH_CUDA_API cudnnHandle_t getCudnnHandle(); + +}} // namespace at::native diff --git a/thirdparty/libtorch/include/ATen/cudnn/Handles.h b/thirdparty/libtorch/include/ATen/cudnn/Handles.h new file mode 100644 index 0000000000..5b9a081f0c --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cudnn/Handles.h @@ -0,0 +1,2 @@ +#pragma once +#include diff --git a/thirdparty/libtorch/include/ATen/cudnn/Types.h b/thirdparty/libtorch/include/ATen/cudnn/Types.h new file mode 100644 index 0000000000..70e5dd4ab7 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cudnn/Types.h @@ -0,0 +1,12 @@ +#pragma once + +#include +#include + +namespace at { namespace native { + +cudnnDataType_t getCudnnDataType(const at::Tensor& tensor); + +int64_t cudnn_version(); + +}} // namespace at::cudnn diff --git a/thirdparty/libtorch/include/ATen/cudnn/Utils.h b/thirdparty/libtorch/include/ATen/cudnn/Utils.h new file mode 100644 index 0000000000..7d6405798d --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cudnn/Utils.h @@ -0,0 +1,27 @@ +#pragma once + +#include +#include +#include +#include +#include + +namespace at { namespace native { + +inline void setCuDNNStreamToCurrent() { + // TODO: Should getCurrentStream be a method on Context? + AT_CUDNN_CHECK(cudnnSetStream(getCudnnHandle(), at::cuda::getCurrentCUDAStream())); +} + +// cuDNN has a buggy check for tensor being contiguous (that is, it does +// not ignore stride for dimension that is equal to 0). This function +// makes tensors which have zero stride contiguous, by setting the +// strides to 1 as cuDNN likes. +inline Tensor contiguousIfZeroInStrides(const Tensor& t) { + for (auto s : t.strides()) { + if (s == 0) return t.contiguous(); + } + return t; +} + +}} diff --git a/thirdparty/libtorch/include/ATen/cudnn/cudnn-wrapper.h b/thirdparty/libtorch/include/ATen/cudnn/cudnn-wrapper.h new file mode 100644 index 0000000000..66a926fe57 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/cudnn/cudnn-wrapper.h @@ -0,0 +1,16 @@ +#pragma once + +#include + +#define STRINGIFY(x) #x +#define STRING(x) STRINGIFY(x) + +#if CUDNN_MAJOR < 6 +#pragma message ("CuDNN v" STRING(CUDNN_MAJOR) " found, but need at least CuDNN v6. You can get the latest version of CuDNN from https://developer.nvidia.com/cudnn or disable CuDNN with USE_CUDNN=0") +#pragma message "We strongly encourage you to move to 6.0 and above." +#pragma message "This message is intended to annoy you enough to update." +#endif + +#undef STRINGIFY +#undef STRING + diff --git a/thirdparty/libtorch/include/ATen/detail/CPUGuardImpl.h b/thirdparty/libtorch/include/ATen/detail/CPUGuardImpl.h new file mode 100644 index 0000000000..65751c15f5 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/detail/CPUGuardImpl.h @@ -0,0 +1,61 @@ +#pragma once + +#include +#include + +namespace at { +namespace detail { + +struct CPUGuardImpl final : public c10::impl::DeviceGuardImplInterface { + CPUGuardImpl() {} + DeviceType type() const override { + return DeviceType::CPU; + } + Device exchangeDevice(Device) const override { + // no-op + return Device(DeviceType::CPU, -1); + + } + Device getDevice() const override { + return Device(DeviceType::CPU, -1); + } + void setDevice(Device) const override { + // no-op + } + void uncheckedSetDevice(Device d) const noexcept override { + // no-op + } + Stream getStream(Device d) const noexcept override { + // no-op + return Stream(Stream::DEFAULT, Device(DeviceType::CPU, -1)); + } + // NB: These do NOT set the current device + Stream exchangeStream(Stream s) const noexcept override { + // no-op + return Stream(Stream::DEFAULT, Device(DeviceType::CPU, -1)); + } + DeviceIndex deviceCount() const noexcept override { + return 1; + } + + // Event-related functions + void record(void** event, + const Stream& stream, + const DeviceIndex device_index, + const EventFlag flag) const override { + TORCH_CHECK(false, "CPU backend doesn't support events."); + } + void block( + void* event, + const Stream& stream) const override { + TORCH_CHECK(false, "CPU backend doesn't support events.") + } + bool queryEvent(void* event) const override { + TORCH_CHECK(false, "CPU backend doesn't support events.") + } + void destroyEvent( + void* event, + const DeviceIndex device_index) const noexcept override { } +}; + +}} // namespace at::detail diff --git a/thirdparty/libtorch/include/ATen/detail/CUDAHooksInterface.h b/thirdparty/libtorch/include/ATen/detail/CUDAHooksInterface.h new file mode 100644 index 0000000000..46cf5b4367 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/detail/CUDAHooksInterface.h @@ -0,0 +1,171 @@ +#pragma once + +#include +#include +#include +#include +#include + +#include +#include +#include + +// Forward-declares THCState +struct THCState; + +// Forward-declares at::cuda::NVRTC +namespace at { namespace cuda { +struct NVRTC; +}} // at::cuda + +namespace at { +class Context; +} + +// NB: Class must live in `at` due to limitations of Registry.h. +namespace at { + +constexpr const char* CUDA_HELP = + "PyTorch splits its backend into two shared libraries: a CPU library " + "and a CUDA library; this error has occurred because you are trying " + "to use some CUDA functionality, but the CUDA library has not been " + "loaded by the dynamic linker for some reason. The CUDA library MUST " + "be loaded, EVEN IF you don't directly use any symbols from the CUDA library! " + "One common culprit is a lack of -Wl,--no-as-needed in your link arguments; many " + "dynamic linkers will delete dynamic library dependencies if you don't " + "depend on any of their symbols. You can check if this has occurred by " + "using ldd on your binary to see if there is a dependency on *_cuda.so " + "library."; + +// The CUDAHooksInterface is an omnibus interface for any CUDA functionality +// which we may want to call into from CPU code (and thus must be dynamically +// dispatched, to allow for separate compilation of CUDA code). How do I +// decide if a function should live in this class? There are two tests: +// +// 1. Does the *implementation* of this function require linking against +// CUDA libraries? +// +// 2. Is this function *called* from non-CUDA ATen code? +// +// (2) should filter out many ostensible use-cases, since many times a CUDA +// function provided by ATen is only really ever used by actual CUDA code. +// +// TODO: Consider putting the stub definitions in another class, so that one +// never forgets to implement each virtual function in the real implementation +// in CUDAHooks. This probably doesn't buy us much though. +struct CAFFE2_API CUDAHooksInterface { + // This should never actually be implemented, but it is used to + // squelch -Werror=non-virtual-dtor + virtual ~CUDAHooksInterface() {} + + // Initialize THCState and, transitively, the CUDA state + virtual std::unique_ptr initCUDA() const { + TORCH_CHECK(false, "Cannot initialize CUDA without ATen_cuda library. ", CUDA_HELP); + } + + virtual Generator* getDefaultCUDAGenerator(DeviceIndex device_index = -1) const { + TORCH_CHECK(false, "Cannot get default CUDA generator without ATen_cuda library. ", CUDA_HELP); + } + + virtual Device getDeviceFromPtr(void* data) const { + TORCH_CHECK(false, "Cannot get device of pointer on CUDA without ATen_cuda library. ", CUDA_HELP); + } + + virtual bool isPinnedPtr(void* data) const { + return false; + } + + virtual bool hasCUDA() const { + return false; + } + + virtual bool hasMAGMA() const { + return false; + } + + virtual bool hasCuDNN() const { + return false; + } + + virtual const at::cuda::NVRTC& nvrtc() const { + TORCH_CHECK(false, "NVRTC requires CUDA. ", CUDA_HELP); + } + + virtual int64_t current_device() const { + return -1; + } + + virtual bool hasPrimaryContext(int64_t device_index) const { + TORCH_CHECK(false, "Cannot call hasPrimaryContext(", device_index, ") without ATen_cuda library. ", CUDA_HELP); + } + + virtual c10::optional getDevceIndexWithPrimaryContext() const { + return c10::nullopt; + } + + virtual Allocator* getPinnedMemoryAllocator() const { + TORCH_CHECK(false, "Pinned memory requires CUDA. ", CUDA_HELP); + } + + virtual bool compiledWithCuDNN() const { + return false; + } + + virtual bool compiledWithMIOpen() const { + return false; + } + + virtual bool supportsDilatedConvolutionWithCuDNN() const { + return false; + } + + virtual bool supportsDepthwiseConvolutionWithCuDNN() const { + return false; + } + + virtual long versionCuDNN() const { + TORCH_CHECK(false, "Cannot query cuDNN version without ATen_cuda library. ", CUDA_HELP); + } + + virtual std::string showConfig() const { + TORCH_CHECK(false, "Cannot query detailed CUDA version without ATen_cuda library. ", CUDA_HELP); + } + + virtual double batchnormMinEpsilonCuDNN() const { + TORCH_CHECK(false, + "Cannot query batchnormMinEpsilonCuDNN() without ATen_cuda library. ", CUDA_HELP); + } + + virtual int64_t cuFFTGetPlanCacheMaxSize(int64_t device_index) const { + TORCH_CHECK(false, "Cannot access cuFFT plan cache without ATen_cuda library. ", CUDA_HELP); + } + + virtual void cuFFTSetPlanCacheMaxSize(int64_t device_index, int64_t max_size) const { + TORCH_CHECK(false, "Cannot access cuFFT plan cache without ATen_cuda library. ", CUDA_HELP); + } + + virtual int64_t cuFFTGetPlanCacheSize(int64_t device_index) const { + TORCH_CHECK(false, "Cannot access cuFFT plan cache without ATen_cuda library. ", CUDA_HELP); + } + + virtual void cuFFTClearPlanCache(int64_t device_index) const { + TORCH_CHECK(false, "Cannot access cuFFT plan cache without ATen_cuda library. ", CUDA_HELP); + } + + virtual int getNumGPUs() const { + return 0; + } +}; + +// NB: dummy argument to suppress "ISO C++11 requires at least one argument +// for the "..." in a variadic macro" +struct CAFFE2_API CUDAHooksArgs {}; + +C10_DECLARE_REGISTRY(CUDAHooksRegistry, CUDAHooksInterface, CUDAHooksArgs); +#define REGISTER_CUDA_HOOKS(clsname) \ + C10_REGISTER_CLASS(CUDAHooksRegistry, clsname, clsname) + +namespace detail { +CAFFE2_API const CUDAHooksInterface& getCUDAHooks(); +} // namespace detail +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/detail/FunctionTraits.h b/thirdparty/libtorch/include/ATen/detail/FunctionTraits.h new file mode 100644 index 0000000000..aab7300b58 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/detail/FunctionTraits.h @@ -0,0 +1,78 @@ +#pragma once + +#include + +// Modified from https://stackoverflow.com/questions/7943525/is-it-possible-to-figure-out-the-parameter-type-and-return-type-of-a-lambda + +// Fallback, anything with an operator() +template +struct function_traits : public function_traits { +}; + +// Pointers to class members that are themselves functors. +// For example, in the following code: +// template +// struct S { +// func_t f; +// }; +// template +// S make_s(func_t f) { +// return S { .f = f }; +// } +// +// auto s = make_s([] (int, float) -> double { /* ... */ }); +// +// function_traits traits; +template +struct function_traits : public function_traits { +}; + +// Const class member functions +template +struct function_traits : public function_traits { +}; + +// Reference types +template +struct function_traits : public function_traits {}; +template +struct function_traits : public function_traits {}; + +// Free functions +template +struct function_traits { + // arity is the number of arguments. + enum { arity = sizeof...(Args) }; + + typedef std::tuple ArgsTuple; + typedef ReturnType result_type; + + template + struct arg + { + typedef typename std::tuple_element>::type type; + // the i-th argument is equivalent to the i-th tuple element of a tuple + // composed of those arguments. + }; +}; + +template +struct nullary_function_traits { + using traits = function_traits; + using result_type = typename traits::result_type; +}; + +template +struct unary_function_traits { + using traits = function_traits; + using result_type = typename traits::result_type; + using arg1_t = typename traits::template arg<0>::type; +}; + +template +struct binary_function_traits { + using traits = function_traits; + using result_type = typename traits::result_type; + using arg1_t = typename traits::template arg<0>::type; + using arg2_t = typename traits::template arg<1>::type; +}; diff --git a/thirdparty/libtorch/include/ATen/detail/HIPHooksInterface.h b/thirdparty/libtorch/include/ATen/detail/HIPHooksInterface.h new file mode 100644 index 0000000000..4c53eb4e92 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/detail/HIPHooksInterface.h @@ -0,0 +1,74 @@ +#pragma once + +#include +#include +#include + +#include + +#include +#include +#include + +// Forward-declares THHState +struct THHState; + +namespace at { +class Context; +} + +// NB: Class must live in `at` due to limitations of Registry.h. +namespace at { + +// The HIPHooksInterface is an omnibus interface for any HIP functionality +// which we may want to call into from CPU code (and thus must be dynamically +// dispatched, to allow for separate compilation of HIP code). See +// CUDAHooksInterface for more detailed motivation. +struct CAFFE2_API HIPHooksInterface { + // This should never actually be implemented, but it is used to + // squelch -Werror=non-virtual-dtor + virtual ~HIPHooksInterface() {} + + // Initialize THHState and, transitively, the HIP state + virtual std::unique_ptr initHIP() const { + AT_ERROR("Cannot initialize HIP without ATen_hip library."); + } + + virtual std::unique_ptr initHIPGenerator(Context*) const { + AT_ERROR("Cannot initialize HIP generator without ATen_hip library."); + } + + virtual bool hasHIP() const { + return false; + } + + virtual int64_t current_device() const { + return -1; + } + + virtual Allocator* getPinnedMemoryAllocator() const { + AT_ERROR("Pinned memory requires HIP."); + } + + virtual void registerHIPTypes(Context*) const { + AT_ERROR("Cannot registerHIPTypes() without ATen_hip library."); + } + + virtual int getNumGPUs() const { + return 0; + } +}; + +// NB: dummy argument to suppress "ISO C++11 requires at least one argument +// for the "..." in a variadic macro" +struct CAFFE2_API HIPHooksArgs {}; + +C10_DECLARE_REGISTRY(HIPHooksRegistry, HIPHooksInterface, HIPHooksArgs); +#define REGISTER_HIP_HOOKS(clsname) \ + C10_REGISTER_CLASS(HIPHooksRegistry, clsname, clsname) + +namespace detail { +CAFFE2_API const HIPHooksInterface& getHIPHooks(); + +} // namespace detail +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/detail/ScalarTypeConversions.h b/thirdparty/libtorch/include/ATen/detail/ScalarTypeConversions.h new file mode 100644 index 0000000000..5c197b2d44 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/detail/ScalarTypeConversions.h @@ -0,0 +1,24 @@ +#pragma once + +#include +#include +#include +#include + +namespace at { namespace detail { + +template +inline T load(const void* data, ScalarType src_type) { + return AT_DISPATCH_ALL_TYPES_AND2(at::ScalarType::Half, at::ScalarType::Bool, src_type, "load", [&]() { + return at::convert(*(scalar_t*)data); + }); +} + +template +inline void store(T value, void* dst, ScalarType dst_type) { + AT_DISPATCH_ALL_TYPES_AND2(at::ScalarType::Half, at::ScalarType::Bool, dst_type, "store", [&]() { + *(scalar_t*)dst = at::convert(value); + }); +} + +}} // namespace at::detail diff --git a/thirdparty/libtorch/include/ATen/div_rtn.h b/thirdparty/libtorch/include/ATen/div_rtn.h new file mode 100644 index 0000000000..7b6beec4b0 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/div_rtn.h @@ -0,0 +1,11 @@ +#pragma once + +// Integer division rounding to -Infinity +template +static inline T div_rtn(T x, T y) { + int q = x/y; + int r = x%y; + if ((r!=0) && ((r<0) != (y<0))) --q; + return q; +} + diff --git a/thirdparty/libtorch/include/ATen/dlpack.h b/thirdparty/libtorch/include/ATen/dlpack.h new file mode 100644 index 0000000000..f8dc8fcd2c --- /dev/null +++ b/thirdparty/libtorch/include/ATen/dlpack.h @@ -0,0 +1,141 @@ +/*! + * Copyright (c) 2017 by Contributors + * \file dlpack.h + * \brief The common header of DLPack. + */ +#ifndef DLPACK_DLPACK_H_ +#define DLPACK_DLPACK_H_ + +#ifdef __cplusplus +#define DLPACK_EXTERN_C extern "C" +#else +#define DLPACK_EXTERN_C +#endif + +/*! \brief The current version of dlpack */ +#define DLPACK_VERSION 010 + +/*! \brief DLPACK_DLL prefix for windows */ +#ifdef _WIN32 +#ifdef DLPACK_EXPORTS +#define DLPACK_DLL __declspec(dllexport) +#else +#define DLPACK_DLL __declspec(dllimport) +#endif +#else +#define DLPACK_DLL +#endif + +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif +/*! + * \brief The device type in DLContext. + */ +typedef enum { + kDLCPU = 1, + kDLGPU = 2, + // kDLCPUPinned = kDLCPU | kDLGPU + kDLCPUPinned = 3, + kDLOpenCL = 4, + kDLMetal = 8, + kDLVPI = 9, + kDLROCM = 10, +} DLDeviceType; + +/*! + * \brief A Device context for Tensor and operator. + */ +typedef struct { + /*! \brief The device type used in the device. */ + DLDeviceType device_type; + /*! \brief The device index */ + int device_id; +} DLContext; + +/*! + * \brief The type code options DLDataType. + */ +typedef enum { + kDLInt = 0U, + kDLUInt = 1U, + kDLFloat = 2U, +} DLDataTypeCode; + +/*! + * \brief The data type the tensor can hold. + * + * Examples + * - float: type_code = 2, bits = 32, lanes=1 + * - float4(vectorized 4 float): type_code = 2, bits = 32, lanes=4 + * - int8: type_code = 0, bits = 8, lanes=1 + */ +typedef struct { + /*! + * \brief Type code of base types. + * We keep it uint8_t instead of DLDataTypeCode for minimal memory + * footprint, but the value should be one of DLDataTypeCode enum values. + * */ + uint8_t code; + /*! + * \brief Number of bits, common choices are 8, 16, 32. + */ + uint8_t bits; + /*! \brief Number of lanes in the type, used for vector types. */ + uint16_t lanes; +} DLDataType; + +/*! + * \brief Plain C Tensor object, does not manage memory. + */ +typedef struct { + /*! + * \brief The opaque data pointer points to the allocated data. + * This will be CUDA device pointer or cl_mem handle in OpenCL. + * This pointer is always aligns to 256 bytes as in CUDA. + */ + void* data; + /*! \brief The device context of the tensor */ + DLContext ctx; + /*! \brief Number of dimensions */ + int ndim; + /*! \brief The data type of the pointer*/ + DLDataType dtype; + /*! \brief The shape of the tensor */ + int64_t* shape; + /*! + * \brief strides of the tensor, + * can be NULL, indicating tensor is compact. + */ + int64_t* strides; + /*! \brief The offset in bytes to the beginning pointer to data */ + uint64_t byte_offset; +} DLTensor; + +/*! + * \brief C Tensor object, manage memory of DLTensor. This data structure is + * intended to faciliate the borrowing of DLTensor by another framework. It is + * not meant to transfer the tensor. When the borrowing framework doesn't need + * the tensor, it should call the deleter to notify the host that the resource + * is no longer needed. + */ +typedef struct DLManagedTensor { + /*! \brief DLTensor which is being memory managed */ + DLTensor dl_tensor; + /*! \brief the context of the original host framework of DLManagedTensor in + * which DLManagedTensor is used in the framework. It can also be NULL. + */ + void * manager_ctx; + /*! \brief Destructor signature void (*)(void*) - this should be called + * to destruct manager_ctx which holds the DLManagedTensor. It can be NULL + * if there is no way for the caller to provide a reasonable destructor. + */ + void (*deleter)(struct DLManagedTensor * self); +} DLManagedTensor; +#ifdef __cplusplus +} // DLPACK_EXTERN_C +#endif +#endif // DLPACK_DLPACK_H_ diff --git a/thirdparty/libtorch/include/ATen/native/quantized/Copy.h b/thirdparty/libtorch/include/ATen/native/quantized/Copy.h new file mode 100644 index 0000000000..a1bd290b3a --- /dev/null +++ b/thirdparty/libtorch/include/ATen/native/quantized/Copy.h @@ -0,0 +1,11 @@ +#pragma once + +#include + +namespace at { +namespace native { + +Tensor& quantized_copy_from_float_(Tensor& self, const Tensor& src); + +} +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/native/quantized/cpu/fake_quantize_core.h b/thirdparty/libtorch/include/ATen/native/quantized/cpu/fake_quantize_core.h new file mode 100644 index 0000000000..b2c5ff3c14 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/native/quantized/cpu/fake_quantize_core.h @@ -0,0 +1,27 @@ +#include +#include +#include +#include + +/* FakeQuantize Op for PerChannelAffine quantization scheme */ +namespace at { +namespace native { +void fake_quantize_slice( + Tensor& output, + const Tensor& input, + float sc, + int64_t z_point, + int64_t quant_min, + int64_t quant_max); + +void fake_quantize_grad_slice( + Tensor& input_grad, + const Tensor& output_grad, + const Tensor& input, + float sc, + int64_t z_point, + int64_t quant_min, + int64_t quant_max); + +} // namespace native +} // namespace at diff --git a/thirdparty/libtorch/include/ATen/native/quantized/cpu/fbgemm_utils.h b/thirdparty/libtorch/include/ATen/native/quantized/cpu/fbgemm_utils.h new file mode 100644 index 0000000000..fe590b8449 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/native/quantized/cpu/fbgemm_utils.h @@ -0,0 +1,109 @@ +#pragma once + +#ifdef USE_FBGEMM +#include "fbgemm/Fbgemm.h" +#include "fbgemm/QuantUtils.h" + +#include +#include + +// The struct for the packed weight matrix (PackBMatrix) and the corresponding +// column offsets used for the fully connect layer, which are both prepared in +// the prepacking step to save the computations in the inference. Note the +// column offsets include the sum of the B columns as well as the scalar term +// B_zero_point * K, whereas the row offsets created by +// PackAWithQuantRowOffset/PackAWithIm2Col/PackAWithRowOffset are only the sum +// of the A rows. The column offsets are needed for the asymmetric quantization +// (affine quantization) of input matrix. +// Note that in JIT mode we can think of a way to fuse col_offsets with bias. +struct FBGEMM_API PackedLinearWeight { + std::unique_ptr> w; + c10::optional bias; + std::vector col_offsets; + std::vector w_scale; + std::vector w_zp; + c10::QScheme q_scheme; +}; + +template +struct FBGEMM_API PackedConvWeight { + std::unique_ptr> w; + c10::optional bias; + std::vector col_offsets; + std::vector kernel; + std::vector w_scale; + std::vector w_zp; + c10::QScheme q_scheme; +}; + +// PackWeight: Convert the weight from uint8 to int8. +inline void convert_uint8_int8( + int len, + const uint8_t* src_uint8, + int8_t* dst_int8) { + for (int i = 0; i < len; ++i) { + dst_int8[i] = static_cast(static_cast(src_uint8[i]) - 128); + } +} + +// UnpackWeight: Convert the weight from int8 to uint8. +inline void convert_int8_uint8( + int len, + const int8_t* src_int8, + uint8_t* dst_uint8) { + for (int i = 0; i < len; ++i) { + dst_uint8[i] = + static_cast(static_cast(src_int8[i]) + 128); + } +} + +namespace at { +namespace native { +namespace fbgemm_utils { + +template +fbgemm::conv_param_t MakeFbgemmConvParam( + int N, + int C, + int M, + const std::vector& image_shape, + int groups, + const std::vector& kernels, + const std::vector& strides, + const std::vector& pads, + const std::vector& dilations); + +// TODO: Remove functions below when ChannelsLast3d is ready. +Tensor MakeStridedQTensorCPU( + const IntArrayRef& sizes, + const IntArrayRef& strides, + const TensorOptions& options, + QuantizerPtr quantizer); + +Tensor MakeEmptyAffineQuantizedChannelsLast3dTensor( + int64_t N, + int64_t C, + int64_t D, + int64_t H, + int64_t W, + const TensorOptions& options, + double scale, + int64_t zero_point); + +Tensor MakeEmptyPerChannelAffineQuantizedChannelsLast3dTensor( + int64_t N, + int64_t C, + int64_t D, + int64_t H, + int64_t W, + const TensorOptions& options, + const Tensor& scales, + const Tensor& zero_points); + +Tensor ConvertToChannelsLast3dTensor(const Tensor& src); + +} // namespace fbgemm_utils +} // namespace native +} // namespace at + +#endif // USE_FBGEMM diff --git a/thirdparty/libtorch/include/ATen/native/quantized/cpu/init_qnnpack.h b/thirdparty/libtorch/include/ATen/native/quantized/cpu/init_qnnpack.h new file mode 100644 index 0000000000..dbfb406ea5 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/native/quantized/cpu/init_qnnpack.h @@ -0,0 +1,13 @@ +#pragma once + +#ifdef USE_PYTORCH_QNNPACK + +namespace at { +namespace native { + +void initQNNPACK(); + +} // namespace native +} // namespace at + +#endif diff --git a/thirdparty/libtorch/include/ATen/native/quantized/cpu/qnnpack_utils.h b/thirdparty/libtorch/include/ATen/native/quantized/cpu/qnnpack_utils.h new file mode 100644 index 0000000000..89e1ee112f --- /dev/null +++ b/thirdparty/libtorch/include/ATen/native/quantized/cpu/qnnpack_utils.h @@ -0,0 +1,86 @@ +#pragma once + +#ifdef USE_PYTORCH_QNNPACK +#include +#include + +struct QnnpackOperatorDeleter { + void operator()(pytorch_qnnp_operator_t op) { + pytorch_qnnp_delete_operator(op); + } +}; + +// PackedWeight struct for QNNPACK stores the original Weight and Bias as +// QNNPACK currently does not support an unpack function. Possible optimiation - +// For PyTorch Mobile, once the model is scripted and serialized we don't need +// to call unpack, so we can save some memory by checking for this case. +// Input scale is set to null in pre-pack step. QNNPACK needs bias quantized with +// input scale which is available at runtime in pytorch. During runtime if input +// scale value changes then we requantize bias with the updated scale. +// For inference we expect the graph to be static so the input scale should +// not change across consecutive inference calls. +struct PackedLinearWeightsQnnp { + std::unique_ptr w; + at::Tensor orig_weight; + at::Tensor bias; + c10::optional input_scale; + double w_scale; + int64_t w_zp; +}; + +struct PackedConvWeightsQnnp { + std::unique_ptr w; + at::Tensor orig_weight; + at::Tensor bias; + c10::optional input_scale; + std::vector kernel; + double w_scale; + int64_t w_zp; +}; + +enum class Activation : uint8_t { NONE = 0, RELU = 1 }; + +#if defined(__ANDROID__) && !defined(__NDK_MAJOR__) +template +inline float Round(const float x) { + return ::nearbyintf(x); +} +inline double Round(const double x) { + return ::nearbyint(x); +} +#else +template +inline T Round(const T x) { + return std::nearbyint(x); +} +#endif + +inline uint8_t QuantizeUint8(float scale, int32_t zero_point, float value) { + const int32_t qmin = std::numeric_limits::min(); + const int32_t qmax = std::numeric_limits::max(); + auto r = zero_point + static_cast(Round(value / scale)); + r = std::max(r, qmin); + r = std::min(r, qmax); + return static_cast(r); +} + +inline std::pair activationLimits( + float scale, + int32_t zero_point, + Activation Ac) { + switch (Ac) { + case Activation::NONE: + return {std::numeric_limits::min(), + std::numeric_limits::max()}; + case Activation::RELU: + return {QuantizeUint8(scale, zero_point, 0.0), + std::numeric_limits::max()}; + default: +#ifdef _MSC_VER + __assume(0); +#else + __builtin_unreachable(); +#endif + } +} +#endif diff --git a/thirdparty/libtorch/include/ATen/native/quantized/cpu/quantized_ops.h b/thirdparty/libtorch/include/ATen/native/quantized/cpu/quantized_ops.h new file mode 100644 index 0000000000..2b742d8483 --- /dev/null +++ b/thirdparty/libtorch/include/ATen/native/quantized/cpu/quantized_ops.h @@ -0,0 +1,91 @@ +#include +#include +#include + +namespace at { +namespace native { + +using qrelu_fn = void (*)(const at::Tensor& /*qx*/, at::Tensor& /*qy*/); +using qadd_fn = + void (*)(Tensor& /*out*/, const Tensor& /*self*/, const Tensor& /*other*/); +using qmaxpool_2d_fn = void (*)( + const Tensor& qx, + int64_t iC, // input/output channels + int64_t iH, + int64_t iW, // input sizes + int64_t oH, + int64_t oW, // output sizes + int64_t kH, + int64_t kW, // kernel size + int64_t sH, + int64_t sW, // strides + int64_t pH, + int64_t pW, // padding + int64_t dH, + int64_t dW, // dilation + Tensor& qy); +using qadaptive_avg_pool2d_fn = void (*)( + const Tensor& qx, + Tensor& qy, + int64_t b, + int64_t sizeD, + int64_t isizeH, + int64_t isizeW, + int64_t osizeH, + int64_t osizeW, + int64_t istrideB, + int64_t istrideD, + int64_t istrideH, + int64_t istrideW); + +using qavg_pool2d_fn = void (*)( + const Tensor& qx, + Tensor& qy, + int64_t b, + int64_t nInputPlane, + int64_t inputWidth, + int64_t inputHeight, + int64_t outputWidth, + int64_t outputHeight, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + bool count_include_pad, + c10::optional divisor_override); + +using qupsample_bilinear2d_fn = void (*)( + Tensor& output, + const Tensor& input, + int64_t input_height, + int64_t input_width, + int64_t output_height, + int64_t output_width, + int64_t nbatch, + int64_t channels, + bool align_corners); + +using qcat_nhwc_fn = Tensor (*)( + const c10::List& qxs, + int64_t dim, + double scale, + int64_t zero_point); +using qtopk_fn = void(*)(Tensor&, Tensor&, const Tensor&, int64_t, int64_t, bool, bool); + +// using qavg_pool2d_fn +DECLARE_DISPATCH(qrelu_fn, qrelu_stub); +DECLARE_DISPATCH(qrelu_fn, qrelu6_stub); +DECLARE_DISPATCH(qadd_fn, qadd_stub); +DECLARE_DISPATCH(qadd_fn, qadd_relu_stub); +DECLARE_DISPATCH(qmaxpool_2d_fn, qmaxpool_2d_nhwc_stub); +DECLARE_DISPATCH(qadaptive_avg_pool2d_fn, qadaptive_avg_pool2d_nhwc_stub); +DECLARE_DISPATCH(qavg_pool2d_fn, qavg_pool2d_nhwc_stub); +DECLARE_DISPATCH(qupsample_bilinear2d_fn, qupsample_bilinear2d_nhwc_stub); +DECLARE_DISPATCH(qcat_nhwc_fn, qcat_nhwc_stub); +DECLARE_DISPATCH(qcat_nhwc_fn, qcat_relu_nhwc_stub); +DECLARE_DISPATCH(qtopk_fn, qtopk_stub); + +} // namespace native +} // namespace at diff --git a/thirdparty/libtorch/include/TH/TH.h b/thirdparty/libtorch/include/TH/TH.h new file mode 100644 index 0000000000..5d863b58d5 --- /dev/null +++ b/thirdparty/libtorch/include/TH/TH.h @@ -0,0 +1,23 @@ +#ifndef TH_INC +#define TH_INC + +#include + +#include +#ifdef USE_LAPACK +#include +#endif + +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include + +#endif diff --git a/thirdparty/libtorch/include/TH/THAllocator.h b/thirdparty/libtorch/include/TH/THAllocator.h new file mode 100644 index 0000000000..b9acf0a754 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THAllocator.h @@ -0,0 +1,100 @@ +#pragma once + +#include + +#include + +#define TH_ALLOCATOR_MAPPED_SHARED 1 +#define TH_ALLOCATOR_MAPPED_SHAREDMEM 2 +#define TH_ALLOCATOR_MAPPED_EXCLUSIVE 4 +#define TH_ALLOCATOR_MAPPED_NOCREATE 8 +#define TH_ALLOCATOR_MAPPED_KEEPFD 16 +#define TH_ALLOCATOR_MAPPED_FROMFD 32 +#define TH_ALLOCATOR_MAPPED_UNLINK 64 + +/* default malloc/free allocator. malloc and realloc raise an error (using + * THError) on allocation failure. + */ +TH_API c10::Allocator* getTHDefaultAllocator(void); + +// Sentinel value/type to help distinguish the file descriptor constructor from +// the non-file descriptor constructor +enum WithFd { WITH_FD }; + +class CAFFE2_API THMapAllocator { + public: + THMapAllocator(const char *filename, int flags, size_t size); + THMapAllocator(WithFd, const char *filename, int fd, int flags, size_t size); + THMapAllocator(const THMapAllocator&) = delete; + THMapAllocator& operator=(const THMapAllocator&) = delete; + THMapAllocator(THMapAllocator&&) = delete; + THMapAllocator& operator=(THMapAllocator&&) = delete; + + const char* filename() const { return filename_.c_str(); } + int fd() const { +#ifdef _WIN32 + AT_ERROR("THMapAllocator::fd() is unsupported on Windows"); +#else + return fd_; +#endif + } + ptrdiff_t size() const { return size_; } + // Return a pointer to the actual data for this allocator + // (in the case of the refcounted allocator, this is offset + // from the base pointer.) + virtual void* data() const { return base_ptr_; } + + static THMapAllocator* fromDataPtr(const at::DataPtr&); + static at::DataPtr makeDataPtr(const char *filename, int flags, size_t size, size_t* actual_size_out); + static at::DataPtr makeDataPtr(WithFd, const char *filename, int fd, int flags, size_t size, size_t* actual_size_out); + + // Closes the data. Helps us avoid destructor shenanigans + virtual void close(); + + // This is very dangerous. You have to redefine this destructor for each + // subclass + virtual ~THMapAllocator() { close(); } + +protected: + bool closed_ = false; + std::string filename_; + int flags_ = 0; + ptrdiff_t size_; /* mapped size */ +#ifdef _WIN32 + void* handle_; + void* event_; + std::string eventname_; +#else + int fd_ = -1; +#endif + void *base_ptr_ = nullptr; +}; + +// Base-from-member idiom +struct CAFFE2_API THRefcountedMapAllocatorArgCheck { + THRefcountedMapAllocatorArgCheck(int flags); +}; + +class CAFFE2_API THRefcountedMapAllocator + : private THRefcountedMapAllocatorArgCheck, + public THMapAllocator { + public: + THRefcountedMapAllocator(const char *filename, int flags, size_t size); + THRefcountedMapAllocator(WithFd, const char *filename, int fd, int flags, size_t size); + + static THRefcountedMapAllocator* fromDataPtr(const at::DataPtr&); + static at::DataPtr makeDataPtr(const char *filename, int flags, size_t size, size_t* actual_size_out); + static at::DataPtr makeDataPtr(WithFd, const char *filename, int fd, int flags, size_t size, size_t* actual_size_out); + + void* data() const override; + + void incref(); + int decref(); + void close() override; + + virtual ~THRefcountedMapAllocator() { close(); } + +protected: + void checkFlags(); + void initializeAlloc(); +}; diff --git a/thirdparty/libtorch/include/TH/THBlas.h b/thirdparty/libtorch/include/TH/THBlas.h new file mode 100644 index 0000000000..ea06c307c1 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THBlas.h @@ -0,0 +1,14 @@ +#ifndef TH_BLAS_INC +#define TH_BLAS_INC + +#include + +#define THBlas_(NAME) TH_CONCAT_4(TH,Real,Blas_,NAME) + +#include +#include + +#include +#include + +#endif diff --git a/thirdparty/libtorch/include/TH/THDiskFile.h b/thirdparty/libtorch/include/TH/THDiskFile.h new file mode 100644 index 0000000000..d6bab2168c --- /dev/null +++ b/thirdparty/libtorch/include/TH/THDiskFile.h @@ -0,0 +1,19 @@ +#ifndef TH_DISK_FILE_INC +#define TH_DISK_FILE_INC + +#include + +TH_API THFile *THDiskFile_new(const char *name, const char *mode, int isQuiet); +TH_API THFile *THPipeFile_new(const char *name, const char *mode, int isQuiet); + +TH_API const char *THDiskFile_name(THFile *self); + +TH_API int THDiskFile_isLittleEndianCPU(void); +TH_API int THDiskFile_isBigEndianCPU(void); +TH_API void THDiskFile_nativeEndianEncoding(THFile *self); +TH_API void THDiskFile_littleEndianEncoding(THFile *self); +TH_API void THDiskFile_bigEndianEncoding(THFile *self); +TH_API void THDiskFile_longSize(THFile *self, int size); +TH_API void THDiskFile_noBuffer(THFile *self); + +#endif diff --git a/thirdparty/libtorch/include/TH/THFile.h b/thirdparty/libtorch/include/TH/THFile.h new file mode 100644 index 0000000000..144cec47ef --- /dev/null +++ b/thirdparty/libtorch/include/TH/THFile.h @@ -0,0 +1,93 @@ +#ifndef TH_FILE_INC +#define TH_FILE_INC + +#include + +typedef struct THFile__ THFile; + +TH_API int THFile_isOpened(THFile *self); +TH_API int THFile_isQuiet(THFile *self); +TH_API int THFile_isReadable(THFile *self); +TH_API int THFile_isWritable(THFile *self); +TH_API int THFile_isBinary(THFile *self); +TH_API int THFile_isAutoSpacing(THFile *self); +TH_API int THFile_hasError(THFile *self); + +TH_API void THFile_binary(THFile *self); +TH_API void THFile_ascii(THFile *self); +TH_API void THFile_autoSpacing(THFile *self); +TH_API void THFile_noAutoSpacing(THFile *self); +TH_API void THFile_quiet(THFile *self); +TH_API void THFile_pedantic(THFile *self); +TH_API void THFile_clearError(THFile *self); + +/* scalar */ +TH_API uint8_t THFile_readByteScalar(THFile *self); +TH_API int8_t THFile_readCharScalar(THFile *self); +TH_API int16_t THFile_readShortScalar(THFile *self); +TH_API int32_t THFile_readIntScalar(THFile *self); +TH_API int64_t THFile_readLongScalar(THFile *self); +TH_API float THFile_readFloatScalar(THFile *self); +TH_API double THFile_readDoubleScalar(THFile *self); + +TH_API void THFile_writeByteScalar(THFile *self, uint8_t scalar); +TH_API void THFile_writeCharScalar(THFile *self, int8_t scalar); +TH_API void THFile_writeShortScalar(THFile *self, int16_t scalar); +TH_API void THFile_writeIntScalar(THFile *self, int32_t scalar); +TH_API void THFile_writeLongScalar(THFile *self, int64_t scalar); +TH_API void THFile_writeFloatScalar(THFile *self, float scalar); +TH_API void THFile_writeDoubleScalar(THFile *self, double scalar); + +/* storage */ +TH_API size_t THFile_readByte(THFile *self, THByteStorage *storage); +TH_API size_t THFile_readChar(THFile *self, THCharStorage *storage); +TH_API size_t THFile_readShort(THFile *self, THShortStorage *storage); +TH_API size_t THFile_readInt(THFile *self, THIntStorage *storage); +TH_API size_t THFile_readLong(THFile *self, THLongStorage *storage); +TH_API size_t THFile_readFloat(THFile *self, THFloatStorage *storage); +TH_API size_t THFile_readDouble(THFile *self, THDoubleStorage *storage); +TH_API size_t THFile_readBool(THFile *self, THBoolStorage *storage); + +TH_API size_t THFile_writeByte(THFile *self, THByteStorage *storage); +TH_API size_t THFile_writeChar(THFile *self, THCharStorage *storage); +TH_API size_t THFile_writeShort(THFile *self, THShortStorage *storage); +TH_API size_t THFile_writeInt(THFile *self, THIntStorage *storage); +TH_API size_t THFile_writeLong(THFile *self, THLongStorage *storage); +TH_API size_t THFile_writeFloat(THFile *self, THFloatStorage *storage); +TH_API size_t THFile_writeDouble(THFile *self, THDoubleStorage *storage); +TH_API size_t THFile_writeBool(THFile *self, THBoolStorage *storage); + +/* raw */ +TH_API size_t THFile_readByteRaw(THFile *self, uint8_t *data, size_t n); +TH_API size_t THFile_readCharRaw(THFile *self, int8_t *data, size_t n); +TH_API size_t THFile_readShortRaw(THFile *self, int16_t *data, size_t n); +TH_API size_t THFile_readIntRaw(THFile *self, int32_t *data, size_t n); +TH_API size_t THFile_readLongRaw(THFile *self, int64_t *data, size_t n); +TH_API size_t THFile_readFloatRaw(THFile *self, float *data, size_t n); +TH_API size_t THFile_readDoubleRaw(THFile *self, double *data, size_t n); +TH_API size_t THFile_readStringRaw(THFile *self, const char *format, char **str_); /* you must deallocate str_ */ + +TH_API size_t THFile_writeByteRaw(THFile *self, uint8_t *data, size_t n); +TH_API size_t THFile_writeCharRaw(THFile *self, int8_t *data, size_t n); +TH_API size_t THFile_writeShortRaw(THFile *self, int16_t *data, size_t n); +TH_API size_t THFile_writeIntRaw(THFile *self, int32_t *data, size_t n); +TH_API size_t THFile_writeLongRaw(THFile *self, int64_t *data, size_t n); +TH_API size_t THFile_writeFloatRaw(THFile *self, float *data, size_t n); +TH_API size_t THFile_writeDoubleRaw(THFile *self, double *data, size_t n); +TH_API size_t THFile_writeStringRaw(THFile *self, const char *str, size_t size); + +TH_API THHalf THFile_readHalfScalar(THFile *self); +TH_API void THFile_writeHalfScalar(THFile *self, THHalf scalar); +TH_API size_t THFile_readHalf(THFile *self, THHalfStorage *storage); +TH_API size_t THFile_writeHalf(THFile *self, THHalfStorage *storage); +TH_API size_t THFile_readHalfRaw(THFile *self, THHalf* data, size_t size); +TH_API size_t THFile_writeHalfRaw(THFile *self, THHalf* data, size_t size); + +TH_API void THFile_synchronize(THFile *self); +TH_API void THFile_seek(THFile *self, size_t position); +TH_API void THFile_seekEnd(THFile *self); +TH_API size_t THFile_position(THFile *self); +TH_API void THFile_close(THFile *self); +TH_API void THFile_free(THFile *self); + +#endif diff --git a/thirdparty/libtorch/include/TH/THFilePrivate.h b/thirdparty/libtorch/include/TH/THFilePrivate.h new file mode 100644 index 0000000000..354d769c70 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THFilePrivate.h @@ -0,0 +1,50 @@ +#include + +#include + + +struct THFile__ +{ + struct THFileVTable *vtable; + + int isQuiet; + int isReadable; + int isWritable; + int isBinary; + int isAutoSpacing; + int hasError; +}; + +/* virtual table definition */ + +struct THFileVTable +{ + int (*isOpened)(THFile *self); + + ssize_t (*readByte)(THFile *self, uint8_t *data, ssize_t n); + ssize_t (*readChar)(THFile *self, int8_t *data, ssize_t n); + ssize_t (*readShort)(THFile *self, int16_t *data, ssize_t n); + ssize_t (*readInt)(THFile *self, int32_t *data, ssize_t n); + ssize_t (*readLong)(THFile *self, int64_t *data, ssize_t n); + ssize_t (*readFloat)(THFile *self, float *data, ssize_t n); + ssize_t (*readDouble)(THFile *self, double *data, ssize_t n); + ssize_t (*readHalf)(THFile *self, THHalf *data, ssize_t n); + ssize_t (*readString)(THFile *self, const char *format, char **str_); + + ssize_t (*writeByte)(THFile *self, uint8_t *data, ssize_t n); + ssize_t (*writeChar)(THFile *self, int8_t *data, ssize_t n); + ssize_t (*writeShort)(THFile *self, int16_t *data, ssize_t n); + ssize_t (*writeInt)(THFile *self, int32_t *data, ssize_t n); + ssize_t (*writeLong)(THFile *self, int64_t *data, ssize_t n); + ssize_t (*writeFloat)(THFile *self, float *data, ssize_t n); + ssize_t (*writeDouble)(THFile *self, double *data, ssize_t n); + ssize_t (*writeHalf)(THFile *self, THHalf *data, ssize_t n); + ssize_t (*writeString)(THFile *self, const char *str, ssize_t size); + + void (*synchronize)(THFile *self); + void (*seek)(THFile *self, ssize_t position); + void (*seekEnd)(THFile *self); + ssize_t (*position)(THFile *self); + void (*close)(THFile *self); + void (*free)(THFile *self); +}; diff --git a/thirdparty/libtorch/include/TH/THGeneral.h b/thirdparty/libtorch/include/TH/THGeneral.h new file mode 100644 index 0000000000..a3b99e6e8e --- /dev/null +++ b/thirdparty/libtorch/include/TH/THGeneral.h @@ -0,0 +1,172 @@ +#ifndef TH_GENERAL_INC +#define TH_GENERAL_INC + +#ifndef __STDC_FORMAT_MACROS +#define __STDC_FORMAT_MACROS +#endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#ifdef TH_BLAS_MKL +#include +#endif + +#define USE_BLAS +#define USE_LAPACK +/* #undef BLAS_F2C */ +#define BLAS_USE_CBLAS_DOT + +# define TH_EXTERNC extern "C" + +// Note(jiayq): copied from ATen/core/Macros.h. Because internal build of TH +// and ATen are not unified yet, we need to duplicate code for now. Long term +// we should merge macros. +#ifdef _WIN32 +#if !defined(AT_CORE_STATIC_WINDOWS) +// TODO: unfiy the controlling macros. +#if defined(CAFFE2_BUILD_MAIN_LIBS) || defined(ATen_cpu_EXPORTS) || defined(caffe2_EXPORTS) +#define TH_CPP_API __declspec(dllexport) +#else // defined(CAFFE2_BUILD_MAIN_LIBS) || defined(ATen_cpu_EXPORTS) || defined(caffe2_EXPORTS) +#define TH_CPP_API __declspec(dllimport) +#endif // defined(CAFFE2_BUILD_MAIN_LIBS) || defined(ATen_cpu_EXPORTS) || defined(caffe2_EXPORTS) +#else // !defined(AT_CORE_STATIC_WINDOWS) +#define TH_CPP_API +#endif // !defined(AT_CORE_STATIC_WINDOWS) +#else // _WIN32 +#if defined(__GNUC__) +#define TH_CPP_API __attribute__((__visibility__("default"))) +#endif // defined(__GNUC__) +#endif // _WIN32 + +#ifdef NO_EXPORT +#undef TH_CPP_API +#define TH_CPP_API +#endif + +#define TH_API TH_EXTERNC TH_CPP_API + +#ifdef _WIN32 +# define TH_NO_RETURN __declspec(noreturn) +# define TH_UNREACHABLE +#else +# define TH_NO_RETURN __attribute__((noreturn)) +# define TH_UNREACHABLE __builtin_unreachable(); +#endif + +#if defined(__GNUC__) && ((__GNUC__ > 2) || (__GNUC__ == 2 && __GNUC_MINOR__ > 4)) +# define TH_UNUSED __attribute__((unused)) +#else +# define TH_UNUSED +#endif + +#if defined(__clang__) +#define __ubsan_ignore_float_divide_by_zero__ __attribute__((no_sanitize("float-divide-by-zero"))) +#else +#define __ubsan_ignore_float_divide_by_zero__ +#endif + +#ifndef M_PI +# define M_PI 3.14159265358979323846 +#endif + +typedef void (*THErrorHandlerFunction)(const char *msg, void *data); +typedef void (*THArgErrorHandlerFunction)(int argNumber, const char *msg, void *data); + +#define TH_DESC_BUFF_LEN 64 +typedef struct { + char str[TH_DESC_BUFF_LEN]; +} THDescBuff; + + +TH_API THDescBuff _THSizeDesc(const int64_t *size, const int64_t ndim); +TH_API TH_NO_RETURN void _THError(const char *file, const int line, const char *fmt, ...); +TH_API void _THAssertionFailed(const char *file, const int line, const char *exp, const char *fmt, ...); +TH_API void THSetErrorHandler(THErrorHandlerFunction new_handler, void *data); +TH_API void THSetDefaultErrorHandler(THErrorHandlerFunction new_handler, void *data); +TH_API void _THArgCheck(const char *file, int line, int condition, int argNumber, const char *fmt, ...); +TH_API void THSetArgErrorHandler(THArgErrorHandlerFunction new_handler, void *data); +TH_API void THSetDefaultArgErrorHandler(THArgErrorHandlerFunction new_handler, void *data); +TH_API void* THAlloc(ptrdiff_t size); +TH_API void* THRealloc(void *ptr, ptrdiff_t size); +TH_API void THFree(void *ptr); +TH_API void THSetGCHandler( void (*torchGCHandlerFunction)(void *data), void *data ); +// this hook should only be called by custom allocator functions +TH_API void THHeapUpdate(ptrdiff_t size); + +#define THError(...) _THError(__FILE__, __LINE__, __VA_ARGS__) + +#define THCleanup(...) __VA_ARGS__ + +#define THArgCheck(...) \ +do { \ + _THArgCheck(__FILE__, __LINE__, __VA_ARGS__); \ +} while(0) + +#define THArgError(...) \ +do { \ + _THArgCheck(__FILE__, __LINE__, false, __VA_ARGS__); \ + TH_UNREACHABLE \ +} while(0) + +#define THArgCheckWithCleanup(condition, cleanup, ...) \ +do if (!(condition)) { \ + cleanup \ + _THArgCheck(__FILE__, __LINE__, 0, __VA_ARGS__); \ +} while(0) + +#define THAssert(exp) \ +do { \ + if (!(exp)) { \ + _THAssertionFailed(__FILE__, __LINE__, #exp, ""); \ + } \ +} while(0) + +#define THAssertMsg(exp, ...) \ +do { \ + if (!(exp)) { \ + _THAssertionFailed(__FILE__, __LINE__, #exp, __VA_ARGS__); \ + } \ +} while(0) + +#define TH_CONCAT_STRING_2(x,y) TH_CONCAT_STRING_2_EXPAND(x,y) +#define TH_CONCAT_STRING_2_EXPAND(x,y) #x #y + +#define TH_CONCAT_STRING_3(x,y,z) TH_CONCAT_STRING_3_EXPAND(x,y,z) +#define TH_CONCAT_STRING_3_EXPAND(x,y,z) #x #y #z + +#define TH_CONCAT_STRING_4(x,y,z,w) TH_CONCAT_STRING_4_EXPAND(x,y,z,w) +#define TH_CONCAT_STRING_4_EXPAND(x,y,z,w) #x #y #z #w + +#define TH_CONCAT_2(x,y) TH_CONCAT_2_EXPAND(x,y) +#define TH_CONCAT_2_EXPAND(x,y) x ## y + +#define TH_CONCAT_3(x,y,z) TH_CONCAT_3_EXPAND(x,y,z) +#define TH_CONCAT_3_EXPAND(x,y,z) x ## y ## z + +#define TH_CONCAT_4_EXPAND(x,y,z,w) x ## y ## z ## w +#define TH_CONCAT_4(x,y,z,w) TH_CONCAT_4_EXPAND(x,y,z,w) + +#define THMin(X, Y) ((X) < (Y) ? (X) : (Y)) +#define THMax(X, Y) ((X) > (Y) ? (X) : (Y)) + +#if (defined(_MSC_VER) || defined(__MINGW32__)) +#define snprintf _snprintf +#define popen _popen +#define pclose _pclose +#include +#if !defined(HAVE_SSIZE_T) +typedef SSIZE_T ssize_t; +#endif +#endif + +#endif diff --git a/thirdparty/libtorch/include/TH/THGenerateAllTypes.h b/thirdparty/libtorch/include/TH/THGenerateAllTypes.h new file mode 100644 index 0000000000..009ca9b60b --- /dev/null +++ b/thirdparty/libtorch/include/TH/THGenerateAllTypes.h @@ -0,0 +1,17 @@ +#ifndef TH_GENERIC_FILE +#error "You must define TH_GENERIC_FILE before including THGenerateAllTypes.h" +#endif + +#ifndef THGenerateManyTypes +#define THAllLocalGenerateManyTypes +#define THGenerateManyTypes +#endif + +#include +#include + +#ifdef THAllLocalGenerateManyTypes +#undef THAllLocalGenerateManyTypes +#undef THGenerateManyTypes +#undef TH_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/TH/THGenerateBFloat16Type.h b/thirdparty/libtorch/include/TH/THGenerateBFloat16Type.h new file mode 100644 index 0000000000..40f0a8c570 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THGenerateBFloat16Type.h @@ -0,0 +1,21 @@ +#ifndef TH_GENERIC_FILE +#error "You must define TH_GENERIC_FILE before including THGenerateBFloat16Type.h" +#endif + +#include +#define scalar_t at::BFloat16 +#define accreal double +#define TH_CONVERT_ACCREAL_TO_REAL(_val) (scalar_t)(_val) +#define Real BFloat16 +#define TH_REAL_IS_BFLOAT16 +#line 1 TH_GENERIC_FILE +#include TH_GENERIC_FILE +#undef accreal +#undef scalar_t +#undef Real +#undef TH_REAL_IS_BFLOAT16 +#undef TH_CONVERT_ACCREAL_TO_REAL + +#ifndef THGenerateManyTypes +#undef TH_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/TH/THGenerateBoolType.h b/thirdparty/libtorch/include/TH/THGenerateBoolType.h new file mode 100644 index 0000000000..12d22b1946 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THGenerateBoolType.h @@ -0,0 +1,24 @@ +#ifndef TH_GENERIC_FILE +#error "You must define TH_GENERIC_FILE before including THGenerateBoolType.h" +#endif + +#define scalar_t bool +#define ureal bool +#define accreal int64_t +#define Real Bool +#define TH_CONVERT_REAL_TO_ACCREAL(_val) (accreal)(_val) +#define TH_CONVERT_ACCREAL_TO_REAL(_val) (scalar_t)(_val) +#define TH_REAL_IS_BOOL +#line 1 TH_GENERIC_FILE +#include TH_GENERIC_FILE +#undef scalar_t +#undef ureal +#undef accreal +#undef Real +#undef TH_REAL_IS_BOOL +#undef TH_CONVERT_REAL_TO_ACCREAL +#undef TH_CONVERT_ACCREAL_TO_REAL + +#ifndef THGenerateManyTypes +#undef TH_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/TH/THGenerateByteType.h b/thirdparty/libtorch/include/TH/THGenerateByteType.h new file mode 100644 index 0000000000..3b03842080 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THGenerateByteType.h @@ -0,0 +1,26 @@ +#ifndef TH_GENERIC_FILE +#error "You must define TH_GENERIC_FILE before including THGenerateByteType.h" +#endif + +#define scalar_t uint8_t +#define ureal uint8_t +#define accreal int64_t +#define Real Byte +#define TH_CONVERT_REAL_TO_ACCREAL(_val) (accreal)(_val) +#define TH_CONVERT_ACCREAL_TO_REAL(_val) (scalar_t)(_val) +#define THInf UCHAR_MAX +#define TH_REAL_IS_BYTE +#line 1 TH_GENERIC_FILE +#include TH_GENERIC_FILE +#undef scalar_t +#undef ureal +#undef accreal +#undef Real +#undef THInf +#undef TH_REAL_IS_BYTE +#undef TH_CONVERT_REAL_TO_ACCREAL +#undef TH_CONVERT_ACCREAL_TO_REAL + +#ifndef THGenerateManyTypes +#undef TH_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/TH/THGenerateCharType.h b/thirdparty/libtorch/include/TH/THGenerateCharType.h new file mode 100644 index 0000000000..57265b0665 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THGenerateCharType.h @@ -0,0 +1,26 @@ +#ifndef TH_GENERIC_FILE +#error "You must define TH_GENERIC_FILE before including THGenerateCharType.h" +#endif + +#define scalar_t int8_t +#define ureal uint8_t +#define accreal int64_t +#define Real Char +#define THInf SCHAR_MAX +#define TH_CONVERT_REAL_TO_ACCREAL(_val) (accreal)(_val) +#define TH_CONVERT_ACCREAL_TO_REAL(_val) (scalar_t)(_val) +#define TH_REAL_IS_CHAR +#line 1 TH_GENERIC_FILE +#include TH_GENERIC_FILE +#undef scalar_t +#undef ureal +#undef accreal +#undef Real +#undef THInf +#undef TH_REAL_IS_CHAR +#undef TH_CONVERT_REAL_TO_ACCREAL +#undef TH_CONVERT_ACCREAL_TO_REAL + +#ifndef THGenerateManyTypes +#undef TH_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/TH/THGenerateDoubleType.h b/thirdparty/libtorch/include/TH/THGenerateDoubleType.h new file mode 100644 index 0000000000..fb67a52b3c --- /dev/null +++ b/thirdparty/libtorch/include/TH/THGenerateDoubleType.h @@ -0,0 +1,24 @@ +#ifndef TH_GENERIC_FILE +#error "You must define TH_GENERIC_FILE before including THGenerateDoubleType.h" +#endif + +#define scalar_t double +#define accreal double +#define TH_CONVERT_REAL_TO_ACCREAL(_val) (accreal)(_val) +#define TH_CONVERT_ACCREAL_TO_REAL(_val) (scalar_t)(_val) +#define Real Double +#define THInf DBL_MAX +#define TH_REAL_IS_DOUBLE +#line 1 TH_GENERIC_FILE +#include TH_GENERIC_FILE +#undef accreal +#undef scalar_t +#undef Real +#undef THInf +#undef TH_REAL_IS_DOUBLE +#undef TH_CONVERT_REAL_TO_ACCREAL +#undef TH_CONVERT_ACCREAL_TO_REAL + +#ifndef THGenerateManyTypes +#undef TH_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/TH/THGenerateFloatType.h b/thirdparty/libtorch/include/TH/THGenerateFloatType.h new file mode 100644 index 0000000000..c4b97b5236 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THGenerateFloatType.h @@ -0,0 +1,24 @@ +#ifndef TH_GENERIC_FILE +#error "You must define TH_GENERIC_FILE before including THGenerateFloatType.h" +#endif + +#define scalar_t float +#define accreal double +#define TH_CONVERT_REAL_TO_ACCREAL(_val) (accreal)(_val) +#define TH_CONVERT_ACCREAL_TO_REAL(_val) (scalar_t)(_val) +#define Real Float +#define THInf FLT_MAX +#define TH_REAL_IS_FLOAT +#line 1 TH_GENERIC_FILE +#include TH_GENERIC_FILE +#undef accreal +#undef scalar_t +#undef Real +#undef THInf +#undef TH_REAL_IS_FLOAT +#undef TH_CONVERT_REAL_TO_ACCREAL +#undef TH_CONVERT_ACCREAL_TO_REAL + +#ifndef THGenerateManyTypes +#undef TH_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/TH/THGenerateFloatTypes.h b/thirdparty/libtorch/include/TH/THGenerateFloatTypes.h new file mode 100644 index 0000000000..f51b658c22 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THGenerateFloatTypes.h @@ -0,0 +1,17 @@ +#ifndef TH_GENERIC_FILE +#error "You must define TH_GENERIC_FILE before including THGenerateFloatTypes.h" +#endif + +#ifndef THGenerateManyTypes +#define THFloatLocalGenerateManyTypes +#define THGenerateManyTypes +#endif + +#include +#include + +#ifdef THFloatLocalGenerateManyTypes +#undef THFloatLocalGenerateManyTypes +#undef THGenerateManyTypes +#undef TH_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/TH/THGenerateHalfType.h b/thirdparty/libtorch/include/TH/THGenerateHalfType.h new file mode 100644 index 0000000000..b075c683e0 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THGenerateHalfType.h @@ -0,0 +1,25 @@ +#ifndef TH_GENERIC_FILE +#error "You must define TH_GENERIC_FILE before including THGenerateHalfType.h" +#endif + +#include +#define scalar_t THHalf +#define accreal float +#define TH_CONVERT_REAL_TO_ACCREAL(_val) (accreal)(_val) +#define TH_CONVERT_ACCREAL_TO_REAL(_val) (scalar_t)(_val) +#define Real Half +#define THInf TH_HALF_BITS_TO_LITERAL(TH_HALF_INF) +#define TH_REAL_IS_HALF +#line 1 TH_GENERIC_FILE +#include TH_GENERIC_FILE +#undef scalar_t +#undef accreal +#undef Real +#undef THInf +#undef TH_REAL_IS_HALF +#undef TH_CONVERT_REAL_TO_ACCREAL +#undef TH_CONVERT_ACCREAL_TO_REAL + +#ifndef THGenerateManyTypes +#undef TH_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/TH/THGenerateIntType.h b/thirdparty/libtorch/include/TH/THGenerateIntType.h new file mode 100644 index 0000000000..2d31760ad9 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THGenerateIntType.h @@ -0,0 +1,26 @@ +#ifndef TH_GENERIC_FILE +#error "You must define TH_GENERIC_FILE before including THGenerateIntType.h" +#endif + +#define scalar_t int32_t +#define ureal uint32_t +#define accreal int64_t +#define TH_CONVERT_REAL_TO_ACCREAL(_val) (accreal)(_val) +#define TH_CONVERT_ACCREAL_TO_REAL(_val) (scalar_t)(_val) +#define Real Int +#define THInf INT_MAX +#define TH_REAL_IS_INT +#line 1 TH_GENERIC_FILE +#include TH_GENERIC_FILE +#undef scalar_t +#undef ureal +#undef accreal +#undef Real +#undef THInf +#undef TH_REAL_IS_INT +#undef TH_CONVERT_REAL_TO_ACCREAL +#undef TH_CONVERT_ACCREAL_TO_REAL + +#ifndef THGenerateManyTypes +#undef TH_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/TH/THGenerateIntTypes.h b/thirdparty/libtorch/include/TH/THGenerateIntTypes.h new file mode 100644 index 0000000000..07445573dc --- /dev/null +++ b/thirdparty/libtorch/include/TH/THGenerateIntTypes.h @@ -0,0 +1,20 @@ +#ifndef TH_GENERIC_FILE +#error "You must define TH_GENERIC_FILE before including THGenerateIntTypes.h" +#endif + +#ifndef THGenerateManyTypes +#define THIntLocalGenerateManyTypes +#define THGenerateManyTypes +#endif + +#include +#include +#include +#include +#include + +#ifdef THIntLocalGenerateManyTypes +#undef THIntLocalGenerateManyTypes +#undef THGenerateManyTypes +#undef TH_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/TH/THGenerateLongType.h b/thirdparty/libtorch/include/TH/THGenerateLongType.h new file mode 100644 index 0000000000..636cf94584 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THGenerateLongType.h @@ -0,0 +1,26 @@ +#ifndef TH_GENERIC_FILE +#error "You must define TH_GENERIC_FILE before including THGenerateLongType.h" +#endif + +#define scalar_t int64_t +#define ureal uint64_t +#define accreal int64_t +#define TH_CONVERT_REAL_TO_ACCREAL(_val) (accreal)(_val) +#define TH_CONVERT_ACCREAL_TO_REAL(_val) (scalar_t)(_val) +#define Real Long +#define THInf LONG_MAX +#define TH_REAL_IS_LONG +#line 1 TH_GENERIC_FILE +#include TH_GENERIC_FILE +#undef scalar_t +#undef ureal +#undef accreal +#undef Real +#undef THInf +#undef TH_REAL_IS_LONG +#undef TH_CONVERT_REAL_TO_ACCREAL +#undef TH_CONVERT_ACCREAL_TO_REAL + +#ifndef THGenerateManyTypes +#undef TH_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/TH/THGenerateQInt32Type.h b/thirdparty/libtorch/include/TH/THGenerateQInt32Type.h new file mode 100644 index 0000000000..b0b45d0b14 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THGenerateQInt32Type.h @@ -0,0 +1,24 @@ +#ifndef TH_GENERIC_FILE +#error "You must define TH_GENERIC_FILE before including THGenerateQInt32Type.h" +#endif + +#define quantized_t c10::qint32 +#define scalar_t int32_t +#define Real QInt32 +#define RealUnderlying Int +#define THQUANTIZED +#define THQINT32 +#define TH_REAL_IS_BYTE +#line 1 TH_GENERIC_FILE +#include TH_GENERIC_FILE +#undef scalar_t +#undef quantized_t +#undef Real +#undef RealUnderlying +#undef TH_REAL_IS_BYTE +#undef THQINT32 +#undef THQUANTIZED + +#ifndef THGenerateManyTypes +#undef TH_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/TH/THGenerateQInt8Type.h b/thirdparty/libtorch/include/TH/THGenerateQInt8Type.h new file mode 100644 index 0000000000..4126fadd90 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THGenerateQInt8Type.h @@ -0,0 +1,24 @@ +#ifndef TH_GENERIC_FILE +#error "You must define TH_GENERIC_FILE before including THGenerateQInt8Type.h" +#endif + +#define quantized_t c10::qint8 +#define scalar_t int8_t +#define Real QInt8 +#define RealUnderlying Char +#define THQUANTIZED +#define THQINT8 +#define TH_REAL_IS_BYTE +#line 1 TH_GENERIC_FILE +#include TH_GENERIC_FILE +#undef scalar_t +#undef quantized_t +#undef Real +#undef RealUnderlying +#undef TH_REAL_IS_BYTE +#undef THQINT8 +#undef THQUANTIZED + +#ifndef THGenerateManyTypes +#undef TH_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/TH/THGenerateQTypes.h b/thirdparty/libtorch/include/TH/THGenerateQTypes.h new file mode 100644 index 0000000000..ee958b3a32 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THGenerateQTypes.h @@ -0,0 +1,18 @@ +#ifndef TH_GENERIC_FILE +#error "You must define TH_GENERIC_FILE before including THGenerateQTypes.h" +#endif + +#ifndef THGenerateManyTypes +#define THQLocalGenerateManyTypes +#define THGenerateManyTypes +#endif + +#include +#include +#include + +#ifdef THQLocalGenerateManyTypes +#undef THQLocalGenerateManyTypes +#undef THGenerateManyTypes +#undef TH_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/TH/THGenerateQUInt8Type.h b/thirdparty/libtorch/include/TH/THGenerateQUInt8Type.h new file mode 100644 index 0000000000..3051bbc35e --- /dev/null +++ b/thirdparty/libtorch/include/TH/THGenerateQUInt8Type.h @@ -0,0 +1,24 @@ +#ifndef TH_GENERIC_FILE +#error "You must define TH_GENERIC_FILE before including THGenerateQUInt8Type.h" +#endif + +#define quantized_t c10::quint8 +#define scalar_t uint8_t +#define Real QUInt8 +#define RealUnderlying Byte +#define THQUANTIZED +#define THQUINT8 +#define TH_REAL_IS_BYTE +#line 1 TH_GENERIC_FILE +#include TH_GENERIC_FILE +#undef scalar_t +#undef quantized_t +#undef Real +#undef RealUnderlying +#undef TH_REAL_IS_BYTE +#undef THQUINT8 +#undef THQUANTIZED + +#ifndef THGenerateManyTypes +#undef TH_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/TH/THGenerateShortType.h b/thirdparty/libtorch/include/TH/THGenerateShortType.h new file mode 100644 index 0000000000..afda4dce8e --- /dev/null +++ b/thirdparty/libtorch/include/TH/THGenerateShortType.h @@ -0,0 +1,26 @@ +#ifndef TH_GENERIC_FILE +#error "You must define TH_GENERIC_FILE before including THGenerateShortType.h" +#endif + +#define scalar_t int16_t +#define ureal uint16_t +#define accreal int64_t +#define TH_CONVERT_REAL_TO_ACCREAL(_val) (accreal)(_val) +#define TH_CONVERT_ACCREAL_TO_REAL(_val) (scalar_t)(_val) +#define Real Short +#define THInf SHRT_MAX +#define TH_REAL_IS_SHORT +#line 1 TH_GENERIC_FILE +#include TH_GENERIC_FILE +#undef scalar_t +#undef ureal +#undef accreal +#undef Real +#undef THInf +#undef TH_REAL_IS_SHORT +#undef TH_CONVERT_REAL_TO_ACCREAL +#undef TH_CONVERT_ACCREAL_TO_REAL + +#ifndef THGenerateManyTypes +#undef TH_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/TH/THGenerator.hpp b/thirdparty/libtorch/include/TH/THGenerator.hpp new file mode 100644 index 0000000000..970f12ac6b --- /dev/null +++ b/thirdparty/libtorch/include/TH/THGenerator.hpp @@ -0,0 +1,39 @@ +#pragma once + +#include + +/** + * THGeneratorState is a POD class needed for memcpys + * in torch.get_rng_state() and torch.set_rng_state(). + * It is a legacy class and even though it is replaced with + * at::CPUGenerator, we need this class and some of its fields + * to support backward compatibility on loading checkpoints. + */ +struct THGeneratorState { + /* The initial seed. */ + uint64_t the_initial_seed; + int left; /* = 1; */ + int seeded; /* = 0; */ + uint64_t next; + uint64_t state[at::MERSENNE_STATE_N]; /* the array for the state vector */ + + /********************************/ + + /* For normal distribution */ + double normal_x; + double normal_y; + double normal_rho; + int normal_is_valid; /* = 0; */ +}; + +/** + * THGeneratorStateNew is a POD class containing + * new data introduced in at::CPUGenerator and the legacy state. It is used + * as a helper for torch.get_rng_state() and torch.set_rng_state() + * functions. + */ +struct THGeneratorStateNew { + THGeneratorState legacy_pod; + float next_float_normal_sample; + bool is_next_float_normal_sample_valid; +}; diff --git a/thirdparty/libtorch/include/TH/THHalf.h b/thirdparty/libtorch/include/TH/THHalf.h new file mode 100644 index 0000000000..ce41080ca7 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THHalf.h @@ -0,0 +1,8 @@ +#ifndef TH_HALF_H +#define TH_HALF_H + +#include + +#define THHalf at::Half + +#endif diff --git a/thirdparty/libtorch/include/TH/THLapack.h b/thirdparty/libtorch/include/TH/THLapack.h new file mode 100644 index 0000000000..e50faa8f45 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THLapack.h @@ -0,0 +1,27 @@ +#ifndef TH_LAPACK_INC +#define TH_LAPACK_INC + +#include + +#define THLapack_(NAME) TH_CONCAT_4(TH,Real,Lapack_,NAME) + +#define THLapackCheck(fmt, func, info , ...) \ +if (info < 0) { \ + THError("Lapack Error in %s : Illegal Argument %d", func, -info); \ +} else if(info > 0) { \ + THError(fmt, func, info, ##__VA_ARGS__); \ +} \ + +#define THLapackCheckWithCleanup(fmt, cleanup, func, info , ...) \ +if (info < 0) { \ + cleanup \ + THError("Lapack Error in %s : Illegal Argument %d", func, -info); \ +} else if(info > 0) { \ + cleanup \ + THError(fmt, func, info, ##__VA_ARGS__); \ +} + +#include +#include + +#endif diff --git a/thirdparty/libtorch/include/TH/THLogAdd.h b/thirdparty/libtorch/include/TH/THLogAdd.h new file mode 100644 index 0000000000..60acaedc14 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THLogAdd.h @@ -0,0 +1,14 @@ +#ifndef TH_LOG_ADD_INC +#define TH_LOG_ADD_INC + +#include + +TH_API const double THLog2Pi; +TH_API const double THLogZero; +TH_API const double THLogOne; + +TH_API double THLogAdd(double log_a, double log_b); +TH_API double THLogSub(double log_a, double log_b); +TH_API double THExpMinusApprox(const double x); + +#endif diff --git a/thirdparty/libtorch/include/TH/THMemoryFile.h b/thirdparty/libtorch/include/TH/THMemoryFile.h new file mode 100644 index 0000000000..d572b6ddca --- /dev/null +++ b/thirdparty/libtorch/include/TH/THMemoryFile.h @@ -0,0 +1,13 @@ +#ifndef TH_MEMORY_FILE_INC +#define TH_MEMORY_FILE_INC + +#include +#include + +TH_API THFile *THMemoryFile_newWithStorage(THCharStorage *storage, const char *mode); +TH_API THFile *THMemoryFile_new(const char *mode); + +TH_API THCharStorage *THMemoryFile_storage(THFile *self); +TH_API void THMemoryFile_longSize(THFile *self, int size); + +#endif diff --git a/thirdparty/libtorch/include/TH/THSize.h b/thirdparty/libtorch/include/TH/THSize.h new file mode 100644 index 0000000000..c190a31077 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THSize.h @@ -0,0 +1,13 @@ +#ifndef TH_SIZE_INC +#define TH_SIZE_INC + +#include +#include + +// THTensor functions that would work on a THSize if we had such a class in C++, +// i.e. THTensor functions that depend only on the shape of the tensor, not the type. + +TH_API int THSize_isSameSizeAs(const int64_t *sizeA, int64_t dimsA, const int64_t *sizeB, int64_t dimsB); +TH_API ptrdiff_t THSize_nElement(int64_t dims, int64_t *size); + +#endif diff --git a/thirdparty/libtorch/include/TH/THStorage.h b/thirdparty/libtorch/include/TH/THStorage.h new file mode 100644 index 0000000000..404d587529 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THStorage.h @@ -0,0 +1,4 @@ +#pragma once +#include + +// Compatability header. Use THStorageFunctions.h instead if you need this. diff --git a/thirdparty/libtorch/include/TH/THStorageFunctions.h b/thirdparty/libtorch/include/TH/THStorageFunctions.h new file mode 100644 index 0000000000..adaccb435a --- /dev/null +++ b/thirdparty/libtorch/include/TH/THStorageFunctions.h @@ -0,0 +1,39 @@ +#pragma once + +#include +#include + +#define THStorage_(NAME) TH_CONCAT_4(TH,Real,Storage_,NAME) + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +// This exists to have a data-type independent way of freeing (necessary for THPPointer). +TH_API void THStorage_free(THStorage *storage); diff --git a/thirdparty/libtorch/include/TH/THStorageFunctions.hpp b/thirdparty/libtorch/include/TH/THStorageFunctions.hpp new file mode 100644 index 0000000000..f26ee62b36 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THStorageFunctions.hpp @@ -0,0 +1,38 @@ +#pragma once + +// STOP!!! Thinking of including this header directly? Please +// read Note [TH abstraction violation] + +#include +#include +#include + +#include + +// Note [Weak references for intrusive refcounting] +// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +// Here's the scheme: +// +// - refcount == number of strong references to the object +// weakcount == number of weak references to the object, +// plus one more if refcount > 0 +// +// - THStorage stays live as long as there are any strong +// or weak pointers to it (weakcount > 0, since strong +// references count as a +1 to weakcount) +// +// - finalizers are called and data_ptr is deallocated when refcount == 0 +// +// - Once refcount == 0, it can never again be > 0 (the transition +// from > 0 to == 0 is monotonic) +// +// - When you access THStorage via a weak pointer, you must +// atomically increment the use count, if it is greater than 0. +// If it is not, you must report that the storage is dead. +// + +TH_CPP_API THStorage* THStorage_new(caffe2::TypeMeta data_type); +TH_API ptrdiff_t THStorage_size(const THStorage *self); + +TH_API void THStorage_retain(THStorage *storage); +TH_API void THStorage_resize(THStorage *storage, ptrdiff_t size); diff --git a/thirdparty/libtorch/include/TH/THTensor.h b/thirdparty/libtorch/include/TH/THTensor.h new file mode 100644 index 0000000000..335000fbef --- /dev/null +++ b/thirdparty/libtorch/include/TH/THTensor.h @@ -0,0 +1,62 @@ +#ifndef TH_TENSOR_INC +#define TH_TENSOR_INC + +#include +#include + +#define THTensor_(NAME) TH_CONCAT_4(TH,Real,Tensor_,NAME) + +/* basics */ +#include +#include + +#include +#include + +#include +#include + +#include +#include + +/* random numbers */ +#include +#include + +#include +#include + +/* maths */ +#include +#include + +#include +#include + +#include +#include + +#include +#include + +/* fill and zero*/ +#include +#include + +#include +#include + +#include +#include + +#include +#include + +/* convolutions */ +#include +#include + +/* lapack support */ +#include +#include +#endif diff --git a/thirdparty/libtorch/include/TH/THTensor.hpp b/thirdparty/libtorch/include/TH/THTensor.hpp new file mode 100644 index 0000000000..699ebaa0ce --- /dev/null +++ b/thirdparty/libtorch/include/TH/THTensor.hpp @@ -0,0 +1,138 @@ +#pragma once + +// STOP!!! Thinking of including this header directly? Please +// read Note [TH abstraction violation] + +#include +#include + +#include +#include + +// Returns a Tensor given a TensorImpl. The TensorImpl remains valid after the +// the Tensor is destroyed. +inline at::Tensor THTensor_wrap(THTensor* tensor) { + c10::raw::intrusive_ptr::incref(tensor); + return at::Tensor(c10::intrusive_ptr::reclaim(tensor)); +} + +inline const int64_t* THTensor_getSizePtr(THTensor* tensor) { + return tensor->sizes().data(); +} + +inline const int64_t* THTensor_getStridePtr(THTensor* tensor) { + return tensor->strides().data(); +} + +// NB: Non-retaining +inline THStorage* THTensor_getStoragePtr(const THTensor* tensor) { + // Within PyTorch, the invariant is that storage_ is always + // initialized; we never have tensors that don't have any storage. + // However, for Caffe2, this is not true, because they have permitted + // tensors to be allocated without specifying what scalar type + // they should be, only to be filled when GetMutableData is called + // for the first time (providing the necessary type). It is an ERROR to + // invoke any PyTorch operations on such a half-constructed storage, + // and this check tests for that case. + TORCH_CHECK(tensor->storage(), "Cannot use PyTorch operations on a half-constructed " + "tensor. If this tensor came from Caffe2, please call GetMutableData on " + "it first; otherwise, this is a bug, please report it."); + return tensor->storage().unsafeGetStorageImpl(); +} + +inline void THTensor_maybe_zero_dim(THTensor *tensor, bool condition_when_zero_dim) { + bool set_zero_dim = condition_when_zero_dim && tensor->sizes().size() == 1 && tensor->size(0) == 1; + if (set_zero_dim) { + tensor->set_sizes_and_strides({}, {}); + } +} + +// [NOTE: nDimension vs nDimensionLegacyNoScalars vs nDimensionLegacyAll] +// nDimension corresponds to the "true" ATen dimension. +// nDimensionLegacyNoScalars correpsonds to the ATen dimension, except scalars are viewed as 1-dimensional tensors. +// nDimensionLegacyAll corresponds to the ATen dimension, except scalars are viewed as 1-dimensional tensors +// and tensors with a dimension of size zero are collapsed to 0-dimensional tensors. +// +// Eventually, everything should go through nDimension or tensor->dim(). +inline int THTensor_nDimension(const THTensor* tensor) { + return tensor->dim(); +} + +inline int THTensor_nDimensionLegacyNoScalars(const THTensor* tensor) { + if (tensor->dim() == 0) { + return 1; + } else { + return tensor->dim(); + } +} + +inline int THTensor_nDimensionLegacyAll(const THTensor* tensor) { + if (tensor->is_empty()) { + return 0; + } else if (tensor->dim() == 0) { + return 1; + } else { + return tensor->dim(); + } +} + +inline int64_t THTensor_strideLegacyNoScalars(const THTensor *self, int dim) { + THArgCheck((dim >= 0) && (dim < THTensor_nDimensionLegacyNoScalars(self)), 2, "dimension %d out of range of %dD tensor", + dim, THTensor_nDimensionLegacyNoScalars(self)); + return self->dim() == 0 ? 1 : self->stride(dim); +} + +inline int64_t THTensor_sizeLegacyNoScalars(const THTensor *self, int dim) +{ + THArgCheck((dim >= 0) && (dim < THTensor_nDimensionLegacyNoScalars(self)), 2, "dimension %d out of range of %dD tensor", + dim, THTensor_nDimensionLegacyNoScalars(self)); + return self->dim() == 0 ? 1 : self->size(dim); +} + +#include +#include + +#include +#include + +inline std::vector THTensor_sizesLegacyNoScalars(const THTensor *self) { + if (self->dim() == 0) { + return {1}; + } else { + return self->sizes().vec(); + } +} + +inline std::vector THTensor_stridesLegacyNoScalars(const THTensor *self) { + if (self->dim() == 0) { + return {1}; + } else { + return self->strides().vec(); + } +} + +// NB: Steals ownership of storage +TH_API void THTensor_stealAndSetStoragePtr(THTensor* tensor, THStorage* storage); + +TH_API void THTensor_free(THTensor *self); +TH_API void THTensor_setStorageNd(THTensor *self, THStorage *storage, ptrdiff_t storageOffset, int nDimension, const int64_t *size, const int64_t *stride); +TH_API void THTensor_resizeNd(THTensor *self, int nDimension, const int64_t *size, const int64_t *stride); + +TH_CPP_API void THTensor_resize(THTensor *self, at::IntArrayRef size, at::IntArrayRef stride); +TH_CPP_API void THTensor_setStorage(THTensor *self, THStorage *storage_, ptrdiff_t storageOffset_, at::IntArrayRef size_, at::IntArrayRef stride_); +TH_CPP_API c10::optional> THTensor_compute_stride( + at::IntArrayRef oldshape, + at::IntArrayRef oldstride, + at::IntArrayRef newshape); + +#include +#include + +#include +#include + +#include +#include + +#include +#include diff --git a/thirdparty/libtorch/include/TH/THTensorApply.h b/thirdparty/libtorch/include/TH/THTensorApply.h new file mode 100644 index 0000000000..230e6d2462 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THTensorApply.h @@ -0,0 +1,309 @@ +#ifndef TH_TENSOR_APPLY_INC +#define TH_TENSOR_APPLY_INC + +#include + +/* + * The basic strategy for apply is as follows: + * + * 1. Starting with the outermost index, loop until we reach a dimension where the + * data is no longer contiguous, i.e. the stride at that dimension is not equal to + * the size of the tensor defined by the outer dimensions. Let's call this outer + * (contiguous) tensor A. Note that if the Tensor is contiguous, then A is equal + * to the entire Tensor. Let's call the inner tensor B. + * + * 2. We loop through the indices in B, starting at its outermost dimension. For + * example, if B is a 2x2 matrix, then we do: + * + * B[0][0] + * B[0][1] + * B[1][0] + * B[1][1] + * + * We set the offset into the underlying storage as (storageOffset + stride_B * index_B), + * i.e. basically we compute the offset into the storage as we would normally for a + * Tensor. But because we are guaranteed the subsequent data is contiguous in memory, we + * can simply loop for sizeof(A) iterations and perform the operation, without having to + * follow the order described by the strides of A. + * + * 3. As an optimization, we merge dimensions of A that are contiguous in memory. For + * example, if A is a 3x3x3x3 tensor narrowed from a 3x3x4x3 tensor, then the first two + * dimensions can be merged for the purposes of APPLY, reducing the number of nested + * loops. + */ + +#define __TH_TENSOR_APPLYX_PREAMBLE(TYPE, TENSOR, DIM, ALLOW_CONTIGUOUS) \ + TYPE *TENSOR##_data = NULL; \ + int64_t *TENSOR##_counter = NULL, *TENSOR##_sizes = NULL, *TENSOR##_strides = NULL, *TENSOR##_dimOffset = NULL; \ + int64_t TENSOR##_stride = 0, TENSOR##_size = 0, TENSOR##_dim = 0, TENSOR##_i, TENSOR##_n; \ + int TENSOR##_contiguous = ALLOW_CONTIGUOUS && DIM < 0; \ + TENSOR##_n = 1; \ + for(TENSOR##_i = 0; TENSOR##_i < TENSOR->dim(); TENSOR##_i++) \ + TENSOR##_n *= TENSOR->size(TENSOR##_i); \ +\ + if(TENSOR->is_empty()) \ + TH_TENSOR_APPLY_hasFinished = 1; \ + else \ + { \ + TENSOR##_data = THTensor_getStoragePtr(TENSOR)->data()+TENSOR->storage_offset(); \ + TENSOR##_size = 1; \ + TENSOR##_stride = 1; \ + for(TENSOR##_i = THTensor_nDimensionLegacyAll(TENSOR)-1; TENSOR##_i >= 0; TENSOR##_i--) { \ + if(THTensor_sizeLegacyNoScalars(TENSOR, TENSOR##_i) != 1) { \ + if(THTensor_strideLegacyNoScalars(TENSOR, TENSOR##_i) == TENSOR##_size && TENSOR##_i != DIM) \ + TENSOR##_size *= THTensor_sizeLegacyNoScalars(TENSOR, TENSOR##_i); \ + else{ \ + TENSOR##_contiguous = 0; \ + break; \ + } \ + } \ + } \ + if (!TENSOR##_contiguous) { \ + /* Find the dimension of contiguous sections */ \ + TENSOR##_dim = 1; \ + for(TENSOR##_i = THTensor_nDimensionLegacyAll(TENSOR)-2; TENSOR##_i >= 0; TENSOR##_i--) \ + { \ + if(TENSOR->stride(TENSOR##_i) != TENSOR->stride(TENSOR##_i+1) * TENSOR->size(TENSOR##_i+1) || TENSOR##_i == DIM || TENSOR##_i+1 == DIM) \ + TENSOR##_dim++; \ + } \ + /* Allocate an array of 3*dim elements, where dim is the number of contiguous sections */ \ + TENSOR##_counter = (int64_t*)THAlloc(sizeof(int64_t)*(3*TENSOR##_dim)); \ + TENSOR##_sizes = TENSOR##_counter + TENSOR##_dim; \ + TENSOR##_strides = TENSOR##_counter + 2*TENSOR##_dim; \ + TH_TENSOR_dim_index = TENSOR##_dim-1; \ + TENSOR##_dimOffset = (DIM == THTensor_nDimensionLegacyAll(TENSOR)-1) ? &TENSOR##_i : &TENSOR##_counter[DIM]; \ + TENSOR##_sizes[TH_TENSOR_dim_index] = THTensor_sizeLegacyNoScalars(TENSOR, THTensor_nDimensionLegacyAll(TENSOR)-1); \ + TENSOR##_strides[TH_TENSOR_dim_index] = THTensor_strideLegacyNoScalars(TENSOR, THTensor_nDimensionLegacyAll(TENSOR)-1); \ + /* TENSOR##_counter tracks where we are in the storage. The offset into the */ \ + /* storage is given by storage_offset + (i * j), where i is the stride */ \ + /* vector and j is tensor_counter vector. This sets the starting position for the loop. */ \ + for(TENSOR##_i = TENSOR##_dim-1; TENSOR##_i >= 0; --TENSOR##_i) { \ + TENSOR##_counter[TENSOR##_i] = 0; \ + } \ + for(TENSOR##_i = THTensor_nDimensionLegacyAll(TENSOR)-2; TENSOR##_i >= 0; --TENSOR##_i) { \ + if (TENSOR->stride(TENSOR##_i) == TENSOR->stride(TENSOR##_i+1) * TENSOR->size(TENSOR##_i+1) && TENSOR##_i != DIM && TENSOR##_i+1 != DIM) { \ + TENSOR##_sizes[TH_TENSOR_dim_index] = TENSOR->size(TENSOR##_i) * TENSOR##_sizes[TH_TENSOR_dim_index]; \ + if (DIM != THTensor_nDimensionLegacyAll(TENSOR)-1 && TENSOR##_i < DIM) \ + TENSOR##_dimOffset--; \ + } else { \ + --TH_TENSOR_dim_index; \ + TENSOR##_sizes[TH_TENSOR_dim_index] = TENSOR->size(TENSOR##_i); \ + TENSOR##_strides[TH_TENSOR_dim_index] = TENSOR->stride(TENSOR##_i); \ + } \ + } \ + /* Size of the inner most section */ \ + TENSOR##_size = TENSOR##_sizes[TENSOR##_dim-1]; \ + /* Stride of the inner most section */ \ + TENSOR##_stride = TENSOR##_strides[TENSOR##_dim-1]; \ + } \ + else{\ + TENSOR##_dim = 1;\ + TENSOR##_counter = (int64_t*)THAlloc(sizeof(int64_t)*3);\ + TENSOR##_sizes = TENSOR##_counter + 1;\ + TENSOR##_strides = TENSOR##_counter + 2;\ + TENSOR##_sizes[0] = TENSOR##_n;\ + TENSOR##_strides[0] = 1;\ + TENSOR##_size = TENSOR##_sizes[0];\ + TENSOR##_stride = TENSOR##_strides[0];\ + }\ + } \ + TENSOR##_i = 0; + +#define __TH_TENSOR_APPLYX_UPDATE_COUNTERS(TENSOR, ALWAYS_UPDATE) \ + if(TENSOR##_i == TENSOR##_size || ALWAYS_UPDATE) \ + { \ + if(TENSOR##_contiguous) \ + break; \ +\ + if(TENSOR##_dim == 1) \ + break; \ +\ + /* Reset pointer to beginning of loop */ \ + TENSOR##_data -= TENSOR##_size*TENSOR##_stride; \ + for(TENSOR##_i = TENSOR##_dim-2; TENSOR##_i >= 0; TENSOR##_i--) \ + { \ + TENSOR##_counter[TENSOR##_i]++; \ + /* Jump ahread by the stride of this dimension */ \ + TENSOR##_data += TENSOR##_strides[TENSOR##_i]; \ +\ + if(TENSOR##_counter[TENSOR##_i] == TENSOR##_sizes[TENSOR##_i]) \ + { \ + if(TENSOR##_i == 0) \ + { \ + TH_TENSOR_APPLY_hasFinished = 1; \ + break; \ + } \ + else \ + { \ + /* Reset the pointer to the beginning of the chunk defined by this dimension */ \ + TENSOR##_data -= TENSOR##_counter[TENSOR##_i]*TENSOR##_strides[TENSOR##_i]; \ + TENSOR##_counter[TENSOR##_i] = 0; \ + } \ + } \ + else \ + break; \ + } \ + TENSOR##_i = 0; \ + } \ + +#define TH_TENSOR_APPLY3_D(TYPE1, TENSOR1, TYPE2, TENSOR2, TYPE3, TENSOR3, DIM, CODE) \ +{ \ + int TH_TENSOR_APPLY_hasFinished = 0; \ + int64_t TH_TENSOR_dim_index = 0; \ + __TH_TENSOR_APPLYX_PREAMBLE(TYPE1, TENSOR1, DIM, 1) \ + __TH_TENSOR_APPLYX_PREAMBLE(TYPE2, TENSOR2, DIM, 1) \ + __TH_TENSOR_APPLYX_PREAMBLE(TYPE3, TENSOR3, DIM, 1) \ + \ + int elements_equal = 1; \ + if(TENSOR1##_n != TENSOR2##_n) { \ + elements_equal = 0; \ + } \ + else if(TENSOR1##_n != TENSOR3##_n) { \ + elements_equal = 0; \ + } \ + if (elements_equal == 0) { \ + AT_ERROR("inconsistent tensor size, expected ", \ + #TENSOR1, " ", TENSOR1->sizes(), ", ", \ + #TENSOR2, " ", TENSOR2->sizes(), " and ", \ + #TENSOR3, " ", TENSOR3->sizes(), " to have the same " \ + "number of elements, but got ", TENSOR1##_n, ", ", \ + TENSOR2##_n, " and ", TENSOR3##_n, " elements respectively"); \ + } \ + \ + while(!TH_TENSOR_APPLY_hasFinished) \ + { \ + /* Loop through the inner most region of the Tensor */ \ + for(; TENSOR1##_i < TENSOR1##_size && TENSOR2##_i < TENSOR2##_size && TENSOR3##_i < TENSOR3##_size; TENSOR1##_i++, TENSOR2##_i++, TENSOR3##_i++, TENSOR1##_data += TENSOR1##_stride, TENSOR2##_data += TENSOR2##_stride, TENSOR3##_data += TENSOR3##_stride) /* 0 et pas TENSOR##_dim! */ \ + { \ + CODE \ + } \ + __TH_TENSOR_APPLYX_UPDATE_COUNTERS(TENSOR1, 0) \ + __TH_TENSOR_APPLYX_UPDATE_COUNTERS(TENSOR2, 0) \ + __TH_TENSOR_APPLYX_UPDATE_COUNTERS(TENSOR3, 0) \ + } \ + if(TENSOR1##_counter != NULL) \ + THFree(TENSOR1##_counter); \ + if(TENSOR2##_counter != NULL) \ + THFree(TENSOR2##_counter); \ + if(TENSOR3##_counter != NULL) \ + THFree(TENSOR3##_counter); \ +} + +#define TH_TENSOR_APPLY3(TYPE1, TENSOR1, TYPE2, TENSOR2, TYPE3, TENSOR3, CODE) \ + TH_TENSOR_APPLY3_D(TYPE1, TENSOR1, TYPE2, TENSOR2, TYPE3, TENSOR3, -1, CODE) + +#define TH_TENSOR_APPLY2_D(TYPE1, TENSOR1, TYPE2, TENSOR2, DIM, CODE) \ +{ \ + int TH_TENSOR_APPLY_hasFinished = 0; \ + int64_t TH_TENSOR_dim_index = 0; \ + __TH_TENSOR_APPLYX_PREAMBLE(TYPE1, TENSOR1, DIM, 1) \ + __TH_TENSOR_APPLYX_PREAMBLE(TYPE2, TENSOR2, DIM, 1) \ +\ + if(TENSOR1##_n != TENSOR2##_n) { \ + AT_ERROR("inconsistent tensor size, expected ", \ + #TENSOR1, " ", TENSOR1->sizes(), " and ", \ + #TENSOR2, " ", TENSOR2->sizes(), \ + " to have the same number of elements, but got ", \ + TENSOR1##_n, " and ", TENSOR2##_n, " elements respectively"); \ + } \ + while(!TH_TENSOR_APPLY_hasFinished) \ + { \ + /* Loop through the inner most region of the Tensor */ \ + for(; TENSOR1##_i < TENSOR1##_size && TENSOR2##_i < TENSOR2##_size; TENSOR1##_i++, TENSOR2##_i++, TENSOR1##_data += TENSOR1##_stride, TENSOR2##_data += TENSOR2##_stride) /* 0 et pas TENSOR##_dim! */ \ + { \ + CODE \ + } \ + __TH_TENSOR_APPLYX_UPDATE_COUNTERS(TENSOR1, 0) \ + __TH_TENSOR_APPLYX_UPDATE_COUNTERS(TENSOR2, 0) \ + } \ + if(TENSOR1##_counter != NULL) \ + THFree(TENSOR1##_counter); \ + if(TENSOR2##_counter != NULL) \ + THFree(TENSOR2##_counter); \ +} + +#define TH_TENSOR_APPLY2(TYPE1, TENSOR1, TYPE2, TENSOR2, CODE) \ + TH_TENSOR_APPLY2_D(TYPE1, TENSOR1, TYPE2, TENSOR2, -1, CODE) + +#define TH_TENSOR_APPLY_D(TYPE, TENSOR, DIM, CODE) \ +{ \ + int TH_TENSOR_APPLY_hasFinished = 0; \ + int64_t TH_TENSOR_dim_index = 0; \ + __TH_TENSOR_APPLYX_PREAMBLE(TYPE, TENSOR, DIM, 0) \ +\ + while(!TH_TENSOR_APPLY_hasFinished) \ + { \ + /* Loop through the inner most region of the Tensor */ \ + for(; TENSOR##_i < TENSOR##_size; TENSOR##_i++, TENSOR##_data += TENSOR##_stride) /* 0 et pas TENSOR##_dim! */ \ + { \ + CODE \ + } \ + __TH_TENSOR_APPLYX_UPDATE_COUNTERS(TENSOR, 1) \ + } \ + THFree(TENSOR##_counter); \ +} + +#define TH_TENSOR_APPLY(TYPE, TENSOR, CODE) \ + TH_TENSOR_APPLY_D(TYPE, TENSOR, -1, CODE) + + +/* + * Calcuate the memory offset of an element in a tensor. The strategy is below: + * + * 1. convert the line index(the index of the element) to the indexs(coordinates) in the tensor. + * It can hinted by a classical problem: Getting each individual digit from a whole integer(Decimal base). + * A N-digit decimal base number could be view as a N-dimension tensor and the sizes of the tensor are 10. + * So the value the whole integer is the line index. And the digits could be viewed as the indexes in + * different dimentions. + * + * 2. convert the indexs(coordinates) in the tensor to the memory offset. + * + * You can get the detailes in the for-statement iterations. + * + * The macro is only used in the first element in each thread. For the rest, the memory offset could update + * according to info of the tensor in order to get better performance. So we should also record the each + * indexs in coresponding dimension of first element. + * The recorded info is stored in the TENSOR##_counter_tmp. + * + */ +#define __TH_TENSOR_APPLYX_CAL_MEMORY_OFFSET(TENSOR) \ + int64_t *TENSOR##_counter_tmp = (int64_t*)THAlloc(sizeof(int64_t) * TENSOR##_dim); \ + ptrdiff_t TENSOR##_memory_offset = 0; \ + ptrdiff_t TENSOR##_quot = line_index_start; \ + for (TENSOR##_i = TENSOR##_dim-1; TENSOR##_i>=0; --TENSOR##_i) { \ + TENSOR##_counter_tmp[TENSOR##_i] = TENSOR##_quot%TENSOR##_sizes[TENSOR##_i]; \ + TENSOR##_quot /= TENSOR##_sizes[TENSOR##_i]; \ + TENSOR##_memory_offset += TENSOR##_counter_tmp[TENSOR##_i] * TENSOR##_strides[TENSOR##_i]; \ + } + +/* + * The macro update the indexes in each dimension of the elements except for the first one allocated in + * each thread. + * For a tensor, if the index of some dimension reaches the size of the corresponding dimension. It will carry and clear. + * If the index of next high dimension does do, the index of next high dimension should carry and clear, too. + * + * The momery offset calculatation is a little confusing. If current index carries, the current index is set to 0. So + * the offset should decrease by size*stride of the last dimension. Then the index next high dimension increases by 1. So + * the offset should increase by stride of next high dimension. + */ +#define __TH_TENSOR_APPLYX_UPDATE_COUNTERS_PARALLEL(TENSOR) \ + if(TENSOR##_i == TENSOR##_size && TENSOR##_dim > 1){ /*reaches the edge*/ \ + int TENSOR##_carry_coord = 1; /*set carry flag to true*/ \ + TENSOR##_start = 0; /*the current index be cleared to 0*/\ + TENSOR##_data -= TENSOR##_size * TENSOR##_stride; /*the momery offset reset to the first one in current dimension */\ + for(TENSOR##_i = TENSOR##_dim - 2; (TENSOR##_i >= 0) && (TENSOR##_carry_coord); TENSOR##_i--){ \ + TENSOR##_counter_tmp[TENSOR##_i]++; /*the index of next high dimension update*/ \ + TENSOR##_data += TENSOR##_strides[TENSOR##_i]; /*memory offset increase by stride of next high dimension*/\ + if(TENSOR##_counter_tmp[TENSOR##_i] == TENSOR##_sizes[TENSOR##_i]){ /*The next high dimension also carry, continue + to clear and carry*/ \ + TENSOR##_data -= TENSOR##_sizes[TENSOR##_i] * TENSOR##_strides[TENSOR##_i]; \ + TENSOR##_counter_tmp[TENSOR##_i] = 0; \ + } else { \ + TENSOR##_carry_coord = 0; \ + } \ + } \ + } else { \ + TENSOR##_start = TENSOR##_i; \ + } + +#endif diff --git a/thirdparty/libtorch/include/TH/THTensorDimApply.h b/thirdparty/libtorch/include/TH/THTensorDimApply.h new file mode 100644 index 0000000000..2bb3f97e60 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THTensorDimApply.h @@ -0,0 +1,329 @@ +#ifndef TH_TENSOR_DIM_APPLY_INC +#define TH_TENSOR_DIM_APPLY_INC + +// This is an example of SIZE_CHECK argument passable to TH_TENSOR_DIM_APPLY3. +// The TENSOR1, TENSOR2, TENSOR3, DIMENSION will be expanded the same way as +// TH_TENSOR_DIM_APPLY3. +// Specifically, this check ensures that TENSOR1, TENSOR2, TENSOR3 have same +// size except for DIMENSION. +#define TH_TENSOR_DIM_APPLY3_SIZE_EQ_EXCEPT_DIM(TENSOR1, TENSOR2, TENSOR3, DIMENSION) \ +{ \ + int shape_check_flag = 0; \ + for(TH_TENSOR_DIM_APPLY_i = 0; TH_TENSOR_DIM_APPLY_i < THTensor_nDimensionLegacyNoScalars(TENSOR1); TH_TENSOR_DIM_APPLY_i++) \ + { \ + if (TH_TENSOR_DIM_APPLY_i == DIMENSION) \ + continue; \ + if (TENSOR1->size(TH_TENSOR_DIM_APPLY_i) != TENSOR2->size(TH_TENSOR_DIM_APPLY_i)) { \ + shape_check_flag = 1; \ + break; \ + } \ + if(TENSOR1->size(TH_TENSOR_DIM_APPLY_i) != TENSOR3->size(TH_TENSOR_DIM_APPLY_i)) { \ + shape_check_flag = 1; \ + break; \ + } \ + } \ + if (shape_check_flag == 1) { \ + AT_ERROR("Expected ", #TENSOR1, " ", TENSOR1->sizes(), ", ", #TENSOR2, " ", TENSOR2->sizes(), " and ", #TENSOR3, " ", TENSOR3->sizes(), " to have the same size apart from dimension ", DIMENSION); \ + } \ +} + +#define TH_TENSOR_DIM_APPLY3(TYPE1, TENSOR1, TYPE2, TENSOR2, TYPE3, TENSOR3, DIMENSION, SIZE_CHECK, CODE) \ +{ \ + TYPE1 *TENSOR1##_data = NULL; \ + TH_UNUSED int64_t TENSOR1##_stride = 0, TENSOR1##_size = 0; \ + TYPE2 *TENSOR2##_data = NULL; \ + TH_UNUSED int64_t TENSOR2##_stride = 0, TENSOR2##_size = 0; \ + TYPE3 *TENSOR3##_data = NULL; \ + TH_UNUSED int64_t TENSOR3##_stride = 0, TENSOR3##_size = 0; \ + int64_t *TH_TENSOR_DIM_APPLY_counter = NULL; \ + int TH_TENSOR_DIM_APPLY_hasFinished = THTensor_(numel)(TENSOR1) == 0; \ + int TH_TENSOR_DIM_APPLY_i; \ +\ + if( (DIMENSION < 0) || (DIMENSION >= THTensor_nDimensionLegacyNoScalars(TENSOR1)) ) \ + THError("invalid dimension %d (expected to be 0 <= dim < %d)", DIMENSION, THTensor_nDimensionLegacyNoScalars(TENSOR1)); \ + int same_dims = 1; \ + if( THTensor_nDimensionLegacyNoScalars(TENSOR1) != THTensor_nDimensionLegacyNoScalars(TENSOR2) ) { \ + same_dims = 0; \ + } \ + if( THTensor_nDimensionLegacyNoScalars(TENSOR1) != THTensor_nDimensionLegacyNoScalars(TENSOR3) ) { \ + same_dims = 0; \ + } \ + if (same_dims == 0) { \ + AT_ERROR("inconsistent tensor size, expected ", #TENSOR1, " ", TENSOR1->sizes(), ", ", #TENSOR2, " ", TENSOR2->sizes(), " and ", #TENSOR3, " ",TENSOR3->sizes() , " to have the same number of dimensions"); \ + } \ + SIZE_CHECK(TENSOR1, TENSOR2, TENSOR3, DIMENSION) \ +\ + if (TH_TENSOR_DIM_APPLY_hasFinished) { \ + return; \ + } \ + TH_TENSOR_DIM_APPLY_counter = (int64_t*)THAlloc(sizeof(int64_t)*(THTensor_nDimensionLegacyNoScalars(TENSOR1))); \ + for(TH_TENSOR_DIM_APPLY_i = 0; TH_TENSOR_DIM_APPLY_i < THTensor_nDimensionLegacyNoScalars(TENSOR1); TH_TENSOR_DIM_APPLY_i++) \ + TH_TENSOR_DIM_APPLY_counter[TH_TENSOR_DIM_APPLY_i] = 0; \ +\ + TENSOR1##_data = THTensor_getStoragePtr(TENSOR1)->data()+(TENSOR1)->storage_offset(); \ + TENSOR1##_stride = THTensor_strideLegacyNoScalars((TENSOR1), DIMENSION); \ + TENSOR1##_size = THTensor_sizeLegacyNoScalars((TENSOR1), DIMENSION); \ +\ + TENSOR2##_data = THTensor_getStoragePtr(TENSOR2)->data()+(TENSOR2)->storage_offset(); \ + TENSOR2##_stride = THTensor_strideLegacyNoScalars((TENSOR2), DIMENSION); \ + TENSOR2##_size = THTensor_sizeLegacyNoScalars((TENSOR2), DIMENSION); \ +\ + TENSOR3##_data = THTensor_getStoragePtr(TENSOR3)->data()+(TENSOR3)->storage_offset(); \ + TENSOR3##_stride = THTensor_strideLegacyNoScalars((TENSOR3), DIMENSION); \ + TENSOR3##_size = THTensor_sizeLegacyNoScalars((TENSOR3), DIMENSION); \ +\ + while(!TH_TENSOR_DIM_APPLY_hasFinished) \ + { \ + CODE \ +\ + if(THTensor_nDimensionLegacyNoScalars(TENSOR1) == 1) \ + break; \ + \ + for(TH_TENSOR_DIM_APPLY_i = 0; TH_TENSOR_DIM_APPLY_i < THTensor_nDimensionLegacyNoScalars(TENSOR1); TH_TENSOR_DIM_APPLY_i++) \ + { \ + if(TH_TENSOR_DIM_APPLY_i == DIMENSION) \ + { \ + if(TH_TENSOR_DIM_APPLY_i == THTensor_nDimensionLegacyNoScalars(TENSOR1)-1) \ + { \ + TH_TENSOR_DIM_APPLY_hasFinished = 1; \ + break; \ + } \ + continue; \ + } \ +\ + TH_TENSOR_DIM_APPLY_counter[TH_TENSOR_DIM_APPLY_i]++; \ + TENSOR1##_data += THTensor_strideLegacyNoScalars(TENSOR1, TH_TENSOR_DIM_APPLY_i); \ + TENSOR2##_data += THTensor_strideLegacyNoScalars(TENSOR2, TH_TENSOR_DIM_APPLY_i); \ + TENSOR3##_data += THTensor_strideLegacyNoScalars(TENSOR3, TH_TENSOR_DIM_APPLY_i); \ +\ + if(TH_TENSOR_DIM_APPLY_counter[TH_TENSOR_DIM_APPLY_i] == THTensor_sizeLegacyNoScalars(TENSOR1, TH_TENSOR_DIM_APPLY_i)) \ + { \ + if(TH_TENSOR_DIM_APPLY_i == THTensor_nDimensionLegacyNoScalars(TENSOR1)-1) \ + { \ + TH_TENSOR_DIM_APPLY_hasFinished = 1; \ + break; \ + } \ + else \ + { \ + TENSOR1##_data -= TH_TENSOR_DIM_APPLY_counter[TH_TENSOR_DIM_APPLY_i]*THTensor_strideLegacyNoScalars(TENSOR1, TH_TENSOR_DIM_APPLY_i); \ + TENSOR2##_data -= TH_TENSOR_DIM_APPLY_counter[TH_TENSOR_DIM_APPLY_i]*THTensor_strideLegacyNoScalars(TENSOR2, TH_TENSOR_DIM_APPLY_i); \ + TENSOR3##_data -= TH_TENSOR_DIM_APPLY_counter[TH_TENSOR_DIM_APPLY_i]*THTensor_strideLegacyNoScalars(TENSOR3, TH_TENSOR_DIM_APPLY_i); \ + TH_TENSOR_DIM_APPLY_counter[TH_TENSOR_DIM_APPLY_i] = 0; \ + } \ + } \ + else \ + break; \ + } \ + } \ + THFree(TH_TENSOR_DIM_APPLY_counter); \ +} + +/** + * Similar to DIM_APPLY(...) but we maintain two sets of pointers: one for the first tensor + * and one for the second. The two tensors must have the same shape, other than at the + * specified DIMENSION. This function makes it easy to store the output from reducing the + * TENSOR at index. For example, in the sum example described below, we could instead do: + * + * int64_t i = 0; + * TYPE1 sum; + * + * for (i = 0; i < TENSOR1##_size; ++i) { + * sum += TENSOR1##_data[i * TENSOR1##_stride] + * } + * *TENSOR2##_data = (TYPE2) sum; + * + * In particular, we guarantee that the offset into TENSOR2 will be what you would get if + * you applied all of the index values used to generate the offset into TENSOR1. + */ +#define TH_TENSOR_DIM_APPLY2(TYPE1, TENSOR1, TYPE2, TENSOR2, DIMENSION, CODE) \ +{ \ + TYPE1 *TENSOR1##_data = NULL; \ + TH_UNUSED int64_t TENSOR1##_stride = 0, TENSOR1##_size = 0; \ + TYPE2 *TENSOR2##_data = NULL; \ + TH_UNUSED int64_t TENSOR2##_stride = 0, TENSOR2##_size = 0; \ + int64_t *TH_TENSOR_DIM_APPLY_counter = NULL; \ + int TH_TENSOR_DIM_APPLY_hasFinished = THTensor_(numel)(TENSOR1) == 0; \ + int TH_TENSOR_DIM_APPLY_i; \ +\ + if( (DIMENSION < 0) || (DIMENSION >= THTensor_nDimensionLegacyNoScalars(TENSOR1)) ) \ + THError("invalid dimension %d (expected to be 0 <= dim < %d)", DIMENSION, THTensor_nDimensionLegacyAll(TENSOR1)); \ + if( THTensor_nDimensionLegacyNoScalars(TENSOR1) != THTensor_nDimensionLegacyNoScalars(TENSOR2)) { \ + AT_ERROR("inconsistent tensor size, expected ", #TENSOR1, " ", TENSOR1->sizes(), " and ", #TENSOR2, " ", TENSOR2->sizes(), " to have the same number of dimensions"); \ + } \ + TH_UNUSED int shape_check_flag = 0; \ + for(TH_TENSOR_DIM_APPLY_i = 0; TH_TENSOR_DIM_APPLY_i < THTensor_nDimensionLegacyNoScalars(TENSOR1); TH_TENSOR_DIM_APPLY_i++) \ + { \ + if(TH_TENSOR_DIM_APPLY_i == DIMENSION) \ + continue; \ + if(THTensor_sizeLegacyNoScalars(TENSOR1, TH_TENSOR_DIM_APPLY_i) != THTensor_sizeLegacyNoScalars(TENSOR2, TH_TENSOR_DIM_APPLY_i)) { \ + AT_ERROR("Expected ", #TENSOR1, " ", TENSOR1->sizes(), " and ", #TENSOR2, " ", TENSOR2->sizes(), " to have the same size in dimension ", DIMENSION); \ + } \ + } \ +\ + if (TH_TENSOR_DIM_APPLY_hasFinished) { \ + return; \ + } \ + TH_TENSOR_DIM_APPLY_counter = (int64_t*)THAlloc(sizeof(int64_t)*(THTensor_nDimensionLegacyNoScalars(TENSOR1))); \ + for(TH_TENSOR_DIM_APPLY_i = 0; TH_TENSOR_DIM_APPLY_i < THTensor_nDimensionLegacyNoScalars(TENSOR1); TH_TENSOR_DIM_APPLY_i++) \ + TH_TENSOR_DIM_APPLY_counter[TH_TENSOR_DIM_APPLY_i] = 0; \ +\ + TENSOR1##_data = THTensor_getStoragePtr(TENSOR1)->data()+(TENSOR1)->storage_offset(); \ + TENSOR1##_stride = THTensor_strideLegacyNoScalars((TENSOR1), DIMENSION); \ + TENSOR1##_size = THTensor_sizeLegacyNoScalars(TENSOR1, DIMENSION); \ +\ + TENSOR2##_data = THTensor_getStoragePtr(TENSOR2)->data()+(TENSOR2)->storage_offset(); \ + TENSOR2##_stride = THTensor_strideLegacyNoScalars((TENSOR2), DIMENSION); \ + TENSOR2##_size = THTensor_sizeLegacyNoScalars(TENSOR2, DIMENSION); \ +\ + while(!TH_TENSOR_DIM_APPLY_hasFinished) \ + { \ + CODE \ +\ + if(THTensor_nDimensionLegacyNoScalars(TENSOR1) == 1) \ + break; \ + \ + for(TH_TENSOR_DIM_APPLY_i = 0; TH_TENSOR_DIM_APPLY_i < THTensor_nDimensionLegacyNoScalars(TENSOR1); TH_TENSOR_DIM_APPLY_i++) \ + { \ + if(TH_TENSOR_DIM_APPLY_i == DIMENSION) \ + { \ + if(TH_TENSOR_DIM_APPLY_i == THTensor_nDimensionLegacyNoScalars(TENSOR1)-1) \ + { \ + TH_TENSOR_DIM_APPLY_hasFinished = 1; \ + break; \ + } \ + continue; \ + } \ +\ + TH_TENSOR_DIM_APPLY_counter[TH_TENSOR_DIM_APPLY_i]++; \ + TENSOR1##_data += THTensor_strideLegacyNoScalars(TENSOR1, TH_TENSOR_DIM_APPLY_i); \ + TENSOR2##_data += THTensor_strideLegacyNoScalars(TENSOR2, TH_TENSOR_DIM_APPLY_i); \ +\ + if(TH_TENSOR_DIM_APPLY_counter[TH_TENSOR_DIM_APPLY_i] == THTensor_sizeLegacyNoScalars(TENSOR1, TH_TENSOR_DIM_APPLY_i)) \ + { \ + if(TH_TENSOR_DIM_APPLY_i == THTensor_nDimensionLegacyNoScalars(TENSOR1)-1) \ + { \ + TH_TENSOR_DIM_APPLY_hasFinished = 1; \ + break; \ + } \ + else \ + { \ + TENSOR1##_data -= TH_TENSOR_DIM_APPLY_counter[TH_TENSOR_DIM_APPLY_i]*THTensor_strideLegacyNoScalars(TENSOR1, TH_TENSOR_DIM_APPLY_i); \ + TENSOR2##_data -= TH_TENSOR_DIM_APPLY_counter[TH_TENSOR_DIM_APPLY_i]*THTensor_strideLegacyNoScalars(TENSOR2, TH_TENSOR_DIM_APPLY_i); \ + TH_TENSOR_DIM_APPLY_counter[TH_TENSOR_DIM_APPLY_i] = 0; \ + } \ + } \ + else \ + break; \ + } \ + } \ + THFree(TH_TENSOR_DIM_APPLY_counter); \ +} + +/** + * The basic idea for DIM_APPLY: Given a TENSOR and a DIMENSION, provide access to the data stored + * at all sets of dimension values other than DIMENSION, such that we can get all the values at those + * fixed indices for the various values at DIMENSION. + * + * Suppose we have a 2x3x4 Tensor A, and we have DIMENSION=2. Then we will hit CODE (2x3) times, and the + * pointer into storage will be at: + * + * A[0][0] + * A[0][1] + * A[0][2] + * A[1][0] + * A[1][1] + * A[1][2] + * + * And at each point, we can access the data for each of the four elements of the Tensor via + * TENSOR##_stride. So for example, if we wanted to sum the elements there, we could do: + * + * int64_t i = 0; + * TYPE sum; + * for (i = 0; i < TENSOR##_size; i++) { + * sum += TENSOR##_data[i * TENSOR##_stride] + * } + * + * Note that we don't have to have DIMENSION be the last tensor. If we have DIMENSION=1, then we will hit the + * code (2x4) times, with pointer into the storage at: + * + * offset + + * stride_0 * 0 + stride_2 * 0 + * stride_0 * 1 + stride_2 * 0 + * stride_0 * 0 + stride_2 * 1 + * stride_0 * 1 + stride_2 * 1 + * stride_0 * 0 + stride_2 * 2 + * stride_0 * 1 + stride_2 * 2 + * stride_0 * 0 + stride_2 * 3 + * stride_0 * 1 + stride_2 * 3 + * + * So we can again sum over the values at DIMENSION with the other indices fixed. + */ +#define TH_TENSOR_DIM_APPLY(TYPE, TENSOR, DIMENSION, CODE) \ +{ \ + TYPE *TENSOR##_data = NULL; \ + int64_t TENSOR##_stride = 0, TENSOR##_size = 0; \ + int64_t *TH_TENSOR_DIM_APPLY_counter = NULL; \ + int TH_TENSOR_DIM_APPLY_hasFinished = 0; \ + int TH_TENSOR_DIM_APPLY_i; \ +\ + if( (DIMENSION < 0) || (DIMENSION >= THTensor_nDimensionLegacyAll(TENSOR)) ) \ + THError("invalid dimension"); \ +\ + TENSOR##_data = THTensor_getStoragePtr(TENSOR)->data()+(TENSOR)->storage_offset(); \ + TENSOR##_stride = THTensor_strideLegacyNoScalars((TENSOR), DIMENSION); \ + TENSOR##_size = THTensor_sizeLegacyNoScalars(TENSOR, DIMENSION); \ + /* Counter stores the indices into the Tensor at any time */ \ + TH_TENSOR_DIM_APPLY_counter = (int64_t*)THAlloc(sizeof(int64_t)*(THTensor_nDimensionLegacyAll(TENSOR))); \ + for(TH_TENSOR_DIM_APPLY_i = 0; TH_TENSOR_DIM_APPLY_i < THTensor_nDimensionLegacyAll(TENSOR); TH_TENSOR_DIM_APPLY_i++) \ + TH_TENSOR_DIM_APPLY_counter[TH_TENSOR_DIM_APPLY_i] = 0; \ +\ + while(!TH_TENSOR_DIM_APPLY_hasFinished) \ + { \ + CODE \ +\ + if(THTensor_nDimensionLegacyAll(TENSOR) == 1) \ + break; \ + \ + for(TH_TENSOR_DIM_APPLY_i = 0; TH_TENSOR_DIM_APPLY_i < THTensor_nDimensionLegacyAll(TENSOR); TH_TENSOR_DIM_APPLY_i++) \ + { \ + /* Check if the index is equal to DIMENSION. We don't need to update the */ \ + /* offset if this is the case, and can consider the next index. However, */ \ + /* in the case that the DIMENSION is the last index in the Tensor, then */ \ + /* we have parsed the entire tensor and can exit */ \ + if(TH_TENSOR_DIM_APPLY_i == DIMENSION) \ + { \ + if(TH_TENSOR_DIM_APPLY_i == THTensor_nDimensionLegacyAll(TENSOR)-1) \ + { \ + TH_TENSOR_DIM_APPLY_hasFinished = 1; \ + break; \ + } \ + continue; \ + } \ +\ + /* Bump the counter at this index, update the pointer */ \ + TH_TENSOR_DIM_APPLY_counter[TH_TENSOR_DIM_APPLY_i]++; \ + TENSOR##_data += THTensor_strideLegacyNoScalars(TENSOR, TH_TENSOR_DIM_APPLY_i); \ +\ + if(TH_TENSOR_DIM_APPLY_counter[TH_TENSOR_DIM_APPLY_i] == THTensor_sizeLegacyNoScalars(TENSOR, TH_TENSOR_DIM_APPLY_i)) \ + { \ + /* Handled TENSOR_size(dim) iterations for DIM_APPLY_i. If this is the last dimension, exit */ \ + if(TH_TENSOR_DIM_APPLY_i == THTensor_nDimensionLegacyAll(TENSOR)-1) \ + { \ + TH_TENSOR_DIM_APPLY_hasFinished = 1; \ + break; \ + } \ + else \ + { \ + /* Reset the counter, and the pointer to the beginning of the storage for this combination of indices */ \ + TENSOR##_data -= TH_TENSOR_DIM_APPLY_counter[TH_TENSOR_DIM_APPLY_i]*THTensor_strideLegacyNoScalars(TENSOR, TH_TENSOR_DIM_APPLY_i); \ + TH_TENSOR_DIM_APPLY_counter[TH_TENSOR_DIM_APPLY_i] = 0; \ + } \ + } \ + else \ + break; \ + } \ + } \ + THFree(TH_TENSOR_DIM_APPLY_counter); \ +} + +#endif diff --git a/thirdparty/libtorch/include/TH/THVector.h b/thirdparty/libtorch/include/TH/THVector.h new file mode 100644 index 0000000000..512ce54900 --- /dev/null +++ b/thirdparty/libtorch/include/TH/THVector.h @@ -0,0 +1,21 @@ +#ifndef TH_VECTOR_INC +#define TH_VECTOR_INC + +#include +#define THVector_(NAME) TH_CONCAT_4(TH,Real,Vector_,NAME) + +/* We are going to use dynamic dispatch, and want only to generate declarations + * of the vector functions */ +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#endif // TH_VECTOR_INC diff --git a/thirdparty/libtorch/include/TH/generic/THBlas.h b/thirdparty/libtorch/include/TH/generic/THBlas.h new file mode 100644 index 0000000000..7d827eb09d --- /dev/null +++ b/thirdparty/libtorch/include/TH/generic/THBlas.h @@ -0,0 +1,19 @@ +#ifndef TH_GENERIC_FILE +#define TH_GENERIC_FILE "TH/generic/THBlas.h" +#else + +/* Level 1 */ +TH_API void THBlas_(swap)(int64_t n, scalar_t *x, int64_t incx, scalar_t *y, int64_t incy); +TH_API void THBlas_(scal)(int64_t n, scalar_t a, scalar_t *x, int64_t incx); +TH_API void THBlas_(copy)(int64_t n, scalar_t *x, int64_t incx, scalar_t *y, int64_t incy); +TH_API void THBlas_(axpy)(int64_t n, scalar_t a, scalar_t *x, int64_t incx, scalar_t *y, int64_t incy); +TH_API scalar_t THBlas_(dot)(int64_t n, scalar_t *x, int64_t incx, scalar_t *y, int64_t incy); + +/* Level 2 */ +TH_API void THBlas_(gemv)(char trans, int64_t m, int64_t n, scalar_t alpha, scalar_t *a, int64_t lda, scalar_t *x, int64_t incx, scalar_t beta, scalar_t *y, int64_t incy); +TH_API void THBlas_(ger)(int64_t m, int64_t n, scalar_t alpha, scalar_t *x, int64_t incx, scalar_t *y, int64_t incy, scalar_t *a, int64_t lda); + +/* Level 3 */ +TH_API void THBlas_(gemm)(char transa, char transb, int64_t m, int64_t n, int64_t k, scalar_t alpha, scalar_t *a, int64_t lda, scalar_t *b, int64_t ldb, scalar_t beta, scalar_t *c, int64_t ldc); + +#endif diff --git a/thirdparty/libtorch/include/TH/generic/THLapack.h b/thirdparty/libtorch/include/TH/generic/THLapack.h new file mode 100644 index 0000000000..287915c74d --- /dev/null +++ b/thirdparty/libtorch/include/TH/generic/THLapack.h @@ -0,0 +1,21 @@ +#ifndef TH_GENERIC_FILE +#define TH_GENERIC_FILE "TH/generic/THLapack.h" +#else + +/* ||AX-B|| */ +TH_API void THLapack_(gels)(char trans, int m, int n, int nrhs, scalar_t *a, int lda, scalar_t *b, int ldb, scalar_t *work, int lwork, int *info); +/* Non-sym eigenvals */ +TH_API void THLapack_(geev)(char jobvl, char jobvr, int n, scalar_t *a, int lda, scalar_t *wr, scalar_t *wi, scalar_t* vl, int ldvl, scalar_t *vr, int ldvr, scalar_t *work, int lwork, int *info); + +/* Positive Definite matrices */ +/* Matrix inverse based on Cholesky factorization */ +TH_API void THLapack_(potri)(char uplo, int n, scalar_t *a, int lda, int *info); + +/* QR decomposition */ +TH_API void THLapack_(geqrf)(int m, int n, scalar_t *a, int lda, scalar_t *tau, scalar_t *work, int lwork, int *info); +/* Build Q from output of geqrf */ +TH_API void THLapack_(orgqr)(int m, int n, int k, scalar_t *a, int lda, scalar_t *tau, scalar_t *work, int lwork, int *info); +/* Multiply Q with a matrix from output of geqrf */ +TH_API void THLapack_(ormqr)(char side, char trans, int m, int n, int k, scalar_t *a, int lda, scalar_t *tau, scalar_t *c, int ldc, scalar_t *work, int lwork, int *info); + +#endif diff --git a/thirdparty/libtorch/include/TH/generic/THStorage.h b/thirdparty/libtorch/include/TH/generic/THStorage.h new file mode 100644 index 0000000000..9f6de2e9d0 --- /dev/null +++ b/thirdparty/libtorch/include/TH/generic/THStorage.h @@ -0,0 +1,74 @@ +#ifndef TH_GENERIC_FILE +#define TH_GENERIC_FILE "TH/generic/THStorage.h" +#else + +#include +#include + +/* on pourrait avoir un liste chainee + qui initialise math, lab structures (or more). + mouais -- complique. + + Pb: THMapStorage is kind of a class + THLab_()... comment je m'en sors? + + en template, faudrait que je les instancie toutes!!! oh boy! + Et comment je sais que c'est pour Cuda? Le type float est le meme dans les <> + + au bout du compte, ca serait sur des pointeurs float/double... etc... = facile. + primitives?? + */ + +// Struct definition is moved to THStorage.hpp (so this file stays C compatible) + +#define THStorage at::StorageImpl + +// These used to be distinct types; for some measure of backwards compatibility and documentation +// alias these to the single THStorage type. +#define THFloatStorage THStorage +#define THDoubleStorage THStorage +#define THHalfStorage THStorage +#define THByteStorage THStorage +#define THCharStorage THStorage +#define THShortStorage THStorage +#define THIntStorage THStorage +#define THLongStorage THStorage +#define THBoolStorage THStorage +#define THBFloat16Storage THStorage +#define THQUInt8Storage THStorage +#define THQInt8Storage THStorage +#define THQInt32Storage THStorage + +TH_API scalar_t* THStorage_(data)(const THStorage*); +TH_API ptrdiff_t THStorage_(size)(const THStorage*); +TH_API size_t THStorage_(elementSize)(void); + +/* slow access -- checks everything */ +TH_API void THStorage_(set)(THStorage*, ptrdiff_t, scalar_t); +TH_API scalar_t THStorage_(get)(const THStorage*, ptrdiff_t); + +TH_API THStorage* THStorage_(new)(void); +TH_API THStorage* THStorage_(newWithSize)(ptrdiff_t size); +TH_API THStorage* THStorage_(newWithSize1)(scalar_t); +TH_API THStorage* THStorage_(newWithSize2)(scalar_t, scalar_t); +TH_API THStorage* THStorage_(newWithSize3)(scalar_t, scalar_t, scalar_t); +TH_API THStorage* THStorage_(newWithSize4)(scalar_t, scalar_t, scalar_t, scalar_t); +TH_API THStorage* THStorage_(newWithMapping)(const char *filename, ptrdiff_t size, int flags); + +TH_API THStorage* THStorage_(newWithAllocator)(ptrdiff_t size, + c10::Allocator* allocator); +TH_API THStorage* THStorage_(newWithDataAndAllocator)( + at::DataPtr&& data, ptrdiff_t size, at::Allocator* allocator); + +/* should not differ with API */ +TH_API void THStorage_(setFlag)(THStorage *storage, const char flag); +TH_API void THStorage_(clearFlag)(THStorage *storage, const char flag); +TH_API void THStorage_(retain)(THStorage *storage); +TH_API void THStorage_(swap)(THStorage *storage1, THStorage *storage2); + +/* might differ with other API (like CUDA) */ +TH_API void THStorage_(free)(THStorage *storage); +TH_API void THStorage_(resize)(THStorage *storage, ptrdiff_t size); +TH_API void THStorage_(fill)(THStorage *storage, scalar_t value); + +#endif diff --git a/thirdparty/libtorch/include/TH/generic/THStorageCopy.h b/thirdparty/libtorch/include/TH/generic/THStorageCopy.h new file mode 100644 index 0000000000..7bab82e454 --- /dev/null +++ b/thirdparty/libtorch/include/TH/generic/THStorageCopy.h @@ -0,0 +1,27 @@ +#ifndef TH_GENERIC_FILE +#define TH_GENERIC_FILE "TH/generic/THStorageCopy.h" +#else + +/* Support for copy between different Storage types */ +TH_API void THStorage_(copy)(THStorage *storage, THStorage *src); +TH_API void THStorage_(copyByte)(THStorage *storage, struct THByteStorage *src); +TH_API void THStorage_(copyChar)(THStorage *storage, struct THCharStorage *src); +TH_API void THStorage_(copyShort)(THStorage *storage, struct THShortStorage *src); +TH_API void THStorage_(copyInt)(THStorage *storage, struct THIntStorage *src); +TH_API void THStorage_(copyLong)(THStorage *storage, struct THLongStorage *src); +TH_API void THStorage_(copyFloat)(THStorage *storage, struct THFloatStorage *src); +TH_API void THStorage_(copyDouble)(THStorage *storage, struct THDoubleStorage *src); +TH_API void THStorage_(copyHalf)(THStorage *storage, struct THHalfStorage *src); +TH_API void THStorage_(copyBool)(THStorage *storage, struct THBoolStorage *src); +TH_API void THStorage_(copyBFloat16)(THStorage *storage, struct THBFloat16Storage *src); +#ifdef THQUINT8 +TH_API void THStorage_(copyQUInt8)(THStorage *storage, struct THQUInt8Storage *src); +#endif +#ifdef THQINT8 +TH_API void THStorage_(copyQInt8)(THStorage *storage, struct THQInt8Storage *src); +#endif +#ifdef THQINT32 +TH_API void THStorage_(copyQInt32)(THStorage *storage, struct THQInt32Storage *src); +#endif + +#endif diff --git a/thirdparty/libtorch/include/TH/generic/THTensor.h b/thirdparty/libtorch/include/TH/generic/THTensor.h new file mode 100644 index 0000000000..b59a08c023 --- /dev/null +++ b/thirdparty/libtorch/include/TH/generic/THTensor.h @@ -0,0 +1,134 @@ +#ifndef TH_GENERIC_FILE +#define TH_GENERIC_FILE "TH/generic/THTensor.h" +#else + +/* a la lua? dim, storageoffset, ... et les methodes ? */ + +#include + +#define THTensor at::TensorImpl + +// These used to be distinct types; for some measure of backwards compatibility and documentation +// alias these to the single THTensor type. +#define THFloatTensor THTensor +#define THDoubleTensor THTensor +#define THHalfTensor THTensor +#define THByteTensor THTensor +#define THCharTensor THTensor +#define THShortTensor THTensor +#define THIntTensor THTensor +#define THLongTensor THTensor +#define THBoolTensor THTensor +#define THBFloat16Tensor THTensor + +/**** access methods ****/ +TH_API THStorage* THTensor_(storage)(const THTensor *self); +TH_API ptrdiff_t THTensor_(storageOffset)(const THTensor *self); + +// See [NOTE: nDimension vs nDimensionLegacyNoScalars vs nDimensionLegacyAll] +TH_API int THTensor_(nDimension)(const THTensor *self); +TH_API int THTensor_(nDimensionLegacyNoScalars)(const THTensor *self); +TH_API int THTensor_(nDimensionLegacyAll)(const THTensor *self); +TH_API int64_t THTensor_(size)(const THTensor *self, int dim); +TH_API int64_t THTensor_(stride)(const THTensor *self, int dim); +TH_API scalar_t *THTensor_(data)(const THTensor *self); + + +/**** creation methods ****/ +TH_API THTensor *THTensor_(new)(void); +TH_API THTensor *THTensor_(newWithTensor)(THTensor *tensor); +TH_API THTensor *THTensor_(newWithStorage1d)(THStorage *storage_, ptrdiff_t storageOffset_, + int64_t size0_, int64_t stride0_); +TH_API THTensor *THTensor_(newWithStorage2d)(THStorage *storage_, ptrdiff_t storageOffset_, + int64_t size0_, int64_t stride0_, + int64_t size1_, int64_t stride1_); +TH_API THTensor *THTensor_(newWithStorage3d)(THStorage *storage_, ptrdiff_t storageOffset_, + int64_t size0_, int64_t stride0_, + int64_t size1_, int64_t stride1_, + int64_t size2_, int64_t stride2_); +TH_API THTensor *THTensor_(newWithStorage4d)(THStorage *storage_, ptrdiff_t storageOffset_, + int64_t size0_, int64_t stride0_, + int64_t size1_, int64_t stride1_, + int64_t size2_, int64_t stride2_, + int64_t size3_, int64_t stride3_); + +/* stride might be NULL */ +TH_API THTensor *THTensor_(newWithSize1d)(int64_t size0_); +TH_API THTensor *THTensor_(newWithSize2d)(int64_t size0_, int64_t size1_); +TH_API THTensor *THTensor_(newWithSize3d)(int64_t size0_, int64_t size1_, int64_t size2_); +TH_API THTensor *THTensor_(newWithSize4d)(int64_t size0_, int64_t size1_, int64_t size2_, int64_t size3_); + +TH_API THTensor *THTensor_(newClone)(THTensor *self); +TH_API THTensor *THTensor_(newContiguous)(THTensor *tensor); +TH_API THTensor *THTensor_(newSelect)(THTensor *tensor, int dimension_, int64_t sliceIndex_); +TH_API THTensor *THTensor_(newNarrow)(THTensor *tensor, int dimension_, int64_t firstIndex_, int64_t size_); +TH_API THTensor *THTensor_(newTranspose)(THTensor *tensor, int dimension1_, int dimension2_); + +// resize* methods simply resize the storage. So they may not retain the current data at current indices. +// This is especially likely to happen when the tensor is not contiguous. In general, if you still need the +// values, unless you are doing some size and stride tricks, do not use resize*. +TH_API void THTensor_(resizeNd)(THTensor *tensor, int nDimension, const int64_t *size, const int64_t *stride); +TH_API void THTensor_(resizeAs)(THTensor *tensor, THTensor *src); +TH_API void THTensor_(resize0d)(THTensor *tensor); +TH_API void THTensor_(resize1d)(THTensor *tensor, int64_t size0_); +TH_API void THTensor_(resize2d)(THTensor *tensor, int64_t size0_, int64_t size1_); +TH_API void THTensor_(resize3d)(THTensor *tensor, int64_t size0_, int64_t size1_, int64_t size2_); +TH_API void THTensor_(resize4d)(THTensor *tensor, int64_t size0_, int64_t size1_, int64_t size2_, int64_t size3_); +TH_API void THTensor_(resize5d)(THTensor *tensor, int64_t size0_, int64_t size1_, int64_t size2_, int64_t size3_, int64_t size4_); +// Note: these are legacy resize functions that treat sizes as size->size() == 0 and size->data() as being 0-terminated. + +TH_API void THTensor_(set)(THTensor *self, THTensor *src); +TH_API void THTensor_(setStorageNd)(THTensor *self, THStorage *storage_, ptrdiff_t storageOffset_, int nDimension, const int64_t *size, const int64_t *stride); +TH_API void THTensor_(setStorage1d)(THTensor *self, THStorage *storage_, ptrdiff_t storageOffset_, + int64_t size0_, int64_t stride0_); +TH_API void THTensor_(setStorage2d)(THTensor *self, THStorage *storage_, ptrdiff_t storageOffset_, + int64_t size0_, int64_t stride0_, + int64_t size1_, int64_t stride1_); +TH_API void THTensor_(setStorage3d)(THTensor *self, THStorage *storage_, ptrdiff_t storageOffset_, + int64_t size0_, int64_t stride0_, + int64_t size1_, int64_t stride1_, + int64_t size2_, int64_t stride2_); +TH_API void THTensor_(setStorage4d)(THTensor *self, THStorage *storage_, ptrdiff_t storageOffset_, + int64_t size0_, int64_t stride0_, + int64_t size1_, int64_t stride1_, + int64_t size2_, int64_t stride2_, + int64_t size3_, int64_t stride3_); + +TH_API void THTensor_(narrow)(THTensor *self, THTensor *src, int dimension_, int64_t firstIndex_, int64_t size_); +TH_API void THTensor_(select)(THTensor *self, THTensor *src, int dimension_, int64_t sliceIndex_); +TH_API void THTensor_(transpose)(THTensor *self, THTensor *src, int dimension1_, int dimension2_); +TH_API int THTensor_(isTransposed)(const THTensor *self); + +TH_API void THTensor_(squeeze1d)(THTensor *self, THTensor *src, int dimension_); +TH_API void THTensor_(unsqueeze1d)(THTensor *self, THTensor *src, int dimension_); + +TH_API int THTensor_(isContiguous)(const THTensor *self); +TH_API int THTensor_(isSameSizeAs)(const THTensor *self, const THTensor *src); +TH_API ptrdiff_t THTensor_(nElement)(const THTensor *self); + +TH_API void THTensor_(retain)(THTensor *self); +TH_API void THTensor_(free)(THTensor *self); +TH_API void THTensor_(freeCopyTo)(THTensor *self, THTensor *dst); + +/* Slow access methods [check everything] */ +TH_API void THTensor_(set0d)(THTensor *tensor, scalar_t value); +TH_API void THTensor_(set1d)(THTensor *tensor, int64_t x0, scalar_t value); +TH_API void THTensor_(set2d)(THTensor *tensor, int64_t x0, int64_t x1, scalar_t value); +TH_API void THTensor_(set3d)(THTensor *tensor, int64_t x0, int64_t x1, int64_t x2, scalar_t value); +TH_API void THTensor_(set4d)(THTensor *tensor, int64_t x0, int64_t x1, int64_t x2, int64_t x3, scalar_t value); + +TH_API scalar_t THTensor_(get0d)(const THTensor *tensor); +TH_API scalar_t THTensor_(get1d)(const THTensor *tensor, int64_t x0); +TH_API scalar_t THTensor_(get2d)(const THTensor *tensor, int64_t x0, int64_t x1); +TH_API scalar_t THTensor_(get3d)(const THTensor *tensor, int64_t x0, int64_t x1, int64_t x2); +TH_API scalar_t THTensor_(get4d)(const THTensor *tensor, int64_t x0, int64_t x1, int64_t x2, int64_t x3); + +/* Shape manipulation methods */ +TH_API void THTensor_(cat)(THTensor *r_, THTensor *ta, THTensor *tb, int dimension); +TH_API void THTensor_(catArray)(THTensor *result, THTensor **inputs, int numInputs, int dimension); + +/* Debug methods */ +TH_API THDescBuff THTensor_(desc)(const THTensor *tensor); +TH_API THDescBuff THTensor_(sizeDesc)(const THTensor *tensor); + +#endif diff --git a/thirdparty/libtorch/include/TH/generic/THTensor.hpp b/thirdparty/libtorch/include/TH/generic/THTensor.hpp new file mode 100644 index 0000000000..5966fd853b --- /dev/null +++ b/thirdparty/libtorch/include/TH/generic/THTensor.hpp @@ -0,0 +1,20 @@ +#ifndef TH_GENERIC_FILE +#define TH_GENERIC_FILE "TH/generic/THTensor.hpp" +#else + +// STOP!!! Thinking of including this header directly? Please +// read Note [TH abstraction violation] + +// NOTE: functions exist here only to support dispatch via Declarations.cwrap. You probably don't want to put +// new functions in here, they should probably be un-genericized. + +TH_CPP_API void THTensor_(setStorage)(THTensor *self, THStorage *storage_, ptrdiff_t storageOffset_, + at::IntArrayRef size_, at::IntArrayRef stride_); +/* strides.data() might be NULL */ +TH_CPP_API THTensor *THTensor_(newWithStorage)(THStorage *storage, ptrdiff_t storageOffset, + at::IntArrayRef sizes, at::IntArrayRef strides); + +TH_CPP_API void THTensor_(resize)(THTensor *self, at::IntArrayRef size, at::IntArrayRef stride); +TH_CPP_API THTensor *THTensor_(newWithSize)(at::IntArrayRef size, at::IntArrayRef stride); + +#endif diff --git a/thirdparty/libtorch/include/TH/generic/THTensorConv.h b/thirdparty/libtorch/include/TH/generic/THTensorConv.h new file mode 100644 index 0000000000..7eab385bef --- /dev/null +++ b/thirdparty/libtorch/include/TH/generic/THTensorConv.h @@ -0,0 +1,79 @@ +#ifndef TH_GENERIC_FILE +#define TH_GENERIC_FILE "TH/generic/THTensorConv.h" +#else + +TH_API void THTensor_(validXCorr2Dptr)(scalar_t *r_, + scalar_t alpha, + scalar_t *t_, int64_t ir, int64_t ic, + scalar_t *k_, int64_t kr, int64_t kc, + int64_t sr, int64_t sc); + +TH_API void THTensor_(validConv2Dptr)(scalar_t *r_, + scalar_t alpha, + scalar_t *t_, int64_t ir, int64_t ic, + scalar_t *k_, int64_t kr, int64_t kc, + int64_t sr, int64_t sc); + +TH_API void THTensor_(fullXCorr2Dptr)(scalar_t *r_, + scalar_t alpha, + scalar_t *t_, int64_t ir, int64_t ic, + scalar_t *k_, int64_t kr, int64_t kc, + int64_t sr, int64_t sc); + +TH_API void THTensor_(fullConv2Dptr)(scalar_t *r_, + scalar_t alpha, + scalar_t *t_, int64_t ir, int64_t ic, + scalar_t *k_, int64_t kr, int64_t kc, + int64_t sr, int64_t sc); + +TH_API void THTensor_(validXCorr2DRevptr)(scalar_t *r_, + scalar_t alpha, + scalar_t *t_, int64_t ir, int64_t ic, + scalar_t *k_, int64_t kr, int64_t kc, + int64_t sr, int64_t sc); + +TH_API void THTensor_(conv2DRevger)(THTensor *r_, scalar_t beta, scalar_t alpha, THTensor *t_, THTensor *k_, int64_t srow, int64_t scol); +TH_API void THTensor_(conv2DRevgerm)(THTensor *r_, scalar_t beta, scalar_t alpha, THTensor *t_, THTensor *k_, int64_t srow, int64_t scol); +TH_API void THTensor_(conv2Dger)(THTensor *r_, scalar_t beta, scalar_t alpha, THTensor *t_, THTensor *k_, int64_t srow, int64_t scol, const char *vf, const char *xc); +TH_API void THTensor_(conv2Dmv)(THTensor *r_, scalar_t beta, scalar_t alpha, THTensor *t_, THTensor *k_, int64_t srow, int64_t scol, const char *vf, const char *xc); +TH_API void THTensor_(conv2Dmm)(THTensor *r_, scalar_t beta, scalar_t alpha, THTensor *t_, THTensor *k_, int64_t srow, int64_t scol, const char *vf, const char *xc); +TH_API void THTensor_(conv2Dmul)(THTensor *r_, scalar_t beta, scalar_t alpha, THTensor *t_, THTensor *k_, int64_t srow, int64_t scol, const char *vf, const char *xc); +TH_API void THTensor_(conv2Dcmul)(THTensor *r_, scalar_t beta, scalar_t alpha, THTensor *t_, THTensor *k_, int64_t srow, int64_t scol, const char *vf, const char *xc); + +TH_API void THTensor_(validXCorr3Dptr)(scalar_t *r_, + scalar_t alpha, + scalar_t *t_, int64_t it, int64_t ir, int64_t ic, + scalar_t *k_, int64_t kt, int64_t kr, int64_t kc, + int64_t st, int64_t sr, int64_t sc); + +TH_API void THTensor_(validConv3Dptr)(scalar_t *r_, + scalar_t alpha, + scalar_t *t_, int64_t it, int64_t ir, int64_t ic, + scalar_t *k_, int64_t kt, int64_t kr, int64_t kc, + int64_t st, int64_t sr, int64_t sc); + +TH_API void THTensor_(fullXCorr3Dptr)(scalar_t *r_, + scalar_t alpha, + scalar_t *t_, int64_t it, int64_t ir, int64_t ic, + scalar_t *k_, int64_t kt, int64_t kr, int64_t kc, + int64_t st, int64_t sr, int64_t sc); + +TH_API void THTensor_(fullConv3Dptr)(scalar_t *r_, + scalar_t alpha, + scalar_t *t_, int64_t it, int64_t ir, int64_t ic, + scalar_t *k_, int64_t kt, int64_t kr, int64_t kc, + int64_t st, int64_t sr, int64_t sc); + +TH_API void THTensor_(validXCorr3DRevptr)(scalar_t *r_, + scalar_t alpha, + scalar_t *t_, int64_t it, int64_t ir, int64_t ic, + scalar_t *k_, int64_t kt, int64_t kr, int64_t kc, + int64_t st, int64_t sr, int64_t sc); + +TH_API void THTensor_(conv3DRevger)(THTensor *r_, scalar_t beta, scalar_t alpha, THTensor *t_, THTensor *k_, int64_t sdepth, int64_t srow, int64_t scol); +TH_API void THTensor_(conv3Dger)(THTensor *r_, scalar_t beta, scalar_t alpha, THTensor *t_, THTensor *k_, int64_t sdepth, int64_t srow, int64_t scol, const char *vf, const char *xc); +TH_API void THTensor_(conv3Dmv)(THTensor *r_, scalar_t beta, scalar_t alpha, THTensor *t_, THTensor *k_, int64_t sdepth, int64_t srow, int64_t scol, const char *vf, const char *xc); +TH_API void THTensor_(conv3Dmul)(THTensor *r_, scalar_t beta, scalar_t alpha, THTensor *t_, THTensor *k_, int64_t sdepth, int64_t srow, int64_t scol, const char *vf, const char *xc); +TH_API void THTensor_(conv3Dcmul)(THTensor *r_, scalar_t beta, scalar_t alpha, THTensor *t_, THTensor *k_, int64_t sdepth, int64_t srow, int64_t scol, const char *vf, const char *xc); + +#endif diff --git a/thirdparty/libtorch/include/TH/generic/THTensorFastGetSet.hpp b/thirdparty/libtorch/include/TH/generic/THTensorFastGetSet.hpp new file mode 100644 index 0000000000..83c2c6481b --- /dev/null +++ b/thirdparty/libtorch/include/TH/generic/THTensorFastGetSet.hpp @@ -0,0 +1,49 @@ +#ifndef TH_GENERIC_FILE +#define TH_GENERIC_FILE "TH/generic/THTensorFastGetSet.hpp" +#else + +static inline scalar_t THTensor_(fastGetLegacy1dNoScalars)(THTensor *self, int64_t x0) { + return self->unsafe_data()[x0*THTensor_strideLegacyNoScalars(self, 0)]; +} + +static inline scalar_t THTensor_(fastGet1d)(THTensor *self, int64_t x0) { + return self->unsafe_data()[x0*self->stride(0)]; +} + +static inline scalar_t THTensor_(fastGet2d)(THTensor *self, int64_t x0, int64_t x1) { + return self->unsafe_data()[x0*self->stride(0)+x1*self->stride(1)]; +} + +static inline scalar_t THTensor_(fastGet3d)(THTensor *self, int64_t x0, int64_t x1, int64_t x2) { + return self->unsafe_data()[x0*self->stride(0)+x1*self->stride(1)+x2*self->stride(2)]; +} + +static inline scalar_t THTensor_(fastGet4d)(THTensor *self, int64_t x0, int64_t x1, int64_t x2, int64_t x3) { + return self->unsafe_data()[x0*self->stride(0)+x1*self->stride(1)+x2*self->stride(2)+x3*self->stride(3)]; +} + +static inline scalar_t THTensor_(fastGet5d)(THTensor *self, int64_t x0, int64_t x1, int64_t x2, int64_t x3, int64_t x4) { + return self->unsafe_data()[x0*self->stride(0)+x1*self->stride(1)+x2*self->stride(2)+x3*self->stride(3)+(x4)*self->stride(4)]; +} + +static inline void THTensor_(fastSet1d)(THTensor *self, int64_t x0, scalar_t value) { + self->unsafe_data()[x0*self->stride(0)] = value; +} + +static inline void THTensor_(fastSet2d)(THTensor *self, int64_t x0, int64_t x1, scalar_t value) { + self->unsafe_data()[x0*self->stride(0)+x1*self->stride(1)] = value; +} + +static inline void THTensor_(fastSet3d)(THTensor *self, int64_t x0, int64_t x1, int64_t x2, scalar_t value) { + self->unsafe_data()[x0*self->stride(0)+x1*self->stride(1)+x2*self->stride(2)] = value; +} + +static inline void THTensor_(fastSet4d)(THTensor *self, int64_t x0, int64_t x1, int64_t x2, int64_t x3, scalar_t value) { + self->unsafe_data()[x0*self->stride(0)+x1*self->stride(1)+x2*self->stride(2)+x3*self->stride(3)] = value; +} + +static inline void THTensor_(fastSet5d)(THTensor *self, int64_t x0, int64_t x1, int64_t x2, int64_t x3, int64_t x4, scalar_t value) { + self->unsafe_data()[x0*self->stride(0)+x1*self->stride(1)+x2*self->stride(2)+x3*self->stride(3)+(x4)*self->stride(4)] = value; +} + +#endif diff --git a/thirdparty/libtorch/include/TH/generic/THTensorFill.h b/thirdparty/libtorch/include/TH/generic/THTensorFill.h new file mode 100644 index 0000000000..4763918565 --- /dev/null +++ b/thirdparty/libtorch/include/TH/generic/THTensorFill.h @@ -0,0 +1,8 @@ +#ifndef TH_GENERIC_FILE +#define TH_GENERIC_FILE "TH/generic/THTensorFill.h" +#else + +TH_API void THTensor_(fill)(THTensor *r_, scalar_t value); +TH_API void THTensor_(zero)(THTensor *r_); + +#endif diff --git a/thirdparty/libtorch/include/TH/generic/THTensorLapack.h b/thirdparty/libtorch/include/TH/generic/THTensorLapack.h new file mode 100644 index 0000000000..05dbbf9f12 --- /dev/null +++ b/thirdparty/libtorch/include/TH/generic/THTensorLapack.h @@ -0,0 +1,12 @@ +#ifndef TH_GENERIC_FILE +#define TH_GENERIC_FILE "TH/generic/THTensorLapack.h" +#else + +TH_API void THTensor_(gels)(THTensor *rb_, THTensor *ra_, THTensor *b_, THTensor *a_); +TH_API void THTensor_(geev)(THTensor *re_, THTensor *rv_, THTensor *a_, bool eigenvectors); +TH_API void THTensor_(potri)(THTensor *ra_, THTensor *a, bool upper); +TH_API void THTensor_(geqrf)(THTensor *ra_, THTensor *rtau_, THTensor *a); +TH_API void THTensor_(orgqr)(THTensor *ra_, THTensor *a, THTensor *tau); +TH_API void THTensor_(ormqr)(THTensor *ra_, THTensor *a, THTensor *tau, THTensor *c, bool left, bool transpose); + +#endif diff --git a/thirdparty/libtorch/include/TH/generic/THTensorMath.h b/thirdparty/libtorch/include/TH/generic/THTensorMath.h new file mode 100644 index 0000000000..b1054dd044 --- /dev/null +++ b/thirdparty/libtorch/include/TH/generic/THTensorMath.h @@ -0,0 +1,169 @@ +#ifndef TH_GENERIC_FILE +#define TH_GENERIC_FILE "TH/generic/THTensorMath.h" +#else + +#include +#include + +TH_API void THTensor_(nonzero)(THLongTensor *subscript, THTensor *tensor); + +#if !defined(TH_REAL_IS_HALF) + +TH_API void THTensor_(ltValue)(THBoolTensor *r_, THTensor* t, scalar_t value); +TH_API void THTensor_(leValue)(THBoolTensor *r_, THTensor* t, scalar_t value); +TH_API void THTensor_(gtValue)(THBoolTensor *r_, THTensor* t, scalar_t value); +TH_API void THTensor_(geValue)(THBoolTensor *r_, THTensor* t, scalar_t value); +TH_API void THTensor_(neValue)(THBoolTensor *r_, THTensor* t, scalar_t value); +TH_API void THTensor_(eqValue)(THBoolTensor *r_, THTensor* t, scalar_t value); + +TH_API void THTensor_(ltValueT)(THTensor *r_, THTensor* t, scalar_t value); +TH_API void THTensor_(leValueT)(THTensor *r_, THTensor* t, scalar_t value); +TH_API void THTensor_(gtValueT)(THTensor *r_, THTensor* t, scalar_t value); +TH_API void THTensor_(geValueT)(THTensor *r_, THTensor* t, scalar_t value); +TH_API void THTensor_(neValueT)(THTensor *r_, THTensor* t, scalar_t value); +TH_API void THTensor_(eqValueT)(THTensor *r_, THTensor* t, scalar_t value); + +TH_API void THTensor_(ltTensor)(THBoolTensor *r_, THTensor *ta, THTensor *tb); +TH_API void THTensor_(leTensor)(THBoolTensor *r_, THTensor *ta, THTensor *tb); +TH_API void THTensor_(gtTensor)(THBoolTensor *r_, THTensor *ta, THTensor *tb); +TH_API void THTensor_(geTensor)(THBoolTensor *r_, THTensor *ta, THTensor *tb); +TH_API void THTensor_(neTensor)(THBoolTensor *r_, THTensor *ta, THTensor *tb); +TH_API void THTensor_(eqTensor)(THBoolTensor *r_, THTensor *ta, THTensor *tb); + +TH_API void THTensor_(ltTensorT)(THTensor *r_, THTensor *ta, THTensor *tb); +TH_API void THTensor_(leTensorT)(THTensor *r_, THTensor *ta, THTensor *tb); +TH_API void THTensor_(gtTensorT)(THTensor *r_, THTensor *ta, THTensor *tb); +TH_API void THTensor_(geTensorT)(THTensor *r_, THTensor *ta, THTensor *tb); +TH_API void THTensor_(neTensorT)(THTensor *r_, THTensor *ta, THTensor *tb); +TH_API void THTensor_(eqTensorT)(THTensor *r_, THTensor *ta, THTensor *tb); + +TH_API void THTensor_(ltValueByte)(THByteTensor *r_, THTensor* t, scalar_t value); +TH_API void THTensor_(leValueByte)(THByteTensor *r_, THTensor* t, scalar_t value); +TH_API void THTensor_(gtValueByte)(THByteTensor *r_, THTensor* t, scalar_t value); +TH_API void THTensor_(geValueByte)(THByteTensor *r_, THTensor* t, scalar_t value); +TH_API void THTensor_(neValueByte)(THByteTensor *r_, THTensor* t, scalar_t value); +TH_API void THTensor_(eqValueByte)(THByteTensor *r_, THTensor* t, scalar_t value); + +TH_API void THTensor_(ltTensorByte)(THByteTensor *r_, THTensor *ta, THTensor *tb); +TH_API void THTensor_(leTensorByte)(THByteTensor *r_, THTensor *ta, THTensor *tb); +TH_API void THTensor_(gtTensorByte)(THByteTensor *r_, THTensor *ta, THTensor *tb); +TH_API void THTensor_(geTensorByte)(THByteTensor *r_, THTensor *ta, THTensor *tb); +TH_API void THTensor_(neTensorByte)(THByteTensor *r_, THTensor *ta, THTensor *tb); +TH_API void THTensor_(eqTensorByte)(THByteTensor *r_, THTensor *ta, THTensor *tb); + +TH_API void THTensor_(maskedSelect)(THTensor *tensor, THTensor* src, THByteTensor *mask); +TH_API void THTensor_(maskedSelectBool)(THTensor *tensor, THTensor* src, THBoolTensor *mask); +TH_API void THTensor_(maskedFill)(THTensor *tensor, THByteTensor *mask, scalar_t value); +TH_API void THTensor_(maskedFillBool)(THTensor *tensor, THBoolTensor *mask, scalar_t value); +TH_API void THTensor_(maskedCopy)(THTensor *tensor, THByteTensor *mask, THTensor* src); +TH_API void THTensor_(maskedCopyBool)(THTensor *tensor, THBoolTensor *mask, THTensor* src); + +TH_API ptrdiff_t THTensor_(numel)(THTensor *t); + +TH_API void THTensor_(addmv)(THTensor *r_, THTensor *t, THTensor *mat, THTensor *vec, scalar_t beta, scalar_t alpha); +TH_API void THTensor_(addmm)(THTensor *r_, THTensor *t, THTensor *mat1, THTensor *mat2, scalar_t beta, scalar_t alpha); +TH_API void THTensor_(addr)(THTensor *r_, THTensor *t, THTensor *vec1, THTensor *vec2, scalar_t beta, scalar_t alpha); + +#if !defined(TH_REAL_IS_BOOL) +TH_API void THTensor_(mul)(THTensor *r_, THTensor *t, scalar_t value); +TH_API void THTensor_(div)(THTensor *r_, THTensor *t, scalar_t value); +#endif + +#if !defined(TH_REAL_IS_BFLOAT16) + +TH_API accreal THTensor_(sumall)(THTensor *t); +TH_API int THTensor_(equal)(THTensor *ta, THTensor *tb); + +TH_API void THTensor_(bitand)(THTensor *r_, THTensor *t, scalar_t value); +TH_API void THTensor_(cbitand)(THTensor *r_, THTensor *t, THTensor *src); +TH_API void THTensor_(bitor)(THTensor *r_, THTensor *t, scalar_t value); +TH_API void THTensor_(cbitor)(THTensor *r_, THTensor *t, THTensor *src); + +void THTensor_(preserveReduceDimSemantics)(THTensor *r_, int in_dims, int reduce_dimension, int keepdim); + +TH_API void THTensor_(max)(THTensor *values_, THLongTensor *indices_, THTensor *t, int dimension, int keepdim); +TH_API void THTensor_(min)(THTensor *values_, THLongTensor *indices_, THTensor *t, int dimension, int keepdim); +TH_API scalar_t THTensor_(minall)(THTensor *t); +TH_API scalar_t THTensor_(maxall)(THTensor *t); + +TH_API void THTensor_(cmax)(THTensor *r, THTensor *t, THTensor *src); +TH_API void THTensor_(cmin)(THTensor *r, THTensor *t, THTensor *src); +TH_API void THTensor_(cmaxValue)(THTensor *r, THTensor *t, scalar_t value); +TH_API void THTensor_(cminValue)(THTensor *r, THTensor *t, scalar_t value); + +TH_API void THTensor_(indexSelect)(THTensor *tensor, THTensor *src, int dim, THLongTensor *index); +TH_API void THTensor_(indexCopy)(THTensor *tensor, int dim, THLongTensor *index, THTensor *src); +TH_API void THTensor_(take)(THTensor *tensor, THTensor *src, THLongTensor *index); +TH_API void THTensor_(put)(THTensor *tensor, THLongTensor *index, THTensor *src, int accumulate); +TH_API void THTensor_(indexFill)(THTensor *tensor, int dim, THLongTensor *index, scalar_t val); + +TH_API void THTensor_(gather)(THTensor *tensor, THTensor *src, int dim, THLongTensor *index); +TH_API void THTensor_(scatter)(THTensor *tensor, int dim, THLongTensor *index, THTensor *src); +TH_API void THTensor_(scatterAdd)(THTensor *tensor, int dim, THLongTensor *index, THTensor *src); +TH_API void THTensor_(scatterFill)(THTensor *tensor, int dim, THLongTensor *index, scalar_t val); + +TH_API void THTensor_(cumsum)(THTensor *r_, THTensor *t, int dimension); +TH_API void THTensor_(cumprod)(THTensor *r_, THTensor *t, int dimension); + +#if !defined(TH_REAL_IS_BOOL) /* non bool only part */ + +TH_API accreal THTensor_(dot)(THTensor *t, THTensor *src); + +TH_API void THTensor_(cinv)(THTensor *self, THTensor *src); + +TH_API void THTensor_(lshift)(THTensor *r_, THTensor *t, scalar_t value); +TH_API void THTensor_(rshift)(THTensor *r_, THTensor *t, scalar_t value); +TH_API void THTensor_(fmod)(THTensor *r_, THTensor *t, scalar_t value); +TH_API void THTensor_(remainder)(THTensor *r_, THTensor *t, scalar_t value); +TH_API void THTensor_(clamp)(THTensor *r_, THTensor *t, scalar_t min_value, scalar_t max_value); + +TH_API void THTensor_(cadd)(THTensor *r_, THTensor *t, scalar_t value, THTensor *src); +TH_API void THTensor_(csub)(THTensor *self, THTensor *src1, scalar_t value, THTensor *src2); +TH_API void THTensor_(cmul)(THTensor *r_, THTensor *t, THTensor *src); +TH_API void THTensor_(cdiv)(THTensor *r_, THTensor *t, THTensor *src); +TH_API void THTensor_(clshift)(THTensor *r_, THTensor *t, THTensor *src); +TH_API void THTensor_(crshift)(THTensor *r_, THTensor *t, THTensor *src); +TH_API void THTensor_(cfmod)(THTensor *r_, THTensor *t, THTensor *src); +TH_API void THTensor_(cremainder)(THTensor *r_, THTensor *t, THTensor *src); + +TH_API void THTensor_(addbmm)(THTensor *r_, THTensor *t, THTensor *batch1, THTensor *batch2, scalar_t beta, scalar_t alpha); +TH_API void THTensor_(baddbmm)(THTensor *r_, THTensor *t, THTensor *batch1, THTensor *batch2, scalar_t beta, scalar_t alpha); + +TH_API void THTensor_(kthvalue)(THTensor *values_, THLongTensor *indices_, THTensor *t, int64_t k, int dimension, int keepdim); +TH_API void THTensor_(mode)(THTensor *values_, THLongTensor *indices_, THTensor *t, int dimension, int keepdim); +TH_API void THTensor_(prod)(THTensor *r_, THTensor *t, int dimension, int keepdim); +TH_API accreal THTensor_(trace)(THTensor *t); + +TH_API void THTensor_(diag)(THTensor *r_, THTensor *t, int k); + +TH_API void THTensor_(sort)(THTensor *rt_, THLongTensor *ri_, THTensor *t, int dimension, int descendingOrder); +TH_API void THTensor_(triu)(THTensor *r_, THTensor *t, int64_t k); + + +#if defined(TH_REAL_IS_FLOAT) || defined(TH_REAL_IS_DOUBLE) + +TH_API void THTensor_(cos)(THTensor *r_, THTensor *t); +TH_API void THTensor_(cosh)(THTensor *r_, THTensor *t); +TH_API void THTensor_(tan)(THTensor *r_, THTensor *t); +TH_API void THTensor_(atan)(THTensor *r_, THTensor *t); +TH_API void THTensor_(tanh)(THTensor *r_, THTensor *t); +TH_API void THTensor_(erf)(THTensor *r_, THTensor *t); +TH_API void THTensor_(erfc)(THTensor *r_, THTensor *t); + +TH_API void THTensor_(std_single)(THTensor *r_, THTensor *t, int dimension, bool unbiased, int keepdim); +TH_API void THTensor_(var_single)(THTensor *r_, THTensor *t, int dimension, bool unbiased, int keepdim); +TH_API void THTensor_(norm)(THTensor *r_, THTensor *t, scalar_t value, int dimension, int keepdim); +TH_API void THTensor_(renorm)(THTensor *r_, THTensor *t, scalar_t value, int dimension, scalar_t maxnorm); +TH_API accreal THTensor_(dist)(THTensor *a, THTensor *b, scalar_t value); +TH_API void THTensor_(histc)(THTensor *hist, THTensor *tensor, int64_t nbins, scalar_t minvalue, scalar_t maxvalue); +TH_API void THTensor_(bhistc)(THTensor *hist, THTensor *tensor, int64_t nbins, scalar_t minvalue, scalar_t maxvalue); + +TH_API accreal THTensor_(meanall)(THTensor *self); +TH_API accreal THTensor_(var_all)(THTensor *self, bool unbiased); +TH_API accreal THTensor_(std_all)(THTensor *self, bool unbiased); +TH_API accreal THTensor_(normall)(THTensor *t, scalar_t value); +#endif +#endif +#endif +#endif +#endif diff --git a/thirdparty/libtorch/include/TH/generic/THTensorRandom.h b/thirdparty/libtorch/include/TH/generic/THTensorRandom.h new file mode 100644 index 0000000000..7bdae7f64a --- /dev/null +++ b/thirdparty/libtorch/include/TH/generic/THTensorRandom.h @@ -0,0 +1,32 @@ +#ifndef TH_GENERIC_FILE +#define TH_GENERIC_FILE "TH/generic/THTensorRandom.h" +#else + +#include +#include + +TH_API void THTensor_(random)(THTensor *self, at::Generator *_generator); +TH_API void THTensor_(clampedRandom)(THTensor *self, int64_t min, int64_t max, at::Generator *_generator); +TH_API void THTensor_(cappedRandom)(THTensor *self, int64_t max, at::Generator *_generator); +TH_API void THTensor_(geometric)(THTensor *self, double p, at::Generator *_generator); + +#if defined(TH_REAL_IS_FLOAT) || defined(TH_REAL_IS_DOUBLE) +TH_API void THTensor_(bernoulli_Tensor)(THTensor *self, at::Generator *_generator, THTensor *p); +TH_API void THTensor_(uniform)(THTensor *self, double a, double b, at::Generator *_generator); +TH_API void THTensor_(normal)(THTensor *self, double mean, double stdv, at::Generator *_generator); +TH_API void THTensor_(normal_means)(THTensor *self, THTensor *means, double stddev, at::Generator *gen); +TH_API void THTensor_(normal_stddevs)(THTensor *self, double mean, THTensor *stddevs, at::Generator *gen); +TH_API void THTensor_(normal_means_stddevs)(THTensor *self, THTensor *means, THTensor *stddevs, at::Generator *gen); +TH_API void THTensor_(exponential)(THTensor *self, double lambda, at::Generator *_generator); +TH_API void THTensor_(cauchy)(THTensor *self, double median, double sigma, at::Generator *_generator); +TH_API void THTensor_(logNormal)(THTensor *self, double mean, double stdv, at::Generator *_generator); +TH_API void THTensor_(multinomialAliasSetup)(THTensor *prob_dist, THLongTensor *J, THTensor *q); +TH_API void THTensor_(multinomialAliasDraw)(THLongTensor *self, THTensor *q, THLongTensor *J, int n_sample, at::Generator *_generator); +#endif + +#if defined(TH_REAL_IS_BYTE) +TH_API void THTensor_(getRNGState)(at::Generator *_generator, THTensor *self); +TH_API void THTensor_(setRNGState)(at::Generator *_generator, THTensor *self); +#endif + +#endif diff --git a/thirdparty/libtorch/include/TH/generic/THVector.h b/thirdparty/libtorch/include/TH/generic/THVector.h new file mode 100644 index 0000000000..564459ddd1 --- /dev/null +++ b/thirdparty/libtorch/include/TH/generic/THVector.h @@ -0,0 +1,43 @@ +#ifndef TH_GENERIC_FILE +#define TH_GENERIC_FILE "TH/generic/THVector.h" +#else + +#include +#include + +TH_API void THVector_(fill)(scalar_t *x, const scalar_t c, const ptrdiff_t n); + +#if !defined(TH_REAL_IS_BOOL) /* non bool only part */ + +TH_API void THVector_(cadd)(scalar_t *z, const scalar_t *x, const scalar_t *y, const scalar_t c, const ptrdiff_t n); +TH_API void THVector_(adds)(scalar_t *y, const scalar_t *x, const scalar_t c, const ptrdiff_t n); +TH_API void THVector_(cmul)(scalar_t *z, const scalar_t *x, const scalar_t *y, const ptrdiff_t n); +TH_API void THVector_(muls)(scalar_t *y, const scalar_t *x, const scalar_t c, const ptrdiff_t n); +TH_API void THVector_(cdiv)(scalar_t *z, const scalar_t *x, const scalar_t *y, const ptrdiff_t n); +TH_API void THVector_(divs)(scalar_t *y, const scalar_t *x, const scalar_t c, const ptrdiff_t n); +TH_API void THVector_(neg)(scalar_t *y, const scalar_t *x, const ptrdiff_t n); +TH_API void THVector_(normal_fill)(scalar_t *data, + const int64_t size, + struct at::Generator *generator, + const scalar_t mean, + const scalar_t stddev); + +#endif /* non bool only part */ + +/* floating point only now */ +#if defined(TH_REAL_IS_FLOAT) || defined(TH_REAL_IS_DOUBLE) + +TH_API void THVector_(exp)(scalar_t *y, const scalar_t *x, const ptrdiff_t n); +TH_API void THVector_(erf)(scalar_t *y, const scalar_t *x, const ptrdiff_t n); +TH_API void THVector_(erfc)(scalar_t *y, const scalar_t *x, const ptrdiff_t n); +TH_API void THVector_(cos)(scalar_t *y, const scalar_t *x, const ptrdiff_t n); +TH_API void THVector_(cosh)(scalar_t *y, const scalar_t *x, const ptrdiff_t n); +TH_API void THVector_(tan)(scalar_t *y, const scalar_t *x, const ptrdiff_t n); +TH_API void THVector_(atan)(scalar_t *y, const scalar_t *x, const ptrdiff_t n); +TH_API void THVector_(tanh)(scalar_t *y, const scalar_t *x, const ptrdiff_t n); +TH_API void THVector_(pow)(scalar_t *y, const scalar_t *x, const scalar_t c, const ptrdiff_t n); +TH_API void THVector_(cinv)(scalar_t *y, const scalar_t *x, const ptrdiff_t n); + +#endif /* floating point only part */ + +#endif diff --git a/thirdparty/libtorch/include/THC/THC.h b/thirdparty/libtorch/include/THC/THC.h new file mode 100644 index 0000000000..79be433e1a --- /dev/null +++ b/thirdparty/libtorch/include/THC/THC.h @@ -0,0 +1,18 @@ +#ifndef THC_INC +#define THC_INC + +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include + +#endif diff --git a/thirdparty/libtorch/include/THC/THCAllocator.h b/thirdparty/libtorch/include/THC/THCAllocator.h new file mode 100644 index 0000000000..a749ee53fe --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCAllocator.h @@ -0,0 +1,17 @@ +#ifndef THC_ALLOCATOR_INC +#define THC_ALLOCATOR_INC + +#include + +// IPC doesn't support (re)allocation + +class TORCH_CUDA_API THCIpcDeleter { + public: + THCIpcDeleter(std::shared_ptr basePtr); + ~THCIpcDeleter(); + static at::DataPtr makeDataPtr(std::shared_ptr basePtr, void* data); +private: + std::shared_ptr basePtr_; +}; + +#endif diff --git a/thirdparty/libtorch/include/THC/THCApply.cuh b/thirdparty/libtorch/include/THC/THCApply.cuh new file mode 100644 index 0000000000..6d275e24d1 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCApply.cuh @@ -0,0 +1,758 @@ +#ifndef THC_APPLY_INC +#define THC_APPLY_INC + +#include +#include +#include +#include +#include + +// +// This file contains pointwise operation functions and kernels that +// work on both contiguous and non-contiguous tensor arguments of +// arbitrary (up to MAX_CUTORCH_DIMS) dimensioned arguments without +// copying or temporary storage. +// + +// Rearrange dimensions for pointwise operations so that strides are in +// decreasing order as much as possible, so that kernels have better memory +// access patterns. +// +// For example, consider a binary operation on two "transposed" 2-dim tensors: +// sizes: 256 512 +// aInfo->strides: 1 256 +// bInfo->strides: 1 256 +// +// Given this, each concurrent memory access inside kernelPointwiseApply2() is +// exactly 256 elements apart, resulting in poor performance. +// +// This function exchanges dimensions so that memory access is contiguous: +// sizes: 512 256 +// aInfo->strides: 256 1 +// bInfo->strides: 256 1 +// +// (Actually, it becomes even better because now collapseDims() can turn each +// input into one contiguous array.) +// +// In general, given M (<=3) TensorInfo's with N dimensions, we can view each +// strides[i] (0 <= i < N) as an M-tuple. Given each pair i < j, we exchange +// strides[i] and [j] if +// (1) strides[i][k] < strides[j][k] for some k (0 <= k < M) +// (exchanging them will benefit input #k), and +// (2) strides[i][k] <= strieds[j][k] for all k +// (exchanging them will not make any input worse). +template +void rearrangeDims(TensorInfo* aInfo, + TensorInfo* bInfo = nullptr, + TensorInfo* cInfo = nullptr) { + int numInfos = 1; + int dims = aInfo->dims; + IndexType *sizes[3] = { aInfo->sizes, }; + IndexType *strides[3] = { aInfo->strides, }; + + if (bInfo != nullptr) { + ++numInfos; + if (bInfo->dims != dims) return; + sizes[1] = bInfo->sizes; + strides[1] = bInfo->strides; + } + + if (cInfo != nullptr) { + ++numInfos; + if (cInfo->dims != dims) return; + sizes[2] = cInfo->sizes; + strides[2] = cInfo->strides; + } + + // Bail out if sizes do not match: we are using "deprecated pointwise + // behavior" among tensors of different shapes but same number of elements. + for (int i = 1; i < numInfos; ++i) { + for (int j = 0; j < dims; ++j) { + if (sizes[i][j] != sizes[0][j]) return; + } + } + + for (int i = 0; i < dims - 1; ++i) { + // No need to consider dimensions of size 1. + if (sizes[0][i] == 1) continue; + + for (int j = i + 1; j < dims; ++j) { + if (sizes[0][j] == 1) continue; + + // Compare the relative sizes of strides between dim #i and dim #j. + bool hasIncreasingStrides = false; + bool hasDecreasingStrides = false; + + for (int k = 0; k < numInfos; k++) { + IndexType stride_i = strides[k][i]; + IndexType stride_j = strides[k][j]; + if (stride_i < stride_j) { + hasIncreasingStrides = true; + } else if (stride_i > stride_j) { + hasDecreasingStrides = true; + } + } + + if (hasIncreasingStrides && !hasDecreasingStrides) { + for (int k = 0; k < numInfos; k++) { + IndexType size = sizes[k][i]; + sizes[k][i] = sizes[k][j]; + sizes[k][j] = size; + + IndexType stride = strides[k][i]; + strides[k][i] = strides[k][j]; + strides[k][j] = stride; + } + } + } + } +} + +// Threads per block for our apply kernel +// FIXME: use occupancy calculator instead +#define THC_APPLY_THREADS_PER_BLOCK (32 * 16) +#define THC_APPLY_BLOCKS_PER_SM 4 +template +#if defined __HIP_PLATFORM_HCC__ +C10_LAUNCH_BOUNDS_2(THC_APPLY_THREADS_PER_BLOCK, THC_APPLY_BLOCKS_PER_SM) +#endif +__global__ void +kernelPointwiseApply1(const OffsetInfo a, + IndexType totalElements, + Op op) { + // NOTE: The two typecasts below are essential when IndexType is 64-bit; + // without them, results are silently truncated to 32 bits! + for (IndexType linearIndex = (IndexType) blockIdx.x * blockDim.x + threadIdx.x; + linearIndex < totalElements; + linearIndex += (IndexType) gridDim.x * blockDim.x) { + op(a.get(linearIndex)); + } +} + +template +#if defined __HIP_PLATFORM_HCC__ +C10_LAUNCH_BOUNDS_2(THC_APPLY_THREADS_PER_BLOCK, THC_APPLY_BLOCKS_PER_SM) +#endif +__global__ void +kernelPointwiseApply2(const OffsetInfo a, + const OffsetInfo b, + IndexType totalElements, + Op op) { + for (IndexType linearIndex = (IndexType) blockIdx.x * blockDim.x + threadIdx.x; + linearIndex < totalElements; + linearIndex += (IndexType) gridDim.x * blockDim.x) { + op(a.get(linearIndex), b.get(linearIndex)); + } +} + +template +#if defined __HIP_PLATFORM_HCC__ +C10_LAUNCH_BOUNDS_2(THC_APPLY_THREADS_PER_BLOCK, THC_APPLY_BLOCKS_PER_SM) +#endif +__global__ void +kernelPointwiseApply3(const OffsetInfo a, + const OffsetInfo b, + const OffsetInfo c, + IndexType totalElements, + Op op) { + for (IndexType linearIndex = (IndexType) blockIdx.x * blockDim.x + threadIdx.x; + linearIndex < totalElements; + linearIndex += (IndexType) gridDim.x * blockDim.x) { + op(a.get(linearIndex), b.get(linearIndex), c.get(linearIndex)); + } +} + +inline dim3 getApplyBlock() { + return dim3(THC_APPLY_THREADS_PER_BLOCK); +} + +inline bool getApplyGrid(THCState* state, uint64_t totalElements, dim3& grid, int curDevice) { + if (curDevice == -1) return false; + + uint64_t numBlocks = THCCeilDiv(totalElements, static_cast(THC_APPLY_THREADS_PER_BLOCK)); + uint64_t maxGridX = at::cuda::getDeviceProperties(curDevice)->maxGridSize[0]; + if (numBlocks > maxGridX) + numBlocks = maxGridX; + + // For 32-bit indices, make sure that gridDim.x * blockDim.x fits in 32 bits. + if (totalElements <= INT32_MAX && + numBlocks > INT32_MAX / THC_APPLY_THREADS_PER_BLOCK) + numBlocks = INT32_MAX / THC_APPLY_THREADS_PER_BLOCK; + + grid = dim3(numBlocks); + return true; +} + +template +bool THC_pointwiseApply1(THCState* state, + TensorTypeA* a, + const Op& op, + TensorArgType aType = ReadWrite) { + if (THCTensor_nDimensionLegacyAll(state, a) > MAX_CUTORCH_DIMS) { + return false; + } + + if (THCTensor_nDimensionLegacyAll(state, a) == 0) { + // Zero-dim tensor; do nothing + return true; + } + + const dim3 block = getApplyBlock(); + + dim3 grid; + ptrdiff_t totalElements = THCTensor_nElement(state, a); + + int curDevice = -1; + cudaGetDevice(&curDevice); + if (!getApplyGrid(state, totalElements, grid, curDevice)) { + return false; + } + + /* + Expands readable/writable tensors whose indices may be "overlapped." + This ensures that each element of the tensor is operated on once and only + once. + */ + TensorTypeA* oldA = NULL; + + if (aType == ReadWrite && + THCTensor_maybeOverlappingIndices(state, a)) { + // Must perform in contiguous space + oldA = a; + a = (TensorTypeA*)THCTensor_newContiguous(state, a); + } + + // It is possible that the tensor dimensions are able to be collapsed, + // and thus we can reduce the actual code complexity of the copy by + // exploiting this knowledge statically, since the div/mod is the + // most expensive part of the operation, more so than memory accesses. + // For instance, when copying a non-contiguous to a contiguous tensor + // (or vice versa), the contiguous tensor can be collapsed to one + // dimension, and the loop to translate the linear index to the array + // index can be similarly collapsed. That is what this unrolling is for. +#define HANDLE_CASE(TYPE, A) \ + kernelPointwiseApply1 \ + <<>>( \ + OffsetInfo \ + (aInfo), \ + (TYPE) totalElements, op); + +#define HANDLE_A_CASE(TYPE, A) { \ + switch (A) { \ + case 1: \ + HANDLE_CASE(TYPE, 1); \ + break; \ + case 2: \ + HANDLE_CASE(TYPE, 2); \ + break; \ + default: \ + HANDLE_CASE(TYPE, -1); \ + break; \ + } \ +} + + // Can we use 32-bit integer math in the kernel (the linear ID for the copy + // and the resulting non-linear offset is all computable using 32-bit math?) + // We also use unsigned index math in the kernel, as signed div/mod has + // additional overhead. + if (THCTensor_canUse32BitIndexMath(state, a)) { + TensorInfo aInfo = + getTensorInfo(state, a); + rearrangeDims(&aInfo); + aInfo.collapseDims(); +#if CUDA_VERSION < 9000 + if (!aInfo.isContiguous()) { + grid.x = min(at::cuda::getCurrentDeviceProperties()->multiProcessorCount * THC_APPLY_BLOCKS_PER_SM , grid.x); + } +#endif + HANDLE_A_CASE(unsigned int, aInfo.dims); + } else { + TensorInfo aInfo = + getTensorInfo(state, a); + rearrangeDims(&aInfo); + aInfo.collapseDims(); + + /* + Only instantiates the all 1D special case and the fallback all nD case for + large (64-bit indexed) tensors to reduce compilation time. + */ + if (aInfo.dims == 1) { + OffsetInfo + aOffset(aInfo); + kernelPointwiseApply1 + <<>>( + aOffset, (uint64_t) totalElements, op); + } else { + +#if CUDA_VERSION < 9000 + grid.x = min(at::cuda::getCurrentDeviceProperties()->multiProcessorCount * THC_APPLY_BLOCKS_PER_SM , grid.x); +#endif + OffsetInfo + aOffset(aInfo); + kernelPointwiseApply1 + <<>>( + aOffset, (uint64_t) totalElements, op); + } + } +#undef HANDLE_CASE +#undef HANDLE_A_CASE + + if (oldA) { + // Ignore overlaps when copying back; if we use THCTensor_copy + // instead, it will recursively try and invoke ourselves to make + // oldA contiguous. + THCTensor_copyIgnoringOverlaps(state, oldA, a); + THCTensor_free(state, a); + a = oldA; + } + + return true; +} + +template +bool THC_pointwiseApply2(THCState* state, + TensorTypeA* a, + TensorTypeB* b, + const Op& op, + TensorArgType aType = ReadWrite, + TensorArgType bType = ReadOnly) { + ptrdiff_t totalElements = THCTensor_nElement(state, a); + if (totalElements != THCTensor_nElement(state, b)) { + return false; + } + + if (THCTensor_nDimensionLegacyAll(state, a) > MAX_CUTORCH_DIMS || + THCTensor_nDimensionLegacyAll(state, b) > MAX_CUTORCH_DIMS) { + return false; + } + + if (THCTensor_nDimensionLegacyAll(state, a) == 0) { + // Zero-dim tensor; do nothing + return true; + } + + const dim3 block = getApplyBlock(); + + dim3 grid; + int curDevice = -1; + cudaGetDevice(&curDevice); + if (!getApplyGrid(state, totalElements, grid, curDevice)) { + return false; + } + + /* + Expands readable/writable tensors whose indices may be "overlapped." + This ensures that each element of the tensor is operated on once and only + once. + */ + TensorTypeA* oldA = NULL; + TensorTypeB* oldB = NULL; + + if (aType == ReadWrite && + THCTensor_maybeOverlappingIndices(state, a)) { + // Must perform in contiguous space + oldA = a; + a = (TensorTypeA*)THCTensor_newContiguous(state, a); + } + if (bType == ReadWrite && + THCTensor_maybeOverlappingIndices(state, b)) { + // Must perform in contiguous space + oldB = b; + b = (TensorTypeB*)THCTensor_newContiguous(state, b); + } + + // It is possible that the tensor dimensions are able to be collapsed, + // and thus we can reduce the actual code complexity of the copy by + // exploiting this knowledge statically, since the div/mod is the + // most expensive part of the operation, more so than memory accesses. + // For instance, when copying a non-contiguous to a contiguous tensor + // (or vice versa), the contiguous tensor can be collapsed to one + // dimension, and the loop to translate the linear index to the array + // index can be similarly collapsed. That is what this unrolling is for. +#define HANDLE_CASE(TYPE, A, B) \ + kernelPointwiseApply2 \ + <<>>( \ + OffsetInfo \ + (aInfo), \ + OffsetInfo \ + (bInfo), \ + (TYPE) totalElements, op); + +#define HANDLE_B_CASE(TYPE, A, B) { \ + switch (B) { \ + case 1: \ + HANDLE_CASE(TYPE, A, 1); \ + break; \ + case 2: \ + HANDLE_CASE(TYPE, A, 2); \ + break; \ + default: \ + HANDLE_CASE(TYPE, A, -1); \ + break; \ + } \ +} + +#define HANDLE_A_CASE(TYPE, A, B) { \ + switch (A) { \ + case 1: \ + HANDLE_B_CASE(TYPE, 1, B); \ + break; \ + case 2: \ + HANDLE_B_CASE(TYPE, 2, B); \ + break; \ + default: \ + HANDLE_B_CASE(TYPE, -1, B); \ + break; \ + } \ +} + + if (THCTensor_canUse32BitIndexMath(state, a) && + THCTensor_canUse32BitIndexMath(state, b)) { + TensorInfo aInfo = + getTensorInfo(state, a); + + TensorInfo bInfo = + getTensorInfo(state, b); + + rearrangeDims(&aInfo, &bInfo); + aInfo.collapseDims(); + bInfo.collapseDims(); +#if CUDA_VERSION < 9000 + if (!(aInfo.isContiguous() && bInfo.isContiguous())) + grid.x = min(at::cuda::getCurrentDeviceProperties()->multiProcessorCount * THC_APPLY_BLOCKS_PER_SM , grid.x); +#endif + + HANDLE_A_CASE(unsigned int, aInfo.dims, bInfo.dims); + } else { + TensorInfo aInfo = + getTensorInfo(state, a); + + TensorInfo bInfo = + getTensorInfo(state, b); + + rearrangeDims(&aInfo, &bInfo); + aInfo.collapseDims(); + bInfo.collapseDims(); + + /* + Only instantiates the all 1D special case and the fallback all nD case for + large (64-bit indexed) tensors to reduce compilation time. + */ + if (aInfo.dims == 1 && bInfo.dims == 1) { + OffsetInfo + aOffset(aInfo); + OffsetInfo + bOffset(bInfo); + kernelPointwiseApply2 + <<>>( + aOffset, bOffset, (uint64_t) totalElements, op); + } else { +#if CUDA_VERSION < 9000 + grid.x = min(at::cuda::getCurrentDeviceProperties()->multiProcessorCount * THC_APPLY_BLOCKS_PER_SM , grid.x); +#endif + OffsetInfo + aOffset(aInfo); + OffsetInfo + bOffset(bInfo); + kernelPointwiseApply2 + <<>>( + aOffset, bOffset, (uint64_t) totalElements, op); + } + } +#undef HANDLE_CASE +#undef HANDLE_B_CASE +#undef HANDLE_A_CASE + + if (oldA) { + // Ignore overlaps when copying back; if we use THCTensor_copy + // instead, it will recursively try and invoke ourselves to make + // oldA contiguous. + THCTensor_copyIgnoringOverlaps(state, oldA, a); + THCTensor_free(state, a); + a = oldA; + } + + if (oldB) { + // Ignore overlaps when copying back; if we use THCTensor_copy + // instead, it will recursively try and invoke ourselves to make + // oldB contiguous. + THCTensor_copyIgnoringOverlaps(state, oldB, b); + THCTensor_free(state, b); + b = oldB; + } + + return true; +} + +template +bool THC_pointwiseApply3(THCState* state, + TensorTypeA* a, + TensorTypeB* b, + TensorTypeC* c, + const Op& op, + TensorArgType aType = ReadWrite, + TensorArgType bType = ReadOnly, + TensorArgType cType = ReadOnly) { + ptrdiff_t totalElements = THCTensor_nElement(state, a); + + if (totalElements != THCTensor_nElement(state, b) || + totalElements != THCTensor_nElement(state, c)) { + return false; + } + + if (THCTensor_nDimensionLegacyAll(state, a) > MAX_CUTORCH_DIMS || + THCTensor_nDimensionLegacyAll(state, b) > MAX_CUTORCH_DIMS || + THCTensor_nDimensionLegacyAll(state, c) > MAX_CUTORCH_DIMS) { + return false; + } + + if (THCTensor_nDimensionLegacyAll(state, a) == 0) { + // Zero-dim tensor; do nothing + return true; + } + + const dim3 block = getApplyBlock(); + + dim3 grid; + int curDevice = -1; + cudaGetDevice(&curDevice); + if (!getApplyGrid(state, totalElements, grid, curDevice)) { + return false; + } + + /* + Expands readable/writable tensors whose indices may be "overlapped." + This ensures that each element of the tensor is operated on once and only + once. + */ + TensorTypeA* oldA = NULL; + TensorTypeB* oldB = NULL; + TensorTypeC* oldC = NULL; + + if (aType == ReadWrite && + THCTensor_maybeOverlappingIndices(state, a)) { + // Must perform in contiguous space + oldA = a; + a = (TensorTypeA*)THCTensor_newContiguous(state, a); + } + if (bType == ReadWrite && + THCTensor_maybeOverlappingIndices(state, b)) { + // Must perform in contiguous space + oldB = b; + b = (TensorTypeB*)THCTensor_newContiguous(state, b); + } + if (cType == ReadWrite && + THCTensor_maybeOverlappingIndices(state, c)) { + // Must perform in contiguous space + oldC = c; + c = (TensorTypeC*)THCTensor_newContiguous(state, c); + } + +#define HANDLE_CASE(TYPE, A, B, C) \ + kernelPointwiseApply3 \ + <<>>( \ + OffsetInfo \ + (aInfo), \ + OffsetInfo \ + (bInfo), \ + OffsetInfo \ + (cInfo), \ + (TYPE) totalElements, op); + +#define HANDLE_C_CASE(TYPE, A, B, C) { \ + switch (C) { \ + case 1: \ + HANDLE_CASE(TYPE, A, B, 1); \ + break; \ + case 2: \ + HANDLE_CASE(TYPE, A, B, 2); \ + break; \ + default: \ + HANDLE_CASE(TYPE, A, B, -1); \ + break; \ + } \ +} + +#define HANDLE_B_CASE(TYPE, A, B, C) { \ + switch (B) { \ + case 1: \ + HANDLE_C_CASE(TYPE, A, 1, C); \ + break; \ + case 2: \ + HANDLE_C_CASE(TYPE, A, 2, C); \ + break; \ + default: \ + HANDLE_C_CASE(TYPE, A, -1, C); \ + break; \ + } \ +} + +#define HANDLE_A_CASE(TYPE, A, B, C) { \ + switch (A) { \ + case 1: \ + HANDLE_B_CASE(TYPE, 1, B, C); \ + break; \ + case 2: \ + HANDLE_B_CASE(TYPE, 2, B, C); \ + break; \ + default: \ + HANDLE_B_CASE(TYPE, -1, B, C); \ + break; \ + } \ +} + + if (THCTensor_canUse32BitIndexMath(state, a) && + THCTensor_canUse32BitIndexMath(state, b) && + THCTensor_canUse32BitIndexMath(state, c)) { + TensorInfo aInfo = + getTensorInfo(state, a); + + TensorInfo bInfo = + getTensorInfo(state, b); + + TensorInfo cInfo = + getTensorInfo(state, c); + + rearrangeDims(&aInfo, &bInfo, &cInfo); + aInfo.collapseDims(); + bInfo.collapseDims(); + cInfo.collapseDims(); + +#if CUDA_VERSION < 9000 + if (!(aInfo.isContiguous() && bInfo.isContiguous() && cInfo.isContiguous())) + grid.x = min(at::cuda::getCurrentDeviceProperties()->multiProcessorCount * THC_APPLY_BLOCKS_PER_SM , grid.x); +#endif + HANDLE_A_CASE(unsigned int, aInfo.dims, bInfo.dims, cInfo.dims); + } else { + TensorInfo aInfo = + getTensorInfo(state, a); + + TensorInfo bInfo = + getTensorInfo(state, b); + + TensorInfo cInfo = + getTensorInfo(state, c); + + rearrangeDims(&aInfo, &bInfo, &cInfo); + aInfo.collapseDims(); + bInfo.collapseDims(); + cInfo.collapseDims(); + + /* + Only instantiates the all 1D special case and the fallback all nD case for + large (64-bit indexed) tensors to reduce compilation time. + */ + if (aInfo.dims == 1 && bInfo.dims == 1 && cInfo.dims == 1) { + OffsetInfo + aOffset(aInfo); + OffsetInfo + bOffset(bInfo); + OffsetInfo + cOffset(cInfo); + kernelPointwiseApply3 + <<>>( + aOffset, bOffset, cOffset, (uint64_t) totalElements, op); + } else { +#if CUDA_VERSION < 9000 + grid.x = min(at::cuda::getCurrentDeviceProperties()->multiProcessorCount * THC_APPLY_BLOCKS_PER_SM , grid.x); +#endif + + OffsetInfo + aOffset(aInfo); + OffsetInfo + bOffset(bInfo); + OffsetInfo + cOffset(cInfo); + kernelPointwiseApply3 + <<>>( + aOffset, bOffset, cOffset, (uint64_t) totalElements, op); + } + } +#undef HANDLE_CASE +#undef HANDLE_C_CASE +#undef HANDLE_B_CASE +#undef HANDLE_A_CASE + + if (oldA) { + // Ignore overlaps when copying back; if we use THCTensor_copy + // instead, it will recursively try and invoke ourselves to make + // oldA contiguous. + THCTensor_copyIgnoringOverlaps(state, oldA, a); + THCTensor_free(state, a); + a = oldA; + } + + if (oldB) { + // Ignore overlaps when copying back; if we use THCTensor_copy + // instead, it will recursively try and invoke ourselves to make + // oldB contiguous. + THCTensor_copyIgnoringOverlaps(state, oldB, b); + THCTensor_free(state, b); + b = oldB; + } + + if (oldC) { + // Ignore overlaps when copying back; if we use THCTensor_copy + // instead, it will recursively try and invoke ourselves to make + // oldC contiguous. + THCTensor_copyIgnoringOverlaps(state, oldC, c); + THCTensor_free(state, c); + c = oldC; + } + + return true; +} + +#undef THC_APPLY_THREADS_PER_BLOCK +#undef THC_APPLY_BLOCKS_PER_SM + +#endif // THC_APPLY_INC diff --git a/thirdparty/libtorch/include/THC/THCAsmUtils.cuh b/thirdparty/libtorch/include/THC/THCAsmUtils.cuh new file mode 100644 index 0000000000..479567fba5 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCAsmUtils.cuh @@ -0,0 +1,145 @@ +#ifndef THC_ASM_UTILS_INC +#define THC_ASM_UTILS_INC + +// Collection of direct PTX functions + +template +struct Bitfield {}; + +template <> +struct Bitfield { + static __device__ __forceinline__ + unsigned int getBitfield(unsigned int val, int pos, int len) { +#if defined(__HIP_PLATFORM_HCC__) + pos &= 0xff; + len &= 0xff; + + unsigned int m = (1u << len) - 1u; + return (val >> pos) & m; +#else + unsigned int ret; + asm("bfe.u32 %0, %1, %2, %3;" : "=r"(ret) : "r"(val), "r"(pos), "r"(len)); + return ret; +#endif + } + + static __device__ __forceinline__ + unsigned int setBitfield(unsigned int val, unsigned int toInsert, int pos, int len) { +#if defined(__HIP_PLATFORM_HCC__) + pos &= 0xff; + len &= 0xff; + + unsigned int m = (1u << len) - 1u; + toInsert &= m; + toInsert <<= pos; + m <<= pos; + + return (val & ~m) | toInsert; +#else + unsigned int ret; + asm("bfi.b32 %0, %1, %2, %3, %4;" : + "=r"(ret) : "r"(toInsert), "r"(val), "r"(pos), "r"(len)); + return ret; +#endif + } +}; + +template <> +struct Bitfield { + static __device__ __forceinline__ + uint64_t getBitfield(uint64_t val, int pos, int len) { +#if defined(__HIP_PLATFORM_HCC__) + pos &= 0xff; + len &= 0xff; + + uint64_t m = (1u << len) - 1u; + return (val >> pos) & m; +#else + uint64_t ret; + asm("bfe.u64 %0, %1, %2, %3;" : "=l"(ret) : "l"(val), "r"(pos), "r"(len)); + return ret; +#endif + } + + static __device__ __forceinline__ + uint64_t setBitfield(uint64_t val, uint64_t toInsert, int pos, int len) { +#if defined(__HIP_PLATFORM_HCC__) + pos &= 0xff; + len &= 0xff; + + uint64_t m = (1u << len) - 1u; + toInsert &= m; + toInsert <<= pos; + m <<= pos; + + return (val & ~m) | toInsert; +#else + uint64_t ret; + asm("bfi.b64 %0, %1, %2, %3, %4;" : + "=l"(ret) : "l"(toInsert), "l"(val), "r"(pos), "r"(len)); + return ret; +#endif + } +}; + +__device__ __forceinline__ int getLaneId() { +#if defined(__HIP_PLATFORM_HCC__) + return __lane_id(); +#else + int laneId; + asm("mov.s32 %0, %laneid;" : "=r"(laneId) ); + return laneId; +#endif +} + +#if defined(__HIP_PLATFORM_HCC__) +__device__ __forceinline__ unsigned long long int getLaneMaskLt() { + std::uint64_t m = (1ull << getLaneId()) - 1ull; + return m; +#else +__device__ __forceinline__ unsigned getLaneMaskLt() { + unsigned mask; + asm("mov.u32 %0, %%lanemask_lt;" : "=r"(mask)); + return mask; +#endif +} + +#if defined (__HIP_PLATFORM_HCC__) +__device__ __forceinline__ unsigned long long int getLaneMaskLe() { + std::uint64_t m = UINT64_MAX >> (sizeof(std::uint64_t) * CHAR_BIT - (getLaneId() + 1)); + return m; +} +#else +__device__ __forceinline__ unsigned getLaneMaskLe() { + unsigned mask; + asm("mov.u32 %0, %%lanemask_le;" : "=r"(mask)); + return mask; +} +#endif + +#if defined(__HIP_PLATFORM_HCC__) +__device__ __forceinline__ unsigned long long int getLaneMaskGt() { + std::uint64_t m = getLaneMaskLe(); + return m ? ~m : m; +#else +__device__ __forceinline__ unsigned getLaneMaskGt() { + unsigned mask; + asm("mov.u32 %0, %%lanemask_gt;" : "=r"(mask)); + return mask; +#endif +} + +#if defined(__HIP_PLATFORM_HCC__) +__device__ __forceinline__ unsigned long long int getLaneMaskGe() { + std::uint64_t m = getLaneMaskLt(); + return ~m; +#else +__device__ __forceinline__ unsigned getLaneMaskGe() { + unsigned mask; + asm("mov.u32 %0, %%lanemask_ge;" : "=r"(mask)); + return mask; +#endif +} + + +#endif // THC_ASM_UTILS_INC diff --git a/thirdparty/libtorch/include/THC/THCAtomics.cuh b/thirdparty/libtorch/include/THC/THCAtomics.cuh new file mode 100644 index 0000000000..91a74bc3a1 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCAtomics.cuh @@ -0,0 +1,169 @@ +#ifndef THC_ATOMICS_INC +#define THC_ATOMICS_INC + +#include +#include +#include +#include + +template +struct AtomicAddIntegerImpl; + +template +struct AtomicAddIntegerImpl { + inline __device__ void operator()(T *address, T val) { + size_t offset = (size_t)address & 3; + uint32_t * address_as_ui = (uint32_t *)((char *)address - offset); + uint32_t old = *address_as_ui; + uint32_t shift = offset * 8; + uint32_t old_byte; + uint32_t newval; + uint32_t assumed; + + do { + assumed = old; + old_byte = (old >> shift) & 0xff; + // preserve size in initial cast. Casting directly to uint32_t pads + // negative signed values with 1's (e.g. signed -1 = unsigned ~0). + newval = static_cast(THCNumerics::add(val, old_byte)); + newval = (old & ~(0x000000ff << shift)) | (newval << shift); + old = atomicCAS(address_as_ui, assumed, newval); + } while (assumed != old); + } +}; + +template +struct AtomicAddIntegerImpl { + inline __device__ void operator()(T *address, T val) { + size_t offset = (size_t)address & 2; + uint32_t * address_as_ui = (uint32_t *)((char *)address - offset); + bool is_32_align = offset; + uint32_t old = *address_as_ui; + uint32_t old_bytes; + uint32_t newval; + uint32_t assumed; + + do { + assumed = old; + old_bytes = is_32_align ? old >> 16 : old & 0xffff; + // preserve size in initial cast. Casting directly to uint32_t pads + // negative signed values with 1's (e.g. signed -1 = unsigned ~0). + newval = static_cast(THCNumerics::add(val, old_bytes)); + newval = is_32_align ? (old & 0xffff) | (newval << 16) : (old & 0xffff0000) | newval; + old = atomicCAS(address_as_ui, assumed, newval); + } while (assumed != old); + } +}; + +template +struct AtomicAddIntegerImpl { + inline __device__ void operator()(T *address, T val) { + uint32_t * address_as_ui = (uint32_t *) (address); + uint32_t old = *address_as_ui; + uint32_t newval; + uint32_t assumed; + + do { + assumed = old; + newval = val + (T)old; + old = atomicCAS(address_as_ui, assumed, newval); + } while (assumed != old); + } +}; + +template +struct AtomicAddIntegerImpl { + inline __device__ void operator()(T *address, T val) { + unsigned long long * address_as_ui = (unsigned long long *) (address); + unsigned long long old = *address_as_ui; + unsigned long long newval; + unsigned long long assumed; + + do { + assumed = old; + newval = val + (T)old; + old = atomicCAS(address_as_ui, assumed, newval); + } while (assumed != old); + } +}; + +static inline __device__ void atomicAdd(uint8_t *address, uint8_t val) { + AtomicAddIntegerImpl()(address, val); +} + +static inline __device__ void atomicAdd(int8_t *address, int8_t val) { + AtomicAddIntegerImpl()(address, val); +} + +static inline __device__ void atomicAdd(int16_t *address, int16_t val) { + AtomicAddIntegerImpl()(address, val); +} + +static inline __device__ void atomicAdd(int64_t *address, int64_t val) { +#ifdef __HIP_PLATFORM_HCC__ + __atomic_fetch_add(address, val, __ATOMIC_RELAXED); +#else + AtomicAddIntegerImpl()(address, val); +#endif +} + +static inline __device__ void atomicAdd(bool *address, bool val) { + *address = address && val; +} + +static inline __device__ void atomicAdd(at::Half *address, at::Half val) { + #if ((CUDA_VERSION < 10000) || (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ < 700))) + unsigned int * address_as_ui = + (unsigned int *) ((char *)address - ((size_t)address & 2)); + unsigned int old = *address_as_ui; + unsigned int assumed; + + do { + assumed = old; + at::Half hsum; + hsum.x = (size_t)address & 2 ? (old >> 16) : (old & 0xffff); + hsum = THCNumerics::add(hsum, val); + old = (size_t)address & 2 ? (old & 0xffff) | (hsum.x << 16) : (old & 0xffff0000) | hsum.x; + old = atomicCAS(address_as_ui, assumed, old); + } while (assumed != old); + #else + atomicAdd(reinterpret_cast<__half*>(address), val); + #endif + +} + +#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ < 600 || CUDA_VERSION < 8000) +// from CUDA C Programmic Guide +static inline __device__ void atomicAdd(double *address, double val) { + unsigned long long int* address_as_ull = (unsigned long long int*)address; + unsigned long long int old = *address_as_ull; + unsigned long long int assumed; + + do { + assumed = old; + old = atomicCAS(address_as_ull, assumed, + __double_as_longlong(val + + __longlong_as_double(assumed))); + + // Note: uses integer comparison to avoid hang in case of NaN (since NaN != NaN) +} while (assumed != old); +} +#elif !defined(__CUDA_ARCH__) && (CUDA_VERSION < 8000) || defined(__HIP_PLATFORM_HCC__) + +/* Note [hip-clang differences to hcc] + * ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + * The upcoming hip-clang compiler for ROCm differs from hcc in a few details. + * It exports the __HIP__ macro, we can hence differentiate between hcc and + * hip-clang. In the below, hcc only received support for atomicAdd with double + * typing after work week 18312. hip-clang had support from the first version. + * In general, the code-visible differences between hip-clang and hcc will be + * minimal. + */ + +#if defined(__HIP_PLATFORM_HCC__) && __hcc_workweek__ < 18312 && !__HIP__ + // This needs to be defined for the host side pass + static inline __device__ void atomicAdd(double *address, double val) { } +#endif +#endif + +#endif // THC_ATOMICS_INC diff --git a/thirdparty/libtorch/include/THC/THCBlas.h b/thirdparty/libtorch/include/THC/THCBlas.h new file mode 100644 index 0000000000..ef2320a283 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCBlas.h @@ -0,0 +1,55 @@ +#ifndef THC_BLAS_INC +#define THC_BLAS_INC + +#include +#include +#include + +/* Level 1 */ +THC_API float THCudaBlas_Sdot(THCState *state, int64_t n, float *x, int64_t incx, float *y, int64_t incy); +THC_API double THCudaBlas_Ddot(THCState *state, int64_t n, double *x, int64_t incx, double *y, int64_t incy); +THC_API THHalf THCudaBlas_Hdot(THCState *state, int64_t n, THHalf *x, int64_t incx, THHalf *y, int64_t incy); + +/* Level 2 */ +THC_API void THCudaBlas_Sgemv(THCState *state, char trans, int64_t m, int64_t n, float alpha, float *a, int64_t lda, float *x, int64_t incx, float beta, float *y, int64_t incy); +THC_API void THCudaBlas_Dgemv(THCState *state, char trans, int64_t m, int64_t n, double alpha, double *a, int64_t lda, double *x, int64_t incx, double beta, double *y, int64_t incy); +THC_API void THCudaBlas_Sger(THCState *state, int64_t m, int64_t n, float alpha, float *x, int64_t incx, float *y, int64_t incy, float *a, int64_t lda); +THC_API void THCudaBlas_Dger(THCState *state, int64_t m, int64_t n, double alpha, double *x, int64_t incx, double *y, int64_t incy, double *a, int64_t lda); + +/* Level 3 */ +THC_API void THCudaBlas_Sgemm(THCState *state, char transa, char transb, int64_t m, int64_t n, int64_t k, float alpha, float *a, int64_t lda, float *b, int64_t ldb, float beta, float *c, int64_t ldc); +THC_API void THCudaBlas_Dgemm(THCState *state, char transa, char transb, int64_t m, int64_t n, int64_t k, double alpha, double *a, int64_t lda, double *b, int64_t ldb, double beta, double *c, int64_t ldc); + +THC_API void THCudaBlas_Hgemm(THCState *state, char transa, char transb, int64_t m, int64_t n, int64_t k, THHalf alpha, THHalf *a, int64_t lda, THHalf *b, int64_t ldb, THHalf beta, THHalf *c, int64_t ldc); +#ifdef __HIP_PLATFORM_HCC__ +THC_API void THCudaBlas_Bgemm(THCState *state, char transa, char transb, int64_t m, int64_t n, int64_t k, at::BFloat16 alpha, at::BFloat16 *a, int64_t lda, at::BFloat16 *b, int64_t ldb, at::BFloat16 beta, at::BFloat16 *c, int64_t ldc); +#endif + +THC_API void THCudaBlas_SgemmBatched(THCState *state, char transa, char transb, int64_t m, int64_t n, int64_t k, + float alpha, const float *a[], int64_t lda, const float *b[], int64_t ldb, + float beta, float *c[], int64_t ldc, int64_t batchCount); +THC_API void THCudaBlas_DgemmBatched(THCState *state, char transa, char transb, int64_t m, int64_t n, int64_t k, + double alpha, const double *a[], int64_t lda, const double *b[], int64_t ldb, + double beta, double *c[], int64_t ldc, int64_t batchCount); +#if CUDA_VERSION >= 8000 || defined __HIP_PLATFORM_HCC__ +THC_API void THCudaBlas_SgemmStridedBatched(THCState *state, char transa, char transb, int64_t m, int64_t n, int64_t k, + float alpha, const float *a, int64_t lda, int64_t strideA, const float *b, int64_t ldb, int64_t strideB, + float beta, float *c, int64_t ldc, int64_t strideC, int64_t batchCount); +THC_API void THCudaBlas_DgemmStridedBatched(THCState *state, char transa, char transb, int64_t m, int64_t n, int64_t k, + double alpha, const double *a, int64_t lda, int64_t strideA, const double *b, int64_t ldb, int64_t strideB, + double beta, double *c, int64_t ldc, int64_t strideC, int64_t batchCount); +#endif + +#if CUDA_VERSION >= 9010 || defined(__HIP_PLATFORM_HCC__) +void THCudaBlas_HgemmStridedBatched(THCState *state, char transa, char transb, int64_t m, int64_t n, int64_t k, + THHalf alpha, const THHalf *a, int64_t lda, int64_t strideA, const THHalf *b, int64_t ldb, int64_t strideB, + THHalf beta, THHalf *c, int64_t ldc, int64_t strideC, int64_t batchCount); +#endif + +#ifdef __HIP_PLATFORM_HCC__ +void THCudaBlas_BgemmStridedBatched(THCState *state, char transa, char transb, int64_t m, int64_t n, int64_t k, + at::BFloat16 alpha, const at::BFloat16 *a, int64_t lda, int64_t strideA, const at::BFloat16 *b, int64_t ldb, int64_t strideB, + at::BFloat16 beta, at::BFloat16 *c, int64_t ldc, int64_t strideC, int64_t batchCount); +#endif + +#endif diff --git a/thirdparty/libtorch/include/THC/THCCachingHostAllocator.h b/thirdparty/libtorch/include/THC/THCCachingHostAllocator.h new file mode 100644 index 0000000000..38688842e1 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCCachingHostAllocator.h @@ -0,0 +1,33 @@ +#ifndef THC_CACHING_HOST_ALLOCATOR_INC +#define THC_CACHING_HOST_ALLOCATOR_INC + +#include + + +#include + +// +// A caching allocator for CUDA host allocations (pinned memory). +// +// This provides a drop-in replacement for THCudaHostAllocator, which re-uses +// freed pinned (page-locked) memory allocations. This avoids device +// synchronizations due to cudaFreeHost calls. +// +// To ensure correct behavior, THCCachingHostAllocator_recordEvent must be +// called anytime a pointer from this allocator is used in a cudaMemcpyAsync +// call between host and device. We implement this for storages and tensors in +// copy_from_cpu_async_ and copy_to_cpu_async_. +// +// Note that this allocator does not split larger allocations into smaller +// blocks, unlike the caching device allocator. +// +THC_API c10::Allocator* getTHCCachingHostAllocator(void); + +// Records an event in the specified stream. The allocation 'ptr' will not be +// re-used until the event has occurred. +THC_API cudaError_t THCCachingHostAllocator_recordEvent(void *ptr, at::cuda::CUDAStream stream); + +// Releases cached pinned memory allocations via cudaHostFree +THC_API void THCCachingHostAllocator_emptyCache(void); + +#endif diff --git a/thirdparty/libtorch/include/THC/THCDeviceTensor-inl.cuh b/thirdparty/libtorch/include/THC/THCDeviceTensor-inl.cuh new file mode 100644 index 0000000000..16e1f94e47 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCDeviceTensor-inl.cuh @@ -0,0 +1,416 @@ +#include + +namespace detail { + +template +__host__ __device__ void copy(T to[N], T from[N]) { + for (int i = 0; i < N; ++i) { + to[i] = from[i]; + } +} + +} // namespace detail + +template class PtrTraits> +__host__ __device__ +THCDeviceTensor::THCDeviceTensor() + : data_(NULL) { + thc_static_assert(Dim > 0); + + for (int i = 0; i < Dim; ++i) { + size_[i] = 0; + stride_[i] = (IndexT) 1; + } +} + +template class PtrTraits> +__host__ __device__ +THCDeviceTensor:: +#ifdef _MSC_VER +THCDeviceTensor(DataPtrType data, const IndexT (&sizes)[Dim]) +#else +THCDeviceTensor(DataPtrType data, const IndexT sizes[Dim]) +#endif + : data_(data) { + thc_static_assert(Dim > 0); + + for (int i = 0; i < Dim; ++i) { + size_[i] = sizes[i]; + } + + stride_[Dim - 1] = (IndexT) 1; + for (int i = Dim - 2; i >= 0; --i) { + stride_[i] = stride_[i + 1] * sizes[i + 1]; + } +} + +template class PtrTraits> +__host__ __device__ +THCDeviceTensor::THCDeviceTensor( +#ifdef _MSC_VER + DataPtrType data, const IndexT (&sizes)[Dim], const IndexT (&strides)[Dim]) +#else + DataPtrType data, const IndexT sizes[Dim], const IndexT strides[Dim]) +#endif + : data_(data) { + thc_static_assert(Dim > 0); + + for (int i = 0; i < Dim; ++i) { + size_[i] = sizes[i]; + stride_[i] = strides[i]; + } +} + +template class PtrTraits> +template +__host__ __device__ bool +THCDeviceTensor::isSameSizeAndStride( + const THCDeviceTensor& rhs) const { + if (Dim != OtherDim) { + return false; + } + + for (int i = 0; i < Dim; ++i) { + if (size_[i] != rhs.size_[i]) { + return false; + } + + if (stride_[i] != rhs.stride_[i]) { + return false; + } + } + + return true; +} + +template class PtrTraits> +template +__host__ __device__ THCDeviceTensor +THCDeviceTensor::cast() { + thc_static_assert(sizeof(U) == sizeof(T)); + + return THCDeviceTensor( + reinterpret_cast(data_), size_, stride_); +} + +template class PtrTraits> +template +__host__ __device__ const THCDeviceTensor +THCDeviceTensor::cast() const { + thc_static_assert(sizeof(U) == sizeof(T)); + + return THCDeviceTensor( + reinterpret_cast(data_), size_, stride_); +} + +template class PtrTraits> +__host__ __device__ ptrdiff_t +THCDeviceTensor::numElements() const { + ptrdiff_t size = getSize(0); + + for (int i = 1; i < Dim; ++i) { + size *= getSize(i); + } + + return size; +} + +template class PtrTraits> +__host__ __device__ bool +THCDeviceTensor::isContiguous() const { + return isContiguousRange(0, Dim); +} + +template class PtrTraits> +__host__ __device__ bool +THCDeviceTensor::isConsistentlySized(int i) const { + if (i == 0 && getStride(i) > 0 && getSize(i) > 0) { + return true; + } else if ((i > 0) && (i < Dim) && (getStride(i) > 0) && + ((getStride(i - 1) / getStride(i)) >= getSize(i))) { + return true; + } + + return false; +} + +template class PtrTraits> +__host__ __device__ bool +THCDeviceTensor::isConsistentlySized() const { + for (int i = 0; i < Dim; ++i) { + if (!isConsistentlySized(i)) { + return false; + } + } + + return true; +} + +template class PtrTraits> +__host__ __device__ bool +THCDeviceTensor::isContiguousRange( + int first, int last) const { + + int64_t prevSize = last < Dim ? getStride(last) * getSize(last) : 1; + + for (int i = last - 1; i >= first; --i) { + if (getSize(i) != (IndexT) 1) { + if (getStride(i) == prevSize) { + prevSize *= getSize(i); + } else { + return false; + } + } + } + + return true; +} + +template class PtrTraits> +__host__ __device__ THCDeviceTensor +THCDeviceTensor::transpose(int dim1, + int dim2) const { +#if defined(__CUDA_ARCH__) || defined(__HIP_PLATFORM_HCC__) + // Device code + assert(dim1 >= 0 && dim1 < Dim); + assert(dim1 >= 0 && dim2 < Dim); +#else + // Host code + if (dim1 < 0 || dim1 >= Dim) { + THError("dim1 out of bounds"); + } + + if (dim2 < 0 || dim2 >= Dim) { + THError("dim2 out of bounds"); + } +#endif + + IndexT newSize[Dim]; + IndexT newStride[Dim]; + + for (int i = 0; i < Dim; ++i) { + newSize[i] = size_[i]; + newStride[i] = stride_[i]; + } + + IndexT tmp = newSize[dim1]; + newSize[dim1] = newSize[dim2]; + newSize[dim2] = tmp; + + tmp = newStride[dim1]; + newStride[dim1] = newStride[dim2]; + newStride[dim2] = tmp; + + return THCDeviceTensor(data_, newSize, newStride); +} + +template class PtrTraits> +template +__host__ __device__ THCDeviceTensor +THCDeviceTensor::upcastOuter() { + // Can only create tensors of greater dimension + thc_static_assert(NewDim > Dim); + + IndexT newSize[NewDim]; + IndexT newStride[NewDim]; + + int shift = NewDim - Dim; + + for (int i = 0; i < NewDim; ++i) { + if (i < shift) { + // These are the extended dimensions + newSize[i] = (IndexT) 1; + newStride[i] = size_[0] * stride_[0]; + } else { + // Shift the remaining dimensions + newSize[i] = size_[i - shift]; + newStride[i] = stride_[i - shift]; + } + } + + return THCDeviceTensor( + data_, newSize, newStride); +} + +template class PtrTraits> +template +__host__ __device__ THCDeviceTensor +THCDeviceTensor::upcastInner() { + // Can only create tensors of greater dimension + thc_static_assert(NewDim > Dim); + + IndexT newSize[NewDim]; + IndexT newStride[NewDim]; + + for (int i = 0; i < NewDim; ++i) { + if (i < Dim) { + // Existing dimensions get copied over + newSize[i] = size_[i]; + newStride[i] = stride_[i]; + } else { + // Extended dimensions + newSize[i] = (IndexT) 1; + newStride[i] = (IndexT) 1; + } + } + + return THCDeviceTensor( + data_, newSize, newStride); +} + +template class PtrTraits> +template +__host__ __device__ THCDeviceTensor +THCDeviceTensor::downcastOuter() { + // Can only create tensors of lesser dimension + thc_static_assert(NewDim < Dim); + + // We can't downcast non-contiguous tensors, since it leaves + // garbage data in the tensor. The tensor needs to be contiguous + // in all of the dimensions we are collapsing (no padding in + // them). + bool cont = isContiguousRange(0, Dim - NewDim); +#if defined(__CUDA_ARCH__) || defined(__HIP_PLATFORM_HCC__) + // Device code + assert(cont); +#else + // Host code + if (!cont) { + THError("Can only downcast contiguous tensors"); + } +#endif + + IndexT newSize[NewDim]; + IndexT newStride[NewDim]; + + int ignoredDims = Dim - NewDim; + IndexT collapsedSize = 1; + + for (int i = 0; i < Dim; ++i) { + if (i < ignoredDims) { + // Collapse these dimensions + collapsedSize *= getSize(i); + } else { + // Non-collapsed dimensions + if (i == ignoredDims) { + // This is the first non-collapsed dimension + newSize[i - ignoredDims] = collapsedSize * getSize(i); + } else { + // Subsequent non-collapsed dimensions + newSize[i - ignoredDims] = getSize(i); + } + + newStride[i - ignoredDims] = getStride(i); + } + } + + return THCDeviceTensor( + data_, newSize, newStride); +} + +template class PtrTraits> +template +__host__ __device__ THCDeviceTensor +THCDeviceTensor::downcastInner() { + // Can only create tensors of lesser dimension + thc_static_assert(NewDim < Dim); + + // We can't downcast non-contiguous tensors, since it leaves + // garbage data in the tensor. The tensor needs to be contiguous + // in all of the dimensions we are collapsing (no padding in + // them). + bool cont = isContiguousRange(NewDim, Dim); +#if defined(__CUDA_ARCH__) || defined(__HIP_PLATFORM_HCC__) + // Device code + assert(cont); +#else + // Host code + if (!cont) { + THError("Can only downcast contiguous tensors"); + } +#endif + + IndexT newSize[NewDim]; + IndexT newStride[NewDim]; + + IndexT collapsedSize = 1; + + for (int i = Dim - 1; i >= 0; --i) { + if (i >= NewDim) { + // Collapse these dimensions + collapsedSize *= getSize(i); + } else { + // Non-collapsed dimensions + if (i == NewDim - 1) { + // This is the first non-collapsed dimension + newSize[i] = collapsedSize * getSize(i); + newStride[i] = getStride(Dim - 1); + } else { + // Subsequent non-collapsed dimensions + newSize[i] = getSize(i); + newStride[i] = getStride(i); + } + } + } + + return THCDeviceTensor( + data_, newSize, newStride); +} + +template class PtrTraits> +template +__host__ __device__ THCDeviceTensor +THCDeviceTensor::view(DataPtrType at) { + thc_static_assert(SubDim >= 1 && SubDim < Dim); + + IndexT viewSizes[SubDim]; + IndexT viewStrides[SubDim]; + + for (int i = 0; i < SubDim; ++i) { + viewSizes[i] = size_[Dim - SubDim + i]; + viewStrides[i] = stride_[Dim - SubDim + i]; + } + + return THCDeviceTensor( + at, viewSizes, viewStrides); +} + +template class PtrTraits> +template +__host__ __device__ THCDeviceTensor +THCDeviceTensor::view() { + return view(data_); +} + +template class PtrTraits> +void +THCDeviceTensor::zero(cudaStream_t stream) { +#if defined(__CUDA_ARCH__) || defined(__HIP_PLATFORM_HCC__) + assert(isContiguous()); +#else + if (!isContiguous()) { + THError("fillAsync only works on contiguous data"); + } +#endif + + cudaMemsetAsync(data(), 0, numElements() * sizeof(T), stream); +} diff --git a/thirdparty/libtorch/include/THC/THCDeviceTensor.cuh b/thirdparty/libtorch/include/THC/THCDeviceTensor.cuh new file mode 100644 index 0000000000..543420ea72 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCDeviceTensor.cuh @@ -0,0 +1,513 @@ +#ifndef THC_DEVICE_TENSOR_INC +#define THC_DEVICE_TENSOR_INC + +#include +#include + +// A CUDA 6.5 compatible version of static_assert. Remove once on CUDA 7.0. +template +struct THCStaticAssert; + +template <> +struct THCStaticAssert { +}; + +#define thc_static_assert(expr) (THCStaticAssert<(expr) != 0>()) + +/// Our tensor type +template class PtrTraits> +class THCDeviceTensor; + +/// Type of a subspace of a tensor +namespace detail { +template class PtrTraits> +class THCDeviceSubTensor; +} + +template +struct RestrictPtrTraits { + typedef T* __restrict__ PtrType; +}; + +template +struct DefaultPtrTraits { + typedef T* PtrType; +}; + +/** + Templated multi-dimensional array that supports strided access of + elements. Main access is through `operator[]`; e.g., + `tensor[x][y][z]`. + +- `T` is the contained type (e.g., `float`) +- `Dim` is the tensor rank +- `IndexT` is the integer type used for size/stride arrays, and for +- all indexing math. Default is `int`, but for large tensors, `int64_t` +- can be used instead. +- `PtrTraits` are traits applied to our data pointer (T*). By default, +- this is just T*, but RestrictPtrTraits can be used to apply T* +- __restrict__ for alias-free analysis. +*/ +template class PtrTraits = DefaultPtrTraits> +class THCDeviceTensor { + public: + enum { NumDim = Dim }; + typedef T DataType; + typedef IndexT IndexType; + typedef typename PtrTraits::PtrType DataPtrType; + typedef THCDeviceTensor TensorType; + + /// Default constructor + __host__ __device__ THCDeviceTensor(); + + /// Constructor that calculates strides with no padding + __host__ __device__ THCDeviceTensor(DataPtrType data, +#ifdef _MSC_VER + const IndexT (&sizes)[Dim]); +#else + const IndexT sizes[Dim]); +#endif + + /// Constructor that takes arbitrary size/stride arrays + __host__ __device__ THCDeviceTensor(DataPtrType data, +#ifdef _MSC_VER + const IndexT (&sizes)[Dim], + const IndexT (&strides)[Dim]); +#else + const IndexT sizes[Dim], + const IndexT strides[Dim]); +#endif + + /// Returns true if the two tensors are of the same dimensionality, + /// size and stride. + template + __host__ __device__ bool + isSameSizeAndStride( + const THCDeviceTensor& rhs) const; + + /// Cast to a tensor of a different type of the same size and stride + template + __host__ __device__ THCDeviceTensor cast(); + + /// Const version of `cast` + template + __host__ __device__ + const THCDeviceTensor cast() const; + + /// Returns a raw pointer to the start of our data. + __host__ __device__ __forceinline__ DataPtrType data() { + return data_; + } + + /// Returns a raw pointer to the start of our data (const). + __host__ __device__ __forceinline__ + const DataPtrType data() const { + return data_; + } + + /// Cast to a different datatype + template + __host__ __device__ __forceinline__ + typename PtrTraits::PtrType dataAs() { + return reinterpret_cast::PtrType>(data_); + } + + /// Cast to a different datatype + template + __host__ __device__ __forceinline__ + const typename PtrTraits::PtrType dataAs() const { + return reinterpret_cast::PtrType>(data_); + } + + /// Returns a read/write view of a portion of our tensor. + __host__ __device__ __forceinline__ + detail::THCDeviceSubTensor + operator[](IndexT); + + /// Returns a read/write view of a portion of our tensor (const). + __host__ __device__ __forceinline__ + const detail::THCDeviceSubTensor + operator[](IndexT) const; + + /// Returns the size of a given dimension, `[0, Dim - 1]`. No bounds + /// checking. + __host__ __device__ __forceinline__ int getSize(int i) const { + return size_[i]; + } + + /// Returns the stride of a given dimension, `[0, Dim - 1]`. No bounds + /// checking. + __host__ __device__ __forceinline__ int getStride(int i) const { + return stride_[i]; + } + + /// Returns the total number of elements contained within our data + /// (product of `getSize(i)`) + __host__ __device__ ptrdiff_t numElements() const; + + /// Returns the size array. + __host__ __device__ __forceinline__ const IndexT* sizes() const { + return size_; + } + + /// Returns the stride array. + __host__ __device__ __forceinline__ const IndexT* strides() const { + return stride_; + } + + /// Returns true if there is no padding within the tensor and no + /// re-ordering of the dimensions. + /// ~~~ + /// (stride(i) == size(i + 1) * stride(i + 1)) && stride(dim - 1) == 0 + /// ~~~ + __host__ __device__ bool isContiguous() const; + + /// Returns whether a given dimension has only increasing stride + /// from the previous dimension. A tensor that was permuted by + /// exchanging size and stride only will fail this check. + /// If `i == 0` just check `size > 0`. Returns `false` if `stride` is `<= 0`. + __host__ __device__ bool isConsistentlySized(int i) const; + + // Returns whether at each dimension `stride <= size`. + // If this is not the case then iterating once over the size space will + // touch the same memory locations multiple times. + __host__ __device__ bool isConsistentlySized() const; + + /// Returns true if the given dimension range [first, last) has no padding. + __host__ __device__ bool isContiguousRange(int first, int last) const; + + /// Returns a tensor of the same dimension after transposing the two + /// dimensions given. Does not actually move elements; transposition + /// is made by permuting the size/stride arrays. + /// If the dimensions are not valid, asserts. + __host__ __device__ THCDeviceTensor + transpose(int dim1, int dim2) const; + + /// Upcast a tensor of dimension `D` to some tensor of dimension + /// D' > D by padding the leading dimensions by 1 + /// e.g., upcasting a 2-d tensor `[2][3]` to a 4-d tensor `[1][1][2][3]` + template + __host__ __device__ THCDeviceTensor + upcastOuter(); + + /// Upcast a tensor of dimension `D` to some tensor of dimension + /// D' > D by padding the lowest/most varying dimensions by 1 + /// e.g., upcasting a 2-d tensor `[2][3]` to a 4-d tensor `[2][3][1][1]` + template + __host__ __device__ THCDeviceTensor + upcastInner(); + + /// Downcast a tensor of dimension `D` to some tensor of dimension + /// D' < D by collapsing the leading dimensions. asserts if there is + /// padding on the leading dimensions. + template + __host__ __device__ + THCDeviceTensor downcastOuter(); + + /// Downcast a tensor of dimension `D` to some tensor of dimension + /// D' < D by collapsing the leading dimensions. asserts if there is + /// padding on the leading dimensions. + template + __host__ __device__ + THCDeviceTensor downcastInner(); + + /// Returns a tensor that is a view of the `SubDim`-dimensional slice + /// of this tensor, starting at `at`. + template + __host__ __device__ THCDeviceTensor + view(DataPtrType at); + + /// Returns a tensor that is a view of the `SubDim`-dimensional slice + /// of this tensor, starting where our data begins + template + __host__ __device__ THCDeviceTensor + view(); + + /// Zeroes out the tensor asynchronously. Asserts if the contents + /// in question are not contiguous. + void zero(cudaStream_t stream = 0); + + private: + /// Raw pointer to where the tensor data begins + DataPtrType data_; + + /// Array of strides (in sizeof(T) terms) per each dimension + IndexT stride_[Dim]; + + /// Size per each dimension + IndexT size_[Dim]; +}; + +namespace detail { + +/// Specialization for a view of a single value (0-dimensional) +template class PtrTraits> +class THCDeviceSubTensor { + public: + __host__ __device__ THCDeviceSubTensor + operator=(typename TensorType::DataType val) { + *data_ = val; + return *this; + } + + // operator T& + __host__ __device__ operator typename TensorType::DataType&() { + return *data_; + } + + // const operator T& returning const T& + __host__ __device__ operator const typename TensorType::DataType&() const { + return *data_; + } + + // operator& returning T* + __host__ __device__ typename TensorType::DataType* operator&() { + return data_; + } + + // const operator& returning const T* + __host__ __device__ const typename TensorType::DataType* operator&() const { + return data_; + } + + /// Returns a raw accessor to our slice. + __host__ __device__ __forceinline__ typename TensorType::DataPtrType data() { + return data_; + } + + /// Returns a raw accessor to our slice (const). + __host__ __device__ __forceinline__ + const typename TensorType::DataPtrType data() const { + return data_; + } + + /// Cast to a different datatype. + template + __host__ __device__ T& as() { + return *dataAs(); + } + + /// Cast to a different datatype (const). + template + __host__ __device__ const T& as() const { + return *dataAs(); + } + + /// Cast to a different datatype + template + __host__ __device__ __forceinline__ + typename PtrTraits::PtrType dataAs() { + return reinterpret_cast::PtrType>(data_); + } + + /// Cast to a different datatype (const) + template + __host__ __device__ __forceinline__ + typename PtrTraits::PtrType dataAs() const { + return reinterpret_cast::PtrType>(data_); + } + + /// Use the texture cache for reads + __device__ __forceinline__ typename TensorType::DataType ldg() const { +#if __CUDA_ARCH__ >= 350 + return __ldg(data_); +#else + return *data_; +#endif + } + + /// Use the texture cache for reads; cast as a particular type + template + __device__ __forceinline__ T ldgAs() const { +#if __CUDA_ARCH__ >= 350 + return __ldg(dataAs()); +#else + return as(); +#endif + } + + private: + /// One dimension greater can create us + friend class THCDeviceSubTensor; + + /// Our parent tensor can create us + friend class THCDeviceTensor; + + __host__ __device__ __forceinline__ THCDeviceSubTensor( + TensorType& t, + typename TensorType::DataPtrType data) + : tensor_(t), + data_(data) { + } + + /// The tensor we're referencing + TensorType& tensor_; + + /// Where our value is located + typename TensorType::DataPtrType const data_; +}; + +/// A `SubDim`-rank slice of a parent THCDeviceTensor +template class PtrTraits> +class THCDeviceSubTensor { + public: + /// Returns a view of the data located at our offset (the dimension + /// `SubDim` - 1 tensor). + __host__ __device__ __forceinline__ + THCDeviceSubTensor + operator[](typename TensorType::IndexType index) { + return THCDeviceSubTensor( + tensor_, + data_ + index * tensor_.getStride(TensorType::NumDim - SubDim)); + } + + /// Returns a view of the data located at our offset (the dimension + /// `SubDim` - 1 tensor) (const). + __host__ __device__ __forceinline__ + const THCDeviceSubTensor + operator[](typename TensorType::IndexType index) const { + return THCDeviceSubTensor( + tensor_, + data_ + index * tensor_.getStride(TensorType::NumDim - SubDim)); + } + + // operator& returning T* + __host__ __device__ typename TensorType::DataType* operator&() { + return data_; + } + + // const operator& returning const T* + __host__ __device__ const typename TensorType::DataType* operator&() const { + return data_; + } + + /// Returns a raw accessor to our slice. + __host__ __device__ __forceinline__ typename TensorType::DataPtrType data() { + return data_; + } + + /// Returns a raw accessor to our slice (const). + __host__ __device__ __forceinline__ + const typename TensorType::DataPtrType data() const { + return data_; + } + + /// Cast to a different datatype. + template + __host__ __device__ T& as() { + return *dataAs(); + } + + /// Cast to a different datatype (const). + template + __host__ __device__ const T& as() const { + return *dataAs(); + } + + /// Cast to a different datatype + template + __host__ __device__ __forceinline__ + typename PtrTraits::PtrType dataAs() { + return reinterpret_cast::PtrType>(data_); + } + + /// Cast to a different datatype (const) + template + __host__ __device__ __forceinline__ + typename PtrTraits::PtrType dataAs() const { + return reinterpret_cast::PtrType>(data_); + } + + /// Use the texture cache for reads + __device__ __forceinline__ typename TensorType::DataType ldg() const { +#if __CUDA_ARCH__ >= 350 + return __ldg(data_); +#else + return *data_; +#endif + } + + /// Use the texture cache for reads; cast as a particular type + template + __device__ __forceinline__ T ldgAs() const { +#if __CUDA_ARCH__ >= 350 + return __ldg(dataAs()); +#else + return as(); +#endif + } + + /// Returns a tensor that is a view of the SubDim-dimensional slice + /// of this tensor, starting where our data begins + THCDeviceTensor view() { + return tensor_.template view(data_); + } + + private: + /// One dimension greater can create us + friend class THCDeviceSubTensor; + + /// Our parent tensor can create us + friend class + THCDeviceTensor; + + __host__ __device__ __forceinline__ THCDeviceSubTensor( + TensorType& t, + typename TensorType::DataPtrType data) + : tensor_(t), + data_(data) { + } + + /// The tensor we're referencing + TensorType& tensor_; + + /// The start of our sub-region + typename TensorType::DataPtrType const data_; +}; + +} // namespace detail + +template class PtrTraits> +__host__ __device__ __forceinline__ +detail::THCDeviceSubTensor, + Dim - 1, PtrTraits> +THCDeviceTensor::operator[](IndexT index) { + return detail::THCDeviceSubTensor( + detail::THCDeviceSubTensor( + *this, data_)[index]); +} + +template class PtrTraits> +__host__ __device__ __forceinline__ +const detail::THCDeviceSubTensor, + Dim - 1, PtrTraits> +THCDeviceTensor::operator[](IndexT index) const { + return detail::THCDeviceSubTensor( + detail::THCDeviceSubTensor( + const_cast(*this), data_)[index]); +} + +#include + +#endif // THC_DEVICE_TENSOR_INC diff --git a/thirdparty/libtorch/include/THC/THCDeviceTensorUtils-inl.cuh b/thirdparty/libtorch/include/THC/THCDeviceTensorUtils-inl.cuh new file mode 100644 index 0000000000..d02e7d087c --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCDeviceTensorUtils-inl.cuh @@ -0,0 +1,118 @@ +namespace detail { + +// Add a layer of SFINAE to support static_assert +template class PtrTraits, + int NewDim, bool B> +struct UpcastTHCRoot { + static THCDeviceTensor + make(THCState* state, THCudaTensor* t); +}; + +template class PtrTraits, + int NewDim, bool B> +struct UpcastTHC : + UpcastTHCRoot { +}; + +// Never instantiated SFINAE purposes only +template class PtrTraits, + int NewDim> +struct UpcastTHC : + UpcastTHCRoot { +}; + +template class PtrTraits, + int NewDim> +struct UpcastTHC : + UpcastTHCRoot { + static THCDeviceTensor + make(THCState* state, THCudaTensor* t) { + thc_static_assert(NewDim > Dim); + return toDeviceTensor(state, t). + template upcastOuter(); + } +}; + +// Add a layer of SFINAE to support static_assert +template class PtrTraits, + int NewDim, bool B> +struct DowncastTHCRoot { + static THCDeviceTensor + make(THCState* state, THCudaTensor* t); +}; + +template class PtrTraits, + int NewDim, bool B> +struct DowncastTHC : + DowncastTHCRoot { +}; + +// Never instantiated SFINAE purposes only +template class PtrTraits, + int NewDim> +struct DowncastTHC : + DowncastTHCRoot { +}; + +template class PtrTraits, + int NewDim> +struct DowncastTHC : + DowncastTHCRoot { + static THCDeviceTensor + make(THCState* state, THCudaTensor* t) { + thc_static_assert(NewDim < Dim); + return toDeviceTensor(state, t). + template downcastOuter(); + } +}; + +} // namespace detail + +#define SWITCH_UNROLL_CUDA_CAST_FACTORY(i) \ + case i: \ + if (NewDim > i) { \ + return detail::UpcastTHC i)>:: \ + make(state, t); \ + } else if (NewDim == i) { \ + return toDeviceTensor(state, t); \ + } else { \ + return detail::DowncastTHC:: \ + make(state, t); \ + } \ + /* break; */ + +template class PtrTraits> +THCDeviceTensor +toDeviceTensorCast(THCState* state, THCudaTensor* t) { + switch (THCudaTensor_nDimensionLegacyAll(state, t)) { + SWITCH_UNROLL_CUDA_CAST_FACTORY(1); + SWITCH_UNROLL_CUDA_CAST_FACTORY(2); + SWITCH_UNROLL_CUDA_CAST_FACTORY(3); + SWITCH_UNROLL_CUDA_CAST_FACTORY(4); + SWITCH_UNROLL_CUDA_CAST_FACTORY(5); + SWITCH_UNROLL_CUDA_CAST_FACTORY(6); + SWITCH_UNROLL_CUDA_CAST_FACTORY(7); + SWITCH_UNROLL_CUDA_CAST_FACTORY(8); + SWITCH_UNROLL_CUDA_CAST_FACTORY(9); + SWITCH_UNROLL_CUDA_CAST_FACTORY(10); + default: + ; + } + + // Not implemented + THError("THCDeviceTensor dimension size not supported"); + return NULL; /* never enters this piece, appeasing compiler warnings */ +} + +#undef SWITCH_UNROLL_CUDA_CAST_FACTORY diff --git a/thirdparty/libtorch/include/THC/THCDeviceTensorUtils.cuh b/thirdparty/libtorch/include/THC/THCDeviceTensorUtils.cuh new file mode 100644 index 0000000000..285aa823c6 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCDeviceTensorUtils.cuh @@ -0,0 +1,80 @@ +#ifndef THC_DEVICE_TENSOR_UTILS_INC +#define THC_DEVICE_TENSOR_UTILS_INC + +#include +#include +#include + +/// Constructs a DeviceTensor initialized from a THCudaTensor by +/// upcasting or downcasting the tensor to that of a different +/// dimension. +template class PtrTraits> +THCDeviceTensor +toDeviceTensorCast(THCState* state, THCudaTensor* t); + +template +THCDeviceTensor +toDeviceTensorCast(THCState* state, THCudaTensor* t) { + return toDeviceTensorCast(state, t); +} + +template +THCDeviceTensor +toDeviceTensorCast(THCState* state, THCudaTensor* t) { + return toDeviceTensorCast(state, t); +} + +/// Constructs a THCDeviceTensor initialized from a THCudaTensor. Will +/// error if the dimensionality does not match exactly. +template class PtrTraits> +THCDeviceTensor +toDeviceTensor(THCState* state, THCTensor* t); + +template +THCDeviceTensor +toDeviceTensor(THCState* state, THCTensor* t) { + return toDeviceTensor(state, t); +} + +template +THCDeviceTensor +toDeviceTensor(THCState* state, THCTensor* t) { + return toDeviceTensor(state, t); +} + +template class PtrTraits> +THCDeviceTensor +toDeviceTensor(THCState* state, THCTensor* t) { + if (Dim != THCTensor_nDimensionLegacyAll(state, t)) { + THError("THCudaTensor dimension mismatch"); + } + // Determine the maximum offset into the tensor achievable; `IndexT` + // must be smaller than this type in order to use it. + ptrdiff_t maxOffset = 0; + IndexT sizes[Dim]; + IndexT strides[Dim]; + + for (int i = 0; i < Dim; ++i) { + int64_t size = THTensor_sizeLegacyNoScalars(t, i); + int64_t stride = THTensor_strideLegacyNoScalars(t, i); + + maxOffset += (size - 1) * stride; + + sizes[i] = (IndexT) size; + strides[i] = (IndexT) stride; + } + + if (maxOffset > std::numeric_limits::max()) { + THError("THCudaTensor sizes too large for THCDeviceTensor conversion"); + } + + return THCDeviceTensor( + t->data(), sizes, strides); +} + +#include + +#endif // THC_DEVICE_TENSOR_UTILS_INC diff --git a/thirdparty/libtorch/include/THC/THCDeviceUtils.cuh b/thirdparty/libtorch/include/THC/THCDeviceUtils.cuh new file mode 100644 index 0000000000..34bc74f7a5 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCDeviceUtils.cuh @@ -0,0 +1,120 @@ +#ifndef THC_DEVICE_UTILS_INC +#define THC_DEVICE_UTILS_INC + +#include +/* The largest consecutive integer representable in float32 (2^24) */ +#define FLOAT32_MAX_CONSECUTIVE_INT 16777216.0f + +/** + Computes ceil(a / b) +*/ +template +__host__ __device__ __forceinline__ T THCCeilDiv(T a, T b) { + return (a + b - 1) / b; +} + +/** + Computes ceil(a / b) * b; i.e., rounds up `a` to the next highest + multiple of b +*/ +template +__host__ __device__ __forceinline__ T THCRoundUp(T a, T b) { + return THCCeilDiv(a, b) * b; +} + +/** + * For CC 3.5+, perform a load using __ldg + */ +template +__device__ __forceinline__ T doLdg(const T* p) { +#if __CUDA_ARCH__ >= 350 + return __ldg(p); +#else + return *p; +#endif +} + +__device__ __forceinline__ unsigned int ACTIVE_MASK() +{ +#if CUDA_VERSION >= 9000 + return __activemask(); +#else +// will be ignored anyway + return 0xffffffff; +#endif +} + +#if defined(__HIP_PLATFORM_HCC__) +__device__ __forceinline__ unsigned long long int WARP_BALLOT(int predicate) +{ + return __ballot(predicate); +} +#else +__device__ __forceinline__ unsigned int WARP_BALLOT(int predicate, unsigned int mask = 0xffffffff) +{ +#if CUDA_VERSION >= 9000 + return __ballot_sync(mask, predicate); +#else + return __ballot(predicate); +#endif +} +#endif + +template +__device__ __forceinline__ T WARP_SHFL_XOR(T value, int laneMask, int width = warpSize, unsigned int mask = 0xffffffff) +{ +#if CUDA_VERSION >= 9000 + return __shfl_xor_sync(mask, value, laneMask, width); +#else + return __shfl_xor(value, laneMask, width); +#endif +} + +template +__device__ __forceinline__ T WARP_SHFL(T value, int srcLane, int width = warpSize, unsigned int mask = 0xffffffff) +{ +#if CUDA_VERSION >= 9000 + return __shfl_sync(mask, value, srcLane, width); +#else + return __shfl(value, srcLane, width); +#endif +} + +template +__device__ __forceinline__ T WARP_SHFL_UP(T value, unsigned int delta, int width = warpSize, unsigned int mask = 0xffffffff) +{ +#if CUDA_VERSION >= 9000 + return __shfl_up_sync(mask, value, delta, width); +#else + return __shfl_up(value, delta, width); +#endif +} + +#ifdef __HIP_PLATFORM_HCC__ +//To handle ambiguity, add a type double version. +__device__ __forceinline__ double WARP_SHFL_DOWN(double value, unsigned int delta, int width = warpSize, unsigned int mask = 0xffffffff) +{ + //(HIP doesn't support double) + return (double) __shfl_down((float) value, delta, width); +} +__device__ __forceinline__ int64_t WARP_SHFL_DOWN(int64_t value, unsigned int delta, int width = warpSize, unsigned int mask = 0xffffffff) +{ + //(HIP doesn't support int64_t). Trick from https://devblogs.nvidia.com/faster-parallel-reductions-kepler/ + int2 a = *reinterpret_cast(&value); + a.x = __shfl_down(a.x, delta); + a.y = __shfl_down(a.y, delta); + return *reinterpret_cast(&a); +} +#endif +template +__device__ __forceinline__ T WARP_SHFL_DOWN(T value, unsigned int delta, int width = warpSize, unsigned int mask = 0xffffffff) +{ +#if CUDA_VERSION >= 9000 + return __shfl_down_sync(mask, value, delta, width); +#else + return __shfl_down(value, delta, width); +#endif +} + + +#endif // THC_DEVICE_UTILS_INC diff --git a/thirdparty/libtorch/include/THC/THCGeneral.h b/thirdparty/libtorch/include/THC/THCGeneral.h new file mode 100644 index 0000000000..fe92f4cebb --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCGeneral.h @@ -0,0 +1,84 @@ +#ifndef THC_GENERAL_INC +#define THC_GENERAL_INC + +#include +#include + +#include + +#include +#include +#include + +#include + +#define USE_MAGMA + +# define THC_EXTERNC extern "C" + +// TH & THC are now part of the same library as ATen and Caffe2 +// NB: However, we are planning to split it out to a torch_cuda library +#define THC_API THC_EXTERNC TORCH_CUDA_API +#define THC_CLASS TORCH_CUDA_API + +#ifndef THAssert +#define THAssert(exp) \ + do { \ + if (!(exp)) { \ + _THError(__FILE__, __LINE__, "assert(%s) failed", #exp); \ + } \ + } while(0) +#endif + +typedef struct THCState THCState; +struct THCState; + +typedef struct _THCCudaResourcesPerDevice { + /* Size of scratch space per each stream on this device available */ + size_t scratchSpacePerStream; +} THCCudaResourcesPerDevice; + +THC_API THCState* THCState_alloc(void); +THC_API void THCState_free(THCState* state); + +THC_API void THCudaInit(THCState* state); +THC_API void THCudaShutdown(THCState* state); + +/* If device `dev` can access allocations on device `devToAccess`, this will return */ +/* 1; otherwise, 0. */ +THC_API int THCState_getPeerToPeerAccess(THCState* state, int dev, int devToAccess); + +THC_API c10::Allocator* THCState_getCudaHostAllocator(THCState* state); + +THC_API void THCMagma_init(THCState *state); + +/* State manipulators and accessors */ +THC_API int THCState_getNumDevices(THCState* state); + +THC_API cudaStream_t THCState_getCurrentStreamOnDevice(THCState *state, int device); +THC_API cudaStream_t THCState_getCurrentStream(THCState *state); + +/* For the current device and stream, returns the allocated scratch space */ +THC_API size_t THCState_getCurrentDeviceScratchSpaceSize(THCState* state); + +#define THCAssertSameGPU(expr) if (!expr) THError("arguments are located on different GPUs") +#define THCudaCheck(err) __THCudaCheck(err, __FILE__, __LINE__) +#define THCudaCheckWarn(err) __THCudaCheckWarn(err, __FILE__, __LINE__) +#define THCublasCheck(err) __THCublasCheck(err, __FILE__, __LINE__) +#define THCusparseCheck(err) __THCusparseCheck(err, __FILE__, __LINE__) + +THC_API void __THCudaCheck(cudaError_t err, const char *file, const int line); +THC_API void __THCudaCheckWarn(cudaError_t err, const char *file, const int line); +THC_API void __THCublasCheck(cublasStatus_t status, const char *file, const int line); +THC_API void __THCusparseCheck(cusparseStatus_t status, const char *file, const int line); + +THC_API void* THCudaMalloc(THCState *state, size_t size); +THC_API void THCudaFree(THCState *state, void* ptr); + +at::DataPtr THCudaHostAlloc(THCState *state, size_t size); + +THC_API void THCudaHostRecord(THCState *state, void *ptr); + +THC_API cudaError_t THCudaMemGetInfo(THCState *state, size_t* freeBytes, size_t* totalBytes, size_t* largestBlock); + +#endif diff --git a/thirdparty/libtorch/include/THC/THCGeneral.hpp b/thirdparty/libtorch/include/THC/THCGeneral.hpp new file mode 100644 index 0000000000..dc12b43cff --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCGeneral.hpp @@ -0,0 +1,23 @@ +#pragma once + +#include + +/* Global state of THC. */ +struct THCState { + /* Set of all allocated resources. */ + THCCudaResourcesPerDevice* resourcesPerDevice; + /* Captured number of devices upon startup; convenience for bounds checking */ + int numDevices; + + /* Allocator using cudaMallocHost. */ + // NB: These allocators (specifically, cudaHostAllocator) MUST implement + // maybeGlobalBoundDeleter, because we have a few use-cases where we need to + // do raw allocations with them (for Thrust). + // TODO: Make this statically obvious + at::Allocator* cudaHostAllocator; + at::Allocator* cudaDeviceAllocator; + + /* Table of enabled peer-to-peer access between directed pairs of GPUs. + If i accessing allocs on j is enabled, p2pAccess[i][j] is 1; 0 otherwise. */ + int** p2pAccessEnabled; +}; diff --git a/thirdparty/libtorch/include/THC/THCGenerateAllTypes.h b/thirdparty/libtorch/include/THC/THCGenerateAllTypes.h new file mode 100644 index 0000000000..48d19e5988 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCGenerateAllTypes.h @@ -0,0 +1,37 @@ +#ifndef THC_GENERIC_FILE +#error "You must define THC_GENERIC_FILE before including THGenerateAllTypes.h" +#endif + +#define THCGenerateAllTypes + +#define THCTypeIdxByte 1 +#define THCTypeIdxChar 2 +#define THCTypeIdxShort 3 +#define THCTypeIdxInt 4 +#define THCTypeIdxLong 5 +#define THCTypeIdxFloat 6 +#define THCTypeIdxDouble 7 +#define THCTypeIdxHalf 8 +#define THCTypeIdx_(T) TH_CONCAT_2(THCTypeIdx,T) + +#include +#include +#include +#include +#include +#include +#include +#include + +#undef THCTypeIdxByte +#undef THCTypeIdxChar +#undef THCTypeIdxShort +#undef THCTypeIdxInt +#undef THCTypeIdxLong +#undef THCTypeIdxFloat +#undef THCTypeIdxDouble +#undef THCTypeIdxHalf +#undef THCTypeIdx_ + +#undef THCGenerateAllTypes +#undef THC_GENERIC_FILE diff --git a/thirdparty/libtorch/include/THC/THCGenerateBFloat16Type.h b/thirdparty/libtorch/include/THC/THCGenerateBFloat16Type.h new file mode 100644 index 0000000000..a19cae3b6f --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCGenerateBFloat16Type.h @@ -0,0 +1,27 @@ +#ifndef THC_GENERIC_FILE +#error "You must define THC_GENERIC_FILE before including THCGenerateBFloat16Type.h" +#endif +#include + +#define scalar_t at::BFloat16 +#define accreal float +#define Real BFloat16 + +#define CReal CudaBFloat16 + +#define THC_REAL_IS_BFLOAT16 +#line 1 THC_GENERIC_FILE +#include THC_GENERIC_FILE +#undef scalar_t +#undef accreal +#undef Real + +#undef CReal + +#undef THC_REAL_IS_BFLOAT16 + +#ifndef THCGenerateAllTypes +#ifndef THCGenerateFloatTypes +#undef THC_GENERIC_FILE +#endif +#endif diff --git a/thirdparty/libtorch/include/THC/THCGenerateBoolType.h b/thirdparty/libtorch/include/THC/THCGenerateBoolType.h new file mode 100644 index 0000000000..6a2ff4bf52 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCGenerateBoolType.h @@ -0,0 +1,22 @@ +#ifndef THC_GENERIC_FILE +#error "You must define THC_GENERIC_FILE before including THCGenerateBoolType.h" +#endif + +#define scalar_t bool +#define ureal bool +#define accreal int64_t +#define Real Bool +#define CReal CudaBool +#define THC_REAL_IS_BOOL +#line 1 THC_GENERIC_FILE +#include THC_GENERIC_FILE +#undef scalar_t +#undef ureal +#undef accreal +#undef Real +#undef CReal +#undef THC_REAL_IS_BOOL + +#ifndef THCGenerateBoolType +#undef THC_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/THC/THCGenerateByteType.h b/thirdparty/libtorch/include/THC/THCGenerateByteType.h new file mode 100644 index 0000000000..e2c5f35d91 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCGenerateByteType.h @@ -0,0 +1,20 @@ +#ifndef THC_GENERIC_FILE +#error "You must define THC_GENERIC_FILE before including THGenerateByteType.h" +#endif + +#define scalar_t uint8_t +#define accreal int64_t +#define Real Byte +#define CReal CudaByte +#define THC_REAL_IS_BYTE +#line 1 THC_GENERIC_FILE +#include THC_GENERIC_FILE +#undef scalar_t +#undef accreal +#undef Real +#undef CReal +#undef THC_REAL_IS_BYTE + +#ifndef THCGenerateAllTypes +#undef THC_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/THC/THCGenerateCharType.h b/thirdparty/libtorch/include/THC/THCGenerateCharType.h new file mode 100644 index 0000000000..f41ffbd36a --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCGenerateCharType.h @@ -0,0 +1,20 @@ +#ifndef THC_GENERIC_FILE +#error "You must define THC_GENERIC_FILE before including THGenerateCharType.h" +#endif + +#define scalar_t int8_t +#define accreal int64_t +#define Real Char +#define CReal CudaChar +#define THC_REAL_IS_CHAR +#line 1 THC_GENERIC_FILE +#include THC_GENERIC_FILE +#undef scalar_t +#undef accreal +#undef Real +#undef CReal +#undef THC_REAL_IS_CHAR + +#ifndef THCGenerateAllTypes +#undef THC_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/THC/THCGenerateDoubleType.h b/thirdparty/libtorch/include/THC/THCGenerateDoubleType.h new file mode 100644 index 0000000000..4c81b36277 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCGenerateDoubleType.h @@ -0,0 +1,22 @@ +#ifndef THC_GENERIC_FILE +#error "You must define THC_GENERIC_FILE before including THGenerateDoubleType.h" +#endif + +#define scalar_t double +#define accreal double +#define Real Double +#define CReal CudaDouble +#define THC_REAL_IS_DOUBLE +#line 1 THC_GENERIC_FILE +#include THC_GENERIC_FILE +#undef scalar_t +#undef accreal +#undef Real +#undef CReal +#undef THC_REAL_IS_DOUBLE + +#ifndef THCGenerateAllTypes +#ifndef THCGenerateFloatTypes +#undef THC_GENERIC_FILE +#endif +#endif diff --git a/thirdparty/libtorch/include/THC/THCGenerateFloatType.h b/thirdparty/libtorch/include/THC/THCGenerateFloatType.h new file mode 100644 index 0000000000..f84abcc837 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCGenerateFloatType.h @@ -0,0 +1,24 @@ +#ifndef THC_GENERIC_FILE +#error "You must define THC_GENERIC_FILE before including THGenerateFloatType.h" +#endif + +#define scalar_t float +/* FIXME: fp64 has bad performance on some platforms; avoid using it unless + we opt into it? */ +#define accreal float +#define Real Float +#define CReal Cuda +#define THC_REAL_IS_FLOAT +#line 1 THC_GENERIC_FILE +#include THC_GENERIC_FILE +#undef scalar_t +#undef accreal +#undef Real +#undef CReal +#undef THC_REAL_IS_FLOAT + +#ifndef THCGenerateAllTypes +#ifndef THCGenerateFloatTypes +#undef THC_GENERIC_FILE +#endif +#endif diff --git a/thirdparty/libtorch/include/THC/THCGenerateFloatTypes.h b/thirdparty/libtorch/include/THC/THCGenerateFloatTypes.h new file mode 100644 index 0000000000..c44666446e --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCGenerateFloatTypes.h @@ -0,0 +1,32 @@ +#ifndef THC_GENERIC_FILE +#error "You must define THC_GENERIC_FILE before including THGenerateFloatTypes.h" +#endif + +#define THCGenerateFloatTypes + +#define THCTypeIdxByte 1 +#define THCTypeIdxChar 2 +#define THCTypeIdxShort 3 +#define THCTypeIdxInt 4 +#define THCTypeIdxLong 5 +#define THCTypeIdxFloat 6 +#define THCTypeIdxDouble 7 +#define THCTypeIdxHalf 8 +#define THCTypeIdx_(T) TH_CONCAT_2(THCTypeIdx,T) + +#include +#include +#include + +#undef THCTypeIdxByte +#undef THCTypeIdxChar +#undef THCTypeIdxShort +#undef THCTypeIdxInt +#undef THCTypeIdxLong +#undef THCTypeIdxFloat +#undef THCTypeIdxDouble +#undef THCTypeIdxHalf +#undef THCTypeIdx_ + +#undef THCGenerateFloatTypes +#undef THC_GENERIC_FILE diff --git a/thirdparty/libtorch/include/THC/THCGenerateHalfType.h b/thirdparty/libtorch/include/THC/THCGenerateHalfType.h new file mode 100644 index 0000000000..e067559d5a --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCGenerateHalfType.h @@ -0,0 +1,28 @@ +#ifndef THC_GENERIC_FILE +#error "You must define THC_GENERIC_FILE before including THGenerateHalfType.h" +#endif + +#include + +#define scalar_t THHalf +#define accreal float +#define Real Half + +#define CReal CudaHalf + +#define THC_REAL_IS_HALF +#line 1 THC_GENERIC_FILE +#include THC_GENERIC_FILE +#undef scalar_t +#undef accreal +#undef Real + +#undef CReal + +#undef THC_REAL_IS_HALF + +#ifndef THCGenerateAllTypes +#ifndef THCGenerateFloatTypes +#undef THC_GENERIC_FILE +#endif +#endif diff --git a/thirdparty/libtorch/include/THC/THCGenerateIntType.h b/thirdparty/libtorch/include/THC/THCGenerateIntType.h new file mode 100644 index 0000000000..083ebce323 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCGenerateIntType.h @@ -0,0 +1,20 @@ +#ifndef THC_GENERIC_FILE +#error "You must define THC_GENERIC_FILE before including THGenerateIntType.h" +#endif + +#define scalar_t int32_t +#define accreal int64_t +#define Real Int +#define CReal CudaInt +#define THC_REAL_IS_INT +#line 1 THC_GENERIC_FILE +#include THC_GENERIC_FILE +#undef scalar_t +#undef accreal +#undef Real +#undef CReal +#undef THC_REAL_IS_INT + +#ifndef THCGenerateAllTypes +#undef THC_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/THC/THCGenerateLongType.h b/thirdparty/libtorch/include/THC/THCGenerateLongType.h new file mode 100644 index 0000000000..7fc587654c --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCGenerateLongType.h @@ -0,0 +1,20 @@ +#ifndef THC_GENERIC_FILE +#error "You must define THC_GENERIC_FILE before including THGenerateLongType.h" +#endif + +#define scalar_t int64_t +#define accreal int64_t +#define Real Long +#define CReal CudaLong +#define THC_REAL_IS_LONG +#line 1 THC_GENERIC_FILE +#include THC_GENERIC_FILE +#undef scalar_t +#undef accreal +#undef Real +#undef CReal +#undef THC_REAL_IS_LONG + +#ifndef THCGenerateAllTypes +#undef THC_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/THC/THCGenerateShortType.h b/thirdparty/libtorch/include/THC/THCGenerateShortType.h new file mode 100644 index 0000000000..5f05d65ce2 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCGenerateShortType.h @@ -0,0 +1,20 @@ +#ifndef THC_GENERIC_FILE +#error "You must define THC_GENERIC_FILE before including THGenerateShortType.h" +#endif + +#define scalar_t int16_t +#define accreal int64_t +#define Real Short +#define CReal CudaShort +#define THC_REAL_IS_SHORT +#line 1 THC_GENERIC_FILE +#include THC_GENERIC_FILE +#undef scalar_t +#undef accreal +#undef Real +#undef CReal +#undef THC_REAL_IS_SHORT + +#ifndef THCGenerateAllTypes +#undef THC_GENERIC_FILE +#endif diff --git a/thirdparty/libtorch/include/THC/THCIntegerDivider.cuh b/thirdparty/libtorch/include/THC/THCIntegerDivider.cuh new file mode 100644 index 0000000000..75c0a5079f --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCIntegerDivider.cuh @@ -0,0 +1,123 @@ +#ifndef THC_INTEGER_DIVIDER_INC +#define THC_INTEGER_DIVIDER_INC + +#include +#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) +#include +#endif + +// A utility class to implement integer division by muliplication, given a fixed +// divisor. +// +// WARNING: The fast divider algorithm is only implemented for unsigned int; +// otherwise we default to plain integer division. For unsigned int, +// we further assume that the dividend is at most INT32_MAX. Thus, +// IntDivider must NOT be used for general integer division. +// +// This reduced range is enough for our purpose, and it allows us to +// slightly simplify the computation. +// +// (NOTE: Below, "2^k" denotes exponentiation, i.e., 1< 0), we can find a "magic number" m (2^N +// <= m < 2^(N+1)) and shift s such that: +// +// \floor(n / d) = \floor((m * n) / 2^(N+s)). +// +// Given such m and s, the integer division can be then implemented as: +// +// let m' = m - 2^N // 0 <= m' < 2^N +// +// fast_integer_division(n): +// // Multiply two N-bit unsigned integers: the result is a 2N-bit unsigned +// // integer. Then take the higher N bits. +// t = (m' * n) >> N +// +// // Here we use the fact that n is less than 2^(N-1): otherwise the value +// // of (t + n) may not fit in an N-bit integer. +// return (t + n) >> s +// +// Finding such a magic number is surprisingly easy: +// +// s = \ceil(\log_2 d) +// m' = \floor(2^N * (2^s - d) / d) + 1 // Need 2N-bit integer arithmetic. +// +// See also: +// - Division by Invariant Integers Using Multiplication, +// Torbjörn Granlund and Peter L. Montgomery, 1994. +// +// - http://www.hackersdelight.org/magic.htm +// +// - http://ridiculousfish.com/blog/posts/labor-of-division-episode-i.html + +// Result of div/mod operation stored together. +template +struct DivMod { + Value div, mod; + + __host__ __device__ DivMod(Value div, Value mod) : div(div), mod(mod) { } +}; + +// Base case: we only have an implementation for uint32_t for now. For +// everything else, we use plain division. +template +struct IntDivider { + IntDivider() { } // Dummy constructor for arrays. + IntDivider(Value d) : divisor(d) { } + + __host__ __device__ inline Value div(Value n) const { return n / divisor; } + __host__ __device__ inline Value mod(Value n) const { return n % divisor; } + __host__ __device__ inline DivMod divmod(Value n) const { + return DivMod(n / divisor, n % divisor); + } + + Value divisor; +}; + +// Implement fast integer division. +template <> +struct IntDivider { + static_assert(sizeof(unsigned int) == 4, "Assumes 32-bit unsigned int."); + + IntDivider() { } // Dummy constructor for arrays. + + IntDivider(unsigned int d) : divisor(d) { + assert(divisor >= 1 && divisor <= INT32_MAX); + + // TODO: gcc/clang has __builtin_clz() but it's not portable. + for (shift = 0; shift < 32; shift++) if ((1U << shift) >= divisor) break; + + uint64_t one = 1; + uint64_t magic = ((one << 32) * ((one << shift) - divisor)) / divisor + 1; + m1 = magic; + assert(m1 > 0 && m1 == magic); // m1 must fit in 32 bits. + } + + __host__ __device__ inline unsigned int div(unsigned int n) const { +#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) + // 't' is the higher 32-bits of unsigned 32-bit multiplication of 'n' and + // 'm1'. + unsigned int t = __umulhi(n, m1); + return (t + n) >> shift; +#else + // Using uint64_t so that the addition does not overflow. + uint64_t t = ((uint64_t) n * m1) >> 32; + return (t + n) >> shift; +#endif + } + + __host__ __device__ inline unsigned int mod(unsigned int n) const { + return n - div(n) * divisor; + } + + __host__ __device__ inline DivMod divmod(unsigned int n) const { + unsigned int q = div(n); + return DivMod(q, n - q * divisor); + } + + unsigned int divisor; // d above. + unsigned int m1; // Magic number: m' above. + unsigned int shift; // Shift amounts. +}; + +#endif // THC_INTEGER_DIVIDER_INC diff --git a/thirdparty/libtorch/include/THC/THCNumerics.cuh b/thirdparty/libtorch/include/THC/THCNumerics.cuh new file mode 100644 index 0000000000..baa54cf6cc --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCNumerics.cuh @@ -0,0 +1,405 @@ +#ifndef THC_NUMERICS_INC +#define THC_NUMERICS_INC + +#include +#include +#include +#include +#include +#include +#include + +// WARNING: THCNumerics is being deprecated. Please follow the comments +// in this file to learn about new usages. +// Comments on usage: +// - lt,le,gt,ge,eq,neg,add,mul,sub,div and other binary ops can +// be implemented using CUDA_apply_utils or binary cuda kernel +// - Check NumericLimits.cuh for specialized math functions. +// - Note how __half and at::Half can be casted. for instance: +// static_cast(std::sin(static_cast(a))); + +template +struct THCNumerics { +}; + +template +static inline __host__ __device__ T powi(T a, T b) { + assert(THCNumerics::ge(b, 0)); + T result = 1; + while (b) { + if (b & 1) { + result *= a; + } + b /= 2; + a *= a; + } + return result; +} + +// DEPRECATED: For integral types, use math functions from std and NumericLimits.cuh. +// Use binary_kernel or CUDA_apply_utils for arithmetic +template <> +struct THCNumerics { + static inline __host__ __device__ uint8_t min() { return at::numeric_limits::lowest(); } + static inline __host__ __device__ uint8_t max() { return at::numeric_limits::max(); } + static inline __host__ __device__ uint8_t lower_bound() { return at::numeric_limits::lower_bound(); } + static inline __host__ __device__ uint8_t upper_bound() { return at::numeric_limits::upper_bound(); } + + static inline __host__ __device__ bool lt(uint8_t a, uint8_t b) { return a < b; } + static inline __host__ __device__ bool le(uint8_t a, uint8_t b) { return a <= b; } + static inline __host__ __device__ bool gt(uint8_t a, uint8_t b) { return a > b; } + static inline __host__ __device__ bool ge(uint8_t a, uint8_t b) { return a >= b; } + static inline __host__ __device__ bool eq(uint8_t a, uint8_t b) { return a == b; } + static inline __device__ bool eq_with_nan(uint8_t a, uint8_t b) { return a == b; } + static inline __host__ __device__ bool ne(uint8_t a, uint8_t b) { return a != b; } + + static inline __host__ __device__ uint8_t add(uint8_t a, uint8_t b) { return a + b; } + static inline __host__ __device__ uint8_t mul(uint8_t a, uint8_t b) { return a * b; } + static inline __host__ __device__ uint8_t sub(uint8_t a, uint8_t b) { return a - b; } + static inline __host__ __device__ uint8_t div(uint8_t a, uint8_t b) { return a / b; } + static inline __host__ __device__ uint8_t pow(uint8_t a, uint8_t b) { return powi(a, b); } + static inline __host__ __device__ bool isnan(uint8_t a) { return false; } + static inline __host__ __device__ bool isinf(uint8_t a) { return false; } +}; + +template <> +struct THCNumerics { + static inline __host__ __device__ bool min() { return at::numeric_limits::lowest(); } + static inline __host__ __device__ bool max() { return at::numeric_limits::max(); } + static inline __host__ __device__ bool lower_bound() { return at::numeric_limits::lower_bound(); } + static inline __host__ __device__ bool upper_bound() { return at::numeric_limits::upper_bound(); } + + static inline __host__ __device__ bool lt(bool a, bool b) { return a < b; } + static inline __host__ __device__ bool le(bool a, bool b) { return a <= b; } + static inline __host__ __device__ bool gt(bool a, bool b) { return a > b; } + static inline __host__ __device__ bool ge(bool a, bool b) { return a >= b; } + static inline __host__ __device__ bool eq(bool a, bool b) { return a == b; } + static inline __host__ __device__ bool ne(bool a, bool b) { return a != b; } + static inline __host__ __device__ bool add(bool a, bool b) { return a + b; } + static inline __host__ __device__ bool mul(bool a, bool b) { return a && b; } + static inline __host__ __device__ bool sub(bool a, bool b) { return a - b; } + static inline __host__ __device__ bool div(bool a, bool b) { return a / b; } + static inline __host__ __device__ bool isnan(bool a) { return false; } + static inline __host__ __device__ bool isinf(bool a) { return false; } +}; + +template <> +struct THCNumerics { + static inline __host__ __device__ int8_t min() { return at::numeric_limits::lowest(); } + static inline __host__ __device__ int8_t max() { return at::numeric_limits::max(); } + static inline __host__ __device__ int8_t lower_bound() { return at::numeric_limits::lower_bound(); } + static inline __host__ __device__ int8_t upper_bound() { return at::numeric_limits::upper_bound(); } + + static inline __host__ __device__ bool lt(int8_t a, int8_t b) { return a < b; } + static inline __host__ __device__ bool le(int8_t a, int8_t b) { return a <= b; } + static inline __host__ __device__ bool gt(int8_t a, int8_t b) { return a > b; } + static inline __host__ __device__ bool ge(int8_t a, int8_t b) { return a >= b; } + static inline __host__ __device__ bool eq(int8_t a, int8_t b) { return a == b; } + static inline __device__ bool eq_with_nan(int8_t a, int8_t b) { return a == b; } + static inline __host__ __device__ bool ne(int8_t a, int8_t b) { return a != b; } + + static inline __host__ __device__ int8_t add(int8_t a, int8_t b) { return a + b; } + static inline __host__ __device__ int8_t mul(int8_t a, int8_t b) { return a * b; } + static inline __host__ __device__ int8_t sub(int8_t a, int8_t b) { return a - b; } + static inline __host__ __device__ int8_t div(int8_t a, int8_t b) { return a / b; } + static inline __host__ __device__ int8_t pow(int8_t a, int8_t b) { return powi(a, b); } + static inline __host__ __device__ bool isnan(int8_t a) { return false; } + static inline __host__ __device__ bool isinf(int8_t a) { return false; } +}; + +template <> +struct THCNumerics { + static inline __host__ __device__ int16_t min() { return at::numeric_limits::lowest(); } + static inline __host__ __device__ int16_t max() { return at::numeric_limits::max(); } + static inline __host__ __device__ int16_t lower_bound() { return at::numeric_limits::lower_bound(); } + static inline __host__ __device__ int16_t upper_bound() { return at::numeric_limits::upper_bound(); } + + static inline __host__ __device__ bool lt(int16_t a, int16_t b) { return a < b; } + static inline __host__ __device__ bool le(int16_t a, int16_t b) { return a <= b; } + static inline __host__ __device__ bool gt(int16_t a, int16_t b) { return a > b; } + static inline __host__ __device__ bool ge(int16_t a, int16_t b) { return a >= b; } + static inline __host__ __device__ bool eq(int16_t a, int16_t b) { return a == b; } + static inline __device__ bool eq_with_nan(int16_t a, int16_t b) { return a == b; } + static inline __host__ __device__ bool ne(int16_t a, int16_t b) { return a != b; } + + static inline __host__ __device__ int16_t add(int16_t a, int16_t b) { return a + b; } + static inline __host__ __device__ int16_t mul(int16_t a, int16_t b) { return a * b; } + static inline __host__ __device__ int16_t sub(int16_t a, int16_t b) { return a - b; } + static inline __host__ __device__ int16_t div(int16_t a, int16_t b) { return a / b; } + static inline __host__ __device__ int16_t pow(int16_t a, int16_t b) { return powi(a, b); } + static inline __host__ __device__ bool isnan(int16_t a) { return false; } + static inline __host__ __device__ bool isinf(int16_t a) { return false; } +}; + +template <> +struct THCNumerics { + static inline __host__ __device__ int32_t min() { return at::numeric_limits::lowest(); } + static inline __host__ __device__ int32_t max() { return at::numeric_limits::max(); } + static inline __host__ __device__ int32_t lower_bound() { return at::numeric_limits::lower_bound(); } + static inline __host__ __device__ int32_t upper_bound() { return at::numeric_limits::upper_bound(); } + + static inline __host__ __device__ bool lt(int32_t a, int32_t b) { return a < b; } + static inline __host__ __device__ bool le(int32_t a, int32_t b) { return a <= b; } + static inline __host__ __device__ bool gt(int32_t a, int32_t b) { return a > b; } + static inline __host__ __device__ bool ge(int32_t a, int32_t b) { return a >= b; } + static inline __host__ __device__ bool eq(int32_t a, int32_t b) { return a == b; } + static inline __device__ bool eq_with_nan(int32_t a, int32_t b) { return a == b; } + static inline __host__ __device__ bool ne(int32_t a, int32_t b) { return a != b; } + + static inline __host__ __device__ int32_t add(int32_t a, int32_t b) { return a + b; } + static inline __host__ __device__ int32_t mul(int32_t a, int32_t b) { return a * b; } + static inline __host__ __device__ int32_t sub(int32_t a, int32_t b) { return a - b; } + static inline __host__ __device__ int32_t div(int32_t a, int32_t b) { return a / b; } + static inline __host__ __device__ int32_t pow(int32_t a, int32_t b) { return powi(a, b); } + static inline __host__ __device__ bool isnan(int32_t a) { return false; } + static inline __host__ __device__ bool isinf(int32_t a) { return false; } +}; + +template <> +struct THCNumerics { + static inline __host__ __device__ int64_t min() { return at::numeric_limits::lowest(); } + static inline __host__ __device__ int64_t max() { return at::numeric_limits::max(); } + static inline __host__ __device__ int64_t lower_bound() { return at::numeric_limits::lower_bound(); } + static inline __host__ __device__ int64_t upper_bound() { return at::numeric_limits::upper_bound(); } + + static inline __host__ __device__ bool lt(int64_t a, int64_t b) { return a < b; } + static inline __host__ __device__ bool le(int64_t a, int64_t b) { return a <= b; } + static inline __host__ __device__ bool gt(int64_t a, int64_t b) { return a > b; } + static inline __host__ __device__ bool ge(int64_t a, int64_t b) { return a >= b; } + static inline __host__ __device__ bool eq(int64_t a, int64_t b) { return a == b; } + static inline __device__ bool eq_with_nan(int64_t a, int64_t b) { return a == b; } + static inline __host__ __device__ bool ne(int64_t a, int64_t b) { return a != b; } + + + static inline __host__ __device__ int64_t add(int64_t a, int64_t b) { return a + b; } + static inline __host__ __device__ int64_t mul(int64_t a, int64_t b) { return a * b; } + static inline __host__ __device__ int64_t sub(int64_t a, int64_t b) { return a - b; } + static inline __host__ __device__ int64_t div(int64_t a, int64_t b) { return a / b; }; + static inline __host__ __device__ int64_t pow(int64_t a, int64_t b) { return powi(a, b); } + static inline __host__ __device__ bool isnan(int64_t a) { return false; } + static inline __host__ __device__ bool isinf(int64_t a) { return false; } +}; + +// DEPRECATED: use math functions from std and NumericLimits.cuh +template <> +struct THCNumerics { + static inline __host__ __device__ at::Half min() { return at::numeric_limits::lowest(); } + static inline __host__ __device__ at::Half max() { return at::numeric_limits::max(); } + static inline __host__ __device__ at::Half lower_bound() { return at::numeric_limits::lower_bound(); } + static inline __host__ __device__ at::Half upper_bound() { return at::numeric_limits::upper_bound(); } + + static inline __host__ __device__ bool lt(at::Half a, at::Half b) { return a < b; } + static inline __host__ __device__ bool le(at::Half a, at::Half b) { return a <= b; } + static inline __host__ __device__ bool gt(at::Half a, at::Half b) { return a > b; } + static inline __host__ __device__ bool ge(at::Half a, at::Half b) { return a >= b; } + static inline __host__ __device__ bool eq(at::Half a, at::Half b) { return a == b; } + static inline __device__ bool eq_with_nan(at::Half a, at::Half b) { return __half_as_ushort(a) == __half_as_ushort(b); } + static inline __host__ __device__ bool ne(at::Half a, at::Half b) { return a != b; } + + static inline __host__ __device__ at::Half exp(at::Half a) { return std::exp(a); } + static inline __host__ __device__ at::Half exp10(at::Half a) { return ::exp10(a); } + static inline __host__ __device__ at::Half cos(at::Half a) { return ::cos(a); } + static inline __host__ __device__ at::Half sqrt(at::Half a) { return ::sqrt(a); } + static inline __host__ __device__ at::Half cosh(at::Half a) { return ::cosh(a); } + static inline __host__ __device__ at::Half tan(at::Half a) { return ::tan(a); } + static inline __host__ __device__ at::Half atan(at::Half a) { return ::atan(a); } + static inline __host__ __device__ at::Half tanh(at::Half a) { return ::tanh(a); } + static inline __host__ __device__ at::Half erf(at::Half a) { return ::erf(a); } + static inline __host__ __device__ at::Half erfc(at::Half a) { return ::erfc(a); } + static inline __host__ __device__ at::Half cinv(at::Half a) { return 1.0f / a; } + static inline __host__ __device__ at::Half add(at::Half a, at::Half b) { return a + b; } + static inline __host__ __device__ at::Half div(at::Half a, at::Half b) { return a / b; } + static inline __host__ __device__ at::Half mul(at::Half a, at::Half b) { return a * b; } + static inline __host__ __device__ at::Half sub(at::Half a, at::Half b) { return a - b; } + static inline __host__ __device__ at::Half pow(at::Half a, at::Half b) { return ::pow(a, b); } + + static inline __host__ __device__ bool isnan(at::Half a) { + #ifdef _MSC_VER + // Windows requires this explicit conversion. The reason is unclear + // related issue with clang: https://reviews.llvm.org/D37906 + return ::isnan((float) a); + #else + return ::isnan(a); + #endif + } + + static inline __host__ __device__ bool isinf(at::Half a) { + #ifdef _MSC_VER + // Windows requires this explicit conversion. The reason is unclear + // related issue with clang: https://reviews.llvm.org/D37906 + return ::isinf((float) a); + #else + return ::isinf(a); + #endif + } + +}; + +// DEPRECATED: use math functions from std and cuda math API (if needed) +// note that the functions exp10,erfinv and cinv +// are not in the std namespace +template <> +struct THCNumerics { + static inline __host__ __device__ float min() { return at::numeric_limits::lowest(); } + static inline __host__ __device__ float max() { return at::numeric_limits::max(); } + static inline __host__ __device__ float lower_bound() { return at::numeric_limits::lower_bound(); } + static inline __host__ __device__ float upper_bound() { return at::numeric_limits::upper_bound(); } + + static inline __host__ __device__ bool lt(float a, float b) { return a < b; } + static inline __host__ __device__ bool le(float a, float b) { return a <= b; } + static inline __host__ __device__ bool gt(float a, float b) { return a > b; } + static inline __host__ __device__ bool ge(float a, float b) { return a >= b; } + static inline __host__ __device__ bool eq(float a, float b) { return a == b; } + static inline __device__ bool eq_with_nan(float a, float b) { return __float_as_int(a) == __float_as_int(b); } + static inline __host__ __device__ bool ne(float a, float b) { return a != b; } + + static inline __host__ __device__ float exp (float a) { return expf(a); } + static inline __host__ __device__ float exp10(float a) { return exp10f(a); } + static inline __host__ __device__ float cos (float a) { return cosf(a); } + static inline __host__ __device__ float sqrt (float a) { return sqrtf(a); } + static inline __host__ __device__ float cosh (float a) { return coshf(a); } + static inline __host__ __device__ float tan (float a) { return tanf(a); } + static inline __host__ __device__ float atan (float a) { return atanf(a); } + static inline __host__ __device__ float tanh (float a) { return tanhf(a); } + static inline __host__ __device__ float erf (float a) { return erff(a); } + static inline __host__ __device__ float erfc (float a) { return erfcf(a); } + static inline __host__ __device__ float cinv (float a) { return 1.0f / a; } + static inline __host__ __device__ float add (float a, float b) { return a + b; } + static inline __host__ __device__ float div (float a, float b) { return a / b; } + static inline __host__ __device__ float mul (float a, float b) { return a * b; } + static inline __host__ __device__ float sub (float a, float b) { return a - b; } + static inline __host__ __device__ float pow (float a, float b) { return powf(a, b); } + static inline __host__ __device__ bool isnan(float a) { return ::isnan(a); } + static inline __host__ __device__ bool isinf(float a) { return ::isinf(a); } +}; + +template <> +struct THCNumerics { + static inline __host__ __device__ at::BFloat16 min() { return at::numeric_limits::lowest(); } + static inline __host__ __device__ at::BFloat16 max() { return at::numeric_limits::max(); } + static inline __host__ __device__ at::BFloat16 lower_bound() { return at::numeric_limits::lower_bound(); } + static inline __host__ __device__ at::BFloat16 upper_bound() { return at::numeric_limits::upper_bound(); } + + static inline __host__ __device__ bool lt(at::BFloat16 a, at::BFloat16 b) { return a < b; } + static inline __host__ __device__ bool le(at::BFloat16 a, at::BFloat16 b) { return a <= b; } + static inline __host__ __device__ bool gt(at::BFloat16 a, at::BFloat16 b) { return a > b; } + static inline __host__ __device__ bool ge(at::BFloat16 a, at::BFloat16 b) { return a >= b; } + static inline __host__ __device__ bool eq(at::BFloat16 a, at::BFloat16 b) { return a == b; } + static inline __host__ __device__ bool ne(at::BFloat16 a, at::BFloat16 b) { return a != b; } + + static inline __host__ __device__ at::BFloat16 lgamma(at::BFloat16 a) { return lgammaf(a);} + static inline __host__ __device__ at::BFloat16 exp (at::BFloat16 a) { return expf(a); } + static inline __host__ __device__ at::BFloat16 exp10(at::BFloat16 a) { return exp10f(a); } + static inline __host__ __device__ at::BFloat16 log (at::BFloat16 a) { return logf(a); } + static inline __host__ __device__ at::BFloat16 log10(at::BFloat16 a) { return log10f(a); } + static inline __host__ __device__ at::BFloat16 log1p(at::BFloat16 a) { return log1pf(a); } + static inline __host__ __device__ at::BFloat16 log2 (at::BFloat16 a) { return log2f(a); } + static inline __host__ __device__ at::BFloat16 expm1(at::BFloat16 a) { return expm1f(a); } + static inline __host__ __device__ at::BFloat16 cos (at::BFloat16 a) { return cosf(a); } + static inline __host__ __device__ at::BFloat16 sin (at::BFloat16 a) { return sinf(a); } + static inline __host__ __device__ at::BFloat16 sqrt (at::BFloat16 a) { return sqrtf(a); } + static inline __host__ __device__ at::BFloat16 rsqrt(at::BFloat16 a) { return rsqrtf(a); } + static inline __host__ __device__ at::BFloat16 floor(at::BFloat16 a) { return floorf(a); } + static inline __host__ __device__ at::BFloat16 trunc(at::BFloat16 a) { return truncf(a); } + static inline __host__ __device__ at::BFloat16 acos (at::BFloat16 a) { return acosf(a); } + static inline __host__ __device__ at::BFloat16 cosh (at::BFloat16 a) { return coshf(a); } + static inline __host__ __device__ at::BFloat16 acosh(at::BFloat16 a) { return acoshf(a); } + static inline __host__ __device__ at::BFloat16 asin (at::BFloat16 a) { return asinf(a); } + static inline __host__ __device__ at::BFloat16 sinh (at::BFloat16 a) { return sinhf(a); } + static inline __host__ __device__ at::BFloat16 asinh(at::BFloat16 a) { return asinhf(a); } + static inline __host__ __device__ at::BFloat16 tan (at::BFloat16 a) { return tanf(a); } + static inline __host__ __device__ at::BFloat16 atan (at::BFloat16 a) { return atanf(a); } + static inline __host__ __device__ at::BFloat16 tanh (at::BFloat16 a) { return tanhf(a); } + static inline __host__ __device__ at::BFloat16 erf (at::BFloat16 a) { return erff(a); } + static inline __host__ __device__ at::BFloat16 erfc (at::BFloat16 a) { return erfcf(a); } + static inline __host__ __device__ at::BFloat16 abs (at::BFloat16 a) { return fabsf(a); } + static inline __host__ __device__ at::BFloat16 round(at::BFloat16 a) { return nearbyintf(a); } + static inline __host__ __device__ at::BFloat16 frac (at::BFloat16 a) { return a - truncf(a); } + static inline __host__ __device__ at::BFloat16 cinv (at::BFloat16 a) { return 1.0f / a; } + static inline __host__ __device__ at::BFloat16 add (at::BFloat16 a, at::BFloat16 b) { return a + b; } + static inline __host__ __device__ at::BFloat16 div (at::BFloat16 a, at::BFloat16 b) { return a / b; } + static inline __host__ __device__ at::BFloat16 mul (at::BFloat16 a, at::BFloat16 b) { return a * b; } + static inline __host__ __device__ at::BFloat16 sub (at::BFloat16 a, at::BFloat16 b) { return a - b; } + static inline __host__ __device__ at::BFloat16 pow (at::BFloat16 a, at::BFloat16 b) { return powf(a, b); } + static inline __host__ __device__ at::BFloat16 atan2(at::BFloat16 a, at::BFloat16 b) { return atan2f(a, b); } + + static inline __host__ __device__ bool isnan(at::BFloat16 a) { + #ifdef _MSC_VER + // Windows requires this explicit conversion. The reason is unclear + // related issue with clang: https://reviews.llvm.org/D37906 + return ::isnan((float) a); + #else + return ::isnan(a); + #endif + } + + static inline __host__ __device__ bool isinf(at::BFloat16 a) { + #ifdef _MSC_VER + // Windows requires this explicit conversion. The reason is unclear + // related issue with clang: https://reviews.llvm.org/D37906 + return ::isinf((float) a); + #else + return ::isinf(a); + #endif + } +}; + +// DEPRECATED: use math functions from std and cuda math API (if needed) +// note that the functions exp10,erfinv and cinv +// are not in the std namespace +template <> +struct THCNumerics { + static inline __host__ __device__ double min() { return at::numeric_limits::lowest(); } + static inline __host__ __device__ double max() { return at::numeric_limits::max(); } + static inline __host__ __device__ double lower_bound() { return at::numeric_limits::lower_bound(); } + static inline __host__ __device__ double upper_bound() { return at::numeric_limits::upper_bound(); } + + static inline __host__ __device__ bool lt(double a, double b) { return a < b; } + static inline __host__ __device__ bool le(double a, double b) { return a <= b; } + static inline __host__ __device__ bool gt(double a, double b) { return a > b; } + static inline __host__ __device__ bool ge(double a, double b) { return a >= b; } + static inline __host__ __device__ bool eq(double a, double b) { return a == b; } + static inline __device__ bool eq_with_nan(double a, double b) { return __double_as_longlong(a) == __double_as_longlong(b); } + static inline __host__ __device__ bool ne(double a, double b) { return a != b; } + + static inline __host__ __device__ double exp (double a) { return ::exp(a); } + static inline __host__ __device__ double exp10(double a) { return ::exp10(a); } + static inline __host__ __device__ double cos (double a) { return ::cos(a); } + static inline __host__ __device__ double sqrt (double a) { return ::sqrt(a); } + static inline __host__ __device__ double cosh (double a) { return ::cosh(a); } + static inline __host__ __device__ double tan (double a) { return ::tan(a); } + static inline __host__ __device__ double atan (double a) { return ::atan(a); } + static inline __host__ __device__ double tanh (double a) { return ::tanh(a); } + static inline __host__ __device__ double erf (double a) { return ::erf(a); } + static inline __host__ __device__ double erfc (double a) { return ::erfc(a); } + static inline __host__ __device__ double cinv (double a) { return 1.0 / a; } + static inline __host__ __device__ double add (double a, double b) { return a + b; } + static inline __host__ __device__ double div (double a, double b) { return a / b; } + static inline __host__ __device__ double mul (double a, double b) { return a * b; } + static inline __host__ __device__ double sub (double a, double b) { return a - b; } + static inline __host__ __device__ double pow (double a, double b) { return ::pow(a, b); } + static inline __host__ __device__ bool isnan(double a) { return ::isnan(a); } + static inline __host__ __device__ bool isinf(double a) { return ::isinf(a); } +}; + +// WARNING: The following note is deprecated +/// `half` has some type conversion issues associated with it, since it +/// is a struct without a constructor/implicit conversion constructor. +/// We use this to convert scalar values to the given type that the +/// tensor expects. +/// +/// at::Half has implicit conversions for float and __half types. Moreover +/// it has constructors for __half and float types. + +template +struct ScalarConvert { + static __host__ __device__ Out to(const In v) { return (Out) v; } +}; + +// DEPRECATED: use static_cast in kernels instead of scalar_cast +template +__host__ __device__ T scalar_cast(U u) { + return ScalarConvert::to(u); +} + +#endif // THC_NUMERICS_INC diff --git a/thirdparty/libtorch/include/THC/THCReduce.cuh b/thirdparty/libtorch/include/THC/THCReduce.cuh new file mode 100644 index 0000000000..856d3226c6 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCReduce.cuh @@ -0,0 +1,643 @@ +#ifndef THC_REDUCE_INC +#define THC_REDUCE_INC + +// +// This file contains dimension reduction operation functions and +// kernels that work on both contiguous and non-contiguous tensor +// arguments of arbitrary (up to MAX_CUTORCH_DIMS) dimensioned +// arguments without copying or temporary storage. +// + +#include +#include +#include +#include + +// Threads per thread block +#define THC_NONCONTIG_REDUCE_BLOCK_SIZE 32 * 16 +#define CHUNKPERBLOCK 256 + +template +__device__ __forceinline__ IndexType getReduceNoncontigDimSliceIndex() { + // Each thread handles one slice + return getLinearBlockId() * THC_NONCONTIG_REDUCE_BLOCK_SIZE + threadIdx.x; +} + +// quick hack to enable two-stage use of reduceChunk +template +struct SimpleCopyOp +{ + __device__ __forceinline__ T operator()(volatile const T val) const volatile + { + return val; + } +}; + +__device__ __forceinline__ int lastpow2(int n) +{ + int out = 1 << (31 - __clz(n)); + if(n == out) + out >>= 1; + return out; +} + +template + +__device__ __forceinline__ void reduceChunk + (T* out, + U* in, + const int& inbounds, + const IndexType& reductionStride, + const IndexType& reductionSize, + const IndexType& inOffset, + const IndexType& outOffset, + const int& shmem_lim, + AccT init, + AccT* shmem, + ModifyOp modifyOp, + ReduceOp reduceOp, + FinalizeOp finalizeOp) +{ + AccT load_reg[4]; + AccT local_reg = init; + + //Unroll this loop + //for(IndexType i=threadIdx.y; i(in[inOffset + i*reductionStride]); + load_reg[0] = modifyOp(val0); + const AccT val1 = scalar_cast(in[inOffset + (i + blockDim.y)*reductionStride]); + load_reg[1] = modifyOp(val1); + const AccT val2 = scalar_cast(in[inOffset + (i + blockDim.y*2)*reductionStride]); + load_reg[2] = modifyOp(val2); + const AccT val3 = scalar_cast(in[inOffset + (i + blockDim.y*3)*reductionStride]); + load_reg[3] = modifyOp(val3); + local_reg = reduceOp(local_reg, load_reg[0]); + local_reg = reduceOp(local_reg, load_reg[1]); + local_reg = reduceOp(local_reg, load_reg[2]); + local_reg = reduceOp(local_reg, load_reg[3]); + } + else if (i + blockDim.y*2 < reductionSize) + { + const AccT val0 = scalar_cast(in[inOffset + i*reductionStride]); + load_reg[0] = modifyOp(val0); + const AccT val1 = scalar_cast(in[inOffset + (i + blockDim.y)*reductionStride]); + load_reg[1] = modifyOp(val1); + const AccT val2 = scalar_cast(in[inOffset + (i + blockDim.y*2)*reductionStride]); + load_reg[2] = modifyOp(val2); + local_reg = reduceOp(local_reg, load_reg[0]); + local_reg = reduceOp(local_reg, load_reg[1]); + local_reg = reduceOp(local_reg, load_reg[2]); + } + else if (i + blockDim.y < reductionSize) + { + const AccT val0 = scalar_cast(in[inOffset + i*reductionStride]); + load_reg[0] = modifyOp(val0); + const AccT val1 = scalar_cast(in[inOffset + (i + blockDim.y)*reductionStride]); + load_reg[1] = modifyOp(val1); + local_reg = reduceOp(local_reg, load_reg[0]); + local_reg = reduceOp(local_reg, load_reg[1]); + } + else if (i < reductionSize) + { + const AccT val0 = scalar_cast(in[inOffset + i*reductionStride]); + local_reg = reduceOp(local_reg, modifyOp(val0)); + } + } + + *shmem = local_reg; + for(int i = lastpow2(shmem_lim); i > 0; i >>= 1) + { + __syncthreads(); + if(threadIdx.y < i && threadIdx.y + i < shmem_lim) + *shmem = reduceOp(*shmem, *(shmem + i*blockDim.x)); + } + + if(threadIdx.y == 0 && inbounds) { + T &&o_ele = static_cast(finalizeOp(*shmem)); + out[outOffset] = o_ele; + } +} + +// Kernel that handles an entire reduction of a slice of a tensor per each thread +template + +#if __CUDA_ARCH__ >= 350 || defined __HIP_PLATFORM_HCC__ +C10_LAUNCH_BOUNDS_2(512, 4) +#endif +__global__ void kernelReduceNoncontigDim_shared + (TensorInfo out, + TensorInfo in, + IndexType reductionStride, + IndexType reductionSize, + IndexType totalSlices, + AccT init, + ModifyOp modifyOp, + ReduceOp reduceOp, + FinalizeOp finalizeOp, + volatile AccT* stagingData, + int* semaphores) +{ + IndexType sliceIndex = blockIdx.x*blockDim.x + threadIdx.x; + + __shared__ int isLastBlockDone; + __shared__ AccT local_reduce[THC_NONCONTIG_REDUCE_BLOCK_SIZE]; + AccT* shmem = &local_reduce[threadIdx.x + threadIdx.y*blockDim.x]; + + // This kernel is intended for the latency-bound case, so we want to launch enough blocks + // to cover the entire output. This means we don't need grid-stride loops. + const IndexType outOffset = + IndexToOffset::get(sliceIndex, out); + const IndexType inOffset = + IndexToOffset::get(sliceIndex, in); + const int inbounds = (sliceIndex < totalSlices); + + if(gridDim.y == 1) + reduceChunk + (out.data, + in.data, + inbounds, + reductionStride, + reductionSize, + inOffset, + outOffset, + reductionSize < blockDim.y ? reductionSize : blockDim.y, + init, + shmem, + modifyOp, + reduceOp, + finalizeOp); + else + { + int* semaphore = semaphores + blockIdx.x; + + const IndexType chunkStart = blockIdx.y*CHUNKPERBLOCK; + const IndexType chunkSize = reductionSize - chunkStart < CHUNKPERBLOCK ? + reductionSize - chunkStart : CHUNKPERBLOCK; + const IndexType reductionStrideStaging = totalSlices; + const IndexType stagingOffset = sliceIndex; + + reduceChunk + (stagingData, + in.data, + inbounds, + reductionStride, + chunkSize, + inOffset + chunkStart*reductionStride, + stagingOffset + blockIdx.y*reductionStrideStaging, + chunkSize < blockDim.y ? chunkSize : blockDim.y, + init, + shmem, + modifyOp, + reduceOp, + SimpleCopyOp()); + + __threadfence(); // make sure writes are globally visible + __syncthreads(); // if multiple warps in this block wrote to staging, make sure they're all done + + if(threadIdx.x == 0 && threadIdx.y == 0) + { + int old = atomicAdd(semaphore, 1); + isLastBlockDone = (old == gridDim.y - 1); + } + + __syncthreads(); + + // The staging area contains gridDim.y elements along each slice. The final reduction + // begins by treating the first blockDim.y elements as "init" values. + if(isLastBlockDone) + { + if(threadIdx.y < gridDim.y) + init = stagingData[stagingOffset + threadIdx.y*reductionStrideStaging]; + IndexType remaining = gridDim.y < blockDim.y ? 0 : gridDim.y - blockDim.y; + reduceChunk + (out.data, + stagingData, + inbounds, + reductionStrideStaging, + remaining, // if 0, loop in reduceChunk is skipped, otherwise... + stagingOffset + blockDim.y*reductionStrideStaging, // ...loop begins at blockDim+1th element + outOffset, + gridDim.y < blockDim.y ? gridDim.y : blockDim.y, + init, + shmem, + SimpleCopyOp(), + reduceOp, + finalizeOp); + } + } +} + + +// Kernel that handles an entire reduction of a slice of a tensor per each thread +template +#if __CUDA_ARCH__ >= 350 || defined __HIP_PLATFORM_HCC__ +C10_LAUNCH_BOUNDS_2(512, 4) +#endif +__global__ void +kernelReduceNoncontigDim(TensorInfo out, + TensorInfo in, + IndexType reductionStride, + IndexType reductionSize, + IndexType totalSlices, + AccT init, + ModifyOp modifyOp, + ReduceOp reduceOp, + FinalizeOp finalizeOp) { + const IndexType sliceIndex = getReduceNoncontigDimSliceIndex(); + + if (sliceIndex >= totalSlices) { + return; + } + + // Each thread picks a point in `out` and `in` for which it is + // producing the reduction + const IndexType outOffset = + IndexToOffset::get(sliceIndex, out); + const IndexType inBaseOffset = + IndexToOffset::get(sliceIndex, in); + + // For each point in reductionSize, reduce into `r` + IndexType inOffset = inBaseOffset; + AccT r = init; + + for (IndexType i = 0; i < reductionSize; ++i) { + const AccT val = scalar_cast(in.data[inOffset]); + r = reduceOp(r, modifyOp(val)); + inOffset += reductionStride; + } + + // Write out reduced value + out.data[outOffset] = scalar_cast(finalizeOp(r)); +} + +template +__device__ __forceinline__ IndexType getReduceContigDimSliceIndex() { + // Each block handles one slice + return getLinearBlockId(); +} + +// Kernel that handles an entire reduction of a slice of a tensor per +// each block +template +__global__ void +kernelReduceContigDim(TensorInfo out, + TensorInfo in, + IndexType reductionSize, + IndexType totalSlices, + AccT init, + ModifyOp modifyOp, + ReduceOp reduceOp, + FinalizeOp finalizeOp) { + const IndexType sliceIndex = getReduceContigDimSliceIndex(); + + if (sliceIndex >= totalSlices) { + return; + } + + // Get the offset in `out` for the reduction + const IndexType outOffset = + IndexToOffset::get(sliceIndex, out); + + // Get the base offset in `in` for this block's reduction + const IndexType inBaseOffset = + IndexToOffset::get(sliceIndex, in); + + // Each thread in the block will reduce some subset of elements in + // the slice. The elements are guaranteed contiguous starting at + // `inBaseOffset`. + AccT r = init; + for (IndexType i = threadIdx.x; i < reductionSize; i += blockDim.x) { + const AccT val = scalar_cast(in.data[inBaseOffset + i]); + r = reduceOp(r, modifyOp(val)); + } + + // Reduce within the block + // FIXME: extern name + extern __shared__ char smemChar[]; + AccT* smem = (AccT*) smemChar; + r = reduceBlock(smem, blockDim.x, r, reduceOp, init); + + if (threadIdx.x == 0) { + // Write out reduced value + out.data[outOffset] = scalar_cast(finalizeOp(r)); + } +} + +inline dim3 getNoncontigReduceBlock() { + return dim3(THC_NONCONTIG_REDUCE_BLOCK_SIZE); +} + +inline dim3 getContigReduceBlock(ptrdiff_t numSlices, int64_t reductionSize) { + // If the number of slices is low but the reduction dimension size + // is high, then we should increase block size for greater parallelism. + // Aim for at least 32 warps per SM (assume 15 SMs; don't bother + // inquiring the real number for now). + int maxWarps = 4; // better occupancy if many blocks are around + // For numSlices > 15 * 8, there are > 32 warps active per SM. + if (numSlices < 15 * 8) { + maxWarps = 8; + if (numSlices < 15 * 4) { + maxWarps = 16; + if (numSlices < 15 * 2) { + maxWarps = 32; + } + } + } + + // Scale up block size based on the reduction dimension size + int64_t warpsInReductionSize = THCCeilDiv(reductionSize, (int64_t) 32); + int numWarps = warpsInReductionSize > (int64_t) maxWarps ? + maxWarps : (int) warpsInReductionSize; + + return dim3(numWarps * 32); +} + +inline bool getNoncontigReduceGrid(ptrdiff_t elements, dim3& grid) { + // One output point per thread + return THC_getGridFromTiles(THCCeilDiv(elements, + (ptrdiff_t) THC_NONCONTIG_REDUCE_BLOCK_SIZE), grid); +} + +inline bool getContigReduceGrid(ptrdiff_t elements, dim3& grid) { + // One output point per block + return THC_getGridFromTiles(elements, grid); +} + +// Performs a reduction out[..., 0, ...] = reduce_i(modify(in[..., i, ...])) for +// all in where i and the out's 0 are indexed at dimension `dim` +template +bool THC_reduceDim(THCState* state, + TensorType* out, + TensorType* in, + const ModifyOp modifyOp, + const ReduceOp reduceOp, + const FinalizeOp finalizeOp, + AccT init, + int dim, + int keepdim) { + ptrdiff_t inElements = THCTensor_nElement(state, in); + + int64_t reductionSize = THTensor_sizeLegacyNoScalars(in, dim); + int64_t reductionStride = THTensor_strideLegacyNoScalars(in, dim); + ptrdiff_t outElements = inElements / reductionSize; + + if (THCTensor_nDimensionLegacyAll(state, out) > MAX_CUTORCH_DIMS || + THCTensor_nDimensionLegacyAll(state, in) > MAX_CUTORCH_DIMS) { + return false; + } + + if (THCTensor_nDimensionLegacyAll(state, in) == 0) { + // Zero-dim tensor; do nothing + return true; + } + + // Is the reduction dimension contiguous? If so, then we can use a + // shared memory reduction kernel to increase performance. + bool contigReduction = (reductionStride == 1); + + dim3 block; + dim3 grid; + int smemSize = 0; // contiguous reduction uses smem + if (contigReduction) { + if (!getContigReduceGrid(outElements, grid)) { + return false; + } + + block = getContigReduceBlock(outElements, reductionSize); + smemSize = sizeof(AccT) * block.x; + } else { + if (!getNoncontigReduceGrid(outElements, grid)) { + return false; + } + + block = getNoncontigReduceBlock(); + + if(outElements <= 4096) + { + // gridDim.x and blockDim.x parallelize work across slices. + // blockDim.y enables some intra-block reduction within slices. + // gridDim.y enables inter-block reduction within slices. + + // Each block covers 32 output elements. + int blockdimx = 32; + int griddimx = THCCeilDiv((int64_t)outElements, (int64_t)blockdimx); + + // Each warp reduces at most 4 slices. This heuristic can be tuned, + // but locking blockdimy to 16 is robust and reasonably performant. + int blockdimy = 16; + + int griddimy = 1; + bool coop = false; + // Rough heuristics to decide if using cooperating blocks is worthwhile + if( outElements <= 32 && reductionSize >= 4096) coop = true; + if( 32 < outElements && outElements <= 64 && reductionSize >= 4096) coop = true; + if( 64 < outElements && outElements <= 128 && reductionSize >= 4096) coop = true; + if( 128 < outElements && outElements <= 256 && reductionSize >= 4096) coop = true; + if( 256 < outElements && outElements <= 512 && reductionSize >= 4096) coop = true; + if( 512 < outElements && outElements <= 1024 && reductionSize >= 4096) coop = true; + if(1024 < outElements && outElements <= 2048 && reductionSize >= 2048) coop = true; + if(2048 < outElements && outElements <= 4096 && reductionSize >= 2048) coop = true; + // Each block reduces at most CHUNKPERBLOCK (currently 256) slices. + if(coop) + griddimy = THCCeilDiv((int64_t)reductionSize, (int64_t)CHUNKPERBLOCK); + + grid = dim3(griddimx, griddimy, 1); + block = dim3(blockdimx, blockdimy, 1); + } + } + + // Resize out to correspond to the reduced size with keepdim=True. + + // Preserve noncontiguities by unsqueezing out if necessary + THCTensor_preserveReduceDimSemantics( + state, out, THCTensor_nDimensionLegacyAll(state, in), dim, keepdim); + + // Resize out + std::vector sizes = THTensor_sizesLegacyNoScalars(in); + sizes[dim] = 1; + THCTensor_resize(state, out, sizes, {}); + + // It is possible that the tensor dimensions are able to be collapsed, + // and thus we can reduce the actual code complexity of the copy by + // exploiting this knowledge statically, since the div/mod is the + // most expensive part of the operation, more so than memory accesses. + // For instance, when copying a non-contiguous to a contiguous tensor + // (or vice versa), the contiguous tensor can be collapsed to one + // dimension, and the loop to translate the linear index to the array + // index can be similarly collapsed. That is what this unrolling is for. +#define HANDLE_CASE(TYPE, OUT, IN) \ + if (contigReduction) { \ + kernelReduceContigDim \ + <<>> \ + (outInfo, inInfo, reductionSize, \ + (TYPE) outElements, init, modifyOp, reduceOp, finalizeOp); \ + } else { \ + if(block.y == 1){ \ + kernelReduceNoncontigDim< \ + ScalarType, \ + TYPE, AccT, ModifyOp, ReduceOp, FinalizeOp, \ + OUT, IN> \ + <<>> \ + (outInfo, inInfo, reductionStride, reductionSize, \ + (TYPE) outElements, init, modifyOp, reduceOp, finalizeOp); \ + } \ + else \ + { \ + void* stagingData = nullptr; \ + void* semaphores = nullptr; \ + \ + if(grid.y > 1) \ + { \ + stagingData = THCudaMalloc(state, sizeof(AccT)*outElements*grid.y);\ + semaphores = THCudaMalloc(state, sizeof(int)*grid.x); \ + THCudaCheck(cudaMemsetAsync \ + (semaphores, \ + 0, \ + sizeof(int)*grid.x, \ + THCState_getCurrentStream(state))); \ + } \ + \ + kernelReduceNoncontigDim_shared \ + \ + <<>> \ + (outInfo, \ + inInfo, \ + reductionStride, \ + reductionSize, \ + (TYPE) outElements, \ + init, \ + modifyOp, \ + reduceOp, \ + finalizeOp, \ + (volatile AccT*)stagingData, \ + (int*)semaphores); \ + \ + if(grid.y > 1) \ + { \ + THCudaFree(state, stagingData); \ + THCudaFree(state, semaphores); \ + } \ + } \ + } + +#define HANDLE_IN_CASE(TYPE, OUT, IN) \ + { \ + switch (IN) { \ + case 1: \ + HANDLE_CASE(TYPE, OUT, 1); \ + break; \ + case 2: \ + HANDLE_CASE(TYPE, OUT, 2); \ + break; \ + default: \ + HANDLE_CASE(TYPE, OUT, -1); \ + break; \ + } \ + } + +#define HANDLE_OUT_CASE(TYPE, OUT, IN) \ + { \ + switch (OUT) { \ + case 1: \ + HANDLE_IN_CASE(TYPE, 1, IN); \ + break; \ + case 2: \ + HANDLE_IN_CASE(TYPE, 2, IN); \ + break; \ + default: \ + HANDLE_IN_CASE(TYPE, -1, IN); \ + break; \ + } \ + } + + if(THCTensor_canUse32BitIndexMath(state, out) && + THCTensor_canUse32BitIndexMath(state, in)) + { + TensorInfo outInfo = + getTensorInfo(state, out); + outInfo.collapseDims(); + + TensorInfo inInfo = + getTensorInfo(state, in); + inInfo.reduceDim(dim); + inInfo.collapseDims(); + HANDLE_OUT_CASE(unsigned int, outInfo.dims, inInfo.dims); + } + else + { + TensorInfo outInfo = + getTensorInfo(state, out); + outInfo.collapseDims(); + + TensorInfo inInfo = + getTensorInfo(state, in); + inInfo.reduceDim(dim); + inInfo.collapseDims(); + + /* + Only instantiates the all 1D special case and the fallback all nD case for + large (64-bit indexed) tensors to reduce compilation time. + */ + if (outInfo.dims == 1 && inInfo.dims == 1) { + HANDLE_CASE(uint64_t, 1, 1); + } else { + HANDLE_CASE(uint64_t, -1, -1); + } + } +#undef HANDLE_CASE +#undef HANDLE_IN_CASE +#undef HANDLE_OUT_CASE + + + if (!keepdim) { + THCTensor_squeeze1d(state, out, out, dim); + } + return true; +} + +#undef THC_NONCONTIG_REDUCE_BLOCK_SIZE +#undef CHUNKPERBLOCK + +#endif // THC_REDUCE_INC diff --git a/thirdparty/libtorch/include/THC/THCReduceAll.cuh b/thirdparty/libtorch/include/THC/THCReduceAll.cuh new file mode 100644 index 0000000000..0e009f3f16 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCReduceAll.cuh @@ -0,0 +1,341 @@ +#ifndef THC_REDUCEALL_INC +#define THC_REDUCEALL_INC + +// +// This file contains dimension reduction operation functions and +// kernels that work on both contiguous and non-contiguous tensor +// arguments of arbitrary (up to MAX_CUTORCH_DIMS) dimensioned +// arguments without copying or temporary storage, for reducing an +// entire tensor to one value. +// + +#include +#include + +// Size per each reduction block +#define THC_REDUCE_ALL_BLOCK_SIZE 1024L + +// Cutoff size for two-pass reduction +#define THC_TWO_PASS_REDUCTION_SIZE 2048L + +// Kernel that handles an entire reduction of a tensor in one pass +template +__global__ void +#if defined(__HIP_PLATFORM_HCC__) +C10_LAUNCH_BOUNDS_1(THC_REDUCE_ALL_BLOCK_SIZE) +#endif +kernelReduceAll(TensorInfo in, + IndexType totalElements, + AccT init, + ModifyOp modifyOp, + ReduceOp reduceOp, + AccT* out) { + // With a block-wide stride, have each thread perform its own reduction. + AccT r = init; + for (IndexType i = threadIdx.x; i < totalElements; i += blockDim.x) { + const IndexType inOffset = IndexToOffset::get(i, in); + const AccT val = scalar_cast(in.data[inOffset]); + r = reduceOp(r, modifyOp(val)); + } + + // Reduce within the block + extern __shared__ char smemChar[]; + AccT* smem = (AccT*) smemChar; + r = reduceBlock(smem, blockDim.x, r, reduceOp, init); + + if (threadIdx.x == 0) { + // Write out reduced value + *out = r; + } +} + +template +__device__ __forceinline__ IndexType getStartIndex(IndexType totalSize) { + IndexType sizePerBlock = THCCeilDiv(totalSize, (IndexType) gridDim.x); + return blockIdx.x * sizePerBlock; +} + +template +__device__ __forceinline__ IndexType getEndIndex(IndexType totalSize) { + IndexType sizePerBlock = THCCeilDiv(totalSize, (IndexType) gridDim.x); + return min((IndexType) ((blockIdx.x + 1) * sizePerBlock), totalSize); +} + +// Kernel that handles an entire reduction of a tensor in two passes +template +#if defined(__HIP_PLATFORM_HCC__) +C10_LAUNCH_BOUNDS_1(THC_REDUCE_ALL_BLOCK_SIZE) +#endif +__global__ void +kernelReduceAllPass1(TensorInfo in, + IndexType totalElements, + AccT init, + ModifyOp modifyOp, + ReduceOp reduceOp, + AccT* scratchSpace) { + const IndexType startIndex = getStartIndex(totalElements); + const IndexType endIndex = getEndIndex(totalElements); + + // With a block-wide stride, have each thread perform its own reduction. + AccT r = init; + for (IndexType i = startIndex + threadIdx.x; i < endIndex; i += blockDim.x) { + const IndexType inOffset = IndexToOffset::get(i, in); + const AccT val = scalar_cast(in.data[inOffset]); + r = reduceOp(r, modifyOp(val)); + } + + // Reduce within the block + extern __shared__ char smemChar[]; + AccT* smem = (AccT*) smemChar; + r = reduceBlock(smem, blockDim.x, r, reduceOp, init); + + if (threadIdx.x == 0) { + // Write out block-wide reduced value + scratchSpace[blockIdx.x] = r; + } +} + +template +#if defined(__HIP_PLATFORM_HCC__) +C10_LAUNCH_BOUNDS_1(THC_REDUCE_ALL_BLOCK_SIZE) +#endif +__global__ void +kernelReduceAllPass2(int numPass1Blocks, + T init, + ReduceOp reduceOp, + T* scratchSpace, + T* out) { + T r = init; + if (threadIdx.x < numPass1Blocks) { + r = scratchSpace[threadIdx.x]; + } + + // Reduce within the block + extern __shared__ char smemChar[]; + T* smem = (T*) smemChar; + r = reduceBlock(smem, numPass1Blocks, r, reduceOp, init); + + if (threadIdx.x == 0) { + *out = r; + } +} + +// Perform a two-pass reduction if the tensor is large enough to +// warrant it. +inline bool isTwoPassReductionSize(ptrdiff_t elements) { + return (elements > THC_TWO_PASS_REDUCTION_SIZE); +} + +template +inline ptrdiff_t getTwoPassBlocks(THCState* state, ptrdiff_t elements) { + ptrdiff_t numBlocks = THCCeilDiv(elements, (ptrdiff_t)THC_REDUCE_ALL_BLOCK_SIZE); + + // We can only have as many blocks as there is scratch space + ptrdiff_t scratchSpace = + THCState_getCurrentDeviceScratchSpaceSize(state) / sizeof(T); + THAssert(scratchSpace > 0); + + // Limit to 1024 due to dimensionality constraint + if (scratchSpace > 1024) { + scratchSpace = 1024; + } + + if (numBlocks > scratchSpace) { + numBlocks = scratchSpace; + } + + return numBlocks; +} + +// Get the block/grid size that we want +template +inline void getPass1ReduceBlockGrid(THCState* state, ptrdiff_t elements, + dim3& grid, dim3& block) { + grid = dim3(getTwoPassBlocks(state, elements)); + block = dim3(THC_REDUCE_ALL_BLOCK_SIZE); +} + +template +inline void getPass2ReduceBlockGrid(THCState* state, ptrdiff_t elements, + dim3& grid, dim3& block) { + grid = dim3(1); + // We only need as many threads as there were blocks originally + block = dim3(getTwoPassBlocks(state, elements)); +} + +inline void getSinglePassReduceBlockGrid(ptrdiff_t elements, + dim3& grid, dim3& block) { + grid = dim3(1); + block = dim3(THC_REDUCE_ALL_BLOCK_SIZE); +} + +template +void callReduceAll(THCState* state, + const TensorInfo& in, + ptrdiff_t totalElements, + AccT init, + const ModifyOp& modifyOp, + const ReduceOp& reduceOp, + AccT* devOut) { + dim3 grid; + dim3 block; + + if (isTwoPassReductionSize(totalElements)) { + void* scratchSpace = THCudaMalloc(state, THCState_getCurrentDeviceScratchSpaceSize(state)); + + getPass1ReduceBlockGrid(state, totalElements, grid, block); + size_t smemSize = block.x * sizeof(AccT); + + kernelReduceAllPass1 + <<>>( + in, (IndexType) totalElements, init, modifyOp, reduceOp, + (AccT*) scratchSpace); + + int numPass1Blocks = grid.x; + getPass2ReduceBlockGrid(state, totalElements, grid, block); + smemSize = block.x * sizeof(AccT); + + kernelReduceAllPass2 + <<>>( + numPass1Blocks, init, reduceOp, + (AccT*) scratchSpace, devOut); + + THCudaFree(state, scratchSpace); + } else { + getSinglePassReduceBlockGrid(totalElements, grid, block); + size_t smemSize = block.x * sizeof(AccT); + + kernelReduceAll + <<>>( + in, (IndexType) totalElements, init, modifyOp, reduceOp, devOut); + } +} + +// Reduces the entire tensor to one value. `out` points to +// host-resident memory. +template +bool THC_reduceAll(THCState* state, + TensorType* in, + const ModifyOp& modifyOp, + const ReduceOp& reduceOp, + AccT init, + AccT* out, + int outOnDevice) { + ptrdiff_t inElements = THCTensor_nElement(state, in); + + if (THCTensor_nDimensionLegacyAll(state, in) > MAX_CUTORCH_DIMS) { + return false; + } + + if (THCTensor_nDimensionLegacyAll(state, in) == 0) { + // Zero-dim tensor; do nothing + *out = init; + return true; + } + + bool freeDevOut = false; + AccT* devOut = out; + if (!outOnDevice) { + // Use the stream-specific scratch space for the reduction kernel + // to write out its value + devOut = static_cast(THCudaMalloc(state, + THCState_getCurrentDeviceScratchSpaceSize(state))); + freeDevOut = true; + } + + // It is possible that the tensor dimensions are able to be collapsed, + // and thus we can reduce the actual code complexity of the copy by + // exploiting this knowledge statically, since the div/mod is the + // most expensive part of the operation, more so than memory accesses. + // For instance, when copying a non-contiguous to a contiguous tensor + // (or vice versa), the contiguous tensor can be collapsed to one + // dimension, and the loop to translate the linear index to the array + // index can be similarly collapsed. That is what this unrolling is for. +#define HANDLE_CASE(TYPE, IN) \ + callReduceAll( \ + state, inInfo, inElements, init, modifyOp, \ + reduceOp, devOut); + +#define HANDLE_IN_CASE(TYPE, IN) \ + { \ + switch (IN) { \ + case 1: \ + HANDLE_CASE(TYPE, 1); \ + break; \ + case 2: \ + HANDLE_CASE(TYPE, 2); \ + break; \ + default: \ + HANDLE_CASE(TYPE, -1); \ + break; \ + } \ + } + + if (THCTensor_canUse32BitIndexMath(state, in)) { + TensorInfo inInfo = + getTensorInfo(state, in); + inInfo.collapseDims(); + + HANDLE_IN_CASE(unsigned int, inInfo.dims); + } else { + TensorInfo inInfo = + getTensorInfo(state, in); + inInfo.collapseDims(); + + /* + Only instantiates the all 1D special case and the fallback all nD case for + large (64-bit indexed) tensors to reduce compilation time. + */ + if (inInfo.dims == 1) { + HANDLE_IN_CASE(uint64_t, 1); + } else { + HANDLE_IN_CASE(uint64_t, -1); + } + } +#undef HANDLE_CASE +#undef HANDLE_IN_CASE + + // If our destination is not on the device, copy the value back to + // the host (synchronous!) + if (!outOnDevice) { + cudaStream_t stream = THCState_getCurrentStream(state); + THCudaCheck(cudaMemcpyAsync(out, + devOut, + sizeof(AccT), + cudaMemcpyDeviceToHost, + stream)); + THCudaCheck(cudaStreamSynchronize(stream)); + } + + if (freeDevOut) { + THCudaFree(state, devOut); + } + + return true; +} + +#undef THC_REDUCE_ALL_BLOCK_SIZE +#undef THC_TWO_PASS_REDUCTION_SIZE + +#endif // THC_REDUCEALL_INC diff --git a/thirdparty/libtorch/include/THC/THCReduceApplyUtils.cuh b/thirdparty/libtorch/include/THC/THCReduceApplyUtils.cuh new file mode 100644 index 0000000000..4324e19597 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCReduceApplyUtils.cuh @@ -0,0 +1,152 @@ +#ifndef THC_REDUCE_APPLY_UTILS_INC +#define THC_REDUCE_APPLY_UTILS_INC + +#include +#include +#include +#include +#include +#include + +// Enum that indicates whether tensor arguments are read/write or +// read-only +enum TensorArgType { ReadWrite, ReadOnly }; + +template +__device__ __forceinline__ IndexType getLinearBlockId() { + return blockIdx.z * gridDim.y * gridDim.x + + blockIdx.y * gridDim.x + + blockIdx.x; +} + +// Reduce N values concurrently, i.e. suppose N = 2, and there are 4 threads: +// (1, 2), (3, 4), (5, 6), (7, 8), then the return in threadVals for thread 0 +// is (1 + 3 + 5 + 7, 2 + 4 + 6 + 8) = (16, 20) +// +// If smem is not used again, there is no need to __syncthreads before this +// call. However, if smem will be used, e.g., this function is called in a loop, +// then __syncthreads is needed either before or afterwards to prevent non-0 +// threads overriding smem in the next loop before num-0 thread reads from it. +template +__device__ void reduceNValuesInBlock(T *smem, + T threadVals[N], + const unsigned int numVals, + ReduceOp reduceOp, + T init) { + if (numVals == 0) { + #pragma unroll + for (int i = 0; i < N; ++i) { + threadVals[i] = init; + } + return; + } + + // We store each of the N values contiguously, so if N = 2, all values for + // the first threadVal for each thread in the block are stored followed by + // all of the values for the second threadVal for each thread in the block + if (threadIdx.x < numVals) { + #pragma unroll + for (int i = 0; i < N; ++i) { + smem[i * numVals + threadIdx.x] = threadVals[i]; + } + } + __syncthreads(); + + // Number of lanes in the final reduction --> this is used to determine + // where to put the outputs of each of the n things we are reducing. If + // nLP = 32, then we have the 32 outputs for the first threadVal, + // followed by the 32 outputs for the second threadVal, etc. + const unsigned int numLanesParticipating = min(numVals, warpSize); + + if (numVals > warpSize && ((threadIdx.x / warpSize) == 0 )) { + #pragma unroll + for (int i = 0; i < N; ++i) { + threadVals[i] = threadIdx.x < numVals ? threadVals[i] : init; + } + + for (int i = warpSize + threadIdx.x; i < numVals; i += warpSize) { + #pragma unroll + for (int j = 0; j < N; ++j) { + threadVals[j] = reduceOp(threadVals[j], smem[j * numVals + i]); + } + } + + #pragma unroll + for (int i = 0; i < N; ++i) { + smem[i * numLanesParticipating + threadIdx.x] = threadVals[i]; + } + } + __syncthreads(); + + if (threadIdx.x == 0) { + if (numLanesParticipating == 32) { + #pragma unroll + for (int i = 0; i < N; ++i) { + #pragma unroll + for (int j = 1; j < 32; ++j) { + threadVals[i] = reduceOp(threadVals[i], smem[i * 32 + j]); + } + } + } else { + #pragma unroll + for (int i = 0; i < N; ++i) { + for (int j = 1; j < numLanesParticipating; ++j) { + threadVals[i] = reduceOp(threadVals[i], smem[i * numVals + j]); + } + } + } + } +} + +// Block-wide reduction in shared memory helper; only threadIdx.x == 0 will +// return the reduced value +// +// If smem is not used again, there is no need to __syncthreads before this +// call. However, if smem will be used, e.g., this function is called in a loop, +// then __syncthreads is needed either before or afterwards to prevent non-0 +// threads overriding smem in the next loop before num-0 thread reads from it. +template +__device__ T reduceBlock(T* smem, + const unsigned int numVals, + T threadVal, + ReduceOp reduceOp, + T init) { + reduceNValuesInBlock(smem, &threadVal, numVals, reduceOp, init); + return threadVal; +} + + +// Block-wide reduction where each thread locally reduces N +// values before letting a single warp take over - assumes +// threadVals is in registers, not shared memory +// +// If smem is not used again, there is no need to __syncthreads before this +// call. However, if smem will be used, e.g., this function is called in a loop, +// then __syncthreads is needed either before or afterwards to prevent non-0 +// threads overriding smem in the next loop before num-0 thread reads from it. +template +__device__ T reduceBlockWithNThreadLocalReductions(T *smem, + T threadVals[N], + const unsigned int numVals, + ReduceOp reduceOp, + T init) { + int offset = threadIdx.x * N; + T local = offset < numVals ? threadVals[0] : init; + + #pragma unroll + for (int i = 1; i < N; ++i) { + ++offset; + T next = offset < numVals ? threadVals[i] : init; + local = reduceOp(local, next); + } + + return reduceBlock(smem, blockDim.x < numVals ? blockDim.x : numVals, local, reduceOp, init); +} + +// Make sure the given tensor doesn't have too many dimensions +void THCCheckTensorDims(THCState* state, THCudaTensor* tensor, int arg); + +// Produces a grid with at least one point per tile +THC_API bool THC_getGridFromTiles(ptrdiff_t gridTiles, dim3& grid); + +#endif // THC_REDUCE_APPLY_UTILS_INC diff --git a/thirdparty/libtorch/include/THC/THCScanUtils.cuh b/thirdparty/libtorch/include/THC/THCScanUtils.cuh new file mode 100644 index 0000000000..75576238ca --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCScanUtils.cuh @@ -0,0 +1,218 @@ +#ifndef THC_SCAN_UTILS_INC +#define THC_SCAN_UTILS_INC + +#include +#include +#include + +// Collection of in-kernel scan / prefix sum utilities + +// Inclusive Scan via an upsweep/downsweep mechanism. Assumes: +// +// 1. Power2ScanSize is a power of 2. This code still works for collections that +// do not exactly contain a power of 2 number of elements, simply round up to the +// nearest power of 2 and then call. +// +// 2. That there are two-elements per thread, i.e. the size of the smem storage +// is 2 * blockDim.x * sizeof(T). +// +// Consider a (+)-Scan on the following elements: +// +// Upsweep: +// +// 0 1 2 3 4 5 6 7 +// 1 5 9 13 +// 6 22 +// 28 +// +// Downsweep: +// 15 +// 3 10 21 +template +__device__ void inclusivePrefixScan(T *smem, BinaryOp binop) { + // Reduce step ("upsweep") +#pragma unroll + for (int stride = 1; stride < Power2ScanSize; stride <<= 1) { + int index = (threadIdx.x + 1) * stride * 2 - 1; + if (index < Power2ScanSize) { + smem[index] = binop(smem[index], smem[index - stride]); + } + __syncthreads(); + } + + // Post-reduce step ("downsweep") +#pragma unroll + for (int stride = Power2ScanSize / 4; stride > 0; stride >>= 1) { + int index = (threadIdx.x + 1) * stride * 2 - 1; + if ((index + stride) < Power2ScanSize) { + smem[index + stride] = binop(smem[index + stride], smem[index]); + } + __syncthreads(); + } +} + +// Generic Op that can be used to support segmented scans by re-using +// the basic inclusiveScanOp. Merely requires that the input data has both +// a flag and val component +template +struct SegmentedScanOp { + __host__ __device__ SegmentedScanOp(BinaryOp binop): _binop(binop) {} + __host__ __device__ inline T operator()(const T& a, const T& b) { + T c; + c.val = a.flag ? a.val : _binop(a.val, b.val); + c.flag = a.flag | b.flag; + return c; + } + + BinaryOp _binop; +}; + +// Extends the above Inclusive Scan to support segments. It has the same properties +// but also takes a flag array that indicates the starts of "segments", i.e. individual +// units to scan. For example, consider the following (+)-scan that is segmented: +// +// Input: [1, 3, 2, 4, 1, 2, 3, 2, 1, 4] +// Flags: [1, 0, 0, 1, 0, 1, 1, 0, 1, 0] +// Output: 1 4 6 4 5 2 3 5 1 5 +// +// So we see that each "flag" resets the scan to that index. +template +__device__ void segmentedInclusivePrefixScan(T *smem, bool *bmem, BinaryOp binop) { + // Reduce step ("upsweep") +#pragma unroll + for (int stride = 1; stride < Power2ScanSize; stride <<= 1) { + int index = (threadIdx.x + 1) * stride * 2 - 1; + if (index < Power2ScanSize) { + smem[index] = bmem[index] ? smem[index] : binop(smem[index], smem[index - stride]); + bmem[index] = bmem[index] | bmem[index - stride]; + } + __syncthreads(); + } + + // Post-reduce step ("downsweep") +#pragma unroll + for (int stride = Power2ScanSize / 4; stride > 0; stride >>= 1) { + int index = (threadIdx.x + 1) * stride * 2 - 1; + if ((index + stride) < Power2ScanSize) { + smem[index + stride] = bmem[index + stride] ? smem[index + stride] : binop(smem[index + stride], smem[index]); + bmem[index + stride] = bmem[index + stride] | bmem[index]; + } + __syncthreads(); + } +} + +// Inclusive prefix sum using shared memory +template +__device__ void inclusivePrefixScan(T* smem, T in, T* out, BinaryFunction binop) { + // FIXME: this is a slow, simple implementation; need up/down sweep, + // prevent smem conflicts + smem[threadIdx.x] = in; + + __syncthreads(); + + for (int offset = 1; offset < blockDim.x; offset *= 2) { + T val = 0; + + if (threadIdx.x >= offset) { + val = binop(smem[threadIdx.x - offset], smem[threadIdx.x]); + } + + __syncthreads(); + if (threadIdx.x >= offset) { + smem[threadIdx.x] = val; + } + + __syncthreads(); + } + + *out = smem[threadIdx.x]; + + // Prevent write-after-read dependencies on smem usage above if necessary + if (KillWARDependency) { + __syncthreads(); + } +} + +// Exclusive prefix sum using shared memory +template +__device__ void exclusivePrefixScan(T* smem, T in, T* out, T* carry, BinaryFunction binop) { + // FIXME: crappy implementation + // We kill write-after-read dependencies separately below, hence the `false` + inclusivePrefixScan(smem, in, out, binop); + + *out -= in; + *carry = smem[blockDim.x - 1]; + + // Prevent write-after-read dependencies on smem usage above if necessary + if (KillWARDependency) { + __syncthreads(); + } +} + +// Inclusive prefix sum for binary vars using intra-warp voting + +// shared memory +template +__device__ void inclusiveBinaryPrefixScan(T* smem, bool in, T* out, BinaryFunction binop) { + // Within-warp, we use warp voting. +#if defined (__HIP_PLATFORM_HCC__) + unsigned long long int vote = WARP_BALLOT(in); + T index = __popcll(getLaneMaskLe() & vote); + T carry = __popcll(vote); +#else + T vote = WARP_BALLOT(in); + T index = __popc(getLaneMaskLe() & vote); + T carry = __popc(vote); +#endif + + int warp = threadIdx.x / C10_WARP_SIZE; + + // Per each warp, write out a value + if (getLaneId() == 0) { + smem[warp] = carry; + } + + __syncthreads(); + + // Sum across warps in one thread. This appears to be faster than a + // warp shuffle scan for CC 3.0+ + if (threadIdx.x == 0) { + int current = 0; + for (int i = 0; i < blockDim.x / C10_WARP_SIZE; ++i) { + T v = smem[i]; + smem[i] = binop(smem[i], current); + current = binop(current, v); + } + } + + __syncthreads(); + + // load the carry from the preceding warp + if (warp >= 1) { + index = binop(index, smem[warp - 1]); + } + + *out = index; + + if (KillWARDependency) { + __syncthreads(); + } +} + +// Exclusive prefix sum for binary vars using intra-warp voting + +// shared memory +template +__device__ void exclusiveBinaryPrefixScan(T* smem, bool in, T* out, T* carry, BinaryFunction binop) { + inclusiveBinaryPrefixScan(smem, in, out, binop); + + // Inclusive to exclusive + *out -= (T) in; + + // The outgoing carry for all threads is the last warp's sum + *carry = smem[THCCeilDiv(blockDim.x, C10_WARP_SIZE) - 1]; + + if (KillWARDependency) { + __syncthreads(); + } +} + +#endif // THC_SCAN_UTILS_INC diff --git a/thirdparty/libtorch/include/THC/THCSleep.h b/thirdparty/libtorch/include/THC/THCSleep.h new file mode 100644 index 0000000000..b3f20a3934 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCSleep.h @@ -0,0 +1,10 @@ +#ifndef THC_SPIN_INC +#define THC_SPIN_INC + +#include +#include + +// enqueues a kernel that spins for the specified number of cycles +THC_API void THC_sleep(THCState* state, int64_t cycles); + +#endif diff --git a/thirdparty/libtorch/include/THC/THCSortUtils.cuh b/thirdparty/libtorch/include/THC/THCSortUtils.cuh new file mode 100644 index 0000000000..7d39b3be53 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCSortUtils.cuh @@ -0,0 +1,229 @@ +#ifndef THC_SORT_UTILS_INC +#define THC_SORT_UTILS_INC + +#include +#include +#include +#include + +// Collection of kernel sort routines +template +struct LTComp { + __device__ inline bool operator()(const T& a, const T& b) const { + return (handleNaN && THCNumerics::isnan(b) && !THCNumerics::isnan(a)) || THCNumerics::lt(a, b); + } +}; + +template +struct GTComp { + __device__ inline bool operator()(const T& a, const T& b) const { + return (handleNaN && THCNumerics::isnan(a) && !THCNumerics::isnan(b)) || THCNumerics::gt(a, b); + } +}; + +template +__device__ inline void swapVars(T& t1, T& t2) { + T tmp = t1; + t1 = t2; + t2 = tmp; +} + +template +__device__ inline void bitonicSwap(K& kA, V& vA, bool& validA, + K& kB, V& vB, bool& validB, + bool dir, + const Comparator& comp) { + // Invalid entries always sort to the end + bool swap = (comp(kA, kB) && validA) || !validB; + if (swap == dir) { + swapVars(kA, kB); + swapVars(vA, vB); + swapVars(validA, validB); + } +}; + +template +__device__ inline void bitonicSwapKeys(K& kA, bool& validA, + K& kB, bool& validB, + bool dir, + const Comparator& comp) { + bool swap = (comp(kA, kB) && validA) || !validB; + if (swap == dir) { + swapVars(kA, kB); + swapVars(validA, validB); + } +} + +template +__device__ inline void bitonicSort(K keys[Power2SortSize], + V values[Power2SortSize], + bool valid[Power2SortSize], + const Comparator& comp) { +#ifndef __HIP_PLATFORM_HCC__ +#pragma unroll +#endif + for (unsigned int size = 2; size < Power2SortSize; size *= 2) { + bool flag = ((threadIdx.x & (size / 2)) != 0); + +#ifndef __HIP_PLATFORM_HCC__ +#pragma unroll +#endif + for (unsigned int stride = size / 2; stride > 0; stride /= 2) { + + __syncthreads(); + + unsigned int pos = 2 * threadIdx.x - (threadIdx.x & (stride - 1)); + bitonicSwap( + keys[pos], values[pos], valid[pos], + keys[pos + stride], values[pos + stride], valid[pos + stride], + flag, comp); + } + } + +#ifndef __HIP_PLATFORM_HCC__ +#pragma unroll +#endif + for (unsigned int stride = Power2SortSize / 2; stride > 0; stride /= 2) { + + __syncthreads(); + + unsigned int pos = 2 * threadIdx.x - (threadIdx.x & (stride - 1)); + bitonicSwap( + keys[pos], values[pos], valid[pos], + keys[pos + stride], values[pos + stride], valid[pos + stride], + false, comp); + } + + __syncthreads(); + +} + +template +__device__ inline void bitonicSortKeys(K keys[Power2SortSize], + bool valid[Power2SortSize], + const Comparator& comp) { +#ifndef __HIP_PLATFORM_HCC__ +#pragma unroll +#endif + for (unsigned int size = 2; size < Power2SortSize; size *= 2) { + bool flag = ((threadIdx.x & (size / 2)) != 0); + +#ifndef __HIP_PLATFORM_HCC__ +#pragma unroll +#endif + for (unsigned int stride = size / 2; stride > 0; stride /= 2) { + + __syncthreads(); + + unsigned int pos = 2 * threadIdx.x - (threadIdx.x & (stride - 1)); + bitonicSwapKeys( + keys[pos], valid[pos], + keys[pos + stride], valid[pos + stride], + flag, comp); + } + } + +#ifndef __HIP_PLATFORM_HCC__ +#pragma unroll +#endif + for (unsigned int stride = Power2SortSize / 2; stride > 0; stride /= 2) { + __syncthreads(); + + unsigned int pos = 2 * threadIdx.x - (threadIdx.x & (stride - 1)); + bitonicSwapKeys( + keys[pos], valid[pos], + keys[pos + stride], valid[pos + stride], + false, comp); + } + + __syncthreads(); + +} + +// Sorts (key, value) pairs (in different tensors) in-place; i.e., +// modifies the input `keys` and `values` +template +C10_LAUNCH_BOUNDS_1(1024) +__global__ void +bitonicSortKVInPlace(TensorInfo keys, + IndexType keySlices, + IndexType keySliceSize, + IndexType keySliceStride, + TensorInfo values, + IndexType valueSliceStride, + Comparator comp) { + // Find the slice of the tensor that we are sorting + const IndexType linearIndex = getLinearBlockId(); + // Tiling the slices could have us be out of bounds, if there are a + // lot of slices to sort + if (linearIndex >= keySlices) { + return; + } + + __shared__ K sharedKeys[Power2SortSize]; + __shared__ V sharedValues[Power2SortSize]; + __shared__ bool sharedValid[Power2SortSize]; + + const IndexType keyStartOffset = + IndexToOffset::get(linearIndex, keys); + const IndexType valueStartOffset = + IndexToOffset::get(linearIndex, values); + + // If the sort size is 1, the data is already sorted + if (Power2SortSize == 1) { + return; + } else { + // Otherwise, each thread is responsible for loading and storing 2 + // elements. The sort size is guaranteed to be >= 2 + const int elem1 = threadIdx.x; + const int elem2 = threadIdx.x + (Power2SortSize / 2); + + bool valid1 = (elem1 < keySliceSize); + K k1 = valid1 ? + keys.data[keyStartOffset + elem1 * keySliceStride] : ScalarConvert::to(0); + V v1 = valid1 ? + values.data[valueStartOffset + elem1 * valueSliceStride] : ScalarConvert::to(0); + + sharedKeys[elem1] = k1; + sharedValues[elem1] = v1; + sharedValid[elem1] = valid1; + + bool valid2 = (elem2 < keySliceSize); + K k2 = valid2 ? + keys.data[keyStartOffset + elem2 * keySliceStride] : ScalarConvert::to(0); + V v2 = valid2 ? + values.data[valueStartOffset + elem2 * valueSliceStride] : ScalarConvert::to(0); + + sharedKeys[elem2] = k2; + sharedValues[elem2] = v2; + sharedValid[elem2] = valid2; + + // Sort! + bitonicSort( + sharedKeys, sharedValues, sharedValid, comp); + + // elem1 and elem2 values might be out-of-range, if the data size we are + // sorting is smaller than half the power2 size + if (valid1) { + keys.data[keyStartOffset + elem1 * keySliceStride] = + sharedKeys[elem1]; + values.data[valueStartOffset + elem1 * valueSliceStride] = + sharedValues[elem1]; + } + + if (valid2) { + keys.data[keyStartOffset + elem2 * keySliceStride] = + sharedKeys[elem2]; + values.data[valueStartOffset + elem2 * valueSliceStride] = + sharedValues[elem2]; + } + } +} + +uint64_t nextHighestPowerOf2(uint64_t n); + +#endif // THC_SORT_UTILS_INC diff --git a/thirdparty/libtorch/include/THC/THCStorage.h b/thirdparty/libtorch/include/THC/THCStorage.h new file mode 100644 index 0000000000..4d4d9abf09 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCStorage.h @@ -0,0 +1,18 @@ +#ifndef THC_STORAGE_INC +#define THC_STORAGE_INC + +#include +#include + +#define THCStorage_(NAME) TH_CONCAT_4(TH,CReal,Storage_,NAME) + +#include +#include + +#include +#include + +#include +#include + +#endif diff --git a/thirdparty/libtorch/include/THC/THCStorage.hpp b/thirdparty/libtorch/include/THC/THCStorage.hpp new file mode 100644 index 0000000000..62a1d950a4 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCStorage.hpp @@ -0,0 +1,26 @@ +#pragma once + +// STOP!!! Thinking of including this header directly? Please +// read Note [TH abstraction violation] + +#include +// Should work with THStorageClass +#include + +#include + +#include +#include +#include + +THC_API THCStorage* THCStorage_new(THCState* state, caffe2::TypeMeta); + +THC_API void THCStorage_retain(THCState *state, THCStorage *storage); + +THC_API void THCStorage_resize(THCState *state, THCStorage *storage, ptrdiff_t size); +THC_API int THCStorage_getDevice(THCState* state, const THCStorage* storage); + +THC_API THCStorage* THCStorage_newWithDataAndAllocator( + THCState *state, at::ScalarType scalar_type, + at::DataPtr&& data, ptrdiff_t size, + at::Allocator* allocator); diff --git a/thirdparty/libtorch/include/THC/THCStorageCopy.h b/thirdparty/libtorch/include/THC/THCStorageCopy.h new file mode 100644 index 0000000000..3a6dc0c7f6 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCStorageCopy.h @@ -0,0 +1,17 @@ +#ifndef THC_STORAGE_COPY_INC +#define THC_STORAGE_COPY_INC + +#include +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#endif diff --git a/thirdparty/libtorch/include/THC/THCTensor.h b/thirdparty/libtorch/include/THC/THCTensor.h new file mode 100644 index 0000000000..c8ebd5d3d6 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCTensor.h @@ -0,0 +1,26 @@ +#ifndef THC_TENSOR_INC +#define THC_TENSOR_INC + +#include +#include +#include + +#define THCTensor_(NAME) TH_CONCAT_4(TH,CReal,Tensor_,NAME) + +#define THC_DESC_BUFF_LEN 64 + +typedef struct THC_CLASS THCDescBuff +{ + char str[THC_DESC_BUFF_LEN]; +} THCDescBuff; + +#include +#include + +#include +#include + +#include +#include + +#endif diff --git a/thirdparty/libtorch/include/THC/THCTensor.hpp b/thirdparty/libtorch/include/THC/THCTensor.hpp new file mode 100644 index 0000000000..b543c0af25 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCTensor.hpp @@ -0,0 +1,64 @@ +#pragma once + +// STOP!!! Thinking of including this header directly? Please +// read Note [TH abstraction violation] + +#include +#include +#include +#include + +#include +#include + +// See [NOTE: nDimension vs nDimensionLegacyNoScalars vs nDimensionLegacyAll] +THC_API int THCTensor_nDimension(THCState *state, const THCTensor *self); +THC_API int THCTensor_nDimensionLegacyNoScalars(THCState *state, const THCTensor *self); +THC_API int THCTensor_nDimensionLegacyAll(THCState *state, const THCTensor *self); + +THC_API int64_t THCTensor_size(THCState *state, const THCTensor *self, int dim); +THC_API int64_t THCTensor_sizeLegacyNoScalars(THCState *state, const THCTensor *self, int dim); +THC_API int64_t THCTensor_stride(THCState *state, const THCTensor *self, int dim); +THC_API int64_t THCTensor_strideLegacyNoScalars(THCState *state, const THCTensor *self, int dim); + +THC_API THCTensor *THCTensor_new(THCState *state, caffe2::TypeMeta type_meta); + +THC_API void THCTensor_resize(THCState *state, THCTensor *tensor, at::IntArrayRef size, at::IntArrayRef stride); +THC_API void THCTensor_resizeNd(THCState *state, THCTensor *tensor, int nDimension, const int64_t *size, const int64_t *stride); +THC_API void THCTensor_resizeAs(THCState *state, THCTensor *tensor, THCTensor *src); + +THC_API void THCTensor_set(THCState *state, THCTensor *self, THCTensor *src); +THC_API void THCTensor_setStorage(THCState *state, THCTensor *self, THCStorage *storage_, ptrdiff_t storageOffset_, at::IntArrayRef size_, at::IntArrayRef stride_); +THC_API void THCTensor_setStorageNd(THCState *state, THCTensor *self, THCStorage *storage, ptrdiff_t storageOffset, int nDimension, const int64_t *size, const int64_t *stride); + +THC_API void THCTensor_squeeze1d(THCState *state, THCTensor *self, THCTensor *src, int dimension_); +THC_API void THCTensor_unsqueeze1d(THCState *state, THCTensor *self, THCTensor *src, int dimension_); + +THC_API bool THCTensor_allContiguous(THCState *state, THCTensor **inputs, int numInputs); +THC_API ptrdiff_t THCTensor_nElement(THCState *state, const THCTensor *self); + +THC_API void THCTensor_retain(THCState *state, THCTensor *self); +THC_API void THCTensor_free(THCState *state, THCTensor *self); + +THC_API int THCTensor_getDevice(THCState* state, const THCTensor* tensor); +THC_API bool THCTensor_allSameDevice(THCState* state, THCTensor ** inputs, int numInputs); + +/* Can we use 32 bit math for indexing? */ +THC_API bool THCTensor_canUse32BitIndexMath(THCState* state, const THCTensor* t, ptrdiff_t max_elem=INT32_MAX); +/* Are all tensors 32-bit indexable? */ +THC_API bool THCTensor_all32BitIndexable(THCState* state, THCTensor** inputs, int numInputs); +THC_API void THCTensor_preserveReduceDimSemantics(THCState *state, THCTensor *tensor, int in_dims, + int64_t dimension, int keepdim); +/* Returns false if there is no possibility that the tensor */ +/* has more than one index that references the same datapoint, */ +/* true otherwise. */ +THC_API bool THCTensor_maybeOverlappingIndices(THCState* state, const THCTensor* t); + +#include +#include + +#include +#include + +#include +#include diff --git a/thirdparty/libtorch/include/THC/THCTensorCopy.h b/thirdparty/libtorch/include/THC/THCTensorCopy.h new file mode 100644 index 0000000000..9366c37b04 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCTensorCopy.h @@ -0,0 +1,18 @@ +#ifndef TH_CUDA_TENSOR_COPY_INC +#define TH_CUDA_TENSOR_COPY_INC + +#include +#include +#include + + +#include +#include + +#include +#include + +#include +#include + +#endif diff --git a/thirdparty/libtorch/include/THC/THCTensorCopy.hpp b/thirdparty/libtorch/include/THC/THCTensorCopy.hpp new file mode 100644 index 0000000000..ff01449ba4 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCTensorCopy.hpp @@ -0,0 +1,18 @@ +#pragma once + +#include + +template +void THC_copyTensor(THCState* state, THCTensor* dst, THCTensor* src); + +template +THCTensor *THCTensor_newClone(THCState *state, THCTensor *self); + +template +THCTensor *THCTensor_newContiguous(THCState *state, THCTensor *self); + +template +void THCTensor_freeCopyTo(THCState *state, THCTensor *self, THCTensor *dst); + +template +void THCTensor_copyIgnoringOverlaps(THCState* state, THCTensor* dst, THCTensor* src); diff --git a/thirdparty/libtorch/include/THC/THCTensorInfo.cuh b/thirdparty/libtorch/include/THC/THCTensorInfo.cuh new file mode 100644 index 0000000000..4ad61c6ad7 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCTensorInfo.cuh @@ -0,0 +1,260 @@ +#ifndef THC_TENSOR_INFO_INC +#define THC_TENSOR_INFO_INC + +#include +#include +#include +#include +#include + +// Maximum number of dimensions allowed for cutorch +#define MAX_CUTORCH_DIMS 25 + +// Warning string for tensor arguments that are too large or have too +// many dimensions +#define CUTORCH_STR(X) #X +#define CUTORCH_DIM_WARNING "tensor too large or too many (>" \ + CUTORCH_STR(MAX_CUTORCH_DIMS) ") dimensions" + +// CUDA kernel argument that defines tensor layout +template +struct TensorInfo { + TensorInfo(T* p, + int dim, + IndexType sz[MAX_CUTORCH_DIMS], + IndexType st[MAX_CUTORCH_DIMS]); + + // Set the size of the given dimension to 1, as if it were a + // reduction dim (allows you to calculate offsets of the reduction + // slice) + void reduceDim(int dim); + + /* + Updates the TensorInfo's dims, sizes, and strides to reflect a "collapse" of + the info, possibly excluding the optional excludeDim. A "collapsed" version + of the info is the fewest dims that order the tensor's elements in the same + way as the original info. If excludeDim is specified, the collapse is the + fewest dims that order the tensor's elements as the original and preserve the + excluded dimension, unless the tensor collapses to a point. + + Returns the (new) index of the preserved dimension if excludeDim is + specified. Returns 0 if the tensor is collapsed to a point. Returns -1 + otherwise. + */ + int collapseDims(const int excludeDim = -1); + + // Contiguous tensors of more than one dimension are collapsed down + // to one tensor + __host__ __device__ inline bool isContiguous() const { + return (dims == 1 && strides[0] == 1); + } + + T* data; + IndexType sizes[MAX_CUTORCH_DIMS]; + IndexType strides[MAX_CUTORCH_DIMS]; + int dims; +}; + +template +TensorInfo::TensorInfo(T* p, + int dim, + IndexType sz[MAX_CUTORCH_DIMS], + IndexType st[MAX_CUTORCH_DIMS]) { + data = p; + dims = dim; + assert(dims > 0 && dims < MAX_CUTORCH_DIMS); + + for (int i = 0; i < dim; ++i) { + sizes[i] = sz[i]; + strides[i] = st[i]; + } +} + +template +void +TensorInfo::reduceDim(int dim) { + assert(dim < dims && dim >= 0); + sizes[dim] = 1; +} + +template +int +TensorInfo::collapseDims(const int excludeDim) { + + assert(excludeDim >= -1 && excludeDim < dims); + + int stopDim = (excludeDim == -1) ? dims : excludeDim; + int newIndex = -1; + int oldIndex = 0; + int remappedExcludedDim = -1; + + while (oldIndex < dims) { + // Finds a dimension to collapse into + for (; oldIndex < stopDim; ++oldIndex) { + if (sizes[oldIndex] == 1) { + continue; + } + ++newIndex; + sizes[newIndex] = sizes[oldIndex]; + strides[newIndex] = strides[oldIndex]; + ++oldIndex; + break; + } + + // Collapses dims + for (; oldIndex < stopDim; ++oldIndex) { + if (sizes[oldIndex] == 1) { + continue; + } + + if (strides[newIndex] == sizes[oldIndex] * strides[oldIndex]) { + sizes[newIndex] *= sizes[oldIndex]; + strides[newIndex] = strides[oldIndex]; + } else { + ++newIndex; + sizes[newIndex] = sizes[oldIndex]; + strides[newIndex] = strides[oldIndex]; + } + } + + // Handles excludeDim being set (oldIndex == excludeDim) + if (oldIndex != dims) { + + // Preserves excluded dimension + ++newIndex; + sizes[newIndex] = sizes[oldIndex]; + strides[newIndex] = strides[oldIndex]; + remappedExcludedDim = newIndex; + + // Restarts iteration after excludeDim + ++oldIndex; + stopDim = dims; + } + } + + // Handles special case of all dims size 1 + if (newIndex == -1 || (newIndex == 0 && sizes[0] == 1)) { + dims = 1; + sizes[0] = 1; + strides[0] = 1; + + return 0; + } + + dims = newIndex + 1; + return remappedExcludedDim; +} + +// Translate a linear index for the apply to a T* offset; +// specialized on `Dims` to reduce nvcc compilation time +template +struct IndexToOffset { + static __host__ __device__ IndexType get( + IndexType linearId, + const TensorInfo& info) { + + IndexType offset = 0; + + // Uses static dims + for (int i = Dims - 1; i > 0; --i) { + IndexType curDimIndex = linearId % info.sizes[i]; + IndexType curDimOffset = curDimIndex * info.strides[i]; + offset += curDimOffset; + linearId /= info.sizes[i]; + } + + return offset + linearId * info.strides[0]; + } +}; + +template +struct IndexToOffset { + static inline __host__ __device__ IndexType get( + IndexType linearId, + const TensorInfo& info) { + + IndexType offset = 0; + + // Uses dynamic dims + for (int i = info.dims - 1; i > 0; --i) { + IndexType curDimIndex = linearId % info.sizes[i]; + IndexType curDimOffset = curDimIndex * info.strides[i]; + offset += curDimOffset; + linearId /= info.sizes[i]; + } + + return offset + linearId * info.strides[0]; + } +}; + +// OffsetInfo is a faster implementation of IndexToOffset that uses faster +// integer division: we transform each division into integer multiplication by a +// pre-computed constant. (See IntDivider for details.) +template +struct OffsetInfo { + explicit OffsetInfo(const TensorInfo& tinfo) { + assert(tinfo.dims == Dims); + data = tinfo.data; + + for (int i = 0; i < Dims; ++i) { + sizes[i] = IntDivider(tinfo.sizes[i]); + strides[i] = tinfo.strides[i]; + } + } + + __host__ __device__ T* get(IndexType linearIndex) const { + IndexType offset = 0; + + for (int i = Dims - 1; i > 0; --i) { + DivMod divmod = sizes[i].divmod(linearIndex); + linearIndex = divmod.div; + offset += divmod.mod * strides[i]; + } + + return &data[offset + linearIndex * strides[0]]; + } + + T* data; + IntDivider sizes[Dims]; + IndexType strides[Dims]; +}; + +// For 1D tensors the offset equals linear index * stride. +template +struct OffsetInfo { + explicit OffsetInfo(const TensorInfo& tinfo) + : data{tinfo.data}, stride{tinfo.strides[0]} {} + + __host__ __device__ T* get(IndexType linearIndex) const { + return &data[linearIndex * stride]; + } + + T* data; + const IndexType stride; +}; + +// Dims=-1 is used when the dimension is unknown at compile time. +// +// Unfortunately, pre-computation does not work here, because of a bug in nvcc +// (tested on CUDA 8.0): if a kernel argument contains an array that is +// dynamically accessed, the whole array is first copied into the local memory. +// (That is, every kernel thread makes its own copy of the argument, even if it +// is never updated.) Pre-computation makes it worse because now we have more +// data to copy. +// +// So let's fall back to vanilla division approach. + +template +struct OffsetInfo { + explicit OffsetInfo(const TensorInfo& tinfo) + : tinfo(tinfo) { } + + __host__ __device__ T* get(IndexType linearIndex) const { + IndexType offset = IndexToOffset::get(linearIndex, tinfo); + return &tinfo.data[offset]; + } + + TensorInfo tinfo; +}; + +#endif // THC_TENSOR_INFO_INC diff --git a/thirdparty/libtorch/include/THC/THCTensorMath.h b/thirdparty/libtorch/include/THC/THCTensorMath.h new file mode 100644 index 0000000000..8c9aa6f837 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCTensorMath.h @@ -0,0 +1,82 @@ +#ifndef TH_CUDA_TENSOR_MATH_INC +#define TH_CUDA_TENSOR_MATH_INC + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#endif diff --git a/thirdparty/libtorch/include/THC/THCTensorMathMagma.cuh b/thirdparty/libtorch/include/THC/THCTensorMathMagma.cuh new file mode 100644 index 0000000000..08124d3d4c --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCTensorMathMagma.cuh @@ -0,0 +1,22 @@ +#ifndef THC_TENSOR_MATH_MAGMA_CUH +#define THC_TENSOR_MATH_MAGMA_CUH + +#ifdef USE_MAGMA +#include +#else +#include +#endif + +#ifdef USE_MAGMA +template +static inline T* th_magma_malloc_pinned(size_t n) +{ + void* ptr; + if (MAGMA_SUCCESS != magma_malloc_pinned(&ptr, n * sizeof(T))) + THError("$ Torch: not enough memory: you tried to allocate %dGB. Buy new RAM!", n/268435456); + return reinterpret_cast(ptr); +} + +#endif + +#endif // THC_TENSOR_MATH_MAGMA_CUH diff --git a/thirdparty/libtorch/include/THC/THCTensorMathPointwise.cuh b/thirdparty/libtorch/include/THC/THCTensorMathPointwise.cuh new file mode 100644 index 0000000000..ece97e715c --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCTensorMathPointwise.cuh @@ -0,0 +1,363 @@ +#ifndef THC_TENSORMATH_POINTWISE_CUH +#define THC_TENSORMATH_POINTWISE_CUH + +#include +#include +#include +#include +#include +#include +#include +#include + +template +struct TensorCAddOp { + TensorCAddOp(T v) : val(v) {} + + __device__ __forceinline__ void operator()(T* out, T* in) { + *out += val * *in; + } + + __device__ __forceinline__ void operator()(T* out, T* in1, T* in2) { + *out = *in1 + val * *in2; + } + + T val; +}; + +template +struct TensorMulOp { + __device__ __forceinline__ void operator()(T* out, T* in) { + *out *= *in; + } + + __device__ __forceinline__ void operator()(T* out, T* in1, T* in2) { + *out = *in1 * *in2; + } +}; + +template +static __device__ __forceinline__ +typename std::enable_if::value, bool>::type +modulo_wrap(T a, T b) { + return (a != 0) && (a < 0) != (b < 0); +} + +template +static __device__ __forceinline__ +typename std::enable_if::value, bool>::type +modulo_wrap(T a, T b) { + return false; +} + +template +struct TensorCRemainderOp { + __device__ __forceinline__ void operator()(T* out, T* in) { + T val = *out % *in; + if (modulo_wrap(val, *in)) { + val += *in; + } + *out = val; + } + + __device__ __forceinline__ void operator()(T* out, T* in1, T* in2) { + T val = *in1 % *in2; + if (modulo_wrap(val, *in2)) { + val += *in2; + } + *out = val; + } +}; + +template <> +struct TensorCRemainderOp { + __device__ __forceinline__ void operator()(float* out, float* in) { + *out = *in != 0.f ? *out - *in * floorf(*out / *in) : NAN; + } + + __device__ __forceinline__ void operator()(float* out, float* in1, float* in2) { + *out = *in2 != 0.f ? *in1 - *in2 * floorf(*in1 / *in2) : NAN; + } +}; + +template <> +struct TensorCRemainderOp { + __device__ __forceinline__ void operator()(double* out, double* in) { + *out = *in != 0. ? *out - *in * floor(*out / *in) : NAN; + } + + __device__ __forceinline__ void operator()(double* out, double* in1, double* in2) { + *out = *in2 != 0. ? *in1 - *in2 * floor(*in1 / *in2) : NAN; + } +}; + +template <> +struct TensorCRemainderOp { + __device__ __forceinline__ void operator()(at::Half* out, at::Half* in) { + *out = *in != 0.f ? *out - *in * floorf(*out / *in) : NAN; + } + + __device__ __forceinline__ void operator()(at::Half* out, at::Half* in1, at::Half* in2) { + *out = *in2 != 0.f ? *in1 - *in2 * floorf(*in1 / *in2) : NAN; + } +}; + +template +struct TensorCFmodOp { + __device__ __forceinline__ void operator()(T* out, T* in) { + *out = *out % *in; + } + + __device__ __forceinline__ void operator()(T* out, T* in1, T* in2) { + *out = *in1 % *in2; + } +}; + +template <> +struct TensorCFmodOp { + __device__ __forceinline__ void operator()(float* out, float* in) { + *out = fmodf(*out, *in); + } + + __device__ __forceinline__ void operator()(float* out, float* in1, float* in2) { + *out = fmodf(*in1, *in2); + } +}; + +template <> +struct TensorCFmodOp { + __device__ __forceinline__ void operator()(double* out, double* in) { + *out = fmod(*out, *in); + } + + __device__ __forceinline__ void operator()(double* out, double* in1, double* in2) { + *out = fmod(*in1, *in2); + } +}; + +template <> +struct TensorCFmodOp { + __device__ __forceinline__ void operator()(at::Half* out, at::Half* in) { + *out = fmodf(*out, *in); + } + + __device__ __forceinline__ void operator()(at::Half* out, at::Half* in1, at::Half* in2) { + *out = fmodf(*in1, *in2); + } +}; + +template +struct TensorClampOp { + TensorClampOp(T min, T max) : minValue(min), maxValue(max) {} + __device__ __forceinline__ void operator()(T* out, T* in) { + T val = THCNumerics::lt(*in, minValue) ? minValue : *in; + *out = THCNumerics::gt(val, maxValue) ? maxValue : val; + } + + __device__ __forceinline__ void operator()(T* v) { + T val = THCNumerics::lt(*v, minValue) ? minValue : *v; + *v = THCNumerics::gt(val, maxValue) ? maxValue : val; + } + + const T minValue; + const T maxValue; +}; + +template +struct TensorCrossOp { + TensorCrossOp(int64_t sx, int64_t sy, int64_t so) : sx(sx), sy(sy), so(so) {} + + __device__ __forceinline__ void operator()(T* out, T* x, T*y) { + T val0 = THCNumerics::sub( + THCNumerics::mul(x[1 * sx], y[2 * sy]), + THCNumerics::mul(x[2 * sx], y[1 * sy]) + ); + + T val1 = THCNumerics::sub( + THCNumerics::mul(x[2 * sx], y[0 * sy]), + THCNumerics::mul(x[0 * sx], y[2 * sy]) + ); + + T val2 = THCNumerics::sub( + THCNumerics::mul(x[0 * sx], y[1 * sy]), + THCNumerics::mul(x[1 * sx], y[0 * sy]) + ); + + out[0 * so] = val0; + out[1 * so] = val1; + out[2 * so] = val2; + } + + const int64_t sx, sy, so; +}; + +template +struct TensorMaxOp { + __device__ __forceinline__ void operator()(T* out, T* in) { + *out = THCNumerics::gt(*out, *in) ? *out : *in; + } + + __device__ __forceinline__ void operator()(T* out, T* in1, T* in2) { + *out = THCNumerics::gt(*in1, *in2) ? *in1 : *in2; + } +}; + +template +struct TensorMinOp { + __device__ __forceinline__ void operator()(T* out, T* in) { + *out = THCNumerics::lt(*out, *in) ? *out : *in; + } + + __device__ __forceinline__ void operator()(T* out, T* in1, T* in2) { + *out = THCNumerics::lt(*in1, *in2) ? *in1 : *in2; + } +}; + +template +struct TensorMaxValueOp { + TensorMaxValueOp(T v) : val(v) {} + + __device__ __forceinline__ void operator()(T* out) { + *out = THCNumerics::lt(*out, val) ? val : *out; // this order propagates NaN + } + + __device__ __forceinline__ void operator()(T* out, T* in) { + *out = THCNumerics::lt(*in, val) ? val : *in; // this order propagates NaN + } + + T val; +}; + +template +struct TensorMinValueOp { + TensorMinValueOp(T v) : val(v) {} + + __device__ __forceinline__ void operator()(T* out) { + *out = THCNumerics::gt(*out, val) ? val : *out; // this order propagates NaN + } + + __device__ __forceinline__ void operator()(T* out, T* in) { + *out = THCNumerics::gt(*in, val) ? val : *in; // this order propagates NaN + } + + T val; +}; + +template +struct TensorLShiftOp { + __device__ __forceinline__ void + operator()(T* out, T* in) { + *out <<= *in; + } + + __device__ __forceinline__ void + operator()(T* out, T* in1, T* in2) { + *out = *in1 << *in2; + } +}; + +template <> +struct TensorLShiftOp { + __device__ __forceinline__ void + operator()(float* out, float* in) { + *out *= powf(2.0f, *in); + } + + __device__ __forceinline__ void + operator()(float* out, float* in1, float* in2) { + *out = *in1 * powf(2.0f, *in2); + } +}; + +template <> +struct TensorLShiftOp { + __device__ __forceinline__ void + operator()(double* out, double* in) { + *out *= pow(2.0, *in); + } + + __device__ __forceinline__ void + operator()(double* out, double* in1, double* in2) { + *out = *in1 * pow(2.0, *in2); + } +}; + +template +struct TensorRShiftOp { + __device__ __forceinline__ void + operator()(T* out, T* in) { + *out >>= *in; + } + + __device__ __forceinline__ void + operator()(T* out, T* in1, T* in2) { + *out = *in1 >> *in2; + } +}; + +template <> +struct TensorRShiftOp { + __device__ __forceinline__ void + operator()(float* out, float* in) { + *out /= powf(2.0f, *in); + } + + __device__ __forceinline__ void + operator()(float* out, float* in1, float* in2) { + *out = *in1 / powf(2.0f, *in2); + } +}; + +template <> +struct TensorRShiftOp { + __device__ __forceinline__ void + operator()(double* out, double* in) { + *out /= pow(2.0, *in); + } + + __device__ __forceinline__ void + operator()(double* out, double* in1, double* in2) { + *out = *in1 / pow(2.0, *in2); + } +}; + +template +struct TensorBitAndOp { + __device__ __forceinline__ void + operator()(T* out, T* in) { + *out &= *in; + } + + __device__ __forceinline__ void + operator()(T* out, T* in1, T* in2) { + *out = *in1 & *in2; + } +}; + +template +struct TensorBitOrOp { + __device__ __forceinline__ void + operator()(T* out, T* in) { + *out |= *in; + } + + __device__ __forceinline__ void + operator()(T* out, T* in1, T* in2) { + *out = *in1 | *in2; + } +}; + +template +struct TensorBitXorOp { + __device__ __forceinline__ void + operator()(T* out, T* in) { + *out ^= *in; + } + + __device__ __forceinline__ void + operator()(T* out, T* in1, T* in2) { + *out = *in1 ^ *in2; + } +}; + +#endif // THC_TENSORMATH_POINTWISE_CUH diff --git a/thirdparty/libtorch/include/THC/THCTensorMathReduce.cuh b/thirdparty/libtorch/include/THC/THCTensorMathReduce.cuh new file mode 100644 index 0000000000..a0602ebfb6 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCTensorMathReduce.cuh @@ -0,0 +1,606 @@ +#ifndef THC_TENSORMATH_REDUCE_CUH +#define THC_TENSORMATH_REDUCE_CUH + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#if CUDA_VERSION >= 7000 || defined __HIP_PLATFORM_HCC__ +#include +#endif + +/* +Reductions that (only) operate on accumulate types. +*/ + +template +struct WelfordData { + T mean_; + T m_2_n_; + int count_; // do we need int64_t? + + __host__ __device__ WelfordData() { + } + + // stripping initialization from default constructor to avoid dynamic + // initialization warning thrown from using this data structure in CUDA kernel + // as static shared memory. + __host__ __device__ void reset() { + mean_ = T(0); + m_2_n_ = T(0); + count_ = 0; + } + + __host__ __device__ WelfordData(const U data_) { + mean_ = static_cast(data_); + m_2_n_ = static_cast(0); + count_ = 1; + } + + __host__ __device__ WelfordData(const WelfordData &t) : + mean_(t.mean_), + m_2_n_(t.m_2_n_), + count_(t.count_) + { + } + + __host__ __device__ WelfordData(const volatile WelfordData &t) : + mean_(t.mean_), + m_2_n_(t.m_2_n_), + count_(t.count_) + { + } + + __host__ __device__ volatile WelfordData& operator = (const volatile WelfordData &t) volatile { + mean_ = t.mean_; + m_2_n_ = t.m_2_n_; + count_ = t.count_; + return *this; + } + + __host__ __device__ WelfordData& operator = (const WelfordData &t) { + mean_ = t.mean_; + m_2_n_ = t.m_2_n_; + count_ = t.count_; + return *this; + } + +}; + + +template +struct ModifyWelford { + inline __device__ T operator()(const T &a) const { + return a; + } +}; + +template +struct ReduceWelford { + inline __device__ WelfordData operator()(const WelfordData &a, const WelfordData &b) const { + WelfordData c; + c.count_ = THCNumerics::add(a.count_, b.count_); + T factor = THCNumerics::div(1.0, max(1, c.count_)); + c.mean_ = THCNumerics::mul(THCNumerics::add(THCNumerics::mul(a.mean_, a.count_), THCNumerics::mul(b.mean_, b.count_)), factor); + c.m_2_n_ = THCNumerics::add(a.m_2_n_, THCNumerics::add(b.m_2_n_, THCNumerics::mul(factor, THCNumerics::mul(a.count_, THCNumerics::mul(b.count_, THCNumerics::pow(THCNumerics::sub(a.mean_, b.mean_), 2) ))))); + return c; + } +}; + +template +struct VarianceWelford { + VarianceWelford(const int _unbiased, const bool _apply_sqrt): unbiased{_unbiased}, apply_sqrt(_apply_sqrt) {} + + inline __device__ T operator()(const WelfordData &a) const { + T res = THCNumerics::div(a.m_2_n_, unbiased ? a.count_ : a.count_-1); + if (apply_sqrt) { + return THCNumerics::sqrt(res); + } + return res; + } + + const int unbiased; + const bool apply_sqrt; +}; + +template +struct ReduceAdd { + inline __device__ T operator()(const T a, const T b) const { + return THCNumerics::add(a, b); + } +}; + +template +struct ReduceMultiply { + inline __device__ T operator()(const T a, const T b) const { + return THCNumerics::mul(a, b); + } +}; + +template +struct ReduceDivide { + ReduceDivide(const T _divisor): divisor{_divisor} {} + + inline __device__ T operator()(const T x) const { + return THCNumerics::div(x, divisor); + } + + const T divisor; +}; + +template +struct ReducePow { + ReducePow(const T _exponent): exponent{_exponent} {} + + inline __device__ T operator()(const T x) const { + return THCNumerics::pow(x, exponent); + } + + const T exponent; +}; + +template +struct SquareFunctor { + SquareFunctor(const T _mean): mean{_mean} {} + + inline __device__ T operator()(const T x) const { + return THCNumerics::mul( + THCNumerics::sub(x, mean), + THCNumerics::sub(x, mean) + ); + } + + const T mean; +}; + +template +struct ReduceMin { + inline __device__ T operator()(T a, T b) const { + return (THCNumerics::lt(a, b) || THCNumerics::isnan(a)) ? a : b; + } +}; + +template +struct ReduceMax { + inline __device__ T operator()(T a, T b) const { + return (THCNumerics::gt(a, b) || THCNumerics::isnan(a)) ? a : b; + } +}; + +struct LogicalAll { + inline __device__ unsigned char operator()(const unsigned char x, + const unsigned char y) const { + return (x && y); + } +}; + +struct LogicalAny { + inline __device__ unsigned char operator()(const unsigned char x, + const unsigned char y) const { + return (x || y); + } +}; + +template +inline __device__ T THCMax(const T a, const T b) { + return THCNumerics::gt(a, b) ? a : b; +} + +template +__global__ void THCTensor_kernel_renorm(T *data, + const AccT value, + const ptrdiff_t size, + const AccT maxnorm) { + __shared__ AccT buffer[32]; + int64_t tx = threadIdx.x; + int64_t bx = blockIdx.x; + int64_t step = blockDim.x; + T *row = data + size * bx; + + buffer[tx] = scalar_cast(0); + AccT norm; + + if (THCNumerics::eq(value, scalar_cast(INFINITY))) { + // get norm of axis + for (ptrdiff_t i = tx; i < size; i += step) { + const AccT val = scalar_cast(row[i]); + buffer[tx] = THCMax(buffer[tx], static_cast(std::abs(val))); + } + // add (reduce) + for (unsigned int stride = blockDim.x >> 1; stride > 0; stride >>= 1) { + __syncthreads(); + if (tx < stride) + buffer[tx] = THCMax(buffer[tx], buffer[tx+stride]); + } + // clip norms + __syncthreads(); + norm = buffer[0]; + } else { + // get norm of axis + for (ptrdiff_t i = tx; i < size; i += step) { + const AccT val = scalar_cast(row[i]); + buffer[tx] = THCNumerics::add( + buffer[tx], + THCNumerics::pow(static_cast(std::abs(val)), value) + ); + } + // add (reduce) + for (unsigned int stride = blockDim.x >> 1; stride > 0; stride >>= 1) { + __syncthreads(); + if (tx < stride) + buffer[tx] = THCNumerics::add(buffer[tx], buffer[tx+stride]); + } + // clip norms + __syncthreads(); + norm = THCNumerics::pow(buffer[0], THCNumerics::cinv(value)); + } + + if (THCNumerics::gt(norm, maxnorm)) { + norm = THCNumerics::div( + maxnorm, + THCNumerics::add(norm, scalar_cast(1e-7)) + ); + // renormalize + for (ptrdiff_t i = tx; i < size; i += step) { + const AccT val = scalar_cast(row[i]); + row[i] = scalar_cast(THCNumerics::mul(val, norm)); + } + } +} + +template +struct TensorNonZeroOp { + TensorNonZeroOp() {} + + __host__ __device__ T operator()(const T lhs) const { + const T zero = scalar_cast(0); + if (THCNumerics::eq(lhs, zero)) return zero; + + return scalar_cast(1); + } +}; + +template +struct TensorNormOp { + TensorNormOp(T _exponent) : exponent{_exponent} {} + + __host__ __device__ T operator()(const T x) const { + switch (StaticExp) { + case 1: return static_cast(std::abs(x)); + case 2: return THCNumerics::mul(x, x); + default: return THCNumerics::pow(static_cast(std::abs(x)), exponent); + } + } + + const T exponent; +}; + +/* + Fuses conversions and a TensorDistOp. Needed for Thrust. +*/ +template +struct ThrustTensorDistOp { + ThrustTensorDistOp(AccT _exponent) : exponent{_exponent} {} + + __host__ __device__ AccT operator()(T _x, T _y) const { + const AccT x = scalar_cast(_x); + const AccT y = scalar_cast(_y); + if (THCNumerics::eq(exponent, scalar_cast(0))) { + const AccT zero = scalar_cast(0); + if (THCNumerics::eq(THCNumerics::sub(x, y), zero))return zero; + return scalar_cast(1); + } + if (THCNumerics::eq(exponent, scalar_cast(1))) { + return static_cast(std::abs(THCNumerics::sub(x, y))); + } else if (THCNumerics::eq(exponent, scalar_cast(2))) { + return THCNumerics::pow( + THCNumerics::sub(x, y), exponent); + } else { + return THCNumerics::pow( + static_cast(std::abs(THCNumerics::sub(x, y))), + exponent); + } + } + + const AccT exponent; +}; + +#include + +// Given the sum of values and the sum of squares, compute the variance or standard deviation. +template +__forceinline__ __device__ T THCTensor_computeVar( + T sum, + T sum2, + const unsigned row_size) { + + T rs2 = scalar_cast(row_size); + T rs2m = scalar_cast(row_size - 1); + T zero = scalar_cast(0); + + if (flag) { + sum = THCNumerics::div(sum, rs2); + sum2 = THCNumerics::div(sum2, rs2); + sum2 = THCNumerics::sub(sum2, THCNumerics::mul(sum, sum)); + sum2 = (THCNumerics::lt(sum2, zero) ? zero : sum2); + } else { + sum = THCNumerics::div(sum, rs2); + sum2 = THCNumerics::div(sum2, rs2m); + sum2 = THCNumerics::sub(sum2, + THCNumerics::mul( + THCNumerics::div(rs2 ,rs2m), + THCNumerics::mul(sum, sum))); + sum2 = (THCNumerics::lt(sum2, zero) ? zero : sum2); + } + + if (apply_sqrt) + return THCNumerics::sqrt(sum2); + + return sum2; +} + +/* A set of reduction kernels that take in binary ops on thrust pairs (of value, index). + These are useful when you not only have to do a reduction, but you might have + to preserve the location of contention (for example min/max operations). + The structure of the kernels follows the structure of the reduction kernels. +*/ +template +__global__ void +kernelTransformReduceOuterDimIndex(K *tgt1, + Index *tgt2, + K *src_, + unsigned num_orows, + unsigned num_irows, + unsigned row_size, + thrust::pair init, + BinaryFunction binary_op) { + for (unsigned orow = blockIdx.x; orow < num_orows; orow += gridDim.x) { + for (unsigned irow = blockIdx.y * blockDim.x + threadIdx.x; + irow < num_irows; + irow += gridDim.y * blockDim.x) { + K *src = src_ + orow * row_size * num_irows + irow; + thrust::pair acc = init; + + for (unsigned col = 0; col < row_size; ++col) { + // +1 for Lua index + acc = binary_op(acc, + thrust::make_pair(*src, col)); + src += num_irows; + } + + tgt1[orow * num_irows + irow] = acc.first; + tgt2[orow * num_irows + irow] = acc.second; + } + } +} + +template +__host__ void +THC_transformReduceOuterDimIndex(THCState *state, + TensorTypeK *tgt1, + TensorTypeIndex *tgt2, + TensorTypeK *src, + int64_t rdim, + const thrust::pair& init, + BinaryFunction binary_op) { + unsigned ndim = THCTensor_nDimensionLegacyAll(state, src); + unsigned num_orows = 1; + for (int64_t dim = 0; dim < rdim; dim++) { + num_orows *= THCTensor_sizeLegacyNoScalars(state, src, dim); + } + unsigned row_size = THCTensor_sizeLegacyNoScalars(state, src, rdim); + unsigned num_irows = 1; + for (unsigned dim = rdim + 1; dim < ndim; dim++) { + num_irows *= THCTensor_sizeLegacyNoScalars(state, src, dim); + } + + dim3 threads(min(512, num_irows)); + unsigned maxGridDim = 1024; + dim3 grid(min(maxGridDim, num_orows), + min(maxGridDim, THCCeilDiv(num_irows, threads.x))); + + kernelTransformReduceOuterDimIndex + <<>>( + tgt1->template data(), + tgt2->template data(), + src->template data(), + num_orows, num_irows, row_size, init, binary_op); + + THCudaCheck(cudaGetLastError()); +} + +/* Reduce the innermost dimension of a tensor (on thrust::pair functors which are (value, index)) + * + * For an n-d tensor (n <= 4) where the reduction is along the innermost dimension: + * + * - block.x is the innermost dimension, i.e. dimension 0; + * - block.y and grid.y make up dimension 1; and + * - grid.x and grid z are the remaining two outer dimensions (if any) + * + * Reduction along other dimensions is handled in a separate kernel. + */ +template +__global__ void +kernelTransformReduceInnermostDimIndex(K *tgt1, + Index* tgt2, + K *src_, + unsigned num_rows, + unsigned row_size, + thrust::pair init, + BinaryFunction binary_op) { + __shared__ K sbuf[32][16 + 1]; // avoid bank conflict + __shared__ Index ibuf[32][16 + 1]; // avoid bank conflict + + for (unsigned block_row = blockIdx.x * blockDim.y; + block_row < num_rows; + block_row += blockDim.y * gridDim.x) { + unsigned row = block_row + threadIdx.y; + thrust::pair acc = init; + if (row < num_rows) { + K *src = src_ + row * row_size; + // Sequential reduction within a thread. + for (unsigned col = threadIdx.x; col < row_size; col += blockDim.x) { + acc = binary_op(acc, thrust::make_pair(src[col], col)); + } + } + + sbuf[threadIdx.y][threadIdx.x] = acc.first; + ibuf[threadIdx.y][threadIdx.x] = acc.second; + + __syncthreads(); + + // Reduce intermediate values to single value. + K* sline = &sbuf[threadIdx.y][0]; + Index* iline = &ibuf[threadIdx.y][0]; + for (unsigned s = 8; s > 0; s >>= 1) { + if (row < num_rows && threadIdx.x < s) { + thrust::pair arg1 = + thrust::make_pair(sline[threadIdx.x], iline[threadIdx.x]); + thrust::pair arg2 = + thrust::make_pair(sline[threadIdx.x + s], iline[threadIdx.x + s]); + thrust::pair res = binary_op(arg1, arg2); + + sline[threadIdx.x] = res.first; + iline[threadIdx.x] = res.second; + } + __syncthreads(); + } + + if (row < num_rows && threadIdx.x == 0) { + tgt1[row] = sline[0]; + tgt2[row] = iline[0]; + } + __syncthreads(); + } +} + +template +__host__ void +THC_transformReduceInnermostDimIndex(THCState *state, + TensorTypeK *tgt1, + TensorTypeIndex *tgt2, + TensorTypeK *src, + const thrust::pair& init, + BinaryFunction binary_op) { + unsigned ndim = THCTensor_nDimensionLegacyAll(state, src); + unsigned num_rows = 1; + for (unsigned dim = 0; dim < ndim - 1; dim++) { + num_rows *= THCTensor_sizeLegacyNoScalars(state, src, dim); + } + unsigned row_size = THCTensor_sizeLegacyNoScalars(state, src, ndim - 1); + + dim3 threads(16, 32); + dim3 grid(min(1024, THCCeilDiv(num_rows, threads.y))); + + kernelTransformReduceInnermostDimIndex + <<>>( + tgt1->template data(), + tgt2->template data(), + src->template data(), + num_rows, row_size, init, binary_op); + + THCudaCheck(cudaGetLastError()); +} + +template +void +THC_reduceDimIndex(THCState *state, + TensorTypeK *tgt1_, + TensorTypeIndex *tgt2_, + TensorTypeK *src, + int64_t dimension, + int keepdim, + const thrust::pair& init, + BinaryFunction binary_op) +{ + THArgCheck(dimension >= 0 && + dimension < THCTensor_nDimensionLegacyAll(state, src), + 3, "dimension out of range"); + + + // Unsqueeze tgt1_/tgt_2 if necessary so that their contiguity traits + // are preserved if they are the same size as the correct reduction output. + int src_dims = THCTensor_nDimensionLegacyAll(state, src); + THCTensor_preserveReduceDimSemantics( + state, tgt1_, src_dims, dimension, keepdim); + THCTensor_preserveReduceDimSemantics( + state, tgt2_, src_dims, dimension, keepdim); + + std::vector dim = THTensor_sizesLegacyNoScalars(src); + dim[dimension] = 1; + THCTensor_resize(state, tgt1_, dim, {}); + THCTensor_resize(state, tgt2_, dim, {}); + + TensorTypeK *tgt1 = (TensorTypeK*)THCTensor_newContiguous(state, tgt1_); + TensorTypeIndex *tgt2 = (TensorTypeIndex*)THCTensor_newContiguous(state, tgt2_); + src = (TensorTypeK*)THCTensor_newContiguous(state, src); + + if (dimension == THCTensor_nDimensionLegacyAll(state, src) - 1) { + THC_transformReduceInnermostDimIndex(state, tgt1, tgt2, src, init, binary_op); + } else { + THC_transformReduceOuterDimIndex(state, tgt1, tgt2, src, dimension, init, binary_op); + } + + THCTensor_free(state, src); + THCTensor_freeCopyTo(state, tgt1, tgt1_); + THCTensor_freeCopyTo(state, tgt2, tgt2_); + if (!keepdim) { + THCTensor_squeeze1d(state, tgt1_, tgt1_, dimension); + THCTensor_squeeze1d(state, tgt2_, tgt2_, dimension); + } +} + +template +struct MaxValuePair { + __host__ __device__ + thrust::pair operator()(const thrust::pair& a, + const thrust::pair& b) { + return (THCNumerics::ge(a.first, b.first) || + THCNumerics::isnan(a.first)) ? a : b; + } +}; + +template +struct MinValuePair { + __host__ __device__ + thrust::pair operator()(const thrust::pair& a, + const thrust::pair& b) { + return (THCNumerics::le(a.first, b.first) || + THCNumerics::isnan(a.first)) ? a : b; + } +}; + +template +struct AddOp { + __device__ __forceinline__ T operator()(T const &lhs, T const &rhs) { + return THCNumerics::add(lhs, rhs); + } +}; + +template +struct MulOp { + __device__ __forceinline__ T operator()(T const &lhs, T const &rhs) { + return THCNumerics::mul(lhs, rhs); + } +}; + +#endif // THC_TENSORMATH_REDUCE_CUH diff --git a/thirdparty/libtorch/include/THC/THCTensorMode.cuh b/thirdparty/libtorch/include/THC/THCTensorMode.cuh new file mode 100644 index 0000000000..ef9126860b --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCTensorMode.cuh @@ -0,0 +1,282 @@ +#ifndef THC_TENSOR_MODE_CUH +#define THC_TENSOR_MODE_CUH + +#include +#include +#include + +struct ThrustHalfLess +{ + __host__ __device__ inline bool operator()(const at::Half& lhs, const at::Half& rhs) { + return THCNumerics::lt(lhs, rhs); + } +}; + +struct ThrustHalfNotEqualTo +{ + __host__ __device__ inline bool operator()(const at::Half& lhs, const at::Half& rhs) { + return THCNumerics::ne(lhs, rhs); + } +}; + +struct ThrustHalfEqualTo +{ + __host__ __device__ inline bool operator()(const at::Half& lhs, const at::Half& rhs) { + return THCNumerics::eq(lhs, rhs); + } +}; + +struct ThrustHalfEqualToPredicate +{ + ThrustHalfEqualToPredicate(at::Half val): val_(val) {} + __host__ __device__ inline bool operator()(at::Half x) { + return THCNumerics::eq(val_, x); + } + + at::Half val_; +}; + +template +struct BinaryAddOp { + __host__ __device__ inline T operator()(const T a, const T b) { + return THCNumerics::add(a, b); + } +}; + +template <> +struct BinaryAddOp { + __host__ __device__ inline unsigned int operator()(const unsigned int a, const unsigned int b) { + return a + b; + } +}; + +// Used for a segmented reduction +struct ModeUnsignedBoolPair { + unsigned int val; + bool flag; +}; + +// In the kernel below, we have a common pattern of reducing (unsigned int, unsigned int) +// pairs of data +struct ModeUnsignedPair { + unsigned int val; + unsigned int index; +}; + +template +struct MaxReduceOp { + __host__ __device__ inline T operator()(const T& a, const T& b) { + return b.val > a.val ? b : a; + } +}; + +template +struct MatchReduceOp { + __host__ __device__ inline T operator()(const T& a, const T& b) { + return b.flag ? b : a; + } +}; + +// The mode kernel has the following characteristics: It uses internal shared memory +// buffers of Power2Size, which must be greater than the number of elements. Additionally, +// there is one block for every slice to calculate the mode for, and in each block there +// is one thread for every two elements. +// +// Both sorted and positions are assumed to be contiguous Tensors with the mode dimension +// as the innermost dim, such that we can get the particular slice for a Tensor via its +// linear block dimension * the slice size. +template +__global__ void computeMode( + T *input, + TensorInfo values, + TensorInfo indices, + int64_t sliceSize) +{ + int tidx = threadIdx.x; + int stidx = blockDim.x + threadIdx.x; // Second index this thread responsible for + + // First, we need to calculate the offset into the sorted Tensor that represents + // the start of the slice for this block to calculate the mode for. This offset + // is a combination of the gridIndices, and the number of elements in the slice. + unsigned int blockId = getLinearBlockId(); + unsigned int linearOffset = blockId * sliceSize; + + // shmem is a dynamically sized buffer we will use throughout the kernel to + // handle computation efficiently. The size of this shmem must be + // sizeof(T) * Power2Size + (2 * sizeof(unsigned int) * Power2Size) + // + // Initially, the buffer will be organized as follows: + // + // [smem (slice elements) | bmem (valid indices) | ] + extern __shared__ char shmem[]; + + // smem represents a proportion of the shared memory buffer that is used to store + // the elements from the slice: + T *smem = reinterpret_cast(shmem); + + // Each thread loads up to two elements from the Tensor into shared memory + if (tidx < sliceSize) { + smem[tidx] = input[linearOffset + tidx]; + } + if (stidx < sliceSize) { + smem[stidx] = input[linearOffset + stidx]; + } + + // Next, we initialize a boolean region of the buffer, offset by the loaded element + // smem region + bool *bmem = reinterpret_cast(&smem[Power2Size]); + + // The first use of this region stores bmem[i] = i < sliceSize to mark the valid + // components in the smem buffer + bmem[tidx] = tidx < sliceSize; + bmem[stidx] = stidx < sliceSize; + __syncthreads(); // barrier for smem, bmem initialization + + // First, sort the input slice in ascending order. smem contains the input + // elements, and bmem marks the valid indices + bitonicSortKeys, T, unsigned int, Power2Size>(smem, bmem, LTComp()); + __syncthreads(); // make no assumptions that the sort syncs at end + + // The next step of our algorithm is performing a block-wide comparison of + // neighboring elements. In particular, given an sorted input slice A, we + // produce an output slice B, such that B[i] = 1 if A[i-i] != A[i], otherwise 0. + // + // Given the input A = [0, 0, 1, 1, 2, 2, 2, 4, 5, 6, 6, 7, 8] + // B = [1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1] + // + // In particular, we can think of B[i] true indicating the start of a sequence of + // equal values in the sorted list. Similarly, we will also store the negation of B, + // which we'll call C. In particular, we can think of C[i] = true iff A[i-1] == A[i] + // in our original sorted slice. + // + // C = [0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0] + + // We overwrite bmem, and treat the rest of shared memory as a buffer of (index, flag) pairs + // where the index represents values from C, and the flag represents values from B. + // + // [smem (sorted slice) | ubpmem (index, flag pairs)] + + struct ModeUnsignedBoolPair *ubpmem = reinterpret_cast( + &smem[Power2Size]); + + if (tidx == 0) { + ubpmem[0].flag = true; + ubpmem[0].val = 0; + } + + // Compares elements (0, 1), (2, 3), ... and sets 1, 3, ... + ubpmem[tidx * 2 + 1].flag = THCNumerics::ne(smem[tidx * 2], smem[tidx * 2 + 1]); // (0, 1), (1, 2), etc. + ubpmem[tidx * 2 + 1].val = !ubpmem[tidx * 2 + 1].flag; + + // Compares elements (1, 2), (3, 4), ... and sets 2, 4, ... + if (((tidx + 1) * 2) < Power2Size) { + ubpmem[(tidx + 1) * 2].flag = THCNumerics::ne(smem[((tidx + 1) * 2) - 1], smem[(tidx + 1) * 2]); + ubpmem[(tidx + 1) * 2].val = !ubpmem[(tidx + 1) * 2].flag; + } + __syncthreads(); // barrier for ubpmem initialization + + // Next, we perform a segmented prefix sum on the neighboring elements, where + // the presence of a one indicates the start of a segment. In this case B acts + // as the segment start flags, and C is the buffer to be summed: + // + // Input (C) = [0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0] + // Flag (B) = [1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1] + // Output (C) = [0, 1, 0, 1, 0, 1, 2, 0, 0, 0, 1, 0, 0] + // + // Afterwards, the (index) components of the ubpmem buffer contain the lengths of the + // segments (minus 1), i.e. the counts of each element in the original input. + + inclusivePrefixScan< + struct ModeUnsignedBoolPair, + struct SegmentedScanOp >, + Power2Size>( + ubpmem, + SegmentedScanOp >(BinaryAddOp())); + // assumes scan syncs at the end + + // Next, we reinterpret the ubpmem buffer as pairs of unsigned integers (i.e. we treat the + // boolean flag regions as integers). We initialize these to represent indices, and we'll call + // this buffer I + struct ModeUnsignedPair *uupmem = reinterpret_cast(ubpmem); + + // At this point, we need to find the maximum element in lengths buffer C. + // This element will represent the count (-1) of the mode. Because of the + // way we have set up the problem, the index where this mode occurs will + // also be the location of the mode value in the sorted array, e.g. + // + // smem = [0, 0, 1, 1, 1, 2] + // C = [0, 1, 0, 1, 2, 0] + // I = [0, 1, 2, 3, 4, 5] + // ^ + // maximum value, also aligned with mode = 1 + // + // We perform a block wide max-reduction of the C buffer, but we also need the + // indices to come along with it, so we utilize the uupmem construction. + // + // At the end we need to return the ModeUnsignedPair containing index = 4, val = 2, + // which represents the max + + // In practice, we will make each thread locally reduce 2 values in its registers prior + // to the global block-wide reduction. Note that instead of tidx/stidx, we utilize tidx * 2, + // tidx * 2 + 1, so each thread deals with adjacent elements. This is because the reduce + // code below relies on thread elements to be adjacent. + struct ModeUnsignedPair uup[2]; + uup[0].index = tidx * 2; + uup[0].val = ubpmem[tidx * 2].val; + uup[1].index = tidx * 2 + 1; + uup[1].val = ubpmem[tidx * 2 + 1].val; + __syncthreads(); + + struct ModeUnsignedPair max = {0, 0}; + + max = reduceBlockWithNThreadLocalReductions, 2> + (uupmem, uup, sliceSize, MaxReduceOp(), max); + + // Store the mode in shared memory for use in finding the mode in the input slice + __shared__ T mode; + + // Given the above constraints, the mode is the value at the reduced index in the + // original sorted element buffer + if (tidx == 0) { + mode = smem[max.index]; + } + __syncthreads(); // broadcast mode + + // Finally, we need to find the "an" index of the mode in the input Tensor. The API does + // not constrain which index we pick, so it can be any of the indices that contain the mode. + // We will do a reduction to find the index. We go back to using the (index, flag) buffer + // arrangement. First, we mark indices that are equal to the mode, i.e B[i] = true if + // input[i] == mode, and initialize C[i] to be the index + // + // Again we reduce 2 elements in the thread's registers prior to the block-wide reduction + struct ModeUnsignedBoolPair ubpp[2]; + if (tidx * 2 < sliceSize) { + ubpp[0].flag = THCNumerics::eq(input[linearOffset + (tidx * 2)], mode); + ubpp[0].val = tidx * 2; + } + if (tidx * 2 + 1 < sliceSize) { + ubpp[1].flag = THCNumerics::eq(input[linearOffset + (tidx * 2 + 1)], mode); + ubpp[1].val = tidx * 2 + 1; + } + + // Then we perform a similar reduction to the one above, except this time we update + // the element if the element at the base position is not equal to the mode and + // the element at the offset position is. At the end, C[0] will contain an index + // with the mode. + struct ModeUnsignedBoolPair match = {0, false}; + + match = reduceBlockWithNThreadLocalReductions, 2> + (ubpmem, ubpp, sliceSize, MatchReduceOp(), match); + + // Finally, we have the mode, and an index where it occurs. We use a single thread + // to place this in the appropriate output position + if (tidx == 0) { + int64_t index = match.val; + + unsigned int outputOffset = IndexToOffset::get(blockId, values); + values.data[outputOffset] = mode; + indices.data[outputOffset] = index; + } +} + +#endif // THC_TENSOR_MODE_CUH diff --git a/thirdparty/libtorch/include/THC/THCTensorRandom.cuh b/thirdparty/libtorch/include/THC/THCTensorRandom.cuh new file mode 100644 index 0000000000..d38cb96801 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCTensorRandom.cuh @@ -0,0 +1,90 @@ +#ifndef THC_TENSOR_RANDOM_CUH +#define THC_TENSOR_RANDOM_CUH + +#include +#include +#include + +#include + +#define MAX_NUM_BLOCKS 200 +#define BLOCK_SIZE 256 + +template +__global__ void +multinomialAliasDrawKernel(int size, int64_t *output, int64_t *J, T *q, int64_t K, T *uniform, T *bernoulli){ + int64_t idx = blockIdx.x * BLOCK_SIZE + threadIdx.x; + if (idx < size) { + int64_t rand_ind = ScalarConvert::to(uniform[idx]); + T bern_uniform = bernoulli[idx]; + int _mask = (int) THCNumerics::lt(bern_uniform, q[rand_ind]); + output[idx] = J[rand_ind]*(1 -_mask) + rand_ind * _mask; + } +} + +template +__global__ void +aliasMultinomialFilter(T *q, T *probs, int64_t *smaller, int64_t *larger, int64_t *J_data, int64_t *larger_short_data, int64_t *smaller_short_data, T one, int64_t inputsize){ + int64_t idx = blockIdx.x * BLOCK_SIZE + threadIdx.x; + if (idx < inputsize) { + larger_short_data[idx] = 0; + smaller_short_data[idx] = 0; + J_data[idx]= -1; + T val = THCNumerics::mul(probs[idx], ScalarConvert::to(inputsize)); + if (THCNumerics::lt(val, one)) { + smaller[idx] = idx+1; + larger[idx] = 0; + } else { + larger[idx] = idx+1; + smaller[idx] = 0; + } + q[idx] = val; + } +} + +template +__global__ void +condDiv(T *q, int64_t *J, int64_t inputsize, T q_max) { + int64_t idx = blockIdx.x * BLOCK_SIZE + threadIdx.x; + T one = ScalarConvert::to(1); + if (idx < inputsize) { + if (J[idx] < 0) { + q[idx] = one; + } else { + if (THCNumerics::gt(q_max, one)) { + q[idx] = THCNumerics::div(q[idx], q_max); + } + } + } +} + + +#undef MAX_NUM_BLOCKS +#undef BLOCK_SIZE + +template +__global__ void +aliasMultinomialSetup(int64_t *J, T*q, int64_t inputsize, int64_t * smaller, int64_t *larger, int small_c, int large_c) { + T one = ScalarConvert::to(1); + // Loop through and create little binary mixtures that + // appropriately allocate the larger outcomes over the + // overall uniform mixture. + int64_t large = 0; + int64_t small = 0; + while (small_c > 0 && large_c > 0) { + large = larger[large_c-1]; + small = smaller[small_c-1]; + J[small] = large; + T q_sum = THCNumerics::add(q[large], q[small]); + q[large] = THCNumerics::sub(q_sum, one); + if (THCNumerics::lt(q[large], one)) { + smaller[small_c-1] = large; + large_c -= 1; + } else { + larger[large_c-1] = large; + small_c -= 1; + } + } +} + +#endif // THC_TENSOR_RANDOM_CUH diff --git a/thirdparty/libtorch/include/THC/THCTensorRandom.h b/thirdparty/libtorch/include/THC/THCTensorRandom.h new file mode 100644 index 0000000000..8422f3fafa --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCTensorRandom.h @@ -0,0 +1,17 @@ +#ifndef TH_CUDA_TENSOR_RANDOM_INC +#define TH_CUDA_TENSOR_RANDOM_INC + +#include + +#include +#include + +#include +#include + +#include + +THC_API void THCRandom_getRNGState(at::Generator *gen_, THByteTensor *rng_state); +THC_API void THCRandom_setRNGState(at::Generator *gen_, THByteTensor *rng_state); + +#endif diff --git a/thirdparty/libtorch/include/THC/THCTensorSort.cuh b/thirdparty/libtorch/include/THC/THCTensorSort.cuh new file mode 100644 index 0000000000..ffda23a000 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCTensorSort.cuh @@ -0,0 +1,103 @@ +#ifndef THC_TENSORSORT_CUH +#define THC_TENSORSORT_CUH + +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#if CUDA_VERSION >= 7000 || defined(__HIP_PLATFORM_HCC__) +#include +#endif + +template +struct ThrustGTOp { + __device__ bool operator()(const T& lhs, const T& rhs) const { + return (handleNaN && THCNumerics::isnan(lhs) && !THCNumerics::isnan(rhs)) || THCNumerics::gt(lhs, rhs); + } +}; + +template +struct ThrustLTOp { + __device__ bool operator()(const T& lhs, const T& rhs) const { + return (handleNaN && THCNumerics::isnan(rhs) && !THCNumerics::isnan(lhs)) || THCNumerics::lt(lhs, rhs); + } +}; + +template +struct ThrustSliceGTOp { +ThrustSliceGTOp(int64_t size) : sliceSize(size) {} + __device__ bool operator()(const thrust::tuple& lhs, const thrust::tuple& rhs) const { + IndT segA = (IndT)thrust::get<0>(lhs) / sliceSize; + IndT segB = (IndT)thrust::get<0>(rhs) / sliceSize; + if (segA != segB) + return segA < segB; + else + return (handleNaN && THCNumerics::isnan(thrust::get<1>(lhs)) && !THCNumerics::isnan(thrust::get<1>(rhs))) || THCNumerics::gt(thrust::get<1>(lhs), thrust::get<1>(rhs)); + } + const IndT sliceSize; +}; + +template +struct ThrustSliceLTOp { +ThrustSliceLTOp(int64_t size) : sliceSize(size) {} + __device__ bool operator()(const thrust::tuple& lhs, const thrust::tuple& rhs) const { + IndT segA = (IndT)thrust::get<0>(lhs) / sliceSize; + IndT segB = (IndT)thrust::get<0>(rhs) / sliceSize; + if (segA != segB) + return segA < segB; + else + return (handleNaN && THCNumerics::isnan(thrust::get<1>(rhs)) && !THCNumerics::isnan(thrust::get<1>(lhs))) || THCNumerics::lt(thrust::get<1>(lhs), thrust::get<1>(rhs)); + } + const IndT sliceSize; +}; + + + + +// `base` is the base address of a tensor +// For each slice (defined as a linear point of `out`, from 0 -> +// (sliceSize - 1) * sliceStride, we fill that slice from `0` to +// `sliceSize - 1`. +template +__global__ void +fillSliceWithIndex(TensorInfo out, + IndexType totalSlices, + IndexType sliceSize, + IndexType sliceStride) { + IndexType slice = getLinearBlockId(); + + if (slice >= totalSlices) { + return; + } + + const uint64_t offset = + IndexToOffset::get(slice, out); + int64_t* base = &out.data[offset]; + + for (int64_t i = threadIdx.x; i < sliceSize; i += blockDim.x) { + // Torch indices are 1-based (hence the +1) + base[i * sliceStride] = i; + } +} + +// For sorting in Thurst; extracts a within-slice index from a linear index +struct GlobalIndexToPerSliceIndex { + GlobalIndexToPerSliceIndex(int64_t size) : sliceSize(size) {} + + __device__ inline void operator()(int64_t& v) const { + v = v % sliceSize; + } + + const int64_t sliceSize; +}; + +void THCudaLongTensor_fillSliceWithIndex(THCState* state, + THCudaLongTensor* t, + int dim); +#endif // THC_TENSORSORT_CUH diff --git a/thirdparty/libtorch/include/THC/THCTensorTopK.cuh b/thirdparty/libtorch/include/THC/THCTensorTopK.cuh new file mode 100644 index 0000000000..4863cf90aa --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCTensorTopK.cuh @@ -0,0 +1,144 @@ +#ifndef THC_TENSOR_TOPK_CUH +#define THC_TENSOR_TOPK_CUH + +#include +#include + +using namespace at::native; + +template +C10_LAUNCH_BOUNDS_1(1024) +__global__ void gatherTopK(TensorInfo input, + IndexType inputSliceSize, + IndexType outputSliceSize, // aka `k` + + IndexType numInputSlices, + IndexType inputWithinSliceStride, + + TensorInfo topK, + IndexType numTopKSlices, + IndexType topKWithinSliceStride, + + TensorInfo indices, + IndexType indicesWithinSliceStride) { + // Indices are limited to integer fp precision, so counts can fit in + // int32, regardless of IndexType +#ifdef __HIP_PLATFORM_HCC__ + __shared__ int smem[64]; +#else + __shared__ int smem[32]; // one per each warp, up to warp limit +#endif + + IndexType slice = getLinearBlockId(); + if (slice >= numInputSlices) { + return; + } + + // Find the start offset for our slice + IndexType sliceStartIndex = + IndexToOffset::get(slice, input); + IndexType topKSliceStartIndex = + IndexToOffset::get(slice, topK); + IndexType indicesSliceStartIndex = + IndexToOffset::get(slice, indices); + + T* inputSliceStart = &input.data[sliceStartIndex]; + T* topKSliceStart = &topK.data[topKSliceStartIndex]; + int64_t* indicesSliceStart = &indices.data[indicesSliceStartIndex]; + + // Find the k-th highest element in our input + T topKValue = ScalarConvert::to(0); + radixSelect::RadixType, IndexType, Order>( + inputSliceStart, outputSliceSize, + inputSliceSize, inputWithinSliceStride, + smem, &topKValue); + + // Every value that is strictly less/greater than `pattern` + // (depending on sort dir) in sorted int format is in the top-K. + // The top-K value itself might not be unique. + // + // Since there are a variable number of elements that we see that + // are within the top-k, we don't know at what index to write out + // the resulting values. + // In order to get this, we perform an exclusive prefix sum of + // `hasTopK`. This will return the resulting index into which we + // need to write the result, if a thread has a result. + + // All threads need to participate in the loop and the prefix sum, + // but not necessarily in the load; hence loop bounds being rounded + // up to a multiple of the block dim. + IndexType numIterations = THCRoundUp(inputSliceSize, (IndexType) blockDim.x); + IndexType writeIndexStart = 0; + + for (IndexType i = threadIdx.x; i < numIterations; i += blockDim.x) { + bool inRange = (i < inputSliceSize); + T v = + inRange ? doLdg(&inputSliceStart[i * inputWithinSliceStride]) : ScalarConvert::to(0); + bool hasTopK; + if (Order) { + hasTopK = inRange && (THCNumerics::gt(v, topKValue)); + } else { + hasTopK = inRange && (THCNumerics::lt(v, topKValue)); + } + + int index; + int carry; + exclusiveBinaryPrefixScan(smem, hasTopK, &index, &carry, AddOp()); + + if (hasTopK) { + int writeIndex = writeIndexStart + index; + assert(writeIndex < outputSliceSize); + + IndexType topKOffset = writeIndex * topKWithinSliceStride; + IndexType indexOffset = writeIndex * indicesWithinSliceStride; + + topKSliceStart[topKOffset] = v; + indicesSliceStart[indexOffset] = i; + } + + writeIndexStart += carry; + } + + // We need to fill in the rest with actual == top-K values. + // The number that we need is outputSliceSize - + // writeIndexStart. There might be more than that number available, + // in which case we have to choose the first seen set. We do this + // via a prefix sum to calculate indices for writing results. + assert(outputSliceSize >= writeIndexStart); + IndexType topKRemaining = (outputSliceSize - writeIndexStart); + + for (IndexType i = threadIdx.x; i < numIterations; i += blockDim.x) { + bool inRange = (i < inputSliceSize); + T v = + inRange ? doLdg(&inputSliceStart[i * inputWithinSliceStride]) : ScalarConvert::to(0); + bool hasTopK = inRange && (THCNumerics::eq(v, topKValue)); + + int index; + int carry; + exclusiveBinaryPrefixScan(smem, hasTopK, &index, &carry, AddOp()); + + if (hasTopK && index < topKRemaining) { + int writeIndex = writeIndexStart + index; + assert(writeIndex < outputSliceSize); + + IndexType topKOffset = writeIndex * topKWithinSliceStride; + IndexType indexOffset = writeIndex * indicesWithinSliceStride; + + topKSliceStart[topKOffset] = v; + indicesSliceStart[indexOffset] = i; + } + + if (carry >= topKRemaining) { + break; + } + + topKRemaining -= carry; + writeIndexStart += carry; + } +} + +#undef RADIX_BITS +#undef RADIX_SIZE +#undef RADIX_MASK + +#endif // THC_TENSOR_TOPK_CUH diff --git a/thirdparty/libtorch/include/THC/THCTensorTypeUtils.cuh b/thirdparty/libtorch/include/THC/THCTensorTypeUtils.cuh new file mode 100644 index 0000000000..23d7c8c232 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCTensorTypeUtils.cuh @@ -0,0 +1,83 @@ +#ifndef THC_TENSOR_TYPE_UTILS_INC +#define THC_TENSOR_TYPE_UTILS_INC + +#include +#include +#include +#include +#include +#include +#include + +/// A utility for accessing THCuda*Tensor types in a generic manner + +/// Equivalent to C++11's type_traits std::is_same; used for comparing +/// equality of types. Don't assume the existence of C++11 +template +struct SameType { + static const bool same = false; +}; + +template +struct SameType { + static const bool same = true; +}; + +template +bool isSameType() { + return SameType::same; +} + +// Utility function for constructing TensorInfo structs. In this case, the +// two template parameters are: +// +// 1. The TensorType, e.g. THCTensor in generic functions, or THCudaTensor, +// THCudaLongTensor etc. +// +// 2. The IndexType. This is always going to be an unsigned integral value, +// but depending on the size of the Tensor you may select uint16_t +// uint32_t, uint64_t etc. +// +// Internally we use the TensorUtils static functions to get the necessary +// dims, sizes, stride etc. +// +// For example, suppose we have a THCudaTensor t, with dim = 2, size = [3, 4], +// stride = [4, 1], offset = 8, and we set our index type to be unsigned int. +// Then we yield a TensorInfo struct templatized with float, unsigned int and +// the following fields: +// +// data is a float* to the underlying storage at position 8 +// dims is 2 +// sizes is a MAX_CUTORCH_DIMS element array with [3, 4] in its first two positions +// strides is a MAX_CUTORCH_DIMS element array with [4, 1] in its first two positions +// +// TensorInfos can then be passed to CUDA kernels, but we can use the static functions +// defined above to perform Tensor Operations that are appropriate for each +// TensorType. +template +TensorInfo +getTensorInfo(THCState* state, TensorType* t) { + IndexType sz[MAX_CUTORCH_DIMS]; + IndexType st[MAX_CUTORCH_DIMS]; + + int dims = THCTensor_nDimensionLegacyNoScalars(state, t); + for (int i = 0; i < dims; ++i) { + sz[i] = THTensor_sizeLegacyNoScalars(t, i); + st[i] = THTensor_strideLegacyNoScalars(t, i); + } + + return TensorInfo( + t->template data(), dims, sz, st); +} + +template +struct ScalarNegate { + static __host__ __device__ T to(const T v) { return -v; } +}; + +template +struct ScalarInv { + static __host__ __device__ T to(const T v) { return ((T) 1) / v; } +}; + +#endif // THC_TENSOR_TYPE_UTILS_INC diff --git a/thirdparty/libtorch/include/THC/THCThrustAllocator.cuh b/thirdparty/libtorch/include/THC/THCThrustAllocator.cuh new file mode 100644 index 0000000000..0e753222d6 --- /dev/null +++ b/thirdparty/libtorch/include/THC/THCThrustAllocator.cuh @@ -0,0 +1,31 @@ +#ifndef THC_THRUST_ALLOCATOR_INC +#define THC_THRUST_ALLOCATOR_INC + +#include + +/// Allocator for Thrust to re-route its internal device allocations +/// to the THC allocator +class THCThrustAllocator { + public: + typedef char value_type; + + THCThrustAllocator(THCState* state) + : state_(state) { + } + + ~THCThrustAllocator() { + } + + char* allocate(std::ptrdiff_t size) { + return static_cast(THCudaMalloc(state_, size)); + } + + void deallocate(char* p, size_t size) { + THCudaFree(state_, p); + } + + private: + THCState* state_; +}; + +#endif // THC_THRUST_ALLOCATOR_INC diff --git a/thirdparty/libtorch/include/THC/generic/THCStorage.h b/thirdparty/libtorch/include/THC/generic/THCStorage.h new file mode 100644 index 0000000000..cbcdaf5f3e --- /dev/null +++ b/thirdparty/libtorch/include/THC/generic/THCStorage.h @@ -0,0 +1,53 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "THC/generic/THCStorage.h" +#else + +#define THCStorage THStorage + +// These used to be distinct types; for some measure of backwards compatibility and documentation +// alias these to the single THCStorage type. +#define THCudaStorage THCStorage +#define THCudaDoubleStorage THCStorage +#define THCudaHalfStorage THCStorage +#define THCudaByteStorage THCStorage +#define THCudaCharStorage THCStorage +#define THCudaShortStorage THCStorage +#define THCudaIntStorage THCStorage +#define THCudaLongStorage THCStorage +#define THCudaBoolStorage THCStorage +#define THCudaBFloat16Storage THCStorage + +THC_API scalar_t* THCStorage_(data)(THCState *state, const THCStorage*); +THC_API ptrdiff_t THCStorage_(size)(THCState *state, const THCStorage*); +THC_API int THCStorage_(elementSize)(THCState *state); + +/* slow access -- checks everything */ +THC_API void THCStorage_(set)(THCState *state, THCStorage*, ptrdiff_t, scalar_t); +THC_API scalar_t THCStorage_(get)(THCState *state, const THCStorage*, ptrdiff_t); + +THC_API THCStorage* THCStorage_(new)(THCState *state); +THC_API THCStorage* THCStorage_(newWithSize)(THCState *state, ptrdiff_t size); +THC_API THCStorage* THCStorage_(newWithSize1)(THCState *state, scalar_t); +THC_API THCStorage* THCStorage_(newWithSize2)(THCState *state, scalar_t, scalar_t); +THC_API THCStorage* THCStorage_(newWithSize3)(THCState *state, scalar_t, scalar_t, scalar_t); +THC_API THCStorage* THCStorage_(newWithSize4)(THCState *state, scalar_t, scalar_t, scalar_t, scalar_t); +THC_API THCStorage* THCStorage_(newWithMapping)(THCState *state, const char *filename, ptrdiff_t size, int shared); + +THC_API THCStorage* THCStorage_(newWithAllocator)( + THCState *state, ptrdiff_t size, + at::Allocator* allocator); +THC_API THCStorage* THCStorage_(newWithDataAndAllocator)( + THCState *state, at::DataPtr&& data, ptrdiff_t size, + at::Allocator* allocator); + +THC_API void THCStorage_(setFlag)(THCState *state, THCStorage *storage, const char flag); +THC_API void THCStorage_(clearFlag)(THCState *state, THCStorage *storage, const char flag); +THC_API void THCStorage_(retain)(THCState *state, THCStorage *storage); + +THC_API void THCStorage_(free)(THCState *state, THCStorage *storage); +THC_API void THCStorage_(resize)(THCState *state, THCStorage *storage, ptrdiff_t size); +THC_API void THCStorage_(fill)(THCState *state, THCStorage *storage, scalar_t value); + +THC_API int THCStorage_(getDevice)(THCState* state, const THCStorage* storage); + +#endif diff --git a/thirdparty/libtorch/include/THC/generic/THCStorageCopy.h b/thirdparty/libtorch/include/THC/generic/THCStorageCopy.h new file mode 100644 index 0000000000..16c5afd147 --- /dev/null +++ b/thirdparty/libtorch/include/THC/generic/THCStorageCopy.h @@ -0,0 +1,45 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "THC/generic/THCStorageCopy.h" +#else + +/* Support for copy between different Storage types */ + +THC_API void THCStorage_(copy)(THCState *state, THCStorage *storage, THCStorage *src); +THC_API void THCStorage_(copyByte)(THCState *state, THCStorage *storage, struct THByteStorage *src); +THC_API void THCStorage_(copyChar)(THCState *state, THCStorage *storage, struct THCharStorage *src); +THC_API void THCStorage_(copyShort)(THCState *state, THCStorage *storage, struct THShortStorage *src); +THC_API void THCStorage_(copyInt)(THCState *state, THCStorage *storage, struct THIntStorage *src); +THC_API void THCStorage_(copyLong)(THCState *state, THCStorage *storage, struct THLongStorage *src); +THC_API void THCStorage_(copyFloat)(THCState *state, THCStorage *storage, struct THFloatStorage *src); +THC_API void THCStorage_(copyDouble)(THCState *state, THCStorage *storage, struct THDoubleStorage *src); +THC_API void THCStorage_(copyHalf)(THCState *state, THCStorage *storage, struct THHalfStorage *src); +THC_API void THCStorage_(copyBool)(THCState *state, THCStorage *storage, struct THBoolStorage *src); +THC_API void THCStorage_(copyBFloat16)(THCState *state, THCStorage *storage, struct THBFloat16Storage *src); + +THC_API void THCStorage_(copyCudaByte)(THCState *state, THCStorage *storage, struct THCudaByteStorage *src); +THC_API void THCStorage_(copyCudaChar)(THCState *state, THCStorage *storage, struct THCudaCharStorage *src); +THC_API void THCStorage_(copyCudaShort)(THCState *state, THCStorage *storage, struct THCudaShortStorage *src); +THC_API void THCStorage_(copyCudaInt)(THCState *state, THCStorage *storage, struct THCudaIntStorage *src); +THC_API void THCStorage_(copyCudaLong)(THCState *state, THCStorage *storage, struct THCudaLongStorage *src); +THC_API void THCStorage_(copyCudaFloat)(THCState *state, THCStorage *storage, struct THCudaStorage *src); +THC_API void THCStorage_(copyCudaDouble)(THCState *state, THCStorage *storage, struct THCudaDoubleStorage *src); +THC_API void THCStorage_(copyCudaHalf)(THCState *state, THCStorage *storage, struct THCudaHalfStorage *src); +THC_API void THCStorage_(copyCudaBool)(THCState *state, THCStorage *storage, struct THCudaBoolStorage *src); +THC_API void THCStorage_(copyCudaBFloat16)(THCState *state, THCStorage *storage, struct THCudaBFloat16Storage *src); + +THC_API void TH_CONCAT_2(THByteStorage_copyCuda , Real)(THCState *state, THByteStorage *self, struct THCStorage *src); +THC_API void TH_CONCAT_2(THCharStorage_copyCuda , Real)(THCState *state, THCharStorage *self, struct THCStorage *src); +THC_API void TH_CONCAT_2(THShortStorage_copyCuda , Real)(THCState *state, THShortStorage *self, struct THCStorage *src); +THC_API void TH_CONCAT_2(THIntStorage_copyCuda , Real)(THCState *state, THIntStorage *self, struct THCStorage *src); +THC_API void TH_CONCAT_2(THLongStorage_copyCuda , Real)(THCState *state, THLongStorage *self, struct THCStorage *src); +THC_API void TH_CONCAT_2(THFloatStorage_copyCuda , Real)(THCState *state, THFloatStorage *self, struct THCStorage *src); +THC_API void TH_CONCAT_2(THDoubleStorage_copyCuda, Real)(THCState *state, THDoubleStorage *self, struct THCStorage *src); +THC_API void TH_CONCAT_2(THHalfStorage_copyCuda, Real)(THCState *state, THHalfStorage *self, struct THCStorage *src); +THC_API void TH_CONCAT_2(THBoolStorage_copyCuda, Real)(THCState *state, THBoolStorage *self, struct THCStorage *src); +THC_API void TH_CONCAT_2(THBFloat16Storage_copyCuda, Real)(THCState *state, THBFloat16Storage *self, struct THCStorage *src); + +THC_API void THStorage_(copyCuda)(THCState *state, THStorage *self, THCStorage *src); +THC_API void THCStorage_(copyCuda)(THCState *state, THCStorage *self, THCStorage *src); +THC_API void THCStorage_(copyCPU)(THCState *state, THCStorage *self, THStorage *src); + +#endif diff --git a/thirdparty/libtorch/include/THC/generic/THCTensor.h b/thirdparty/libtorch/include/THC/generic/THCTensor.h new file mode 100644 index 0000000000..79e5b14d09 --- /dev/null +++ b/thirdparty/libtorch/include/THC/generic/THCTensor.h @@ -0,0 +1,134 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "THC/generic/THCTensor.h" +#else + +#define THCTensor THTensor + +// These used to be distinct types; for some measure of backwards compatibility and documentation +// alias these to the single THCTensor type. +#define THCudaTensor THCTensor +#define THCudaDoubleTensor THCTensor +#define THCudaHalfTensor THCTensor +#define THCudaByteTensor THCTensor +#define THCudaCharTensor THCTensor +#define THCudaShortTensor THCTensor +#define THCudaIntTensor THCTensor +#define THCudaLongTensor THCTensor +#define THCudaBoolTensor THCTensor +#define THCudaBFloat16Tensor THCTensor + +/**** access methods ****/ +THC_API THCStorage* THCTensor_(storage)(THCState *state, const THCTensor *self); +THC_API ptrdiff_t THCTensor_(storageOffset)(THCState *state, const THCTensor *self); + +// See [NOTE: nDimension vs nDimensionLegacyNoScalars vs nDimensionLegacyAll] +THC_API int THCTensor_(nDimension)(THCState *state, const THCTensor *self); +THC_API int THCTensor_(nDimensionLegacyNoScalars)(THCState *state, const THCTensor *self); +THC_API int THCTensor_(nDimensionLegacyAll)(THCState *state, const THCTensor *self); + +THC_API int64_t THCTensor_(size)(THCState *state, const THCTensor *self, int dim); +THC_API int64_t THCTensor_(sizeLegacyNoScalars)(THCState *state, const THCTensor *self, int dim); +THC_API int64_t THCTensor_(stride)(THCState *state, const THCTensor *self, int dim); +THC_API int64_t THCTensor_(strideLegacyNoScalars)(THCState *state, const THCTensor *self, int dim); +THC_API scalar_t *THCTensor_(data)(THCState *state, const THCTensor *self); + +THC_API void THCTensor_(setFlag)(THCState *state, THCTensor *self, const char flag); +THC_API void THCTensor_(clearFlag)(THCState *state, THCTensor *self, const char flag); + + +/**** creation methods ****/ +THC_API THCTensor *THCTensor_(new)(THCState *state); +THC_API THCTensor *THCTensor_(newWithTensor)(THCState *state, THCTensor *tensor); +THC_API THCTensor *THCTensor_(newWithStorage1d)(THCState *state, THCStorage *storage_, ptrdiff_t storageOffset_, + int64_t size0_, int64_t stride0_); +THC_API THCTensor *THCTensor_(newWithStorage2d)(THCState *state, THCStorage *storage_, ptrdiff_t storageOffset_, + int64_t size0_, int64_t stride0_, + int64_t size1_, int64_t stride1_); +THC_API THCTensor *THCTensor_(newWithStorage3d)(THCState *state, THCStorage *storage_, ptrdiff_t storageOffset_, + int64_t size0_, int64_t stride0_, + int64_t size1_, int64_t stride1_, + int64_t size2_, int64_t stride2_); +THC_API THCTensor *THCTensor_(newWithStorage4d)(THCState *state, THCStorage *storage_, ptrdiff_t storageOffset_, + int64_t size0_, int64_t stride0_, + int64_t size1_, int64_t stride1_, + int64_t size2_, int64_t stride2_, + int64_t size3_, int64_t stride3_); + +/* stride might be NULL */ +THC_API THCTensor *THCTensor_(newWithSize1d)(THCState *state, int64_t size0_); +THC_API THCTensor *THCTensor_(newWithSize2d)(THCState *state, int64_t size0_, int64_t size1_); +THC_API THCTensor *THCTensor_(newWithSize3d)(THCState *state, int64_t size0_, int64_t size1_, int64_t size2_); +THC_API THCTensor *THCTensor_(newWithSize4d)(THCState *state, int64_t size0_, int64_t size1_, int64_t size2_, int64_t size3_); + +THC_API THCTensor *THCTensor_(newClone)(THCState *state, THCTensor *self); +THC_API THCTensor *THCTensor_(newContiguous)(THCState *state, THCTensor *tensor); +THC_API THCTensor *THCTensor_(newSelect)(THCState *state, THCTensor *tensor, int dimension_, int64_t sliceIndex_); +THC_API THCTensor *THCTensor_(newNarrow)(THCState *state, THCTensor *tensor, int dimension_, int64_t firstIndex_, int64_t size_); +THC_API THCTensor *THCTensor_(newTranspose)(THCState *state, THCTensor *tensor, int dimension1_, int dimension2_); +THC_API THCTensor *THCTensor_(newFoldBatchDim)(THCState *state, THCTensor *input); + +// resize* methods simply resize the storage. So they may not retain the current data at current indices. +// This is especially likely to happen when the tensor is not contiguous. In general, if you still need the +// values, unless you are doing some size and stride tricks, do not use resize*. +THC_API void THCTensor_(resizeNd)(THCState *state, THCTensor *tensor, int nDimension, const int64_t *size, const int64_t *stride); +THC_API void THCTensor_(resizeAs)(THCState *state, THCTensor *tensor, THCTensor *src); +THC_API void THCTensor_(resize0d)(THCState *state, THCTensor *tensor); +THC_API void THCTensor_(resize1d)(THCState *state, THCTensor *tensor, int64_t size0_); +THC_API void THCTensor_(resize2d)(THCState *state, THCTensor *tensor, int64_t size0_, int64_t size1_); +THC_API void THCTensor_(resize3d)(THCState *state, THCTensor *tensor, int64_t size0_, int64_t size1_, int64_t size2_); +THC_API void THCTensor_(resize4d)(THCState *state, THCTensor *tensor, int64_t size0_, int64_t size1_, int64_t size2_, int64_t size3_); +THC_API void THCTensor_(resize5d)(THCState *state, THCTensor *tensor, int64_t size0_, int64_t size1_, int64_t size2_, int64_t size3_, int64_t size4_); + +THC_API void THCTensor_(set)(THCState *state, THCTensor *self, THCTensor *src); +THC_API void THCTensor_(setStorageNd)(THCState *state, THCTensor *self, THCStorage *storage, ptrdiff_t storageOffset, int nDimension, const int64_t *size, const int64_t *stride); +THC_API void THCTensor_(setStorage1d)(THCState *state, THCTensor *self, THCStorage *storage_, ptrdiff_t storageOffset_, + int64_t size0_, int64_t stride0_); +THC_API void THCTensor_(setStorage2d)(THCState *state, THCTensor *self, THCStorage *storage_, ptrdiff_t storageOffset_, + int64_t size0_, int64_t stride0_, + int64_t size1_, int64_t stride1_); +THC_API void THCTensor_(setStorage3d)(THCState *state, THCTensor *self, THCStorage *storage_, ptrdiff_t storageOffset_, + int64_t size0_, int64_t stride0_, + int64_t size1_, int64_t stride1_, + int64_t size2_, int64_t stride2_); +THC_API void THCTensor_(setStorage4d)(THCState *state, THCTensor *self, THCStorage *storage_, ptrdiff_t storageOffset_, + int64_t size0_, int64_t stride0_, + int64_t size1_, int64_t stride1_, + int64_t size2_, int64_t stride2_, + int64_t size3_, int64_t stride3_); + +THC_API void THCTensor_(narrow)(THCState *state, THCTensor *self, THCTensor *src, int dimension_, int64_t firstIndex_, int64_t size_); +THC_API void THCTensor_(select)(THCState *state, THCTensor *self, THCTensor *src, int dimension_, int64_t sliceIndex_); +THC_API void THCTensor_(transpose)(THCState *state, THCTensor *self, THCTensor *src, int dimension1_, int dimension2_); + +THC_API void THCTensor_(squeeze1d)(THCState *state, THCTensor *self, THCTensor *src, int dimension_); +THC_API void THCTensor_(unsqueeze1d)(THCState *state, THCTensor *self, THCTensor *src, int dimension_); + +THC_API int THCTensor_(isContiguous)(THCState *state, const THCTensor *self); +THC_API int THCTensor_(isSameSizeAs)(THCState *state, const THCTensor *self, const THCTensor *src); +THC_API ptrdiff_t THCTensor_(nElement)(THCState *state, const THCTensor *self); + +THC_API void THCTensor_(retain)(THCState *state, THCTensor *self); +THC_API void THCTensor_(free)(THCState *state, THCTensor *self); +THC_API void THCTensor_(freeCopyTo)(THCState *state, THCTensor *self, THCTensor *dst); + +/* Slow access methods [check everything] */ +THC_API void THCTensor_(set0d)(THCState *state, THCTensor *tensor, scalar_t value); +THC_API void THCTensor_(set1d)(THCState *state, THCTensor *tensor, int64_t x0, scalar_t value); +THC_API void THCTensor_(set2d)(THCState *state, THCTensor *tensor, int64_t x0, int64_t x1, scalar_t value); +THC_API void THCTensor_(set3d)(THCState *state, THCTensor *tensor, int64_t x0, int64_t x1, int64_t x2, scalar_t value); +THC_API void THCTensor_(set4d)(THCState *state, THCTensor *tensor, int64_t x0, int64_t x1, int64_t x2, int64_t x3, scalar_t value); + +THC_API scalar_t THCTensor_(get0d)(THCState *state, const THCTensor *tensor); +THC_API scalar_t THCTensor_(get1d)(THCState *state, const THCTensor *tensor, int64_t x0); +THC_API scalar_t THCTensor_(get2d)(THCState *state, const THCTensor *tensor, int64_t x0, int64_t x1); +THC_API scalar_t THCTensor_(get3d)(THCState *state, const THCTensor *tensor, int64_t x0, int64_t x1, int64_t x2); +THC_API scalar_t THCTensor_(get4d)(THCState *state, const THCTensor *tensor, int64_t x0, int64_t x1, int64_t x2, int64_t x3); + +/* CUDA-specific functions */ +THC_API int THCTensor_(getDevice)(THCState *state, const THCTensor *self); +THC_API int THCTensor_(checkGPU)(THCState *state, unsigned int nTensors, ...); + +/* debug methods */ +THC_API THCDescBuff THCTensor_(sizeDesc)(THCState *state, const THCTensor *tensor); + +#endif diff --git a/thirdparty/libtorch/include/THC/generic/THCTensorCopy.h b/thirdparty/libtorch/include/THC/generic/THCTensorCopy.h new file mode 100644 index 0000000000..896b2635d7 --- /dev/null +++ b/thirdparty/libtorch/include/THC/generic/THCTensorCopy.h @@ -0,0 +1,11 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "THC/generic/THCTensorCopy.h" +#else + +THC_API void THCTensor_(copy)(THCState *state, THCTensor *self, THCTensor *src); +THC_API void THCTensor_(copyIgnoringOverlaps)(THCState *state, THCTensor *self, THCTensor *src); + +THC_API void THCTensor_(copyAsyncCPU)(THCState *state, THCTensor *self, THTensor *src); +THC_API void THTensor_(copyAsyncCuda)(THCState *state, THTensor *self, THCTensor *src); + +#endif diff --git a/thirdparty/libtorch/include/THC/generic/THCTensorIndex.h b/thirdparty/libtorch/include/THC/generic/THCTensorIndex.h new file mode 100644 index 0000000000..c97e3b373a --- /dev/null +++ b/thirdparty/libtorch/include/THC/generic/THCTensorIndex.h @@ -0,0 +1,15 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "THC/generic/THCTensorIndex.h" +#else + +THC_API void THCTensor_(indexCopy)(THCState *state, THCTensor *res_, int dim, THCudaLongTensor *indices, THCTensor *src); +THC_API void THCTensor_(indexFill)(THCState *state, THCTensor *tensor, int dim, THCudaLongTensor *index, scalar_t val); +THC_API void THCTensor_(indexSelect)(THCState *state, THCTensor *tensor, THCTensor *src, int dim, THCudaLongTensor *index); +THC_API void THCTensor_(take)(THCState *state, THCTensor *res_, THCTensor *src, THCudaLongTensor *index); +THC_API void THCTensor_(put)(THCState *state, THCTensor *res_, THCudaLongTensor *indices, THCTensor *src, int accumulate); + +#if !defined(THC_REAL_IS_BOOL) /* non bool only part */ +THC_API void THCTensor_(indexAdd)(THCState *state, THCTensor *res_, int dim, THCudaLongTensor *indices, THCTensor *src); +#endif + +#endif diff --git a/thirdparty/libtorch/include/THC/generic/THCTensorMasked.h b/thirdparty/libtorch/include/THC/generic/THCTensorMasked.h new file mode 100644 index 0000000000..57eac2b4f6 --- /dev/null +++ b/thirdparty/libtorch/include/THC/generic/THCTensorMasked.h @@ -0,0 +1,54 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "THC/generic/THCTensorMasked.h" +#else + +THC_API void THCTensor_(maskedFill)(THCState *state, + THCTensor *tensor, + THCudaByteTensor *mask, + scalar_t value); + + +THC_API void THCTensor_(maskedFillBool)(THCState *state, + THCTensor *tensor, + THCudaBoolTensor *mask, + scalar_t value); + +// FIXME: remove now that we have THCudaByteTensor? +THC_API void THCTensor_(maskedFillByte)(THCState *state, + THCTensor *tensor, + THByteTensor *mask, + scalar_t value); + +THC_API void THCTensor_(maskedCopy)(THCState *state, + THCTensor *tensor, + THCudaByteTensor *mask, + THCTensor *src); + +THC_API void THCTensor_(maskedCopyBool)(THCState *state, + THCTensor *tensor, + THCudaBoolTensor *mask, + THCTensor *src); + +// FIXME: remove now that we have THCudaByteTensor? +THC_API void THCTensor_(maskedCopyByte)(THCState *state, + THCTensor *tensor, + THByteTensor *mask, + THCTensor *src); + +THC_API void THCTensor_(maskedSelect)(THCState *state, + THCTensor *tensor, + THCTensor *src, + THCudaByteTensor *mask); + +THC_API void THCTensor_(maskedSelectBool)(THCState *state, + THCTensor *tensor, + THCTensor *src, + THCudaBoolTensor *mask); + +// FIXME: remove now that we have THCudaByteTensor? +THC_API void THCTensor_(maskedSelectByte)(THCState *state, + THCTensor *tensor, + THCTensor *src, + THByteTensor *mask); + +#endif diff --git a/thirdparty/libtorch/include/THC/generic/THCTensorMath.h b/thirdparty/libtorch/include/THC/generic/THCTensorMath.h new file mode 100644 index 0000000000..a565cec208 --- /dev/null +++ b/thirdparty/libtorch/include/THC/generic/THCTensorMath.h @@ -0,0 +1,20 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "THC/generic/THCTensorMath.h" +#else + +THC_API void THCTensor_(fill)(THCState *state, THCTensor *self, scalar_t value); +THC_API void THCTensor_(zero)(THCState *state, THCTensor *self); +THC_API void THCTensor_(cat)(THCState *state, THCTensor *result, THCTensor *ta, THCTensor *tb, int dimension); +THC_API void THCTensor_(catArray)(THCState *state, THCTensor *result, THCTensor **inputs, int numInputs, int dimension); +THC_API void THCTensor_(nonzero)(THCState* state, THCudaLongTensor *tensor, THCTensor *self); +THC_API ptrdiff_t THCTensor_(numel)(THCState *state, THCTensor *t); + +#if !defined(THC_REAL_IS_BOOL) /* non bool only part */ + +THC_API void THCTensor_(triu)(THCState *state, THCTensor *self, THCTensor *src, int64_t k); +THC_API void THCTensor_(diag)(THCState *state, THCTensor *self, THCTensor *src, int64_t k); +THC_API accreal THCTensor_(trace)(THCState *state, THCTensor *self); + +#endif + +#endif diff --git a/thirdparty/libtorch/include/THC/generic/THCTensorMathBlas.h b/thirdparty/libtorch/include/THC/generic/THCTensorMathBlas.h new file mode 100644 index 0000000000..98608b016b --- /dev/null +++ b/thirdparty/libtorch/include/THC/generic/THCTensorMathBlas.h @@ -0,0 +1,12 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "THC/generic/THCTensorMathBlas.h" +#else + +THC_API accreal THCTensor_(dot)(THCState *state, THCTensor *self, THCTensor *src); +THC_API void THCTensor_(addmv)(THCState *state, THCTensor *self, THCTensor *t, THCTensor *mat, THCTensor *vec, scalar_t beta, scalar_t alpha); +THC_API void THCTensor_(addmm)(THCState *state, THCTensor *self, THCTensor *t, THCTensor *mat1, THCTensor *mat2, scalar_t beta, scalar_t alpha); +THC_API void THCTensor_(addr)(THCState *state, THCTensor *self, THCTensor *t, THCTensor *vec1, THCTensor *vec2, scalar_t beta, scalar_t alpha); +THC_API void THCTensor_(addbmm)(THCState *state, THCTensor *result, THCTensor *t, THCTensor *batch1, THCTensor *batch2, scalar_t beta, scalar_t alpha); +THC_API void THCTensor_(baddbmm)(THCState *state, THCTensor *result, THCTensor *t, THCTensor *batch1, THCTensor *batch2, scalar_t beta, scalar_t alpha); + +#endif diff --git a/thirdparty/libtorch/include/THC/generic/THCTensorMathMagma.h b/thirdparty/libtorch/include/THC/generic/THCTensorMathMagma.h new file mode 100644 index 0000000000..ae46a62c9e --- /dev/null +++ b/thirdparty/libtorch/include/THC/generic/THCTensorMathMagma.h @@ -0,0 +1,15 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "THC/generic/THCTensorMathMagma.h" +#else + +#if defined(THC_REAL_IS_FLOAT) || defined(THC_REAL_IS_DOUBLE) + +// MAGMA (i.e. CUDA implementation of LAPACK functions) +THC_API void THCTensor_(gels)(THCState *state, THCTensor *rb_, THCTensor *ra_, THCTensor *b_, THCTensor *a_); +THC_API void THCTensor_(geev)(THCState *state, THCTensor *re_, THCTensor *rv_, THCTensor *a_, bool eigenvectors); +THC_API void THCTensor_(potri)(THCState *state, THCTensor *ra_, THCTensor *a, bool upper); +THC_API void THCTensor_(geqrf)(THCState *state, THCTensor *ra_, THCTensor *rtau_, THCTensor *a_); + +#endif // defined(THC_REAL_IS_FLOAT) || defined(THC_REAL_IS_DOUBLE) + +#endif diff --git a/thirdparty/libtorch/include/THC/generic/THCTensorMathPairwise.h b/thirdparty/libtorch/include/THC/generic/THCTensorMathPairwise.h new file mode 100644 index 0000000000..246be6be70 --- /dev/null +++ b/thirdparty/libtorch/include/THC/generic/THCTensorMathPairwise.h @@ -0,0 +1,21 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "THC/generic/THCTensorMathPairwise.h" +#else + +THC_API int THCTensor_(equal)(THCState *state, THCTensor *self, THCTensor *src); + +THC_API void THCTensor_(bitand)(THCState *state, THCTensor *self, THCTensor *src, scalar_t value); +THC_API void THCTensor_(bitor)(THCState *state, THCTensor *self, THCTensor *src, scalar_t value); + +#if !defined(THC_REAL_IS_BOOL) + +THC_API void THCTensor_(mul)(THCState *state, THCTensor *self, THCTensor *src, scalar_t value); +THC_API void THCTensor_(div)(THCState *state, THCTensor *self, THCTensor *src, scalar_t value); +THC_API void THCTensor_(lshift)(THCState *state, THCTensor *self, THCTensor *src, scalar_t value); +THC_API void THCTensor_(rshift)(THCState *state, THCTensor *self, THCTensor *src, scalar_t value); +THC_API void THCTensor_(fmod)(THCState *state, THCTensor *self, THCTensor *src, scalar_t value); +THC_API void THCTensor_(remainder)(THCState *state, THCTensor *self, THCTensor *src, scalar_t value); + +#endif + +#endif diff --git a/thirdparty/libtorch/include/THC/generic/THCTensorMathPointwise.h b/thirdparty/libtorch/include/THC/generic/THCTensorMathPointwise.h new file mode 100644 index 0000000000..5eb631b90c --- /dev/null +++ b/thirdparty/libtorch/include/THC/generic/THCTensorMathPointwise.h @@ -0,0 +1,43 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "THC/generic/THCTensorMathPointwise.h" +#else + +THC_API void THCTensor_(cbitand)(THCState *state, THCTensor *self, THCTensor *src1, THCTensor *src2); +THC_API void THCTensor_(cbitor)(THCState *state, THCTensor *self, THCTensor *src1, THCTensor *src2); + +THC_API void THCTensor_(cmax)(THCState *state, THCTensor *self, THCTensor *src1, THCTensor *src2); +THC_API void THCTensor_(cmin)(THCState *state, THCTensor *self, THCTensor *src1, THCTensor *src2); +THC_API void THCTensor_(cmaxValue)(THCState *state, THCTensor *self, THCTensor *src, scalar_t value); +THC_API void THCTensor_(cminValue)(THCState *state, THCTensor *self, THCTensor *src, scalar_t value); + +#if !defined(THC_REAL_IS_BOOL) + +#if defined(THC_REAL_IS_FLOAT) || defined(THC_REAL_IS_DOUBLE) || defined(THC_REAL_IS_HALF) + +THC_API void THCTensor_(exp)(THCState *state, THCTensor *self, THCTensor *src); +THC_API void THCTensor_(cos)(THCState *state, THCTensor *self, THCTensor *src); +THC_API void THCTensor_(cosh)(THCState *state, THCTensor *self, THCTensor *src); +THC_API void THCTensor_(tan)(THCState *state, THCTensor *self, THCTensor *src); +THC_API void THCTensor_(atan)(THCState *state, THCTensor *self, THCTensor *src); +THC_API void THCTensor_(tanh)(THCState *state, THCTensor *self, THCTensor *src); +THC_API void THCTensor_(erf)(THCState *state, THCTensor *self, THCTensor *src); +THC_API void THCTensor_(erfc)(THCState *state, THCTensor *self, THCTensor *src); +THC_API void THCTensor_(sqrt)(THCState *state, THCTensor *self, THCTensor *src); +THC_API void THCTensor_(cinv)(THCState *state, THCTensor *self, THCTensor *src); + +#endif + +THC_API void THCTensor_(clamp)(THCState *state, THCTensor *self, THCTensor *src, scalar_t min_value, scalar_t max_value); +THC_API void THCTensor_(crossKernel)(THCState *state, THCTensor *self, THCTensor *src1, THCTensor *src2, int dimension); + +THC_API void THCTensor_(cadd)(THCState *state, THCTensor *self, THCTensor *src1, scalar_t value, THCTensor *src2); +THC_API void THCTensor_(csub)(THCState *state, THCTensor *self, THCTensor *src1, scalar_t value, THCTensor *src2); +THC_API void THCTensor_(cmul)(THCState *state, THCTensor *self, THCTensor *src1, THCTensor *src2); +THC_API void THCTensor_(cdiv)(THCState *state, THCTensor *self, THCTensor *src1, THCTensor *src2); +THC_API void THCTensor_(clshift)(THCState *state, THCTensor *self, THCTensor *src1, THCTensor *src2); +THC_API void THCTensor_(crshift)(THCState *state, THCTensor *self, THCTensor *src1, THCTensor *src2); +THC_API void THCTensor_(cfmod)(THCState *state, THCTensor *self, THCTensor *src1, THCTensor *src2); +THC_API void THCTensor_(cremainder)(THCState *state, THCTensor *self, THCTensor *src1, THCTensor *src2); + +#endif +#endif diff --git a/thirdparty/libtorch/include/THC/generic/THCTensorMathReduce.h b/thirdparty/libtorch/include/THC/generic/THCTensorMathReduce.h new file mode 100644 index 0000000000..dd0e188a47 --- /dev/null +++ b/thirdparty/libtorch/include/THC/generic/THCTensorMathReduce.h @@ -0,0 +1,50 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "THC/generic/THCTensorMathReduce.h" +#else + +THC_API accreal THCTensor_(sumall)(THCState *state, THCTensor *self); + +THC_API void THCTensor_(min)(THCState *state, + THCTensor *values, + THCudaLongTensor *indices, + THCTensor *src, int dim, int keepdim); +THC_API void THCTensor_(max)(THCState *state, + THCTensor *values, + THCudaLongTensor *indices, + THCTensor *src, int dim, int keepdim); + +THC_API scalar_t THCTensor_(minall)(THCState *state, THCTensor *self); +THC_API scalar_t THCTensor_(maxall)(THCState *state, THCTensor *self); + +#if !defined(THC_REAL_IS_BOOL) + +#if defined(THC_REAL_IS_FLOAT) || defined(THC_REAL_IS_DOUBLE) || defined(THC_REAL_IS_HALF) + +THC_API void THCTensor_(renorm)(THCState *state, THCTensor* self, THCTensor* src, scalar_t value, int dimension, scalar_t max_norm); +THC_API void THCTensor_(std_single)(THCState *state, THCTensor *self, THCTensor *src, int dim, bool unbiased, int keepdim); +THC_API void THCTensor_(norm)(THCState *state, THCTensor* self, THCTensor* src, scalar_t value, int dimension, int keepdim); +THC_API void THCTensor_(var_single)(THCState *state, THCTensor *self, THCTensor *src, int dim, bool unbiased, int keepdim); + +THC_API accreal THCTensor_(std_all)(THCState *state, THCTensor *self, bool unbiased); +THC_API accreal THCTensor_(normall)(THCState *state, THCTensor *self, scalar_t value); +THC_API accreal THCTensor_(var_all)(THCState *state, THCTensor *self, bool unbiased); + +#endif + +THC_API void THCTensor_(prod)(THCState *state, THCTensor *self, THCTensor *src, int dim, int keepdim); + +THC_API accreal THCTensor_(meanall)(THCState *state, THCTensor *self); + +THC_API scalar_t THCTensor_(medianall)(THCState *state, THCTensor *self); + +THC_API void THCTensor_(median)(THCState *state, + THCTensor *values, + THCudaLongTensor *indices, + THCTensor *src, int dim, int keepdim); + +THC_API accreal THCTensor_(dist)(THCState *state, THCTensor *self, THCTensor *src, + scalar_t value); + +#endif + +#endif diff --git a/thirdparty/libtorch/include/THC/generic/THCTensorMathScan.h b/thirdparty/libtorch/include/THC/generic/THCTensorMathScan.h new file mode 100644 index 0000000000..9346dbce71 --- /dev/null +++ b/thirdparty/libtorch/include/THC/generic/THCTensorMathScan.h @@ -0,0 +1,8 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "THC/generic/THCTensorMathScan.h" +#else + +THC_API void THCTensor_(cumsum)(THCState *state, THCTensor *self, THCTensor *src, int dim); +THC_API void THCTensor_(cumprod)(THCState *state, THCTensor *self, THCTensor *src, int dim); + +#endif diff --git a/thirdparty/libtorch/include/THC/generic/THCTensorMode.h b/thirdparty/libtorch/include/THC/generic/THCTensorMode.h new file mode 100644 index 0000000000..796eb66e53 --- /dev/null +++ b/thirdparty/libtorch/include/THC/generic/THCTensorMode.h @@ -0,0 +1,14 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "THC/generic/THCTensorMode.h" +#else + +/* Returns the mode, and index of the mode, for the set of values + * along a given dimension in the input tensor. */ +THC_API void THCTensor_(mode)(THCState *state, + THCTensor *values, + THCudaLongTensor *indices, + THCTensor *input, + int dimension, + int keepdim); + +#endif // THC_GENERIC_FILE diff --git a/thirdparty/libtorch/include/THC/generic/THCTensorRandom.h b/thirdparty/libtorch/include/THC/generic/THCTensorRandom.h new file mode 100644 index 0000000000..cb359001af --- /dev/null +++ b/thirdparty/libtorch/include/THC/generic/THCTensorRandom.h @@ -0,0 +1,13 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "THC/generic/THCTensorRandom.h" +#else + +#include "ATen/core/Generator.h" + +#if defined(THC_REAL_IS_FLOAT) || defined(THC_REAL_IS_DOUBLE) || defined(THC_REAL_IS_HALF) + +THC_API void THCTensor_(multinomialAliasSetup)(struct THCState *state, THCTensor *probs, THCudaLongTensor *J, THCTensor *q); +THC_API void THCTensor_(multinomialAliasDraw)(THCState *state, THCudaLongTensor *self, THCTensor *_q, THCudaLongTensor *_J, int n_sample, at::Generator* gen_); + +#endif +#endif diff --git a/thirdparty/libtorch/include/THC/generic/THCTensorScatterGather.h b/thirdparty/libtorch/include/THC/generic/THCTensorScatterGather.h new file mode 100644 index 0000000000..fb87cfc3e4 --- /dev/null +++ b/thirdparty/libtorch/include/THC/generic/THCTensorScatterGather.h @@ -0,0 +1,10 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "THC/generic/THCTensorScatterGather.h" +#else + +THC_API void THCTensor_(gather)(THCState* state, THCTensor *tensor, THCTensor *src, int dim, THCudaLongTensor *index); +THC_API void THCTensor_(scatter)(THCState* state, THCTensor *tensor, int dim, THCudaLongTensor *index, THCTensor *src); +THC_API void THCTensor_(scatterAdd)(THCState* state, THCTensor *tensor, int dim, THCudaLongTensor *index, THCTensor *src); +THC_API void THCTensor_(scatterFill)(THCState* state, THCTensor *tensor, int dim, THCudaLongTensor *index, scalar_t value); + +#endif diff --git a/thirdparty/libtorch/include/THC/generic/THCTensorSort.h b/thirdparty/libtorch/include/THC/generic/THCTensorSort.h new file mode 100644 index 0000000000..d6569a3dd7 --- /dev/null +++ b/thirdparty/libtorch/include/THC/generic/THCTensorSort.h @@ -0,0 +1,20 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "THC/generic/THCTensorSort.h" +#else + +/* Performs an in-place sort of (keys, values). Only works for slice sizes + <= 2048 at the moment (slice size == size of keys/values dim `dim`) */ +THC_API void THCTensor_(sortKeyValueInplace)(THCState* state, + THCTensor* keys, + THCudaLongTensor* values, + int dim, bool dir); + +/* Performs an out-of-place sort of `input`, returning the per-slice indices + in `indices` and the sorted values in `sorted` */ +THC_API void THCTensor_(sort)(THCState* state, + THCTensor* sorted, + THCudaLongTensor* indices, + THCTensor* input, + int dim, int order); + +#endif diff --git a/thirdparty/libtorch/include/THC/generic/THCTensorTopK.h b/thirdparty/libtorch/include/THC/generic/THCTensorTopK.h new file mode 100644 index 0000000000..ffe6e959ea --- /dev/null +++ b/thirdparty/libtorch/include/THC/generic/THCTensorTopK.h @@ -0,0 +1,13 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "THC/generic/THCTensorTopK.h" +#else + +/* Returns the set of all kth smallest (or largest) elements, depending */ +/* on `dir` */ +THC_API void THCTensor_(topk)(THCState* state, + THCTensor* topK, + THCudaLongTensor* indices, + THCTensor* input, + int64_t k, int dim, int dir, int sorted); + +#endif // THC_GENERIC_FILE diff --git a/thirdparty/libtorch/include/THCUNN/SharedMem.cuh b/thirdparty/libtorch/include/THCUNN/SharedMem.cuh new file mode 100644 index 0000000000..8d83d9f9a9 --- /dev/null +++ b/thirdparty/libtorch/include/THCUNN/SharedMem.cuh @@ -0,0 +1,43 @@ +// Based on the simpleTempltes CUDA example + +#ifndef THCUNN_SHAREDMEM_H +#define THCUNN_SHAREDMEM_H + +template +struct SharedMem { + __device__ T *getPointer() + { + extern __device__ void error(void); + error(); + return NULL; + } +}; + +template <> +struct SharedMem +{ + __device__ half *getPointer() { + extern __shared__ half s_half[]; + return s_half; + } +}; + +template <> +struct SharedMem +{ + __device__ float *getPointer() { + extern __shared__ float s_float[]; + return s_float; + } +}; + +template <> +struct SharedMem +{ + __device__ double *getPointer() { + extern __shared__ double s_double[]; + return s_double; + } +}; + +#endif diff --git a/thirdparty/libtorch/include/THCUNN/THCHalfAutoNumerics.cuh b/thirdparty/libtorch/include/THCUNN/THCHalfAutoNumerics.cuh new file mode 100644 index 0000000000..05ffab5ff2 --- /dev/null +++ b/thirdparty/libtorch/include/THCUNN/THCHalfAutoNumerics.cuh @@ -0,0 +1,46 @@ +#ifndef THC_HALF_AUTO_NUMERICS_INC +#define THC_HALF_AUTO_NUMERICS_INC + +#include +#include + +// WARNING: THCNumerics is being deprecated. Read the comments and function usage +// in THCNumerics to learn about the deprecation +// +// Half numerics functions defined as free functions, so cunn code can be +// written generically, i.e. without excessive calling of THCNumerics functions. + +// these functions should move to THCNumerics + +inline __host__ __device__ THHalf fmaxType(THHalf x, THHalf y) { + return THCNumerics::ge(x, y) ? x : y; +} + +inline __host__ __device__ float fmaxType(float x, THHalf y) { + return fmaxf(x, ScalarConvert::to(y)); +} + +inline __host__ __device__ float fmaxType(float x, float y) { + return fmaxf(x, y); +} + +inline __host__ __device__ double fmaxType(double x, double y) { + return fmax(x, y); +} + + +// arithmetic functions + +inline __host__ __device__ THHalf exp(THHalf a) { + return THCNumerics::exp(a); +} + +inline __host__ __device__ THHalf pow(THHalf a, THHalf b) { + return THCNumerics::pow(a, b); +} + +inline __host__ __device__ THHalf tanh(THHalf a) { + return THCNumerics::tanh(a); +} + +#endif diff --git a/thirdparty/libtorch/include/THCUNN/generic/THCUNN.h b/thirdparty/libtorch/include/THCUNN/generic/THCUNN.h new file mode 100644 index 0000000000..fc329b6e93 --- /dev/null +++ b/thirdparty/libtorch/include/THCUNN/generic/THCUNN.h @@ -0,0 +1,326 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "THCUNN/generic/THCUNN.h" +#else + +#include + +THC_API void THNN_(BCECriterion_updateOutput)( + THCState *state, + THCTensor *input, + THCTensor *target, + THCTensor *output, + int64_t reduction, + THCTensor *weights); // [OPTIONAL] + +THC_API void THNN_(BCECriterion_updateGradInput)( + THCState *state, + THCTensor *input, + THCTensor *target, + THCTensor *gradOutput, + THCTensor *gradInput, + int64_t reduction, + THCTensor *weights); // [OPTIONAL] + +THC_API void THNN_(ClassNLLCriterion_updateOutput)( + THCState *state, + THCTensor *input, + THCIndexTensor *target, + THCTensor *output, + int64_t reduction, + THCTensor *weights, // [OPTIONAL] + THCTensor *total_weight, + int64_t ignore_index); + +THC_API void THNN_(ClassNLLCriterion_updateGradInput)( + THCState *state, + THCTensor *input, + THCIndexTensor *target, + THCTensor *gradOutput, + THCTensor *gradInput, + int64_t reduction, + THCTensor *weights, // [OPTIONAL] + THCTensor *total_weight, + int64_t ignore_index); + +THC_API void THNN_(ELU_updateOutput)( + THCState *state, + THCTensor *input, + THCTensor *output, + accreal alpha, + accreal scale, + accreal input_scale, + bool inplace); + +THC_API void THNN_(ELU_updateGradInput)( + THCState *state, + THCTensor *gradOutput, + THCTensor *gradInput, + THCTensor *output, + accreal alpha, + accreal scale, + accreal input_scale); + +THC_API void THNN_(HardTanh_updateOutput)( + THCState *state, + THCTensor *input, + THCTensor *output, + accreal min_val, + accreal max_val, + bool inplace); + +THC_API void THNN_(HardTanh_updateGradInput)( + THCState *state, + THCTensor *input, + THCTensor *gradOutput, + THCTensor *gradInput, + accreal min_val, + accreal max_val, + bool inplace); + +THC_API void THNN_(GatedLinear_updateOutput)( + THCState *state, + THCTensor *input, + THCTensor *output, + int dim); + +THC_API void THNN_(GatedLinear_updateGradInput)( + THCState *state, + THCTensor *input, + THCTensor *gradOutput, + THCTensor *gradInput, + int dim); + +THC_API void THNN_(LeakyReLU_updateOutput)( + THCState *state, + THCTensor *input, + THCTensor *output, + accreal negval, + bool inplace); + +THC_API void THNN_(LeakyReLU_updateGradInput)( + THCState *state, + THCTensor *input, + THCTensor *gradOutput, + THCTensor *gradInput, + accreal negval, + bool inplace); + +THC_API void THNN_(LogSigmoid_updateOutput)( + THCState *state, + THCTensor *input, + THCTensor *output, + THCTensor *buffer); + +THC_API void THNN_(LogSigmoid_updateGradInput)( + THCState *state, + THCTensor *input, + THCTensor *gradOutput, + THCTensor *gradInput, + THCTensor *buffer); + +THC_API void THNN_(MultiLabelMarginCriterion_updateOutput)( + THCState *state, + THCTensor *input, + THCIndexTensor *target, + THCTensor *output, + THCTensor *is_target, + int64_t reduction); + +THC_API void THNN_(MultiLabelMarginCriterion_updateGradInput)( + THCState *state, + THCTensor *input, + THCIndexTensor *target, + THCTensor *gradOutput, + THCTensor *gradInput, + THCTensor *is_target, + int64_t reduction); + +THC_API void THNN_(MultiMarginCriterion_updateOutput)( + THCState *state, + THCTensor *input, + THCIndexTensor *target, + THCTensor *output, + int64_t reduction, + int p, + THCTensor *weights, // [OPTIONAL] + accreal margin); + +THC_API void THNN_(MultiMarginCriterion_updateGradInput)( + THCState *state, + THCTensor *input, + THCIndexTensor *target, + THCTensor *gradOutput, + THCTensor *gradInput, + int64_t reduction, + int p, + THCTensor *weights, // [OPTIONAL] + accreal margin); + +THC_API void THNN_(SpatialClassNLLCriterion_updateOutput)( + THCState *state, + THCTensor *input, + THCIndexTensor *target, + THCTensor *output, + int64_t reduction, + THCTensor *weights, // [OPTIONAL] + THCTensor *total_weight, + int64_t ignore_index); + +THC_API void THNN_(SpatialClassNLLCriterion_updateGradInput)( + THCState *state, + THCTensor *input, + THCIndexTensor *target, + THCTensor *gradOutput, + THCTensor *gradInput, + int64_t reduction, + THCTensor *weights, // [OPTIONAL] + THCTensor *total_weight, + int64_t ignore_index); + +THC_API void THNN_(SpatialConvolutionMM_updateOutput)( + THCState *state, + THCTensor *input, + THCTensor *output, + THCTensor *weight, + THCTensor *bias, // [OPTIONAL] + THCTensor *columns, + THCTensor *ones, + int kW, int kH, + int dW, int dH, + int padW, int padH); + +THC_API void THNN_(SpatialConvolutionMM_updateGradInput)( + THCState *state, + THCTensor *input, + THCTensor *gradOutput, + THCTensor *gradInput, + THCTensor *weight, + THCTensor *columns, + THCTensor *ones, + int kW, int kH, + int dW, int dH, + int padW, int padH); + +THC_API void THNN_(SpatialConvolutionMM_accGradParameters)( + THCState *state, + THCTensor *input, + THCTensor *gradOutput, + THCTensor *gradWeight, + THCTensor *gradBias, // [OPTIONAL] + THCTensor *columns, + THCTensor *ones, + int kW, int kH, + int dW, int dH, + int padW, int padH, + accreal scale); + +THC_API void THNN_(SpatialDepthwiseConvolution_updateOutput)( + THCState *state, + THCTensor *input, + THCTensor *output, + THCTensor *weight, + THCTensor *bias, // [OPTIONAL] + int kW, int kH, + int dW, int dH, + int padW, int padH, + int dilationW, int dilationH); + +THC_API void THNN_(SpatialDepthwiseConvolution_updateGradInput)( + THCState *state, + THCTensor *input, + THCTensor *gradOutput, + THCTensor *gradInput, + THCTensor *weight, + int kW, int kH, + int dW, int dH, + int padW, int padH, + int dilationW, int dilationH); + +THC_API void THNN_(SpatialDepthwiseConvolution_accGradParameters)( + THCState *state, + THCTensor *input, + THCTensor *gradOutput, + THCTensor *gradWeight, + int kW, int kH, + int dW, int dH, + int padW, int padH, + int dilationW, int dilationH); + +THC_API void THNN_(RReLU_updateOutput)( + THCState *state, + THCTensor *input, + THCTensor *output, + THCTensor *noise, + double lower, + double upper, + bool train, + bool inplace, + void *generator); + +THC_API void THNN_(RReLU_updateGradInput)( + THCState *state, + THCTensor *input, + THCTensor *gradOutput, + THCTensor *gradInput, + THCTensor *noise, + double lower, + double upper, + bool train, + bool inplace); + +THC_API void THNN_(SoftMarginCriterion_updateOutput)( + THCState *state, + THCTensor *input, + THCTensor *target, + THCTensor *output, + int64_t reduction); + +THC_API void THNN_(SoftMarginCriterion_updateGradInput)( + THCState *state, + THCTensor *input, + THCTensor *target, + THCTensor *gradOutput, + THCTensor *gradInput, + int64_t reduction); + +THC_API void THNN_(SoftPlus_updateOutput)( + THCState *state, + THCTensor *input, + THCTensor *output, + accreal beta, + accreal threshold); + +THC_API void THNN_(SoftPlus_updateGradInput)( + THCState *state, + THCTensor *input, + THCTensor *gradOutput, + THCTensor *gradInput, + THCTensor *output, + accreal beta, + accreal threshold); + +THC_API void THNN_(SoftShrink_updateOutput)( + THCState *state, + THCTensor *input, + THCTensor *output, + accreal lambda); + +THC_API void THNN_(SoftShrink_updateGradInput)( + THCState *state, + THCTensor *input, + THCTensor *gradOutput, + THCTensor *gradInput, + accreal lambda); + +THC_API void THNN_(Tanh_updateOutput)( + THCState *state, + THCTensor *input, + THCTensor *output); + +THC_API void THNN_(Tanh_updateGradInput)( + THCState *state, + THCTensor *gradOutput, + THCTensor *gradInput, + THCTensor *output); + +#endif diff --git a/thirdparty/libtorch/include/THNN/generic/THNN.h b/thirdparty/libtorch/include/THNN/generic/THNN.h new file mode 100644 index 0000000000..7113805ec5 --- /dev/null +++ b/thirdparty/libtorch/include/THNN/generic/THNN.h @@ -0,0 +1,172 @@ +#ifndef TH_GENERIC_FILE +#define TH_GENERIC_FILE "THNN/generic/THNN.h" +#else + +#include +#include +#include + +#if !defined(TH_REAL_IS_LONG) + +TH_API void THNN_(BCECriterion_updateOutput)( + THNNState *state, + THTensor *input, + THTensor *target, + THTensor *output, + int64_t reduction, + THTensor *weights); // [OPTIONAL] +TH_API void THNN_(BCECriterion_updateGradInput)( + THNNState *state, + THTensor *input, + THTensor *target, + THTensor *gradOutput, + THTensor *gradInput, + int64_t reduction, + THTensor *weights); // [OPTIONAL] + +TH_API void THNN_(ELU_updateOutput)( + THNNState *state, // library's state + THTensor *input, // input tensor + THTensor *output, // [OUT] ELU output + accreal alpha, // an ELU parameter (as in paper) + accreal scale, // scaling factor for output + accreal input_scale, // scaling factor for input + bool inplace); // if true, modifies gradOutput and sets gradInput onto it (no additional memory is allocated) +TH_API void THNN_(ELU_updateGradInput)( + THNNState *state, // library's state + THTensor *gradOutput, // gradient w.r.t. output + THTensor *gradInput, // [OUT] gradient w.r.t. input + THTensor *output, // output from a forward pass + accreal alpha, // an ELU parameter (as in paper) + accreal scale, + accreal input_scale); + +TH_API void THNN_(GatedLinear_updateOutput)( + THNNState *state, // library's state + THTensor *input, // input tensor + THTensor *output, // [OUT] output tensor, half size of input along dimension dim + int dim); // dimension for halving operation +TH_API void THNN_(GatedLinear_updateGradInput)( + THNNState *state, // library's state + THTensor *input, // input tensor + THTensor *gradOutput, // gradient w.r.t module's output + THTensor *gradInput, // [OUT] gradient w.r.t input + int dim); // dimension for halving operation + +// HardTanh clamps the values to the interval [min_val; max_val]. +TH_API void THNN_(HardTanh_updateOutput)( + THNNState *state, // library's state + THTensor *input, // input tensor + THTensor *output, // [OUT] output tensor + accreal min_val, // lower threshold + accreal max_val, // upper threshold + bool inplace); +TH_API void THNN_(HardTanh_updateGradInput)( + THNNState *state, // library's state + THTensor *input, // input tensor + THTensor *gradOutput, // gradient w.r.t. module's output + THTensor *gradInput, // [OUT] gradient w.r.t. the input + accreal min_val, // lower threshold + accreal max_val, // upper threshold + bool inplace); + +TH_API void THNN_(LeakyReLU_updateOutput)( + THNNState *state, // library's state + THTensor *input, // [MODIFIED] input tensor + THTensor *output, // [OUT] output tensor + accreal negval, // negative part slope + bool inplace); // if true, modifies the input tensor and sets the output tensor on it (no additional memory is allocated) +TH_API void THNN_(LeakyReLU_updateGradInput)( + THNNState *state, // library's state + THTensor *input, // input tensor + THTensor *gradOutput, // [MODIFIED] gradient w.r.t. module's output + THTensor *gradInput, // [OUT] gradient w.r.t. the input + accreal negval, // negative part slope + bool inplace); // if true, modifies gradOutput and sets gradInput onto it (no additional memory is allocated) + +TH_API void THNN_(LogSigmoid_updateOutput)( + THNNState *state, // library's state + THTensor *input, // input tensor + THTensor *output, // output tensor + THTensor *buffer); // [BUFFER] +TH_API void THNN_(LogSigmoid_updateGradInput)( + THNNState *state, // library's state + THTensor *input, // input + THTensor *gradOutput, // gradient w.r.t. module's output + THTensor *gradInput, // [OUT] gradient w.r.t. input + THTensor *buffer); // [BUFFER] + +TH_API void THNN_(SoftMarginCriterion_updateOutput)( + THNNState *state, + THTensor *input, + THTensor *target, + THTensor *output, + int64_t reduction); + +TH_API void THNN_(SoftMarginCriterion_updateGradInput)( + THNNState *state, + THTensor *input, + THTensor *target, + THTensor *gradOutput, + THTensor *gradInput, + int64_t reduction); + +TH_API void THNN_(RReLU_updateOutput)( + THNNState *state, + THTensor *input, + THTensor *output, + THTensor *noise, + accreal lower, + accreal upper, + bool train, + bool inplace, + at::Generator *generator); +TH_API void THNN_(RReLU_updateGradInput)( + THNNState *state, + THTensor *input, + THTensor *gradOutput, + THTensor *gradInput, + THTensor *noise, + accreal lower, + accreal upper, + bool train, + bool inplace); + +TH_API void THNN_(SoftPlus_updateOutput)( + THNNState *state, + THTensor *input, THTensor *output, + accreal beta, + accreal threshold); +TH_API void THNN_(SoftPlus_updateGradInput)( + THNNState *state, + THTensor *input, + THTensor *gradOutput, + THTensor *gradInput, + THTensor *output, + accreal beta, + accreal threshold); + +TH_API void THNN_(SoftShrink_updateOutput)( + THNNState *state, + THTensor *input, + THTensor *output, + accreal lambda); +TH_API void THNN_(SoftShrink_updateGradInput)( + THNNState *state, + THTensor *input, + THTensor *gradOutput, + THTensor *gradInput, + accreal lambda); + +TH_API void THNN_(Tanh_updateOutput)( + THNNState *state, + THTensor *input, + THTensor *output); +TH_API void THNN_(Tanh_updateGradInput)( + THNNState *state, + THTensor *gradOutput, + THTensor *gradInput, + THTensor *output); + +#endif +#endif diff --git a/thirdparty/libtorch/include/c10/core/Allocator.h b/thirdparty/libtorch/include/c10/core/Allocator.h new file mode 100644 index 0000000000..06b77c7b95 --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/Allocator.h @@ -0,0 +1,217 @@ +#pragma once + +#include +#include + +#include +#include +#include + +namespace c10 { + +// A DataPtr is a unique pointer (with an attached deleter and some +// context for the deleter) to some memory, which also records what +// device is for its data. +// +// nullptr DataPtrs can still have a nontrivial device; this allows +// us to treat zero-size allocations uniformly with non-zero allocations. +// +class C10_API DataPtr { + private: + c10::detail::UniqueVoidPtr ptr_; + Device device_; + + public: + // Choice of CPU here is arbitrary; if there's an "undefined" device + // we could use that too + DataPtr() : ptr_(), device_(DeviceType::CPU) {} + DataPtr(void* data, Device device) : ptr_(data), device_(device) {} + DataPtr(void* data, void* ctx, DeleterFnPtr ctx_deleter, Device device) + : ptr_(data, ctx, ctx_deleter), device_(device) {} + void* operator->() const { + return ptr_.get(); + } + void clear() { + ptr_.clear(); + } + void* get() const { + return ptr_.get(); + } + void* get_context() const { + return ptr_.get_context(); + } + void* release_context() { + return ptr_.release_context(); + } + std::unique_ptr&& move_context() { + return ptr_.move_context(); + } + operator bool() const { + return static_cast(ptr_); + } + template + T* cast_context(DeleterFnPtr expected_deleter) const { + return ptr_.cast_context(expected_deleter); + } + DeleterFnPtr get_deleter() const { + return ptr_.get_deleter(); + } + /** + * Compare the deleter in a DataPtr to expected_deleter. + * If it matches, replace the deleter with new_deleter + * and return true; otherwise, does nothing and returns + * false. + * + * In general, it is not safe to unconditionally set the + * deleter on a DataPtr, because you don't know what + * the deleter is, and thus will have a hard time properly + * disposing of the deleter without storing the original + * deleter (this is difficult to do, because DeleterFnPtr + * is not a closure, and because the context on DataPtr is + * only a single word, you generally don't have enough + * space to store both the original deleter and its context). + * However, in some cases, you know /exactly/ what the deleter + * is, and you have a new deleter that manually wraps + * the old one. In this case, you can safely swap the deleter + * after asserting that the deleters line up. + * + * What are the requirements on new_deleter? It must still + * properly dispose of the void* pointer passed in as its argument, + * where void* is whatever the context of the original deleter + * is. So in general, you expect the new deleter to look something + * like this: + * + * [](void* ptr) { + * some_new_stuff(ptr); + * get_orig_allocator()->raw_deleter(ptr); + * } + * + * Note that it won't work to close over the original + * allocator; you don't have enough space to do that! Also, + * it's unsafe to assume that the passed in pointer in + * question is the memory pointer in question; it might not + * be; be sure to read the source code of the Allocator + * in question to confirm this. + */ + C10_NODISCARD bool compare_exchange_deleter(DeleterFnPtr expected_deleter, DeleterFnPtr new_deleter) { + return ptr_.compare_exchange_deleter(expected_deleter, new_deleter); + } + Device device() const { + return device_; + } + // Unsafely mutates the device on a DataPtr. Under normal use, + // you should never actually need to call this function. + // We need this for the implementation of the hack detailed + // in Note [Masquerading as CUDA] + void unsafe_set_device(Device device) { + device_ = device; + } +}; + +// NB: Device is NOT tested for here; a CUDA nullptr is as much a nullptr as a +// CPU nullptr + +inline bool operator==(const DataPtr& dp, std::nullptr_t) noexcept { + return !dp; +} +inline bool operator==(std::nullptr_t, const DataPtr& dp) noexcept { + return !dp; +} +inline bool operator!=(const DataPtr& dp, std::nullptr_t) noexcept { + return dp; +} +inline bool operator!=(std::nullptr_t, const DataPtr& dp) noexcept { + return dp; +} + +// Note [raw_allocate/raw_deallocate and Thrust] +// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +// Thrust's support for custom allocators requires us to write something +// like this: +// +// class ThrustAllocator { +// char* allocate(size_t); +// void deallocate(char*, size_t); +// }; +// +// This is not good for our unique_ptr based allocator interface, as +// there is no way to get to the context when we free. +// +// However, in some cases the context is exactly the same as +// the data pointer. In this case, we can support the "raw" +// allocate and deallocate interface. This is what +// raw_deleter signifies. By default, it returns a nullptr, which means that +// the raw interface is not implemented. Be sure to implement it whenever +// possible, or the raw interface will incorrectly reported as unsupported, +// when it is actually possible. + +struct C10_API Allocator { + virtual ~Allocator() = default; + + virtual DataPtr allocate(size_t n) const = 0; + + // If this returns a non nullptr, it means that allocate() + // is guaranteed to return a unique_ptr with this deleter attached; + // it means the rawAllocate and rawDeallocate APIs are safe to use. + // This function MUST always return the same BoundDeleter. + virtual DeleterFnPtr raw_deleter() const { + return nullptr; + } + void* raw_allocate(size_t n) { + auto dptr = allocate(n); + AT_ASSERT(dptr.get() == dptr.get_context()); + return dptr.release_context(); + } + void raw_deallocate(void* ptr) { + auto d = raw_deleter(); + AT_ASSERT(d); + d(ptr); + } +}; + +// This context is used to generate DataPtr which have arbitrary +// std::function deleters associated with them. In some user facing +// functions, we give a (user-friendly) interface for constructing +// tensors from external data which take an arbitrary std::function +// deleter. Grep for InefficientStdFunctionContext to find these +// occurrences. +// +// This context is inefficient because we have to do a dynamic +// allocation InefficientStdFunctionContext, on top of the dynamic +// allocation which is implied by std::function itself. +struct C10_API InefficientStdFunctionContext { + std::unique_ptr> ptr_; + InefficientStdFunctionContext( + std::unique_ptr>&& ptr) + : ptr_(std::move(ptr)) {} + static DataPtr makeDataPtr( + void* ptr, + const std::function& deleter, + Device device); +}; + +/** Set the allocator for DeviceType `t`. The passed in allocator pointer is + * expected to have static lifetime; this function does NOT take ownership + * of the raw pointer. (The reason for this is to prevent existing pointers + * to an allocator of a particular device from being invalidated when + * SetAllocator is called.) + * + * Also note that this is not thread-safe, and we assume this function will + * only be called during initialization. + */ +C10_API void SetAllocator(DeviceType t, Allocator* alloc); +C10_API Allocator* GetAllocator(const DeviceType& t); + +template +struct AllocatorRegisterer { + explicit AllocatorRegisterer(Allocator* alloc) { + SetAllocator(t, alloc); + } +}; + +#define REGISTER_ALLOCATOR(t, f) \ + namespace { \ + static AllocatorRegisterer g_allocator_d(f); \ + } + +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/core/Backend.h b/thirdparty/libtorch/include/c10/core/Backend.h new file mode 100644 index 0000000000..26a277374c --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/Backend.h @@ -0,0 +1,289 @@ +#pragma once + +#include +#include +#include + +#include + +namespace c10 { + +/** + * This legacy enum class defines the set of backends supported by old school, + * code generated Type-based ATen. A "backend" in this sense roughly + * corresponds to the cartesian product of (device type, layout), but restricted + * only to combinations which we actually have kernels for. Backend does NOT + * include dtype. + * + * The reason we are sunsetting this enum class is because it doesn't allow for + * open registration; e.g., if you want to add SparseXLA, you'd have to + * edit this enum; you wouldn't be able to do it out of tree. TensorTypeId is + * the replacement for Backend which supports open registration. + * + * NB: The concept of 'Backend' here disagrees with the notion of backend + * exposed to users in torch.backends. Backend here is something like "CPU" + * or "SparseCUDA"; backend in torch.backends is something like "MKL" or + * "CUDNN". + */ +enum class Backend { CPU, CUDA, HIP, SparseCPU, SparseCUDA, SparseHIP, MSNPU, XLA, QuantizedCPU, ComplexCPU, ComplexCUDA, Undefined, MkldnnCPU, NumOptions }; + +static inline Backend toSparse(Backend b) { + switch (b) { + case Backend::CPU: + return Backend::SparseCPU; + case Backend::CUDA: + return Backend::SparseCUDA; + case Backend::HIP: + return Backend::SparseHIP; + case Backend::SparseCPU: + return Backend::SparseCPU; + case Backend::SparseCUDA: + return Backend::SparseCUDA; + case Backend::SparseHIP: + return Backend::SparseHIP; + default: + throw std::runtime_error("Unknown backend"); + } +} + +static inline Backend toDense(Backend b) { + switch (b) { + case Backend::CPU: + return Backend::CPU; + case Backend::CUDA: + return Backend::CUDA; + case Backend::HIP: + return Backend::HIP; + case Backend::MSNPU: + return Backend::MSNPU; + case Backend::XLA: + return Backend::XLA; + case Backend::SparseCPU: + return Backend::CPU; + case Backend::SparseCUDA: + return Backend::CUDA; + case Backend::SparseHIP: + return Backend::HIP; + case Backend::QuantizedCPU: + return Backend::QuantizedCPU; + case Backend::ComplexCPU: + return Backend::ComplexCPU; + case Backend::ComplexCUDA: + return Backend::ComplexCUDA; + default: + throw std::runtime_error("Unknown backend"); + } +} + +static inline Backend tensorTypeIdToBackend(TensorTypeId t) { + if (t == TensorTypeId::CPUTensorId) { + return Backend::CPU; + } else if (t == TensorTypeId::CUDATensorId) { + return Backend::CUDA; + } else if (t == TensorTypeId::HIPTensorId) { + return Backend::HIP; + } else if (t == TensorTypeId::MSNPUTensorId) { + return Backend::MSNPU; + } else if (t == TensorTypeId::XLATensorId) { + return Backend::XLA; + } else if (t == TensorTypeId::SparseCPUTensorId) { + return Backend::SparseCPU; + } else if (t == TensorTypeId::SparseCUDATensorId) { + return Backend::SparseCUDA; + } else if (t == TensorTypeId::SparseHIPTensorId) { + return Backend::SparseHIP; + } else if (t == TensorTypeId::MkldnnCPUTensorId) { + return Backend::MkldnnCPU; + } else if (t == TensorTypeId::QuantizedCPUTensorId) { + return Backend::QuantizedCPU; + } else if (t == TensorTypeId::ComplexCPUTensorId) { + return Backend::ComplexCPU; + } else if (t == TensorTypeId::ComplexCUDATensorId) { + return Backend::ComplexCUDA; + } else if (t == TensorTypeId::UndefinedTensorId) { + return Backend::Undefined; + } else { + AT_ERROR("Unrecognized tensor type ID: ", t); + } +} + +static inline TensorTypeId backendToTensorTypeId(Backend b) { + switch (b) { + case Backend::CPU: + return TensorTypeId::CPUTensorId; + case Backend::CUDA: + return TensorTypeId::CUDATensorId; + case Backend::HIP: + return TensorTypeId::HIPTensorId; + case Backend::MSNPU: + return TensorTypeId::MSNPUTensorId; + case Backend::XLA: + return TensorTypeId::XLATensorId; + case Backend::SparseCPU: + return TensorTypeId::SparseCPUTensorId; + case Backend::SparseCUDA: + return TensorTypeId::SparseCUDATensorId; + case Backend::SparseHIP: + return TensorTypeId::SparseHIPTensorId; + case Backend::MkldnnCPU: + return TensorTypeId::MkldnnCPUTensorId; + case Backend::QuantizedCPU: + return TensorTypeId::QuantizedCPUTensorId; + case Backend::ComplexCPU: + return TensorTypeId::ComplexCPUTensorId; + case Backend::ComplexCUDA: + return TensorTypeId::ComplexCUDATensorId; + case Backend::Undefined: + return TensorTypeId::UndefinedTensorId; + default: + throw std::runtime_error("Unknown backend"); + } +} + +static inline DeviceType backendToDeviceType(Backend b) { + switch (b) { + case Backend::CPU: + return DeviceType::CPU; + case Backend::CUDA: + return DeviceType::CUDA; + case Backend::HIP: + return DeviceType::HIP; + case Backend::MSNPU: + return DeviceType::MSNPU; + case Backend::XLA: + return DeviceType::XLA; + case Backend::SparseCPU: + return DeviceType::CPU; + case Backend::SparseCUDA: + return DeviceType::CUDA; + case Backend::SparseHIP: + return DeviceType::HIP; + case Backend::MkldnnCPU: + case Backend::QuantizedCPU: + case Backend::ComplexCPU: + return DeviceType::CPU; + case Backend::ComplexCUDA: + return DeviceType::CUDA; + case Backend::Undefined: + AT_ERROR("Undefined backend is not a valid device type"); + default: + AT_ERROR("Unknown backend"); + } +} + +static inline Backend backendToCPU(Backend b) { + switch (b) { + case Backend::CPU: + return Backend::CPU; + case Backend::CUDA: + return Backend::CPU; + case Backend::HIP: + return Backend::CPU; + case Backend::SparseCPU: + return Backend::SparseCPU; + case Backend::SparseCUDA: + return Backend::SparseCPU; + case Backend::SparseHIP: + return Backend::SparseCPU; + case Backend::MSNPU: + case Backend::XLA: + return Backend::CPU; + case Backend::MkldnnCPU: + return Backend::MkldnnCPU; + case Backend::QuantizedCPU: + return Backend::QuantizedCPU; + case Backend::ComplexCPU: + case Backend::ComplexCUDA: + return Backend::ComplexCPU; + case Backend::Undefined: + return Backend::Undefined; + default: + AT_ERROR("Unknown backend"); + } +} + +static inline Backend backendToCUDA(Backend b) { + switch (b) { + case Backend::CPU: + case Backend::CUDA: + case Backend::HIP: + case Backend::MSNPU: + case Backend::XLA: + return Backend::CUDA; + case Backend::SparseCPU: + case Backend::SparseCUDA: + case Backend::SparseHIP: + return Backend::SparseCUDA; + case Backend::ComplexCPU: + case Backend::ComplexCUDA: + return Backend::ComplexCUDA; + case Backend::Undefined: + return Backend::Undefined; + default: + AT_ERROR("Unknown backend"); + } +} + +static inline Backend backendToHIP(Backend b) { + switch (b) { + case Backend::CPU: + case Backend::CUDA: + case Backend::HIP: + case Backend::MSNPU: + case Backend::XLA: + return Backend::HIP; + case Backend::SparseCPU: + case Backend::SparseCUDA: + case Backend::SparseHIP: + return Backend::SparseHIP; + case Backend::Undefined: + return Backend::Undefined; + default: + AT_ERROR("Unknown backend"); + } +} + +// TODO: This probably shouldn't actually be static inline +static inline const char* toString(Backend b) { + switch (b) { + case Backend::CPU: + return "CPU"; + case Backend::CUDA: + return "CUDA"; + case Backend::HIP: + return "HIP"; + case Backend::MSNPU: + return "MSNPU"; + case Backend::XLA: + return "XLA"; + case Backend::SparseCPU: + return "SparseCPU"; + case Backend::SparseCUDA: + return "SparseCUDA"; + case Backend::SparseHIP: + return "SparseHIP"; + case Backend::MkldnnCPU: + return "MkldnnCPU"; + case Backend::QuantizedCPU: + return "QuantizedCPU"; + case Backend::ComplexCPU: + return "ComplexCPU"; + case Backend::ComplexCUDA: + return "ComplexCUDA"; + default: + return "UNKNOWN_BACKEND"; + } +} + +static inline bool isSparse(Backend b) { + switch (b) { + case Backend::SparseCPU: + case Backend::SparseCUDA: + case Backend::SparseHIP: + return true; + default: + return false; + } +} + +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/core/CPUAllocator.h b/thirdparty/libtorch/include/c10/core/CPUAllocator.h new file mode 100644 index 0000000000..c0fe490a07 --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/CPUAllocator.h @@ -0,0 +1,42 @@ +#pragma once + +#include +#include + +#include +#include +#include + +// TODO: rename to c10 +C10_DECLARE_bool(caffe2_report_cpu_memory_usage); +C10_DECLARE_bool(caffe2_cpu_allocator_do_zero_fill); +C10_DECLARE_bool(caffe2_cpu_allocator_do_junk_fill); + +namespace c10 { + +// Use 64-byte alignment should be enough for computation up to AVX512. +constexpr size_t gAlignment = 64; + +using MemoryDeleter = void (*)(void*); + +// A helper function that is basically doing nothing. +C10_API void NoDelete(void*); + +// Fill the data memory region of num bytes with a particular garbage pattern. +// The garbage value is chosen to be NaN if interpreted as floating point value, +// or a very large integer. +C10_API void memset_junk(void* data, size_t num); + +C10_API void* alloc_cpu(size_t nbytes); +C10_API void free_cpu(void* data); + +// Get the CPU Allocator. +C10_API at::Allocator* GetCPUAllocator(); +// Sets the CPU allocator to the given allocator: the caller gives away the +// ownership of the pointer. +C10_API void SetCPUAllocator(at::Allocator* alloc); + +// Get the Default CPU Allocator +C10_API at::Allocator* GetDefaultCPUAllocator(); + +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/core/CopyBytes.h b/thirdparty/libtorch/include/c10/core/CopyBytes.h new file mode 100644 index 0000000000..c49763f69d --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/CopyBytes.h @@ -0,0 +1,44 @@ +#pragma once + +#include + +namespace c10 { + +using CopyBytesFunction = void (*)( + size_t nbytes, + const void* src, + Device src_device, + void* dst, + Device dst_device); + +struct C10_API _CopyBytesFunctionRegisterer { + _CopyBytesFunctionRegisterer( + DeviceType from, + DeviceType to, + CopyBytesFunction func_sync, + CopyBytesFunction func_async = nullptr); +}; + +#define REGISTER_COPY_BYTES_FUNCTION(from, to, ...) \ + namespace { \ + static _CopyBytesFunctionRegisterer C10_ANONYMOUS_VARIABLE( \ + g_copy_function)(from, to, __VA_ARGS__); \ + } + +/* + * WARNING: Implementations for this function are currently registered from + * ATen and caffe2, not yet from c10. Don't use this if not either ATen + * or caffe2 is present as well. + * We can't move them yet, because the CUDA implementations aren't unified yet + * between ATen and caffe2. + * We're planning to move the implementations into c10/backend/xxx + * to make c10 self contained again. + */ +C10_API void CopyBytes( + size_t nbytes, + const void* src, + Device src_device, + void* dst, + Device dst_device, + bool async); +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/core/DefaultDtype.h b/thirdparty/libtorch/include/c10/core/DefaultDtype.h new file mode 100644 index 0000000000..ad0f3cb4ef --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/DefaultDtype.h @@ -0,0 +1,12 @@ +#pragma once + +#include + +namespace caffe2 { +class TypeMeta; +} // namespace caffe2 + +namespace c10 { +C10_API void set_default_dtype(caffe2::TypeMeta dtype); +C10_API const caffe2::TypeMeta& get_default_dtype(); +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/core/DefaultTensorOptions.h b/thirdparty/libtorch/include/c10/core/DefaultTensorOptions.h new file mode 100644 index 0000000000..7bf96525c1 --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/DefaultTensorOptions.h @@ -0,0 +1,36 @@ +#pragma once + +#include +#include +#include +#include + +namespace c10 { + +struct TensorOptions; + +/// Like TensorOptions, but all fields are guaranteed to be filled. +struct DefaultTensorOptions { + DefaultTensorOptions() = default; + + caffe2::TypeMeta dtype() const noexcept { return dtype_; } + Device device() const noexcept { return device_; } + Layout layout() const noexcept { return layout_; } + bool requires_grad() const noexcept { return requires_grad_; } + + // Defined in TensorOptions.h + inline DefaultTensorOptions& merge(const TensorOptions& options); + + private: + caffe2::TypeMeta dtype_ = caffe2::TypeMeta::Make(); // 64-bit + Device device_ = at::kCPU; // 32-bit + Layout layout_ = at::kStrided; // 8-bit + bool requires_grad_ = false; // 8-bit +}; + +inline const DefaultTensorOptions& getDefaultTensorOptions() { + static const auto options = DefaultTensorOptions(); + return options; +} + +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/core/Device.h b/thirdparty/libtorch/include/c10/core/Device.h new file mode 100644 index 0000000000..f1249e865f --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/Device.h @@ -0,0 +1,132 @@ +#pragma once + +#include +#include +#include + +#include +#include +#include +#include + +namespace c10 { + +/// An index representing a specific device; e.g., the 1 in GPU 1. +/// A DeviceIndex is not independently meaningful without knowing +/// the DeviceType it is associated; try to use Device rather than +/// DeviceIndex directly. +using DeviceIndex = int16_t; + +/// Represents a a compute device on which a tensor is located. A device is +/// uniquely identified by a type, which specifies the type of machine it is +/// (e.g. CPU or CUDA GPU), and a device index or ordinal, which identifies the +/// specific compute device when there is more than one of a certain type. The +/// device index is optional, and in its defaulted state represents (abstractly) +/// "the current device". Further, there are two constraints on the value of the +/// device index, if one is explicitly stored: +/// 1. A negative index represents the current device, a non-negative index +/// represents a specific, concrete device, +/// 2. When the device type is CPU, the device index must be zero. +struct C10_API Device final { + using Type = DeviceType; + + /// Constructs a new `Device` from a `DeviceType` and an optional device + /// index. + /* implicit */ Device(DeviceType type, DeviceIndex index = -1) + : type_(type), index_(index) { + validate(); + } + + /// Constructs a `Device` from a string description, for convenience. + /// The string supplied must follow the following schema: + /// `(cpu|cuda)[:]` + /// where `cpu` or `cuda` specifies the device type, and + /// `:` optionally specifies a device index. + /* implicit */ Device(const std::string& device_string); + + /// Returns true if the type and index of this `Device` matches that of + /// `other`. + bool operator==(const Device& other) const noexcept { + return this->type_ == other.type_ && this->index_ == other.index_; + } + + /// Returns true if the type or index of this `Device` differs from that of + /// `other`. + bool operator!=(const Device& other) const noexcept { + return !(*this == other); + } + + /// Sets the device index. + void set_index(DeviceIndex index) { + index_ = index; + } + + /// Returns the type of device this is. + DeviceType type() const noexcept { + return type_; + } + + /// Returns the optional index. + DeviceIndex index() const noexcept { + return index_; + } + + /// Returns true if the device has a non-default index. + bool has_index() const noexcept { + return index_ != -1; + } + + /// Return true if the device is of CUDA type. + bool is_cuda() const noexcept { + return type_ == DeviceType::CUDA; + } + + /// Return true if the device is of CPU type. + bool is_cpu() const noexcept { + return type_ == DeviceType::CPU; + } + + /// Same string as returned from operator<<. + std::string str() const; + + private: + DeviceType type_; + DeviceIndex index_ = -1; + void validate() { + TORCH_CHECK(index_ == -1 || index_ >= 0, + "Device index must be -1 or non-negative, got ", index_); + TORCH_CHECK(!is_cpu() || index_ <= 0, + "CPU device index must be -1 or zero, got ", index_); + } +}; + +C10_API std::ostream& operator<<( + std::ostream& stream, + const Device& device); + +} // namespace c10 + +namespace std { +template <> +struct hash { + size_t operator()(c10::Device d) const noexcept { + // Are you here because this static assert failed? Make sure you ensure + // that the bitmasking code below is updated accordingly! + static_assert(sizeof(c10::DeviceType) == 2, "DeviceType is not 16-bit"); + static_assert(sizeof(c10::DeviceIndex) == 2, "DeviceIndex is not 16-bit"); + // Note [Hazard when concatenating signed integers] + // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + // We must first convert to a same-sized unsigned type, before promoting to + // the result type, to prevent sign extension when any of the values is -1. + // If sign extension occurs, you'll clobber all of the values in the MSB + // half of the resulting integer. + // + // Technically, by C/C++ integer promotion rules, we only need one of the + // uint32_t casts to the result type, but we put in both for explicitness's sake. + uint32_t bits = + static_cast(static_cast(d.type())) << 16 + | static_cast(static_cast(d.index())); + return std::hash{}(bits); + } +}; +} // namespace std diff --git a/thirdparty/libtorch/include/c10/core/DeviceGuard.h b/thirdparty/libtorch/include/c10/core/DeviceGuard.h new file mode 100644 index 0000000000..852d6366eb --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/DeviceGuard.h @@ -0,0 +1,184 @@ +#pragma once + +#include + +namespace c10 { + +/// RAII guard that sets a certain default device in its constructor, and +/// changes it back to the device that was originally active upon destruction. +/// +/// The device is always reset to the one that was active at the time of +/// construction of the guard. Even if you `set_device` after construction, the +/// destructor will still reset the device to the one that was active at +/// construction time. +/// +/// This device guard does NOT have an uninitialized state; it is guaranteed +/// to reset a device on exit. If you are in a situation where you *might* +/// want to setup a guard (i.e., are looking for the moral equivalent +/// of optional), see OptionalDeviceGuard. +class DeviceGuard { +public: + /// No default constructor; see Note [Omitted default constructor from RAII] + explicit DeviceGuard() = delete; + + /// Set the current device to the passed Device. + explicit DeviceGuard(Device device) : guard_(device) {} + + /// This constructor is for testing only. + explicit DeviceGuard(Device device, const impl::DeviceGuardImplInterface* impl) : guard_(device, impl) {} + + /// Copy is disallowed + DeviceGuard(const DeviceGuard&) = delete; + DeviceGuard& operator=(const DeviceGuard&) = delete; + + /// Move is disallowed, as DeviceGuard does not have an uninitialized state, + /// which is required for moves on types with nontrivial destructors. + DeviceGuard(DeviceGuard&& other) = delete; + DeviceGuard& operator=(DeviceGuard&& other) = delete; + + /// Sets the device to the given one. The specified device must be consistent + /// with the device type originally specified during guard construction. + /// + /// TODO: The consistency check here is inconsistent with StreamGuard's + /// behavior with set_stream, where a stream on a different device than + /// the original one isn't an error; we just reset the stream and then + /// switch devices. + void reset_device(at::Device device) { + guard_.reset_device(device); + } + + /// This method is for testing only. + void reset_device(at::Device device, const impl::DeviceGuardImplInterface* impl) { + guard_.reset_device(device, impl); + } + + /// Sets the device index to the given one. The device type is inferred + /// from the original device type the guard was constructed with. + void set_index(DeviceIndex index) { + guard_.set_index(index); + } + + /// Returns the device that was set at the time the guard was constructed. + Device original_device() const { + return guard_.original_device(); + } + + /// Returns the most recent device that was set using this device guard, + /// either from construction, or via set_device. + Device current_device() const { + return guard_.current_device(); + } + +private: + impl::InlineDeviceGuard guard_; +}; + +/** + * A OptionalDeviceGuard is an RAII class that sets a device to some value on + * initialization, and resets the device to its original value on destruction. + * Morally, a OptionalDeviceGuard is equivalent to optional, but + * with extra constructors and methods as appropriate. + * + * Besides its obvious use (optionally applying a DeviceGuard), OptionalDeviceGuard + * is often also used for the following idiom: + * + * OptionalDeviceGuard g; + * for (const auto& t : tensors) { + * g.set_device(t.device()); + * do_something_with(t); + * } + * + * This usage is marginally more efficient than constructing a DeviceGuard every + * iteration of the for loop, as it avoids an unnecessary device reset. + * + * Unlike DeviceGuard, a OptionalDeviceGuard may be uninitialized. This occurs + * when you use the nullary constructor, or pass a nullopt to the constructor. + * Uninitialized OptionalDeviceGuards do *nothing*; they do not know what the + * original device was and they do not reset on destruction. This is why + * original_device() and current_device() return optional rather than + * Device (as they do in DeviceGuard), and also is why we didn't just + * provide OptionalDeviceGuard by default and hide DeviceGuard from users. + * + * The semantics of an OptionalDeviceGuard are exactly explained by thinking + * of it as an optional. In particular, an initialized + * OptionalDeviceGuard doesn't restore device to its value at construction; it + * restores device to its value *at initialization*. So if you have the + * program: + * + * setDevice(1); + * OptionalDeviceGuard g; + * setDevice(2); + * g.reset_device(Device(DeviceType::CUDA, 3)); // initializes! + * + * On destruction, g will reset device to 2, rather than 1. + * + * An uninitialized OptionalDeviceGuard is distinct from a (initialized) + * DeviceGuard whose original_device_ and current_device_ match, since the + * DeviceGuard will still reset the device to original_device_. + */ +class OptionalDeviceGuard { +public: + /// Create an uninitialized guard. Set the guard later using reset_device. + explicit OptionalDeviceGuard() : guard_() {} + + /// Initialize the guard, setting the current device to the passed Device. + explicit OptionalDeviceGuard(Device device) : guard_(device) {} + + /// Initialize the guard if a Device is passed; otherwise leave the + /// guard uninitialized. + explicit OptionalDeviceGuard(optional device) : guard_(device) {} + + /// Constructor for testing only. + explicit OptionalDeviceGuard(Device device, const impl::DeviceGuardImplInterface* impl) : guard_(device, impl) {} + + /// Copy is disallowed + OptionalDeviceGuard(const OptionalDeviceGuard&) = delete; + OptionalDeviceGuard& operator=(const OptionalDeviceGuard&) = delete; + + /// Move is disallowed + /// See Note [Explicit initialization of optional fields] + /// and // Note [Move construction for RAII guards is tricky] + /// for rationale. + OptionalDeviceGuard(OptionalDeviceGuard&& other) = delete; + OptionalDeviceGuard& operator=(OptionalDeviceGuard&& other) = delete; + + /// Sets the device to the given one. The specified device must be consistent + /// with the device type originally specified during guard construction. + void reset_device(at::Device device) { + guard_.reset_device(device); + } + + /// For testing only + void reset_device(at::Device device, const impl::DeviceGuardImplInterface* impl) { + guard_.reset_device(device, impl); + } + + /// Returns the device that was set at the time the guard was constructed. + optional original_device() const { + return guard_.original_device(); + } + + /// Returns the most recent device that was set using this device guard, + /// either from construction, or via reset_device. + optional current_device() const { + return guard_.current_device(); + } + +private: + impl::InlineOptionalDeviceGuard guard_; +}; + +// Note [Whither the DeviceGuard boilerplate] +// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +// Design note: in principle, we could avoid these wrappers using: +// +// using DeviceGuard = impl::InlineDeviceGuard; +// using OptionalDeviceGuard = impl::InlineOptionalDeviceGuard; +// +// But the error messages are worse, and our users can't just look at the +// header file to find out what's going on. Furthermore, for specializations +// like CUDAStreamGuard, it can be profitable to replace some interfaces with +// refined types (e.g., return CUDAStream instead of Stream). So, we eat +// the boilerplate and write out the API explicitly. + +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/core/DeviceType.h b/thirdparty/libtorch/include/c10/core/DeviceType.h new file mode 100644 index 0000000000..9f759666d7 --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/DeviceType.h @@ -0,0 +1,70 @@ +#pragma once + +// This is directly synchronized with caffe2/proto/caffe2.proto, but +// doesn't require me to figure out how to get Protobuf headers into +// ATen/core (which would require a lot more build system hacking.) +// If you modify me, keep me synchronized with that file. + +#include + +#include +#include + +namespace c10 { + +enum class DeviceType : int16_t { + CPU = 0, + CUDA = 1, // CUDA. + MKLDNN = 2, // Reserved for explicit MKLDNN + OPENGL = 3, // OpenGL + OPENCL = 4, // OpenCL + IDEEP = 5, // IDEEP. + HIP = 6, // AMD HIP + FPGA = 7, // FPGA + MSNPU = 8, // MSNPU + XLA = 9, // XLA / TPU + // NB: If you add more devices: + // - Change the implementations of DeviceTypeName and isValidDeviceType + // in DeviceType.cpp + // - Change the number below + COMPILE_TIME_MAX_DEVICE_TYPES = 10, + ONLY_FOR_TEST = 20901, // This device type is only for test. +}; + +constexpr DeviceType kCPU = DeviceType::CPU; +constexpr DeviceType kCUDA = DeviceType::CUDA; +constexpr DeviceType kHIP = DeviceType::HIP; +constexpr DeviceType kMSNPU = DeviceType::MSNPU; +constexpr DeviceType kXLA = DeviceType::XLA; + +// define explicit int constant +constexpr int COMPILE_TIME_MAX_DEVICE_TYPES = + static_cast(DeviceType::COMPILE_TIME_MAX_DEVICE_TYPES); + +static_assert(COMPILE_TIME_MAX_DEVICE_TYPES <= 16, + "Hey! You seem to be adding a lot of new DeviceTypes. The intent was " + "for this constant to reflect the actual number of DeviceTypes we support " + "in PyTorch; it's important that this number is not too large as we " + "use this to allocate stack arrays in some places in our code. If you " + "are indeed just adding the 17th device type, feel free to change " + "the check to 32; but if you are adding some sort of extensible device " + "types registration, please be aware that you are affecting code that " + "this number is small. Try auditing uses of this constant."); + +C10_API std::string DeviceTypeName( + DeviceType d, + bool lower_case = false); + +C10_API bool isValidDeviceType(DeviceType d); + +C10_API std::ostream& operator<<(std::ostream& stream, DeviceType type); + +} // namespace c10 + +namespace std { +template <> struct hash { + std::size_t operator()(c10::DeviceType k) const { + return std::hash()(static_cast(k)); + } +}; +} // namespace std diff --git a/thirdparty/libtorch/include/c10/core/Event.h b/thirdparty/libtorch/include/c10/core/Event.h new file mode 100644 index 0000000000..9b4d7ddf12 --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/Event.h @@ -0,0 +1,113 @@ +#pragma once + +#include "c10/core/impl/InlineEvent.h" +#include "c10/core/impl/VirtualGuardImpl.h" + +namespace c10 { + +/** + * A backend-generic movable, not copyable, not thread-safe event. + * + * The design of this event follows that of CUDA and HIP events. These events + * are recorded and waited on by streams and can be rerecorded to, + * each rerecording essentially creating a new version of the event. + * For example, if (in CPU time), stream X is asked to record E, + * stream Y waits on E, and stream X is asked to record E again, then Y will + * wait for X to finish the first call to record and not the second, because + * it's waiting on the first version of event E, not the second. + * Querying an event only returns the status of its most recent version. + * + * Backend-generic events are implemented by this class and + * impl::InlineEvent. In addition to these events there are also + * some backend-specific events, like ATen's CUDAEvent. Each of these + * classes has its own use. + * + * impl::InlineEvent<...> or a backend-specific event should be + * preferred when the backend is known at compile time and known to + * be compiled. Backend-specific events may have additional functionality. + * + * This Event should be used if a particular backend may not be available, + * or the backend required is not known at compile time. + * + * These generic events are built on top of DeviceGuardImpls, analogous + * to DeviceGuard and InlineDeviceGuard. The name "DeviceGuardImpls," + * is no longer entirely accurate, as these classes implement the + * backend-specific logic for a generic backend interface. + * + * See DeviceGuardImplInterface.h for a list of all supported flags. + */ + +struct Event final { + // Constructors + Event() = delete; + Event( + const DeviceType _device_type, + const EventFlag _flag = EventFlag::PYTORCH_DEFAULT) + : impl_{_device_type, _flag} { } + + // Copy constructor and copy assignment operator (deleted) + Event(const Event&) = delete; + Event& operator=(const Event&) = delete; + + // Move constructor and move assignment operator + Event(Event&& other) : impl_{std::move(other.impl_)} { } + Event& operator=(Event&& other) { + impl_.swap(std::move(other.impl_)); + return *this; + } + + // Destructor + ~Event() = default; + + // Getters + DeviceType device_type() const noexcept { return impl_.device_type(); } + DeviceIndex device_index() const noexcept { return impl_.device_index(); } + EventFlag flag() const noexcept { return impl_.flag(); } + bool was_marked_for_recording() const noexcept { return impl_.was_marked_for_recording(); } + +/** + * Calls record() if and only if record() has never been called for this event. + * Note: because Event is not thread-safe recordOnce() may call record() + * multiple times if called from multiple threads. + */ + void recordOnce(const Stream& stream) { + impl_.recordOnce(stream); + } + +/** + * Increments the event's version and enqueues a job with this version + * in the stream's work queue. When the stream process that job + * it nofifies all streams waiting on / blocked by that version of the + * event to continue and marks that version as recorded. + * */ + void record(const Stream& stream) { + impl_.record(stream); + } + +/** + * Does nothing if the event has not been scheduled to be recorded. + * If the event was previously enqueued to be recorded, a command + * to wait for the version of the event that exists at the time of this call + * is inserted in the stream's work queue. + * When the stream reaches this command it will stop processing + * additional commands until that version of the event is marked as recorded. + */ + void block(const Stream& stream) const { + impl_.block(stream); + } + +/** + * Returns true if (and only if) + * (1) the event has never been scheduled to be recorded + * (2) the current version is marked as recorded. + * Returns false otherwise. + */ + bool query() const { + return impl_.query(); + } + +private: + impl::InlineEvent impl_; +}; + +} // c10 diff --git a/thirdparty/libtorch/include/c10/core/Layout.h b/thirdparty/libtorch/include/c10/core/Layout.h new file mode 100644 index 0000000000..c5ecc8908a --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/Layout.h @@ -0,0 +1,41 @@ +#pragma once + +#include +#include + +#include + +namespace c10 { +enum class Layout : int8_t { Strided, Sparse, Mkldnn }; + +constexpr auto kStrided = Layout::Strided; +constexpr auto kSparse = Layout::Sparse; +constexpr auto kMkldnn = Layout::Mkldnn; + +inline Layout layout_from_backend(Backend backend) { + switch (backend) { + case Backend::SparseCPU: + case Backend::SparseCUDA: + case Backend::SparseHIP: + return Layout::Sparse; + case Backend::MkldnnCPU: + return Layout::Mkldnn; + default: + return Layout::Strided; + } +} + +inline std::ostream& operator<<(std::ostream& stream, at::Layout layout) { + switch (layout) { + case at::kStrided: + return stream << "Strided"; + case at::kSparse: + return stream << "Sparse"; + case at::kMkldnn: + return stream << "Mkldnn"; + default: + AT_ERROR("Unknown layout"); + } +} + +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/core/MemoryFormat.h b/thirdparty/libtorch/include/c10/core/MemoryFormat.h new file mode 100644 index 0000000000..87f5c590eb --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/MemoryFormat.h @@ -0,0 +1,63 @@ +#pragma once + +#include +#include +#include + +#include + +// Memory format is not the property of a Tensor. It is the way to tell an +// operator how the result should be organized in memory and nothing more. That +// means memory format should never be used as return value for any tensor state +// interrogation functions (internally and externally). +// +// Possible options are: +// Preserve: +// If any of the input tensors is in channels_last format, operator output +// should be in channels_last format +// +// Contiguous: +// Regardless of input tensors format, the output should be contiguous Tensor. +// +// ChannelsLast: +// Regardless of input tensors format, the output should be in channels_last format. + + +namespace c10 { +enum class MemoryFormat : int8_t { Contiguous, Preserve, ChannelsLast }; + +// If you are seeing this, it means that this call site was not checked if +// the memory format could be preserved, and it was switched to old default +// behaviour of contiguous +#define LEGACY_CONTIGUOUS_MEMORY_FORMAT c10::get_contiguous_memory_format() + +C10_DEPRECATED inline MemoryFormat get_contiguous_memory_format() { + return MemoryFormat::Contiguous; +} + +inline std::ostream& operator<<( + std::ostream& stream, + at::MemoryFormat memory_format) { + switch (memory_format) { + case MemoryFormat::Preserve: + return stream << "Preserve"; + case MemoryFormat::Contiguous: + return stream << "Contiguous"; + case MemoryFormat::ChannelsLast: + return stream << "ChannelsLast"; + default: + AT_ERROR("Unknown memory format"); + } +} + +inline std::vector get_channels_last_strides(IntArrayRef sizes) { + AT_ASSERT(sizes.size() == 4); + std::vector strides(sizes.size()); + strides[1] = 1; + strides[3] = sizes[1]; + strides[2] = strides[3] * sizes[3]; + strides[0] = strides[2] * sizes[2]; + return strides; +} + +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/core/QEngine.h b/thirdparty/libtorch/include/c10/core/QEngine.h new file mode 100644 index 0000000000..082b85dffa --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/QEngine.h @@ -0,0 +1,40 @@ +#pragma once + +#include +#include +#include + +namespace c10 { + +/** + * QEngine is an enum that is used to select the engine to run quantized ops. + * Keep this enum in sync with get_qengine_id() in + * torch/backends/quantized/__init__.py + */ +enum class QEngine : uint8_t { + NoQEngine = 0, + FBGEMM = 1, + QNNPACK = 2, +}; + +constexpr auto kNoQEngine = QEngine::NoQEngine; +constexpr auto kFBGEMM = QEngine::FBGEMM; +constexpr auto kQNNPACK = QEngine::QNNPACK; + +inline std::string toString(QEngine qengine) { + switch (qengine) { + case kNoQEngine: + return "NoQEngine"; + case kFBGEMM: + return "FBGEMM"; + case kQNNPACK: + return "QNNPACK"; + default: + TORCH_CHECK( + false, + "Unrecognized Quantized Engine: ", + static_cast(qengine)); + } +} + +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/core/QScheme.h b/thirdparty/libtorch/include/c10/core/QScheme.h new file mode 100644 index 0000000000..96c00b7bd6 --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/QScheme.h @@ -0,0 +1,45 @@ +#pragma once + +#include +#include +#include + +namespace c10 { + +/** + * QScheme is an enum that specifies the type of quantization. This has a one + * to one correspondence with Quantizer + * Please refer to ATen/quantized/Quantizer.h to see the Quantizers classes. + * Keep this file in sync with torch/nn/_qscheme.py + */ +enum class QScheme : uint8_t { + PER_TENSOR_AFFINE = 0, + PER_CHANNEL_AFFINE = 1, + PER_TENSOR_SYMMETRIC = 2, + PER_CHANNEL_SYMMETRIC = 3, + COMPILE_TIME_NUM_QSCHEMES = 4, +}; + +constexpr auto kPerTensorAffine = QScheme::PER_TENSOR_AFFINE; +constexpr auto kPerChannelAffine = QScheme::PER_CHANNEL_AFFINE; +constexpr auto kPerTensorSymmetric = QScheme::PER_TENSOR_SYMMETRIC; +constexpr auto kPerChannelSymmetric = QScheme::PER_CHANNEL_SYMMETRIC; +constexpr int COMPILE_TIME_NUM_QSCHEMES = + static_cast(QScheme::COMPILE_TIME_NUM_QSCHEMES); + +inline std::string toString(QScheme qscheme) { + switch(qscheme) { + case kPerTensorAffine: + return "per_tensor_affine"; + case kPerChannelAffine: + return "per_channel_affine"; + case kPerTensorSymmetric: + return "per_tensor_symmetric"; + case kPerChannelSymmetric: + return "per_channel_symmetric"; + default: + TORCH_CHECK(false, "Unrecognized qscheme: ", static_cast(qscheme)); + } +} + +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/core/Scalar.h b/thirdparty/libtorch/include/c10/core/Scalar.h new file mode 100644 index 0000000000..6d79f3004c --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/Scalar.h @@ -0,0 +1,143 @@ +#pragma once + +#include +#include +#include +#include +#include + +#include +#include +#include + +namespace c10 { + +/** + * Scalar represents a 0-dimensional tensor which contains a single element. + * Unlike a tensor, numeric literals (in C++) are implicitly convertible to + * Scalar (which is why, for example, we provide both add(Tensor) and + * add(Scalar) overloads for many operations). It may also be used in + * circumstances where you statically know a tensor is 0-dim and single size, + * but don't know its type. + */ +class C10_API Scalar { + public: + Scalar() : Scalar(int64_t(0)) {} + +#define DEFINE_IMPLICIT_CTOR(type, name) \ + Scalar(type vv) : Scalar(vv, true) { } + + AT_FORALL_SCALAR_TYPES_AND2(Half, BFloat16, DEFINE_IMPLICIT_CTOR) + +#undef DEFINE_IMPLICIT_CTOR + + // Value* is both implicitly convertible to SymbolicVariable and bool which + // causes ambiguosity error. Specialized constructor for bool resolves this + // problem. + template < + typename T, + typename std::enable_if::value, bool>::type* = + nullptr> + Scalar(T vv) : tag(Tag::HAS_b) { + v.i = convert(vv); + } + +#define DEFINE_IMPLICIT_COMPLEX_CTOR(type, name, member) \ + Scalar(type vv) : tag(Tag::HAS_##member) { \ + v.member[0] = c10::convert(vv.real()); \ + v.member[1] = c10::convert(vv.imag()); \ + } + + DEFINE_IMPLICIT_COMPLEX_CTOR(at::ComplexHalf, ComplexHalf, z) + DEFINE_IMPLICIT_COMPLEX_CTOR(std::complex, ComplexFloat, z) + DEFINE_IMPLICIT_COMPLEX_CTOR(std::complex, ComplexDouble, z) + +#undef DEFINE_IMPLICIT_COMPLEX_CTOR + +#define DEFINE_ACCESSOR(type, name) \ + type to##name() const { \ + if (Tag::HAS_d == tag) { \ + return checked_convert(v.d, #type); \ + } else if (Tag::HAS_z == tag) { \ + return checked_convert>( \ + {v.z[0], v.z[1]}, #type); \ + } if (Tag::HAS_b == tag) { \ + return checked_convert(v.i, #type); \ + } else { \ + return checked_convert(v.i, #type); \ + } \ + } + + // TODO: Support ComplexHalf accessor + AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_EXCEPT_COMPLEX_HALF(DEFINE_ACCESSOR) + + // also support scalar.to(); + template + T to() const; + +#undef DEFINE_ACCESSOR + bool isFloatingPoint() const { + return Tag::HAS_d == tag; + } + + C10_DEPRECATED_MESSAGE("isIntegral is deprecated. Please use the overload with 'includeBool' parameter instead.") + bool isIntegral() const { + return Tag::HAS_i == tag; + } + bool isIntegral(bool includeBool) const { + return Tag::HAS_i == tag || (includeBool && isBoolean()); + } + + bool isComplex() const { + return Tag::HAS_z == tag; + } + bool isBoolean() const { + return Tag::HAS_b == tag; + } + + Scalar operator-() const; + + private: + template::is_integer && ! std::is_same::value, bool>::type* = + nullptr> + Scalar(T vv, bool) : tag(Tag::HAS_i) { + v.i = convert(vv); + } + + template::is_integer, bool>::type* = + nullptr> + Scalar(T vv, bool) : tag(Tag::HAS_d) { + v.d = convert(vv); + } + + // We can't set v in the initializer list using the + // syntax v{ .member = ... } because it doesn't work on MSVC + + enum class Tag { HAS_d, HAS_i, HAS_z, HAS_b }; + Tag tag; + union { + double d; + int64_t i; + // Can't do put std::complex in the union, because it triggers + // an nvcc bug: + // error: designator may not specify a non-POD subobject + double z[2]; + } v; +}; + +// define the scalar.to() specializations +template +inline T Scalar::to() const { + throw std::runtime_error("to() cast to unexpected type."); +} + +#define DEFINE_TO(T, name) \ + template <> \ + inline T Scalar::to() const { \ + return to##name(); \ + } +AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_EXCEPT_COMPLEX_HALF(DEFINE_TO) +#undef DEFINE_TO +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/core/ScalarType.h b/thirdparty/libtorch/include/c10/core/ScalarType.h new file mode 100644 index 0000000000..4dfffc8ec7 --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/ScalarType.h @@ -0,0 +1,430 @@ +#pragma once + +#include +#include +#include +#include + +#include +#include +#include + +namespace c10 { + +// For the macros below: +// NB: If you want to macro some code for all non-QInt scalar types (i.e. types +// with complete information, you probably want one of the +// AT_FORALL_SCALAR_TYPES / AT_FORALL_SCALAR_TYPES_AND +// macros below, which are designed to behave similarly to the Dispatch macros +// with the same name. + +// NB: Order matters for this macro; it is relied upon in +// _promoteTypesLookup and the serialization format. +#define AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(_) \ + _(uint8_t, Byte) /* 0 */ \ + _(int8_t, Char) /* 1 */ \ + _(int16_t, Short) /* 2 */ \ + _(int, Int) /* 3 */ \ + _(int64_t, Long) /* 4 */ \ + _(at::Half, Half) /* 5 */ \ + _(float, Float) /* 6 */ \ + _(double, Double) /* 7 */ \ + _(at::ComplexHalf, ComplexHalf) /* 8 */ \ + _(std::complex, ComplexFloat) /* 9 */ \ + _(std::complex, ComplexDouble) /* 10 */ \ + _(bool, Bool) /* 11 */ \ + _(c10::qint8, QInt8) /* 12 */ \ + _(c10::quint8, QUInt8) /* 13 */ \ + _(c10::qint32, QInt32) /* 14 */ \ + _(at::BFloat16, BFloat16) /* 15 */ + + +// If you want to support ComplexHalf for real, add ComplexHalf +// into this macro (and change the name). But beware: convert() +// doesn't work for all the conversions you need... +#define AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_EXCEPT_COMPLEX_HALF(_) \ + _(uint8_t, Byte) \ + _(int8_t, Char) \ + _(int16_t, Short) \ + _(int, Int) \ + _(int64_t, Long) \ + _(at::Half, Half) \ + _(float, Float) \ + _(double, Double) \ + _(std::complex, ComplexFloat) \ + _(std::complex, ComplexDouble) \ + _(bool, Bool) \ + _(at::BFloat16, BFloat16) + + +enum class ScalarType : int8_t { +#define DEFINE_ENUM(_1, n) n, + AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_ENUM) +#undef DEFINE_ENUM + Undefined, + NumOptions +}; + +namespace impl { + +// These are used to map ScalarTypes to C++ types. Feel free to add more or even +// macro generate this; the examples here are just those we have found to be +// necessary. + +template +struct ScalarTypeToCPPType; + +template<> +struct ScalarTypeToCPPType { + using type = c10::Half; + + // This is a workaround for the CUDA bug which prevents ::detail::ScalarTypeToCType::type being used directly + // due to ambiguous reference which can't to be resolved. For some reason it cant pick between at::detail and at::cuda::detail. + // For repro example, please see: https://gist.github.com/izdeby/952ae7cf256ddb740a73776d39a7e7ba + // TODO: remove once the bug is fixed. + static type t; +}; + +template<> +struct ScalarTypeToCPPType { + using type = c10::BFloat16; + + // This is a workaround for the CUDA bug which prevents ::detail::ScalarTypeToCType::type being used directly + // due to ambiguous reference which can't to be resolved. For some reason it cant pick between at::detail and at::cuda::detail. + // For repro example, please see: https://gist.github.com/izdeby/952ae7cf256ddb740a73776d39a7e7ba + // TODO: remove once the bug is fixed. + static type t; +}; + +template<> +struct ScalarTypeToCPPType { + using type = bool; + + // This is a workaround for the CUDA bug which prevents ::detail::ScalarTypeToCType::type being used directly + // due to ambiguous reference which can't to be resolved. For some reason it cant pick between at::detail and at::cuda::detail. + // For repro example, please see: https://gist.github.com/izdeby/952ae7cf256ddb740a73776d39a7e7ba + // TODO: remove once the bug is fixed. + static type t; +}; + +template<> +struct ScalarTypeToCPPType { + using type = int64_t; + + // This is a workaround for the CUDA bug which prevents ::detail::ScalarTypeToCType::type being used directly + // due to ambiguous reference which can't to be resolved. For some reason it cant pick between at::detail and at::cuda::detail. + // For repro example, please see: https://gist.github.com/izdeby/952ae7cf256ddb740a73776d39a7e7ba + // TODO: remove once the bug is fixed. + static type t; +}; +} + +#define AT_FORALL_SCALAR_TYPES(_) \ + _(uint8_t, Byte) \ + _(int8_t, Char) \ + _(int16_t, Short) \ + _(int, Int) \ + _(int64_t, Long) \ + _(float, Float) \ + _(double, Double) + +#define AT_FORALL_SCALAR_TYPES_AND(SCALARTYPE, _) \ + _(uint8_t, Byte) \ + _(int8_t, Char) \ + _(int16_t, Short) \ + _(int, Int) \ + _(int64_t, Long) \ + _(float, Float) \ + _(double, Double) \ + _(decltype(::c10::impl::ScalarTypeToCPPType<::c10::ScalarType::SCALARTYPE>::t), SCALARTYPE) + +#define AT_FORALL_SCALAR_TYPES_AND2(SCALARTYPE1, SCALARTYPE2, _) \ + _(uint8_t, Byte) \ + _(int8_t, Char) \ + _(int16_t, Short) \ + _(int, Int) \ + _(int64_t, Long) \ + _(float, Float) \ + _(double, Double) \ + _(decltype(::c10::impl::ScalarTypeToCPPType<::c10::ScalarType::SCALARTYPE1>::t), SCALARTYPE1) \ + _(decltype(::c10::impl::ScalarTypeToCPPType<::c10::ScalarType::SCALARTYPE2>::t), SCALARTYPE2) + +#define AT_FORALL_SCALAR_TYPES_AND3(SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, _) \ + _(uint8_t, Byte) \ + _(int8_t, Char) \ + _(int16_t, Short) \ + _(int, Int) \ + _(int64_t, Long) \ + _(float, Float) \ + _(double, Double) \ + _(decltype(::c10::impl::ScalarTypeToCPPType<::c10::ScalarType::SCALARTYPE1>::t), SCALARTYPE1) \ + _(decltype(::c10::impl::ScalarTypeToCPPType<::c10::ScalarType::SCALARTYPE2>::t), SCALARTYPE2) \ + _(decltype(::c10::impl::ScalarTypeToCPPType<::c10::ScalarType::SCALARTYPE3>::t), SCALARTYPE3) + +#define AT_FORALL_QINT_TYPES(_) \ + _(c10::qint8, QInt8) \ + _(c10::quint8, QUInt8) \ + _(c10::qint32, QInt32) + +#define AT_FORALL_COMPLEX_TYPES(_) \ + _(std::complex, ComplexFloat) \ + _(std::complex, ComplexDouble) + +static inline caffe2::TypeMeta scalarTypeToTypeMeta(ScalarType scalar_type) { +#define DEFINE_CASE(ctype, name) \ + case ScalarType::name: \ + return caffe2::TypeMeta::Make(); + + switch (scalar_type) { + AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_CASE) + case ScalarType::Undefined: + return caffe2::TypeMeta(); + default: + AT_ERROR( + "Unrecognized Scalartype ", + scalar_type, + " (please report this error)"); + } +#undef DEFINE_CASE +} + +static inline c10::optional tryTypeMetaToScalarType( + caffe2::TypeMeta dtype) { +#define DEFINE_IF(ctype, name) \ + if (dtype == caffe2::TypeMeta::Make()) { \ + return {ScalarType::name}; \ + } + AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_IF) +#undef DEFINE_IF + if (dtype == caffe2::TypeMeta()) { + return {ScalarType::Undefined}; + } + return c10::nullopt; +} + +static inline ScalarType typeMetaToScalarType(caffe2::TypeMeta dtype) { + if (auto scalar_type = tryTypeMetaToScalarType(dtype)) { + return *scalar_type; + } + AT_ERROR( + "Unsupported TypeMeta in ATen: ", dtype, " (please report this error)"); +} + +static inline bool operator==(ScalarType t, caffe2::TypeMeta m) { + if (auto mt = tryTypeMetaToScalarType(m)) { + return (*mt) == t; + } + return false; +} + +static inline bool operator==(caffe2::TypeMeta m, ScalarType t) { + return t == m; +} + +#define DEFINE_CONSTANT(_, name) \ + constexpr ScalarType k##name = ScalarType::name; + +AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_CONSTANT) +#undef DEFINE_CONSTANT + +static inline const char* toString(ScalarType t) { +#define DEFINE_CASE(_, name) \ + case ScalarType::name: \ + return #name; + + switch (t) { + AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_CASE) + default: + return "UNKNOWN_SCALAR"; + } +#undef DEFINE_CASE +} + +static inline size_t elementSize(ScalarType t) { +#define CASE_ELEMENTSIZE_CASE(ctype, name) \ + case ScalarType::name: \ + return sizeof(ctype); + + switch (t) { + AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(CASE_ELEMENTSIZE_CASE) + default: + AT_ERROR("Unknown ScalarType"); + } +#undef CASE_ELEMENTSIZE_CASE +} + +C10_DEPRECATED_MESSAGE("isIntegralType is deprecated. Please use the overload with 'includeBool' parameter instead.") +static inline bool isIntegralType(ScalarType t) { + return ( + t == ScalarType::Byte || t == ScalarType::Char || t == ScalarType::Int || + t == ScalarType::Long || t == ScalarType::Short); +} + +static inline bool isIntegralType(ScalarType t, bool includeBool) { + bool isIntegral = ( + t == ScalarType::Byte || t == ScalarType::Char || t == ScalarType::Int || + t == ScalarType::Long || t == ScalarType::Short); + + return includeBool ? isIntegral || (t == ScalarType::Bool) : isIntegral; +} + +static inline bool isFloatingType(ScalarType t) { + return ( + t == ScalarType::Double || t == ScalarType::Float || + t == ScalarType::Half || t == ScalarType::BFloat16); +} + +static inline bool isComplexType(ScalarType t) { + return ( + t == ScalarType::ComplexHalf || t == ScalarType::ComplexFloat || + t == ScalarType::ComplexDouble); +} + +static inline bool isQIntType(ScalarType t) { + // Don't forget to extend this when adding new QInt types + return t == ScalarType:: QInt8 || t == ScalarType::QUInt8 || t == ScalarType::QInt32; +} + +static inline ScalarType toQIntType(ScalarType t) { + switch (t) { + case ScalarType::Byte: + return ScalarType::QUInt8; + case ScalarType::Char: + return ScalarType::QInt8; + case ScalarType::Int: + return ScalarType::QInt32; + default: + return t; + } +} + +static inline ScalarType toUnderlying(ScalarType t) { + switch (t) { + case ScalarType::QUInt8: + return ScalarType::Byte; + case ScalarType::QInt8: + return ScalarType::Char; + case ScalarType::QInt32: + return ScalarType::Int; + default: + return t; + } +} + +static inline bool isSignedType(ScalarType t) { + #define CASE_SIGNED(ctype, name) \ + case ScalarType::name: \ + return std::numeric_limits::is_signed; + + switch (toUnderlying(t)) { + AT_FORALL_SCALAR_TYPES_AND3(Half, Bool, BFloat16, CASE_SIGNED) + default: + AT_ERROR("Unknown ScalarType"); + } + #undef CASE_SIGNED +} + +static inline bool isUnderlying(ScalarType type, ScalarType qtype) { + return type == toUnderlying(qtype); +} + +static inline ScalarType toValueType(ScalarType t) { + switch (t) { + case ScalarType::ComplexFloat: + return ScalarType::Float; + case ScalarType::ComplexDouble: + return ScalarType::Double; + default: + return t; + } +} + +// see tensor_attributes.rst for detailed explanation and examples +// of casting rules. +static inline bool canCast(const ScalarType from, const ScalarType to) { + // We disallow float -> integral, e.g., int_tensor *= float is disallowed. + if (isFloatingType(from) && isIntegralType(to, false)) { + return false; + } + + // Treat bool as a distinct "category," to be consistent with type promotion + // rules (e.g. `bool_tensor + 5 -> int64_tensor`). If `5` was in the same category + // as `bool_tensor`, we would not promote. + // Differing categories implies `bool_tensor += 5` is disallowed. + // + // NB: numpy distinguishes "unsigned" as a category to get the desired + // `bool_tensor + 5 -> int64_tensor` behavior. We don't, because: + // * We don't want the performance hit of checking the runtime sign of Scalars. + // * `uint8_tensor + 5 -> int64_tensor` would be undesirable. + if (from != ScalarType::Bool && to == ScalarType::Bool) { + return false; + } + return true; +} + +static inline ScalarType promoteTypes(ScalarType a, ScalarType b) { + // This is generated according to NumPy's promote_types + constexpr auto u1 = ScalarType::Byte; + constexpr auto i1 = ScalarType::Char; + constexpr auto i2 = ScalarType::Short; + constexpr auto i4 = ScalarType::Int; + constexpr auto i8 = ScalarType::Long; + constexpr auto f2 = ScalarType::Half; + constexpr auto f4 = ScalarType::Float; + constexpr auto f8 = ScalarType::Double; + constexpr auto c2 = ScalarType::ComplexHalf; + constexpr auto c4 = ScalarType::ComplexFloat; + constexpr auto c8 = ScalarType::ComplexDouble; + constexpr auto b1 = ScalarType::Bool; + constexpr auto bf = ScalarType::BFloat16; + constexpr auto ud = ScalarType::Undefined; + if (a == ud || b == ud) { + return ScalarType::Undefined; + } + + // For QInt types, we only allow exact match + if (isQIntType(a) && a == b) { + return a; + } + + if (isQIntType(a) || isQIntType(b)) { + AT_ERROR( + "promoteTypes with quantized numbers is not handled yet; figure out what the correct rules should be, offending types: ", + toString(a), + " ", + toString(b)); + } + + // this matrix has to be consistent with AT_FORALL_SCALAR_TYPES_WITH_COMPLEX + // so that's why we have to add undefined as we are not sure what is the + // corrent values for the type promotions in complex type cases. + static constexpr ScalarType _promoteTypesLookup[static_cast( + ScalarType::NumOptions)][static_cast(ScalarType::NumOptions)] = { + /* u1 i1 i2 i4 i8 f2 f4 f8 c2 c4 c8 b1 q1 q2 q3 bf*/ + /* u1 */ {u1, i2, i2, i4, i8, f2, f4, f8, ud, c4, c8, u1, ud, ud, ud, ud}, + /* i1 */ {i2, i1, i2, i4, i8, f2, f4, f8, ud, c4, c8, i1, ud, ud, ud, ud}, + /* i2 */ {i2, i2, i2, i4, i8, f2, f4, f8, ud, c4, c8, i2, ud, ud, ud, ud}, + /* i4 */ {i4, i4, i4, i4, i8, f2, f4, f8, ud, c4, c8, i4, ud, ud, ud, ud}, + /* i8 */ {i8, i8, i8, i8, i8, f2, f4, f8, ud, c4, c8, i8, ud, ud, ud, ud}, + /* f2 */ {f2, f2, f2, f2, f2, f2, f4, f8, ud, c4, c8, f2, ud, ud, ud, ud}, + /* f4 */ {f4, f4, f4, f4, f4, f4, f4, f8, ud, c4, c8, f4, ud, ud, ud, ud}, + /* f8 */ {f8, f8, f8, f8, f8, f8, f8, f8, ud, c8, c8, f8, ud, ud, ud, ud}, + /* c2 */ {ud, ud, ud, ud, ud, ud, ud, ud, c2, c4, c8, ud, ud, ud, ud, ud}, + /* c4 */ {c4, c4, c4, c4, c4, c4, c4, c8, c4, c4, c8, ud, ud, ud, ud, ud}, + /* c8 */ {c8, c8, c8, c8, c8, c8, c8, c8, c8, c8, c8, ud, ud, ud, ud, ud}, + /* b1 */ {u1, i1, i2, i4, i8, f2, f4, f8, ud, ud, ud, b1, ud, ud, ud, ud}, + /* q1 */ {ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud}, + /* q2 */ {ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud}, + /* q3 */ {ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud}, + /* bf */ {ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, ud, bf}, + }; + return _promoteTypesLookup[static_cast(a)][static_cast(b)]; +} + +inline std::ostream& operator<<( + std::ostream& stream, + at::ScalarType scalar_type) { + return stream << toString(scalar_type); +} + +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/core/Storage.h b/thirdparty/libtorch/include/c10/core/Storage.h new file mode 100644 index 0000000000..6d86119eff --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/Storage.h @@ -0,0 +1,184 @@ +#pragma once + +#include + +namespace c10 { + +struct C10_API Storage { + public: + Storage() {} + Storage(c10::intrusive_ptr ptr) : storage_impl_(std::move(ptr)) {} + + // Allocates memory buffer using given allocator and creates a storage with it + Storage( + caffe2::TypeMeta data_type, + size_t size, + Allocator* allocator, + bool resizable) + : storage_impl_(c10::make_intrusive( + data_type, + size, + allocator, + resizable)) {} + + // Creates storage with pre-allocated memory buffer. Allocator is given for + // potential future reallocations, however it can be nullptr if the storage + // is non-resizable + Storage( + caffe2::TypeMeta data_type, + int64_t numel, + at::DataPtr data_ptr, + at::Allocator* allocator, + bool resizable) + : storage_impl_(c10::make_intrusive( + data_type, + numel, + std::move(data_ptr), + allocator, + resizable)) {} + + // Legacy constructor for partially initialized (dtype or memory) storages + // that can be temporarily created with Caffe2 APIs. See the note on top of + // TensorImpl.h for details. + static Storage create_legacy(at::Device device, caffe2::TypeMeta data_type) { + auto allocator = GetAllocator(device.type()); + return Storage(c10::make_intrusive( + data_type, + 0, + allocator->allocate(0), // materialize a non-default Device. + allocator, + true)); + } + + template + inline bool IsType() const { + return storage_impl_->IsType(); + } + + template + T* data() const { return storage_impl_->data(); } + + template + T* unsafe_data() const { return storage_impl_->unsafe_data(); } + + size_t elementSize() const { + return storage_impl_->itemsize(); + } + + inline size_t itemsize() const { + return storage_impl_->itemsize(); + } + + ptrdiff_t size() const { + return storage_impl_->numel(); + } + + int64_t numel() const { + return storage_impl_->numel(); + } + + // TODO: remove later + void set_numel(int64_t numel) const { + storage_impl_.get()->set_numel(numel); + } + + bool resizable() const { + return storage_impl_->resizable(); + } + + size_t capacity() const { + return storage_impl_->capacity(); + } + // get() use here is to get const-correctness + + void* data() const { + return storage_impl_.get()->data(); + } + + const caffe2::TypeMeta& dtype() const { + return storage_impl_->dtype(); + } + + at::DataPtr& data_ptr() { + return storage_impl_->data_ptr(); + } + + const at::DataPtr& data_ptr() const { + return storage_impl_->data_ptr(); + } + + // Returns the previous data_ptr + at::DataPtr set_data_ptr(at::DataPtr&& data_ptr) const { + return storage_impl_.get()->set_data_ptr(std::move(data_ptr)); + }; + + void set_dtype(const caffe2::TypeMeta& data_type) const { + storage_impl_.get()->set_dtype(data_type); + } + + DeviceType device_type() const { + return storage_impl_->device_type(); + } + + at::Allocator* allocator() const { + return storage_impl_.get()->allocator(); + } + + at::Device device() const { + return storage_impl_->device(); + } + + StorageImpl* unsafeReleaseStorageImpl() { + return storage_impl_.release(); + } + + StorageImpl* unsafeGetStorageImpl() const noexcept { + return storage_impl_.get(); + } + + operator bool() const { + return storage_impl_; + } + + size_t use_count() const { + return storage_impl_.use_count(); + } + + inline bool unique() const { + return storage_impl_.unique(); + } + + bool is_alias_of(const Storage& other) const { + return storage_impl_ == other.storage_impl_; + } + + void UniqueStorageShareExternalPointer( + void* src, + const caffe2::TypeMeta& data_type, + size_t capacity, + DeleterFnPtr d = nullptr) { + if (!storage_impl_.unique()) { + AT_ERROR( + "UniqueStorageShareExternalPointer can only be called when use_count == 1"); + } + storage_impl_->UniqueStorageShareExternalPointer( + src, data_type, capacity, d); + } + + void UniqueStorageShareExternalPointer( + at::DataPtr&& data_ptr, + const caffe2::TypeMeta& data_type, + size_t capacity) { + if (!storage_impl_.unique()) { + AT_ERROR( + "UniqueStorageShareExternalPointer can only be called when use_count == 1"); + } + storage_impl_->UniqueStorageShareExternalPointer( + std::move(data_ptr), data_type, capacity); + } + + protected: + c10::intrusive_ptr storage_impl_; +}; + +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/core/StorageImpl.h b/thirdparty/libtorch/include/c10/core/StorageImpl.h new file mode 100644 index 0000000000..579ef00820 --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/StorageImpl.h @@ -0,0 +1,234 @@ +#pragma once + +#include +#include + +#include + +namespace c10 { + +struct C10_API StorageImpl final : public c10::intrusive_ptr_target { + public: + StorageImpl( + caffe2::TypeMeta data_type, + int64_t numel, + at::DataPtr data_ptr, + at::Allocator* allocator, + bool resizable) + : data_type_(data_type), + data_ptr_(std::move(data_ptr)), + numel_(numel), + resizable_(resizable), + received_cuda_(false), + allocator_(allocator) { + if (resizable) { + AT_ASSERTM( + allocator_, "For resizable storage, allocator must be provided"); + } + if (numel > 0) { + if (data_type_.id() == caffe2::TypeIdentifier::uninitialized()) { + AT_ERROR( + "Constructing a storage with meta of unknown type and non-zero numel"); + } + } + } + + StorageImpl( + caffe2::TypeMeta data_type, + int64_t numel, + at::Allocator* allocator, + bool resizable) + : StorageImpl( + data_type, + numel, + allocator->allocate(data_type.itemsize() * numel), + allocator, + resizable) {} + + StorageImpl& operator=(StorageImpl&& other) = default; + StorageImpl& operator=(const StorageImpl&) = delete; + StorageImpl() = delete; + StorageImpl(StorageImpl&& other) = default; + StorageImpl(const StorageImpl&) = delete; + ~StorageImpl() = default; + + void reset() { + data_ptr_.clear(); + numel_ = 0; + } + + template + inline bool IsType() const { + return data_type_.Match(); + } + + template + inline T* data() const { + auto data_type = caffe2::TypeMeta::Make(); + if (dtype() != data_type) { + AT_ERROR( + "Attempt to access StorageImpl having data type ", + dtype(), + " as data type ", + data_type); + } + return unsafe_data(); + } + + template + inline T* unsafe_data() const { + return static_cast(this->data_ptr_.get()); + } + + void release_resources() override { + data_ptr_.clear(); + } + + size_t itemsize() const { + return data_type_.itemsize(); + } + + size_t capacity() const { + return numel_ * itemsize(); + } + + int64_t numel() const { + return numel_; + }; + + // TODO: remove later + void set_numel(int64_t numel) { + numel_ = numel; + }; + + bool resizable() const { + return resizable_; + }; + + at::DataPtr& data_ptr() { + return data_ptr_; + }; + + const at::DataPtr& data_ptr() const { + return data_ptr_; + }; + + // Returns the previous data_ptr + at::DataPtr set_data_ptr(at::DataPtr&& data_ptr) { + std::swap(data_ptr_, data_ptr); + return std::move(data_ptr); + }; + + // XXX: TERRIBLE! DONT USE UNLESS YOU HAVE TO! AND EVEN THEN DONT, JUST DONT! + // Setting the data_type will require you to audit many other parts of the + // struct again to make sure it's still valid. + void set_dtype(const caffe2::TypeMeta& data_type) { + int64_t capacity = numel_ * data_type_.itemsize(); + data_type_ = data_type; + numel_ = capacity / data_type_.itemsize(); + } + + // TODO: Return const ptr eventually if possible + void* data() { + return data_ptr_.get(); + } + + void* data() const { + return data_ptr_.get(); + } + + at::DeviceType device_type() const { + return data_ptr_.device().type(); + } + + at::Allocator* allocator() { + return allocator_; + } + + const caffe2::TypeMeta& dtype() const { + return data_type_; + } + + const at::Allocator* allocator() const { + return allocator_; + }; + + // You generally shouldn't use this method, but it is occasionally + // useful if you want to override how a tensor will be reallocated, + // after it was already allocated (and its initial allocator was + // set) + void set_allocator(at::Allocator* allocator) { + allocator_ = allocator; + } + + Device device() const { + return data_ptr_.device(); + } + + void set_resizable(bool resizable) { + if (resizable) { + // We need an allocator to be resizable + AT_ASSERT(allocator_); + } + resizable_ = resizable; + } + + /** + * Can only be called when use_count is 1 + */ + void UniqueStorageShareExternalPointer( + void* src, + const caffe2::TypeMeta& data_type, + size_t capacity, + DeleterFnPtr d = nullptr) { + UniqueStorageShareExternalPointer( + at::DataPtr(src, src, d, data_ptr_.device()), data_type, capacity); + } + + /** + * Can only be called when use_count is 1 + */ + void UniqueStorageShareExternalPointer( + at::DataPtr&& data_ptr, + const caffe2::TypeMeta& data_type, + size_t capacity) { + data_type_ = data_type; + // TODO: Use CAFFE_ENFORCE_WITH_CALLER equivalent + // For now causes lots of redefine issues if caffe2/core/logging.h is used + if (data_type_.id() == caffe2::TypeIdentifier::uninitialized()) { + AT_ERROR( + "To share with a raw external pointer you need to have meta " + "already set."); + } + data_ptr_ = std::move(data_ptr); + // NOTE: data_type might change and so it's also possible that capacity + // might not be divisible by itemsize. There is no way for us to keep track + // of the exact capacity if we're not explicity storing is. More conrectely + // capacity() might not return the value that was set here, if itemsize does + // not evenly divide it. + numel_ = capacity / data_type_.itemsize(); + allocator_ = nullptr; + resizable_ = false; + } + + // This method can be used only after storage construction and cannot be used + // to modify storage status + void set_received_cuda(bool received_cuda) { + received_cuda_ = received_cuda; + } + + bool received_cuda() { + return received_cuda_; + } + + private: + caffe2::TypeMeta data_type_; + DataPtr data_ptr_; + int64_t numel_; + bool resizable_; + // Identifies that Storage was received from another process and doesn't have + // local to process cuda memory allocation + bool received_cuda_; + Allocator* allocator_; +}; +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/core/Stream.h b/thirdparty/libtorch/include/c10/core/Stream.h new file mode 100644 index 0000000000..6962be72bf --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/Stream.h @@ -0,0 +1,154 @@ +#pragma once + +#include + +namespace c10 { + +/// An index representing a specific stream. A StreamId is not independently +/// meaningful without knowing the Device it is associated with; try to +/// use Stream rather than StreamId directly. +/// +/// StreamIds are opaque; they are assigned by some DeviceType-specific +/// numbering system which is not visible to the user. HOWEVER, we +/// guarantee that StreamId 0 is always a valid stream, and corresponds +/// to some sort of "default" stream. +using StreamId = int32_t; + +// NB: I decided not to call the above StreamIndex to avoid confusion with +// DeviceIndex. This way, you access device index with index(), and stream id +// with id() + +/** + * A stream is a software mechanism used to synchronize launched kernels + * without requiring explicit synchronizations between kernels. The basic + * model is that every kernel launch is associated with a stream: every + * kernel on the same stream is implicitly synchronized so that if I launch + * kernels A and B on the same stream, A is guaranteed to finish before B + * launches. If I want B to run concurrently with A, I must schedule + * it on a different stream. + * + * The Stream class is a backend agnostic value class representing a stream + * which I may schedule a kernel on. Every stream is associated with a device, + * which is recorded in stream, which is used to avoid confusion about which + * device a stream refers to. + * + * Streams are explicitly thread-safe, in the sense that it is OK to pass + * a Stream from one thread to another, and kernels queued from two different + * threads will still get serialized appropriately. (Of course, the + * time when the kernels get queued is undetermined unless you synchronize + * host side ;) + * + * Stream does NOT have a default constructor. Streams are for expert + * users; if you want to use Streams, we're going to assume you know + * how to deal with C++ template error messages if you try to + * resize() a vector of Streams. + * + * Known instances of streams in backends: + * + * - cudaStream_t (CUDA) + * - hipStream_t (HIP) + * - cl_command_queue (OpenCL) (NB: Caffe2's existing OpenCL integration + * does NOT support command queues.) + * + * Because this class is device agnostic, it cannot provide backend-specific + * functionality (e.g., get the cudaStream_t of a CUDA stream.) There are + * wrapper classes which provide this functionality, e.g., CUDAStream. + */ +class Stream final { +private: + Device device_; + StreamId id_; +public: + enum Unsafe { UNSAFE }; + enum Default { DEFAULT }; + + /// Unsafely construct a stream from a Device and a StreamId. In + /// general, only specific implementations of streams for a + /// backend should manufacture Stream directly in this way; other users + /// should use the provided APIs to get a stream. In particular, + /// we don't require backends to give any guarantees about non-zero + /// StreamIds; they are welcome to allocate in whatever way they like. + explicit Stream(Unsafe, Device device, StreamId id) + : device_(device) + , id_(id) {} + + /// Construct the default stream of a Device. The default stream is + /// NOT the same as the current stream; default stream is a fixed stream + /// that never changes, whereas the current stream may be changed by + /// StreamGuard. + explicit Stream(Default, Device device) + : device_(device) + , id_(0) {} + + bool operator==(const Stream& other) const noexcept { + return this->device_ == other.device_ && this->id_ == other.id_; + } + bool operator!=(const Stream& other) const noexcept { + return !(*this == other); + } + + Device device() const noexcept { return device_; } + DeviceType device_type() const noexcept { return device_.type(); } + DeviceIndex device_index() const noexcept { return device_.index(); } + StreamId id() const noexcept { return id_; } + + // Enqueues a wait instruction in the stream's work queue. + // This instruction is a no-op unless the event is marked + // for recording. In that case the stream stops processing + // until the event is recorded. + template + void wait(const T& event) const { + event.block(*this); + } + + // The purpose of this function is to more conveniently permit binding + // of Stream to and from Python. Without packing, I have to setup a whole + // class with two fields (device and stream id); with packing I can just + // store a single uint64_t. + // + // The particular way we pack streams into a uint64_t is considered an + // implementation detail and should not be relied upon. + uint64_t pack() const noexcept { + // Are you here because this static assert failed? Make sure you ensure + // that the bitmasking code below is updated accordingly! + static_assert(sizeof(DeviceType) == 2, "DeviceType is not 16-bit"); + static_assert(sizeof(DeviceIndex) == 2, "DeviceIndex is not 16-bit"); + static_assert(sizeof(StreamId) == 4, "DeviceIndex is not 32-bit"); + // Concat these together into a 64-bit integer + // See Note [Hazard when concatenating signed integers] + uint64_t bits = + static_cast(static_cast(device_type())) << 48 + | static_cast(static_cast(device_index())) << 32 + | static_cast(static_cast(id())); + return bits; + } + + static Stream unpack(uint64_t bits) { + auto stream_id = static_cast(bits) & 0xFFFFFFFFull; + bits >>= 32; + auto device_index = static_cast(bits) & 0xFFFFull; + bits >>= 16; + auto device_type = static_cast(bits); + TORCH_CHECK(isValidDeviceType(device_type)); + // Unfortunately, we can't check if the StreamId is valid here; it + // will be checked upon first use. + return Stream(UNSAFE, Device(device_type, device_index), stream_id); + } + + // I decided NOT to provide setters on this class, because really, + // why would you change the device of a stream? Just construct + // it correctly from the beginning dude. +}; + +C10_API std::ostream& operator<<(std::ostream& stream, const Stream& s); + +} // namespace c10 + +namespace std { + template <> + struct hash { + size_t operator()(c10::Stream s) const noexcept { + return std::hash{}(s.pack()); + } + }; +} // namespace std diff --git a/thirdparty/libtorch/include/c10/core/StreamGuard.h b/thirdparty/libtorch/include/c10/core/StreamGuard.h new file mode 100644 index 0000000000..e2bdac928b --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/StreamGuard.h @@ -0,0 +1,126 @@ +#pragma once + +#include + +namespace c10 { + +/** + * A StreamGuard is an RAII class that changes the current device + * to the device corresponding to some stream, and changes the + * default stream on that device to be this stream. + * + * Use of StreamGuard is HIGHLY discouraged in operator definitions. In + * a single operator, you probably don't know enough about the global + * state of the world to profitably decide how to set streams. Let + * the caller handle this appropriately, and just use the current stream + * in your operator code. + * + * This StreamGuard does NOT have an uninitialized state; it is guaranteed + * to reset the stream and device on exit. If you are in a situation + * where you *might* want to setup a stream guard, see OptionalStreamGuard. + */ +struct StreamGuard { + /// No default constructor, see Note [Omitted default constructor from RAII] + explicit StreamGuard() = delete; + + /// Set the current device to the device associated with the passed stream, + /// and set the current stream on that device to the passed stream. + explicit StreamGuard(Stream stream) : guard_(stream) {} + + /// Copy is disallowed + StreamGuard(const StreamGuard&) = delete; + StreamGuard& operator=(const StreamGuard&) = delete; + + /// Move is disallowed, as StreamGuard does not have an uninitialized state, + /// which is required for moves on types with nontrivial destructors. + StreamGuard(StreamGuard&& other) = delete; + StreamGuard& operator=(StreamGuard&& other) = delete; + + /// Resets the currently set stream to the original stream and + /// the currently set device to the original device. Then, + /// set the current device to the device associated with the passed stream, + /// and set the current stream on that device to the passed stream. + /// + /// NOTE: this implementation may skip some stream/device setting if + /// it can prove that it is unnecessary. + /// + /// WARNING: reset_stream does NOT preserve previously set streams on + /// different devices. If you need to set streams on multiple devices + /// on , use MultiStreamGuard instead. + void reset_stream(Stream stream) { guard_.reset_stream(stream); } + + /// Returns the stream that was set at the time the guard was constructed. + Stream original_stream() const { + return guard_.original_stream(); + } + + /// Returns the most recent stream that was set using this device guard, + /// either from construction, or via set_stream. + Stream current_stream() const { + return guard_.current_stream(); + } + + /// Returns the most recent device that was set using this device guard, + /// either from construction, or via set_device/reset_device/set_index. + Device current_device() const { return guard_.current_device(); } + + /// Returns the device that was set at the most recent reset_stream(), + /// or otherwise the device at construction time. + Device original_device() const { return guard_.original_device(); } + +private: + c10::impl::InlineStreamGuard guard_; +}; + +/** + * An OptionalStreamGuard is an RAII class that sets a device to some value on + * initialization, and resets the device to its original value on destruction. + * See OptionalDeviceGuard for more guidance on how to use this class. + */ +struct OptionalStreamGuard { + /// Create an uninitialized guard. + explicit OptionalStreamGuard() : guard_() {} + + /// Set the current device to the device associated with the passed stream, + /// and set the current stream on that device to the passed stream. + explicit OptionalStreamGuard(Stream stream) : guard_(stream) {} + + /// Set the current device to the device associated with the passed stream, + /// and set the current stream on that device to the passed stream, + /// if the passed stream is not nullopt. + explicit OptionalStreamGuard(optional stream_opt) : guard_(stream_opt) {} + + /// Copy is disallowed + OptionalStreamGuard(const OptionalStreamGuard&) = delete; + OptionalStreamGuard& operator=(const OptionalStreamGuard&) = delete; + + // See Note [Move construction for RAII guards is tricky] + OptionalStreamGuard(OptionalStreamGuard&& other) = delete; + + // See Note [Move assignment for RAII guards is tricky] + OptionalStreamGuard& operator=(OptionalStreamGuard&& other) = delete; + + /// Resets the currently set stream to the original stream and + /// the currently set device to the original device. Then, + /// set the current device to the device associated with the passed stream, + /// and set the current stream on that device to the passed stream. + /// Initializes the guard if it was not previously initialized. + void reset_stream(Stream stream) { guard_.reset_stream(stream); } + + /// Returns the stream that was set at the time the guard was most recently + /// initialized, or nullopt if the guard is uninitialized. + optional original_stream() const { return guard_.original_stream(); } + + /// Returns the most recent stream that was set using this stream guard, + /// either from construction, or via reset_stream, if the guard is initialized, + /// or nullopt if the guard is uninitialized. + optional current_stream() const { return guard_.current_stream(); } + + /// Restore the original device and stream, resetting this guard to uninitialized state. + void reset() { guard_.reset(); } + +private: + c10::impl::InlineOptionalStreamGuard guard_; +}; + +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/core/TensorImpl.h b/thirdparty/libtorch/include/c10/core/TensorImpl.h new file mode 100644 index 0000000000..2e4f338819 --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/TensorImpl.h @@ -0,0 +1,1738 @@ +#pragma once + +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +// A global boolean variable to control whether we free memory when a Tensor +// is shrinked to a smaller size. As a result, a Tensor is always going to +// keep the memory allocated for its maximum capacity reshaped to so far. +// +// This parameter is respected "upper-case" methods which call Resize() +// (e.g., CopyFrom, ResizeLike); it is NOT respected by Tensor::resize_ +// or ShrinkTo, both of which guarantee to never to free memory. +C10_DECLARE_bool(caffe2_keep_on_shrink); + +// Since we can have high variance in blob memory allocated across different +// inputs in the same run, we will shrink the blob only if the memory gain +// is larger than this flag in bytes. This only applies to functions which +// respect caffe2_keep_on_shrink. +C10_DECLARE_int64(caffe2_max_keep_on_shrink_memory); + + +namespace at { +class Tensor; +} + +namespace c10 { +class Scalar; +struct Storage; + +/** + * A utility function to convert vector to vector. + */ +inline std::vector ToVectorint64_t(ArrayRef src) { + return std::vector(src.begin(), src.end()); +} + +/** + * Return product of all dimensions starting from k + */ +inline int64_t size_from_dim_(int k, IntArrayRef dims) { + int64_t r = 1; + for (size_t i = k; i < dims.size(); ++i) { + r *= dims[i]; + } + return r; +} + +// Product of all dims up to k (not including dims[k]) +inline int64_t size_to_dim_(int k, IntArrayRef dims) { + TORCH_CHECK((unsigned)k <= dims.size()); + int64_t r = 1; + for (int i = 0; i < k; ++i) { + r *= dims[i]; + } + return r; +} + +// Product of all dims between k and l (not including dims[k] and dims[l]) +inline int64_t size_between_dim_(int k, int l, IntArrayRef dims) { + TORCH_CHECK((unsigned)l < dims.size()); + int64_t r = 1; + if (k < l) { + for (int i = k + 1; i < l; ++i) { + r *= dims[i]; + } + } else { + for (int i = l + 1; i < k; ++i) { + r *= dims[i]; + } + } + return r; +} + +// Wrap around axis_index if it is negative, s.t., -1 is the last dim +inline int canonical_axis_index_(int axis_index, int ndims) { + TORCH_CHECK(axis_index >= -ndims); + TORCH_CHECK(axis_index < ndims); + if (axis_index < 0) { + return axis_index + ndims; + } + return axis_index; +} + +using PlacementDtor = void (*)(void*, size_t); + +/* + * A Context that will call extra placement deleter during + * deconstruction. + * + * Accept a already constructed DataPtr and store it as member + * during destruction, we'll call extra deleter on the underlying + * data pointer before the DataPtr is destructed. + * `data_ptr_` owns the memory. + */ +struct C10_API PlacementDeleteContext { + DataPtr data_ptr_; + PlacementDtor placement_dtor_; + size_t size_; + PlacementDeleteContext( + DataPtr&& data_ptr, + PlacementDtor placement_dtor, + size_t size) + : data_ptr_(std::move(data_ptr)), + placement_dtor_(placement_dtor), + size_(size) {} + static DataPtr makeDataPtr( + DataPtr&& data_ptr, + PlacementDtor placement_dtor, + size_t size, + Device device); + ~PlacementDeleteContext() { + placement_dtor_(data_ptr_.get(), size_); + // original memory will be freed when data_ptr_ is destructed + } +}; + +struct TensorImpl; + +struct C10_API AutogradMetaInterface { + virtual void set_requires_grad(bool requires_grad, at::TensorImpl* self_impl) = 0; + virtual bool requires_grad() const = 0; + virtual at::Tensor& grad() = 0; + virtual const at::Tensor& grad() const = 0; + virtual ~AutogradMetaInterface(); +}; + +namespace impl { + +// Unfortunately, the definition of AutogradMeta lives in a separate +// compilation unit than TensorImpl (libtorch.so versus libc10.so) +// which means that we cannot construct an AutogradMeta from TensorImpl, +// not even from the cpp file. So we have to indirect it through a factory +// function which will be initialized when we load libtorch.so. + +struct C10_API AutogradMetaFactory { + virtual ~AutogradMetaFactory() = default; + virtual std::unique_ptr make() const = 0; + // This method is the dumbest method. But I don't have access + // to Tensor (not TensorImpl) which is undefined in this header. + virtual const at::Tensor& undefined_tensor() const = 0; +}; + +C10_API void SetAutogradMetaFactory(AutogradMetaFactory* factory); +C10_API AutogradMetaFactory* GetAutogradMetaFactory(); + +struct C10_API AutogradMetaFactoryRegisterer { + explicit AutogradMetaFactoryRegisterer(AutogradMetaFactory* factory) { + SetAutogradMetaFactory(factory); + } +}; + +} // namespace impl + +struct C10_API NamedTensorMetaInterface { + virtual ~NamedTensorMetaInterface() {}; + virtual std::unique_ptr clone() const { + TORCH_INTERNAL_ASSERT( + false, + "Not implemented: NamedTensorMetaInterface::clone"); + }; + virtual int64_t slow_dim() const { + TORCH_INTERNAL_ASSERT( + false, + "Not implemented: NamedTensorMetaInterface::slow_dim"); + }; +}; + +// NOTE [ Version Counter Sharing ] +// +// Every Tensor has a version counter. Version counters are incremented whenever the +// data or size of a tensor changes through in-place Variable operations. Version +// counters are used to detect modifications to saved variables which would result in +// incorrect gradient calculations. Version counters may be shared between Variables: +// +// 1. A view shares the version counter of the base Variable, +// 2. `x.detach()` shares the version counter of `x`, +// 3. Unpacked saved variables share the version counter of the source. +// +// Version counters are not shared in these scenarios: +// +// 1. When we replace a `Variable`'s underlying `Tensor` by calling `set_data(...)`, +// 2. `x.data` does not share the version counter of `x`. (See discussion at +// https://github.com/pytorch/pytorch/issues/5396) +// +// Question: Why do we put the version counter in TensorImpl instead of AutogradMeta? +// +// Answer: After the Variable/Tensor merge, a tensor will not have AutogradMeta when +// its `requires_grad_` is false, but when we use this tensor in the forward pass of +// a function that requires saving this tensor for backward, we need to keep track of +// this tensor's version to make sure it's always valid in the autograd graph. +// +// To achieve this goal, we put the version counter in TensorImpl instead of AutogradMeta, +// and have it always be available. This allows us to have the optimization of not +// carrying AutogradMeta when a tensor doesn't require gradient. +// +// A hypothetical alternative way to achieve this goal is to initialize AutogradMeta and +// create the version counter for the non-requires-grad tensor only when it's saved for +// backward. However, since saving a tensor for backward happens in the forward pass, and +// our invariant is that forward pass needs to be thread-safe, lazy-initializing AutogradMeta +// when saving a tensor can introduce race conditions when we are running the forward +// pass in multi-thread scenarios, thus making the forward pass not thread-safe anymore, +// which breaks the invariant. +struct C10_API VariableVersion { + private: + struct VersionCounter : intrusive_ptr_target { + VersionCounter(uint32_t version) : version_(version) {} + std::atomic version_; + }; + c10::intrusive_ptr version_counter_; + + public: + bool unique() const { + return 1 == version_counter_.use_count(); + } + // NOTE: As of C++11 and 14, default-constructing a std::atomic variable + // leaves it in a persistently undefined state. See + // https://cplusplus.github.io/LWG/issue2334. + VariableVersion(uint32_t version = 0) + : version_counter_(c10::make_intrusive(version)) {} + + void bump() noexcept { + ++version_counter_->version_; + } + + uint32_t current_version() const noexcept { + return version_counter_->version_; + } +}; + +/** + * The low-level representation of a tensor, which contains a pointer + * to a storage (which contains the actual data) and metadata (e.g., sizes and + * strides) describing this particular view of the data as a tensor. + * + * Some basic characteristics about our in-memory representation of + * tensors: + * + * - It contains a pointer to a storage struct (Storage/StorageImpl) + * which contains the pointer to the actual data and records the + * data type and device of the view. This allows multiple tensors + * to alias the same underlying data, which allows to efficiently + * implement differing *views* on a tensor. + * + * - The tensor struct itself records view-specific metadata about + * the tensor, e.g., sizes, strides and offset into storage. + * Each view of a storage can have a different size or offset. + * + * - This class is intrusively refcounted. It is refcounted so that + * we can support prompt deallocation of large tensors; it is + * intrusively refcounted so that we can still perform reference + * counted operations on raw pointers, which is often more convenient + * when passing tensors across language boundaries. + * + * - For backwards-compatibility reasons, a tensor may be in an + * uninitialized state. A tensor may be uninitialized in the following + * two ways: + * + * - A tensor may be DTYPE UNINITIALIZED. A tensor of this + * form has an uninitialized dtype. This situation most + * frequently arises when a user writes Tensor x(CPU). The dtype and + * is subsequently initialized when mutable_data() is + * invoked for the first time. + * + * - A tensor may be STORAGE UNINITIALIZED. A tensor of this form + * has non-zero size, but has a storage with a null data pointer. + * This situation most frequently arises when a user calls + * Resize() or FreeMemory(). This is because Caffe2 historically + * does lazy allocation: allocation of data doesn't occur until + * mutable_data() is invoked. A tensor with zero size is + * always storage initialized, because no allocation is necessary + * in this case. + * + * All combinations of these two uninitialized states are possible. + * Consider the following transcript in idiomatic Caffe2 API: + * + * Tensor x(CPU); // x is storage-initialized, dtype-UNINITIALIZED + * x.Resize(4); // x is storage-UNINITIALIZED, dtype-UNINITIALIZED + * x.mutable_data(); // x is storage-initialized, dtype-initialized + * x.FreeMemory(); // x is storage-UNINITIALIZED, dtype-initialized. + * + * All other fields on tensor are always initialized. In particular, + * size is always valid. (Historically, a tensor declared as Tensor x(CPU) + * also had uninitialized size, encoded as numel == -1, but we have now + * decided to default to zero size, resulting in numel == 0). + * + * Uninitialized storages MUST be uniquely owned, to keep our model + * simple. Thus, we will reject operations which could cause an + * uninitialized storage to become shared (or a shared storage to + * become uninitialized, e.g., from FreeMemory). + * + * In practice, tensors which are storage-UNINITIALIZED and + * dtype-UNINITIALIZED are *extremely* ephemeral: essentially, + * after you do a Resize(), you basically always call mutable_data() + * immediately afterwards. Most functions are not designed to + * work if given a storage-UNINITIALIZED, dtype-UNINITIALIZED tensor. + * + * We intend to eliminate all uninitialized states, so that every + * tensor is fully initialized in all fields. Please do not write new code + * that depends on these uninitialized states. + */ +struct C10_API TensorImpl : public c10::intrusive_ptr_target { + TensorImpl() = delete; + + /** + * Construct a 1-dim 0-size tensor backed by the given storage. + */ + TensorImpl(Storage&& storage, TensorTypeSet); + + /** + * Construct a 1-dim 0 size tensor that doesn't have a storage. + */ + TensorImpl(TensorTypeSet, const caffe2::TypeMeta& data_type, c10::optional device_opt); + + // Legacy constructors so I don't have to go update call sites. + // TODO: When Variable is added, delete these constructors + TensorImpl(Storage&& storage, TensorTypeId type_id) + : TensorImpl(std::move(storage), TensorTypeSet(type_id)) {} + TensorImpl(TensorTypeId type_id, const caffe2::TypeMeta& data_type, c10::optional device_opt) + : TensorImpl(TensorTypeSet(type_id), data_type, device_opt) {} + + private: + // This constructor is private, because the data_type is redundant with + // storage. Still, we pass it in separately because it's easier to write + // the initializer list if we're not worried about storage being moved out + // from under us. + TensorImpl(Storage&& storage, TensorTypeSet, const caffe2::TypeMeta& data_type, c10::optional); + + public: + TensorImpl(const TensorImpl&) = delete; + TensorImpl& operator=(const TensorImpl&) = delete; + TensorImpl(TensorImpl&&) = default; + TensorImpl& operator=(TensorImpl&&) = default; + + /** + * Release (decref) storage, and any other external allocations. This + * override is for `intrusive_ptr_target` and is used to implement weak + * tensors. + */ + virtual void release_resources() override; + + /** + * Return the TensorTypeSet corresponding to this Tensor, specifying + * all of the TensorTypeIds that this Tensor identifies as. This is the + * information used to dispatch operations on this tensor. + */ + TensorTypeSet type_set() const { return type_set_; } + + /** + * Return a reference to the sizes of this tensor. This reference remains + * valid as long as the tensor is live and not resized. + */ + virtual IntArrayRef sizes() const; + + /** + * Return a reference to the strides of this tensor. This reference remains + * valid as long as the tensor is live and not restrided. + */ + virtual IntArrayRef strides() const; + + /** + * Return the number of dimensions of this tensor. Note that 0-dimension + * represents a Tensor that is a Scalar, e.g., one that has a single element. + */ + virtual int64_t dim() const; + + /** + * True if this tensor has storage. See storage() for details. + */ + virtual bool has_storage() const; + + /** + * Return the underlying storage of a Tensor. Multiple tensors may share + * a single storage. A Storage is an impoverished, Tensor-like class + * which supports far less operations than Tensor. + * + * Avoid using this method if possible; try to use only Tensor APIs to perform + * operations. + */ + virtual const Storage& storage() const; + + /** + * The number of elements in a tensor. + * + * WARNING: Previously, if you were using the Caffe2 API, you could + * test numel() == -1 to see if a tensor was uninitialized. This + * is no longer true; numel always accurately reports the product + * of sizes of a tensor. + */ + virtual int64_t numel() const { +#ifdef DEBUG + TORCH_INTERNAL_ASSERT(compute_numel() == numel_); +#endif + return numel_; + } + + bool unique_version() const { + return version_counter_.unique(); + } + + /** + * Whether or not a tensor is laid out in contiguous memory. + * + * Tensors with non-trivial strides are not contiguous. See + * compute_contiguous() for the exact definition of whether or not + * a tensor is contiguous or not. + */ + virtual bool is_contiguous(at::MemoryFormat memory_format=at::MemoryFormat::Contiguous) const; + + bool is_sparse() const { + // NB: This method is not virtual and avoid dispatches for performance reasons. + return type_set_.has(TensorTypeId::SparseCPUTensorId) || + type_set_.has(TensorTypeId::SparseCUDATensorId) || + type_set_.has(TensorTypeId::SparseHIPTensorId); + } + + bool is_quantized() const { + // NB: This method is not virtual and avoid dispatches for performance reasons. + return type_set_.has(TensorTypeId::QuantizedCPUTensorId); + } + + bool is_cuda() const { + // NB: This method is not virtual and avoid dispatches for performance reasons. + return type_set_.has(TensorTypeId::CUDATensorId) || + type_set_.has(TensorTypeId::SparseCUDATensorId); + } + + bool is_hip() const { + // NB: This method is not virtual and avoid dispatches for performance reasons. + return type_set_.has(TensorTypeId::HIPTensorId) || + type_set_.has(TensorTypeId::SparseHIPTensorId); + } + + bool is_mkldnn() const { + return type_set_.has(TensorTypeId::MkldnnCPUTensorId); + } + + int64_t get_device() const { + TORCH_CHECK( + device_opt_.has_value(), + "tensor does not have a device"); + // See NOTE [c10::optional operator usage in CUDA] + return (*device_opt_).index(); + } + + Device device() const { + TORCH_CHECK( + device_opt_.has_value(), + "tensor does not have a device"); + // See NOTE [c10::optional operator usage in CUDA] + return *device_opt_; + } + + Layout layout() const { + // NB: This method is not virtual and avoid dispatches for perf. + if (is_sparse()) { + return kSparse; + } else if (is_mkldnn()) { + return kMkldnn; + } else { + return kStrided; + } + } + + /** + * If `condition_when_zero_dim` is true, and the tensor is a 1-dim, 1-size + * tensor, reshape the tensor into a 0-dim tensor (scalar). + * + * This helper function is called from generated wrapper code, to help + * "fix up" tensors that legacy code didn't generate in the correct shape. + * For example, suppose that we have a legacy function 'add' which produces + * a tensor which is the same shape as its inputs; however, if the inputs + * were zero-dimensional, it produced a 1-dim 1-size tensor (don't ask). + * result->maybe_zero_dim(lhs->dim() == 0 && rhs->dim() == 0) will be called, + * correctly resetting the dimension to 0 when when the inputs had 0-dim. + * + * As we teach more and more of TH to handle 0-dim correctly, this function + * will become less necessary. At the moment, it is often called from functions + * that correctly handle the 0-dim case, and is just dead code in this case. + * In the glorious future, this function will be eliminated entirely. + */ + virtual TensorImpl* maybe_zero_dim(bool condition_when_zero_dim); + + /** + * True if a tensor was auto-wrapped from a C++ or Python number. + * For example, when you write 't + 2', 2 is auto-wrapped into a Tensor + * with `is_wrapped_number_` set to true. + * + * Wrapped numbers do not participate in the result type computation for + * mixed-type operations if there are any Tensors that are not wrapped + * numbers. This is useful, because we want 't + 2' to work with + * any type of tensor, not just LongTensor (which is what integers + * in Python represent). + * + * Otherwise, they behave like their non-wrapped equivalents. + * See [Result type computation] in TensorIterator.h. + * + * Why did we opt for wrapped numbers, as opposed to just having + * an extra function add(Tensor, Scalar)? This helps greatly reduce + * the amount of code we have to write for add, when actually + * a Tensor-Scalar addition is really just a Tensor-Tensor + * addition when the RHS is 0-dim (except for promotion behavior.) + */ + bool is_wrapped_number() const { + return is_wrapped_number_; + } + + /** + * Set whether or not a tensor was auto-wrapped from a C++ or Python + * number. You probably don't want to call this, unless you are + * writing binding code. + */ + void set_wrapped_number(bool value) { + TORCH_INTERNAL_ASSERT(dim() == 0); + is_wrapped_number_ = value; + } + + // ~~~~~ Autograd API ~~~~~ + // Some methods below are defined in TensorImpl.cpp because Tensor is an + // incomplete type. + + /** + * Set whether or not a tensor requires gradient. + * + * It is only valid to call this method on a Variable. + * See Note [Tensor versus Variable in C++]. + */ + void set_requires_grad(bool requires_grad); + + /** + * True if a tensor requires gradient. Tensors which require gradient + * have history tracked for any operations performed on them, so that + * we can automatically differentiate back to them. A tensor that + * requires gradient and has no history is a "leaf" tensor, which we + * accumulate gradients into. + * + * It is only valid to call this method on a Variable. + * See Note [Tensor versus Variable in C++]. + */ + bool requires_grad() const; + + /** + * Return a mutable reference to the gradient. This is conventionally + * used as `t.grad() = x` to set a gradient to a completely new tensor. + * + * It is only valid to call this method on a Variable. + * See Note [Tensor versus Variable in C++]. + */ + at::Tensor& grad(); + + /** + * Return the accumulated gradient of a tensor. This gradient is written + * into when performing backwards, when this tensor is a leaf tensor. + * + * It is only valid to call this method on a Variable. + * See Note [Tensor versus Variable in C++]. + */ + const at::Tensor& grad() const; + + /** + * Return a typed data pointer to the actual data which this tensor refers to. + * This checks that the requested type (from the template parameter) matches + * the internal type of the tensor. + * + * It is invalid to call data() on a dtype-uninitialized tensor, even if + * the size is 0. + * + * WARNING: If a tensor is not contiguous, you MUST use strides when + * performing index calculations to determine the location of elements in + * the tensor. We recommend using 'TensorAccessor' to handle this computation + * for you; this class is available from 'Tensor'. + */ + template + inline T * data() const { + TORCH_CHECK(has_storage(), + "Cannot access data pointer of Tensor that doesn't have storage"); + TORCH_CHECK( + storage_initialized(), + "The tensor has a non-zero number of elements, but its data is not allocated yet. " + "Caffe2 uses a lazy allocation, so you will need to call " + "mutable_data() or raw_mutable_data() to actually allocate memory."); + TORCH_CHECK( + storage_.IsType(), + "Tensor type mismatch, caller expects elements to be ", + caffe2::TypeMeta::TypeName(), + ", while tensor contains ", + data_type_.name(), + ". "); + // We managed the type check ourselves + return storage_.unsafe_data() + storage_offset_; + } + + /** + * Return a void* data pointer to the actual data which this tensor refers to. + * + * It is invalid to call data() on a dtype-uninitialized tensor, even if the + * size is 0. + * + * WARNING: The data pointed to by this tensor may not contiguous; do NOT + * assume that itemsize() * numel() is sufficient to compute the bytes that + * can be validly read from this tensor. + */ + inline void* data() const { + TORCH_CHECK(has_storage(), + "Cannot access data pointer of Tensor that doesn't have storage"); + TORCH_CHECK(dtype_initialized(), + "Cannot access data pointer of Tensor that doesn't have initialized dtype " + "(e.g., caffe2::Tensor x(CPU), prior to calling mutable_data() on x)"); + return static_cast( + static_cast(storage_.data()) + + data_type_.itemsize() * storage_offset_); + } + + /** + * Like data(), but performs no checks. You are responsible for ensuring + * that all invariants required by data() are upheld here. + */ + template + inline T * unsafe_data() const { + return storage_.unsafe_data() + storage_offset_; + } + + /** + * Returns the TypeMeta of a tensor, which describes what data type + * it is (e.g., int, float, ...) + */ + const caffe2::TypeMeta& dtype() const { + return data_type_; + } + + /** + * Return the size of a single element of this tensor in bytes. + */ + size_t itemsize() const { + TORCH_CHECK(dtype_initialized(), + "Cannot report itemsize of Tensor that doesn't have initialized dtype " + "(e.g., caffe2::Tensor x(CPU), prior to calling mutable_data() on x)"); + return data_type_.itemsize(); + } + + /** + * Return the offset in number of elements into the storage that this + * tensor points to. Most tensors have storage_offset() == 0, but, + * for example, an index into a tensor will have a non-zero storage_offset(). + * + * WARNING: This is NOT computed in bytes. + * + * XXX: The only thing stopping this function from being virtual is Variable. + */ + virtual int64_t storage_offset() const { + return storage_offset_; + } + + /** + * True if a tensor has no elements (e.g., numel() == 0). + */ + inline bool is_empty() const { + return numel() == 0; + } + + /** + * Change the dimensionality of a tensor. This is truly a resize: + * old sizes, if they are still valid, are preserved (this invariant + * is utilized by some call-sites, e.g., the implementation of squeeze, which + * mostly wants the sizes to stay the same). New dimensions are given zero + * size and zero stride; this is probably not what you want--you should + * set_size/set_stride afterwards. + * + * TODO: This should be jettisoned in favor of `set_sizes_and_strides`, + * which is harder to misuse. + */ + virtual void resize_dim(int64_t ndim) { + TORCH_CHECK(allow_tensor_metadata_change(), "resize_dim ", err_msg_tensor_metadata_change_not_allowed); + sizes_.resize(ndim, 0); + strides_.resize(ndim, 0); + refresh_numel(); + refresh_contiguous(); + } + + /** + * Change the size at some dimension. This DOES NOT update strides; + * thus, most changes to size will not preserve contiguity. You probably + * also want to call set_stride() when you call this. + * + * TODO: This should be jettisoned in favor of `set_sizes_and_strides`, + * which is harder to misuse. + */ + virtual void set_size(int64_t dim, int64_t new_size) { + TORCH_CHECK(allow_tensor_metadata_change(), "set_size ", err_msg_tensor_metadata_change_not_allowed); + sizes_.at(dim) = new_size; + refresh_numel(); + refresh_contiguous(); + } + + /** + * Change the stride at some dimension. + * + * TODO: This should be jettisoned in favor of `set_sizes_and_strides`, + * which is harder to misuse. + */ + virtual void set_stride(int64_t dim, int64_t new_stride) { + TORCH_CHECK(allow_tensor_metadata_change(), "set_stride ", err_msg_tensor_metadata_change_not_allowed); + strides_[dim] = new_stride; + refresh_numel(); + refresh_contiguous(); + } + + /** + * Set the offset into the storage of this tensor. + * + * WARNING: This does NOT check if the tensor is in bounds for the new + * location at the storage; the caller is responsible for checking this + * (and resizing if necessary.) + */ + virtual void set_storage_offset(int64_t storage_offset) { + TORCH_CHECK(allow_tensor_metadata_change(), "set_storage_offset ", err_msg_tensor_metadata_change_not_allowed); + storage_offset_ = storage_offset; + } + + /** + * Like set_sizes_and_strides but assumes contiguous strides. + * + * WARNING: This function does not check if the requested + * sizes/strides are in bounds for the storage that is allocated; + * this is the responsibility of the caller + */ + void set_sizes_contiguous(IntArrayRef new_size) { + TORCH_CHECK(allow_tensor_metadata_change(), "set_sizes_contiguous ", err_msg_tensor_metadata_change_not_allowed); + auto new_dim = new_size.size(); + + sizes_.resize(new_dim); + for (size_t dim = 0; dim < new_dim; ++dim) { + sizes_[dim] = new_size[dim]; + } + + refresh_numel(); + empty_tensor_restride(MemoryFormat::Contiguous); + } + + /** + * Set the sizes and strides of a tensor. + * + * WARNING: This function does not check if the requested + * sizes/strides are in bounds for the storage that is allocated; + * this is the responsibility of the caller + */ + void set_sizes_and_strides(IntArrayRef new_size, IntArrayRef new_stride) { + TORCH_CHECK(allow_tensor_metadata_change(), "set_sizes_and_strides ", err_msg_tensor_metadata_change_not_allowed); + TORCH_CHECK( + new_size.size() == new_stride.size(), + "dimensionality of sizes (", + new_size.size(), + ") must match dimensionality of strides (", + new_stride.size(), + ")"); + auto new_dim = new_size.size(); + + sizes_.resize(new_dim); + for (size_t dim = 0; dim < new_dim; ++dim) { + sizes_[dim] = new_size[dim]; + } + + strides_.resize(new_dim); + if (new_dim > 0) { + for (size_t dim = new_dim - 1; ; dim--) { + if (new_stride[dim] >= 0) { + strides_[dim] = new_stride[dim]; + } else { + // XXX: This behavior is surprising and may need to be removed to + // support negative strides. Some pytorch functions rely on it: + // for example, torch.cat (run TestTorch.test_cat_empty). + if (dim == new_dim - 1) { + strides_[dim] = 1; + } else { + // Keep stride monotonically increasing to match NumPy. + strides_[dim] = std::max(sizes_[dim + 1], 1) * strides_[dim + 1]; + } + } + if (dim == 0) break; + } + } + + refresh_numel(); + refresh_contiguous(); + } + + /** + * Return the size of a tensor at some dimension. + */ + virtual int64_t size(int64_t d) const; + + /** + * Return the stride of a tensor at some dimension. + */ + virtual int64_t stride(int64_t d) const; + + /** + * Set whether a tensor allows changes to its metadata (e.g. sizes / strides / storage / storage_offset). + * See NOTE [ Metadata Change for a Detached Tensor ] for details. + */ + void set_allow_tensor_metadata_change(bool value) { + allow_tensor_metadata_change_ = value; + } + + /** + * True if a tensor allows changes to its metadata (e.g. sizes / strides / storage / storage_offset). + * See NOTE [ Metadata Change for a Detached Tensor ] for details. + */ + bool allow_tensor_metadata_change() const { + return allow_tensor_metadata_change_; + } + + /** + * Set the pointer to autograd metadata. + */ + void set_autograd_meta(std::unique_ptr autograd_meta); + + /** + * Return the pointer to autograd metadata. May return nullptr if the + * tensor does not track gradients. + */ + c10::AutogradMetaInterface* autograd_meta() const; + + /** + * Set the pointer to named tensor metadata. + */ + void set_named_tensor_meta(std::unique_ptr named_tensor_meta) { + TORCH_WARN_ONCE( + "Named tensors and all their associated APIs are an experimental feature ", + "and subject to change. Please do not use them for anything important ", + "until they are released as stable."); +#ifdef DEBUG + if (named_tensor_meta) { + TORCH_INTERNAL_ASSERT(named_tensor_meta->slow_dim() == dim()); + } +#endif + named_tensor_meta_ = std::move(named_tensor_meta); + } + + /** + * Return the pointer to named tensor metadata. + */ + const c10::NamedTensorMetaInterface* named_tensor_meta() const { + return named_tensor_meta_.get(); + } + + c10::NamedTensorMetaInterface* named_tensor_meta() { + return named_tensor_meta_.get(); + } + + + // NOTE [ TensorImpl Shallow-Copying ] + // + // TensorImpl shallow-copying is used when we want to have two Variables share the same tensor metadata + // (e.g. sizes / strides / storage pointer / storage_offset), but each with a different autograd history. + // Example call sites: + // + // 1. `var_detached = var.detach()` uses `shallow_copy_and_detach()` to create `var_detached` that shares + // the same tensor metadata with `var`, but with a completely new autograd history. + // 2. `var.set_data(tensor)` uses `shallow_copy_from()` to copy tensor metadata from + // `tensor` into `var`, while keeping `var`'s original AutogradMeta. + // + // Functions that shallow-copy a TensorImpl (such as `shallow_copy_and_detach()` / `shallow_copy_from()` / + // `copy_tensor_metadata()`) copy the tensor metadata fields (e.g. sizes / strides / storage pointer / + // storage_offset) by value. However, the following fields are not copied: + // + // 1. the AutogradMeta pointer, because it is unique for each Variable. + // 2. the version counter, because the destination TensorImpl's version counter is either set to the + // passed-in `version_counter` (in `shallow_copy_and_detach()` and `copy_tensor_metadata()`), or it is kept + // intact (in `shallow_copy_from()`). See NOTE [ Version Counter Sharing ] for details. + // + // In `shallow_copy_and_detach()` and `copy_tensor_metadata()`, the passed-in `allow_tensor_metadata_change` + // determines whether the TensorImpl shallow-copy allows changes to its metadata (e.g. sizes / strides / + // storage / storage_offset). See NOTE [ Metadata Change for a Detached Tensor ] for details. + // + // In `shallow_copy_from()`, we don't check the destination TensorImpl's `allow_tensor_metadata_change_`, + // because `shallow_copy_from()` is used for implementing functions such as `var.set_data(tensor)`, which + // changes `var`'s tensor metadata and expects its `allow_tensor_metadata_change_` to be ignored. + + /** + * One TensorImpl can be copied to another TensorImpl if they have the same + * TensorTypeSet. The only two special cases (for legacy reason) are: + * CPUTensorId is compatible with CUDATensorId and SparseCPUTensorId is + * compatible with SparseCUDATensorId. + */ + inline bool has_compatible_shallow_copy_type(TensorTypeSet from) { + auto is_dense = [](TensorTypeSet ts) { + return ts.has(TensorTypeId::CPUTensorId) || + ts.has(TensorTypeId::CUDATensorId) || + ts.has(TensorTypeId::HIPTensorId); + }; + auto is_sparse = [](TensorTypeSet ts) { + return ts.has(TensorTypeId::SparseCPUTensorId) || + ts.has(TensorTypeId::SparseCUDATensorId) || + ts.has(TensorTypeId::SparseHIPTensorId); + }; + return (type_set_ == from) || (is_dense(type_set_) && is_dense(from)) || (is_sparse(type_set_) && is_sparse(from)); + } + + /** + * Return a TensorImpl that is a shallow-copy of this TensorImpl. + * + * For usage of `version_counter` and `allow_tensor_metadata_change`, + * see NOTE [ TensorImpl Shallow-Copying ]. + */ + virtual c10::intrusive_ptr shallow_copy_and_detach( + const c10::VariableVersion& version_counter, + bool allow_tensor_metadata_change) const { + auto impl = c10::make_intrusive(Storage(storage()), type_set_); + copy_tensor_metadata( + /*src_impl=*/this, + /*dest_impl=*/impl.get(), + /*version_counter=*/version_counter, + /*allow_tensor_metadata_change=*/allow_tensor_metadata_change); + impl->refresh_numel(); + impl->refresh_contiguous(); + return impl; + } + + /** + * Shallow-copies data from another TensorImpl into this TensorImpl. + * + * For why this function doesn't check this TensorImpl's `allow_tensor_metadata_change_`, + * see NOTE [ TensorImpl Shallow-Copying ]. + */ + virtual void shallow_copy_from(const c10::intrusive_ptr& impl) { + copy_tensor_metadata( + /*src_impl=*/impl.get(), + /*dest_impl=*/this, + /*version_counter=*/version_counter(), + /*allow_tensor_metadata_change=*/allow_tensor_metadata_change()); + refresh_numel(); + refresh_contiguous(); + } + + void set_version_counter( + const c10::VariableVersion& version_counter) noexcept { + version_counter_ = version_counter; + } + + const c10::VariableVersion& version_counter() const noexcept { + return version_counter_; + } + + void bump_version() noexcept { + version_counter_.bump(); + } + + inline void set_pyobj(PyObject* pyobj) noexcept { + pyobj_ = pyobj; + } + + inline PyObject* pyobj() const noexcept { + return pyobj_; + } + + private: + // See NOTE [c10::optional operator usage in CUDA] + // We probably don't want to expose this publically until + // the note is addressed. + c10::optional device_opt() const { + return device_opt_; + } + + public: + + /** + * The device type of a Tensor, e.g., DeviceType::CPU or DeviceType::CUDA. + */ + DeviceType device_type() const { + // TODO: A useful internal assert would be to show that device_opt_ is null + // only if you are an undefined tensor + TORCH_CHECK(device_opt_.has_value(), "device_type cannot be run on undefined Tensor"); + // See NOTE [c10::optional operator usage in CUDA] + return (*device_opt_).type(); + } + + /** + * @brief Extends the outer-most dimension of this tensor by num elements, + * preserving the existing data. + * + * The underlying data may be reallocated in order to accommodate the new + * elements, in which case this tensors' capacity is grown at a factor of + * growthPct. This ensures that Extend runs on an amortized O(1) time + * complexity. + * + * This op is auto-asynchronous if the underlying device (CUDA) supports it. + */ + void Extend(int64_t num, float growthPct) { + TORCH_CHECK(sizes_.size() >= 1u); + TORCH_CHECK(num >= 0, "`num` must be non-negative for Extend"); + TORCH_CHECK( + is_contiguous_, + "Right now Extend is only supported for contiguous Tensor."); + auto newDims = sizes_; + newDims[0] += num; + if (!storage_.data()) { + Resize(newDims); + return; + } + auto newNumel = std::accumulate( + newDims.begin(), + newDims.end(), + static_cast(1), + std::multiplies()); + if (newNumel * storage_.itemsize() <= storage_.capacity()) { + sizes_ = newDims; + numel_ = newNumel; + return; + } + auto newCapacity = sizes_; + newCapacity[0] = std::max( + newDims[0], std::ceil(sizes_[0] * (growthPct + 100) / 100)); + auto oldData = std::move(storage_.data_ptr()); + auto oldSize = numel_; + auto oldDims = sizes_; + Resize(newCapacity); + auto* newData = raw_mutable_data(data_type_); + if (data_type_.copy()) { + TORCH_CHECK( + device_type() == DeviceType::CPU, + "non-POD types work only on CPU"); + data_type_.copy()(oldData.get(), newData, oldSize); + } else { + // The following copy uses the current (thread local) stream for copying + // and also takes the GPU id from the device() field passed in. + // + // TODO: Potentially more enforcements are necessary to avoid accidental + // switch to sync copy if the currently set device is wrong. + // + // Specifically, we might need to switch to a different context device + // here explicitly to avoid relying on user synchronizing things + // properly. + CopyBytes( + oldSize * itemsize(), + oldData.get(), + device(), + newData, + device(), + true); // non-blocking + } + reserved_ = true; + sizes_ = newDims; + numel_ = newNumel; + } + + /** + * @brief Reserve space for the underlying tensor. + * + * This must be called after Resize(), since we only specify the first + * dimension This does not copy over the old data to the newly allocated space + */ + template + void ReserveSpace(const T& outer_dim) { + TORCH_CHECK( + is_contiguous_, + "Right now ReserveSpace is only supported for contiguous Tensor."); + TORCH_CHECK( + storage_.unique(), "Can't call ReserveSpace on shared storage."); + auto newCapacity = sizes_; + newCapacity[0] = outer_dim; + auto newNumel = std::accumulate( + newCapacity.begin(), + newCapacity.end(), + static_cast(1), + std::multiplies()); + if (newNumel * storage_.itemsize() <= storage_.capacity()) { + return; + } + // Old data is discarded + storage_.data_ptr().clear(); + auto oldSize = numel_; + auto oldDims = sizes_; + Resize(newCapacity); + // Allocate new memory but don't copy over the data + raw_mutable_data(data_type_); + sizes_ = oldDims; + numel_ = oldSize; + reserved_ = true; + } + + /** + * @brief Resizes a tensor. + * + * Resize takes in a vector of ints specifying the dimensions of the tensor. + * You can pass in an empty vector to specify that it is a scalar (i.e. + * containing one single item). + * + * The underlying storage may be deleted after calling Resize: if the new + * shape leads to a different number of items in the tensor, the old memory + * is deleted and new memory will be allocated next time you call + * mutable_data(). However, if the shape is different but the total number of + * items is the same, the underlying storage is kept. + * + * This method respects caffe2_keep_on_shrink. Consult the internal logic + * of this method to see exactly under what circumstances this flag matters. + */ + template + void Resize(Ts... dim_source) { + bool size_changed = SetDims(dim_source...); + if (size_changed) { + // If needed, we will free the data. the next mutable_data() call + // will create the data storage. + bool reset_tensor = false; + if (reserved_) { + // If tensor is reserved then don't claim its memeory unless capacity() + // is smaller than new size + reset_tensor = storage_.capacity() < (storage_offset_ + numel_) * storage_.itemsize(); + } else { + reset_tensor = storage_.capacity() < + (storage_offset_ + numel_) * storage_.itemsize() || + !FLAGS_caffe2_keep_on_shrink || + storage_.capacity() - + (storage_offset_ + numel_) * storage_.itemsize() > + static_cast(FLAGS_caffe2_max_keep_on_shrink_memory); + } + + if (reset_tensor && storage_initialized()) { + FreeMemory(); + } + } + } + + /** + * Resizes the tensor without touching underlying storage. + * This requires the total size of the tensor to remains constant. + */ + inline void Reshape(const std::vector& dims) { + TORCH_CHECK( + is_contiguous_, + "Right now Reshape is only supported for contiguous Tensor."); + int64_t new_size = 1; + for (auto d : dims) { + TORCH_CHECK(d >= 0); + new_size *= d; + } + TORCH_CHECK( + new_size == numel_, + "New size and old size are not equal. You cannot use Reshape, " + "but should use Resize." + // TODO(jiayq): remove the following warning after pending diffs + // stabilize. + " The old caffe2 mixes Reshape and Resize but this behavior has " + "been changed. If you find this error, most likely you will need " + "to change corresponding code from Reshape to Resize."); + sizes_ = dims; + empty_tensor_restride(MemoryFormat::Contiguous); + } + + /** + * Release whatever memory the tensor was holding but keep size and type + * information. Subsequent call to mutable_data will trigger new memory + * allocation. + */ + inline void FreeMemory() { + // We'll detach from the old Storage and create a new one + storage_ = Storage::create_legacy(storage_.device(), data_type_); + storage_offset_ = 0; + } + + /** + * @brief Shares the data with another tensor. + * + * To share data between two tensors, the sizes of the two tensors must be + * equal already. The reason we do not implicitly do a Resize to make the two + * tensors have the same shape is that we want to allow tensors of different + * shapes but the same number of items to still be able to share data. This + * allows one to e.g. have a n-dimensional Tensor and a flattened version + * sharing the same underlying storage. + * + * The source tensor should already have its data allocated. + */ + // To be deprecated + void ShareData(const TensorImpl& src) { + // Right now, we are assuming the device_type are the same, since it is + // inherently the same in the non-templatized code. We should probably add + // an assert here which might affect perf a little bit. + TORCH_CHECK( + src.numel_ == numel_, + "Size mismatch - did you call reshape before sharing the data?"); + // It is possible that the source tensor hasn't called mutable_data() yet, + // in which case ShareData() doesn't make much sense since we don't really + // know what to share yet. + // TODO: Add the assert after all uninitialized states are eliminated + // TORCH_CHECK(src.dtype_initialized(), + // "Source tensor don't have a data type (did you call mutable_data on the tensor?)"); + if (!src.dtype_initialized()) { + C10_LOG_EVERY_MS(WARNING, 1000) << + "Source tensor don't have a data type (did you call mutable_data on the tensor?)"; + } + TORCH_CHECK( + src.storage_initialized(), + "Source tensor has no content and has size > 0"); + // Finally, do sharing. + /* Since we create new Storage whenever we need to change data_type/capacity + * this still keeps the original semantics + */ + storage_ = src.storage(); + data_type_ = src.dtype(); + device_opt_ = src.device_opt(); + storage_offset_ = src.storage_offset(); + } + + void ShareExternalPointer( + DataPtr&& data_ptr, + const caffe2::TypeMeta& data_type, + size_t capacity) { + TORCH_CHECK( + data_type.id() != caffe2::TypeIdentifier::uninitialized(), + "To share with a raw external pointer you need to pass in an " + "initialized data_type(TypeMeta)."); + if (!capacity) { + capacity = numel_ * data_type.itemsize(); + } + if (storage_.unique()) { + storage_.UniqueStorageShareExternalPointer( + std::move(data_ptr), data_type, capacity); + data_type_ = data_type; + device_opt_ = storage_.device(); + storage_offset_ = 0; + } else { + int64_t numel = capacity / data_type.itemsize(); + // Create a new Storage + storage_ = Storage( + data_type, + numel, + std::move(data_ptr), + /*allocator=*/nullptr, + /*resizable=*/false); + data_type_ = data_type; + device_opt_ = storage_.device(); + storage_offset_ = 0; + } + } + + /** + * Returns a mutable raw pointer of the underlying storage. Since we will need + * to know the type of the data for allocation, a TypeMeta object is passed in + * to specify the necessary information. This is conceptually equivalent of + * calling mutable_data() where the TypeMeta parameter meta is derived from + * the type T. This function differs from mutable_data() in the sense that + * the type T can be specified during runtime via the TypeMeta object. + * + * If the existing data does not match the desired type, it will be deleted + * and a new storage will be created. + */ + inline void* raw_mutable_data(const caffe2::TypeMeta& meta) { + // For 0-size tensors it's fine to return any pointer (including nullptr) + if (data_type_ == meta && storage_initialized()) { + return static_cast(static_cast(storage_.data()) + storage_offset_ * meta.itemsize()); + } else { + bool had_special_dtor = data_type_.placementDelete() != nullptr; + storage_offset_ = 0; + if (storage_.unique()) { + storage_.set_dtype(meta); + } else { + if (data_type_ != meta) { + storage_ = Storage::create_legacy(storage_.device(), meta); + } + } + data_type_ = meta; + // NB: device is not changed + + // We can reuse the existing buffer if the current data does not have + // a special destructor and the new data doesn't have a special + // constructor. + if (numel_ == 0 || + (meta.placementNew() == nullptr && !had_special_dtor && + storage_.numel() >= numel_)) { + TORCH_INTERNAL_ASSERT(storage_offset_ == 0); // because we just reallocated + return storage_.data(); + } + const Allocator* allocator = storage_.allocator(); + // Storage might have nullptr allocator in rare cases, for example, if + // an external memory segment has been wrapped with Tensor and we don't + // know how to reallocate it. However, in order to preserve legacy C2 + // behavior, we allow reallocating the memory using default allocator. + if (allocator == nullptr) { + allocator = GetAllocator(storage_.device_type()); + } + if (meta.placementNew()) { + // For types that need placement new, we will call it, as well as + // making sure that when the data is freed, it calls the right + // destruction procedure. + auto size = numel_; + auto dtor = data_type_.placementDelete(); + auto data_ptr = allocator->allocate(numel_ * storage_.itemsize()); + storage_.set_data_ptr(PlacementDeleteContext::makeDataPtr( + std::move(data_ptr), dtor, size, storage_.device())); + data_type_.placementNew()(storage_.data(), numel_); + } else { + // For fundamental type, new and delete is easier. + storage_.set_data_ptr( + allocator->allocate(numel_ * storage_.itemsize())); + } + storage_.set_numel(numel_); + TORCH_INTERNAL_ASSERT(storage_offset_ == 0); // because we just reallocated + device_opt_ = storage_.device(); + return storage_.data(); + } + } + + /** + * Returns a typed pointer of the underlying storage. + * + * For fundamental types, we reuse possible existing storage if there + * is sufficient capacity. + */ + template + inline T* mutable_data() { + if (storage_initialized() && storage_.IsType()) { + return static_cast(storage_.data()) + storage_offset_; + } + // Check it here statically - otherwise TypeMeta would throw the runtime + // error in attempt to invoke TypeMeta::ctor() + static_assert( + std::is_default_constructible::value, + "Tensor can't hold non-default-constructible types"); + return static_cast(raw_mutable_data(caffe2::TypeMeta::Make())); + } + + /** + * True if a tensor is storage initialized. A tensor may become + * storage UNINITIALIZED after a Resize() or FreeMemory() + */ + bool storage_initialized() const { + TORCH_CHECK(has_storage(), "cannot call storage_initialized on tensor that does not have storage"); + return storage_.data() || numel_ == 0; + } + + /** + * True if a tensor is dtype initialized. A tensor allocated with + * Caffe2-style constructors is dtype uninitialized until the + * first time mutable_data() is called. + */ + bool dtype_initialized() const noexcept { + return data_type_ != caffe2::TypeMeta(); + } + + void set_storage(at::Storage storage) { + TORCH_CHECK(allow_tensor_metadata_change(), "set_storage ", err_msg_tensor_metadata_change_not_allowed); + storage_ = std::move(storage); + data_type_ = storage_.dtype(); + device_opt_ = storage_.device(); + } + + /** + * Set the strides of the tensor to match memory_format + * + * WARNING: This function doesn't rearrange data and assumes tensor is a memory + * contiguous + */ + virtual void empty_tensor_restride(MemoryFormat memory_format) { + #ifdef DEBUG + TORCH_INTERNAL_ASSERT(compute_numel() == numel_, + "If you are seeing this error, that means empty_tensor_restride was " + "called before setting correct numel"); + #endif + switch (memory_format) { + case MemoryFormat::Contiguous: { + strides_.resize(sizes_.size(), 0); + if (dim() > 0) { + int last_idx = dim() - 1; + strides_[last_idx] = 1; + for (auto i = last_idx - 1; i >= 0; --i) { + strides_[i] = strides_[i + 1] * std::max(sizes_[i + 1], 1); + } + } + break; + } + case MemoryFormat::ChannelsLast: { + TORCH_CHECK( + dim() == 4, + "required rank 4 tensor to use channels_last format"); + set_sizes_and_strides(sizes(), get_channels_last_strides(sizes())); + break; + } + case MemoryFormat::Preserve: + TORCH_CHECK(false, "unsupported memory format ", memory_format); + // Cleaning warning messages, no need to break as TORCH_CHECK(false) + // terminates flow. + // break; + } + // recompute contiguous flag, as currently NHWC/NCHW flags are not mutually + // exclusive see #24090 + refresh_contiguous(); + } + + bool is_strides_like_channels_last() const { + return is_channels_last_; + } + + bool is_non_overlapping_and_dense() const { + return is_non_overlapping_and_dense_; + } + +private: + + // The Caffe2 Resize() method supports being called both as Resize({2,2}) as + // well as variadic with Resize(2, 2). These overloads provide all of the + // supported calling configurations, while being overloads (and not templates) + // so that implicit conversions still work. + // + // SetDims on ArrayRef is internally implemented as a template, so we can + // handle both ArrayRefs of different types (there are some uses of + // Resize in Caffe2 which pass in int, not int64_t.) + + template < + typename T, + typename = typename std::enable_if::value>::type> + bool SetDimsTemplate(ArrayRef src) { + auto old_numel = numel_; + sizes_.resize(src.size()); + int64_t new_numel = 1; + for (size_t i = 0; i < src.size(); ++i) { + new_numel *= src[i]; + sizes_[i] = src[i]; + } + numel_ = new_numel; + empty_tensor_restride(MemoryFormat::Contiguous); + return numel_ != old_numel; + } + + bool SetDims(ArrayRef s) { + return SetDimsTemplate(s); + } + + bool SetDims(ArrayRef s) { + return SetDimsTemplate(s); + } + + bool SetDims(ArrayRef s) { + return SetDimsTemplate(s); + } + + bool SetDims() { + return SetDims(IntArrayRef{}); + } + + bool SetDims(const int64_t d0) { + return SetDims(IntArrayRef{d0}); + } + + bool SetDims(const int64_t d0, const int64_t d1) { + return SetDims(IntArrayRef{d0, d1}); + } + + bool SetDims(const int64_t d0, const int64_t d1, const int64_t d2) { + return SetDims(IntArrayRef{d0, d1, d2}); + } + + bool SetDims(const int64_t d0, const int64_t d1, const int64_t d2, const int64_t d3) { + return SetDims(IntArrayRef{d0, d1, d2, d3}); + } + + /** + * Compute the number of elements based on the sizes of a tensor. + */ + int64_t compute_numel() const { + int64_t n = 1; + for (auto s : sizes()) { + n *= s; + } + return n; + } + + /** + * Compute whether or not a tensor is contiguous based on the sizes and + * strides of a tensor. + */ + bool compute_contiguous() const; + + bool compute_channels_last_contiguous() const; + + bool compute_strides_like_channels_last() const; + + bool compute_non_overlapping_and_dense() const; + +protected: + /** + * Recompute the cached numel of a tensor. Call this if you modify sizes. + */ + void refresh_numel() { + numel_ = compute_numel(); + } + + /** + * Recompute the cached contiguity of a tensor. Call this if you modify sizes + * or strides. + */ + void refresh_contiguous() { + is_contiguous_ = compute_contiguous(); + is_channels_last_contiguous_ = compute_channels_last_contiguous(); + is_channels_last_ = is_channels_last_contiguous_ || compute_strides_like_channels_last(); + is_non_overlapping_and_dense_ = is_contiguous_ || is_channels_last_contiguous_ || compute_non_overlapping_and_dense(); + } + + /** + * Copy the tensor metadata fields (e.g. sizes / strides / storage pointer / storage_offset) + * from one TensorImpl to another TensorImpl. + * + * For usage of `version_counter` and `allow_tensor_metadata_change`, see NOTE [ TensorImpl Shallow-Copying ]. + */ + static void copy_tensor_metadata( + const TensorImpl* src_impl, + TensorImpl* dest_impl, + const c10::VariableVersion& version_counter, + bool allow_tensor_metadata_change); + +protected: + // Error message to show when the user tries to change tensor metadata on + // Tensor created from .data or .detach(). + // + // See NOTE [ Metadata Change for a Detached Tensor ] for details. + static const char * const err_msg_tensor_metadata_change_not_allowed; + + Storage storage_; + +private: + // This pointer points to an AutogradMeta struct that stores autograd-specific fields + // (such as grad_ / grad_fn_ / grad_accumulator_). + // This pointer always has unique ownership (meaning only one TensorImpl can own it + // at a time). + // + // autograd_meta_ can be nullptr, as an optimization. When this occurs, it is + // equivalent to having an autograd_meta_ pointing to a default constructed + // AutogradMeta; intuitively, tensors which don't require grad will have this + // field set to null. If !type_set_.has(VariableTensorId), then + // autograd_meta == nullptr (but not vice versa, due to the nullptr + // optimization) + // + // This means accessors on autograd_meta_ have to be careful to test if they + // got a nullptr, and handle default behavior appropriately in that case. + // + // Note that we don't enforce the invariant that if the AutogradMeta is + // default constructed, it is nullptr (to do this, we'd have to continuously + // check if an AutogradMeta became, by mutation, equal to the default + // constructed form. (This might be useful, but it seems rare enough that + // a requires_grad=True variable will turn back into the requires_grad=False + // version.) So there are three representable states: + // + // 1. autograd_meta_ == nullptr + // 2. autograd_meta_ is default constructed (semantically, same as (1)) + // 3. autograd_meta_ has nontrivial information content + // + std::unique_ptr autograd_meta_ = nullptr; + +protected: + std::unique_ptr named_tensor_meta_ = nullptr; + + c10::VariableVersion version_counter_; + + // This field contains a weak reference to a PyObject representing + // this Tensor. It MUST NOT be a strong reference, as that would + // create a reference cycle between Tensor and the PyObject. If + // pyobj is nullptr, when we transfer Tensor to Python, we allocate + // a new PyObject for it and set this field. This is thread safe + // because all Python code is protected under the GIL. This design does + // NOT WORK for Tensors which are shared across multiple Python + // subinterpreters (introduced in Python 3.8) since you don't have + // enough space to store the separate PyObject per subinterpreter. + // When a PyObject dies, you are obligated to clear this field + // (otherwise, you will try to use-after-free the pyobj); this currently + // occurs in THPVariable_clear in torch/csrc/autograd/python_variable.cpp + PyObject* pyobj_ = nullptr; + + // We could save a word or two by combining the SmallVector structs, + // since their size is redundant, and if we need to overflow the buffer space + // we could keep the two pointers together. However, that would require + // implementing another struct from scratch, so only do this if we're desperate. + SmallVector sizes_; + SmallVector strides_; + + int64_t storage_offset_ = 0; + // If sizes and strides are empty, the numel is 1!! However, most of the + // time, we will immediately set sizes to {0} and reset numel to 0. + // (Can't do that in the default initializers, because there's no way to + // spell "allocate a one-element array" for strides_). + int64_t numel_ = 1; + + // INVARIANT: When storage is non-null, this type meta must + // agree with the type meta in storage + caffe2::TypeMeta data_type_; + + // NOTE [c10::optional operator usage in CUDA] + // Our optional definition doesn't compile in .cu file if `value()` or + // `operator->` are used. Instead, we always use `operator*`. + // See https://github.com/pytorch/pytorch/issues/18496 for more info. + // If this is too burdensome to maintain, we can just + // manually implement this with an additional bool. + + // INVARIANT: When storage is non-null, this Device must + // agree with the type meta in storage. + // + // INVARIANT: device_opt_ is only nullopt for undefined tensors + // (which do not have a device.) + c10::optional device_opt_; + + // The set of TensorTypeIds which describe this tensor + // + // INVARIANT: type_set_.has(TensorTypeId::VariableTensorId) (every tensor + // is a variable). Historically this was not the case (there was a + // distinction between plain tensors and variables), but because + // we merged Variable and Tensor, this invariant now always holds. + // This invariant is currently enforced in the constructor of TensorImpl. + // + // You might be wondering why we don't just not include VariableTensorId + // from the type set, if it is always set. The answer is, we still need + // to dispatch differently from variables, and then mask out the variable + // id once we are done handling autograd. If the boolean here was + // inverted, we wouldn't be able to get autograd codepath (since there's + // be no TensorTypeId to dispatch to!) We cannot set VariableTensorId + // as the default value contained in the *included* tensor type id set + // as TLS requires our state to be zero-initialized (i.e., it is not + // included). + TensorTypeSet type_set_; + + // You get to have eight byte-size fields here, before you + // should pack this into a bitfield. + bool is_contiguous_ = true; + + // Tensor is stored in the channels last memory format, when dimensions + // order is NCHW and C-strides < W-strides < H-strides < N-strides + // (If size of any dimension is equal to 1, this dimension strides value + // is not taken into account). + bool is_channels_last_ = false; + + // Channels last contiguous tensor is channel last tensor which occupies + // contiguous memory block. + bool is_channels_last_contiguous_ = false; + + // Dense tensor is the tensor that store values in a contiguous block of memory. + // Non-overlapping tensor is the tensor in which elements occupy individual + // non-repetitive memory. + bool is_non_overlapping_and_dense_ = false; + + bool is_wrapped_number_ = false; + + // NOTE [ Metadata Change for a Detached Tensor ] + // + // Normally, a user is allowed to change the tensor metadata + // (e.g. sizes / strides / storage / storage_offset) of a tensor. + // However, if the tensor is created by `t1_detached = t1.data` in Python + // or `t1_detached = t1.detach()` in Python/C++, those changes to the + // tensor metadata of `t1_detached` will not be propagated back to the + // original tensor `t1`. In order to make such changes explicitly illegal, + // we created the `allow_tensor_metadata_change_` flag, to prevent users + // from changing metadata of the detached tensor and expecting the original + // tensor to also be updated. + // + // NOTE: For a full list of tensor metadata fields, please see + // `copy_tensor_metadata()` in TensorImpl and its subclasses to find + // which fields are copied by value. + bool allow_tensor_metadata_change_ = true; + + // we decide to keep reserved_ and it will + // live in Tensor after the split + // The logic is that if Extend() or ReserveSpace() were ever called, + // then subsequent Resize()s will not free up Storage. + bool reserved_ = false; + +}; + +// Note [TensorImpl size constraints] +// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +// Changed the size of TensorImpl? If the size went down, good for +// you! Adjust the documentation below and the expected size. +// Did it go up? Read on... +// +// Struct size matters. In some production systems at Facebook, we have +// 400M live tensors during a training run. Do the math: every 64-bit +// word you add to Tensor is an extra 3.2 gigabytes in RAM. +// +// If you are a Facebook employee, you can check if the run in question +// has tipped you over the point using the command here: +// https://fburl.com/q5enpv98 +// +// For reference, we OOMed at 160 bytes (20 words) per TensorImpl. +// This is not counting overhead from strides out-of-line allocation and +// StorageImpl space and this is from before we inlined sizes and strides +// directly into TensorImpl as SmallVectors. +// +// Our memory usage on 32-bit systems is suboptimal, but we're not checking +// for it at the moment (to help avoid rage inducing cycles when the +// 32-bit number is wrong). +// +// Current breakdown: +// +// vtable pointer +// strong refcount TODO: pack these into one word +// weak refcount +// storage pointer +// autograd metadata pointer +// version counter pointer +// PyObject pointer +// sizes SmallVector (begin) +// sizes SmallVector (end) +// sizes SmallVector (capacity) +// sizes SmallVector (pre-allocated 0) +// sizes SmallVector (pre-allocated 1) +// sizes SmallVector (pre-allocated 2) +// sizes SmallVector (pre-allocated 3) +// sizes SmallVector (pre-allocated 4) +// strides SmallVector (begin) +// strides SmallVector (end) +// strides SmallVector (capacity) +// strides SmallVector (pre-allocated 0) +// strides SmallVector (pre-allocated 1) +// strides SmallVector (pre-allocated 2) +// strides SmallVector (pre-allocated 3) +// strides SmallVector (pre-allocated 4) +// storage offset +// numel +// data type pointer +// (optional) device +// tensor type id +// miscellaneous bitfield +// +static_assert(sizeof(void*) != sizeof(int64_t) || // if 64-bit... + sizeof(TensorImpl) == sizeof(int64_t) * 30, + "You changed the size of TensorImpl on 64-bit arch." + "See Note [TensorImpl size constraints] on how to proceed."); + +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/core/TensorOptions.h b/thirdparty/libtorch/include/c10/core/TensorOptions.h new file mode 100644 index 0000000000..bdae437877 --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/TensorOptions.h @@ -0,0 +1,590 @@ +#pragma once + +#include +#include +#include +#include +#include +#include + +#include +#include +#include + +#include +#include +#include + +namespace c10 { +/// A class to encapsulate construction axes of an Tensor. TensorOptions was +/// designed to support the Python style API for specifying construction options +/// on factory functions, e.g., +/// +/// torch.zeros(2, 3, dtype=torch.int32) +/// +/// Because C++ doesn't natively support keyword arguments, there must be +/// another way of specifying keyword-like arguments. TensorOptions is a +/// builder class which can be used to construct this "dictionary" of keyword +/// arguments: functions which support TensorOptions conventionally take this +/// argument optionally as their last argument. +/// +/// WARNING: In PyTorch, there are `torch::` variants of factory functions, +/// e.g., torch::zeros for at::zeros. These return Variables (while the +/// stock ATen functions return plain Tensors). If you mix these functions +/// up, you WILL BE SAD. +/// +/// Rather than use the constructor of this class directly, you should prefer to +/// use the constructor functions, and then chain setter methods on top of them. +/// +/// at::device(at::kCUDA).dtype(kInt) +/// at::dtype(at::kInt) +/// +/// Additionally, anywhere a TensorOptions is expected, you can directly +/// pass at::kCUDA / at::kInt, and it will implicitly convert to a TensorOptions. +/// +/// Here are some recommended ways to create a 2x2 tensor of zeros +/// with certain properties. These all *implicitly* make use of +/// TensorOptions, even if they don't mention the class explicitly: +/// +/// at::zeros({2,2}, at::kCUDA); +/// at::zeros({2,2}, at::kLong); +/// at::zeros({2,2}, at::device(at::kCUDA).dtype(at::kLong())); +/// at::zeros({2,2}, at::device({at::kCUDA, 1})); // place on device 1 +/// at::zeros({2,2}, at::requires_grad()); +/// + +/// NOTE [ TensorOptions Constructors ] +/// +/// TensorOptions is like a dictionary with entries from the set: +/// {requires_grad, device, dtype, layout}, where each entry may be +/// unspecified (i.e., is optional). It is used to specify the properties of +/// tensors in many places both in C++ internal and API, e.g., tensor factory +/// methods like `at::empty({10}, options)`, tensor conversions like +/// `tensor.to(...)`, etc. +/// +/// To provide a simple API that is consistent with Python, where one can do +/// `torch.empty(sizes, X)` with `X` being a `torch.device`, `torch.dtype`, or a +/// `torch.layout`, we want TensorOptions to be implicitly convertible from +/// `ScalarType dtype`, `Layout layout` and `Device device`. Therefore, we have +/// three implicit constructors from each of these three types. +/// +/// This is sufficient for `ScalarType` and `Layout` as they are simple Enum +/// classes. However, `Device` is an ordinary class with implicit constructors +/// `Device(DeviceType, DeviceIndex = -1)` and `Device(std::string)` to be +/// consistent with Python API, where strings are treated as equivalent with a +/// `torch.device` object (e.g., "cuda:1" can be passed to everywhere a +/// `torch.device("cuda:1")` is accepted). To support the syntax +/// `at::empty({10}, {kCUDA, 1})` and `tensor.to(kCUDA)`, we need to make sure +/// that `TensorOptions` is implicitly constructible with any argments that a +/// `Device` can constructed from. So we have, +/// +/// /* implicit */ TensorOptions(T&& device) : TensorOptions() { +/// this->set_device(device); +/// } +/// +/// template ::value>> +/// /* implicit */ TensorOptions(Args&&... args) +/// : TensorOptions(Device(std::forward(args)...)) {} +/// +/// +/// But this will be problematic. Consider this: `TensorOptions({kCUDA, 1})`. +/// Compiler will compain about ambiguity between the copy constructor and the +/// `Device` constructor because `{kCUDA, 1}` can be converted to both a +/// `TensorOption` and a `Device`. +/// +/// To get around this, we templatize the `Device` constructor. Since overload +/// resolution is done before template resolution, our problem is solved. + + +struct C10_API TensorOptions { + TensorOptions() + : requires_grad_(false) + , pinned_memory_(false) + , has_device_(false) + , has_dtype_(false) + , has_layout_(false) + , has_requires_grad_(false) + , has_pinned_memory_(false) + {} + + /// Constructs a `TensorOptions` object with the given layout. + /* implicit */ TensorOptions(Layout layout) : TensorOptions() { + this->set_layout(layout); + } + + /// Constructs a `TensorOptions` object with the given device. + /// See NOTE [ TensorOptions Constructors ] on why this is templatized. + template, Device>::value>> + /* implicit */ TensorOptions(T&& device) : TensorOptions() { + this->set_device(std::forward(device)); + } + + /// Constructs a `TensorOptions` object from arguments allowed in `Device` + /// constructors. + /// + /// See NOTE [ TensorOptions Constructors ]. + /// + /// NB: Ideally we only allow implicit constructors here. But there is no easy + /// way to detect them. So we have this one that allows explicit + /// constructors too. + template ::value>> + /* implicit */ TensorOptions(Args&&... args) + : TensorOptions(Device(std::forward(args)...)) {} + + /// Constructs a `TensorOptions` object with the given dtype. + /* implicit */ TensorOptions(caffe2::TypeMeta dtype) : TensorOptions() { + this->set_dtype(dtype); + } + + /// legacy constructor to support ScalarType + /* implicit */ TensorOptions(ScalarType dtype) : TensorOptions() { + this->set_dtype(dtype); + } + + /// Return a copy of `TensorOptions` with `device` set to the given one, or + /// cleared if `device` is `nullopt`. + C10_NODISCARD TensorOptions device(c10::optional device) const noexcept { + TensorOptions r = *this; + r.set_device(device); + return r; + } + + /// Return a copy of `TensorOptions` with `device` set to the given one. + /// (This overload ensures that variadic template c10::optional constructor + /// for Device work correctly.) + template + C10_NODISCARD TensorOptions device(Args&&... args) const noexcept { + return device(c10::optional(c10::in_place, std::forward(args)...)); + } + + /// Return a copy of `TensorOptions`, but with device set to CUDA, and the + /// device index set to the given one. + /// + /// TODO: This function encourages bad behavior (assuming CUDA is + /// the only device that matters). Get rid of it / rename it. + C10_NODISCARD TensorOptions device_index(int16_t device_index) const noexcept { + return device(Device::Type::CUDA, device_index); + } + + /// Return a copy of `TensorOptions` with `dtype` set to the given one. + C10_NODISCARD TensorOptions dtype(c10::optional dtype) const noexcept { + TensorOptions r = *this; + r.set_dtype(dtype); + return r; + } + + // legacy function to support ScalarType + C10_NODISCARD TensorOptions dtype(c10::optional dtype) const noexcept { + TensorOptions r = *this; + r.set_dtype(dtype); + return r; + } + + // Since dtype is taken... + template + TensorOptions& dtype() { + dtype_ = caffe2::TypeMeta::Make(); + has_dtype_ = true; + return *this; + } + + /// Sets the layout of the `TensorOptions`. + C10_NODISCARD TensorOptions layout(c10::optional layout) const noexcept { + TensorOptions r = *this; + r.set_layout(layout); + return r; + } + + /// Sets the `requires_grad` property of the `TensorOptions`. + C10_NODISCARD TensorOptions requires_grad(c10::optional requires_grad) const noexcept { + TensorOptions r = *this; + r.set_requires_grad(requires_grad); + return r; + } + + /// Sets the `pinned_memory` property on the `TensorOptions`. + C10_NODISCARD TensorOptions pinned_memory(c10::optional pinned_memory) const noexcept { + TensorOptions r = *this; + r.set_pinned_memory(pinned_memory); + return r; + } + + /// Returns the device of the `TensorOptions`. + Device device() const noexcept { + return has_device_ ? device_ : Device(kCPU); + } + + /// Returns whether the device is specified. + bool has_device() const noexcept { + return has_device_; + } + + /// Returns the device of the `TensorOptions`, or `c10::nullopt` if + /// device is not specified. + c10::optional device_opt() const noexcept { + return has_device_ ? c10::make_optional(device_) : c10::nullopt; + } + + /// Returns the device index of the `TensorOptions`. + int32_t device_index() const noexcept { + return device().index(); + } + + /// Returns the dtype of the `TensorOptions`. + caffe2::TypeMeta dtype() const noexcept { + return has_dtype_ ? dtype_ : get_default_dtype(); + } + + /// Returns whether the dtype is specified. + bool has_dtype() const noexcept { + return has_dtype_; + } + + /// Returns the dtype of the `TensorOptions`, or `c10::nullopt` if + /// device is not specified. + c10::optional dtype_opt() const noexcept { + return has_dtype_ ? c10::make_optional(dtype_) : c10::nullopt; + } + + /// Returns the layout of the `TensorOptions`. + Layout layout() const noexcept { + return has_layout_ ? layout_ : kStrided; + } + + /// Returns whether the layout is specified. + bool has_layout() const noexcept { + return has_layout_; + } + + /// Returns the layout of the `TensorOptions`, or `c10::nullopt` if + /// layout is not specified. + c10::optional layout_opt() const noexcept { + return has_layout_ ? c10::make_optional(layout_) : c10::nullopt; + } + + /// Returns the `requires_grad` property of the `TensorOptions`. + bool requires_grad() const noexcept { + return has_requires_grad_ ? requires_grad_ : false; + } + + /// Returns whether the `requires_grad` is specified. + bool has_requires_grad() const noexcept { + return has_requires_grad_; + } + + /// Returns the `requires_grad` property of the `TensorOptions`, or + /// `c10::nullopt` if `requires_grad` is not specified. + c10::optional requires_grad_opt() const noexcept { + return has_requires_grad_ ? c10::make_optional(requires_grad_) + : c10::nullopt; + } + + /// Returns the `pinned_memory` property of the `TensorOptions`. + bool pinned_memory() const noexcept { + return has_pinned_memory_ ? pinned_memory_ : false; + } + + /// Returns whether the `pinned_memory` is specified. + bool has_pinned_memory() const noexcept { + return has_pinned_memory_; + } + + + /// Returns the `pinned_memory` property of the `TensorOptions`, or + /// `c10::nullopt` if `pinned_memory` is not specified. + c10::optional pinned_memory_opt() const noexcept { + return has_pinned_memory_ ? c10::make_optional(pinned_memory_) : c10::nullopt; + } + + // Resolves the ATen backend specified by the current construction axes. + // TODO: Deprecate this + Backend backend() const noexcept { + return at::tensorTypeIdToBackend(computeTensorTypeId()); + } + + /// Return the right-biased merge of two TensorOptions. This has the + /// effect of overwriting settings from self with specified options + /// of options. + /// + /// NB: This merging operation does NOT respect device merges. + /// For example, if you device({kCUDA, 1}).merge_in(kCUDA) + /// you will get kCUDA in the end! Functions like Tensor.new_empty + /// ensure the right device is selected anyway by way of a + /// device guard. + /// + TensorOptions merge_in(TensorOptions options) const noexcept { + TensorOptions r = options; + if (!r.has_device()) r.set_device(device()); + if (!r.has_dtype()) r.set_dtype(dtype()); + if (!r.has_layout()) r.set_layout(layout()); + // NB: requires grad is right biased; not a logical AND/OR! + if (!r.has_requires_grad()) r.set_requires_grad(requires_grad()); + if (!r.has_pinned_memory()) r.set_pinned_memory(pinned_memory()); + return r; + } + + // Resolves the tensor type set specified by the current construction axes. + TensorTypeSet type_set() const noexcept { + return TensorTypeSet(computeTensorTypeId()).add(TensorTypeId::VariableTensorId); + } + + inline TensorTypeId computeTensorTypeId() const { + switch (layout()) { + case Layout::Strided: + switch (device().type()) { + case DeviceType::CPU: { + auto dtype_tmp = typeMetaToScalarType(dtype()); + if (isComplexType(dtype_tmp)) { + return TensorTypeId::ComplexCPUTensorId; + } + if (isQIntType(dtype_tmp)) { + return TensorTypeId::QuantizedCPUTensorId; + } + return TensorTypeId::CPUTensorId; + } + case DeviceType::CUDA: + if (isComplexType(typeMetaToScalarType(dtype()))) { + return TensorTypeId::ComplexCUDATensorId; + } + return TensorTypeId::CUDATensorId; + case DeviceType::MKLDNN: + return TensorTypeId::MKLDNNTensorId; + case DeviceType::OPENGL: + return TensorTypeId::OpenGLTensorId; + case DeviceType::OPENCL: + return TensorTypeId::OpenCLTensorId; + case DeviceType::IDEEP: + return TensorTypeId::IDEEPTensorId; + case DeviceType::HIP: + return TensorTypeId::HIPTensorId; + case DeviceType::MSNPU: + return TensorTypeId::MSNPUTensorId; + case DeviceType::XLA: + return TensorTypeId::XLATensorId; + default: + AT_ERROR("Unsupported device type for dense layout: ", device().type()); + } + case Layout::Sparse: + switch (device().type()) { + case DeviceType::CPU: + return TensorTypeId::SparseCPUTensorId; + case DeviceType::CUDA: + return TensorTypeId::SparseCUDATensorId; + case DeviceType::HIP: + return TensorTypeId::SparseHIPTensorId; + default: + AT_ERROR("Unsupported device type for sparse layout: ", device().type()); + } + case Layout::Mkldnn: + switch (device().type()) { + case DeviceType::CPU: + return TensorTypeId::MkldnnCPUTensorId; + default: + AT_ERROR("Unsupported device type for mkldnn layout: ", device().type()); + } + default: + AT_ERROR("Unsupported layout: ", layout()); + } + } + + private: + + // These methods are currently private because I'm not sure if it's wise + // to actually publish them. They are methods because I need them in + // the constructor and the functional API implementation. + // + // If you really, really need it, you can make these public, but check if you + // couldn't just do what you need with the functional API. Similarly, these + // methods are not chainable, because if you wanted chaining, you probably + // want to use the functional API instead. (It's probably OK to make + // these chainable, because these functions are all explicitly annotated + // with a ref-qualifier, the trailing &, that makes them illegal to call + // on temporaries.) + + /// Mutably set the device of `TensorOptions`. + void set_device(c10::optional device) & noexcept { + if (device) { + device_ = *device; + has_device_ = true; + } else { + has_device_ = false; + } + } + + /// Mutably set the dtype of `TensorOptions`. + void set_dtype(c10::optional dtype) & noexcept { + if (dtype) { + dtype_ = *dtype; + has_dtype_ = true; + } else { + has_dtype_ = false; + } + } + + // legacy function to support ScalarType + void set_dtype(c10::optional dtype) & noexcept { + if (dtype) { + dtype_ = scalarTypeToTypeMeta(*dtype); + has_dtype_ = true; + } else { + has_dtype_ = false; + } + } + + /// Mutably set the layout of `TensorOptions`. + void set_layout(c10::optional layout) & noexcept { + if (layout) { + layout_ = *layout; + has_layout_ = true; + } else { + has_layout_ = false; + } + } + + /// Mutably set the `requires_grad` property of `TensorOptions`. + void set_requires_grad(c10::optional requires_grad) & noexcept { + if (requires_grad) { + requires_grad_ = *requires_grad; + has_requires_grad_ = true; + } else { + has_requires_grad_ = false; + } + } + + /// Mutably set the `pinned_memory` property of `TensorOptions`. + void set_pinned_memory(c10::optional pinned_memory) & noexcept { + if (pinned_memory) { + pinned_memory_ = *pinned_memory; + has_pinned_memory_ = true; + } else { + has_pinned_memory_ = false; + } + } + + // WARNING: If you edit TensorOptions to add more options, you + // must adjust the implementation of Tensor::options + + // NB: We didn't use c10::optional here, because then we can't pack + // the has_***_ boolean fields. + + caffe2::TypeMeta dtype_ = caffe2::TypeMeta::Make(); // 64-bit + Device device_ = at::kCPU; // 32-bit + Layout layout_ = at::kStrided; // 8-bit + + // Bitmask required here to get this to fit inside 32 bits (or even 64 bits, + // for that matter) + + bool requires_grad_ : 1; + bool pinned_memory_ : 1; + + + bool has_device_ : 1; + bool has_dtype_ : 1; + bool has_layout_ : 1; + bool has_requires_grad_ : 1; + bool has_pinned_memory_ : 1; +}; + +// We should aspire to fit in one machine-size word; but a size greater than two +// words is too much. (We are doing terribly on 32-bit archs, where we require +// three machine size words to store tensor options. Eek!) +static_assert( sizeof(TensorOptions) <= sizeof(int64_t) * 2, + "TensorOptions must fit in 128-bits" ); + +/// Convenience function that returns a `TensorOptions` object with the `dtype` +/// set to the given one. +inline TensorOptions dtype(caffe2::TypeMeta dtype) { + return TensorOptions().dtype(dtype); +} + +// legacy function to support ScalarType +inline TensorOptions dtype(ScalarType dtype) { + return TensorOptions().dtype(scalarTypeToTypeMeta(dtype)); +} + +/// Convenience function that returns a `TensorOptions` object with the `layout` +/// set to the given one. +inline TensorOptions layout(Layout layout) { + return TensorOptions().layout(layout); +} + +/// Convenience function that returns a `TensorOptions` object with the `device` +/// set to the given one. +inline TensorOptions device(Device device) { + return TensorOptions().device(std::move(device)); +} + +/// Convenience function that returns a `TensorOptions` object with the +/// `device` set to CUDA and the `device_index` set to the given one. +inline TensorOptions device_index(int16_t device_index) { + return TensorOptions().device_index(device_index); +} + +/// Convenience function that returns a `TensorOptions` object with the +/// `requires_grad` set to the given one. +inline TensorOptions requires_grad(bool requires_grad = true) { + return TensorOptions().requires_grad(requires_grad); +} + +C10_API std::ostream& operator<<( + std::ostream& stream, + const TensorOptions& options); + +template +inline TensorOptions dtype() { + return dtype(caffe2::TypeMeta::Make()); +} + +// This is intended to be a centralized location by which we can determine +// what an appropriate TensorTypeId for a tensor is. +// +// This takes a TensorOptions, rather than just a DeviceType and Layout, because +// we reserve the right to change dispatch based on *any* aspect of +// TensorOptions. WARNING: If you do this, you need to fix the calls +// to computeTensorTypeId in caffe2/tensor.h +inline TensorTypeId computeTensorTypeId(TensorOptions options) { + return options.computeTensorTypeId(); +} + +inline DeviceType computeDeviceType(TensorTypeId tid) { + if (tid == TensorTypeId::CPUTensorId) { + return DeviceType::CPU; + } else if (tid == TensorTypeId::CUDATensorId) { + return DeviceType::CUDA; + } else if (tid == TensorTypeId::HIPTensorId) { + return DeviceType::HIP; + } else if (tid == TensorTypeId::MKLDNNTensorId) { + return DeviceType::MKLDNN; + } else if (tid == TensorTypeId::OpenGLTensorId) { + return DeviceType::IDEEP; + } else if (tid == TensorTypeId::OpenCLTensorId) { + return DeviceType::OPENCL; + } else if (tid == TensorTypeId::IDEEPTensorId) { + return DeviceType::IDEEP; + } else if (tid == TensorTypeId::HIPTensorId) { + return DeviceType::HIP; + } else if (tid == TensorTypeId::MSNPUTensorId) { + return DeviceType::MSNPU; + } else if (tid == TensorTypeId::XLATensorId) { + return DeviceType::XLA; + } else if (tid == TensorTypeId::SparseCPUTensorId) { + return DeviceType::CPU; + } else if (tid == TensorTypeId::SparseCUDATensorId) { + return DeviceType::CUDA; + } else if (tid == TensorTypeId::SparseHIPTensorId) { + return DeviceType::HIP; + } else if (tid == TensorTypeId::MkldnnCPUTensorId) { + return DeviceType::CPU; + } else if (tid == TensorTypeId::ComplexCPUTensorId) { + return DeviceType::CPU; + } else if (tid == TensorTypeId::ComplexCUDATensorId) { + return DeviceType::CUDA; + } else { + AT_ASSERTM(false, "Unknown TensorTypeId: ", tid); + } +} + +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/core/TensorTypeId.h b/thirdparty/libtorch/include/c10/core/TensorTypeId.h new file mode 100644 index 0000000000..e1bcbc2d26 --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/TensorTypeId.h @@ -0,0 +1,93 @@ +#pragma once + +#include +#include +#include "c10/macros/Macros.h" + +namespace c10 { + +// A "bit" in a TensorTypeSet, which may have a unique dispatch handler +// for it. Higher bit indexes get handled by dispatching first (because +// we "count leading zeros") +enum class TensorTypeId : uint8_t { + // This is not a "real" tensor id, but it exists to give us a "nullopt" + // element we can return for cases when a TensorTypeSet contains no elements. + // You can think a more semantically accurate definition of TensorTypeId is: + // + // using TensorTypeId = optional + // + // and UndefinedTensorId == nullopt. We didn't actually represent + // it this way because optional would take two + // words, when TensorTypeId fits in eight bits. + UndefinedTensorId = 0, + + // This pool of IDs is not really ordered, but it is merged into + // the hierarchy for convenience and performance + CPUTensorId, // PyTorch/Caffe2 supported + CUDATensorId, // PyTorch/Caffe2 supported + MKLDNNTensorId, // Caffe2 only + OpenGLTensorId, // Caffe2 only + OpenCLTensorId, // Caffe2 only + IDEEPTensorId, // Caffe2 only + HIPTensorId, // PyTorch/Caffe2 supported + SparseHIPTensorId, // PyTorch only + MSNPUTensorId, // PyTorch only + XLATensorId, // PyTorch only + MkldnnCPUTensorId, + QuantizedCPUTensorId, // PyTorch only + ComplexCPUTensorId, // PyTorch only + ComplexCUDATensorId, // PyTorch only + + // Sparse has multi-dispatch with dense; handle it first + SparseCPUTensorId, // PyTorch only + SparseCUDATensorId, // PyTorch only + + // WARNING! If you add more "wrapper" style tensor ids (tensor + // ids which don't get kernels directly defined in native_functions.yaml; + // examples are tracing or profiling) here, you need to also adjust + // legacyExtractTypeId in c10/core/TensorTypeId.h to mask them out. + + VariableTensorId, + + // TESTING: This is intended to be a generic testing tensor type id. + // Don't use it for anything real; its only acceptible use is within a single + // process test. Use it by creating a TensorImpl with this TensorTypeId, and + // then registering operators to operate on this type id. + TESTING_ONLY_GenericWrapperTensorId, + + // TESTING: This is intended to be a generic testing tensor type id. + // Don't use it for anything real; its only acceptible use is within a ingle + // process test. Use it by toggling the mode on and off via + // TESTING_ONLY_tls_generic_mode_set_enabled and then registering operators + // to operate on this type id. + TESTING_ONLY_GenericModeTensorId, + + NumTensorIds, // Sentinel +}; + +static_assert( + static_cast(TensorTypeId::NumTensorIds) < 64, + "TensorTypeId is used as index into 64-bit bitmask; you must have less than 64 entries"); + +C10_API const char* toString(TensorTypeId); +C10_API std::ostream& operator<<(std::ostream&, TensorTypeId); + +// For backwards compatibility with XLA repository +// (I don't want to fix this in XLA right now because there might be +// more renaming coming in the future.) +static inline TensorTypeId XLATensorId() { + return TensorTypeId::XLATensorId; +} + +} // namespace c10 + +// NB: You really shouldn't use this instance; this enum is guaranteed +// to be pretty small so a regular array should be acceptable. +namespace std { +template <> +struct hash { + size_t operator()(c10::TensorTypeId x) const { + return static_cast(x); + } +}; +} diff --git a/thirdparty/libtorch/include/c10/core/TensorTypeSet.h b/thirdparty/libtorch/include/c10/core/TensorTypeSet.h new file mode 100644 index 0000000000..f5ce1af70b --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/TensorTypeSet.h @@ -0,0 +1,128 @@ +#pragma once + +#include +#include +#include +#include + +namespace c10 { + +// A representation of a set of TensorTypeIds. A tensor may have multiple +// tensor type ids, e.g., a Variable tensor can also be a CPU tensor; the +// TensorTypeSet specifies what type ids apply. The internal representation is +// as a 64-bit bit set (this means only 64 tensor type ids are supported). +// +// Note that TensorTypeIds are ordered; thus, we can ask questions like "what is +// the highest priority TensorTypeId in the set"? (The set itself is not +// ordered; two sets with the same ids will always have the ids ordered in the +// same way.) +// +// At the moment, there are no nontrivial uses of this set; tensors are always +// singletons. In the near future, this set will represent variable? + tensor +// type id. In the far future, it will be requires grad? + profiling? + +// tracing? + lazy? + tensor type id. +// +// (The difference between variable and requires grad, is that +// there are currently three states a tensor can be: +// 1. Not a variable +// 2. Variable with requires_grad=False +// 3. Variable with requires_grad=True +// Eventually, we want to kill state (1), and only dispatch to autograd +// handling code if one of the inputs requires grad.) +// +// An undefined tensor is one with an empty tensor type set. +class TensorTypeSet final { +public: + enum Full { FULL }; + enum Raw { RAW }; + + // NB: default constructor representation as zero is MANDATORY as + // use of TensorTypeSet in TLS requires this. + TensorTypeSet() + : repr_(0) {} + TensorTypeSet(Full) + : repr_(-1) {} + // Public version of TensorTypeSet(uint64_t) API; external users + // must be explicit when they do this! + TensorTypeSet(Raw, uint64_t x) + : repr_(x) {} + explicit TensorTypeSet(TensorTypeId t) + : repr_(t == TensorTypeId::UndefinedTensorId + ? 0 + : 1ULL << (static_cast(t) - 1)) {} + // Test if a TensorTypeId is in the set + bool has(TensorTypeId t) const { + TORCH_INTERNAL_ASSERT(t != TensorTypeId::UndefinedTensorId); + return static_cast(repr_ & TensorTypeSet(t).repr_); + } + // Perform set union + TensorTypeSet operator|(TensorTypeSet other) const { + return TensorTypeSet(repr_ | other.repr_); + } + // Perform set intersection + TensorTypeSet operator&(TensorTypeSet other) const { + return TensorTypeSet(repr_ & other.repr_); + } + // Compute the set difference self - other + TensorTypeSet operator-(TensorTypeSet other) const { + return TensorTypeSet(repr_ & ~other.repr_); + } + // Perform set equality + bool operator==(TensorTypeSet other) const { + return repr_ == other.repr_; + } + // Add a TensorTypeId to the TensorTypeId set. Does NOT mutate, + // returns the extended TensorTypeSet! + C10_NODISCARD TensorTypeSet add(TensorTypeId t) const { + return *this | TensorTypeSet(t); + } + // Remove a TensorTypeId from the TensorTypeId set. This is + // generally not an operation you should be doing (it's + // used to implement operator<<) + C10_NODISCARD TensorTypeSet remove(TensorTypeId t) const { + return TensorTypeSet(repr_ & ~TensorTypeSet(t).repr_); + } + // Is the set empty? (AKA undefined tensor) + bool empty() const { + return repr_ == 0; + } + uint64_t raw_repr() { return repr_; } + // Return the type id in this set with the highest priority (i.e., + // is the largest in the TensorTypeId enum). Intuitively, this + // type id is the one that should handle dispatch (assuming there + // aren't any further exclusions or inclusions). + TensorTypeId highestPriorityTypeId() const { + // TODO: If I put UndefinedTensorId as entry 64 and then adjust the + // singleton constructor to shift from the right, we can get rid of the + // subtraction here. It's modestly more complicated to get right so I + // didn't do it for now. + return static_cast(64 - llvm::countLeadingZeros(repr_)); + } +private: + TensorTypeSet(uint64_t repr) : repr_(repr) {} + uint64_t repr_ = 0; +}; + +C10_API std::string toString(TensorTypeSet); +C10_API std::ostream& operator<<(std::ostream&, TensorTypeSet); + +// Historically, every tensor only had a single TensorTypeId, and it was +// always something like CPUTensorId and not something weird like VariableId. +// For the forseeable future, it will still be possible to extract /that/ +// TensorTypeId, and that's what this function does. It should be used +// for legacy code that is still using TensorTypeId for things like instanceof +// checks; if at all possible, refactor the code to stop using TensorTypeId +// in those cases. +// +// What's the difference between 'legacyExtractTypeId(s) == id' +// and 's.has(id)'? legacyExtractTypeId will NEVER return VariableTensorId; +// but s.has(VariableTensorId) will evaluate to true if s has VariableTensorId. +// For non-VariableTensorId equality tests, they are indistinguishable. +// +// NB: If you add other non-VariableTensorId other keys to this set, you'll +// have to adjust this some more (sorry.) +static inline TensorTypeId legacyExtractTypeId(TensorTypeSet s) { + return s.remove(TensorTypeId::VariableTensorId).highestPriorityTypeId(); +} + +} diff --git a/thirdparty/libtorch/include/c10/core/UndefinedTensorImpl.h b/thirdparty/libtorch/include/c10/core/UndefinedTensorImpl.h new file mode 100644 index 0000000000..9f1cb93c10 --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/UndefinedTensorImpl.h @@ -0,0 +1,35 @@ +#pragma once + +#include + +namespace c10 { + +struct C10_API UndefinedTensorImpl final : public TensorImpl { + public: + // Without this, we get: + // error: identifier "at::UndefinedTensorImpl::_singleton" is undefined in device code + // (ostensibly because the constexpr tricks MSVC into trying to compile this + // function for device as well). +#ifdef _WIN32 + static inline TensorImpl * singleton() { +#else + static constexpr inline TensorImpl * singleton() { +#endif + return &_singleton; + } + IntArrayRef sizes() const override; + IntArrayRef strides() const override; + int64_t size(int64_t d) const override; + int64_t stride(int64_t d) const override; + int64_t dim() const override; + bool has_storage() const override; + const Storage& storage() const override; + int64_t storage_offset() const override; +private: + UndefinedTensorImpl(); + static UndefinedTensorImpl _singleton; +public: + friend struct UndefinedType; +}; + +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/core/WrapDimMinimal.h b/thirdparty/libtorch/include/c10/core/WrapDimMinimal.h new file mode 100644 index 0000000000..40090239e8 --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/WrapDimMinimal.h @@ -0,0 +1,26 @@ +#pragma once + +#include + +namespace c10 { + +static inline int64_t maybe_wrap_dim(int64_t dim, int64_t dim_post_expr, bool wrap_scalar=true) { + if (dim_post_expr <= 0) { + if (!wrap_scalar) { + AT_INDEX_ERROR("dimension specified as ", dim, " but tensor has no dimensions"); + } + dim_post_expr = 1; // this will make range [-1, 0] + } + + int64_t min = -dim_post_expr; + int64_t max = dim_post_expr - 1; + if (dim < min || dim > max) { + AT_INDEX_ERROR( + "Dimension out of range (expected to be in range of [", + min, ", ", max, "], but got ", dim, ")"); + } + if (dim < 0) dim += dim_post_expr; + return dim; +} + +} diff --git a/thirdparty/libtorch/include/c10/core/impl/DeviceGuardImplInterface.h b/thirdparty/libtorch/include/c10/core/impl/DeviceGuardImplInterface.h new file mode 100644 index 0000000000..516aebba07 --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/impl/DeviceGuardImplInterface.h @@ -0,0 +1,224 @@ +#pragma once + +#include +#include +#include +#include + +// Just for C10_ANONYMOUS_VARIABLE +#include + +#include + +namespace c10 { + +/** + * Flags defining the behavior of events. + * + * PYTORCH_DEFAULT and BACKEND_DEFAULT are valid for all backends. The + * BACKEND_DEFAULT is what a particular backend would select if no + * flags were given. PYTORCH_DEFAULT is the PyTorch's framework default + * choice for events on that backend, which may not be the same. For example, + * when PyTorch creates a CUDA event it sets the flag + * CUDA_EVENT_DISABLING_TIMING by default to improve performance. + * + * The mapping of PYTORCH_DEFAULT and BACKEND_DEFAULT is done by each + * backend implementation. Backend-specific flags, like CUDA_EVENT_DEFAULT, + * should map one-to-one with actual event flags for those backends. + */ +enum class EventFlag { + PYTORCH_DEFAULT, + BACKEND_DEFAULT, + // CUDA flags + CUDA_EVENT_DEFAULT, + CUDA_EVENT_DISABLE_TIMING, // PyTorch-default for CUDA + // HIP flags + HIP_EVENT_DEFAULT, + HIP_EVENT_DISABLE_TIMING, // PyTorch-default for HIP + // FOR TESTING ONLY + INVALID +}; + +namespace impl { + +/** + * DeviceGuardImplInterface represents the virtual interface which provides + * functionality to provide an RAII class for device and stream switching, + * via DeviceGuard. Every distinct device type, e.g., CUDA and HIP, is + * expected to implement and register an implementation of this interface. + * All classes which inherit from DeviceGuardImplInterface should be declared + * 'final'. + * + * This class exists because we provide a unified interface for performing + * device guards via DeviceGuard, but we cannot assume that we have actually + * compiled against the, e.g., CUDA library, which actually implements + * this guard functionality. In this case, a dynamic dispatch is required + * to cross the library boundary. + * + * If possible, you should directly use implementations of this interface; + * those uses will be devirtualized. + */ +struct C10_API DeviceGuardImplInterface { + /** + * Return the type of device managed by this guard implementation. + */ + virtual DeviceType type() const = 0; + + /** + * Set the current device to Device, and return the previous Device. + */ + virtual Device exchangeDevice(Device) const = 0; + // NB: Implementations of exchangeDevice can be a bit boilerplatey. You might + // consider replacing exchangeDevice with a non-virtual function with a baked + // in implementation; however, note that this will triple the number of + // virtual calls (when you implement exchangeDevice in a final subclass, + // the compiler gets to devirtualize everything; it won't do that if you don't + // define it in the subclass!) A common way to solve this problem is to use + // some sort of CRTP; however, we can template DeviceGuardImplInterface since + // we really *do* need it to be virtual. A little boilerplate seems easiest + // to explain. (Another way around this problem is to provide inline + // functions that provide the default implementations, but this seems a little + // hard to explain. In any case, we're only going to have on order of ten + // implementations of this anyway.) + + /** + * Get the current device. + */ + virtual Device getDevice() const = 0; + + /** + * Set the current device to Device. + */ + virtual void setDevice(Device) const = 0; + + /** + * Set the current device to Device, without checking for errors + * (so, e.g., this can be called from a destructor). + */ + virtual void uncheckedSetDevice(Device) const noexcept = 0; + + /** + * Get the current stream for a given device. + */ + virtual Stream getStream(Device) const noexcept = 0; + + /** + * Get the default stream for a given device. + */ + virtual Stream getDefaultStream(Device) const { + TORCH_CHECK(false, "Backend doesn't support acquiring a default stream.") + } + + /** + * Set a stream to be the thread local current stream for its device. + * Return the previous stream for that device. You are NOT required + * to set the current device to match the device of this stream. + */ + virtual Stream exchangeStream(Stream) const noexcept = 0; + +/** + * Destroys the given event. + */ + virtual void destroyEvent ( + void* event, + const DeviceIndex device_index) const noexcept { } + +/** + * Increments the event's version and enqueues a job with this version + * in the stream's work queue. When the stream process that job + * it nofifies all streams waiting on / blocked by that version of the + * event to continue and marks that version as recorded. + * */ + virtual void record( + void** event, + const Stream& stream, + const DeviceIndex device_index, + const c10::EventFlag flag) const { + TORCH_CHECK(false, "Backend doesn't support events."); + } + +/** + * Does nothing if the event has not been scheduled to be recorded. + * If the event was previously enqueued to be recorded, a command + * to wait for the version of the event that exists at the time of this call + * is inserted in the stream's work queue. + * When the stream reaches this command it will stop processing + * additional commands until that version of the event is marked as recorded. + */ + virtual void block( + void* event, + const Stream& stream) const { + TORCH_CHECK(false, "Backend doesn't support events."); + } + +/** + * Returns true if (and only if) + * (1) the event has never been scheduled to be recorded + * (2) the current version is marked as recorded. + * Returns false otherwise. + */ + virtual bool queryEvent(void* event) const { + TORCH_CHECK(false, "Backend doesn't support events."); + } + + /** + * Get the number of devices. WARNING: This is REQUIRED to not raise + * an exception. If there is some sort of problem, e.g., driver error, + * you should report that there are zero available devices. + */ + virtual DeviceIndex deviceCount() const noexcept = 0; + + /** + * Intended use of this class is to leak the DeviceGuardImpl at program end. + * So you better not call the destructor, buster! + */ + virtual ~DeviceGuardImplInterface() = default; +}; + +// The registry is NON-owning. Each stored pointer is std::atomic so +// that under all interleavings of registry calls the structure is +// race-free. This doesn't cost us anything on reads in X86. (An +// unsynchronized implementation probably is OK too, but I didn't want +// to prove that we never read from device_guard_impl_registry at the +// same time some registration is occurring. Shiver.) +// +// I'd like this registry to be valid even at program destruction time +// (in case someone uses a DeviceGuard in a destructor to do some cleanup +// in the CUDA API.) Since there are no direct accesses of the underlying +// owning objects which I can use to enforce initialization order (unlike +// in a Meyer singleton), it implies that you must *leak* objects when +// putting them in the registry. This is done by deleting the destructor +// on DeviceGuardImplInterface. +extern C10_API std::atomic +device_guard_impl_registry[static_cast(DeviceType::COMPILE_TIME_MAX_DEVICE_TYPES)]; + +// I can't conveniently use c10/util/Registry.h for the following reason: +// c10/util/Registry.h gives me a slow way of Create'ing a object of some +// interface from the registry, but no way of quickly accessing an already +// created object. I'll be banging on getDeviceGuardImpl every time we do a +// DeviceGuard, so I really don't want to be doing an unordered_map lookup. +// Better if the registration mechanism directly drops its implementation +// into device_guard_impl_registry. + +class C10_API DeviceGuardImplRegistrar { +public: + DeviceGuardImplRegistrar(DeviceType, const DeviceGuardImplInterface*); +}; + +#define C10_REGISTER_GUARD_IMPL(DevType, DeviceGuardImpl) \ + static ::c10::impl::DeviceGuardImplRegistrar C10_ANONYMOUS_VARIABLE(g_##DeviceType)(::c10::DeviceType::DevType, new DeviceGuardImpl()); + +inline const DeviceGuardImplInterface* getDeviceGuardImpl(DeviceType type) { + auto p = device_guard_impl_registry[static_cast(type)].load(); + // This seems to be the first place where you make use of a device + // when you pass devices to factory functions. Give a nicer error + // message in this case. + TORCH_CHECK(p, "PyTorch is not linked with support for ", type, " devices"); + return p; +} + +inline bool hasDeviceGuardImpl(DeviceType type) { + return device_guard_impl_registry[static_cast(type)].load(); +} + +}} // namespace c10::impl diff --git a/thirdparty/libtorch/include/c10/core/impl/FakeGuardImpl.h b/thirdparty/libtorch/include/c10/core/impl/FakeGuardImpl.h new file mode 100644 index 0000000000..24f48fb8fe --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/impl/FakeGuardImpl.h @@ -0,0 +1,105 @@ +#pragma once + +#include + +#include + +namespace c10 { +namespace impl { + +// FakeGuardImpl is hardcoded to have eight devices. Not for +// any good reason, just to simplify code. +constexpr size_t kFakeGuardImplMaxDevices = 8; + +/** + * A fake implementation of DeviceGuardImplInterface suitable for testing. + * The current device is modeled as a mutable field in the guard implementation + * class. See DeviceGuard_test.cpp for an example use. + */ +template +struct FakeGuardImpl final : public DeviceGuardImplInterface { + static constexpr DeviceType static_type = T; + // Runtime device type is not used + FakeGuardImpl(DeviceType) {} + FakeGuardImpl() {} + DeviceType type() const override { + return T; + } + Device exchangeDevice(Device d) const override { + AT_ASSERT(d.type() == type()); + AT_ASSERT(d.index() < kFakeGuardImplMaxDevices); + Device old_device = getDevice(); + if (old_device.index() != d.index()) { + current_device_ = d.index(); + } + return old_device; + } + Device getDevice() const override { + return Device(type(), current_device_); + } + void setDevice(Device d) const override { + AT_ASSERT(d.type() == type()); + AT_ASSERT(d.index() >= 0); + AT_ASSERT(d.index() < kFakeGuardImplMaxDevices); + current_device_ = d.index(); + } + void uncheckedSetDevice(Device d) const noexcept override { + current_device_ = d.index(); + } + Stream getStream(Device d) const noexcept override { + return Stream(Stream::UNSAFE, d, current_streams_[d.index()]); + } + Stream exchangeStream(Stream s) const noexcept override { + auto old_id = current_streams_[s.device_index()]; + current_streams_[s.device_index()] = s.id(); + return Stream(Stream::UNSAFE, s.device(), old_id); + } + DeviceIndex deviceCount() const noexcept override { + return kFakeGuardImplMaxDevices; + } + + // Event-related functions + void record( + void** event, + const Stream& stream, + const DeviceIndex device_index, + const EventFlag flag) const override { } + void block( + void* event, + const Stream& stream) const override { } + bool queryEvent(void* event) const override { return true; } + void destroyEvent( + void* event, + const DeviceIndex device_index) const noexcept override { } + + // Convenience methods for testing + static DeviceIndex getDeviceIndex() { + return current_device_; + } + static void setDeviceIndex(DeviceIndex i) { + AT_ASSERT(i >= 0); + AT_ASSERT(i < kFakeGuardImplMaxDevices); + current_device_ = i; + } + static StreamId getCurrentStreamIdFor(DeviceIndex i) { + return current_streams_.at(i); + } + static void resetStreams() { + current_streams_.fill(0); + } +private: + thread_local static DeviceIndex current_device_; + thread_local static std::array current_streams_; +}; + +template +thread_local DeviceIndex FakeGuardImpl::current_device_ = 0; + +template +constexpr DeviceType FakeGuardImpl::static_type; + +template +thread_local std::array FakeGuardImpl::current_streams_ = {0,0,0,0,0,0,0,0}; + + +}} // namespace c10::impl diff --git a/thirdparty/libtorch/include/c10/core/impl/InlineDeviceGuard.h b/thirdparty/libtorch/include/c10/core/impl/InlineDeviceGuard.h new file mode 100644 index 0000000000..4ffe9830d5 --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/impl/InlineDeviceGuard.h @@ -0,0 +1,391 @@ +#pragma once + +// This file provides implementations of InlineDeviceGuard and InlineOptionalDeviceGuard. + +#include +#include +#include +#include +#include + +namespace c10 { +namespace impl { + + + +/** + * A DeviceGuard is an RAII class that sets a device to some value + * on construction, and resets the device to its original value on + * destruction. + * + * InlineDeviceGuard is a helper class for implementing DeviceGuards. + * It is templated over a DeviceGuardImpl (anything that implements + * DeviceGuardImplInterface). There are two primary ways to instantiate + * InlineDeviceGuard: + * + * - With a concrete implementation of DeviceGuardImpl, e.g., CUDAGuardImpl. + * This is the best way to use InlineDeviceGuard, as all calls are + * devirtualized, giving you code as efficient as straight line + * calls to cudaGetDevice/cudaSetDevice. + * + * - With VirtualGuardImpl, which does a virtual dispatch to a DeviceGuardImpl + * retrieved from a DeviceType registry. We have explicitly instantiated + * InlineDeviceGuard this way as c10::DeviceGuard. + * + * If you are in a hurry, you can use InlineDeviceGuard directly: + * + * using CUDAGuard = impl::InlineDeviceGuard; + * + * However, you can provide a better user experience if you explicitly write a + * wrapper class that itself contains the template instantiation: + * + * class CUDAGuard { + * public: + * // ... the API ... + * private: + * impl::InlineDeviceGuard guard_; + * } + * + * The wrapper class provides a good place to write documentation, and helps + * avoid weird template instantiation errors when a user incorrectly uses the + * class. + * + * If you need to test this class, consider instantiating it with FakeGuardImpl. + */ +template +class InlineDeviceGuard { +public: + // Note [Omitted default constructor from RAII] + // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + // In principle, we could add a default constructor to + // DeviceGuard which reads the current device and promises to + // restore to that device on exit. However, most cases where you + // would have written this, you probably meant to actually just + // use OptionalDeviceGuard (since you don't actually need the + // restore to happen if you don't ever actually set the device). + // We remove the constructor here to encourage you to think about + // what you actually want to happen. + explicit InlineDeviceGuard() = delete; + + /// Set the current device to the passed Device. + explicit InlineDeviceGuard(Device device) + : impl_(device.type()) + , original_device_(device.index() == -1 ? impl_.getDevice() : impl_.exchangeDevice(device)) + , current_device_(device.index() == -1 ? original_device_ : device) + {} + + /// Set the current device index to the passed DeviceIndex. (The + /// device type is inferred from the template parameter T). + template ::value>::type> + explicit InlineDeviceGuard(DeviceIndex device_index) + : InlineDeviceGuard(Device(U::static_type, device_index)) {} + + /// Construct an InlineDeviceGuard using VirtualGuardImpl with an explicit + /// DeviceGuardImplInterface pointer. + template ::value>::type> + explicit InlineDeviceGuard(Device device, const DeviceGuardImplInterface* impl) + : impl_(VirtualGuardImpl(impl ? impl : getDeviceGuardImpl(device.type()))) + , original_device_(device.index() == -1 ? impl_.getDevice() : impl_.exchangeDevice(device)) + , current_device_(device.index() == -1 ? original_device_ : device) + {} + + /// Copy is disallowed + InlineDeviceGuard(const InlineDeviceGuard&) = delete; + InlineDeviceGuard& operator=(const InlineDeviceGuard&) = delete; + + /// Move is disallowed, as DeviceGuard does not have an uninitialized state, + /// which is required for moves on types with nontrivial destructors. + InlineDeviceGuard(InlineDeviceGuard&& other) = delete; + InlineDeviceGuard& operator=(InlineDeviceGuard&& other) = delete; + + ~InlineDeviceGuard() { + impl_.uncheckedSetDevice(original_device_); + } + + /// Sets the device to the given one. + template ::value, int>::type = 0> + void set_device(at::Device device) { + AT_ASSERT((U::static_type == DeviceType::HIP && device.type() == DeviceType::CUDA) || + device.type() == U::static_type); + auto index = device.index(); + if (index == -1) return; + impl_.setDevice(device); + current_device_ = device; + } + + /// Resets the currently set device to its original device, and then sets the + /// current device to the passed device. This is effectively equivalent to + /// set_device when a guard supports only a single device type. + template + typename std::enable_if::value >::type + reset_device(at::Device device) { + set_device(device); + } + + /// Resets the currently set device to its original device, and then sets the + /// current device to the passed device (for a possibly different device + /// type). + /// + /// This method is named reset_device to highlight the fact that previous + /// device settings from this guard are NOT preserved, even if the device + /// has a different device type. For example: + /// + /// // CUDA device is 0 + /// DeviceGuard g(Device(kCUDA, 1)); + /// g.reset_device(Device(kHIP, 2)); + /// // CUDA device is 0 (!!) + /// + /// NOTE: this implementation may skip some device setting if it can prove + /// that it is unnecessary. + /// + /// Optional argument is for testing only. + template + typename std::enable_if::value >::type + reset_device(at::Device device, const impl::DeviceGuardImplInterface* impl = nullptr) { + auto index = device.index(); + if (index == -1) return; + if (device.type() == original_device_.type()) { + AT_ASSERT(impl == nullptr || impl->type() == device.type()); + impl_.setDevice(device); + current_device_ = device; + } else { + // Destruct and reconstruct the DeviceGuard in place + impl_.setDevice(original_device_); + impl_ = !impl ? VirtualGuardImpl(device.type()) : VirtualGuardImpl(impl); + original_device_ = impl_.exchangeDevice(device); + current_device_ = device; + } + } + + /// Sets the device index to the given one. The device type is inferred + /// from the original device type. + void set_index(DeviceIndex index) { + reset_device(Device(original_device_.type(), index)); + } + + /// Returns the device that was set at the time the most recent + /// reset_device(), or otherwise the device at construction time. + Device original_device() const { + return original_device_; + } + + /// Returns the most recent device that was set using this device guard, + /// either from construction, or via set_device/reset_device/set_index. + Device current_device() const { + return current_device_; + } + +protected: + T impl_; + +private: + Device original_device_; + Device current_device_; +}; + +/** + * A OptionalDeviceGuard is an RAII class that sets a device to some value on + * initialization, and resets the device to its original value on destruction. + * + * InlineOptionalDeviceGuard is a helper class for implementing + * OptionalDeviceGuards. See guidance in InlineDeviceGuard on how to + * use this. See OptionalDeviceGuard for user-oriented usage notes. + */ +template +class InlineOptionalDeviceGuard { +public: + // Note [Explicit initialization of optional fields] + // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + // Explicit initialization of optional fields + // required to workaround an nvcc bug; see https://github.com/pytorch/pytorch/issues/12117 + + /// Creates an uninitialized OptionalDeviceGuard. + explicit InlineOptionalDeviceGuard() + : guard_() // See Note [Explicit initialization of optional fields] + {} + + /// Set the current device to the passed Device, if it is not nullopt. + explicit InlineOptionalDeviceGuard(optional device_opt) + : guard_() { // See Note [Explicit initialization of optional fields] + if (device_opt.has_value()) { + guard_.emplace(device_opt.value()); + } + } + + /// Set the current device to the passed DeviceIndex, if it is not nullopt. + template ::value>::type> + explicit InlineOptionalDeviceGuard(optional device_index_opt) + : guard_() { // See Note [Explicit initialization of optional fields] + if (device_index_opt.has_value()) { + guard_.emplace(device_index_opt.value()); + } + } + + /// All constructors of DeviceGuard are valid for OptionalDeviceGuard + /// and result in initialized OptionalDeviceGuard. + template + explicit InlineOptionalDeviceGuard(Args&&... args) + : guard_(in_place, std::forward(args)...) {} + + // TODO: Consider readding Tensor and TensorList constructors here, when + // Tensor moves to c10. (These are only valid on OptionalDeviceGuard, + // because a Tensor may be undefined, in which case we need an uninitialized + // tensor guard.) + + // Note [Move construction for RAII guards is tricky] + // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + // In principle, move construction is useful for terminating + // the lifetime of a `OptionalDeviceGuard` early; for example: + // + // // current device is d0 + // OptionalDeviceGuard g1(d1); + // // current device is d1 + // { + // OptionalDeviceGuard g2(std::move(g1)); + // } + // // current device is d0!! + // + // However, it's difficult to implement the move constructor + // in a way that works in all situations. For example, consider + // the following example: + // + // OptionalDeviceGuard g1(d1); + // { + // OptionalDeviceGuard g2(d2); + // { + // OptionalDeviceGuard g3(std::move(g1)); // !!! + // } + // } + // + // What should the current device be while g3 in scope... and what + // should it be after it goes out of scope? What about g2? + // There don't seem to be satisfactory answers for these questions. + // + // It's in principle possible to raise an error when this occurs + // by doing some extra thread-local bookkeeping. But why bother? + // Just don't provide the constructor. + InlineOptionalDeviceGuard(InlineOptionalDeviceGuard&& other) = delete; + + // Note [Move assignment for RAII guards is tricky] + // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + // Move assignment is deleted, because you need to know which guard was + // defined "first", as that guard's original_device_ wins--with the current + // representation, we have no way of telling which is the case. (Move + // construction does not have this problem, as one guard is always + // uninitialized.) + // + // We can make this clear by way of a pair of examples: + // + // Example 1: + // + // // initial device is n0 + // { + // CUDAGuard g1(n1); + // { + // CUDAGuard g2(n2); + // // current device should be n2 + // g1 = std::move(g2); + // // current device should still be n2 + // } + // // current device should still be n2 + // } + // // current device should be n0 + // + // Example 2 (flip the order of the two guards): + // + // // initial device is n0 + // { + // CUDAGuard g2(n2); + // { + // CUDAGuard g1(n1); + // // current device should be n1 + // g1 = std::move(g2); + // // current device should be n2 + // } + // // current device should be n0 (since g2 has been vacated) + // } + // + // In both examples, we need g1 to restore to n0 after move assignment. + // However, in example 1, this is determined by the restore value of g1 + // (prior to the move). In example 2, however, it is determined by the the + // restore value of g2(!!). We don't know which one should win, without having + // a way of telling which guard was allocated first. + // + // We could solve this with an extra thread-local variable. But no one is + // actually using move-assignment. So just get rid of it. + InlineOptionalDeviceGuard& operator=(InlineOptionalDeviceGuard&& other) = delete; + + /// Sets the device to the given one. Initializes OptionalDeviceGuard if it + /// is not already initialized. + template ::value>::type> + void set_device(at::Device device) { + if (!guard_.has_value()) { + guard_.emplace(device); + } else { + guard_->set_device(device); + } + } + + /// Resets the currently set device to its original device, and then sets the + /// current device to the passed device (for a possibly different device + /// type). Initializes OptionalDeviceGuard if it is not already initialized. + /// + /// See notes on why this is called reset_device on InlineDeviceGuard. + /// + /// Optional argument is for testing only. + template ::value>::type> + void reset_device(at::Device device, const DeviceGuardImplInterface* impl = nullptr) { + if (!guard_.has_value()) { + guard_.emplace(device, impl); + } else { + guard_->reset_device(device, impl); + } + } + + /// Resets the currently set device to its original device, and then sets the + /// current device to the passed device. Initializes the guard if it is + /// not already initialized. This is effectively equivalent to set_device + /// when a guard supports only a single device type. + template ::value>::type> + void reset_device(at::Device device) { + if (!guard_.has_value()) { + guard_.emplace(device); + } else { + guard_->reset_device(device); + } + } + + /// Sets the device index to the given one. The device type is statically + /// known. + template ::value >::type> + void set_index(DeviceIndex index) { + if (!guard_.has_value()) { + guard_.emplace(index); + } else { + guard_->set_index(index); + } + } + + /// Returns the device that was set immediately prior to initialization of the, + /// guard, or nullopt if the guard is uninitialized. + optional original_device() const { + return guard_.has_value() ? make_optional(guard_->original_device()) : nullopt; + } + + /// Returns the most recent device that was set using this device guard, + /// either from construction, or via set_device, if the guard is initialized, + /// or nullopt if the guard is uninitialized. + optional current_device() const { + return guard_.has_value() ? make_optional(guard_->current_device()) : nullopt; + } + + /// Restore the original device, resetting this guard to uninitialized state. + void reset() { + guard_.reset(); + } + +private: + optional> guard_; +}; + +}} // namespace c10::impl diff --git a/thirdparty/libtorch/include/c10/core/impl/InlineEvent.h b/thirdparty/libtorch/include/c10/core/impl/InlineEvent.h new file mode 100644 index 0000000000..dfc86173c6 --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/impl/InlineEvent.h @@ -0,0 +1,102 @@ +#pragma once + +#include "c10/core/DeviceType.h" +#include "c10/core/Stream.h" +#include "c10/util/Exception.h" +#include "c10/core/impl/DeviceGuardImplInterface.h" + +namespace c10 { +namespace impl { + +template +struct InlineEvent final { + + InlineEvent() = delete; + InlineEvent( + const DeviceType _device_type, + const EventFlag _flag = EventFlag::PYTORCH_DEFAULT) + : backend_{_device_type}, + device_type_{_device_type}, + flag_{_flag} { } + + // Copy constructor and copy assignment operator (deleted) + InlineEvent(const InlineEvent&) = delete; + InlineEvent& operator=(const InlineEvent&) = delete; + + // Move constructor and move assignment operator + InlineEvent(InlineEvent&& other) + : InlineEvent(other.device_type_, other.flag_) { + swap(std::move(other)); + } + InlineEvent& operator=(InlineEvent&& other) { + swap(std::move(other)); + return *this; + } + + void swap(InlineEvent&& other) { + std::swap(event_, other.event_); + std::swap(backend_, other.backend_); + std::swap(device_type_, other.device_type_); + std::swap(device_index_, other.device_index_); + std::swap(flag_, other.flag_); + std::swap(was_marked_for_recording_, other.was_marked_for_recording_); + } + + ~InlineEvent() noexcept { + if (event_) backend_.destroyEvent(event_, device_index_); + } + + DeviceType device_type() const noexcept { return device_type_; } + DeviceIndex device_index() const noexcept { return device_index_; } + EventFlag flag() const noexcept { return flag_; } + bool was_marked_for_recording() const noexcept { return was_marked_for_recording_; } + + + void recordOnce(const Stream& stream) { + if (!was_marked_for_recording_) record(stream); + } + + void record(const Stream& stream) { + TORCH_CHECK( + stream.device_type() == device_type_, + "Event device type ", + DeviceTypeName(device_type_), + " does not match recording stream's device type ", + DeviceTypeName(stream.device_type()), + "."); + + backend_.record(&event_, stream, device_index_, flag_); + was_marked_for_recording_ = true; + device_index_ = stream.device_index(); + } + + void block(const Stream& stream) const { + if (!was_marked_for_recording_) return; + + TORCH_CHECK( + stream.device_type() == device_type_, + "Event device type ", + DeviceTypeName(device_type_), + " does not match blocking stream's device type ", + DeviceTypeName(stream.device_type()), + "."); + + backend_.block(event_, stream); + } + + bool query() const { + if (!was_marked_for_recording_) return true; + return backend_.queryEvent(event_); + } + +private: + void* event_ = nullptr; + T backend_; + DeviceType device_type_; + DeviceIndex device_index_ = -1; + EventFlag flag_ = EventFlag::PYTORCH_DEFAULT; + bool was_marked_for_recording_ = false; +}; + +} // impl +} // c10 diff --git a/thirdparty/libtorch/include/c10/core/impl/InlineStreamGuard.h b/thirdparty/libtorch/include/c10/core/impl/InlineStreamGuard.h new file mode 100644 index 0000000000..4fbb61d60e --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/impl/InlineStreamGuard.h @@ -0,0 +1,187 @@ +#pragma once + +#include + +namespace c10 { +namespace impl { + +/** + * A StreamGuard is an RAII class that changes the current device + * to the device corresponding to some stream, and changes the + * default stream on that device to be this stream. + * + * InlineStreamGuard is a helper class for implementing StreamGuards. + * See InlineDeviceGuard for guidance on how to use this class. + */ +template +class InlineStreamGuard : private InlineDeviceGuard { +public: + /// No default constructor, see Note [Omitted default constructor from RAII] + explicit InlineStreamGuard() = delete; + + /// Set the current device to the device associated with the passed stream, + /// and set the current stream on that device to the passed stream. + explicit InlineStreamGuard(Stream stream) + : InlineDeviceGuard(stream.device()) + , original_stream_of_original_device_(this->impl_.getStream(original_device())) + , original_stream_of_current_device_(this->impl_.exchangeStream(stream)) + , current_stream_(stream) + {} + + /// This constructor exists purely for testing + template ::value>::type> + explicit InlineStreamGuard(Stream stream, const DeviceGuardImplInterface* impl) + : InlineDeviceGuard(stream.device(), impl ? impl : getDeviceGuardImpl(stream.device_type())) + , original_stream_of_original_device_(this->impl_.getStream(original_device())) + , original_stream_of_current_device_(this->impl_.exchangeStream(stream)) + , current_stream_(stream) + {} + + /// Copy is disallowed + InlineStreamGuard(const InlineStreamGuard&) = delete; + InlineStreamGuard& operator=(const InlineStreamGuard&) = delete; + + /// Move is disallowed, as StreamGuard does not have an uninitialized state, + /// which is required for moves on types with nontrivial destructors. + InlineStreamGuard(InlineStreamGuard&& other) = delete; + InlineStreamGuard& operator=(InlineStreamGuard&& other) = delete; + + ~InlineStreamGuard() { + this->impl_.exchangeStream(original_stream_of_current_device_); + } + + /// Resets the currently set stream to the original stream and + /// the currently set device to the original device. Then, + /// set the current device to the device associated with the passed stream, + /// and set the current stream on that device to the passed stream. + /// + /// NOTE: this implementation may skip some stream/device setting if + /// it can prove that it is unnecessary. + /// + /// WARNING: reset_stream does NOT preserve previously set streams on + /// different devices. If you need to set streams on multiple devices + /// on CUDA, use CUDAMultiStreamGuard instead. + void reset_stream(Stream stream) { + // TODO: make a version that takes an impl argument. Unfortunately, + // that will require SFINAE because impl is only valid for the + // VirtualGuardImpl specialization. + if (stream.device() == this->current_device()) { + this->impl_.exchangeStream(stream); + current_stream_ = stream; + } else { + // Destruct and reconstruct the StreamGuard in-place + this->impl_.exchangeStream(original_stream_of_current_device_); + this->reset_device(stream.device()); + original_stream_of_current_device_ = this->impl_.exchangeStream(stream); + current_stream_ = stream; + } + } + + // It's not clear if set_device should also reset the current stream + // if the device is unchanged; therefore, we don't provide it. + // The situation is somewhat clearer with reset_device, but it's still + // a pretty weird thing to do, so haven't added this either. + + /// Returns the stream of the original device prior to this guard. Subtly, + /// the stream returned here is the original stream of the *original* + /// device; i.e., it's the stream that your computation *would* have + /// been put on, if it hadn't been for this meddling stream guard. + /// This is usually what you want. + Stream original_stream() const { + return original_stream_of_original_device_; + } + + /// Returns the most recent stream that was set using this device guard, + /// either from construction, or via set_stream. + Stream current_stream() const { + return current_stream_; + } + + /// Returns the most recent device that was set using this device guard, + /// either from construction, or via set_device/reset_device/set_index. + Device current_device() const { + return InlineDeviceGuard::current_device(); + } + + /// Returns the device that was set at the most recent reset_stream(), + /// or otherwise the device at construction time. + Device original_device() const { + return InlineDeviceGuard::original_device(); + } + +private: + Stream original_stream_of_original_device_; // what the user probably cares about + Stream original_stream_of_current_device_; // what we need to restore + Stream current_stream_; +}; + +/** + * An OptionalStreamGuard is an RAII class that sets a device to some value on + * initialization, and resets the device to its original value on destruction. + * See InlineOptionalDeviceGuard for more guidance on how to use this class. + */ +template +class InlineOptionalStreamGuard { +public: + /// Creates an uninitialized stream guard. + explicit InlineOptionalStreamGuard() + : guard_() // See Note [Explicit initialization of optional fields] + {} + + /// Set the current device to the device associated with the passed stream, + /// and set the current stream on that device to the passed stream, + /// if the passed stream is not nullopt. + explicit InlineOptionalStreamGuard(optional stream_opt) + : guard_() { + if (stream_opt.has_value()) { + guard_.emplace(stream_opt.value()); + } + } + + /// All constructors of StreamGuard are valid for OptionalStreamGuard + template + explicit InlineOptionalStreamGuard(Args&&... args) + : guard_(in_place, std::forward(args)...) {} + + // See Note [Move construction for RAII guards is tricky] + InlineOptionalStreamGuard(InlineOptionalStreamGuard&& other) = delete; + + // See Note [Move assignment for RAII guards is tricky] + InlineOptionalStreamGuard& operator=(InlineOptionalStreamGuard&& other) = delete; + + /// Resets the currently set stream to the original stream and + /// the currently set device to the original device. Then, + /// set the current device to the device associated with the passed stream, + /// and set the current stream on that device to the passed stream. + /// Initializes the OptionalStreamGuard if it was not previously initialized. + void reset_stream(Stream stream) { + if (guard_.has_value()) { + guard_->reset_stream(stream); + } else { + guard_.emplace(stream); + } + } + + /// Returns the stream that was set at the time the guard was most recently + /// initialized, or nullopt if the guard is uninitialized. + optional original_stream() const { + return guard_.has_value() ? make_optional(guard_->original_stream()) : nullopt; + } + + /// Returns the most recent stream that was set using this stream guard, + /// either from construction, or via reset_stream, if the guard is initialized, + /// or nullopt if the guard is uninitialized. + optional current_stream() const { + return guard_.has_value() ? make_optional(guard_->current_stream()) : nullopt; + } + + /// Restore the original device and stream, resetting this guard to uninitialized state. + void reset() { + guard_.reset(); + } + +private: + optional> guard_; +}; + +}} // namespace c10::impl diff --git a/thirdparty/libtorch/include/c10/core/impl/LocalTensorTypeSet.h b/thirdparty/libtorch/include/c10/core/impl/LocalTensorTypeSet.h new file mode 100644 index 0000000000..728cc8afe3 --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/impl/LocalTensorTypeSet.h @@ -0,0 +1,100 @@ +#pragma once + +#include + +// TLS management for TensorTypeSet (the "local" TensorTypeSet(s)) +// +// This manages two thread-local TensorTypeSets: +// +// - The included type set, which adds a tensor type for consideration +// in dispatch. (For example, you might add ProfilingTensorId to +// the included type set to turn on profiling on all tensor operations.) +// +// - The excluded type set, which disqualifies a tensor type from dispatch. +// (For example, after redispatching on variable, we disqualify +// VariableTensorId so we don't attempt to handle variable again.) +// (Exclusion wins over inclusion.) +// +// NB: Originally, I implemented the excluded type set as storing the inverted +// set, but TLS is defined to be zero-initialized, so this doesn't actually work +// (if it's inverted, you want the set to be -1 initialized). + +namespace c10 { +namespace impl { + +// POD version of LocalTensorTypeSet. Declared here just so that +// we can put it in the guards. +struct C10_API PODLocalTensorTypeSet { + uint64_t included_; + uint64_t excluded_; + + TensorTypeSet included() const { + return TensorTypeSet(TensorTypeSet::RAW, included_); + } + TensorTypeSet excluded() const { + return TensorTypeSet(TensorTypeSet::RAW, excluded_); + } + + void set_included(TensorTypeSet x) { + included_ = x.raw_repr(); + } + void set_excluded(TensorTypeSet x) { + excluded_ = x.raw_repr(); + } +}; +static_assert(std::is_pod::value, "PODLocalTensorTypeSet must be a POD type."); + +struct C10_API LocalTensorTypeSet { + /* implicit */ LocalTensorTypeSet(PODLocalTensorTypeSet x) + : included_(x.included()), excluded_(x.excluded()) {} + TensorTypeSet included_; + TensorTypeSet excluded_; +}; + +C10_API LocalTensorTypeSet tls_local_tensor_type_set(); + +// RAII API for manipulating the thread-local dispatch state. + +class C10_API IncludeTensorTypeIdGuard { +public: + IncludeTensorTypeIdGuard(TensorTypeId); + ~IncludeTensorTypeIdGuard(); +private: + // A little micro-optimization to save us from tls_get_addr call + // on destruction + PODLocalTensorTypeSet* tls_; + TensorTypeId id_; + bool prev_state_; +}; + +class C10_API ExcludeTensorTypeIdGuard { +public: + ExcludeTensorTypeIdGuard(TensorTypeId); + ~ExcludeTensorTypeIdGuard(); +private: + // A little micro-optimization to save us from tls_get_addr call + // on destruction + PODLocalTensorTypeSet* tls_; + TensorTypeId id_; + bool prev_state_; +}; + +// Non-RAII API for manipulating the thread-local dispatch state. +// Please prefer the RAII API. The non-RAII API may be useful when +// the included/excluded state of a given TensorTypeId must span +// many calls from the Python to the C++, so you cannot conveniently +// use an RAII guard. +// +// Example use case: a Python context manager that includes a certain +// TensorTypeId, to ensure ops running under the context manager dispatch +// through that TensorTypeId's registered overrides. +// +// The non-RAII API is less efficient than the RAII guards because both the +// getter and setter will do a tls_getaddr lookup (the RAII struct only needs one!) + +bool tls_is_tensor_type_id_excluded(TensorTypeId x); +void tls_set_tensor_type_id_excluded(TensorTypeId x, bool desired_state); +bool tls_is_tensor_type_id_included(TensorTypeId x); +void tls_set_tensor_type_id_included(TensorTypeId x, bool desired_state); + +}} // namespace c10::impl diff --git a/thirdparty/libtorch/include/c10/core/impl/VirtualGuardImpl.h b/thirdparty/libtorch/include/c10/core/impl/VirtualGuardImpl.h new file mode 100644 index 0000000000..c6b6420501 --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/impl/VirtualGuardImpl.h @@ -0,0 +1,75 @@ +#pragma once + +#include + +namespace c10 { +namespace impl { + +/** + * An implementation of DeviceGuardImplInterface which delegates + * to virtual dispatch on the DeviceGuardImpl registry. + */ +class VirtualGuardImpl final : public DeviceGuardImplInterface { +public: + VirtualGuardImpl(DeviceType device_type) + : impl_(getDeviceGuardImpl(device_type)) {} + // This constructor exists purely for testing + VirtualGuardImpl(const DeviceGuardImplInterface* impl) + : impl_(impl) {} + + // Copying and moving is OK! + + DeviceType type() const override { + return impl_->type(); + } + Device exchangeDevice(Device d) const override { + return impl_->exchangeDevice(d); + } + Device getDevice() const override { + return impl_->getDevice(); + } + void setDevice(Device d) const override { + impl_->setDevice(d); + } + void uncheckedSetDevice(Device d) const noexcept override { + impl_->uncheckedSetDevice(d); + } + Stream getStream(Device d) const noexcept override { + return impl_->getStream(d); + } + Stream getDefaultStream(Device d) const override { + return impl_->getDefaultStream(d); + } + Stream exchangeStream(Stream s) const noexcept override { + return impl_->exchangeStream(s); + } + DeviceIndex deviceCount() const noexcept override { + return impl_->deviceCount(); + } + + // Event functions + void record(void** event, + const Stream& stream, + const DeviceIndex device_index, + const EventFlag flag) const override { + impl_->record(event, stream, device_index, flag); + } + void block( + void* event, + const Stream& stream) const override { + impl_->block(event, stream); + } + bool queryEvent(void* event) const override { + return impl_->queryEvent(event); + } + void destroyEvent( + void* event, + const DeviceIndex device_index) const noexcept override { + impl_->destroyEvent(event, device_index); + } + +private: + const DeviceGuardImplInterface* impl_ = nullptr; +}; + +}} // namespace c10::impl diff --git a/thirdparty/libtorch/include/c10/core/thread_pool.h b/thirdparty/libtorch/include/c10/core/thread_pool.h new file mode 100644 index 0000000000..28cb5f3503 --- /dev/null +++ b/thirdparty/libtorch/include/c10/core/thread_pool.h @@ -0,0 +1,125 @@ +#pragma once + +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include + +namespace c10 { + +// TODO: move this to C10 and make it C10_API +class C10_API TaskThreadPoolBase { + public: + virtual void run(const std::function& func) = 0; + + virtual size_t size() const = 0; + + /** + * The number of available (i.e. idle) threads in this thread pool. + */ + virtual size_t numAvailable() const = 0; + + /** + * Check if the current thread is from the thread pool. + */ + virtual bool inThreadPool() const = 0; + + virtual ~TaskThreadPoolBase() noexcept {} + + static size_t defaultNumThreads() { + auto num_threads = std::thread::hardware_concurrency(); +#if defined(_M_X64) || defined(__x86_64__) + num_threads /= 2; +#endif + return num_threads; + } +}; + +class C10_API ThreadPool : public c10::TaskThreadPoolBase { + protected: + struct task_element_t { + bool run_with_id; + const std::function no_id; + const std::function with_id; + + explicit task_element_t(const std::function& f) + : run_with_id(false), no_id(f), with_id(nullptr) {} + explicit task_element_t(const std::function& f) + : run_with_id(true), no_id(nullptr), with_id(f) {} + }; + + std::queue tasks_; + std::vector threads_; + std::mutex mutex_; + std::condition_variable condition_; + std::condition_variable completed_; + std::atomic_bool running_; + bool complete_; + std::size_t available_; + std::size_t total_; + int numa_node_id_; + + public: + ThreadPool() = delete; + + explicit ThreadPool( + int pool_size, + int numa_node_id = -1, + std::function init_thread = nullptr); + + ~ThreadPool(); + + size_t size() const override; + + size_t numAvailable() const override; + + bool inThreadPool() const override; + + void run(const std::function& func) override; + + template + void runTaskWithID(Task task) { + std::unique_lock lock(mutex_); + + // Set task and signal condition variable so that a worker thread will + // wake up and use the task. + tasks_.push( + task_element_t(static_cast>(task))); + complete_ = false; + condition_.notify_one(); + } + + /// @brief Wait for queue to be empty + void waitWorkComplete(); + + private: + // @brief Entry point for pool threads. + void main_loop(std::size_t index); +}; + +class C10_API TaskThreadPool : public c10::ThreadPool { + public: + explicit TaskThreadPool( + std::size_t pool_size, + int numa_node_id = -1) + : ThreadPool(pool_size, numa_node_id, [numa_node_id](){ + setThreadName("CaffeTaskThread"); + NUMABind(numa_node_id); + }) {} +}; + +C10_DECLARE_SHARED_REGISTRY( + ThreadPoolRegistry, + TaskThreadPoolBase, + int, + int, + bool); + +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/cuda/CUDACachingAllocator.h b/thirdparty/libtorch/include/c10/cuda/CUDACachingAllocator.h new file mode 100644 index 0000000000..31f9d3a167 --- /dev/null +++ b/thirdparty/libtorch/include/c10/cuda/CUDACachingAllocator.h @@ -0,0 +1,125 @@ +#ifndef THC_DEVICE_ALLOCATOR_INC +#define THC_DEVICE_ALLOCATOR_INC + +#include +#include +#include +#include + +#include +#include + +namespace c10 { + +// Caching allocator will execute every registered callback if it unable to find +// block inside of already allocated area. +class C10_CUDA_API FreeMemoryCallback { + public: + virtual ~FreeMemoryCallback() {}; + virtual bool Execute() = 0; +}; + +C10_DECLARE_REGISTRY(FreeCudaMemoryCallbacksRegistry, FreeMemoryCallback); +#define REGISTER_FREE_MEMORY_CALLBACK(name, ...) \ + C10_REGISTER_CLASS(FreeCudaMemoryCallbacksRegistry, name, __VA_ARGS__); + +namespace cuda { + +// TODO: Turn this into an honest to goodness class. I briefly attempted to do +// this, but it was a bit irritating to figure out how to also correctly +// apply pimpl pattern so I didn't have to leak any internal implementation +// details in the header (CUDACachingAllocator could be made a pimpl, but +// you also need to appropriately define a class which is a subclass +// of Allocator. Not impossible, but required a bit more surgery than +// I wanted to do at the time.) +// +// Why is this using a namespace rather than old-style THCCachingAllocator_ +// prefix? Mostly because it made the HIPify rules easier to write; _ is +// not counted as a word boundary, so you would otherwise have to list each +// of these functions. + +namespace CUDACachingAllocator { + +struct Stat { + int64_t current = 0; + int64_t peak = 0; + int64_t allocated = 0; + int64_t freed = 0; +}; + +enum struct StatType : uint64_t { + AGGREGATE = 0, + SMALL_POOL = 1, + LARGE_POOL = 2, + NUM_TYPES = 3 // remember to update this whenever a new stat type is added +}; + +typedef std::array(StatType::NUM_TYPES)> StatArray; + +// Struct containing memory allocator summary statistics for a device. +struct DeviceStats { + // COUNT: allocations requested by client code + StatArray allocation; + // COUNT: number of allocated segments from cudaMalloc(). + StatArray segment; + // COUNT: number of active memory blocks (allocated or used by stream) + StatArray active; + // COUNT: number of inactive, split memory blocks (unallocated but can't be released via cudaFree) + StatArray inactive_split; + + // SUM: bytes requested by client code + StatArray allocated_bytes; + // SUM: bytes reserved by this memory allocator (both free and used) + StatArray reserved_bytes; + // SUM: bytes within active memory blocks + StatArray active_bytes; + // SUM: bytes within inactive, split memory blocks + StatArray inactive_split_bytes; + + // COUNT: total number of failed calls to CUDA malloc necessitating cache flushes. + int64_t num_alloc_retries = 0; + + // COUNT: total number of OOMs (i.e. failed calls to CUDA after cache flush) + int64_t num_ooms = 0; +}; + +// Struct containing info of an allocation block (i.e. a fractional part of a cudaMalloc).. +struct BlockInfo { + int64_t size = 0; + bool allocated = false; + bool active = false; +}; + +// Struct containing info of a memory segment (i.e. one contiguous cudaMalloc). +struct SegmentInfo { + int64_t device = 0; + int64_t address = 0; + int64_t total_size = 0; + int64_t allocated_size = 0; + int64_t active_size = 0; + bool is_large = false; + std::vector blocks; +}; + +C10_CUDA_API void* raw_alloc(size_t nbytes); +C10_CUDA_API void raw_delete(void* ptr); + +C10_CUDA_API Allocator* get(); +C10_CUDA_API void emptyCache(); +C10_CUDA_API void cacheInfo(int dev_id, size_t* cachedAndFree, size_t* largestBlock); +C10_CUDA_API void* getBaseAllocation(void *ptr, size_t *size); +C10_CUDA_API void recordStream(void *ptr, CUDAStream stream); +C10_CUDA_API DeviceStats getDeviceStats(int device); +C10_CUDA_API void resetAccumulatedStats(int device); +C10_CUDA_API void resetPeakStats(int device); +C10_CUDA_API std::vector snapshot(); + +C10_CUDA_API std::mutex* getFreeMutex(); + +C10_CUDA_API std::shared_ptr getIpcDevPtr(std::string handle); + +} // namespace CUDACachingAllocator + +}} // namespace c10::cuda + +#endif diff --git a/thirdparty/libtorch/include/c10/cuda/CUDAException.h b/thirdparty/libtorch/include/c10/cuda/CUDAException.h new file mode 100644 index 0000000000..bdce44f1c6 --- /dev/null +++ b/thirdparty/libtorch/include/c10/cuda/CUDAException.h @@ -0,0 +1,31 @@ +#pragma once + +#include +#include +#include + +// Note [CHECK macro] +// ~~~~~~~~~~~~~~~~~~ +// This is a macro so that AT_ERROR can get accurate __LINE__ +// and __FILE__ information. We could split this into a short +// macro and a function implementation if we pass along __LINE__ +// and __FILE__, but no one has found this worth doing. + +// For CUDA Runtime API +#define C10_CUDA_CHECK(EXPR) \ + do { \ + cudaError_t __err = EXPR; \ + if (__err != cudaSuccess) { \ + auto error_unused C10_UNUSED = cudaGetLastError(); \ + TORCH_CHECK(false, "CUDA error: ", cudaGetErrorString(__err)); \ + } \ + } while (0) + + #define C10_CUDA_CHECK_WARN(EXPR) \ + do { \ + cudaError_t __err = EXPR; \ + if (__err != cudaSuccess) { \ + auto error_unused C10_UNUSED = cudaGetLastError(); \ + TORCH_WARN("CUDA warning: ", cudaGetErrorString(__err)); \ + } \ + } while (0) diff --git a/thirdparty/libtorch/include/c10/cuda/CUDAFunctions.h b/thirdparty/libtorch/include/c10/cuda/CUDAFunctions.h new file mode 100644 index 0000000000..2af6925ce0 --- /dev/null +++ b/thirdparty/libtorch/include/c10/cuda/CUDAFunctions.h @@ -0,0 +1,48 @@ +#pragma once + +// This header provides C++ wrappers around commonly used CUDA API functions. +// The benefit of using C++ here is that we can raise an exception in the +// event of an error, rather than explicitly pass around error codes. This +// leads to more natural APIs. +// +// The naming convention used here matches the naming convention of torch.cuda + +#include + +#include +#include +#include + +namespace c10 { +namespace cuda { + +inline DeviceIndex device_count() noexcept { + int count; + // NB: In the past, we were inconsistent about whether or not this reported + // an error if there were driver problems are not. Based on experience + // interacting with users, it seems that people basically ~never want this + // function to fail; it should just return zero if things are not working. + // Oblige them. + cudaError_t err = cudaGetDeviceCount(&count); + if (err != cudaSuccess) { + // Clear out the error state, so we don't spuriously trigger someone else. + // (This shouldn't really matter, since we won't be running very much CUDA + // code in this regime.) + cudaError_t last_err = cudaGetLastError(); + (void)last_err; + return 0; + } + return static_cast(count); +} + +inline DeviceIndex current_device() { + int cur_device; + C10_CUDA_CHECK(cudaGetDevice(&cur_device)); + return static_cast(cur_device); +} + +inline void set_device(DeviceIndex device) { + C10_CUDA_CHECK(cudaSetDevice(static_cast(device))); +} + +}} // namespace c10::cuda diff --git a/thirdparty/libtorch/include/c10/cuda/CUDAGuard.h b/thirdparty/libtorch/include/c10/cuda/CUDAGuard.h new file mode 100644 index 0000000000..0635922101 --- /dev/null +++ b/thirdparty/libtorch/include/c10/cuda/CUDAGuard.h @@ -0,0 +1,236 @@ +#pragma once + +#include +#include +#include +#include +#include + +#include + +namespace c10 { namespace cuda { + +// This code is kind of boilerplatey. See Note [Whither the DeviceGuard boilerplate] + +/// A variant of DeviceGuard that is specialized for CUDA. It accepts +/// integer indices (interpreting them as CUDA devices) and is a little +/// more efficient than DeviceGuard (it compiles to straight line +/// cudaSetDevice/cudaGetDevice calls); however, it can only be used +/// from code that links against CUDA directly. +struct CUDAGuard { + /// No default constructor; see Note [Omitted default constructor from RAII] + explicit CUDAGuard() = delete; + + /// Set the current CUDA device to the passed device index. + explicit CUDAGuard(DeviceIndex device_index) : guard_(device_index) {} + + /// Sets the current CUDA device to the passed device. Errors if the passed + /// device is not a CUDA device. + explicit CUDAGuard(Device device) : guard_(device) {} + + // Copy is not allowed + CUDAGuard(const CUDAGuard&) = delete; + CUDAGuard& operator=(const CUDAGuard&) = delete; + + // Move is not allowed (there is no uninitialized state) + CUDAGuard(CUDAGuard&& other) = delete; + CUDAGuard& operator=(CUDAGuard&& other) = delete; + + /// Sets the CUDA device to the given device. Errors if the given device + /// is not a CUDA device. + void set_device(Device device) { guard_.set_device(device); } + + /// Sets the CUDA device to the given device. Errors if the given device + /// is not a CUDA device. (This method is provided for uniformity with + /// DeviceGuard). + void reset_device(Device device) { guard_.reset_device(device); } + + /// Sets the CUDA device to the given device index. + void set_index(DeviceIndex device_index) { guard_.set_index(device_index); } + + /// Returns the device that was set upon construction of the guard + Device original_device() const { return guard_.original_device(); } + + /// Returns the last device that was set via `set_device`, if any, otherwise the + /// device passed during construction. + Device current_device() const { return guard_.current_device(); } + + private: + /// The guard for the current device. + c10::impl::InlineDeviceGuard guard_; +}; + +/// A variant of OptionalDeviceGuard that is specialized for CUDA. See +/// CUDAGuard for when you can use this. +struct OptionalCUDAGuard { + /// Create an uninitialized OptionalCUDAGuard. + explicit OptionalCUDAGuard() : guard_() {} + + /// Set the current CUDA device to the passed Device, if it is not nullopt. + explicit OptionalCUDAGuard(optional device_opt) : guard_(device_opt) {} + + /// Set the current CUDA device to the passed device index, if it is not + /// nullopt + explicit OptionalCUDAGuard(optional device_index_opt) : guard_(device_index_opt) {} + + // Copy is not allowed + OptionalCUDAGuard(const OptionalCUDAGuard&) = delete; + OptionalCUDAGuard& operator=(const OptionalCUDAGuard&) = delete; + + // See Note [Move construction for RAII guards is tricky] + OptionalCUDAGuard(OptionalCUDAGuard&& other) = delete; + + // See Note [Move assignment for RAII guards is tricky] + OptionalCUDAGuard& operator=(OptionalCUDAGuard&& other) = delete; + + /// Sets the CUDA device to the given device, initializing the guard if it + /// is not already initialized. Errors if the given device is not a CUDA device. + void set_device(Device device) { guard_.set_device(device); } + + /// Sets the CUDA device to the given device, initializing the guard if it is + /// not already initialized. Errors if the given device is not a CUDA device. + /// (This method is provided for uniformity with OptionalDeviceGuard). + void reset_device(Device device) { guard_.reset_device(device); } + + /// Sets the CUDA device to the given device index, initializing the guard if + /// it is not already initialized. + void set_index(DeviceIndex device_index) { guard_.set_index(device_index); } + + /// Returns the device that was set immediately prior to initialization of the + /// guard, or nullopt if the guard is uninitialized. + optional original_device() const { return guard_.original_device(); } + + /// Returns the most recent device that was set using this device guard, + /// either from construction, or via set_device, if the guard is initialized, + /// or nullopt if the guard is uninitialized. + optional current_device() const { return guard_.current_device(); } + + /// Restore the original CUDA device, resetting this guard to uninitialized state. + void reset() { guard_.reset(); } + +private: + c10::impl::InlineOptionalDeviceGuard guard_; +}; + +/// A variant of StreamGuard that is specialized for CUDA. See CUDAGuard +/// for when you can use this. +struct CUDAStreamGuard { + /// No default constructor, see Note [Omitted default constructor from RAII] + explicit CUDAStreamGuard() = delete; + + /// Set the current CUDA device to the device associated with the passed stream, + /// and set the current CUDA stream on that device to the passed stream. + /// Errors if the Stream is not a CUDA stream. + explicit CUDAStreamGuard(Stream stream) : guard_(stream) {} + + /// Copy is disallowed + CUDAStreamGuard(const CUDAStreamGuard&) = delete; + CUDAStreamGuard& operator=(const CUDAStreamGuard&) = delete; + + /// Move is disallowed, as CUDAStreamGuard does not have an uninitialized state, + /// which is required for moves on types with nontrivial destructors. + CUDAStreamGuard(CUDAStreamGuard&& other) = delete; + CUDAStreamGuard& operator=(CUDAStreamGuard&& other) = delete; + + /// Resets the currently set stream to the original stream and + /// the currently set device to the original device. Then, + /// set the current device to the device associated with the passed stream, + /// and set the current stream on that device to the passed stream. + /// Errors if the stream passed is not a CUDA stream. + /// + /// NOTE: this implementation may skip some stream/device setting if + /// it can prove that it is unnecessary. + /// + /// WARNING: reset_stream does NOT preserve previously set streams on + /// different devices. If you need to set streams on multiple devices + /// on CUDA, use CUDAMultiStreamGuard instead. + void reset_stream(Stream stream) { guard_.reset_stream(stream); } + + /// Returns the CUDA stream that was set at the time the guard was constructed. + CUDAStream original_stream() const { + return CUDAStream(CUDAStream::UNCHECKED, guard_.original_stream()); + } + + /// Returns the most recent CUDA stream that was set using this device guard, + /// either from construction, or via set_stream. + CUDAStream current_stream() const { + return CUDAStream(CUDAStream::UNCHECKED, guard_.current_stream()); + } + + /// Returns the most recent CUDA device that was set using this device guard, + /// either from construction, or via set_device/reset_device/set_index. + Device current_device() const { return guard_.current_device(); } + + /// Returns the CUDA device that was set at the most recent reset_stream(), + /// or otherwise the device at construction time. + Device original_device() const { return guard_.original_device(); } + +private: + c10::impl::InlineStreamGuard guard_; +}; + +/// A variant of OptionalStreamGuard that is specialized for CUDA. See CUDAGuard +/// for when you can use this. +struct OptionalCUDAStreamGuard { + /// Create an uninitialized guard. + explicit OptionalCUDAStreamGuard() : guard_() {} + + /// Set the current CUDA device to the device associated with the passed stream, + /// and set the current CUDA stream on that device to the passed stream. + /// Errors if the Stream is not a CUDA stream. + explicit OptionalCUDAStreamGuard(Stream stream) : guard_(stream) {} + + /// Set the current device to the device associated with the passed stream, + /// and set the current stream on that device to the passed stream, + /// if the passed stream is not nullopt. + explicit OptionalCUDAStreamGuard(optional stream_opt) : guard_(stream_opt) {} + + /// Copy is disallowed + OptionalCUDAStreamGuard(const OptionalCUDAStreamGuard&) = delete; + OptionalCUDAStreamGuard& operator=(const OptionalCUDAStreamGuard&) = delete; + + // See Note [Move construction for RAII guards is tricky] + OptionalCUDAStreamGuard(OptionalCUDAStreamGuard&& other) = delete; + + // See Note [Move assignment for RAII guards is tricky] + OptionalCUDAStreamGuard& operator=(OptionalCUDAStreamGuard&& other) = delete; + + /// Resets the currently set CUDA stream to the original stream and + /// the currently set device to the original device. Then, + /// set the current device to the device associated with the passed stream, + /// and set the current stream on that device to the passed stream. + /// Initializes the guard if it was not previously initialized. + void reset_stream(Stream stream) { guard_.reset_stream(stream); } + + /// Returns the CUDA stream that was set at the time the guard was most recently + /// initialized, or nullopt if the guard is uninitialized. + optional original_stream() const { + auto r = guard_.original_stream(); + if (r.has_value()) { + return make_optional(CUDAStream(CUDAStream::UNCHECKED, r.value())); + } else { + return nullopt; + } + } + + /// Returns the most recent CUDA stream that was set using this stream guard, + /// either from construction, or via reset_stream, if the guard is initialized, + /// or nullopt if the guard is uninitialized. + optional current_stream() const { + auto r = guard_.current_stream(); + if (r.has_value()) { + return make_optional(CUDAStream(CUDAStream::UNCHECKED, r.value())); + } else { + return nullopt; + } + } + + /// Restore the original CUDA device and stream, resetting this guard to uninitialized state. + void reset() { guard_.reset(); } + +private: + c10::impl::InlineOptionalStreamGuard guard_; +}; + +} // namespace cuda +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/cuda/CUDAMacros.h b/thirdparty/libtorch/include/c10/cuda/CUDAMacros.h new file mode 100644 index 0000000000..cd63cec850 --- /dev/null +++ b/thirdparty/libtorch/include/c10/cuda/CUDAMacros.h @@ -0,0 +1,38 @@ +#pragma once + +#ifndef C10_USING_CUSTOM_GENERATED_MACROS +#include +#endif + +// See c10/macros/Export.h for a detailed explanation of what the function +// of these macros are. We need one set of macros for every separate library +// we build. + +#ifdef _WIN32 +#if defined(C10_CUDA_BUILD_SHARED_LIBS) +#define C10_CUDA_EXPORT __declspec(dllexport) +#define C10_CUDA_IMPORT __declspec(dllimport) +#else +#define C10_CUDA_EXPORT +#define C10_CUDA_IMPORT +#endif +#else // _WIN32 +#if defined(__GNUC__) +#define C10_CUDA_EXPORT __attribute__((__visibility__("default"))) +#else // defined(__GNUC__) +#define C10_CUDA_EXPORT +#endif // defined(__GNUC__) +#define C10_CUDA_IMPORT C10_CUDA_EXPORT +#endif // _WIN32 + +// This one is being used by libc10_cuda.so +#ifdef C10_CUDA_BUILD_MAIN_LIB +#define C10_CUDA_API C10_CUDA_EXPORT +#else +#define C10_CUDA_API C10_CUDA_IMPORT +#endif + +/** + * The maximum number of GPUs that we recognizes. + */ +#define C10_COMPILE_TIME_MAX_GPUS 16 diff --git a/thirdparty/libtorch/include/c10/cuda/CUDAMathCompat.h b/thirdparty/libtorch/include/c10/cuda/CUDAMathCompat.h new file mode 100644 index 0000000000..a3b8f6f4ab --- /dev/null +++ b/thirdparty/libtorch/include/c10/cuda/CUDAMathCompat.h @@ -0,0 +1,105 @@ +#pragma once + +/* This file defines math functions compatible across different gpu + * platforms (currently CUDA and HIP). + */ +#if defined(__CUDACC__) || defined(__HIPCC__) + +#include + +#ifdef __HIPCC__ +#define __MATH_FUNCTIONS_DECL__ inline C10_DEVICE +#else /* __HIPCC__ */ +#ifdef __CUDACC_RTC__ +#define __MATH_FUNCTIONS_DECL__ C10_HOST_DEVICE +#else /* __CUDACC_RTC__ */ +#define __MATH_FUNCTIONS_DECL__ static inline C10_HOST_DEVICE +#endif /* __CUDACC_RTC__ */ +#endif /* __HIPCC__ */ + +namespace c10 { +namespace cuda { +namespace compat { + +__MATH_FUNCTIONS_DECL__ float abs(float x) { + return ::fabsf(x); +} +__MATH_FUNCTIONS_DECL__ double abs(double x) { + return ::fabs(x); +} + +__MATH_FUNCTIONS_DECL__ float exp(float x) { + return ::expf(x); +} +__MATH_FUNCTIONS_DECL__ double exp(double x) { + return ::exp(x); +} + +__MATH_FUNCTIONS_DECL__ float floor(float x) { + return ::floorf(x); +} +__MATH_FUNCTIONS_DECL__ double floor(double x) { + return ::floor(x); +} + +__MATH_FUNCTIONS_DECL__ float log(float x) { + return ::logf(x); +} +__MATH_FUNCTIONS_DECL__ double log(double x) { + return ::log(x); +} + +__MATH_FUNCTIONS_DECL__ float max(float x, float y) { + return ::fmaxf(x, y); +} +__MATH_FUNCTIONS_DECL__ double max(double x, double y) { + return ::fmax(x, y); +} + +__MATH_FUNCTIONS_DECL__ float pow(float x, float y) { + return ::powf(x, y); +} +__MATH_FUNCTIONS_DECL__ double pow(double x, double y) { + return ::pow(x, y); +} + +__MATH_FUNCTIONS_DECL__ void sincos(float x, float* sptr, float* cptr) { + return ::sincosf(x, sptr, cptr); +} +__MATH_FUNCTIONS_DECL__ void sincos(double x, double* sptr, double* cptr) { + return ::sincos(x, sptr, cptr); +} + +__MATH_FUNCTIONS_DECL__ float sqrt(float x) { + return ::sqrtf(x); +} +__MATH_FUNCTIONS_DECL__ double sqrt(double x) { + return ::sqrt(x); +} + +__MATH_FUNCTIONS_DECL__ float rsqrt(float x) { + return ::rsqrtf(x); +} +__MATH_FUNCTIONS_DECL__ double rsqrt(double x) { + return ::rsqrt(x); +} + +__MATH_FUNCTIONS_DECL__ float tan(float x) { + return ::tanf(x); +} +__MATH_FUNCTIONS_DECL__ double tan(double x) { + return ::tan(x); +} + +__MATH_FUNCTIONS_DECL__ float normcdf(float x) { + return ::normcdff(x); +} +__MATH_FUNCTIONS_DECL__ double normcdf(double x) { + return ::normcdf(x); +} + +} // namespace compat +} // namespace cuda +} // namespace c10 + +#endif diff --git a/thirdparty/libtorch/include/c10/cuda/CUDAStream.h b/thirdparty/libtorch/include/c10/cuda/CUDAStream.h new file mode 100644 index 0000000000..b23f8aa1c6 --- /dev/null +++ b/thirdparty/libtorch/include/c10/cuda/CUDAStream.h @@ -0,0 +1,228 @@ +#pragma once + +#include +#include + +#include + +#include +#include +#include +#include +#include + +/* +* Stream pool note. +* +* A CUDAStream is an abstraction of an actual cuStream on the GPU. CUDAStreams +* are backed by cuStreams, but they use several pools to minimize the costs +* associated with creating, retaining, and destroying cuStreams. +* +* There are three pools per device, and a device's pools are lazily created. +* +* The first pool contains only the default stream. When the default stream +* is requested it's returned. +* +* The second pool is the "low priority" or "default priority" streams. In +* HIP builds there is no distinction between streams in this pool and streams +* in the third pool (below). There are 32 of these streams per device, and +* when a stream is requested one of these streams is returned round-robin. +* That is, the first stream requested is at index 0, the second at index 1... +* to index 31, then index 0 again. +* +* This means that if 33 low priority streams are requested, the first and +* last streams requested are actually the same stream (under the covers) +* and kernels enqueued on them cannot run concurrently. +* +* The third pool is the "high priority" streams. The third pool acts like +* the second pool except the streams are created with a higher priority. +* +* These pools suggest that stream users should prefer many short-lived streams, +* as the cost of acquiring and releasing streams is effectively zero. If +* many longer-lived streams are required in performance critical scenarios +* then the functionality here may need to be extended to allow, for example, +* "reserving" a subset of the pool so that other streams do not accidentally +* overlap the performance critical streams. +* +* Note: although the notion of "current stream for device" is thread local +* (every OS thread has a separate current stream, as one might expect), +* the stream pool is global across all threads; stream 0 is always stream 0 +* no matter which thread you use it on. Multiple threads can synchronize +* on the same stream. Although the CUDA documentation is not very clear +* on the matter, streams are thread safe; e.g., it is safe to enqueue +* a kernel on the same stream from two different threads. +*/ + +namespace c10 { +namespace cuda { + +// Value object representing a CUDA stream. This is just a wrapper +// around c10::Stream, but it comes with a little extra CUDA-specific +// functionality (conversion to cudaStream_t), and a guarantee that +// the wrapped c10::Stream really is a CUDA stream. +class C10_CUDA_API CUDAStream { +public: + + enum Unchecked { UNCHECKED }; + + /// Construct a CUDAStream from a Stream. This construction is checked, + /// and will raise an error if the Stream is not, in fact, a CUDA stream. + explicit CUDAStream(Stream stream) : stream_(stream) { + TORCH_CHECK(stream_.device_type() == DeviceType::CUDA); + } + + /// Construct a CUDAStream from a Stream with no error checking. + /// This constructor uses the "named" constructor idiom, and can + /// be invoked as: CUDAStream(CUDAStream::UNCHECKED, stream) + explicit CUDAStream(Unchecked, Stream stream) : stream_(stream) {} + + bool operator==(const CUDAStream& other) const noexcept { + return unwrap() == other.unwrap(); + } + + bool operator!=(const CUDAStream& other) const noexcept { + return unwrap() != other.unwrap(); + } + + /// Implicit conversion to cudaStream_t. + operator cudaStream_t() const { return stream(); } + + /// Implicit conversion to Stream (a.k.a., forget that the stream is a + /// CUDA stream). + operator Stream() const { return unwrap(); } + + /// Get the CUDA device index that this stream is associated with. + DeviceIndex device_index() const { return stream_.device_index(); } + + /// Get the full Device that this stream is associated with. The Device + /// is guaranteed to be a CUDA device. + Device device() const { return Device(DeviceType::CUDA, device_index()); } + + /// Return the stream ID corresponding to this particular stream. + StreamId id() const { return stream_.id(); } + + bool query() const { + DeviceGuard guard{stream_.device()}; + cudaError_t err = cudaStreamQuery(stream()); + + if (err == cudaSuccess) { + return true; + } else if (err != cudaErrorNotReady) { + C10_CUDA_CHECK(err); + } + + return false; + } + + void synchronize() const { + DeviceGuard guard{stream_.device()}; + C10_CUDA_CHECK(cudaStreamSynchronize(stream())); + } + + int priority() const { + #ifndef __HIP_PLATFORM_HCC__ + DeviceGuard guard{stream_.device()}; + int priority = 0; + C10_CUDA_CHECK(cudaStreamGetPriority(stream(), &priority)); + return priority; + #else + AT_ERROR("cuStreamGetPriority with HIP is not supported"); + #endif + } + + /// Explicit conversion to cudaStream_t. + cudaStream_t stream() const; + + /// Explicit conversion to Stream. + Stream unwrap() const { return stream_; } + + /// Reversibly pack a CUDAStream into a uint64_t representation. This may + /// be helpful when storing a CUDAStream in a C struct, where you cannot + /// conveniently place the CUDAStream object itself (which is morally + /// equivalent, but unfortunately is not POD due to the fact that it + /// has constructors.) + /// + /// The CUDAStream can be unpacked using unpack(). The format of + /// the uint64_t is unspecified and may be changed. + uint64_t pack() const noexcept { + return stream_.pack(); + } + + // Unpack a CUDAStream from the uint64_t representation generated by pack(). + static CUDAStream unpack(uint64_t bits) { + return CUDAStream(Stream::unpack(bits)); + } + + static std::tuple priority_range() { + #ifndef __HIP_PLATFORM_HCC__ + int least_priority, greatest_priority; + C10_CUDA_CHECK( + cudaDeviceGetStreamPriorityRange(&least_priority, &greatest_priority)); + return std::make_tuple(least_priority, greatest_priority); + #else + AT_ERROR("cuDeviceGetStreamPriorityRange with HIP is not supported"); + #endif + } + + // Deleted for now; use CUDAEvent::block instead + // void synchronize_with(const CUDAEvent& event) const; + +private: + Stream stream_; +}; + +/** + * Get a new stream from the CUDA stream pool. You can think of this + * as "creating" a new stream, but no such creation actually happens; + * instead, streams are preallocated from the pool and returned in a + * round-robin fashion. + * + * You can request a stream from the high priority pool by setting + * isHighPriority to true, or a stream for a specific device by setting device + * (defaulting to the current CUDA stream.) + */ +CAFFE2_API CUDAStream +getStreamFromPool(const bool isHighPriority = false, DeviceIndex device = -1); + +/** + * Get the default CUDA stream, for the passed CUDA device, or for the + * current device if no device index is passed. The default stream is + * where most computation occurs when you aren't explicitly using + * streams. + */ +CAFFE2_API CUDAStream getDefaultCUDAStream(DeviceIndex device_index = -1); + +/** + * Get the current CUDA stream, for the passed CUDA device, or for the + * current device if no device index is passed. The current CUDA stream + * will usually be the default CUDA stream for the device, but it may + * be different if someone called 'setCurrentCUDAStream' or used 'StreamGuard' + * or 'CUDAStreamGuard'. + */ +CAFFE2_API CUDAStream getCurrentCUDAStream(DeviceIndex device_index = -1); + +/** + * Set the current stream on the device of the passed in stream to be + * the passed in stream. Yes, you read that right: this function + * has *nothing* to do with the current device: it toggles the current + * stream of the device of the passed stream. + * + * Confused? Avoid using this function; prefer using 'CUDAStreamGuard' instead + * (which will switch both your current device and current stream in the way you + * expect, and reset it back to its original state afterwards). + */ +CAFFE2_API void setCurrentCUDAStream(CUDAStream stream); + +C10_API std::ostream& operator<<(std::ostream& stream, const CUDAStream& s); + +} // namespace cuda +} // namespace at + +namespace std { + template <> + struct hash { + size_t operator()(c10::cuda::CUDAStream s) const noexcept { + return std::hash{}(s.unwrap()); + } + }; +} // namespace std diff --git a/thirdparty/libtorch/include/c10/cuda/impl/CUDAGuardImpl.h b/thirdparty/libtorch/include/c10/cuda/impl/CUDAGuardImpl.h new file mode 100644 index 0000000000..47bcaf4bbc --- /dev/null +++ b/thirdparty/libtorch/include/c10/cuda/impl/CUDAGuardImpl.h @@ -0,0 +1,154 @@ +#pragma once + +#include +#include +#include + +#include +#include +#include + +#include + +namespace c10 { +namespace cuda { +namespace impl { + +struct CUDAGuardImpl final : public c10::impl::DeviceGuardImplInterface { + static constexpr DeviceType static_type = DeviceType::CUDA; + + CUDAGuardImpl() {} + explicit CUDAGuardImpl(DeviceType t) { + TORCH_INTERNAL_ASSERT(t == DeviceType::CUDA); + } + DeviceType type() const override { + return DeviceType::CUDA; + } + Device exchangeDevice(Device d) const override { + TORCH_INTERNAL_ASSERT(d.type() == DeviceType::CUDA); + Device old_device = getDevice(); + if (old_device.index() != d.index()) { + C10_CUDA_CHECK(cudaSetDevice(d.index())); + } + return old_device; + } + Device getDevice() const override { + int device; + C10_CUDA_CHECK(cudaGetDevice(&device)); + return Device(DeviceType::CUDA, device); + } + void setDevice(Device d) const override { + TORCH_INTERNAL_ASSERT(d.type() == DeviceType::CUDA); + C10_CUDA_CHECK(cudaSetDevice(d.index())); + } + void uncheckedSetDevice(Device d) const noexcept override { + C10_CUDA_CHECK_WARN(cudaSetDevice(d.index())); + } + Stream getStream(Device d) const noexcept override { + return getCurrentCUDAStream(d.index()).unwrap(); + } + Stream getDefaultStream(Device d) const override { + return getDefaultCUDAStream(d.index()); + } + // NB: These do NOT set the current device + Stream exchangeStream(Stream s) const noexcept override { + CUDAStream cs(s); + auto old_stream = getCurrentCUDAStream(s.device().index()); + setCurrentCUDAStream(cs); + return old_stream.unwrap(); + } + DeviceIndex deviceCount() const noexcept override { + return device_count(); + } + + // Event-related functions + void createEvent( + cudaEvent_t* cuda_event, + const EventFlag flag) const { + // Maps PyTorch's Event::Flag to CUDA flag + auto cuda_flag = cudaEventDefault; + switch (flag) { + case EventFlag::PYTORCH_DEFAULT: + case EventFlag::CUDA_EVENT_DISABLE_TIMING: + cuda_flag = cudaEventDisableTiming; + break; + case EventFlag::BACKEND_DEFAULT: + case EventFlag::CUDA_EVENT_DEFAULT: + cuda_flag = cudaEventDefault; + break; + default: + TORCH_CHECK(false, "CUDA event received unknown flag"); + } + + C10_CUDA_CHECK(cudaEventCreateWithFlags(cuda_event, cuda_flag)); + } + + void destroyEvent( + void* event, + const DeviceIndex device_index) const noexcept override { + if (!event) return; + auto cuda_event = static_cast(event); + int orig_device; + C10_CUDA_CHECK_WARN(cudaGetDevice(&orig_device)); + C10_CUDA_CHECK_WARN(cudaSetDevice(device_index)); + C10_CUDA_CHECK_WARN(cudaEventDestroy(cuda_event)); + C10_CUDA_CHECK_WARN(cudaSetDevice(orig_device)); + } + + void record( + void** event, + const Stream& stream, + const DeviceIndex device_index, + const EventFlag flag) const override { + TORCH_CHECK(device_index == -1 || device_index == stream.device_index(), + "Event device index ", + device_index, + " does not match recording stream's device index ", + stream.device_index(), + "."); + + cudaEvent_t cuda_event = static_cast(*event); + CUDAStream cuda_stream{stream}; + + // Moves to stream's device to record + const auto orig_device = getDevice(); + setDevice(stream.device()); + + // Creates the event (lazily) + if (!cuda_event) createEvent(&cuda_event, flag); + C10_CUDA_CHECK(cudaEventRecord(cuda_event, cuda_stream)); + // Makes the void* point to the (possibly just allocated) CUDA event + *event = cuda_event; + + // Resets device + setDevice(orig_device); + } + + void block( + void* event, + const Stream& stream) const override { + if (!event) return; + cudaEvent_t cuda_event = static_cast(event); + CUDAStream cuda_stream{stream}; + const auto orig_device = getDevice(); + setDevice(stream.device()); + C10_CUDA_CHECK(cudaStreamWaitEvent( + cuda_stream, + cuda_event, + /*flags (must be zero)=*/ 0)); + setDevice(orig_device); + } + + // May be called from any device + bool queryEvent(void* event) const override { + if (!event) return true; + cudaEvent_t cuda_event = static_cast(event); + const cudaError_t err = cudaEventQuery(cuda_event); + if (err != cudaErrorNotReady) { + C10_CUDA_CHECK(err); + } + return (err == cudaSuccess); + } +}; + +}}} // namespace c10::cuda::impl diff --git a/thirdparty/libtorch/include/c10/cuda/impl/CUDATest.h b/thirdparty/libtorch/include/c10/cuda/impl/CUDATest.h new file mode 100644 index 0000000000..ccfe38e020 --- /dev/null +++ b/thirdparty/libtorch/include/c10/cuda/impl/CUDATest.h @@ -0,0 +1,11 @@ +#pragma once + +#include + +namespace c10 { +namespace cuda { +namespace impl { + +C10_CUDA_API int c10_cuda_test(); + +}}} /// namespace c10::cuda::impl diff --git a/thirdparty/libtorch/include/c10/cuda/impl/cuda_cmake_macros.h b/thirdparty/libtorch/include/c10/cuda/impl/cuda_cmake_macros.h new file mode 100644 index 0000000000..c363c63d5d --- /dev/null +++ b/thirdparty/libtorch/include/c10/cuda/impl/cuda_cmake_macros.h @@ -0,0 +1,6 @@ +#pragma once + +// Automatically generated header file for the C10 CUDA library. Do not +// include this file directly. Instead, include c10/cuda/CUDAMacros.h + +#define C10_CUDA_BUILD_SHARED_LIBS diff --git a/thirdparty/libtorch/include/c10/macros/Export.h b/thirdparty/libtorch/include/c10/macros/Export.h new file mode 100644 index 0000000000..0a213edd7f --- /dev/null +++ b/thirdparty/libtorch/include/c10/macros/Export.h @@ -0,0 +1,110 @@ +#ifndef C10_MACROS_EXPORT_H_ +#define C10_MACROS_EXPORT_H_ + +/* Header file to define the common scaffolding for exported symbols. + * + * Export is by itself a quite tricky situation to deal with, and if you are + * hitting this file, make sure you start with the background here: + * - Linux: https://gcc.gnu.org/wiki/Visibility + * - Windows: + * https://docs.microsoft.com/en-us/cpp/cpp/dllexport-dllimport?view=vs-2017 + * + * Do NOT include this file directly. Instead, use c10/macros/Macros.h + */ + +// You do not need to edit this part of file unless you are changing the core +// pytorch export abstractions. +// +// This part defines the C10 core export and import macros. This is controlled +// by whether we are building shared libraries or not, which is determined +// during build time and codified in c10/core/cmake_macros.h. +// When the library is built as a shared lib, EXPORT and IMPORT will contain +// visibility attributes. If it is being built as a static lib, then EXPORT +// and IMPORT basically have no effect. + +// As a rule of thumb, you should almost NEVER mix static and shared builds for +// libraries that depend on c10. AKA, if c10 is built as a static library, we +// recommend everything dependent on c10 to be built statically. If c10 is built +// as a shared library, everything dependent on it should be built as shared. In +// the PyTorch project, all native libraries shall use the macro +// C10_BUILD_SHARED_LIB to check whether pytorch is building shared or static +// libraries. + +// For build systems that do not directly depend on CMake and directly build +// from the source directory (such as Buck), one may not have a cmake_macros.h +// file at all. In this case, the build system is responsible for providing +// correct macro definitions corresponding to the cmake_macros.h.in file. +// +// In such scenarios, one should define the macro +// C10_USING_CUSTOM_GENERATED_MACROS +// to inform this header that it does not need to include the cmake_macros.h +// file. + +#ifndef C10_USING_CUSTOM_GENERATED_MACROS +#include "c10/macros/cmake_macros.h" +#endif // C10_USING_CUSTOM_GENERATED_MACROS + +#ifdef _WIN32 +#define C10_HIDDEN +#if defined(C10_BUILD_SHARED_LIBS) +#define C10_EXPORT __declspec(dllexport) +#define C10_IMPORT __declspec(dllimport) +#else +#define C10_EXPORT +#define C10_IMPORT +#endif +#else // _WIN32 +#if defined(__GNUC__) +#define C10_EXPORT __attribute__((__visibility__("default"))) +#define C10_HIDDEN __attribute__((__visibility__("hidden"))) +#else // defined(__GNUC__) +#define C10_EXPORT +#define C10_HIDDEN +#endif // defined(__GNUC__) +#define C10_IMPORT C10_EXPORT +#endif // _WIN32 + +#ifdef NO_EXPORT +#undef C10_EXPORT +#define C10_EXPORT +#endif + +// Definition of an adaptive XX_API macro, that depends on whether you are +// building the library itself or not, routes to XX_EXPORT and XX_IMPORT. +// Basically, you will need to do this for each shared library that you are +// building, and the instruction is as follows: assuming that you are building +// a library called libawesome.so. You should: +// (1) for your cmake target (usually done by "add_library(awesome, ...)"), +// define a macro called AWESOME_BUILD_MAIN_LIB using +// target_compile_options. +// (2) define the AWESOME_API macro similar to the one below. +// And in the source file of your awesome library, use AWESOME_API to +// annotate public symbols. + +// Here, for the C10 library, we will define the macro C10_API for both import +// and export. + +// This one is being used by libc10.so +#ifdef C10_BUILD_MAIN_LIB +#define C10_API C10_EXPORT +#else +#define C10_API C10_IMPORT +#endif + +// This one is being used by libtorch.so +// TODO: rename this to TORCH_API +#ifdef CAFFE2_BUILD_MAIN_LIB +#define CAFFE2_API C10_EXPORT +#else +#define CAFFE2_API C10_IMPORT +#endif + +// This one will eventually be used by libtorch_cuda.so, but for +// now it has the same function as CAFFE2_API +#ifdef CAFFE2_BUILD_MAIN_LIB +#define TORCH_CUDA_API C10_EXPORT +#else +#define TORCH_CUDA_API C10_IMPORT +#endif + +#endif // C10_MACROS_MACROS_H_ diff --git a/thirdparty/libtorch/include/c10/macros/Macros.h b/thirdparty/libtorch/include/c10/macros/Macros.h new file mode 100644 index 0000000000..2b75f09f67 --- /dev/null +++ b/thirdparty/libtorch/include/c10/macros/Macros.h @@ -0,0 +1,275 @@ +#ifndef C10_MACROS_MACROS_H_ +#define C10_MACROS_MACROS_H_ + +/* Main entry for c10/macros. + * + * In your code, include c10/macros/Macros.h directly, instead of individual + * files in this folder. + */ + +// For build systems that do not directly depend on CMake and directly build +// from the source directory (such as Buck), one may not have a cmake_macros.h +// file at all. In this case, the build system is responsible for providing +// correct macro definitions corresponding to the cmake_macros.h.in file. +// +// In such scenarios, one should define the macro +// C10_USING_CUSTOM_GENERATED_MACROS +// to inform this header that it does not need to include the cmake_macros.h +// file. + +#ifndef C10_USING_CUSTOM_GENERATED_MACROS +#include "c10/macros/cmake_macros.h" +#endif // C10_USING_CUSTOM_GENERATED_MACROS + +#include "c10/macros/Export.h" + +// Disable the copy and assignment operator for a class. Note that this will +// disable the usage of the class in std containers. +#define C10_DISABLE_COPY_AND_ASSIGN(classname) \ + classname(const classname&) = delete; \ + classname& operator=(const classname&) = delete + +#define C10_CONCATENATE_IMPL(s1, s2) s1##s2 +#define C10_CONCATENATE(s1, s2) C10_CONCATENATE_IMPL(s1, s2) + +#define C10_MACRO_EXPAND(args) args + +/** + * C10_ANONYMOUS_VARIABLE(str) introduces an identifier starting with + * str and ending with a number that varies with the line. + */ +#ifdef __COUNTER__ +#define C10_ANONYMOUS_VARIABLE(str) C10_CONCATENATE(str, __COUNTER__) +#else +#define C10_ANONYMOUS_VARIABLE(str) C10_CONCATENATE(str, __LINE__) +#endif + + +/// C10_NODISCARD - Warn if a type or return value is discarded. + +// Technically, we should check if __cplusplus > 201402L here, because +// [[nodiscard]] is only defined in C++17. However, some compilers +// we care about don't advertise being C++17 (e.g., clang), but +// support the attribute anyway. In fact, this is not just a good idea, +// it's the law: clang::warn_unused_result doesn't work on nvcc + clang +// and the best workaround for this case is to use [[nodiscard]] +// instead; see https://github.com/pytorch/pytorch/issues/13118 +// +// Note to future editors: if you have noticed that a compiler is +// misbehaving (e.g., it advertises support, but the support doesn't +// actually work, or it is emitting warnings). Some compilers which +// are strict about the matter include MSVC, which will complain: +// +// error C2429: attribute 'nodiscard' requires compiler flag '/std:c++latest' +// +// Exhibits: +// - MSVC 19.14: https://godbolt.org/z/Dzd7gn (requires /std:c++latest) +// - Clang 8.0.0: https://godbolt.org/z/3PYL4Z (always advertises support) +// - gcc 8.3: https://godbolt.org/z/4tLMQS (always advertises support) +#define C10_NODISCARD +#if defined(__has_cpp_attribute) +# if __has_cpp_attribute(nodiscard) +# undef C10_NODISCARD +# define C10_NODISCARD [[nodiscard]] +# endif +// Workaround for llvm.org/PR23435, since clang 3.6 and below emit a spurious +// error when __has_cpp_attribute is given a scoped attribute in C mode. +#elif __cplusplus && defined(__has_cpp_attribute) +# if __has_cpp_attribute(clang::warn_unused_result) +// TODO: It's possible this is still triggering https://github.com/pytorch/pytorch/issues/13118 +// on Windows; if it is, better fix it. +# undef C10_NODISCARD +# define C10_NODISCARD [[clang::warn_unused_result]] +# endif +#endif + +// suppress an unused variable. +#ifdef _MSC_VER +#define C10_UNUSED +#else +#define C10_UNUSED __attribute__((__unused__)) +#endif //_MSC_VER + +#define C10_RESTRICT __restrict + +// Simply define the namespace, in case a dependent library want to refer to +// the c10 namespace but not any nontrivial files. +namespace c10 {} // namespace c10 +namespace c10 { namespace cuda {} } +namespace c10 { namespace hip {} } + +// Since C10 is the core library for caffe2 (and aten), we will simply reroute +// all abstractions defined in c10 to be available in caffe2 as well. +// This is only for backwards compatibility. Please use the symbols from the +// c10 namespace where possible. +namespace caffe2 { using namespace c10; } +namespace at { using namespace c10; } +namespace at { namespace cuda { using namespace c10::cuda; }} + +// WARNING!!! THIS IS A GIANT HACK!!! +// This line means you cannot simultaneously include c10/hip +// and c10/cuda and then use them from the at::cuda namespace. +// This is true in practice, because HIPIFY works inplace on +// files in ATen/cuda, so it assumes that c10::hip is available +// from at::cuda. This namespace makes that happen. When +// HIPIFY is no longer out-of-place, we can switch the cuda +// here to hip and everyone is happy. +namespace at { namespace cuda { using namespace c10::hip; }} + +// C10_NORETURN +#if defined(_MSC_VER) +#define C10_NORETURN __declspec(noreturn) +#else +#define C10_NORETURN __attribute__((noreturn)) +#endif + +// C10_LIKELY/C10_UNLIKELY +// +// These macros provide parentheses, so you can use these macros as: +// +// if C10_LIKELY(some_expr) { +// ... +// } +// +// NB: static_cast to boolean is mandatory in C++, because __builtin_expect +// takes a long argument, which means you may trigger the wrong conversion +// without it. +// +#if defined(__GNUC__) || defined(__ICL) || defined(__clang__) +#define C10_LIKELY(expr) (__builtin_expect(static_cast(expr), 1)) +#define C10_UNLIKELY(expr) (__builtin_expect(static_cast(expr), 0)) +#else +#define C10_LIKELY(expr) (expr) +#define C10_UNLIKELY(expr) (expr) +#endif + +#include +#include + +#if defined(__CUDACC__) || defined(__HIPCC__) +// Designates functions callable from the host (CPU) and the device (GPU) +#define C10_HOST_DEVICE __host__ __device__ +#define C10_DEVICE __device__ +#define C10_HOST __host__ +// constants from (https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#features-and-technical-specifications) +// The maximum number of threads per multiprocessor is 1024 for Turing architecture (7.5) +// but 2048 for previous architectures. You'll get warnings if you exceed these constants. +// Hence, the following macros adjust the input values from the user to resolve potential warnings. +#if __CUDA_ARCH__ >= 750 +constexpr uint32_t CUDA_MAX_THREADS_PER_SM = 1024; +#else +constexpr uint32_t CUDA_MAX_THREADS_PER_SM = 2048; +#endif +// CUDA_MAX_THREADS_PER_BLOCK is same for all architectures currently +constexpr uint32_t CUDA_MAX_THREADS_PER_BLOCK = 1024; +// CUDA_THREADS_PER_BLOCK_FALLBACK is the "canonical fallback" choice of block size. +// 256 is a good number for this fallback and should give good occupancy and +// versatility across all architectures. +constexpr uint32_t CUDA_THREADS_PER_BLOCK_FALLBACK = 256; +// NOTE: if you are thinking of constexpr-ify the inputs to launch bounds, it +// turns out that although __launch_bounds__ can take constexpr, it +// can't take a constexpr that has anything to do with templates. +// Currently we use launch_bounds that depend on template arguments in +// Loops.cuh, Reduce.cuh and LossCTC.cuh. Hence, C10_MAX_THREADS_PER_BLOCK and +// C10_MIN_BLOCKS_PER_SM are kept as macros. +// Suppose you were planning to write __launch_bounds__(a, b), based on your performance tuning on a modern GPU. +// Instead, you should write __launch_bounds__(C10_MAX_THREADS_PER_BLOCK(a), C10_MIN_BLOCKS_PER_SM(a, b)), +// which will also properly respect limits on old architectures. +#define C10_MAX_THREADS_PER_BLOCK(val) (((val) <= CUDA_MAX_THREADS_PER_BLOCK) ? (val) : CUDA_THREADS_PER_BLOCK_FALLBACK) +#define C10_MIN_BLOCKS_PER_SM(threads_per_block, blocks_per_sm) ((((threads_per_block)*(blocks_per_sm) <= CUDA_MAX_THREADS_PER_SM) ? (blocks_per_sm) : ((CUDA_MAX_THREADS_PER_SM + (threads_per_block) - 1) / (threads_per_block)))) +// C10_LAUNCH_BOUNDS is analogous to __launch_bounds__ +#define C10_LAUNCH_BOUNDS_0 __launch_bounds__(256, 4) // default launch bounds that should give good occupancy and versatility across all architectures. +#define C10_LAUNCH_BOUNDS_1(max_threads_per_block) __launch_bounds__((C10_MAX_THREADS_PER_BLOCK((max_threads_per_block)))) +#define C10_LAUNCH_BOUNDS_2(max_threads_per_block, min_blocks_per_sm) __launch_bounds__((C10_MAX_THREADS_PER_BLOCK((max_threads_per_block))), (C10_MIN_BLOCKS_PER_SM((max_threads_per_block), (min_blocks_per_sm)))) +#else +#define C10_HOST_DEVICE +#define C10_HOST +#define C10_DEVICE +#endif + +#ifdef __HIP_PLATFORM_HCC__ +#define C10_HIP_HOST_DEVICE __host__ __device__ +#else +#define C10_HIP_HOST_DEVICE +#endif + +#ifdef __HIP_PLATFORM_HCC__ +#define C10_WARP_SIZE 64 +#else +#define C10_WARP_SIZE 32 +#endif + +#ifdef __APPLE__ +#include +#endif + +#if defined(__ANDROID__) +#define C10_ANDROID 1 +#define C10_MOBILE 1 +#elif ( \ + defined(__APPLE__) && \ + (TARGET_IPHONE_SIMULATOR || TARGET_OS_SIMULATOR || TARGET_OS_IPHONE)) +#define C10_IOS 1 +#define C10_MOBILE 1 +#elif (defined(__APPLE__) && TARGET_OS_MAC) +#define C10_IOS 1 +#endif // ANDROID / IOS / MACOS + +// Portably determine if a type T is trivially copyable or not. +#if __GNUG__ && __GNUC__ < 5 +#define C10_IS_TRIVIALLY_COPYABLE(T) __has_trivial_copy(T) +#else +#define C10_IS_TRIVIALLY_COPYABLE(T) std::is_trivially_copyable::value +#endif + +// AT_CPP14_CONSTEXPR: Make it constexpr if we're in C++14 or later +#if defined(_MSC_VER) && defined(__CUDACC__) && \ + (__CUDACC_VER_MAJOR__ >= 10 || \ + (__CUDACC_VER_MAJOR__ == 9 && __CUDACC_VER_MINOR__ >= 2)) +// workaround: CUDA >= v9.2 compiler cannot compile correctly on Windows. +#define AT_CPP14_CONSTEXPR +#define AT_IS_CPP14_CONSTEXPR 0 +#else +#if defined(__cpp_constexpr) && __cpp_constexpr >= 201304 +#define AT_CPP14_CONSTEXPR constexpr +#define AT_IS_CPP14_CONSTEXPR 1 +#else +#define AT_CPP14_CONSTEXPR +#define AT_IS_CPP14_CONSTEXPR 0 +#endif +#endif + +// We need --expt-relaxed-constexpr in CUDA because of Eigen. This flag allows +// device code in CUDA to call host constexpr functions. Unfortunately, +// the CUDA compiler (at least for CUDA 9.0, 9.1 and 9.2) isn't compatible +// with many of the constexpr things we'd like to do and the device code +// compiler crashes when it sees one of these host-only functions. +// It works when nvcc builds host code, but not when it builds device code +// and notices it can call these constexpr functions from device code. +// As a workaround, we use C10_HOST_CONSTEXPR instead of constexpr for these +// functions. This enables constexpr when compiled on the host and applies +// __host__ when it is compiled on the device in an attempt to stop it from +// being called from device functions. Not sure if the latter works, but +// even if not, it not being constexpr anymore should be enough to stop +// it from being called from device code. +// TODO This occurred in CUDA 9 (9.0 to 9.2). Test if this is fixed in CUDA 10. +#if defined(__CUDA_ARCH__) +#define C10_HOST_CONSTEXPR __host__ +#define C10_HOST_CONSTEXPR_VAR +#define C10_CPP14_HOST_CONSTEXPR __host__ +#else +#define C10_HOST_CONSTEXPR constexpr +#define C10_HOST_CONSTEXPR_VAR constexpr +#define C10_CPP14_HOST_CONSTEXPR AT_CPP14_CONSTEXPR +#endif + +#if !defined(__clang__) && !defined(_MSC_VER) && defined(__GNUC__) && \ + __GNUC__ < 6 +#define CONSTEXPR_EXCEPT_GCC5 +#define IS_NOT_GCC5_CONSTEXPR 0 +#else +#define CONSTEXPR_EXCEPT_GCC5 AT_CPP14_CONSTEXPR +#define IS_NOT_GCC5_CONSTEXPR AT_IS_CPP14_CONSTEXPR +#endif + +#endif // C10_MACROS_MACROS_H_ diff --git a/thirdparty/libtorch/include/c10/macros/cmake_macros.h b/thirdparty/libtorch/include/c10/macros/cmake_macros.h new file mode 100644 index 0000000000..d770487e37 --- /dev/null +++ b/thirdparty/libtorch/include/c10/macros/cmake_macros.h @@ -0,0 +1,22 @@ +#ifndef C10_MACROS_CMAKE_MACROS_H_ +#define C10_MACROS_CMAKE_MACROS_H_ + +// Automatically generated header file for the C10 library. +// Do not include this file directly. Instead, include c10/macros/Macros.h. + +#define C10_BUILD_SHARED_LIBS +/* #undef C10_USE_GLOG */ +/* #undef C10_USE_GFLAGS */ +#define C10_DISABLE_NUMA + +// Used by libtorch mobile build to enable features that are not enabled by +// caffe2 mobile build. Should only use it when necessary as we are committed +// to converging libtorch and caffe2 mobile builds and removing it eventually. +/* #undef FEATURE_TORCH_MOBILE */ + +// If defined it will use static dispatch for ATen operators. +// Should expose this macro for projects including ATen headers to inherient +// the same option. +/* #undef USE_STATIC_DISPATCH */ + +#endif // C10_MACROS_CMAKE_MACROS_H_ diff --git a/thirdparty/libtorch/include/c10/util/AlignOf.h b/thirdparty/libtorch/include/c10/util/AlignOf.h new file mode 100644 index 0000000000..7f99a0718b --- /dev/null +++ b/thirdparty/libtorch/include/c10/util/AlignOf.h @@ -0,0 +1,173 @@ +//===--- AlignOf.h - Portable calculation of type alignment -----*- C++ -*-===// +// +// The LLVM Compiler Infrastructure +// +// This file is distributed under the University of Illinois Open Source +// License. See LICENSE.TXT for details. +// +//===----------------------------------------------------------------------===// +// +// This file defines the AlignedCharArray and AlignedCharArrayUnion classes. +// +//===----------------------------------------------------------------------===// + +// ATen: modified from llvm::AlignOf +// replaced LLVM_ALIGNAS with alignas + +#pragma once + +#include + +namespace c10 { + +/// \struct AlignedCharArray +/// \brief Helper for building an aligned character array type. +/// +/// This template is used to explicitly build up a collection of aligned +/// character array types. We have to build these up using a macro and explicit +/// specialization to cope with MSVC (at least till 2015) where only an +/// integer literal can be used to specify an alignment constraint. Once built +/// up here, we can then begin to indirect between these using normal C++ +/// template parameters. + +// MSVC requires special handling here. +#ifndef _MSC_VER + +template +struct AlignedCharArray { + alignas(Alignment) char buffer[Size]; +}; + +#else // _MSC_VER + +/// \brief Create a type with an aligned char buffer. +template +struct AlignedCharArray; + +// We provide special variations of this template for the most common +// alignments because __declspec(align(...)) doesn't actually work when it is +// a member of a by-value function argument in MSVC, even if the alignment +// request is something reasonably like 8-byte or 16-byte. Note that we can't +// even include the declspec with the union that forces the alignment because +// MSVC warns on the existence of the declspec despite the union member forcing +// proper alignment. + +template +struct AlignedCharArray<1, Size> { + union { + char aligned; + char buffer[Size]; + }; +}; + +template +struct AlignedCharArray<2, Size> { + union { + short aligned; + char buffer[Size]; + }; +}; + +template +struct AlignedCharArray<4, Size> { + union { + int aligned; + char buffer[Size]; + }; +}; + +template +struct AlignedCharArray<8, Size> { + union { + double aligned; + char buffer[Size]; + }; +}; + +// The rest of these are provided with a __declspec(align(...)) and we simply +// can't pass them by-value as function arguments on MSVC. + +#define AT_ALIGNEDCHARARRAY_TEMPLATE_ALIGNMENT(x) \ + template \ + struct AlignedCharArray { \ + __declspec(align(x)) char buffer[Size]; \ + }; + +AT_ALIGNEDCHARARRAY_TEMPLATE_ALIGNMENT(16) +AT_ALIGNEDCHARARRAY_TEMPLATE_ALIGNMENT(32) +AT_ALIGNEDCHARARRAY_TEMPLATE_ALIGNMENT(64) +AT_ALIGNEDCHARARRAY_TEMPLATE_ALIGNMENT(128) + +#undef AT_ALIGNEDCHARARRAY_TEMPLATE_ALIGNMENT + +#endif // _MSC_VER + +namespace detail { +template < + typename T1, + typename T2 = char, + typename T3 = char, + typename T4 = char, + typename T5 = char, + typename T6 = char, + typename T7 = char, + typename T8 = char, + typename T9 = char, + typename T10 = char> +class AlignerImpl { + T1 t1; + T2 t2; + T3 t3; + T4 t4; + T5 t5; + T6 t6; + T7 t7; + T8 t8; + T9 t9; + T10 t10; + + AlignerImpl() = delete; +}; + +template < + typename T1, + typename T2 = char, + typename T3 = char, + typename T4 = char, + typename T5 = char, + typename T6 = char, + typename T7 = char, + typename T8 = char, + typename T9 = char, + typename T10 = char> +union SizerImpl { + char arr1[sizeof(T1)], arr2[sizeof(T2)], arr3[sizeof(T3)], arr4[sizeof(T4)], + arr5[sizeof(T5)], arr6[sizeof(T6)], arr7[sizeof(T7)], arr8[sizeof(T8)], + arr9[sizeof(T9)], arr10[sizeof(T10)]; +}; +} // end namespace detail + +/// \brief This union template exposes a suitably aligned and sized character +/// array member which can hold elements of any of up to ten types. +/// +/// These types may be arrays, structs, or any other types. The goal is to +/// expose a char array buffer member which can be used as suitable storage for +/// a placement new of any of these types. Support for more than ten types can +/// be added at the cost of more boilerplate. +template < + typename T1, + typename T2 = char, + typename T3 = char, + typename T4 = char, + typename T5 = char, + typename T6 = char, + typename T7 = char, + typename T8 = char, + typename T9 = char, + typename T10 = char> +struct AlignedCharArrayUnion + : AlignedCharArray< + alignof(detail::AlignerImpl), + sizeof(::c10::detail:: + SizerImpl)> {}; +} // end namespace c10 diff --git a/thirdparty/libtorch/include/c10/util/Array.h b/thirdparty/libtorch/include/c10/util/Array.h new file mode 100644 index 0000000000..ce4c3aa22d --- /dev/null +++ b/thirdparty/libtorch/include/c10/util/Array.h @@ -0,0 +1,323 @@ +/** +* This file is based on the std::array implementation of libstdc++ at +* https://gcc.gnu.org/onlinedocs/gcc-7.1.0/libstdc++/api/a01056_source.html +* +* Changes: +* - isolate, i.e. remove dependencies on internal libstdc++ stuff +* - use c++17 behavior even in c++11 or c++14 +* - remove std::swappable special case because that doesn't work with MSVC +* - constexpr more things +* - add some features like prepend/tail +* +* If using std::array at runtime, feel free to either keep using std::array or use this one - it doesn't really matter. +* For compile time computations, this one here is preferred because std::array in C++11 +* misses some constexpr specifiers, forcing these methods to be called at runtime instead of compile time. +*/ + +// Copyright (C) 2007-2017 Free Software Foundation, Inc. +// +// This file is part of the GNU ISO C++ Library. This library is free +// software; you can redistribute it and/or modify it under the +// terms of the GNU General Public License as published by the +// Free Software Foundation; either version 3, or (at your option) +// any later version. + +// This library is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +// GNU General Public License for more details. + +// Under Section 7 of GPL version 3, you are granted additional +// permissions described in the GCC Runtime Library Exception, version +// 3.1, as published by the Free Software Foundation. + +// You should have received a copy of the GNU General Public License and +// a copy of the GCC Runtime Library Exception along with this program; +// see the files COPYING3 and COPYING.RUNTIME respectively. If not, see +// . + +#pragma once + +#include +#include +#include +#include +#include + +namespace c10 { namespace guts { + +namespace detail { +template +struct __array_traits final { + using _Type = _Tp[_Nm]; + + static constexpr _Tp& _S_ref(const _Type& __t, std::size_t __n) noexcept { + return const_cast<_Tp&>(__t[__n]); + } + + static constexpr _Tp* _S_ptr(const _Type& __t) noexcept { + return const_cast<_Tp*>(__t); + } +}; + +template +struct __array_traits<_Tp, 0> final { + struct _Type final {}; + + static constexpr _Tp& _S_ref(const _Type& __t, std::size_t) noexcept { + return *_S_ptr(__t); + } + + static constexpr _Tp* _S_ptr(const _Type&) noexcept { + return nullptr; + } +}; + +[[noreturn]] inline void __throw_out_of_range(std::string msg) { + throw std::out_of_range(std::move(msg)); +} +} + +template +class array final { +public: + using value_type = _Tp; + using pointer = value_type*; + using const_pointer = const value_type*; + using reference = value_type&; + using const_reference = const value_type&; + using iterator = value_type*; + using const_iterator = const value_type*; + using size_type = std::size_t; + using difference_type = std::ptrdiff_t; + using reverse_iterator = std::reverse_iterator; + using const_reverse_iterator = std::reverse_iterator; + +private: + using _AT_Type = detail::__array_traits<_Tp, _Nm>; +public: // needs to be public member for aggregate initialization + typename _AT_Type::_Type _M_elems; + +public: + // No explicit construct/copy/destroy for aggregate type. + + // DR 776. + AT_CPP14_CONSTEXPR void fill(const value_type& __u) + { std::fill_n(begin(), size(), __u); } + + AT_CPP14_CONSTEXPR void swap(array& __other) + { std::swap_ranges(begin(), end(), __other.begin()); } + + // Iterators. + AT_CPP14_CONSTEXPR iterator begin() noexcept + { return iterator(data()); } + + constexpr const_iterator begin() const noexcept + { return const_iterator(data()); } + + AT_CPP14_CONSTEXPR iterator end() noexcept + { return iterator(data() + _Nm); } + + constexpr const_iterator end() const noexcept + { return const_iterator(data() + _Nm); } + + AT_CPP14_CONSTEXPR reverse_iterator rbegin() noexcept + { return reverse_iterator(end()); } + + constexpr const_reverse_iterator rbegin() const noexcept + { return const_reverse_iterator(end()); } + + AT_CPP14_CONSTEXPR reverse_iterator rend() noexcept + { return reverse_iterator(begin()); } + + constexpr const_reverse_iterator rend() const noexcept + { return const_reverse_iterator(begin()); } + + constexpr const_iterator cbegin() const noexcept + { return const_iterator(data()); } + + constexpr const_iterator cend() const noexcept + { return const_iterator(data() + _Nm); } + + constexpr const_reverse_iterator crbegin() const noexcept + { return const_reverse_iterator(end()); } + + constexpr const_reverse_iterator crend() const noexcept + { return const_reverse_iterator(begin()); } + + // Capacity. + constexpr size_type size() const noexcept { return _Nm; } + + constexpr size_type max_size() const noexcept { return _Nm; } + + constexpr bool empty() const noexcept { return size() == 0; } + + // Element access. + AT_CPP14_CONSTEXPR reference operator[](size_type __n) noexcept + { return _AT_Type::_S_ref(_M_elems, __n); } + + constexpr const_reference operator[](size_type __n) const noexcept + { return _AT_Type::_S_ref(_M_elems, __n); } + + AT_CPP14_CONSTEXPR reference at(size_type __n) { + if (__n >= _Nm) { + detail::__throw_out_of_range(std::string() + + "array::at: __n (which is " + to_string(__n) + ") " + + ">= _Nm (which is " + to_string(_Nm) + ")"); + } + return _AT_Type::_S_ref(_M_elems, __n); + } + + constexpr const_reference at(size_type __n) const { + // Result of conditional expression must be an lvalue so use + // boolean ? lvalue : (throw-expr, lvalue) + return __n < _Nm ? _AT_Type::_S_ref(_M_elems, __n) + : (detail::__throw_out_of_range(std::string() + + "array::at: __n (which is " + to_string(__n) + ") " + + ">= _Nm (which is " + to_string(_Nm) + ")"), + _AT_Type::_S_ref(_M_elems, 0)); + } + + AT_CPP14_CONSTEXPR reference front() noexcept + { return *begin(); } + + constexpr const_reference front() const noexcept + { return _AT_Type::_S_ref(_M_elems, 0); } + + AT_CPP14_CONSTEXPR reference back() noexcept + { return _Nm ? *(end() - 1) : *end(); } + + constexpr const_reference back() const noexcept + { + return _Nm ? _AT_Type::_S_ref(_M_elems, _Nm - 1) + : _AT_Type::_S_ref(_M_elems, 0); + } + + AT_CPP14_CONSTEXPR pointer data() noexcept + { return _AT_Type::_S_ptr(_M_elems); } + + constexpr const_pointer data() const noexcept + { return _AT_Type::_S_ptr(_M_elems); } +}; + +#if defined(__cpp_deduction_guides) && __cpp_deduction_guides >= 201606 + template + array(_Tp, _Up...) -> + array::value && ...), _Tp>, 1 + sizeof...(_Up)>; +#endif + +// Array comparisons. +namespace detail { +template +constexpr inline bool array_equals_(const array& lhs, const array& rhs, size_t current_index) { + return (current_index == N) + ? true + : (lhs.at(current_index) == rhs.at(current_index) && array_equals_(lhs, rhs, current_index + 1)); +} +template +constexpr inline bool array_less_(const array& lhs, const array& rhs, size_t current_index) { + return (current_index == N) + ? false + : (lhs.at(current_index) < rhs.at(current_index) || array_less_(lhs, rhs, current_index + 1)); +} +} +template +constexpr inline bool operator==(const array<_Tp, _Nm>& __one, const array<_Tp, _Nm>& __two) +{ return detail::array_equals_(__one, __two, 0); } + +template +constexpr inline bool operator!=(const array<_Tp, _Nm>& __one, const array<_Tp, _Nm>& __two) +{ return !(__one == __two); } + +template +constexpr inline bool operator<(const array<_Tp, _Nm>& __a, const array<_Tp, _Nm>& __b) +{ return detail::array_less_(__a, __b, 0); } + +template +constexpr inline bool operator>(const array<_Tp, _Nm>& __one, const array<_Tp, _Nm>& __two) +{ return __two < __one; } + +template +constexpr inline bool operator<=(const array<_Tp, _Nm>& __one, const array<_Tp, _Nm>& __two) +{ return !(__one > __two); } + +template +constexpr inline bool operator>=(const array<_Tp, _Nm>& __one, const array<_Tp, _Nm>& __two) +{ return !(__one < __two); } + +// Specialized algorithms. +template +inline void swap(array<_Tp, _Nm>& __one, array<_Tp, _Nm>& __two) noexcept(noexcept(__one.swap(__two))) +{ __one.swap(__two); } + +template +constexpr _Tp& get(array<_Tp, _Nm>& __arr) noexcept { + static_assert(_Int < _Nm, "array index is within bounds"); + return detail::__array_traits<_Tp, _Nm>::_S_ref(__arr._M_elems, _Int); +} + +template +constexpr _Tp&& get(array<_Tp, _Nm>&& __arr) noexcept +{ + static_assert(_Int < _Nm, "array index is within bounds"); + return guts::move(get<_Int>(__arr)); +} + +template +constexpr const _Tp& get(const array<_Tp, _Nm>& __arr) noexcept +{ + static_assert(_Int < _Nm, "array index is within bounds"); + return detail::__array_traits<_Tp, _Nm>::_S_ref(__arr._M_elems, _Int); +} + +/** + * Some added features not available in std::array. + * Only call these at compile time, they're slow if called at runtime. + * Examples: + * tail({2, 3, 4}) == {3, 4} + * prepend(2, {3, 4}) == {2, 3, 4} + */ +namespace detail { +template +constexpr inline array tail_(const array& arg, guts::index_sequence) { + static_assert(sizeof...(INDEX) == N-1, "invariant"); + return {{get(arg)...}}; +} +} +template +constexpr inline array tail(const array& arg) { + static_assert(N > 0, "Can only call tail() on an array with at least one element"); + return detail::tail_(arg, guts::make_index_sequence()); +} + +namespace detail { +template +constexpr inline array prepend_(T&& head, const array& tail, guts::index_sequence) { + return {{guts::forward(head), get(tail)...}}; +} +} +template +constexpr inline array prepend(T&& head, const array& tail) { + return detail::prepend_(guts::forward(head), tail, guts::make_index_sequence()); +} + +/** + * Convert a C array into a std::array. + * Example: + * int source[3] = {2, 3, 4}; + * std::array target = to_std_array(source); + */ + +namespace detail { +template +constexpr array to_array_(const T (&arr)[N], guts::index_sequence) { + return {{arr[INDEX]...}}; +} +} + +template +constexpr array to_array(const T (&arr)[N]) { + return detail::to_array_(arr, guts::make_index_sequence()); +} + +}} diff --git a/thirdparty/libtorch/include/c10/util/ArrayRef.h b/thirdparty/libtorch/include/c10/util/ArrayRef.h new file mode 100644 index 0000000000..5b1298fa21 --- /dev/null +++ b/thirdparty/libtorch/include/c10/util/ArrayRef.h @@ -0,0 +1,280 @@ +//===--- ArrayRef.h - Array Reference Wrapper -------------------*- C++ -*-===// +// +// The LLVM Compiler Infrastructure +// +// This file is distributed under the University of Illinois Open Source +// License. See LICENSE.TXT for details. +// +//===----------------------------------------------------------------------===// + +// ATen: modified from llvm::ArrayRef. +// removed llvm-specific functionality +// removed some implicit const -> non-const conversions that rely on +// complicated std::enable_if meta-programming +// removed a bunch of slice variants for simplicity... + +#pragma once + +#include +#include +#include +#include + +#include +#include +#include + +namespace c10 { + +/// ArrayRef - Represent a constant reference to an array (0 or more elements +/// consecutively in memory), i.e. a start pointer and a length. It allows +/// various APIs to take consecutive elements easily and conveniently. +/// +/// This class does not own the underlying data, it is expected to be used in +/// situations where the data resides in some other buffer, whose lifetime +/// extends past that of the ArrayRef. For this reason, it is not in general +/// safe to store an ArrayRef. +/// +/// This is intended to be trivially copyable, so it should be passed by +/// value. +template +class ArrayRef final { + public: + using iterator = const T*; + using const_iterator = const T*; + using size_type = size_t; + using value_type = T; + + using reverse_iterator = std::reverse_iterator; + + private: + /// The start of the array, in an external buffer. + const T* Data; + + /// The number of elements. + size_type Length; + + public: + /// @name Constructors + /// @{ + + /// Construct an empty ArrayRef. + /* implicit */ constexpr ArrayRef() : Data(nullptr), Length(0) {} + + /// Construct an ArrayRef from a single element. + // TODO Make this explicit + constexpr ArrayRef(const T& OneElt) : Data(&OneElt), Length(1) {} + + /// Construct an ArrayRef from a pointer and length. + constexpr ArrayRef(const T* data, size_t length) + : Data(data), Length(length) {} + + /// Construct an ArrayRef from a range. + constexpr ArrayRef(const T* begin, const T* end) + : Data(begin), Length(end - begin) {} + + /// Construct an ArrayRef from a SmallVector. This is templated in order to + /// avoid instantiating SmallVectorTemplateCommon whenever we + /// copy-construct an ArrayRef. + template + /* implicit */ ArrayRef(const SmallVectorTemplateCommon& Vec) + : Data(Vec.data()), Length(Vec.size()) {} + + /// Construct an ArrayRef from a std::vector. + // The enable_if stuff here makes sure that this isn't used for std::vector, + // because ArrayRef can't work on a std::vector bitfield. + template + /* implicit */ ArrayRef(const std::vector& Vec) + : Data(Vec.data()), Length(Vec.size()) { + static_assert(!std::is_same::value, "ArrayRef cannot be constructed from a std::vector bitfield."); + } + + /// Construct an ArrayRef from a std::array + template + /* implicit */ constexpr ArrayRef(const std::array& Arr) + : Data(Arr.data()), Length(N) {} + + /// Construct an ArrayRef from a C array. + template + /* implicit */ constexpr ArrayRef(const T (&Arr)[N]) : Data(Arr), Length(N) {} + + /// Construct an ArrayRef from a std::initializer_list. + /* implicit */ constexpr ArrayRef(const std::initializer_list& Vec) + : Data(std::begin(Vec) == std::end(Vec) ? static_cast(nullptr) : std::begin(Vec)), + Length(Vec.size()) {} + + /// @} + /// @name Simple Operations + /// @{ + + constexpr iterator begin() const { + return Data; + } + constexpr iterator end() const { + return Data + Length; + } + + // These are actually the same as iterator, since ArrayRef only + // gives you const iterators. + constexpr const_iterator cbegin() const { + return Data; + } + constexpr const_iterator cend() const { + return Data + Length; + } + + constexpr reverse_iterator rbegin() const { + return reverse_iterator(end()); + } + constexpr reverse_iterator rend() const { + return reverse_iterator(begin()); + } + + /// empty - Check if the array is empty. + constexpr bool empty() const { + return Length == 0; + } + + constexpr const T* data() const { + return Data; + } + + /// size - Get the array size. + constexpr size_t size() const { + return Length; + } + + /// front - Get the first element. + C10_CPP14_HOST_CONSTEXPR const T& front() const { + TORCH_CHECK(!empty(), "ArrayRef: attempted to access front() of empty list"); + return Data[0]; + } + + /// back - Get the last element. + C10_CPP14_HOST_CONSTEXPR const T& back() const { + TORCH_CHECK(!empty(), "ArrayRef: attempted to access back() of empty list"); + return Data[Length - 1]; + } + + /// equals - Check for element-wise equality. + constexpr bool equals(ArrayRef RHS) const { + return Length == RHS.Length && std::equal(begin(), end(), RHS.begin()); + } + + /// slice(n, m) - Chop off the first N elements of the array, and keep M + /// elements in the array. + C10_CPP14_HOST_CONSTEXPR ArrayRef slice(size_t N, size_t M) const { + TORCH_CHECK( + N + M <= size(), + "ArrayRef: invalid slice, N = ", + N, + "; M = ", + M, + "; size = ", + size()); + return ArrayRef(data() + N, M); + } + + /// slice(n) - Chop off the first N elements of the array. + constexpr ArrayRef slice(size_t N) const { + return slice(N, size() - N); + } + + /// @} + /// @name Operator Overloads + /// @{ + constexpr const T& operator[](size_t Index) const { + return Data[Index]; + } + + /// Vector compatibility + C10_CPP14_HOST_CONSTEXPR const T& at(size_t Index) const { + TORCH_CHECK( + Index < Length, + "ArrayRef: invalid index Index = ", + Index, + "; Length = ", + Length); + return Data[Index]; + } + + /// Disallow accidental assignment from a temporary. + /// + /// The declaration here is extra complicated so that "arrayRef = {}" + /// continues to select the move assignment operator. + template + typename std::enable_if::value, ArrayRef>::type& + operator=(U&& Temporary) = delete; + + /// Disallow accidental assignment from a temporary. + /// + /// The declaration here is extra complicated so that "arrayRef = {}" + /// continues to select the move assignment operator. + template + typename std::enable_if::value, ArrayRef>::type& + operator=(std::initializer_list) = delete; + + /// @} + /// @name Expensive Operations + /// @{ + std::vector vec() const { + return std::vector(Data, Data + Length); + } + + /// @} +}; + +template +std::ostream& operator<<(std::ostream & out, ArrayRef list) { + int i = 0; + out << "["; + for(auto e : list) { + if (i++ > 0) + out << ", "; + out << e; + } + out << "]"; + return out; +} + +// WARNING: Template instantiation will NOT be willing to do an implicit +// conversions to get you to an c10::ArrayRef, which is why we need so +// many overloads. + +template +bool operator==(c10::ArrayRef a1, c10::ArrayRef a2) { + return a1.equals(a2); +} + +template +bool operator!=(c10::ArrayRef a1, c10::ArrayRef a2) { + return !a1.equals(a2); +} + +template +bool operator==(const std::vector& a1, c10::ArrayRef a2) { + return c10::ArrayRef(a1).equals(a2); +} + +template +bool operator!=(const std::vector& a1, c10::ArrayRef a2) { + return !c10::ArrayRef(a1).equals(a2); +} + +template +bool operator==(c10::ArrayRef a1, const std::vector& a2) { + return a1.equals(c10::ArrayRef(a2)); +} + +template +bool operator!=(c10::ArrayRef a1, const std::vector& a2) { + return !a1.equals(c10::ArrayRef(a2)); +} + +using IntArrayRef = ArrayRef; + +// This alias is deprecated because it doesn't make ownership +// semantics obvious. Use IntArrayRef instead! +C10_DEFINE_DEPRECATED_USING(IntList, ArrayRef) + +} // namespace c10 diff --git a/thirdparty/libtorch/include/c10/util/BFloat16-inl.h b/thirdparty/libtorch/include/c10/util/BFloat16-inl.h new file mode 100644 index 0000000000..ab366d000d --- /dev/null +++ b/thirdparty/libtorch/include/c10/util/BFloat16-inl.h @@ -0,0 +1,267 @@ +#pragma once + +#include +#include + +namespace c10 { + +/// Constructors +inline C10_HOST_DEVICE BFloat16::BFloat16(float value) { + // RNE by default + x = detail::round_to_nearest_even(value); +} + +/// Implicit conversions +inline C10_HOST_DEVICE BFloat16::operator float() const { + return detail::f32_from_bits(x); +} + +/// Arithmetic + +inline C10_HOST_DEVICE BFloat16 operator+(const BFloat16& a, const BFloat16& b) { + return static_cast(a) + static_cast(b); +} + +inline C10_HOST_DEVICE BFloat16 operator-(const BFloat16& a, const BFloat16& b) { + return static_cast(a) - static_cast(b); +} + +inline C10_HOST_DEVICE BFloat16 operator*(const BFloat16& a, const BFloat16& b) { + return static_cast(a) * static_cast(b); +} + +inline C10_HOST_DEVICE BFloat16 operator/(const BFloat16& a, const BFloat16& b) { + return static_cast(a) / static_cast(b); +} + +inline C10_HOST_DEVICE BFloat16 operator-(const BFloat16& a) { + return -static_cast(a); +} + +inline C10_HOST_DEVICE BFloat16& operator+=(BFloat16& a, const BFloat16& b) { + a = a + b; + return a; +} + +inline C10_HOST_DEVICE BFloat16& operator-=(BFloat16& a, const BFloat16& b) { + a = a - b; + return a; +} + +inline C10_HOST_DEVICE BFloat16& operator*=(BFloat16& a, const BFloat16& b) { + a = a * b; + return a; +} + +inline C10_HOST_DEVICE BFloat16& operator/=(BFloat16& a, const BFloat16& b) { + a = a / b; + return a; +} + +inline C10_HOST_DEVICE BFloat16& operator|(BFloat16& a, const BFloat16& b) { + a.x = a.x | b.x; + return a; +} + +inline C10_HOST_DEVICE BFloat16& operator^(BFloat16& a, const BFloat16& b) { + a.x = a.x ^ b.x; + return a; +} + +inline C10_HOST_DEVICE BFloat16& operator&(BFloat16& a, const BFloat16& b) { + a.x = a.x & b.x; + return a; +} + +/// Arithmetic with floats + +inline C10_HOST_DEVICE float operator+(BFloat16 a, float b) { + return static_cast(a) + b; +} +inline C10_HOST_DEVICE float operator-(BFloat16 a, float b) { + return static_cast(a) - b; +} +inline C10_HOST_DEVICE float operator*(BFloat16 a, float b) { + return static_cast(a) * b; +} +inline C10_HOST_DEVICE float operator/(BFloat16 a, float b) { + return static_cast(a) / b; +} + +inline C10_HOST_DEVICE float operator+(float a, BFloat16 b) { + return a + static_cast(b); +} +inline C10_HOST_DEVICE float operator-(float a, BFloat16 b) { + return a - static_cast(b); +} +inline C10_HOST_DEVICE float operator*(float a, BFloat16 b) { + return a * static_cast(b); +} +inline C10_HOST_DEVICE float operator/(float a, BFloat16 b) { + return a / static_cast(b); +} + +inline C10_HOST_DEVICE float& operator+=(float& a, const BFloat16& b) { + return a += static_cast(b); +} +inline C10_HOST_DEVICE float& operator-=(float& a, const BFloat16& b) { + return a -= static_cast(b); +} +inline C10_HOST_DEVICE float& operator*=(float& a, const BFloat16& b) { + return a *= static_cast(b); +} +inline C10_HOST_DEVICE float& operator/=(float& a, const BFloat16& b) { + return a /= static_cast(b); +} + +/// Arithmetic with doubles + +inline C10_HOST_DEVICE double operator+(BFloat16 a, double b) { + return static_cast(a) + b; +} +inline C10_HOST_DEVICE double operator-(BFloat16 a, double b) { + return static_cast(a) - b; +} +inline C10_HOST_DEVICE double operator*(BFloat16 a, double b) { + return static_cast(a) * b; +} +inline C10_HOST_DEVICE double operator/(BFloat16 a, double b) { + return static_cast(a) / b; +} + +inline C10_HOST_DEVICE double operator+(double a, BFloat16 b) { + return a + static_cast(b); +} +inline C10_HOST_DEVICE double operator-(double a, BFloat16 b) { + return a - static_cast(b); +} +inline C10_HOST_DEVICE double operator*(double a, BFloat16 b) { + return a * static_cast(b); +} +inline C10_HOST_DEVICE double operator/(double a, BFloat16 b) { + return a / static_cast(b); +} + +/// Arithmetic with ints + +inline C10_HOST_DEVICE BFloat16 operator+(BFloat16 a, int b) { + return a + static_cast(b); +} +inline C10_HOST_DEVICE BFloat16 operator-(BFloat16 a, int b) { + return a - static_cast(b); +} +inline C10_HOST_DEVICE BFloat16 operator*(BFloat16 a, int b) { + return a * static_cast(b); +} +inline C10_HOST_DEVICE BFloat16 operator/(BFloat16 a, int b) { + return a / static_cast(b); +} + +inline C10_HOST_DEVICE BFloat16 operator+(int a, BFloat16 b) { + return static_cast(a) + b; +} +inline C10_HOST_DEVICE BFloat16 operator-(int a, BFloat16 b) { + return static_cast(a) - b; +} +inline C10_HOST_DEVICE BFloat16 operator*(int a, BFloat16 b) { + return static_cast(a) * b; +} +inline C10_HOST_DEVICE BFloat16 operator/(int a, BFloat16 b) { + return static_cast(a) / b; +} + +//// Arithmetic with int64_t + +inline C10_HOST_DEVICE BFloat16 operator+(BFloat16 a, int64_t b) { + return a + static_cast(b); +} +inline C10_HOST_DEVICE BFloat16 operator-(BFloat16 a, int64_t b) { + return a - static_cast(b); +} +inline C10_HOST_DEVICE BFloat16 operator*(BFloat16 a, int64_t b) { + return a * static_cast(b); +} +inline C10_HOST_DEVICE BFloat16 operator/(BFloat16 a, int64_t b) { + return a / static_cast(b); +} + +inline C10_HOST_DEVICE BFloat16 operator+(int64_t a, BFloat16 b) { + return static_cast(a) + b; +} +inline C10_HOST_DEVICE BFloat16 operator-(int64_t a, BFloat16 b) { + return static_cast(a) - b; +} +inline C10_HOST_DEVICE BFloat16 operator*(int64_t a, BFloat16 b) { + return static_cast(a) * b; +} +inline C10_HOST_DEVICE BFloat16 operator/(int64_t a, BFloat16 b) { + return static_cast(a) / b; +} + +} // namespace c10 + +namespace std { + +template <> +class numeric_limits { +public: + static constexpr bool is_signed = true; + static constexpr bool is_specialized = true; + static constexpr bool is_integer = false; + static constexpr bool is_exact = false; + static constexpr bool has_infinity = true; + static constexpr bool has_quiet_NaN = true; + static constexpr bool has_signaling_NaN = true; + static constexpr auto has_denorm = numeric_limits::has_denorm; + static constexpr auto has_denorm_loss = + numeric_limits::has_denorm_loss; + static constexpr auto round_style = numeric_limits::round_style; + static constexpr bool is_iec559 = false; + static constexpr bool is_bounded = true; + static constexpr bool is_modulo = false; + static constexpr int digits = 8; + static constexpr int digits10 = 2; + static constexpr int max_digits10 = 4; + static constexpr int radix = 2; + static constexpr int min_exponent = -125; + static constexpr int min_exponent10 = -37; + static constexpr int max_exponent = 128; + static constexpr int max_exponent10 = 38; + static constexpr auto traps = numeric_limits::traps; + static constexpr auto tinyness_before = + numeric_limits::tinyness_before; + + static constexpr c10::BFloat16 min() { + return c10::BFloat16(0x0080, c10::BFloat16::from_bits()); + } + static constexpr c10::BFloat16 lowest() { + return c10::BFloat16(0xFF7F, c10::BFloat16::from_bits()); + } + static constexpr c10::BFloat16 max() { + return c10::BFloat16(0x7F7F, c10::BFloat16::from_bits()); + } + static constexpr c10::BFloat16 epsilon() { + return c10::BFloat16(0x3C00, c10::BFloat16::from_bits()); + } + static constexpr c10::BFloat16 round_error() { + return c10::BFloat16(0x3F00, c10::BFloat16::from_bits()); + } + static constexpr c10::BFloat16 infinity() { + return c10::BFloat16(0x7F80, c10::BFloat16::from_bits()); + } + static constexpr c10::BFloat16 quiet_NaN() { + return c10::BFloat16(0x7FC0, c10::BFloat16::from_bits()); + } + static constexpr c10::BFloat16 signaling_NaN() { + return c10::BFloat16(0x7F80, c10::BFloat16::from_bits()); + } + static constexpr c10::BFloat16 denorm_min() { + return c10::BFloat16(0x0001, c10::BFloat16::from_bits()); + } +}; + +/// Used by vec256::map +inline c10::BFloat16 exp(c10::BFloat16 a) { return std::exp(float(a)); } +inline c10::BFloat16 log(c10::BFloat16 a) { return std::log(float(a)); } + +} // namespace std diff --git a/thirdparty/libtorch/include/c10/util/BFloat16.h b/thirdparty/libtorch/include/c10/util/BFloat16.h new file mode 100644 index 0000000000..f66fb5971e --- /dev/null +++ b/thirdparty/libtorch/include/c10/util/BFloat16.h @@ -0,0 +1,86 @@ +#pragma once + +// Defines the bloat16 type (brain floating-point). This representation uses +// 1 bit for the sign, 8 bits for the exponent and 7 bits for the mantissa. + +#include +#include +#include + +namespace c10 { + +namespace detail { + inline C10_HOST_DEVICE float f32_from_bits(uint16_t src) { + float res = 0; + uint32_t tmp = src; + tmp <<= 16; + +#ifdef __HIP_PLATFORM_HCC__ + float* tempRes; + + // We should be using memcpy in order to respect the strict aliasing rule + // but it fails in the HIP environment. + tempRes = reinterpret_cast(&tmp); + res = *tempRes; +#else + std::memcpy(&res, &tmp, sizeof(tmp)); +#endif + + return res; + } + + inline C10_HOST_DEVICE uint16_t bits_from_f32(float src) { + uint32_t res = 0; + +#ifdef __HIP_PLATFORM_HCC__ + // We should be using memcpy in order to respect the strict aliasing rule + // but it fails in the HIP environment. + uint32_t* tempRes = reinterpret_cast(&src); + res = *tempRes; +#else + std::memcpy(&res, &src, sizeof(res)); +#endif + + return res >> 16; + } + + inline C10_HOST_DEVICE uint16_t round_to_nearest_even(float src) { + if (std::isnan(src)) { + return 0x7FC0; + } else { + union { + uint32_t U32; + float F32; + }; + + F32 = src; + uint32_t rounding_bias = ((U32 >> 16) & 1) + 0x7FFF; + return static_cast((U32 + rounding_bias) >> 16); + } + } +} // namespace detail + +struct alignas(2) BFloat16 { + uint16_t x; + + // HIP wants __host__ __device__ tag, CUDA does not +#ifdef __HIP_PLATFORM_HCC__ + C10_HOST_DEVICE BFloat16() = default; +#else + BFloat16() = default; +#endif + + struct from_bits_t {}; + static constexpr from_bits_t from_bits() { + return from_bits_t(); + } + + constexpr C10_HOST_DEVICE BFloat16(unsigned short bits, from_bits_t) : x(bits){}; + inline C10_HOST_DEVICE BFloat16(float value); + inline C10_HOST_DEVICE operator float() const; +}; + +} // namespace c10 + + +#include diff --git a/thirdparty/libtorch/include/c10/util/Backtrace.h b/thirdparty/libtorch/include/c10/util/Backtrace.h new file mode 100644 index 0000000000..21afac0eb6 --- /dev/null +++ b/thirdparty/libtorch/include/c10/util/Backtrace.h @@ -0,0 +1,17 @@ +#ifndef C10_UTIL_BACKTRACE_H_ +#define C10_UTIL_BACKTRACE_H_ + +#include +#include +#include + +#include "c10/macros/Macros.h" + +namespace c10 { +C10_API std::string get_backtrace( + size_t frames_to_skip = 0, + size_t maximum_number_of_frames = 64, + bool skip_python_frames = true); +} // namespace c10 + +#endif // C10_UTIL_BACKTRACE_H_ diff --git a/thirdparty/libtorch/include/c10/util/C++17.h b/thirdparty/libtorch/include/c10/util/C++17.h new file mode 100644 index 0000000000..9b8458cce2 --- /dev/null +++ b/thirdparty/libtorch/include/c10/util/C++17.h @@ -0,0 +1,308 @@ +#pragma once +#ifndef C10_UTIL_CPP17_H_ +#define C10_UTIL_CPP17_H_ + +#include +#include +#include +#include +#include +#include +#include +#include + + +#if !defined(__clang__) && !defined(_MSC_VER) && defined(__GNUC__) && \ + __GNUC__ < 5 +#error "You're trying to build PyTorch with a too old version of GCC. We need GCC 5 or later." +#endif + +/* + * This header adds some polyfills with C++14 and C++17 functionality + */ + +namespace c10 { namespace guts { + + + +#ifdef __cpp_lib_transformation_trait_aliases +template using conditional_t = std::conditional_t; +template using enable_if_t = std::enable_if_t; +template using add_lvalue_reference_t = std::add_lvalue_reference_t; +template using remove_reference_t = std::remove_reference_t; +template using remove_cv_t = std::remove_cv_t; +template using result_of_t = std::result_of_t; +template using decay_t = std::decay_t; +template using remove_const_t = std::remove_const_t; +template using remove_pointer_t = std::remove_pointer_t; +template using common_type_t = std::common_type_t; +#else +template using conditional_t = typename std::conditional::type; +template using enable_if_t = typename std::enable_if::type; +template using add_lvalue_reference_t = typename std::add_lvalue_reference::type; +template using remove_reference_t = typename std::remove_reference::type; +template using remove_cv_t = typename std::remove_cv::type; +template using result_of_t = typename std::result_of::type; +template using decay_t = typename std::decay::type; +template using remove_const_t = typename std::remove_const::type; +template using remove_pointer_t = typename std::remove_pointer::type; +template using common_type_t = typename std::common_type::type; +#endif + + + + +// C++11 doesn't have constexpr std::move / std::forward. +// Implementation taken from libc++. +template +constexpr inline guts::remove_reference_t&& move(T&& t) noexcept { + return static_cast&&>(t); +} +template +constexpr inline T&& forward(guts::remove_reference_t& t) noexcept { + return static_cast(t); +} +template +constexpr inline T&& forward(guts::remove_reference_t&& t) noexcept { + static_assert(!std::is_lvalue_reference::value, + "can not forward an rvalue as an lvalue."); + return static_cast(t); +} + + + + +#if __cplusplus >= 201402L || defined(__cpp_lib_make_unique) && __cpp_lib_make_unique >= 201304L || \ + (defined(__ANDROID__) && __ANDROID__ && __cplusplus >= 201300L) || defined(_MSC_VER) && _MSC_VER >= 1900 + +/* using override */ using std::make_unique; + +#else + +// Implementation taken from folly +template +typename std::enable_if::value, std::unique_ptr>::type +make_unique(Args&&... args) { + return std::unique_ptr(new T(c10::guts::forward(args)...)); +} +// Allows 'make_unique(10)'. (N3690 s20.9.1.4 p3-4) +template +typename std::enable_if::value, std::unique_ptr>::type +make_unique(const size_t n) { + return std::unique_ptr(new typename std::remove_extent::type[n]()); +} +// Disallows 'make_unique()'. (N3690 s20.9.1.4 p5) +template +typename std::enable_if::value != 0, std::unique_ptr>::type +make_unique(Args&&...) = delete; + +#endif + +template +typename std::enable_if::value && !std::is_array::value && std::is_base_of::value, std::unique_ptr>::type +make_unique_base(Args&&... args) { + return std::unique_ptr(new Child(c10::guts::forward(args)...)); +} + + + +#ifdef __cpp_lib_integer_sequence + +template using integer_sequence = std::integer_sequence; +template using index_sequence = std::index_sequence; +template using make_integer_sequence = std::make_integer_sequence; +template using make_index_sequence = std::make_index_sequence; +template using index_sequence_for = std::index_sequence_for; + +#else + +template struct integer_sequence { + using value_type = T; + static constexpr std::size_t size() noexcept {return sizeof...(Ints);} +}; +template using index_sequence = integer_sequence; +namespace detail { + template + struct make_integer_sequence_ { + using type = typename make_integer_sequence_::type; + }; + template + struct make_integer_sequence_ { + using type = integer_sequence; + }; +} +template using make_integer_sequence = typename detail::make_integer_sequence_::type; +template using make_index_sequence = make_integer_sequence; +static_assert(std::is_same, make_index_sequence<0>>::value, ""); +static_assert(std::is_same, make_index_sequence<3>>::value, ""); +template using index_sequence_for = make_index_sequence; + +#endif + + + + +#ifdef __cpp_lib_logical_traits + +template +using conjunction = std::conjunction; +template +using disjunction = std::disjunction; +template +using bool_constant = std::bool_constant; +template +using negation = std::negation; + +#else + +// Implementation taken from http://en.cppreference.com/w/cpp/types/conjunction +template struct conjunction : std::true_type { }; +template struct conjunction : B1 { }; +template +struct conjunction + : conditional_t, B1> {}; + +// Implementation taken from http://en.cppreference.com/w/cpp/types/disjunction +template struct disjunction : std::false_type { }; +template struct disjunction : B1 { }; +template +struct disjunction + : conditional_t> { }; + +// Implementation taken from http://en.cppreference.com/w/cpp/types/integral_constant +template +using bool_constant = std::integral_constant; + +// Implementation taken from http://en.cppreference.com/w/cpp/types/negation +template +struct negation : bool_constant { }; + +#endif + + + +#ifdef __cpp_lib_void_t + +template using void_t = std::void_t; + +#else + +// Implementation taken from http://en.cppreference.com/w/cpp/types/void_t +// (it takes CWG1558 into account and also works for older compilers) +template struct make_void { typedef void type;}; +template using void_t = typename make_void::type; + +#endif + + + +#ifdef __cpp_lib_apply + +template +inline constexpr decltype(auto) apply(F&& f, Tuple&& t) { + return std::apply(std::forward(f), std::forward(t)); +} + +#else + +// Implementation from http://en.cppreference.com/w/cpp/utility/apply (but modified) +// TODO This is an incomplete implementation of std::apply, not working for member functions. +namespace detail { +template +#if defined(_MSC_VER) +// MSVC has a problem with the decltype() return type, but it also doesn't need it +// Also, nvcc on Windows needs C10_HOST_DEVICE here. +C10_HOST_DEVICE constexpr auto apply_impl(F&& f, Tuple&& t, guts::index_sequence) +#else +// GCC/Clang need the decltype() return type and rocm doesn't like the C10_HOST_DEVICE +constexpr auto apply_impl(F&& f, Tuple&& t, guts::index_sequence) +-> decltype(c10::guts::forward(f)(std::get(c10::guts::forward(t))...)) +#endif +{ + return c10::guts::forward(f)(std::get(c10::guts::forward(t))...); +} +} // namespace detail + +template +#if defined(_MSC_VER) +C10_HOST_DEVICE // rocm doesn't like the C10_HOST_DEVICE +#endif +constexpr auto apply(F&& f, Tuple&& t) -> decltype(detail::apply_impl( + c10::guts::forward(f), c10::guts::forward(t), + guts::make_index_sequence>::value>{})) +{ + return detail::apply_impl( + c10::guts::forward(f), c10::guts::forward(t), + guts::make_index_sequence>::value>{}); +} + +#endif + + + + +template +typename std::enable_if< + std::is_member_pointer::type>::value, + typename std::result_of::type>::type +invoke(Functor&& f, Args&&... args) { + return std::mem_fn(f)(std::forward(args)...); +} + +template +typename std::enable_if< + !std::is_member_pointer::type>::value, + typename std::result_of::type>::type +invoke(Functor&& f, Args&&... args) { + return std::forward(f)(std::forward(args)...); +} + + + +// GCC 4.8 doesn't define std::to_string, even though that's in C++11. Let's define it. +namespace detail { +class DummyClassForToString final {}; +}}} +namespace std { +// We use SFINAE to detect if std::to_string exists for a type, but that only works +// if the function name is defined. So let's define a std::to_string for a dummy type. +// If you're getting an error here saying that this overload doesn't match your +// std::to_string() call, then you're calling std::to_string() but should be calling +// c10::guts::to_string(). +inline std::string to_string(c10::guts::detail::DummyClassForToString) { return ""; } + +} +namespace c10 { namespace guts { namespace detail { + +template +struct to_string_ final { + static std::string call(T value) { + std::ostringstream str; + str << value; + return str.str(); + } +}; +// If a std::to_string exists, use that instead +template +struct to_string_()))>> final { + static std::string call(T value) { + return std::to_string(value); + } +}; +} +template inline std::string to_string(T value) { + return detail::to_string_::call(value); +} + +template +constexpr const T& min(const T& a, const T& b) { + return (b < a) ? b : a; +} + +template +constexpr const T& max(const T& a, const T& b) { + return (a < b) ? b : a; +} +}} + +#endif // C10_UTIL_CPP17_H_ diff --git a/thirdparty/libtorch/include/c10/util/Complex.h b/thirdparty/libtorch/include/c10/util/Complex.h new file mode 100644 index 0000000000..c35e7c7ab9 --- /dev/null +++ b/thirdparty/libtorch/include/c10/util/Complex.h @@ -0,0 +1,17 @@ +#pragma once + +#include + + +namespace std { + +template <> +class numeric_limits> : public numeric_limits {}; + +template <> +class numeric_limits> : public numeric_limits {}; + +template <> +class numeric_limits : public numeric_limits {}; + +} // namespace std diff --git a/thirdparty/libtorch/include/c10/util/ConstexprCrc.h b/thirdparty/libtorch/include/c10/util/ConstexprCrc.h new file mode 100644 index 0000000000..d61091da80 --- /dev/null +++ b/thirdparty/libtorch/include/c10/util/ConstexprCrc.h @@ -0,0 +1,137 @@ +#pragma once + +#include +#include +#include + +namespace c10 { +namespace util { + +namespace detail { +constexpr uint64_t crc64_table[] = { + 0x0000000000000000, 0x7ad870c830358979, 0xf5b0e190606b12f2, + 0x8f689158505e9b8b, 0xc038e5739841b68f, 0xbae095bba8743ff6, + 0x358804e3f82aa47d, 0x4f50742bc81f2d04, 0xab28ecb46814fe75, + 0xd1f09c7c5821770c, 0x5e980d24087fec87, 0x24407dec384a65fe, + 0x6b1009c7f05548fa, 0x11c8790fc060c183, 0x9ea0e857903e5a08, + 0xe478989fa00bd371, 0x7d08ff3b88be6f81, 0x07d08ff3b88be6f8, + 0x88b81eabe8d57d73, 0xf2606e63d8e0f40a, 0xbd301a4810ffd90e, + 0xc7e86a8020ca5077, 0x4880fbd87094cbfc, 0x32588b1040a14285, + 0xd620138fe0aa91f4, 0xacf86347d09f188d, 0x2390f21f80c18306, + 0x594882d7b0f40a7f, 0x1618f6fc78eb277b, 0x6cc0863448deae02, + 0xe3a8176c18803589, 0x997067a428b5bcf0, 0xfa11fe77117cdf02, + 0x80c98ebf2149567b, 0x0fa11fe77117cdf0, 0x75796f2f41224489, + 0x3a291b04893d698d, 0x40f16bccb908e0f4, 0xcf99fa94e9567b7f, + 0xb5418a5cd963f206, 0x513912c379682177, 0x2be1620b495da80e, + 0xa489f35319033385, 0xde51839b2936bafc, 0x9101f7b0e12997f8, + 0xebd98778d11c1e81, 0x64b116208142850a, 0x1e6966e8b1770c73, + 0x8719014c99c2b083, 0xfdc17184a9f739fa, 0x72a9e0dcf9a9a271, + 0x08719014c99c2b08, 0x4721e43f0183060c, 0x3df994f731b68f75, + 0xb29105af61e814fe, 0xc849756751dd9d87, 0x2c31edf8f1d64ef6, + 0x56e99d30c1e3c78f, 0xd9810c6891bd5c04, 0xa3597ca0a188d57d, + 0xec09088b6997f879, 0x96d1784359a27100, 0x19b9e91b09fcea8b, + 0x636199d339c963f2, 0xdf7adabd7a6e2d6f, 0xa5a2aa754a5ba416, + 0x2aca3b2d1a053f9d, 0x50124be52a30b6e4, 0x1f423fcee22f9be0, + 0x659a4f06d21a1299, 0xeaf2de5e82448912, 0x902aae96b271006b, + 0x74523609127ad31a, 0x0e8a46c1224f5a63, 0x81e2d7997211c1e8, + 0xfb3aa75142244891, 0xb46ad37a8a3b6595, 0xceb2a3b2ba0eecec, + 0x41da32eaea507767, 0x3b024222da65fe1e, 0xa2722586f2d042ee, + 0xd8aa554ec2e5cb97, 0x57c2c41692bb501c, 0x2d1ab4dea28ed965, + 0x624ac0f56a91f461, 0x1892b03d5aa47d18, 0x97fa21650afae693, + 0xed2251ad3acf6fea, 0x095ac9329ac4bc9b, 0x7382b9faaaf135e2, + 0xfcea28a2faafae69, 0x8632586aca9a2710, 0xc9622c4102850a14, + 0xb3ba5c8932b0836d, 0x3cd2cdd162ee18e6, 0x460abd1952db919f, + 0x256b24ca6b12f26d, 0x5fb354025b277b14, 0xd0dbc55a0b79e09f, + 0xaa03b5923b4c69e6, 0xe553c1b9f35344e2, 0x9f8bb171c366cd9b, + 0x10e3202993385610, 0x6a3b50e1a30ddf69, 0x8e43c87e03060c18, + 0xf49bb8b633338561, 0x7bf329ee636d1eea, 0x012b592653589793, + 0x4e7b2d0d9b47ba97, 0x34a35dc5ab7233ee, 0xbbcbcc9dfb2ca865, + 0xc113bc55cb19211c, 0x5863dbf1e3ac9dec, 0x22bbab39d3991495, + 0xadd33a6183c78f1e, 0xd70b4aa9b3f20667, 0x985b3e827bed2b63, + 0xe2834e4a4bd8a21a, 0x6debdf121b863991, 0x1733afda2bb3b0e8, + 0xf34b37458bb86399, 0x8993478dbb8deae0, 0x06fbd6d5ebd3716b, + 0x7c23a61ddbe6f812, 0x3373d23613f9d516, 0x49aba2fe23cc5c6f, + 0xc6c333a67392c7e4, 0xbc1b436e43a74e9d, 0x95ac9329ac4bc9b5, + 0xef74e3e19c7e40cc, 0x601c72b9cc20db47, 0x1ac40271fc15523e, + 0x5594765a340a7f3a, 0x2f4c0692043ff643, 0xa02497ca54616dc8, + 0xdafce7026454e4b1, 0x3e847f9dc45f37c0, 0x445c0f55f46abeb9, + 0xcb349e0da4342532, 0xb1eceec59401ac4b, 0xfebc9aee5c1e814f, + 0x8464ea266c2b0836, 0x0b0c7b7e3c7593bd, 0x71d40bb60c401ac4, + 0xe8a46c1224f5a634, 0x927c1cda14c02f4d, 0x1d148d82449eb4c6, + 0x67ccfd4a74ab3dbf, 0x289c8961bcb410bb, 0x5244f9a98c8199c2, + 0xdd2c68f1dcdf0249, 0xa7f41839ecea8b30, 0x438c80a64ce15841, + 0x3954f06e7cd4d138, 0xb63c61362c8a4ab3, 0xcce411fe1cbfc3ca, + 0x83b465d5d4a0eece, 0xf96c151de49567b7, 0x76048445b4cbfc3c, + 0x0cdcf48d84fe7545, 0x6fbd6d5ebd3716b7, 0x15651d968d029fce, + 0x9a0d8ccedd5c0445, 0xe0d5fc06ed698d3c, 0xaf85882d2576a038, + 0xd55df8e515432941, 0x5a3569bd451db2ca, 0x20ed197575283bb3, + 0xc49581ead523e8c2, 0xbe4df122e51661bb, 0x3125607ab548fa30, + 0x4bfd10b2857d7349, 0x04ad64994d625e4d, 0x7e7514517d57d734, + 0xf11d85092d094cbf, 0x8bc5f5c11d3cc5c6, 0x12b5926535897936, + 0x686de2ad05bcf04f, 0xe70573f555e26bc4, 0x9ddd033d65d7e2bd, + 0xd28d7716adc8cfb9, 0xa85507de9dfd46c0, 0x273d9686cda3dd4b, + 0x5de5e64efd965432, 0xb99d7ed15d9d8743, 0xc3450e196da80e3a, + 0x4c2d9f413df695b1, 0x36f5ef890dc31cc8, 0x79a59ba2c5dc31cc, + 0x037deb6af5e9b8b5, 0x8c157a32a5b7233e, 0xf6cd0afa9582aa47, + 0x4ad64994d625e4da, 0x300e395ce6106da3, 0xbf66a804b64ef628, + 0xc5bed8cc867b7f51, 0x8aeeace74e645255, 0xf036dc2f7e51db2c, + 0x7f5e4d772e0f40a7, 0x05863dbf1e3ac9de, 0xe1fea520be311aaf, + 0x9b26d5e88e0493d6, 0x144e44b0de5a085d, 0x6e963478ee6f8124, + 0x21c640532670ac20, 0x5b1e309b16452559, 0xd476a1c3461bbed2, + 0xaeaed10b762e37ab, 0x37deb6af5e9b8b5b, 0x4d06c6676eae0222, + 0xc26e573f3ef099a9, 0xb8b627f70ec510d0, 0xf7e653dcc6da3dd4, + 0x8d3e2314f6efb4ad, 0x0256b24ca6b12f26, 0x788ec2849684a65f, + 0x9cf65a1b368f752e, 0xe62e2ad306bafc57, 0x6946bb8b56e467dc, + 0x139ecb4366d1eea5, 0x5ccebf68aecec3a1, 0x2616cfa09efb4ad8, + 0xa97e5ef8cea5d153, 0xd3a62e30fe90582a, 0xb0c7b7e3c7593bd8, + 0xca1fc72bf76cb2a1, 0x45775673a732292a, 0x3faf26bb9707a053, + 0x70ff52905f188d57, 0x0a2722586f2d042e, 0x854fb3003f739fa5, + 0xff97c3c80f4616dc, 0x1bef5b57af4dc5ad, 0x61372b9f9f784cd4, + 0xee5fbac7cf26d75f, 0x9487ca0fff135e26, 0xdbd7be24370c7322, + 0xa10fceec0739fa5b, 0x2e675fb4576761d0, 0x54bf2f7c6752e8a9, + 0xcdcf48d84fe75459, 0xb71738107fd2dd20, 0x387fa9482f8c46ab, + 0x42a7d9801fb9cfd2, 0x0df7adabd7a6e2d6, 0x772fdd63e7936baf, + 0xf8474c3bb7cdf024, 0x829f3cf387f8795d, 0x66e7a46c27f3aa2c, + 0x1c3fd4a417c62355, 0x935745fc4798b8de, 0xe98f353477ad31a7, + 0xa6df411fbfb21ca3, 0xdc0731d78f8795da, 0x536fa08fdfd90e51, + 0x29b7d047efec8728, +}; + +inline C10_HOST_CONSTEXPR uint64_t +crc64impl(uint64_t accumulator, const char* data, size_t size) { +#if __cpp_constexpr >= 201304 + // if we are in C++14, just use a for loop. This compiles faster. + for (size_t i = 0; i < size; ++i) { + accumulator = + crc64_table[(accumulator ^ data[i]) & 0xFF] ^ (accumulator >> 8); + } + return accumulator; +#else + // if we are in C++11, we need to do it recursively because of constexpr + // restrictions. + return (size == 0) + ? accumulator + : crc64impl( + crc64_table[(accumulator ^ *data) & 0xFF] ^ (accumulator >> 8), + data + 1, + size - 1); +#endif +} +} // namespace detail + +struct crc64_t final : IdWrapper { + constexpr crc64_t(uint64_t checksum) : IdWrapper(checksum) {} + constexpr uint64_t checksum() const { + return this->underlyingId(); + } +}; + +// CRC64 with Jones coefficients and an init value of 0. +inline C10_HOST_CONSTEXPR crc64_t crc64(const char* data, size_t size) { + return crc64_t{detail::crc64impl(0, data, size)}; +} +} // namespace util +} // namespace c10 + +// Allow usage of crc64_t in std::unordered_set +C10_DEFINE_HASH_FOR_IDWRAPPER(c10::util::crc64_t); diff --git a/thirdparty/libtorch/include/c10/util/Deprecated.h b/thirdparty/libtorch/include/c10/util/Deprecated.h new file mode 100644 index 0000000000..59770ac60d --- /dev/null +++ b/thirdparty/libtorch/include/c10/util/Deprecated.h @@ -0,0 +1,101 @@ +#pragma once + +/** + * This file provides portable macros for marking declarations + * as deprecated. You should generally use C10_DEPRECATED, + * except when marking 'using' declarations as deprecated, + * in which case you should use C10_DEFINE_DEPRECATED_USING + * (due to portability concerns). + */ + +// Sample usage: +// +// C10_DEPRECATED void bad_func(); +// struct C10_DEPRECATED BadStruct { +// ... +// }; + +// NB: In PyTorch, this block is not actually used at the moment +// because we are C++11. However, aspirationally, we would like +// to use this version, because as of C++14 it is the correct and +// portable way to declare something deprecated. +// NB: __cplusplus doesn't work for MSVC, so for now MSVC always uses +// the "__declspec(deprecated)" implementation and not the C++14 "[[deprecated]]" +// attribute. We tried enabling "[[deprecated]]" for C++14 on MSVC, but +// ran into issues with some older MSVC versions. +#if (defined(__cplusplus) && __cplusplus >= 201402L) +# define C10_DEPRECATED [[deprecated]] +# define C10_DEPRECATED_MESSAGE(message) [[deprecated(message)]] +#elif defined(__GNUC__) +# define C10_DEPRECATED __attribute__((deprecated)) +// TODO Is there some way to implement this? +# define C10_DEPRECATED_MESSAGE(message) __attribute__((deprecated)) + +#elif defined(_MSC_VER) +# define C10_DEPRECATED __declspec(deprecated) +# define C10_DEPRECATED_MESSAGE(message) __declspec(deprecated(message)) +#else +# warning "You need to implement C10_DEPRECATED for this compiler" +# define C10_DEPRECATED +#endif + + +// Sample usage: +// +// C10_DEFINE_DEPRECATED_USING(BadType, int) +// +// which is the portable version of +// +// using BadType [[deprecated]] = int; + +// technically [[deprecated]] syntax is from c++14 standard, but it works in +// many compilers. +#if defined(__has_cpp_attribute) +#if __has_cpp_attribute(deprecated) +# define C10_DEFINE_DEPRECATED_USING(TypeName, TypeThingy) using TypeName [[deprecated]] = TypeThingy; +#endif +#endif + +#if defined(_MSC_VER) +#if defined(__CUDACC__) +// neither [[deprecated]] nor __declspec(deprecated) work on nvcc on Windows; +// you get the error: +// +// error: attribute does not apply to any entity +// +// So we just turn the macro off in this case. +#if defined(C10_DEFINE_DEPRECATED_USING) +# undef C10_DEFINE_DEPRECATED_USING +#endif +# define C10_DEFINE_DEPRECATED_USING(TypeName, TypeThingy) using TypeName = TypeThingy; +#else +// [[deprecated]] does work in windows without nvcc, though msc doesn't support +// `__has_cpp_attribute` when c++14 is supported, otherwise __declspec(deprecated) +// is used as the alternative. +#ifndef C10_DEFINE_DEPRECATED_USING +#if defined(_MSVC_LANG) && _MSVC_LANG >= 201402L +# define C10_DEFINE_DEPRECATED_USING(TypeName, TypeThingy) using TypeName [[deprecated]] = TypeThingy; +#else +# define C10_DEFINE_DEPRECATED_USING(TypeName, TypeThingy) using TypeName = __declspec(deprecated) TypeThingy; +#endif +#endif +#endif +#endif + +#if !defined(C10_DEFINE_DEPRECATED_USING) && defined(__GNUC__) +// nvcc has a bug where it doesn't understand __attribute__((deprecated)) +// declarations even when the host compiler supports it. We'll only use this gcc +// attribute when not cuda, and when using a GCC compiler that doesn't support +// the c++14 syntax we checked for above (availble in __GNUC__ >= 5) +#if !defined(__CUDACC__) +# define C10_DEFINE_DEPRECATED_USING(TypeName, TypeThingy) using TypeName __attribute__((deprecated)) = TypeThingy; +#else +// using cuda + gcc < 5, neither deprecated syntax is available so turning off. +# define C10_DEFINE_DEPRECATED_USING(TypeName, TypeThingy) using TypeName = TypeThingy; +#endif +#endif + +#if ! defined(C10_DEFINE_DEPRECATED_USING) +# warning "You need to implement C10_DEFINE_DEPRECATED_USING for this compiler" +# define C10_DEFINE_DEPRECATED_USING +#endif diff --git a/thirdparty/libtorch/include/c10/util/Exception.h b/thirdparty/libtorch/include/c10/util/Exception.h new file mode 100644 index 0000000000..e1effd1704 --- /dev/null +++ b/thirdparty/libtorch/include/c10/util/Exception.h @@ -0,0 +1,409 @@ +#ifndef C10_UTIL_EXCEPTION_H_ +#define C10_UTIL_EXCEPTION_H_ + +#include "c10/macros/Macros.h" +#include "c10/util/StringUtil.h" +#include "c10/util/Deprecated.h" + +#include +#include +#include +#include +#include +#include + +#if defined(_MSC_VER) && _MSC_VER <= 1900 +#define __func__ __FUNCTION__ +#endif + +namespace c10 { + +/// The primary ATen error class. +/// Provides a complete error message with source location information via +/// `what()`, and a more concise message via `what_without_backtrace()`. +/// Don't throw this directly; use TORCH_CHECK/TORCH_INTERNAL_ASSERT instead. +/// +/// NB: c10::Error is handled specially by the default torch to suppress the +/// backtrace, see torch/csrc/Exceptions.h +class C10_API Error : public std::exception { + std::vector msg_stack_; + std::string backtrace_; + + // These two are derived fields from msg_stack_ and backtrace_, but we need + // fields for the strings so that we can return a const char* (as the + // signature of std::exception requires). + std::string msg_; + std::string msg_without_backtrace_; + + // This is a little debugging trick: you can stash a relevant pointer + // in caller, and then when you catch the exception, you can compare + // against pointers you have on hand to get more information about + // where the exception came from. In Caffe2, this is used to figure + // out which operator raised an exception. + const void* caller_; + + public: + Error( + const std::string& msg, + const std::string& backtrace, + const void* caller = nullptr); + Error(SourceLocation source_location, const std::string& msg); + Error( + const char* file, + const uint32_t line, + const char* condition, + const std::string& msg, + const std::string& backtrace, + const void* caller = nullptr); + + void AppendMessage(const std::string& msg); + + // Compute the full message from msg_ and msg_without_backtrace_ + // TODO: Maybe this should be private + std::string msg() const; + std::string msg_without_backtrace() const; + + const std::vector& msg_stack() const { + return msg_stack_; + } + + /// Returns the complete error message, including the source location. + const char* what() const noexcept override { + return msg_.c_str(); + } + + const void* caller() const noexcept { + return caller_; + } + + /// Returns only the error message string, without source location. + const char* what_without_backtrace() const noexcept { + return msg_without_backtrace_.c_str(); + } +}; + +class C10_API WarningHandler { + public: + virtual ~WarningHandler() noexcept(false) {} + /// The default warning handler. Prints the message to stderr. + virtual void process( + const SourceLocation& source_location, + const std::string& msg); +}; + +namespace Warning { + +/// Issue a warning with a given message. Dispatched to the current +/// warning handler. +C10_API void warn(SourceLocation source_location, const std::string& msg); +/// Sets the global warning handler. This is not thread-safe, so it should +/// generally be called once during initialization or while holding the GIL +/// for programs that use python. +/// User is responsible for keeping the WarningHandler alive until +/// it is not needed. +C10_API void set_warning_handler(WarningHandler* handler) noexcept(true); +/// Gets the global warning handler. +C10_API WarningHandler* get_warning_handler() noexcept(true); + +} // namespace Warning + +// Used in ATen for out-of-bound indices that can reasonably only be detected +// lazily inside a kernel (See: advanced indexing). These turn into +// IndexError when they cross to Python. +class C10_API IndexError : public Error { + using Error::Error; +}; + + +// A utility function to return an exception std::string by prepending its +// exception type before its what() content +C10_API std::string GetExceptionString(const std::exception& e); + +namespace detail { + +// Return x if it is non-empty; otherwise return y. +inline std::string if_empty_then(std::string x, std::string y) { + if (x.empty()) { + return y; + } else { + return x; + } +} + +} + + +} // namespace c10 + +// Private helper macro for implementing TORCH_INTERNAL_ASSERT and TORCH_CHECK +// +// Note: In the debug build With MSVC, __LINE__ might be of long type (a.k.a int32_t), +// which is different from the definition of `SourceLocation` that requires +// unsigned int (a.k.a uint32_t) and may cause a compile error with the message: +// error C2397: conversion from 'long' to 'uint32_t' requires a narrowing conversion +// Here the static cast is used to pass the build. +// if this is used inside a lambda the __func__ macro expands to operator(), +// which isn't very useful, but hard to fix in a macro so suppressing the warning. +#define C10_THROW_ERROR(err_type, msg) \ + throw ::c10::err_type({__func__, __FILE__, static_cast(__LINE__)}, msg) + +// Private helper macro for workaround MSVC misexpansion of nested macro +// invocations involving __VA_ARGS__. See +// https://stackoverflow.com/questions/5134523/msvc-doesnt-expand-va-args-correctly +#define C10_EXPAND_MSVC_WORKAROUND(x) x + +// On nvcc, C10_UNLIKELY thwarts missing return statement analysis. In cases +// where the unlikely expression may be a constant, use this macro to ensure +// return statement analysis keeps working (at the cost of not getting the +// likely/unlikely annotation on nvcc). https://github.com/pytorch/pytorch/issues/21418 +// +// Currently, this is only used in the error reporting macros below. If you +// want to use it more generally, move me to Macros.h +// +// TODO: Brian Vaughan observed that we might be able to get this to work on nvcc +// by writing some sort of C++ overload that distinguishes constexpr inputs +// from non-constexpr. Since there isn't any evidence that losing C10_UNLIKELY +// in nvcc is causing us perf problems, this is not yet implemented, but this +// might be an interesting piece of C++ code for an intrepid bootcamper to +// write. +#if defined(__CUDACC__) +#define C10_UNLIKELY_OR_CONST(e) e +#else +#define C10_UNLIKELY_OR_CONST(e) C10_UNLIKELY(e) +#endif + + +// ---------------------------------------------------------------------------- +// Error reporting macros +// ---------------------------------------------------------------------------- + +// A utility macro to provide assert()-like functionality; that is, enforcement +// of internal invariants in code. It supports an arbitrary number of extra +// arguments (evaluated only on failure), which will be printed in the assert +// failure message using operator<< (this is useful to print some variables +// which may be useful for debugging.) +// +// Usage: +// TORCH_INTERNAL_ASSERT(should_be_true); +// TORCH_INTERNAL_ASSERT(x == 0, "x = ", x); +// +// Assuming no bugs in PyTorch, the conditions tested by this macro should +// always be true; e.g., it should be possible to disable all of these +// conditions without changing observable user behavior. If you would like to +// do error reporting for user input, please use TORCH_CHECK instead. +// +// NOTE: It is SAFE to use this macro in production code; on failure, this +// simply raises an exception, it does NOT unceremoniously quit the process +// (unlike assert()). +// +#ifdef STRIP_ERROR_MESSAGES +#define TORCH_INTERNAL_ASSERT(cond, ...) \ + if (C10_UNLIKELY_OR_CONST(!(cond))) { \ + C10_THROW_ERROR(Error, \ + #cond " INTERNAL ASSERT FAILED at" \ + __FILE__ \ + ); \ + } +#else +#define TORCH_INTERNAL_ASSERT(cond, ...) \ + if (C10_UNLIKELY_OR_CONST(!(cond))) { \ + C10_THROW_ERROR(Error, ::c10::str( \ + #cond " INTERNAL ASSERT FAILED at ", \ + __FILE__, \ + ":", \ + __LINE__, \ + ", please report a bug to PyTorch. ", \ + ::c10::str(__VA_ARGS__) \ + )); \ + } +#endif + +// A utility macro to make it easier to test for error conditions from user +// input. Like TORCH_INTERNAL_ASSERT, it supports an arbitrary number of extra +// arguments (evaluated only on failure), which will be printed in the error +// message using operator<< (e.g., you can pass any object which has +// operator<< defined. Most objects in PyTorch have these definitions!) +// +// Usage: +// TORCH_CHECK(should_be_true); // A default error message will be provided +// // in this case; but we recommend writing an +// // explicit error message, as it is more +// // user friendly. +// TORCH_CHECK(x == 0, "Expected x to be 0, but got ", x); +// +// On failure, this macro will raise an exception. If this exception propagates +// to Python, it will convert into a Python RuntimeError. +// +// NOTE: It is SAFE to use this macro in production code; on failure, this +// simply raises an exception, it does NOT unceremoniously quit the process +// (unlike CHECK() from glog.) +// +#ifdef STRIP_ERROR_MESSAGES +#define TORCH_CHECK(cond, ...) \ + if (C10_UNLIKELY_OR_CONST(!(cond))) { \ + C10_THROW_ERROR(Error, \ + #cond " CHECK FAILED at " \ + __FILE__ \ + ); \ + } +#else +#define TORCH_CHECK(cond, ...) \ + if (C10_UNLIKELY_OR_CONST(!(cond))) { \ + C10_THROW_ERROR(Error, \ + ::c10::detail::if_empty_then( \ + ::c10::str(__VA_ARGS__), \ + "Expected " #cond " to be true, but got false. " \ + "(Could this error message be improved? If so, " \ + "please report an enhancement request to PyTorch.)" \ + ) \ + ); \ + } +#endif +// TODO: We're going to get a lot of similar looking string literals +// this way; check if this actually affects binary size. + +// Like TORCH_CHECK, but raises IndexErrors instead of Errors. +#ifdef STRIP_ERROR_MESSAGES +#define TORCH_CHECK_INDEX(cond, ...) \ + if (C10_UNLIKELY_OR_CONST(!(cond))) { \ + C10_THROW_ERROR(Error, \ + #cond " INDEX CHECK FAILED at " \ + __FILE__ \ + ); \ + } +#else +#define TORCH_CHECK_INDEX(cond, ...) \ + if (C10_UNLIKELY_OR_CONST(!(cond))) { \ + C10_THROW_ERROR(IndexError, \ + ::c10::detail::if_empty_then( \ + ::c10::str(__VA_ARGS__), \ + "Expected " #cond " to be true, but got false. " \ + "(Could this error message be improved? If so, " \ + "please report an enhancement request to PyTorch.)" \ + ) \ + ); \ + } +#endif + + +// Report a warning to the user. Accepts an arbitrary number of extra +// arguments which are concatenated into the warning message using operator<< +// +#define TORCH_WARN(...) \ + ::c10::Warning::warn({__func__, __FILE__, static_cast(__LINE__)}, ::c10::str(__VA_ARGS__)) + +// Report a warning to the user only once. Accepts an arbitrary number of extra +// arguments which are concatenated into the warning message using operator<< +// +#define TORCH_WARN_ONCE(...) \ + C10_UNUSED static const auto C10_ANONYMOUS_VARIABLE(torch_warn_once_) = [&] { \ + ::c10::Warning::warn({__func__, __FILE__, static_cast(__LINE__)}, ::c10::str(__VA_ARGS__)); \ + return true; \ + }() + + +// ---------------------------------------------------------------------------- +// Deprecated macros +// ---------------------------------------------------------------------------- + +namespace c10 { namespace detail { + +/* +// Deprecation disabled until we fix sites in our codebase +C10_DEPRECATED_MESSAGE("AT_ERROR(msg) is deprecated, use TORCH_CHECK(false, msg) instead.") +*/ +inline void deprecated_AT_ERROR() {} + +/* +// Deprecation disabled until we fix sites in our codebase +C10_DEPRECATED_MESSAGE("AT_INDEX_ERROR(msg) is deprecated, use TORCH_CHECK_INDEX(false, msg) instead.") +*/ +inline void deprecated_AT_INDEX_ERROR() {} + +/* +// Deprecation disabled until we fix sites in our codebase +C10_DEPRECATED_MESSAGE("AT_WARN is deprecated, use TORCH_WARN instead.") +*/ +inline void deprecated_AT_WARN() {} + +C10_DEPRECATED_MESSAGE("AT_CHECK is deprecated, use TORCH_CHECK instead.") +inline void deprecated_AT_CHECK() {} + +/* +// Deprecation disabled until we fix sites in our codebase +C10_DEPRECATED_MESSAGE("AT_ASSERT is deprecated, if you mean to indicate an internal invariant failure, use " \ + "TORCH_INTERNAL_ASSERT instead; if you mean to do user error checking, use " \ + "TORCH_CHECK. See https://github.com/pytorch/pytorch/issues/20287 for more details.") +*/ +inline void deprecated_AT_ASSERT() {} + +/* +// Deprecation disabled until we fix sites in our codebase +C10_DEPRECATED_MESSAGE("AT_ASSERTM is deprecated, if you mean to indicate an internal invariant failure, use " \ + "TORCH_INTERNAL_ASSERT instead; if you mean to do user error checking, use " \ + "TORCH_CHECK. See https://github.com/pytorch/pytorch/issues/20287 for more details.") +*/ +inline void deprecated_AT_ASSERTM() {} + +}} // namespace c10::detail + +// Deprecated alias; this alias was deprecated because it wasn't clear to +// people that you should use a macro with AT_ prefix inside the torch/csrc +// directory. Use TORCH_CHECK instead. +#define AT_CHECK(...) \ + do { \ + ::c10::detail::deprecated_AT_CHECK(); \ + C10_EXPAND_MSVC_WORKAROUND(TORCH_CHECK(__VA_ARGS__)); \ + } while (false) + +// Deprecated alias; this alias was deprecated because people kept mistakenly +// using it for user error checking. Use TORCH_INTERNAL_ASSERT or TORCH_CHECK +// instead. See https://github.com/pytorch/pytorch/issues/20287 for more details. +#define AT_ASSERT(...) \ + do { \ + ::c10::detail::deprecated_AT_ASSERT(); \ + C10_EXPAND_MSVC_WORKAROUND(TORCH_INTERNAL_ASSERT(__VA_ARGS__)); \ + } while (false) + +// Deprecated alias, like AT_ASSERT. The new TORCH_INTERNAL_ASSERT macro supports +// both 0-ary and variadic calls, so having a separate message-accepting macro +// is not necessary. +// +// NB: we MUST include cond explicitly here, as MSVC will miscompile the macro +// expansion, shunting all of __VA_ARGS__ to cond. An alternate workaround +// can be seen at +// https://stackoverflow.com/questions/5134523/msvc-doesnt-expand-va-args-correctly +#define AT_ASSERTM(cond, ...) \ + do { \ + ::c10::detail::deprecated_AT_ASSERTM(); \ + C10_EXPAND_MSVC_WORKAROUND(TORCH_INTERNAL_ASSERT(cond, __VA_ARGS__)); \ + } while (false) + +// Deprecated alias; this alias was deprecated because it represents extra API +// surface that makes it hard for people to understand what macro to use. +// Use TORCH_CHECK(false, ...) or TORCH_INTERNAL_ASSERT(false, ...) to +// unconditionally fail at a line of code. +#define AT_ERROR(...) \ + do { \ + ::c10::detail::deprecated_AT_ERROR(); \ + C10_EXPAND_MSVC_WORKAROUND(TORCH_CHECK(false, ::c10::str(__VA_ARGS__))); \ + } while (false) + +// Deprecated alias; this alias was deprecated for consistency with TORCH_CHECK. +#define AT_INDEX_ERROR(...) \ + do { \ + ::c10::detail::deprecated_AT_INDEX_ERROR(); \ + C10_EXPAND_MSVC_WORKAROUND(TORCH_CHECK_INDEX(false, ::c10::str(__VA_ARGS__))); \ + } while (false) + +// Deprecated alias; this alias was deprecated because it wasn't clear to +// people that you should use a macro with AT_ prefix inside the torch/csrc +// directory. Use TORCH_WARN instead. +#define AT_WARN(...) \ + do { \ + ::c10::detail::deprecated_AT_WARN(); \ + C10_EXPAND_MSVC_WORKAROUND(TORCH_WARN(__VA_ARGS__)); \ + } while (false) + + +#endif // C10_UTIL_EXCEPTION_H_ diff --git a/thirdparty/libtorch/include/c10/util/Flags.h b/thirdparty/libtorch/include/c10/util/Flags.h new file mode 100644 index 0000000000..b26d4a724d --- /dev/null +++ b/thirdparty/libtorch/include/c10/util/Flags.h @@ -0,0 +1,228 @@ +#ifndef C10_UTIL_FLAGS_H_ +#define C10_UTIL_FLAGS_H_ + +/* Commandline flags support for C10. + * + * This is a portable commandline flags tool for c10, so we can optionally + * choose to use gflags or a lightweighted custom implementation if gflags is + * not possible on a certain platform. If you have gflags installed, set the + * macro C10_USE_GFLAGS will seamlessly route everything to gflags. + * + * To define a flag foo of type bool default to true, do the following in the + * *global* namespace: + * C10_DEFINE_bool(foo, true, "An example."); + * + * To use it in another .cc file, you can use C10_DECLARE_* as follows: + * C10_DECLARE_bool(foo); + * + * In both cases, you can then access the flag via FLAGS_foo. + * + * It is recommended that you build with gflags. To learn more about the flags + * usage, refer to the gflags page here: + * + * https://gflags.github.io/gflags/ + * + * Note about Python users / devs: gflags is initiated from a C++ function + * ParseCommandLineFlags, and is usually done in native binaries in the main + * function. As Python does not have a modifiable main function, it is usually + * difficult to change the flags after Python starts. Hence, it is recommended + * that one sets the default value of the flags to one that's acceptable in + * general - that will allow Python to run without wrong flags. + */ + +#include + +#include "c10/macros/Macros.h" +#include "c10/util/Registry.h" + +namespace c10 { +/** + * Sets the usage message when a commandline tool is called with "--help". + */ +C10_API void SetUsageMessage(const std::string& str); + +/** + * Returns the usage message for the commandline tool set by SetUsageMessage. + */ +C10_API const char* UsageMessage(); + +/** + * Parses the commandline flags. + * + * This command parses all the commandline arguments passed in via pargc + * and argv. Once it is finished, partc and argv will contain the remaining + * commandline args that c10 does not deal with. Note that following + * convention, argv[0] contains the binary name and is not parsed. + */ +C10_API bool ParseCommandLineFlags(int* pargc, char*** pargv); + +/** + * Checks if the commandline flags has already been passed. + */ +C10_API bool CommandLineFlagsHasBeenParsed(); + +} // namespace c10 + +//////////////////////////////////////////////////////////////////////////////// +// Below are gflags and non-gflags specific implementations. +// In general, they define the following macros for one to declare (use +// C10_DECLARE) or define (use C10_DEFINE) flags: +// C10_{DECLARE,DEFINE}_{int,int64,double,bool,string} +//////////////////////////////////////////////////////////////////////////////// + +#ifdef C10_USE_GFLAGS + +//////////////////////////////////////////////////////////////////////////////// +// Begin gflags section: most functions are basically rerouted to gflags. +//////////////////////////////////////////////////////////////////////////////// +#include + +// C10 uses hidden visibility by default. However, in gflags, it only uses +// export on Windows platform (with dllexport) but not on linux/mac (with +// default visibility). As a result, to ensure that we are always exporting +// global variables, we will redefine the GFLAGS_DLL_DEFINE_FLAG macro if we +// are building C10 as a shared libray. +// This has to be done after the inclusion of gflags, because some early +// versions of gflags.h (e.g. 2.0 on ubuntu 14.04) directly defines the +// macros, so we need to do definition after gflags is done. +#ifdef GFLAGS_DLL_DEFINE_FLAG +#undef GFLAGS_DLL_DEFINE_FLAG +#endif // GFLAGS_DLL_DEFINE_FLAG +#ifdef GFLAGS_DLL_DECLARE_FLAG +#undef GFLAGS_DLL_DECLARE_FLAG +#endif // GFLAGS_DLL_DECLARE_FLAG +#define GFLAGS_DLL_DEFINE_FLAG C10_EXPORT +#define GFLAGS_DLL_DECLARE_FLAG C10_IMPORT + +// gflags before 2.0 uses namespace google and after 2.1 uses namespace gflags. +// Using GFLAGS_GFLAGS_H_ to capture this change. +#ifndef GFLAGS_GFLAGS_H_ +namespace gflags = google; +#endif // GFLAGS_GFLAGS_H_ + +// Motivation about the gflags wrapper: +// (1) We would need to make sure that the gflags version and the non-gflags +// version of C10 are going to expose the same flags abstraction. One should +// explicitly use FLAGS_flag_name to access the flags. +// (2) For flag names, it is recommended to start with c10_ to distinguish it +// from regular gflags flags. For example, do +// C10_DEFINE_BOOL(c10_my_flag, true, "An example"); +// to allow one to use FLAGS_c10_my_flag. +// (3) Gflags has a design issue that does not properly expose the global flags, +// if one builds the library with -fvisibility=hidden. The current gflags (as of +// Aug 2018) only deals with the Windows case using dllexport, and not the Linux +// counterparts. As a result, we will explciitly use C10_EXPORT to export the +// flags defined in C10. This is done via a global reference, so the flag +// itself is not duplicated - under the hood it is the same global gflags flag. +#define C10_GFLAGS_DEF_WRAPPER(type, real_type, name, default_value, help_str) \ + DEFINE_##type(name, default_value, help_str); \ + +#define C10_DEFINE_int(name, default_value, help_str) \ + C10_GFLAGS_DEF_WRAPPER(int32, gflags::int32, name, default_value, help_str) +#define C10_DEFINE_int32(name, default_value, help_str) \ + C10_DEFINE_int(name, default_value, help_str) +#define C10_DEFINE_int64(name, default_value, help_str) \ + C10_GFLAGS_DEF_WRAPPER(int64, gflags::int64, name, default_value, help_str) +#define C10_DEFINE_double(name, default_value, help_str) \ + C10_GFLAGS_DEF_WRAPPER(double, double, name, default_value, help_str) +#define C10_DEFINE_bool(name, default_value, help_str) \ + C10_GFLAGS_DEF_WRAPPER(bool, bool, name, default_value, help_str) +#define C10_DEFINE_string(name, default_value, help_str) \ + C10_GFLAGS_DEF_WRAPPER(string, ::fLS::clstring, name, default_value, help_str) + +// DECLARE_typed_var should be used in header files and in the global namespace. +#define C10_GFLAGS_DECLARE_WRAPPER(type, real_type, name) \ + DECLARE_##type(name); \ + +#define C10_DECLARE_int(name) \ + C10_GFLAGS_DECLARE_WRAPPER(int32, gflags::int32, name) +#define C10_DECLARE_int32(name) C10_DECLARE_int(name) +#define C10_DECLARE_int64(name) \ + C10_GFLAGS_DECLARE_WRAPPER(int64, gflags::int64, name) +#define C10_DECLARE_double(name) \ + C10_GFLAGS_DECLARE_WRAPPER(double, double, name) +#define C10_DECLARE_bool(name) C10_GFLAGS_DECLARE_WRAPPER(bool, bool, name) +#define C10_DECLARE_string(name) \ + C10_GFLAGS_DECLARE_WRAPPER(string, ::fLS::clstring, name) + +//////////////////////////////////////////////////////////////////////////////// +// End gflags section. +//////////////////////////////////////////////////////////////////////////////// + +#else // C10_USE_GFLAGS + +//////////////////////////////////////////////////////////////////////////////// +// Begin non-gflags section: providing equivalent functionality. +//////////////////////////////////////////////////////////////////////////////// + +namespace c10 { + +class C10_API C10FlagParser { + public: + C10FlagParser() {} + bool success() { + return success_; + } + + protected: + template + bool Parse(const std::string& content, T* value); + bool success_; +}; + +C10_DECLARE_REGISTRY(C10FlagsRegistry, C10FlagParser, const std::string&); + +} // namespace c10 + +// The macros are defined outside the c10 namespace. In your code, you should +// write the C10_DEFINE_* and C10_DECLARE_* macros outside any namespace +// as well. + +#define C10_DEFINE_typed_var(type, name, default_value, help_str) \ + C10_EXPORT type FLAGS_##name = default_value; \ + namespace c10 { \ + namespace { \ + class C10FlagParser_##name : public C10FlagParser { \ + public: \ + explicit C10FlagParser_##name(const std::string& content) { \ + success_ = C10FlagParser::Parse(content, &FLAGS_##name); \ + } \ + }; \ + } \ + RegistererC10FlagsRegistry g_C10FlagsRegistry_##name( \ + #name, \ + C10FlagsRegistry(), \ + RegistererC10FlagsRegistry::DefaultCreator, \ + "(" #type ", default " #default_value ") " help_str); \ + } + +#define C10_DEFINE_int(name, default_value, help_str) \ + C10_DEFINE_typed_var(int, name, default_value, help_str) +#define C10_DEFINE_int32(name, default_value, help_str) \ + C10_DEFINE_int(name, default_value, help_str) +#define C10_DEFINE_int64(name, default_value, help_str) \ + C10_DEFINE_typed_var(int64_t, name, default_value, help_str) +#define C10_DEFINE_double(name, default_value, help_str) \ + C10_DEFINE_typed_var(double, name, default_value, help_str) +#define C10_DEFINE_bool(name, default_value, help_str) \ + C10_DEFINE_typed_var(bool, name, default_value, help_str) +#define C10_DEFINE_string(name, default_value, help_str) \ + C10_DEFINE_typed_var(std::string, name, default_value, help_str) + +// DECLARE_typed_var should be used in header files and in the global namespace. +#define C10_DECLARE_typed_var(type, name) C10_IMPORT extern type FLAGS_##name + +#define C10_DECLARE_int(name) C10_DECLARE_typed_var(int, name) +#define C10_DECLARE_int32(name) C10_DECLARE_int(name) +#define C10_DECLARE_int64(name) C10_DECLARE_typed_var(int64_t, name) +#define C10_DECLARE_double(name) C10_DECLARE_typed_var(double, name) +#define C10_DECLARE_bool(name) C10_DECLARE_typed_var(bool, name) +#define C10_DECLARE_string(name) C10_DECLARE_typed_var(std::string, name) + +//////////////////////////////////////////////////////////////////////////////// +// End non-gflags section. +//////////////////////////////////////////////////////////////////////////////// + +#endif // C10_USE_GFLAGS + +#endif // C10_UTIL_FLAGS_H_ diff --git a/thirdparty/libtorch/include/c10/util/FunctionRef.h b/thirdparty/libtorch/include/c10/util/FunctionRef.h new file mode 100644 index 0000000000..a3730476b7 --- /dev/null +++ b/thirdparty/libtorch/include/c10/util/FunctionRef.h @@ -0,0 +1,65 @@ +//===- llvm/ADT/STLExtras.h - Useful STL related functions ------*- C++ -*-===// +// +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +// See https://llvm.org/LICENSE.txt for license information. +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +// +//===----------------------------------------------------------------------===// +// +// This file contains some templates that are useful if you are working with the +// STL at all. +// +// No library is required when using these functions. +// +//===----------------------------------------------------------------------===// + +// c10: modified from llvm::function_ref +// c10: added more SFINAE to enable use in overloaded functions + +#pragma once + +namespace c10 { + +/// An efficient, type-erasing, non-owning reference to a callable. This is +/// intended for use as the type of a function parameter that is not used +/// after the function in question returns. +/// +/// This class does not own the callable, so it is not in general safe to store +/// a function_ref. +template class function_ref; + +template +class function_ref { +Ret (*callback)(intptr_t callable, Params ...params) = nullptr; +intptr_t callable; + +template +static Ret callback_fn(intptr_t callable, Params ...params) { + return (*reinterpret_cast(callable))( + std::forward(params)...); +} + +public: +function_ref() = default; +function_ref(std::nullptr_t) {} + +template +function_ref(Callable &&callable, + typename std::enable_if< + !std::is_same::type, + function_ref>::value>::type * = nullptr, + typename std::enable_if< + std::is_convertible< + typename std::result_of::type, + Ret>::value>::type * = nullptr) + : callback(callback_fn::type>), + callable(reinterpret_cast(&callable)) {} + +Ret operator()(Params ...params) const { + return callback(callable, std::forward(params)...); +} + +operator bool() const { return callback; } +}; + +} diff --git a/thirdparty/libtorch/include/c10/util/Half-inl.h b/thirdparty/libtorch/include/c10/util/Half-inl.h new file mode 100644 index 0000000000..f3ef954a58 --- /dev/null +++ b/thirdparty/libtorch/include/c10/util/Half-inl.h @@ -0,0 +1,289 @@ +#pragma once + +#include +#include +#include + +#ifdef __CUDACC__ +#include +#endif + +#ifdef __HIPCC__ +#include +#endif + +namespace c10 { + +/// Constructors + +inline C10_HOST_DEVICE Half::Half(float value) { +#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) + x = __half_as_short(__float2half(value)); +#else + x = detail::fp16_ieee_from_fp32_value(value); +#endif +} + +/// Implicit conversions + +inline C10_HOST_DEVICE Half::operator float() const { +#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) + return __half2float(*reinterpret_cast(&x)); +#else + return detail::fp16_ieee_to_fp32_value(x); +#endif +} + +#if defined(__CUDACC__) || defined(__HIPCC__) +inline C10_HOST_DEVICE Half::Half(const __half& value) { + x = *reinterpret_cast(&value); +} +inline C10_HOST_DEVICE Half::operator __half() const { + return *reinterpret_cast(&x); +} +#endif + +// CUDA intrinsics + +#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 350) +inline __device__ Half __ldg(const Half* ptr) { + return __ldg(reinterpret_cast(ptr)); +} +#endif + +/// Arithmetic + +inline C10_HOST_DEVICE Half operator+(const Half& a, const Half& b) { + return static_cast(a) + static_cast(b); +} + +inline C10_HOST_DEVICE Half operator-(const Half& a, const Half& b) { + return static_cast(a) - static_cast(b); +} + +inline C10_HOST_DEVICE Half operator*(const Half& a, const Half& b) { + return static_cast(a) * static_cast(b); +} + +inline C10_HOST_DEVICE Half operator/(const Half& a, const Half& b) { + return static_cast(a) / static_cast(b); +} + +inline C10_HOST_DEVICE Half operator-(const Half& a) { +#if (defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530) || defined(__HIP_DEVICE_COMPILE__) + return __hneg(a); +#else + return -static_cast(a); +#endif +} + +inline C10_HOST_DEVICE Half& operator+=(Half& a, const Half& b) { + a = a + b; + return a; +} + +inline C10_HOST_DEVICE Half& operator-=(Half& a, const Half& b) { + a = a - b; + return a; +} + +inline C10_HOST_DEVICE Half& operator*=(Half& a, const Half& b) { + a = a * b; + return a; +} + +inline C10_HOST_DEVICE Half& operator/=(Half& a, const Half& b) { + a = a / b; + return a; +} + +/// Arithmetic with floats + +inline C10_HOST_DEVICE float operator+(Half a, float b) { + return static_cast(a) + b; +} +inline C10_HOST_DEVICE float operator-(Half a, float b) { + return static_cast(a) - b; +} +inline C10_HOST_DEVICE float operator*(Half a, float b) { + return static_cast(a) * b; +} +inline C10_HOST_DEVICE float operator/(Half a, float b) { + return static_cast(a) / b; +} + +inline C10_HOST_DEVICE float operator+(float a, Half b) { + return a + static_cast(b); +} +inline C10_HOST_DEVICE float operator-(float a, Half b) { + return a - static_cast(b); +} +inline C10_HOST_DEVICE float operator*(float a, Half b) { + return a * static_cast(b); +} +inline C10_HOST_DEVICE float operator/(float a, Half b) { + return a / static_cast(b); +} + +inline C10_HOST_DEVICE float& operator+=(float& a, const Half& b) { + return a += static_cast(b); +} +inline C10_HOST_DEVICE float& operator-=(float& a, const Half& b) { + return a -= static_cast(b); +} +inline C10_HOST_DEVICE float& operator*=(float& a, const Half& b) { + return a *= static_cast(b); +} +inline C10_HOST_DEVICE float& operator/=(float& a, const Half& b) { + return a /= static_cast(b); +} + +/// Arithmetic with doubles + +inline C10_HOST_DEVICE double operator+(Half a, double b) { + return static_cast(a) + b; +} +inline C10_HOST_DEVICE double operator-(Half a, double b) { + return static_cast(a) - b; +} +inline C10_HOST_DEVICE double operator*(Half a, double b) { + return static_cast(a) * b; +} +inline C10_HOST_DEVICE double operator/(Half a, double b) { + return static_cast(a) / b; +} + +inline C10_HOST_DEVICE double operator+(double a, Half b) { + return a + static_cast(b); +} +inline C10_HOST_DEVICE double operator-(double a, Half b) { + return a - static_cast(b); +} +inline C10_HOST_DEVICE double operator*(double a, Half b) { + return a * static_cast(b); +} +inline C10_HOST_DEVICE double operator/(double a, Half b) { + return a / static_cast(b); +} + +/// Arithmetic with ints + +inline C10_HOST_DEVICE Half operator+(Half a, int b) { + return a + static_cast(b); +} +inline C10_HOST_DEVICE Half operator-(Half a, int b) { + return a - static_cast(b); +} +inline C10_HOST_DEVICE Half operator*(Half a, int b) { + return a * static_cast(b); +} +inline C10_HOST_DEVICE Half operator/(Half a, int b) { + return a / static_cast(b); +} + +inline C10_HOST_DEVICE Half operator+(int a, Half b) { + return static_cast(a) + b; +} +inline C10_HOST_DEVICE Half operator-(int a, Half b) { + return static_cast(a) - b; +} +inline C10_HOST_DEVICE Half operator*(int a, Half b) { + return static_cast(a) * b; +} +inline C10_HOST_DEVICE Half operator/(int a, Half b) { + return static_cast(a) / b; +} + +//// Arithmetic with int64_t + +inline C10_HOST_DEVICE Half operator+(Half a, int64_t b) { + return a + static_cast(b); +} +inline C10_HOST_DEVICE Half operator-(Half a, int64_t b) { + return a - static_cast(b); +} +inline C10_HOST_DEVICE Half operator*(Half a, int64_t b) { + return a * static_cast(b); +} +inline C10_HOST_DEVICE Half operator/(Half a, int64_t b) { + return a / static_cast(b); +} + +inline C10_HOST_DEVICE Half operator+(int64_t a, Half b) { + return static_cast(a) + b; +} +inline C10_HOST_DEVICE Half operator-(int64_t a, Half b) { + return static_cast(a) - b; +} +inline C10_HOST_DEVICE Half operator*(int64_t a, Half b) { + return static_cast(a) * b; +} +inline C10_HOST_DEVICE Half operator/(int64_t a, Half b) { + return static_cast(a) / b; +} + +/// NOTE: we do not define comparisons directly and instead rely on the implicit +/// conversion from c10::Half to float. + +} // namespace c10 + +namespace std { + +template <> +class numeric_limits { + public: + static constexpr bool is_specialized = true; + static constexpr bool is_signed = true; + static constexpr bool is_integer = false; + static constexpr bool is_exact = false; + static constexpr bool has_infinity = true; + static constexpr bool has_quiet_NaN = true; + static constexpr bool has_signaling_NaN = true; + static constexpr auto has_denorm = numeric_limits::has_denorm; + static constexpr auto has_denorm_loss = + numeric_limits::has_denorm_loss; + static constexpr auto round_style = numeric_limits::round_style; + static constexpr bool is_iec559 = true; + static constexpr bool is_bounded = true; + static constexpr bool is_modulo = false; + static constexpr int digits = 11; + static constexpr int digits10 = 3; + static constexpr int max_digits10 = 5; + static constexpr int radix = 2; + static constexpr int min_exponent = -13; + static constexpr int min_exponent10 = -4; + static constexpr int max_exponent = 16; + static constexpr int max_exponent10 = 4; + static constexpr auto traps = numeric_limits::traps; + static constexpr auto tinyness_before = + numeric_limits::tinyness_before; + static constexpr c10::Half min() { + return c10::Half(0x0400, c10::Half::from_bits()); + } + static constexpr c10::Half lowest() { + return c10::Half(0xFBFF, c10::Half::from_bits()); + } + static constexpr c10::Half max() { + return c10::Half(0x7BFF, c10::Half::from_bits()); + } + static constexpr c10::Half epsilon() { + return c10::Half(0x1400, c10::Half::from_bits()); + } + static constexpr c10::Half round_error() { + return c10::Half(0x3800, c10::Half::from_bits()); + } + static constexpr c10::Half infinity() { + return c10::Half(0x7C00, c10::Half::from_bits()); + } + static constexpr c10::Half quiet_NaN() { + return c10::Half(0x7E00, c10::Half::from_bits()); + } + static constexpr c10::Half signaling_NaN() { + return c10::Half(0x7D00, c10::Half::from_bits()); + } + static constexpr c10::Half denorm_min() { + return c10::Half(0x0001, c10::Half::from_bits()); + } +}; + +} // namespace std diff --git a/thirdparty/libtorch/include/c10/util/Half.h b/thirdparty/libtorch/include/c10/util/Half.h new file mode 100644 index 0000000000..338f271627 --- /dev/null +++ b/thirdparty/libtorch/include/c10/util/Half.h @@ -0,0 +1,510 @@ +#pragma once + +/// Defines the Half type (half-precision floating-point) including conversions +/// to standard C types and basic arithmetic operations. Note that arithmetic +/// operations are implemented by converting to floating point and +/// performing the operation in float32, instead of using CUDA half intrinisics. +/// Most uses of this type within ATen are memory bound, including the +/// element-wise kernels, and the half intrinisics aren't efficient on all GPUs. +/// If you are writing a compute bound kernel, you can use the CUDA half +/// intrinsics directly on the Half type from device code. + +#include +#include + +#if defined(__cplusplus) && (__cplusplus >= 201103L) +#include +#include +#elif !defined(__OPENCL_VERSION__) +#include +#include +#endif + +#ifdef _MSC_VER +#include +#endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#ifdef __CUDACC__ +#include +#endif + +#ifdef __HIPCC__ +#include +#endif + +namespace c10 { + +namespace detail { + + inline float fp32_from_bits(uint32_t w) { + #if defined(__OPENCL_VERSION__) + return as_float(w); + #elif defined(__CUDA_ARCH__) + return __uint_as_float((unsigned int)w); + #elif defined(__INTEL_COMPILER) + return _castu32_f32(w); + #else + union { + uint32_t as_bits; + float as_value; + } fp32 = {w}; + return fp32.as_value; + #endif + } + + inline uint32_t fp32_to_bits(float f) { + #if defined(__OPENCL_VERSION__) + return as_uint(f); + #elif defined(__CUDA_ARCH__) + return (uint32_t)__float_as_uint(f); + #elif defined(__INTEL_COMPILER) + return _castf32_u32(f); + #else + union { + float as_value; + uint32_t as_bits; + } fp32 = {f}; + return fp32.as_bits; + #endif + } + + /* + * Convert a 16-bit floating-point number in IEEE half-precision format, in bit representation, to + * a 32-bit floating-point number in IEEE single-precision format, in bit representation. + * + * @note The implementation doesn't use any floating-point operations. + */ + inline uint32_t fp16_ieee_to_fp32_bits(uint16_t h) { + /* + * Extend the half-precision floating-point number to 32 bits and shift to the upper part of the 32-bit word: + * +---+-----+------------+-------------------+ + * | S |EEEEE|MM MMMM MMMM|0000 0000 0000 0000| + * +---+-----+------------+-------------------+ + * Bits 31 26-30 16-25 0-15 + * + * S - sign bit, E - bits of the biased exponent, M - bits of the mantissa, 0 - zero bits. + */ + const uint32_t w = (uint32_t) h << 16; + /* + * Extract the sign of the input number into the high bit of the 32-bit word: + * + * +---+----------------------------------+ + * | S |0000000 00000000 00000000 00000000| + * +---+----------------------------------+ + * Bits 31 0-31 + */ + const uint32_t sign = w & UINT32_C(0x80000000); + /* + * Extract mantissa and biased exponent of the input number into the bits 0-30 of the 32-bit word: + * + * +---+-----+------------+-------------------+ + * | 0 |EEEEE|MM MMMM MMMM|0000 0000 0000 0000| + * +---+-----+------------+-------------------+ + * Bits 30 27-31 17-26 0-16 + */ + const uint32_t nonsign = w & UINT32_C(0x7FFFFFFF); + /* + * Renorm shift is the number of bits to shift mantissa left to make the half-precision number normalized. + * If the initial number is normalized, some of its high 6 bits (sign == 0 and 5-bit exponent) equals one. + * In this case renorm_shift == 0. If the number is denormalize, renorm_shift > 0. Note that if we shift + * denormalized nonsign by renorm_shift, the unit bit of mantissa will shift into exponent, turning the + * biased exponent into 1, and making mantissa normalized (i.e. without leading 1). + */ +#ifdef _MSC_VER + unsigned long nonsign_bsr; + _BitScanReverse(&nonsign_bsr, (unsigned long)nonsign); + uint32_t renorm_shift = (uint32_t)nonsign_bsr ^ 31; +#else + uint32_t renorm_shift = __builtin_clz(nonsign); +#endif + renorm_shift = renorm_shift > 5 ? renorm_shift - 5 : 0; + /* + * Iff half-precision number has exponent of 15, the addition overflows + * it into bit 31, and the subsequent shift turns the high 9 bits + * into 1. Thus inf_nan_mask == 0x7F800000 if the half-precision number + * had exponent of 15 (i.e. was NaN or infinity) 0x00000000 otherwise + */ + const int32_t inf_nan_mask = + ((int32_t)(nonsign + 0x04000000) >> 8) & INT32_C(0x7F800000); + /* + * Iff nonsign is 0, it overflows into 0xFFFFFFFF, turning bit 31 + * into 1. Otherwise, bit 31 remains 0. The signed shift right by 31 + * broadcasts bit 31 into all bits of the zero_mask. Thus zero_mask == + * 0xFFFFFFFF if the half-precision number was zero (+0.0h or -0.0h) + * 0x00000000 otherwise + */ + const int32_t zero_mask = (int32_t)(nonsign - 1) >> 31; + /* + * 1. Shift nonsign left by renorm_shift to normalize it (if the input + * was denormal) + * 2. Shift nonsign right by 3 so the exponent (5 bits originally) + * becomes an 8-bit field and 10-bit mantissa shifts into the 10 high + * bits of the 23-bit mantissa of IEEE single-precision number. + * 3. Add 0x70 to the exponent (starting at bit 23) to compensate the + * different in exponent bias (0x7F for single-precision number less 0xF + * for half-precision number). + * 4. Subtract renorm_shift from the exponent (starting at bit 23) to + * account for renormalization. As renorm_shift is less than 0x70, this + * can be combined with step 3. + * 5. Binary OR with inf_nan_mask to turn the exponent into 0xFF if the + * input was NaN or infinity. + * 6. Binary ANDNOT with zero_mask to turn the mantissa and exponent + * into zero if the input was zero. + * 7. Combine with the sign of the input number. + */ + return sign | + ((((nonsign << renorm_shift >> 3) + ((0x70 - renorm_shift) << 23)) | + inf_nan_mask) & + ~zero_mask); + } + + /* + * Convert a 16-bit floating-point number in IEEE half-precision format, in bit representation, to + * a 32-bit floating-point number in IEEE single-precision format. + * + * @note The implementation relies on IEEE-like (no assumption about rounding mode and no operations on denormals) + * floating-point operations and bitcasts between integer and floating-point variables. + */ + inline float fp16_ieee_to_fp32_value(uint16_t h) { + /* + * Extend the half-precision floating-point number to 32 bits and shift to the upper part of the 32-bit word: + * +---+-----+------------+-------------------+ + * | S |EEEEE|MM MMMM MMMM|0000 0000 0000 0000| + * +---+-----+------------+-------------------+ + * Bits 31 26-30 16-25 0-15 + * + * S - sign bit, E - bits of the biased exponent, M - bits of the mantissa, 0 - zero bits. + */ + const uint32_t w = (uint32_t) h << 16; + /* + * Extract the sign of the input number into the high bit of the 32-bit word: + * + * +---+----------------------------------+ + * | S |0000000 00000000 00000000 00000000| + * +---+----------------------------------+ + * Bits 31 0-31 + */ + const uint32_t sign = w & UINT32_C(0x80000000); + /* + * Extract mantissa and biased exponent of the input number into the high bits of the 32-bit word: + * + * +-----+------------+---------------------+ + * |EEEEE|MM MMMM MMMM|0 0000 0000 0000 0000| + * +-----+------------+---------------------+ + * Bits 27-31 17-26 0-16 + */ + const uint32_t two_w = w + w; + + /* + * Shift mantissa and exponent into bits 23-28 and bits 13-22 so they become mantissa and exponent + * of a single-precision floating-point number: + * + * S|Exponent | Mantissa + * +-+---+-----+------------+----------------+ + * |0|000|EEEEE|MM MMMM MMMM|0 0000 0000 0000| + * +-+---+-----+------------+----------------+ + * Bits | 23-31 | 0-22 + * + * Next, there are some adjustments to the exponent: + * - The exponent needs to be corrected by the difference in exponent bias between single-precision and half-precision + * formats (0x7F - 0xF = 0x70) + * - Inf and NaN values in the inputs should become Inf and NaN values after conversion to the single-precision number. + * Therefore, if the biased exponent of the half-precision input was 0x1F (max possible value), the biased exponent + * of the single-precision output must be 0xFF (max possible value). We do this correction in two steps: + * - First, we adjust the exponent by (0xFF - 0x1F) = 0xE0 (see exp_offset below) rather than by 0x70 suggested + * by the difference in the exponent bias (see above). + * - Then we multiply the single-precision result of exponent adjustment by 2**(-112) to reverse the effect of + * exponent adjustment by 0xE0 less the necessary exponent adjustment by 0x70 due to difference in exponent bias. + * The floating-point multiplication hardware would ensure than Inf and NaN would retain their value on at least + * partially IEEE754-compliant implementations. + * + * Note that the above operations do not handle denormal inputs (where biased exponent == 0). However, they also do not + * operate on denormal inputs, and do not produce denormal results. + */ + const uint32_t exp_offset = UINT32_C(0xE0) << 23; + // const float exp_scale = 0x1.0p-112f; + uint32_t scale_bits = (uint32_t) 15 << 23; + float exp_scale_val; + std::memcpy(&exp_scale_val, &scale_bits, sizeof(exp_scale_val)); + const float exp_scale = exp_scale_val; + const float normalized_value = fp32_from_bits((two_w >> 4) + exp_offset) * exp_scale; + + /* + * Convert denormalized half-precision inputs into single-precision results (always normalized). + * Zero inputs are also handled here. + * + * In a denormalized number the biased exponent is zero, and mantissa has on-zero bits. + * First, we shift mantissa into bits 0-9 of the 32-bit word. + * + * zeros | mantissa + * +---------------------------+------------+ + * |0000 0000 0000 0000 0000 00|MM MMMM MMMM| + * +---------------------------+------------+ + * Bits 10-31 0-9 + * + * Now, remember that denormalized half-precision numbers are represented as: + * FP16 = mantissa * 2**(-24). + * The trick is to construct a normalized single-precision number with the same mantissa and thehalf-precision input + * and with an exponent which would scale the corresponding mantissa bits to 2**(-24). + * A normalized single-precision floating-point number is represented as: + * FP32 = (1 + mantissa * 2**(-23)) * 2**(exponent - 127) + * Therefore, when the biased exponent is 126, a unit change in the mantissa of the input denormalized half-precision + * number causes a change of the constructud single-precision number by 2**(-24), i.e. the same ammount. + * + * The last step is to adjust the bias of the constructed single-precision number. When the input half-precision number + * is zero, the constructed single-precision number has the value of + * FP32 = 1 * 2**(126 - 127) = 2**(-1) = 0.5 + * Therefore, we need to subtract 0.5 from the constructed single-precision number to get the numerical equivalent of + * the input half-precision number. + */ + const uint32_t magic_mask = UINT32_C(126) << 23; + const float magic_bias = 0.5f; + const float denormalized_value = fp32_from_bits((two_w >> 17) | magic_mask) - magic_bias; + + /* + * - Choose either results of conversion of input as a normalized number, or as a denormalized number, depending on the + * input exponent. The variable two_w contains input exponent in bits 27-31, therefore if its smaller than 2**27, the + * input is either a denormal number, or zero. + * - Combine the result of conversion of exponent and mantissa with the sign of the input number. + */ + const uint32_t denormalized_cutoff = UINT32_C(1) << 27; + const uint32_t result = sign | + (two_w < denormalized_cutoff ? fp32_to_bits(denormalized_value) : fp32_to_bits(normalized_value)); + return fp32_from_bits(result); + } + + /* + * Convert a 32-bit floating-point number in IEEE single-precision format to a 16-bit floating-point number in + * IEEE half-precision format, in bit representation. + * + * @note The implementation relies on IEEE-like (no assumption about rounding mode and no operations on denormals) + * floating-point operations and bitcasts between integer and floating-point variables. + */ + inline uint16_t fp16_ieee_from_fp32_value(float f) { + // const float scale_to_inf = 0x1.0p+112f; + // const float scale_to_zero = 0x1.0p-110f; + uint32_t scale_to_inf_bits = (uint32_t) 239 << 23; + uint32_t scale_to_zero_bits = (uint32_t) 17 << 23; + float scale_to_inf_val, scale_to_zero_val; + std::memcpy(&scale_to_inf_val, &scale_to_inf_bits, sizeof(scale_to_inf_val)); + std::memcpy(&scale_to_zero_val, &scale_to_zero_bits, sizeof(scale_to_zero_val)); + const float scale_to_inf = scale_to_inf_val; + const float scale_to_zero = scale_to_zero_val; + + float base = (fabsf(f) * scale_to_inf) * scale_to_zero; + + const uint32_t w = fp32_to_bits(f); + const uint32_t shl1_w = w + w; + const uint32_t sign = w & UINT32_C(0x80000000); + uint32_t bias = shl1_w & UINT32_C(0xFF000000); + if (bias < UINT32_C(0x71000000)) { + bias = UINT32_C(0x71000000); + } + + base = fp32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base; + const uint32_t bits = fp32_to_bits(base); + const uint32_t exp_bits = (bits >> 13) & UINT32_C(0x00007C00); + const uint32_t mantissa_bits = bits & UINT32_C(0x00000FFF); + const uint32_t nonsign = exp_bits + mantissa_bits; + return (sign >> 16) | (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign); + } + +} // namespace detail + +struct alignas(2) Half { + unsigned short x; + + struct from_bits_t {}; + static constexpr from_bits_t from_bits() { + return from_bits_t(); + } + + // HIP wants __host__ __device__ tag, CUDA does not +#ifdef __HIP_PLATFORM_HCC__ + C10_HOST_DEVICE Half() = default; +#else + Half() = default; +#endif + + constexpr C10_HOST_DEVICE Half(unsigned short bits, from_bits_t) : x(bits){}; + inline C10_HOST_DEVICE Half(float value); + inline C10_HOST_DEVICE operator float() const; + +#if defined(__CUDACC__) || defined(__HIPCC__) + inline C10_HOST_DEVICE Half(const __half& value); + inline C10_HOST_DEVICE operator __half() const; +#endif +}; + +// This is just a placeholder for whatever complex representation we +// end up deciding to use for half-precision complex numbers. +struct alignas(4) ComplexHalf { + Half real_; + Half imag_; + ComplexHalf() = default; + Half real() const { + return real_; + } + Half imag() const { + return imag_; + } + inline ComplexHalf(std::complex value) + : real_(value.real()), imag_(value.imag()) {} + inline operator std::complex() const { + return {real_, imag_}; + } +}; + +template +struct is_complex_t : public std::false_type {}; + +template +struct is_complex_t> : public std::true_type {}; + +template <> +struct is_complex_t : public std::true_type {}; + +// Extract double from std::complex; is identity otherwise +// TODO: Write in more idiomatic C++17 +template +struct scalar_value_type { + using type = T; +}; +template +struct scalar_value_type> { + using type = T; +}; +template <> +struct scalar_value_type { + using type = Half; +}; + +// The old implementation of Converter as a function made nvcc's head explode +// when we added std::complex on top of the specializations for CUDA-only types +// like __half, so I rewrote it as a templated class (so, no more overloads, +// just (partial) specialization). + +template +struct Converter { + To operator()(From f) { + return static_cast(f); + } +}; + +template +To convert(From from) { + return Converter()(from); +} + +template +struct Converter< + To, + std::complex, + typename std::enable_if< + c10::guts::negation>::value>::type> { + To operator()(std::complex f) { + return static_cast(f.real()); + } +}; + +// In some versions of MSVC, there will be a compiler error when building. +// C4146: unary minus operator applied to unsigned type, result still unsigned +// C4804: unsafe use of type 'bool' in operation +// It can be addressed by disabling the following warning. +#ifdef _MSC_VER +#pragma warning( push ) +#pragma warning( disable : 4146 ) +#pragma warning( disable : 4804 ) +#endif + + +// bool can be converted to any type. +// Without specializing on bool, in pytorch_linux_trusty_py2_7_9_build: +// `error: comparison of constant '255' with boolean expression is always false` +// for `f > limit::max()` below +template +typename std::enable_if::value, bool>::type overflows( + From f) { + return false; +} + +// skip isnan and isinf check for integral types +template +typename std::enable_if::value && !std::is_same::value, bool>::type overflows( + From f) { + using limit = std::numeric_limits::type>; + if (!limit::is_signed && std::numeric_limits::is_signed) { + // allow for negative numbers to wrap using two's complement arithmetic. + // For example, with uint8, this allows for `a - b` to be treated as + // `a + 255 * b`. + return f > limit::max() || + (f < 0 && -static_cast(f) > limit::max()); + } else { + return f < limit::lowest() || f > limit::max(); + } +} + +template +typename std::enable_if::value, bool>::type +overflows(From f) { + using limit = std::numeric_limits::type>; + if (limit::has_infinity && std::isinf(static_cast(f))) { + return false; + } + if (!limit::has_quiet_NaN && (f != f)) { + return true; + } + return f < limit::lowest() || f > limit::max(); +} + +#ifdef _MSC_VER +#pragma warning( pop ) +#endif + +template +typename std::enable_if::value, bool>::type overflows( + From f) { + // casts from complex to real are considered to overflow if the + // imaginary component is non-zero + if (!is_complex_t::value && f.imag() != 0) { + return true; + } + // Check for overflow componentwise + // (Technically, the imag overflow check is guaranteed to be false + // when !is_complex_t, but any optimizer worth its salt will be + // able to figure it out.) + return overflows< + typename scalar_value_type::type, + typename From::value_type>(f.real()) || + overflows< + typename scalar_value_type::type, + typename From::value_type>(f.imag()); +} + +template +To checked_convert(From f, const char* name) { + // Converting to bool can't overflow so we exclude this case from checking. + if (!std::is_same::value && overflows(f)) { + std::ostringstream oss; + oss << "value cannot be converted to type " << name + << " without overflow: " << f; + throw std::domain_error(oss.str()); + } + return convert(f); +} + +C10_API std::ostream& operator<<(std::ostream& out, const Half& value); + +} // namespace c10 + +#include diff --git a/thirdparty/libtorch/include/c10/util/IdWrapper.h b/thirdparty/libtorch/include/c10/util/IdWrapper.h new file mode 100644 index 0000000000..dc28141e53 --- /dev/null +++ b/thirdparty/libtorch/include/c10/util/IdWrapper.h @@ -0,0 +1,77 @@ +#pragma once + +#include +#include +#include + +namespace c10 { + +/** + * This template simplifies generation of simple classes that wrap an id + * in a typesafe way. Namely, you can use it to create a very lightweight + * type that only offers equality comparators and hashing. Example: + * + * struct MyIdType final : IdWrapper { + * constexpr explicit MyIdType(uint32_t id): IdWrapper(id) {} + * }; + * + * Then in the global top level namespace: + * + * C10_DEFINE_HASH_FOR_IDWRAPPER(MyIdType); + * + * That's it - equality operators and hash functions are automatically defined + * for you, given the underlying type supports it. + */ +template +class IdWrapper { + public: + using underlying_type = UnderlyingType; + using concrete_type = ConcreteType; + + protected: + constexpr explicit IdWrapper(underlying_type id) noexcept( + noexcept(underlying_type(std::declval()))) + : id_(id) {} + + constexpr underlying_type underlyingId() const + noexcept(noexcept(underlying_type(std::declval()))) { + return id_; + } + + private: + friend size_t hash_value(const concrete_type& v) { + return std::hash()(v.id_); + } + + // TODO Making operator== noexcept if underlying type is noexcept equality + // comparable doesn't work with GCC 4.8. + // Fix this once we don't need GCC 4.8 anymore. + friend constexpr bool operator==( + const concrete_type& lhs, + const concrete_type& rhs) noexcept { + return lhs.id_ == rhs.id_; + } + + // TODO Making operator!= noexcept if operator== is noexcept doesn't work with + // GCC 4.8. + // Fix this once we don't need GCC 4.8 anymore. + friend constexpr bool operator!=( + const concrete_type& lhs, + const concrete_type& rhs) noexcept { + return !(lhs == rhs); + } + + underlying_type id_; +}; + +} // namespace c10 + +#define C10_DEFINE_HASH_FOR_IDWRAPPER(ClassName)\ + namespace std { \ + template <> \ + struct hash { \ + size_t operator()(ClassName x) const { \ + return hash_value(x); \ + } \ + }; \ + } diff --git a/thirdparty/libtorch/include/c10/util/LeftRight.h b/thirdparty/libtorch/include/c10/util/LeftRight.h new file mode 100644 index 0000000000..ad6cc81413 --- /dev/null +++ b/thirdparty/libtorch/include/c10/util/LeftRight.h @@ -0,0 +1,177 @@ +#include +#include +#include +#include +#include +#include + +namespace c10 { + +namespace detail { + +struct IncrementRAII final { +public: + explicit IncrementRAII(std::atomic *counter): _counter(counter) { + ++(*_counter); + } + + ~IncrementRAII() { + --(*_counter); + } +private: + std::atomic *_counter; + + C10_DISABLE_COPY_AND_ASSIGN(IncrementRAII); +}; + +} + +// LeftRight wait-free readers synchronization primitive +// https://hal.archives-ouvertes.fr/hal-01207881/document +template +class LeftRight final { +public: + template + explicit LeftRight(const Args& ...args) + : _counters{{{0}, {0}}} + , _foregroundCounterIndex(0) + , _foregroundDataIndex(0) + , _inDestruction(false) + , _data{{T{args...}, T{args...}}} + , _writeMutex() + {} + + // Copying and moving would not be threadsafe. + // Needs more thought and careful design to make that work. + LeftRight(const LeftRight&) = delete; + LeftRight(LeftRight&&) noexcept = delete; + LeftRight& operator=(const LeftRight&) = delete; + LeftRight& operator=(LeftRight&&) noexcept= delete; + + ~LeftRight() { + // from now on, no new readers/writers will be accepted (see asserts in read()/write()) + _inDestruction = true; + + // wait until any potentially running writers are finished + { + std::unique_lock lock(_writeMutex); + } + + // wait until any potentially running readers are finished + while (_counters[0].load() != 0 || _counters[1].load() != 0) { + std::this_thread::yield(); + } + } + + template + auto read(F&& readFunc) const -> typename std::result_of::type { + detail::IncrementRAII _increment_counter(&_counters[_foregroundCounterIndex.load()]); + + if(C10_UNLIKELY(_inDestruction.load())) { + throw std::logic_error("Issued LeftRight::read() after the destructor started running"); + } + + return readFunc(_data[_foregroundDataIndex.load()]); + } + + // Throwing an exception in writeFunc is ok but causes the state to be either the old or the new state, + // depending on if the first or the second call to writeFunc threw. + template + auto write(F&& writeFunc) -> typename std::result_of::type { + std::unique_lock lock(_writeMutex); + + if(C10_UNLIKELY(_inDestruction.load())) { + throw std::logic_error("Issued LeftRight::write() after the destructor started running"); + } + + return _write(writeFunc); + } + +private: + template + auto _write(const F& writeFunc) -> typename std::result_of::type { + /* + * Assume, A is in background and B in foreground. In simplified terms, we want to do the following: + * 1. Write to A (old background) + * 2. Switch A/B + * 3. Write to B (new background) + * + * More detailed algorithm (explanations on why this is important are below in code): + * 1. Write to A + * 2. Switch A/B data pointers + * 3. Wait until A counter is zero + * 4. Switch A/B counters + * 5. Wait until B counter is zero + * 6. Write to B + */ + + auto localDataIndex = _foregroundDataIndex.load(); + + // 1. Write to A + _callWriteFuncOnBackgroundInstance(writeFunc, localDataIndex); + + // 2. Switch A/B data pointers + localDataIndex = localDataIndex ^ 1; + _foregroundDataIndex = localDataIndex; + + /* + * 3. Wait until A counter is zero + * + * In the previous write run, A was foreground and B was background. + * There was a time after switching _foregroundDataIndex (B to foreground) and before switching _foregroundCounterIndex, + * in which new readers could have read B but incremented A's counter. + * + * In this current run, we just switched _foregroundDataIndex (A back to foreground), but before writing to + * the new background B, we have to make sure A's counter was zero briefly, so all these old readers are gone. + */ + auto localCounterIndex = _foregroundCounterIndex.load(); + _waitForBackgroundCounterToBeZero(localCounterIndex); + + /* + * 4. Switch A/B counters + * + * Now that we know all readers on B are really gone, we can switch the counters and have new readers + * increment A's counter again, which is the correct counter since they're reading A. + */ + localCounterIndex = localCounterIndex ^ 1; + _foregroundCounterIndex = localCounterIndex; + + /* + * 5. Wait until B counter is zero + * + * This waits for all the readers on B that came in while both data and counter for B was in foreground, + * i.e. normal readers that happened outside of that brief gap between switching data and counter. + */ + _waitForBackgroundCounterToBeZero(localCounterIndex); + + // 6. Write to B + return _callWriteFuncOnBackgroundInstance(writeFunc, localDataIndex); + } + + template + auto _callWriteFuncOnBackgroundInstance(const F& writeFunc, uint8_t localDataIndex) -> typename std::result_of::type { + try { + return writeFunc(_data[localDataIndex ^ 1]); + } catch (...) { + // recover invariant by copying from the foreground instance + _data[localDataIndex ^ 1] = _data[localDataIndex]; + // rethrow + throw; + } + } + + void _waitForBackgroundCounterToBeZero(uint8_t counterIndex) { + while (_counters[counterIndex ^ 1].load() != 0) { + std::this_thread::yield(); + } + } + + mutable std::array, 2> _counters; + std::atomic _foregroundCounterIndex; + std::atomic _foregroundDataIndex; + std::atomic _inDestruction; + std::array _data; + std::mutex _writeMutex; +}; + +} diff --git a/thirdparty/libtorch/include/c10/util/Logging.h b/thirdparty/libtorch/include/c10/util/Logging.h new file mode 100644 index 0000000000..0ede427b4e --- /dev/null +++ b/thirdparty/libtorch/include/c10/util/Logging.h @@ -0,0 +1,295 @@ +#ifndef C10_UTIL_LOGGING_H_ +#define C10_UTIL_LOGGING_H_ + +#include +#include +#include +#include +#include + +#include "c10/macros/Macros.h" +#include "c10/util/Exception.h" +#include "c10/util/Flags.h" +#include "c10/util/StringUtil.h" + +// CAFFE2_LOG_THRESHOLD is a compile time flag that would allow us to turn off +// logging at compile time so no logging message below that level is produced +// at all. The value should be between INT_MIN and CAFFE_FATAL. +#ifndef CAFFE2_LOG_THRESHOLD +// If we have not defined the compile time log threshold, we keep all the +// log cases. +#define CAFFE2_LOG_THRESHOLD INT_MIN +#endif // CAFFE2_LOG_THRESHOLD + +// Below are different implementations for glog and non-glog cases. +#ifdef C10_USE_GLOG +#include "c10/util/logging_is_google_glog.h" +#else // !C10_USE_GLOG +#include "c10/util/logging_is_not_google_glog.h" +#endif // C10_USE_GLOG + +C10_DECLARE_int(caffe2_log_level); +C10_DECLARE_bool(caffe2_use_fatal_for_enforce); + +// Some versions of GLOG support less-spammy version of LOG_EVERY_MS. If it's +// not available - just short-circuit to the always working one one. +// We define the C10_ name to avoid confusing other files +#ifdef LOG_EVERY_MS +#define C10_LOG_EVERY_MS(severity, ms) LOG_EVERY_MS(severity, ms) +#else +#define C10_LOG_EVERY_MS(severity, ms) LOG(severity) +#endif + +// Same for LOG_FIRST_N +#ifdef LOG_FIRST_N +#define C10_LOG_FIRST_N(severity, n) LOG_FIRST_N(severity, n) +#else +#define C10_LOG_FIRST_N(severity, n) LOG(severity) +#endif + +// Same for LOG_EVERY_N +#ifdef LOG_EVERY_N +#define C10_LOG_EVERY_N(severity, n) LOG_EVERY_N(severity, n) +#else +#define C10_LOG_EVERY_N(severity, n) LOG(severity) +#endif + +namespace c10 { + +using std::string; + +// Functions that we use for initialization. +C10_API bool InitCaffeLogging(int* argc, char** argv); +C10_API void UpdateLoggingLevelsFromFlags(); + +C10_API C10_NORETURN void ThrowEnforceNotMet( + const char* file, + const int line, + const char* condition, + const std::string& msg, + const void* caller = nullptr); + +constexpr bool IsUsingGoogleLogging() { +#ifdef C10_USE_GLOG + return true; +#else + return false; +#endif +} + +/** + * A utility to allow one to show log info to stderr after the program starts. + * + * This is similar to calling GLOG's --logtostderr, or setting caffe2_log_level + * to smaller than INFO. You are recommended to only use this in a few sparse + * cases, such as when you want to write a tutorial or something. Normally, use + * the commandline flags to set the log level. + */ +C10_API void ShowLogInfoToStderr(); + +C10_API void SetStackTraceFetcher(std::function fetcher); + +using EnforceNotMet = ::c10::Error; + +#define CAFFE_ENFORCE(condition, ...) \ + do { \ + if (C10_UNLIKELY(!(condition))) { \ + ::c10::ThrowEnforceNotMet( \ + __FILE__, __LINE__, #condition, ::c10::str(__VA_ARGS__)); \ + } \ + } while (false) + +#define CAFFE_ENFORCE_WITH_CALLER(condition, ...) \ + do { \ + if (C10_UNLIKELY(!(condition))) { \ + ::c10::ThrowEnforceNotMet( \ + __FILE__, __LINE__, #condition, ::c10::str(__VA_ARGS__), this); \ + } \ + } while (false) + +#define CAFFE_THROW(...) \ + ::c10::ThrowEnforceNotMet(__FILE__, __LINE__, "", ::c10::str(__VA_ARGS__)) + +/** + * Rich logging messages + * + * CAFFE_ENFORCE_THAT can be used with one of the "checker functions" that + * capture input argument values and add it to the exception message. E.g. + * `CAFFE_ENFORCE_THAT(Equals(foo(x), bar(y)), "Optional additional message")` + * would evaluate both foo and bar only once and if the results are not equal - + * include them in the exception message. + * + * Some of the basic checker functions like Equals or Greater are already + * defined below. Other header might define customized checkers by adding + * functions to caffe2::enforce_detail namespace. For example: + * + * namespace caffe2 { namespace enforce_detail { + * inline EnforceFailMessage IsVector(const vector& shape) { + * if (shape.size() == 1) { return EnforceOK(); } + * return c10::str("Shape ", shape, " is not a vector"); + * } + * }} + * + * With further usages like `CAFFE_ENFORCE_THAT(IsVector(Input(0).dims()))` + * + * Convenient wrappers for binary operations like CAFFE_ENFORCE_EQ are provided + * too. Please use them instead of CHECK_EQ and friends for failures in + * user-provided input. + */ + +namespace enforce_detail { + +struct C10_API EnforceOK {}; + +class C10_API EnforceFailMessage { + public: +#ifdef _MSC_VER + // MSVC + NVCC ignores constexpr and will issue a warning if included. + /* implicit */ EnforceFailMessage(EnforceOK) : msg_(nullptr) {} +#else + constexpr /* implicit */ EnforceFailMessage(EnforceOK) : msg_(nullptr) {} +#endif + EnforceFailMessage(EnforceFailMessage&&) = default; + EnforceFailMessage(const EnforceFailMessage&) = delete; + EnforceFailMessage& operator=(EnforceFailMessage&&) = delete; + EnforceFailMessage& operator=(const EnforceFailMessage&) = delete; + + // Catch all wrong usages like CAFFE_ENFORCE_THAT(x < y) + template + /* implicit */ EnforceFailMessage(Args...) { + static_assert( + // This stands for an "impossible" condition. Plain `false` doesn't + // trick compiler enough. + sizeof...(Args) == std::numeric_limits::max(), + "CAFFE_ENFORCE_THAT has to be used with one of special check functions " + "like `Equals`. Use CAFFE_ENFORCE for simple boolean checks."); + } + + /* implicit */ EnforceFailMessage(std::string&& msg); + + inline bool bad() const { + return msg_ != nullptr; + } + std::string get_message_and_free(std::string&& extra) const { + std::string r; + if (extra.empty()) { + r = std::move(*msg_); + } else { + r = ::c10::str(std::move(*msg_), ". ", std::move(extra)); + } + delete msg_; + return r; + } + + private: + std::string* msg_{}; +}; + +#define BINARY_COMP_HELPER(name, op) \ + template \ + inline EnforceFailMessage name(const T1& x, const T2& y) { \ + if (x op y) { \ + return EnforceOK(); \ + } \ + return c10::str(x, " vs ", y); \ + } +BINARY_COMP_HELPER(Equals, ==) +BINARY_COMP_HELPER(NotEquals, !=) +BINARY_COMP_HELPER(Greater, >) +BINARY_COMP_HELPER(GreaterEquals, >=) +BINARY_COMP_HELPER(Less, <) +BINARY_COMP_HELPER(LessEquals, <=) +#undef BINARY_COMP_HELPER + +#define CAFFE_ENFORCE_THAT_IMPL(condition, expr, ...) \ + do { \ + using namespace ::c10::enforce_detail; \ + const EnforceFailMessage& CAFFE_ENFORCE_THAT_IMPL_r_ = (condition); \ + if (C10_UNLIKELY(CAFFE_ENFORCE_THAT_IMPL_r_.bad())) { \ + ::c10::ThrowEnforceNotMet( \ + __FILE__, \ + __LINE__, \ + expr, \ + CAFFE_ENFORCE_THAT_IMPL_r_.get_message_and_free( \ + ::c10::str(__VA_ARGS__))); \ + } \ + } while (false) + +#define CAFFE_ENFORCE_THAT_IMPL_WITH_CALLER(condition, expr, ...) \ + do { \ + using namespace ::c10::enforce_detail; \ + const EnforceFailMessage& CAFFE_ENFORCE_THAT_IMPL_WITH_CALLER_r_ = \ + (condition); \ + if (C10_UNLIKELY(CAFFE_ENFORCE_THAT_IMPL_WITH_CALLER_r_.bad())) { \ + ::c10::ThrowEnforceNotMet( \ + __FILE__, \ + __LINE__, \ + expr, \ + CAFFE_ENFORCE_THAT_IMPL_WITH_CALLER_r_.get_message_and_free( \ + ::c10::str(__VA_ARGS__)), \ + this); \ + } \ + } while (false) +} // namespace enforce_detail + +#define CAFFE_ENFORCE_THAT(condition, ...) \ + CAFFE_ENFORCE_THAT_IMPL((condition), #condition, __VA_ARGS__) + +#define CAFFE_ENFORCE_EQ(x, y, ...) \ + CAFFE_ENFORCE_THAT_IMPL(Equals((x), (y)), #x " == " #y, __VA_ARGS__) +#define CAFFE_ENFORCE_NE(x, y, ...) \ + CAFFE_ENFORCE_THAT_IMPL(NotEquals((x), (y)), #x " != " #y, __VA_ARGS__) +#define CAFFE_ENFORCE_LE(x, y, ...) \ + CAFFE_ENFORCE_THAT_IMPL(LessEquals((x), (y)), #x " <= " #y, __VA_ARGS__) +#define CAFFE_ENFORCE_LT(x, y, ...) \ + CAFFE_ENFORCE_THAT_IMPL(Less((x), (y)), #x " < " #y, __VA_ARGS__) +#define CAFFE_ENFORCE_GE(x, y, ...) \ + CAFFE_ENFORCE_THAT_IMPL(GreaterEquals((x), (y)), #x " >= " #y, __VA_ARGS__) +#define CAFFE_ENFORCE_GT(x, y, ...) \ + CAFFE_ENFORCE_THAT_IMPL(Greater((x), (y)), #x " > " #y, __VA_ARGS__) +#define CAFFE_ENFORCE_EQ_WITH_CALLER(x, y, ...) \ + CAFFE_ENFORCE_THAT_IMPL_WITH_CALLER( \ + Equals((x), (y)), #x " == " #y, __VA_ARGS__) +#define CAFFE_ENFORCE_NE_WITH_CALLER(x, y, ...) \ + CAFFE_ENFORCE_THAT_IMPL_WITH_CALLER( \ + NotEquals((x), (y)), #x " != " #y, __VA_ARGS__) +#define CAFFE_ENFORCE_LE_WITH_CALLER(x, y, ...) \ + CAFFE_ENFORCE_THAT_IMPL_WITH_CALLER( \ + LessEquals((x), (y)), #x " <= " #y, __VA_ARGS__) +#define CAFFE_ENFORCE_LT_WITH_CALLER(x, y, ...) \ + CAFFE_ENFORCE_THAT_IMPL_WITH_CALLER(Less((x), (y)), #x " < " #y, __VA_ARGS__) +#define CAFFE_ENFORCE_GE_WITH_CALLER(x, y, ...) \ + CAFFE_ENFORCE_THAT_IMPL_WITH_CALLER( \ + GreaterEquals((x), (y)), #x " >= " #y, __VA_ARGS__) +#define CAFFE_ENFORCE_GT_WITH_CALLER(x, y, ...) \ + CAFFE_ENFORCE_THAT_IMPL_WITH_CALLER( \ + Greater((x), (y)), #x " > " #y, __VA_ARGS__) + +/** + * Very lightweight logging for the first time API usage. It's beneficial for + * tracking of individual functionality usage in larger applications. + * + * In order to ensure light-weightness of logging, we utilize static variable + * trick - LogAPIUsage will be invoked only once and further invocations will + * just do an atomic check. + * + * Example: + * // Logs caller info with an arbitrary text event, if there is a usage. + * C10_LOG_API_USAGE_ONCE("my_api"); + */ +#define C10_LOG_API_USAGE_ONCE(...) \ + C10_UNUSED static bool C10_ANONYMOUS_VARIABLE(logFlag) = \ + ::c10::detail::LogAPIUsageFakeReturn(__VA_ARGS__); + +// API usage logging capabilities +C10_API void SetAPIUsageLogger(std::function logger); +C10_API void LogAPIUsage(const std::string& context); + +namespace detail { +// Return value is needed to do the static variable initialization trick +C10_API bool LogAPIUsageFakeReturn(const std::string& context); +} + +} // namespace c10 + +#endif // C10_UTIL_LOGGING_H_ diff --git a/thirdparty/libtorch/include/c10/util/Metaprogramming.h b/thirdparty/libtorch/include/c10/util/Metaprogramming.h new file mode 100644 index 0000000000..b1056a9b10 --- /dev/null +++ b/thirdparty/libtorch/include/c10/util/Metaprogramming.h @@ -0,0 +1,146 @@ +#pragma once + +#include +#include +#include +#include +#include + +namespace c10 { namespace guts { + +/** + * Access information about result type or arguments from a function type. + * Example: + * using A = function_traits::return_type // A == int + * using A = function_traits::parameter_types::tuple_type // A == tuple + */ +template struct function_traits { + static_assert(!std::is_same::value, "In function_traits, Func must be a plain function type."); +}; +template +struct function_traits { + using func_type = Result (Args...); + using return_type = Result; + using parameter_types = typelist::typelist; + static constexpr auto number_of_parameters = sizeof...(Args); +}; + +/** + * infer_function_traits: creates a `function_traits` type for a simple + * function (pointer) or functor (lambda/struct). Currently does not support + * class methods. + */ + +template +struct infer_function_traits { + using type = function_traits>; +}; + +template +struct infer_function_traits { + using type = function_traits; +}; + +template +struct infer_function_traits { + using type = function_traits; +}; + +template +using infer_function_traits_t = typename infer_function_traits::type; + +/** + * Use extract_arg_by_filtered_index to return the i-th argument whose + * type fulfills a given type trait. The argument itself is perfectly forwarded. + * + * Example: + * std::string arg1 = "Hello"; + * std::string arg2 = "World"; + * std::string&& result = extract_arg_by_filtered_index(0, arg1, 2.0, std::move(arg2)); + * + * Warning: Taking the result by rvalue reference can cause segfaults because ownership will not be passed on + * from the original reference. The original reference dies after the expression and the resulting + */ +namespace detail { +template