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Generator.cpp
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Generator.cpp
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#include <torch/csrc/Generator.h>
#include <structmember.h>
#include <ATen/ATen.h>
#include <ATen/CPUGenerator.h>
#include <TH/TH.h>
#include <torch/csrc/THP.h>
#include <torch/csrc/Device.h>
#include <torch/csrc/Exceptions.h>
#include <torch/csrc/autograd/python_variable.h>
#include <torch/csrc/autograd/generated/VariableType.h>
#include <torch/csrc/utils/tensor_types.h>
#include "torch/csrc/utils/python_arg_parser.h"
#include <torch/csrc/autograd/generated/variable_factories.h>
#ifdef USE_CUDA
#include <THC/THCTensorRandom.h>
#include <ATen/CUDAGenerator.h>
#endif
using namespace at;
using namespace torch;
PyObject *THPGeneratorClass = nullptr;
PyObject * THPGenerator_initDefaultGenerator(at::Generator* cdata)
{
auto type = (PyTypeObject*)THPGeneratorClass;
auto self = THPObjectPtr{type->tp_alloc(type, 0)};
if (!self) throw python_error();
auto self_ = reinterpret_cast<THPGenerator*>(self.get());
self_->cdata = cdata;
self_->owner = false;
return self.release();
}
static void THPGenerator_dealloc(THPGenerator* self)
{
if (self->owner) {
delete self->cdata;
}
Py_TYPE(self)->tp_free((PyObject*)self);
}
static PyObject * THPGenerator_pynew(PyTypeObject *type, PyObject *args, PyObject *kwargs)
{
HANDLE_TH_ERRORS
static torch::PythonArgParser parser({
"Generator(Device device=None)"
});
torch::ParsedArgs<1> parsed_args;
auto r = parser.parse(args, kwargs, parsed_args);
auto device = r.deviceWithDefault(0, at::Device(at::kCPU));
THPGeneratorPtr self((THPGenerator *)type->tp_alloc(type, 0));
#ifdef USE_CUDA
if (device.type() == at::kCPU) {
self->cdata = new CPUGenerator();
} else if (device.type() == at::kCUDA){
self->cdata = new CUDAGenerator(device.index());
} else {
AT_ERROR("Device type ", c10::DeviceTypeName(device.type()),
" is not supported for torch.Generator() api.");
}
#else
TORCH_CHECK(device.type() == at::kCPU,
"Device type ", c10::DeviceTypeName(device.type()),
" is not supported for torch.Generator() api.");
self->cdata = new CPUGenerator();
#endif
self->owner = true;
return (PyObject*)self.release();
END_HANDLE_TH_ERRORS
}
static PyObject * THPGenerator_getState(THPGenerator *self, PyObject *noargs)
{
using namespace torch::autograd;
HANDLE_TH_ERRORS
Variable var = torch::empty({0}, at::device(at::kCPU).dtype(at::kByte));
if (self->cdata->device().type() == at::kCPU) {
THByteTensor_getRNGState(self->cdata, (THByteTensor*)(var.unsafeGetTensorImpl()));
} else {
#ifdef USE_CUDA
TORCH_INTERNAL_ASSERT(self->cdata->device().type() == at::kCUDA);
THCRandom_getRNGState(self->cdata, (THByteTensor*)(var.unsafeGetTensorImpl()));
#else
TORCH_INTERNAL_ASSERT(false, "PyTorch not compiled with CUDA");
#endif
}
return THPVariable_Wrap(std::move(var));
END_HANDLE_TH_ERRORS
}
static PyObject * THPGenerator_setState(THPGenerator *self, PyObject *_new_state)
{
using namespace torch::autograd;
HANDLE_TH_ERRORS
if (!THPVariable_Check(_new_state)) {
throw TypeError("expected a torch.ByteTensor, but got %s", Py_TYPE(_new_state)->tp_name);
}
auto& tensor = ((THPVariable*)_new_state)->cdata;
if (tensor.layout() != kStrided || tensor.device().type() != kCPU || tensor.scalar_type() != kByte) {
auto type_name = torch::utils::options_to_string(tensor.options());
throw TypeError("expected a torch.ByteTensor, but got %s", type_name.c_str());
}
if (self->cdata->device().type() == at::kCPU) {
THByteTensor_setRNGState(self->cdata, (THByteTensor*)tensor.unsafeGetTensorImpl());
} else {
#ifdef USE_CUDA
TORCH_INTERNAL_ASSERT(self->cdata->device().type() == at::kCUDA);
THCRandom_setRNGState(self->cdata, (THByteTensor*)tensor.unsafeGetTensorImpl());
#else
TORCH_INTERNAL_ASSERT(false, "PyTorch not compiled with CUDA");
#endif
}
Py_INCREF(self);
return (PyObject*)self;
END_HANDLE_TH_ERRORS
}
static PyObject * THPGenerator_manualSeed(THPGenerator *self, PyObject *seed)
{
HANDLE_TH_ERRORS
auto generator = self->cdata;
THPUtils_assert(THPUtils_checkLong(seed), "manual_seed expected a long, "
"but got %s", THPUtils_typename(seed));
// See Note [Acquire lock when using random generators]
std::lock_guard<std::mutex> lock(generator->mutex_);
generator->set_current_seed(THPUtils_unpackLong(seed));
Py_INCREF(self);
return (PyObject*)self;
END_HANDLE_TH_ERRORS
}
static PyObject * THPGenerator_seed(THPGenerator *self, PyObject *noargs)
{
HANDLE_TH_ERRORS
// See Note [Acquire lock when using random generators]
std::lock_guard<std::mutex> lock(self->cdata->mutex_);
uint64_t seed_val = self->cdata->seed();
return THPUtils_packUInt64(seed_val);
END_HANDLE_TH_ERRORS
}
static PyObject * THPGenerator_initialSeed(THPGenerator *self, PyObject *noargs)
{
HANDLE_TH_ERRORS
return THPUtils_packUInt64(self->cdata->current_seed());
END_HANDLE_TH_ERRORS
}
static PyObject * THPGenerator_get_device(THPGenerator *self, void *unused) {
HANDLE_TH_ERRORS
return THPDevice_New(self->cdata->device());
END_HANDLE_TH_ERRORS
}
static struct PyGetSetDef THPGenerator_properties[] = {
{"device", (getter)THPGenerator_get_device, nullptr, nullptr, nullptr},
{nullptr}
};
static PyMethodDef THPGenerator_methods[] = {
{"get_state", (PyCFunction)THPGenerator_getState, METH_NOARGS, nullptr},
{"set_state", (PyCFunction)THPGenerator_setState, METH_O, nullptr},
{"manual_seed", (PyCFunction)THPGenerator_manualSeed, METH_O, nullptr},
{"seed", (PyCFunction)THPGenerator_seed, METH_NOARGS, nullptr},
{"initial_seed", (PyCFunction)THPGenerator_initialSeed, METH_NOARGS, nullptr},
{nullptr}
};
static struct PyMemberDef THPGenerator_members[] = {
{(char*)"_cdata", T_ULONGLONG, offsetof(THPGenerator, cdata), READONLY, nullptr},
{nullptr}
};
PyTypeObject THPGeneratorType = {
PyVarObject_HEAD_INIT(nullptr, 0)
"torch._C.Generator", /* tp_name */
sizeof(THPGenerator), /* tp_basicsize */
0, /* tp_itemsize */
(destructor)THPGenerator_dealloc, /* tp_dealloc */
0, /* tp_vectorcall_offset */
nullptr, /* tp_getattr */
nullptr, /* tp_setattr */
nullptr, /* tp_reserved */
nullptr, /* tp_repr */
nullptr, /* tp_as_number */
nullptr, /* tp_as_sequence */
nullptr, /* tp_as_mapping */
nullptr, /* tp_hash */
nullptr, /* tp_call */
nullptr, /* tp_str */
nullptr, /* tp_getattro */
nullptr, /* tp_setattro */
nullptr, /* tp_as_buffer */
Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /* tp_flags */
nullptr, /* tp_doc */
nullptr, /* tp_traverse */
nullptr, /* tp_clear */
nullptr, /* tp_richcompare */
0, /* tp_weaklistoffset */
nullptr, /* tp_iter */
nullptr, /* tp_iternext */
THPGenerator_methods, /* tp_methods */
THPGenerator_members, /* tp_members */
THPGenerator_properties, /* tp_getset */
nullptr, /* tp_base */
nullptr, /* tp_dict */
nullptr, /* tp_descr_get */
nullptr, /* tp_descr_set */
0, /* tp_dictoffset */
nullptr, /* tp_init */
nullptr, /* tp_alloc */
THPGenerator_pynew, /* tp_new */
};
bool THPGenerator_init(PyObject *module)
{
THPGeneratorClass = (PyObject*)&THPGeneratorType;
if (PyType_Ready(&THPGeneratorType) < 0)
return false;
Py_INCREF(&THPGeneratorType);
PyModule_AddObject(module, "Generator", (PyObject *)&THPGeneratorType);
return true;
}