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profiler_kineto.cpp
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profiler_kineto.cpp
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#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
#include <torch/csrc/autograd/profiler_kineto.h>
#include <c10/macros/Export.h>
#include <c10/util/flat_hash_map.h>
#include <c10/util/irange.h>
#include <c10/util/overloaded.h>
#include <c10/util/variant.h>
#include <c10/util/C++17.h>
#include <torch/csrc/profiler/api.h>
#include <torch/csrc/profiler/collection.h>
#include <torch/csrc/profiler/containers.h>
#include <torch/csrc/profiler/kineto_shim.h>
#include <torch/csrc/profiler/nvtx_observer.h>
#include <ATen/Context.h>
#include <deque>
#include <limits>
#include <sstream>
#include <stdexcept>
#ifdef USE_KINETO
#include <libkineto.h>
#include <time_since_epoch.h>
#ifndef _MSC_VER
// TODO: TO be removed, once this properly works from libkineto
// Literal copy-n-paste from third_party/kineto/libkineto/src/WeakSymbols.cpp
extern "C" {
// This function is needed to avoid superfluous dependency on GNU OpenMP library
// when cuPTI is linked statically For more details see
// https://github.com/pytorch/pytorch/issues/51026
__attribute__((weak)) int acc_get_device_type() {
throw std::runtime_error(
"Dummy implementation of acc_get_device_type is not supposed to be called!");
}
} // extern "C"
#endif // _MSC_VER
#endif // USE_KINETO
namespace torch {
namespace autograd {
namespace profiler {
namespace {
const std::string kMemoryEventName = "[memory]";
// TODO: consider TLS (tid + tls counter)
uint64_t next_correlation_id() {
static std::atomic<uint64_t> corr_id_{1};
return corr_id_++;
}
inline int64_t getTimeUs() {
#ifdef USE_KINETO
return libkineto::timeSinceEpoch(std::chrono::system_clock::now());
#else
return torch::profiler::impl::getTime() / 1000;
#endif // USE_KINETO
}
} // namespace
namespace python_tracer {
namespace {
CallFn call_fn;
TraceEventsFn get_events_fn;
} // namespace
void registerFunctions(CallFn call, TraceEventsFn get_events) {
call_fn = call;
get_events_fn = get_events;
}
void call(Command c) {
if (call_fn != nullptr) {
call_fn(c);
}
}
std::vector<std::unique_ptr<PyTraceEvent>> get_events() {
return get_events_fn != nullptr
? get_events_fn()
: std::vector<std::unique_ptr<PyTraceEvent>>();
}
// We do not want `getTimeUs` to be directly visible, but we need a way for
// the python tracer to use the same timing convention as the profiler.
int64_t now() {
return getTimeUs();
}
struct Replay {
PyTraceEvent* frame_;
bool enter_;
C10_NODISCARD int64_t t() const {
return enter_ ? frame_->startTime_ : frame_->endTime_;
}
C10_NODISCARD size_t idx() const {
return enter_ ? frame_->call_idx_ : frame_->return_idx_;
}
bool operator<(const Replay& other) const {
return idx() < other.idx();
}
};
void _push_reverse_order(PyTraceEvent* e, std::vector<std::string>& names) {
if (e != nullptr) {
_push_reverse_order(e->parent_, names);
names.push_back(e->name_);
}
}
} // namespace python_tracer
namespace {
using torch::profiler::impl::ProfilerThreadLocalStateBase;
using torch::profiler::impl::ActiveProfilerType;
using torch::profiler::impl::Result;
using torch::profiler::impl::kineto::annotation_t;
using torch::profiler::impl::shapesToStr;
using torch::profiler::impl::dtypesToStr;
using torch::profiler::impl::stacksToStr;
struct MemoryEventData {
torch::profiler::impl::approx_time_t start_time;
void* ptr;
int64_t alloc_size;
int64_t total_allocated;
int64_t total_reserved;
uint64_t threadID;
torch::profiler::impl::kineto::DeviceAndResource kineto_info;
c10::DeviceType device_type;
c10::DeviceIndex device_index;
};
static_assert(std::is_pod<MemoryEventData>::value, "Non-POD member of MemoryEventData.");
struct EventFieldsVisitor {
EventFieldsVisitor(const Result& result, KinetoEvent& kineto_event)
: result_{result}, kineto_event_{kineto_event} {
handleJIT(result_.get().jit_stack_, result_.get().jit_modules_);
c10::visit(*this, result.event_);
}
void operator()(const torch::profiler::impl::OpEvent& op_event) {
kineto_event_.get()
.endThreadId(op_event.end_thread_id_)
.scope(op_event.record_function_scope_)
.setAsync(op_event.is_async_)
.debugHandle(op_event.debug_handle_);
auto& shapes = result_.get().inputs_.shapes_;
if (!shapes.empty()) {
kineto_event_.get().shapes(shapes);
annotations_.emplace_back("Input Dims", shapesToStr(shapes));
}
auto& dtypes = result_.get().inputs_.dtypes_;
if (!dtypes.empty()) {
kineto_event_.get().dtypes(dtypes);
annotations_.emplace_back("Input type", dtypesToStr(dtypes));
}
if (!result_.get().extra_args_.empty()) {
kineto_event_.get().flops(
computeFlops(result_.get().name(), result_.get().extra_args_));
}
kineto_event_.get().cuda_event_start_ =
result_.get().gpu_fallback_.cuda_event_start_;
kineto_event_.get().cuda_event_end_ =
result_.get().gpu_fallback_.cuda_event_end_;
// add information about an associated forward op, if a sequence number
// is available (e.g. during training)
if (op_event.sequence_number_ >= 0) {
kineto_event_.get()
.sequenceNr(op_event.sequence_number_)
.fwdThreadId(op_event.forward_thread_id_);
annotations_.emplace_back(
"Fwd thread id", std::to_string(op_event.forward_thread_id_));
annotations_.emplace_back(
"Sequence number", std::to_string(op_event.sequence_number_));
}
}
void operator()(const torch::profiler::impl::BackendEvent& backend_event) {
kineto_event_.get()
.endThreadId(result_.get().start_tid_)
.scope(backend_event.record_function_scope_)
.debugHandle(backend_event.debug_handle_)
.backend(backend_event.backend_);
if (!backend_event.backend_.empty()) {
annotations_.emplace_back(
"Backend", "\"" + backend_event.backend_ + "\"");
}
}
void handleJIT(
const std::vector<std::string>& jit_stack,
const std::vector<std::string>& jit_modules) {
if (!jit_stack.empty()) {
// NB: This is only for the JIT stack. The python stack (if applicable)
// is constructed later.
kineto_event_.get().stack(jit_stack);
annotations_.emplace_back(
"Call stack", torch::profiler::impl::stacksToStr(jit_stack, ";"));
}
if (!jit_modules.empty()) {
kineto_event_.get().moduleHierarchy(jit_modules);
annotations_.emplace_back(
"Module Hierarchy",
torch::profiler::impl::stacksToStr(jit_modules, "."));
}
}
std::reference_wrapper<const Result> result_;
std::reference_wrapper<KinetoEvent> kineto_event_;
annotation_t annotations_;
};
auto getAnnotations(const MemoryEventData& event) {
torch::profiler::impl::kineto::annotation_t out{
{"Device Type", std::to_string((int8_t)event.device_type)},
{"Device Id", std::to_string(event.device_index)},
{"Addr", std::to_string(reinterpret_cast<intptr_t>(event.ptr))},
{"Bytes", std::to_string(event.alloc_size)}};
if (event.total_allocated >= 0) {
out.emplace_back("Total Allocated", std::to_string(event.total_allocated));
}
if (event.total_reserved >= 0) {
out.emplace_back("Total Reserved", std::to_string(event.total_reserved));
}
return out;
}
// Assumption: Total threads number will not exceed 2^16-1, and total ops will
// not exceed 2^48 -1.
static inline uint64_t getForwardThreadKey(uint64_t tid, uint64_t seqNr) {
return (((tid) << 48) | ((seqNr) & (((uint64_t)1 << 48) - 1)));
}
struct KinetoThreadLocalState : public ProfilerThreadLocalStateBase {
explicit KinetoThreadLocalState(
const ProfilerConfig& config,
std::set<torch::profiler::impl::ActivityType> activities)
: ProfilerThreadLocalStateBase(config),
start_time_(getTimeUs()),
activities_(std::move(activities)),
record_queue_(config),
cpu_trace_(start_time_, "PyTorch Profiler") {}
~KinetoThreadLocalState() override = default;
static KinetoThreadLocalState* getTLS() {
auto tls = ProfilerThreadLocalStateBase::getTLS();
TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
tls == nullptr || tls->profilerType() == ActiveProfilerType::KINETO);
return static_cast<KinetoThreadLocalState*>(tls);
}
ActiveProfilerType profilerType() override {
return ActiveProfilerType::KINETO;
}
bool tracePython() {
return config().with_stack && activities_.count(ActivityType::CPU);
}
void reportMemoryUsage(
void* ptr,
int64_t alloc_size,
int64_t total_allocated,
int64_t total_reserved,
c10::Device device) override {
if (config_.profile_memory && config_.state != ProfilerState::Disabled) {
std::lock_guard<std::mutex> guard(state_mutex_);
memory_events_.emplace_back(
torch::profiler::impl::getApproximateTime(),
ptr,
alloc_size,
total_allocated,
total_reserved,
at::RecordFunction::currentThreadId(),
torch::profiler::impl::kineto::kineto_ids(),
device.type(),
device.index());
}
}
const post_process_t& getEventPostProcessingCallback() const {
return event_post_process_cb_;
}
void setEventPostProcessingCallback(post_process_t&& cb) {
event_post_process_cb_ = std::move(cb);
}
torch::profiler::impl::kineto::ActivityTraceWrapper finalizeTrace() {
auto end_time = getTimeUs();
materializeOpEvents();
finalizeCPUTrace(cpu_trace_.get());
{
std::lock_guard<std::mutex> guard(state_mutex_);
cpu_trace_.transferCpuTrace(end_time);
}
if (config().state != ProfilerState::KINETO_ONDEMAND) {
auto trace = torch::profiler::impl::kineto::stopTrace();
TORCH_CHECK(trace || !torch::profiler::kKinetoAvailable);
addTraceEvents(trace);
return trace;
} else {
return torch::profiler::impl::kineto::ActivityTraceWrapper();
}
}
void materializeOpEvents() {
std::lock_guard<std::mutex> guard(state_mutex_);
auto converter = clock_converter_.makeConverter();
for (const auto& e : memory_events_) {
auto start_time_us = converter(e.start_time) / 1000;
cpu_trace_.addCPUActivity(
kMemoryEventName,
torch::profiler::impl::kineto::KinetoActivityType::CPU_INSTANT_EVENT,
e.kineto_info,
/*correlation_id=*/0,
start_time_us,
start_time_us,
getAnnotations(e));
kineto_events_.emplace_back();
auto& evt = kineto_events_.back();
evt.name(kMemoryEventName)
.startUs(start_time_us)
.deviceIndex(e.device_index)
.deviceType(e.device_type)
.nBytes(e.alloc_size)
.startThreadId(e.threadID);
}
memory_events_.clear();
for (auto& e : record_queue_.getRecords(converter)) {
// `take_data` handles time conversion.
int64_t start_us = e.start_time_us_;
int64_t end_us = e.end_time_us_;
if (end_us < start_us) {
// We initialize end_us_ to the smallest int64_t, so this means that
// the op did not finish before we stopped profiling.
continue;
}
// Call events post processing callback before finalizing trace, if there
// is one.
if (getEventPostProcessingCallback()) {
getEventPostProcessingCallback()(
c10::visit([](const auto& i) { return i.debug_handle_; }, e.event_),
e.jit_stack_,
e.jit_modules_);
}
kineto_events_.emplace_back();
kineto_events_.back()
.name(e.name())
.startUs(start_us)
.durationUs(end_us - start_us)
.correlationId(e.correlation_id())
.deviceType(c10::DeviceType::CPU)
.startThreadId(e.start_tid_);
// NB: also sets fields on `kineto_events_.back()`.
auto annotations =
EventFieldsVisitor(e, kineto_events_.back()).annotations_;
cpu_trace_.addCPUActivity(
e.name(),
e.kinetoType(),
e.kineto_info_,
e.correlation_id(),
start_us,
end_us,
annotations);
}
}
void finalizeCPUTrace(std::unique_ptr<torch::profiler::impl::kineto::trace_t>& cpu_trace) {
#ifndef USE_KINETO
}
#else // USE_KINETO
TORCH_INTERNAL_ASSERT(
cpu_trace->activities.size() == kineto_events_.size());
// startThreadId_seqNum to pointer of activity.
// Low-16bits of startThreadId and low-48bits seqNum are concatenated into
// one uint64_t variable as key.
// From the time being, we need disable the forward/backward correlation feature to
// workaround the crash bug.
// TODO: by Mike Guo
// reenable the forward/backward correlation when kineto fix the following raw pointer
// GenericTraceActivity.flow.linkedActivity
/*
std::unordered_map<uint64_t, libkineto::GenericTraceActivity*>
tidSeq2activity;
for (const auto idx : c10::irange(cpu_trace->activities.size())) {
auto& kineto_event = kineto_events_[idx];
auto& activity = cpu_trace->activities[idx];
// add information about an associated forward op, if a sequence number
// is available (e.g. during training)
if (kineto_event.sequenceNr() >= 0) {
generateForwardBackwardLink(
kineto_event, fwd_bwd_link_id, activity, tidSeq2activity);
}
}
*/
addPythonEvents(cpu_trace);
}
void addPythonEvents(std::unique_ptr<torch::profiler::impl::kineto::trace_t>& cpu_trace) {
if (!tracePython()) {
return;
}
auto py_events = python_tracer::get_events();
for (const auto& e : py_events) {
TORCH_INTERNAL_ASSERT(
!e->thread_id_,
"Profiler expects only single threaded Python tracing.");
}
// The remainder of this function merges the Python and Kineto event
// streams into a single stream. If Python tracing is not enabled, we want
// to avoid this process altogether to cut down on processing time.
if (!py_events.size()) {
return;
}
// Kineto event times
std::vector<int64_t> op_start_times;
for (const auto& a : cpu_trace->activities) {
op_start_times.push_back(a.startTime);
}
std::sort(op_start_times.begin(), op_start_times.end());
// Map PyTraceEvent* to sequential integers for JSON export.
ska::flat_hash_map<python_tracer::PyTraceEvent*, std::string>
py_event_indices_{
{ nullptr,
std::string("null") }};
for (const auto i : c10::irange(py_events.size())) {
py_event_indices_.insert({py_events[i].get(), std::to_string(i)});
}
ska::flat_hash_map<std::string, size_t> module_counter_;
ska::flat_hash_map<size_t, std::string> module_id_map_;
auto record_module_id = [&](python_tracer::PyTraceEvent* e) {
if (e->call_type_ == python_tracer::CallType::kPyModuleCall &&
module_id_map_.find(e->module_id_) == module_id_map_.end()) {
// We use the fact that operator[] will default initialize new keys.
module_id_map_[e->module_id_] =
std::to_string(module_counter_[e->name_]++);
}
};
// Python events
std::vector<python_tracer::Replay> py_replay;
for (const auto& e : py_events) {
py_replay.push_back({e.get(), true});
py_replay.push_back({e.get(), false});
}
std::sort(py_replay.begin(), py_replay.end());
// In order to determine the state of the python interpreter when a
// particular op is called, we have to replay the python events and note
// timestamps which are associated with op start times.
std::vector<python_tracer::PyTraceEvent*> py_stack;
ska::flat_hash_map<int64_t, python_tracer::PyTraceEvent*> op_py_map;
auto replay_it = py_replay.begin();
for (auto t : op_start_times) {
while (replay_it != py_replay.end() && replay_it->t() <= t) {
if (replay_it->enter_) {
py_stack.push_back(replay_it->frame_);
record_module_id(replay_it->frame_);
} else {
TORCH_INTERNAL_ASSERT(py_stack.size());
TORCH_INTERNAL_ASSERT(py_stack.back() == replay_it->frame_);
py_stack.pop_back();
}
replay_it++;
}
op_py_map.insert({t, py_stack.size() ? py_stack.back() : nullptr});
}
std::vector<libkineto::GenericTraceActivity> py_activities;
auto py_events_it = py_events.begin();
auto py_device = libkineto::processId();
auto main_thread = libkineto::systemThreadId();
auto push_py_event = [&]() {
auto e = (*py_events_it).get();
libkineto::GenericTraceActivity op(
cpu_trace->span, libkineto::ActivityType::PYTHON_FUNCTION, e->name_);
op.device = py_device;
op.resource = main_thread;
op.startTime = e->startTime_;
op.endTime = e->endTime_;
op.addMetadata("Python id", py_event_indices_.at(e));
op.addMetadata("Python parent id", py_event_indices_.at(e->parent_));
op.addMetadata("Python thread", std::to_string(e->thread_id_));
if (e->call_type_ == python_tracer::CallType::kPyModuleCall) {
op.addMetadata("Python module id", module_id_map_.at(e->module_id_));
}
py_activities.push_back(op);
py_events_it++;
};
TORCH_INTERNAL_ASSERT(cpu_trace->activities.size() == kineto_events_.size());
for (const auto idx : c10::irange(cpu_trace->activities.size())) {
auto& activity = cpu_trace->activities[idx];
// Add any python events that occurred between this Kineto event and the
// previous Kineto event.
while (py_events_it != py_events.end() &&
(*py_events_it)->endTime_ <= activity.endTime) {
push_py_event();
}
auto python_caller = op_py_map.at(activity.startTime);
activity.addMetadata(
"python_caller_id", py_event_indices_.at(python_caller));
// If the kineto event has a stack that means the JIT model has a stack
// associated with it that we need to respect.
if (!kineto_events_[idx].hasStack()) {
std::vector<std::string> py_names;
_push_reverse_order(python_caller, py_names);
kineto_events_[idx].stack(py_names);
activity.addMetadata("Call stack", torch::profiler::impl::stacksToStr(py_names, ";"));
}
}
// Add any Python events which finish after the last Kineto event.
while (py_events_it != py_events.end()) {
push_py_event();
}
cpu_trace->activities.insert(cpu_trace->activities.end(), py_activities.begin(), py_activities.end());
}
void generateForwardBackwardLink(
const KinetoEvent& kineto_event,
uint64_t& fwd_bwd_link_id,
libkineto::GenericTraceActivity& activity,
std::unordered_map<uint64_t, libkineto::GenericTraceActivity*>&
tidSeq2activity) {
if (kineto_event.fwdThreadId() > 0) {
// act is backward op.
uint64_t key = getForwardThreadKey(
kineto_event.fwdThreadId(), kineto_event.sequenceNr());
auto iter = tidSeq2activity.find(key);
if (iter != tidSeq2activity.end()) {
libkineto::GenericTraceActivity* fwd = iter->second;
fwd->flow.start = true;
activity.flow.id = fwd->flow.id = fwd_bwd_link_id;
activity.flow.type = fwd->flow.type = libkineto::kLinkFwdBwd;
++fwd_bwd_link_id;
}
} else if (kineto_event.startThreadId() != 0) {
// act is forward op.
uint64_t key = getForwardThreadKey(
kineto_event.startThreadId(), kineto_event.sequenceNr());
// Assumption: Among all ops with same sequence number,
// the one with biggest start time is most likely launching backward op.
auto iter = tidSeq2activity.find(key);
if (iter == tidSeq2activity.end()) {
tidSeq2activity[key] = &activity;
} else {
// Now the sequence number is only incremented on creating a "Node"
// object for backward pass, by calling
// "at::sequence_number::get_and_increment()". Among all ops with same
// sequence number, the one with biggest startTime is the one launching
// backward op.
if (activity.startTime >= iter->second->startTime) {
tidSeq2activity[key] = &activity;
}
}
}
}
#endif // USE_KINETO
void addTraceEvents(torch::profiler::impl::kineto::ActivityTraceWrapper& trace) {
#ifdef USE_KINETO
const auto& events = *(trace.get()->activities());
for (const auto& ev_ptr : events) {
if (ev_ptr == nullptr) {
continue;
}
const auto& activity = *ev_ptr;
// These events are already processed
if (activity.type() != libkineto::ActivityType::CPU_OP &&
activity.type() != libkineto::ActivityType::CPU_INSTANT_EVENT &&
activity.type() != libkineto::ActivityType::USER_ANNOTATION &&
activity.type() != libkineto::ActivityType::PYTHON_FUNCTION) {
kineto_events_.emplace_back();
auto& kineto_event = kineto_events_.back();
kineto_event.name(activity.name())
.deviceIndex(activity.deviceId())
.deviceResourceId(activity.resourceId())
.startUs(activity.timestamp())
.durationUs(activity.duration())
.activityType((uint8_t)activity.type());
if (activity.linkedActivity()) {
kineto_event.linkedCorrelationId(
activity.linkedActivity()->correlationId());
}
kineto_event.deviceType(deviceTypeFromActivity(activity.type()));
}
}
#endif // USE_KINETO
}
uint64_t start_time_;
torch::profiler::impl::ApproximateClockToUnixTimeConverter clock_converter_;
std::set<torch::profiler::impl::ActivityType> activities_;
torch::profiler::impl::RecordQueue record_queue_;
torch::profiler::impl::AppendOnlyList<MemoryEventData, 1024> memory_events_;
torch::profiler::impl::kineto::TraceWrapper cpu_trace_;
std::vector<KinetoEvent> kineto_events_;
// Optional, if event post-processing is enabled.
post_process_t event_post_process_cb_;
};
static std::unique_ptr<KinetoThreadLocalState> globalStatePtr;
template<typename... Args>
static void initGlobalState(Args... args) {
if (globalStatePtr) {
LOG(WARNING) << "GlobalStatePtr already exists!";
} else {
globalStatePtr = std::make_unique<KinetoThreadLocalState>(std::forward<Args>(args)...);
}
}
static void resetGlobalState() {
TORCH_INTERNAL_ASSERT(globalStatePtr != nullptr, "Global state ptr cannot be null before resetting");
globalStatePtr.reset();
}
template<bool use_global>
static KinetoThreadLocalState* getStatePtr() {
return c10::guts::if_constexpr<use_global>(
[] { return globalStatePtr.get(); },
[] { return KinetoThreadLocalState::getTLS(); });
}
template<bool use_global_state_ptr = false>
std::unique_ptr<at::ObserverContext> onFunctionEnter(const at::RecordFunction& fn) {
auto state_ptr = getStatePtr<use_global_state_ptr>();
if (!state_ptr) {
return nullptr;
}
auto corr_id = next_correlation_id();
if (fn.scope() == at::RecordScope::USER_SCOPE) {
torch::profiler::impl::kineto::pushUserCorrelationId(corr_id);
} else {
torch::profiler::impl::kineto::pushCorrelationId(corr_id);
}
return state_ptr->record_queue_.getSubqueue()->begin_op(fn, corr_id);
}
// @lint-ignore CLANGTIDY clang-diagnostic-unused-parameter
template<bool use_global_state_ptr = false>
void onFunctionExit(const at::RecordFunction& fn, at::ObserverContext* ctx_ptr) {
auto state_ptr = getStatePtr<use_global_state_ptr>();
if (!state_ptr) {
return;
}
const auto& config = state_ptr->config();
auto* kineto_ctx_ptr =
static_cast<torch::profiler::impl::KinetoObserverContext*>(ctx_ptr);
TORCH_INTERNAL_ASSERT(kineto_ctx_ptr != nullptr);
kineto_ctx_ptr->event_->end_time_ = torch::profiler::impl::getApproximateTime();
kineto_ctx_ptr->event_->end_thread_id_ = at::RecordFunction::currentThreadId();
if (config.state == ProfilerState::KINETO_GPU_FALLBACK) {
try {
auto fallback = kineto_ctx_ptr->fallback_;
TORCH_INTERNAL_ASSERT(fallback != nullptr);
torch::profiler::impl::cudaStubs()->record(
nullptr, &fallback->cuda_event_end_, nullptr);
} catch (const std::exception& e) {
LOG(WARNING) << "Failed to record CUDA event. " << e.what();
}
}
if (fn.scope() == at::RecordScope::USER_SCOPE) {
torch::profiler::impl::kineto::popUserCorrelationId();
} else {
torch::profiler::impl::kineto::popCorrelationId();
}
}
template <bool use_global_callback = false>
void pushProfilingCallbacks(const std::unordered_set<at::RecordScope>& scopes) {
auto registration_state_ptr = getStatePtr<use_global_callback>();
TORCH_INTERNAL_ASSERT(registration_state_ptr, "Expected profiler state set");
auto recordFunctionCallback =
at::RecordFunctionCallback(
onFunctionEnter<use_global_callback>,
onFunctionExit<use_global_callback>)
.needsInputs(registration_state_ptr->config().report_input_shapes)
.scopes(scopes);
auto handle = c10::guts::if_constexpr<use_global_callback>(
[&] { return at::addGlobalCallback(recordFunctionCallback); },
[&] { return at::addThreadLocalCallback(recordFunctionCallback);
});
registration_state_ptr->setCallbackHandle(handle);
}
} // namespace
void reportBackendEventToActiveKinetoProfiler(
const int64_t start_time_us,
const int64_t end_time_us,
const int64_t debug_handle,
const at::RecordScope scope,
const std::string& event_name,
const std::string& backend_name) {
TORCH_INTERNAL_ASSERT(globalStatePtr == nullptr, "On-demand profiling does not support post processing callback");
auto state_ptr = KinetoThreadLocalState::getTLS();
if (!state_ptr) {
return;
}
state_ptr->record_queue_.getSubqueue()->emplace_backend_event(
torch::profiler::impl::BackendEvent {
start_time_us,
end_time_us,
(uint8_t)scope,
debug_handle,
event_name,
backend_name});
/* no support for input shapes now?
if (config.report_input_shapes) {
ctx_ptr->shapes = inputSizes(fn);
ctx_ptr->dtypes = inputTypes(fn);
}
*/
}
void prepareProfiler(
const torch::profiler::impl::ProfilerConfig& config,
const std::set<torch::profiler::impl::ActivityType>& activities) {
if (config.state == ProfilerState::NVTX) {
return;
}
TORCH_CHECK(
config.state == ProfilerState::KINETO ||
config.state == ProfilerState::KINETO_GPU_FALLBACK,
"Supported only in Kineto profiler");
torch::profiler::impl::kineto::prepareTrace(
/*cpuOnly=*/!at::hasCUDA(), activities, config.experimental_config);
}
void enableProfilerWithEventPostProcess(
const torch::profiler::impl::ProfilerConfig& config,
const std::set<torch::profiler::impl::ActivityType>& activities,
post_process_t&& cb,
const std::unordered_set<at::RecordScope>& scopes) {
TORCH_CHECK(
config.state != ProfilerState::NVTX,
"NVTX does not support post processing callback.");
TORCH_INTERNAL_ASSERT(globalStatePtr == nullptr, "On-demand profiling does not support post processing callback");
enableProfiler(config, activities, scopes);
auto state_ptr = KinetoThreadLocalState::getTLS();
state_ptr->setEventPostProcessingCallback(std::move(cb));
}
void enableProfiler(
const torch::profiler::impl::ProfilerConfig& config,
const std::set<torch::profiler::impl::ActivityType>& activities,
const std::unordered_set<at::RecordScope>& scopes) {
TORCH_CHECK(!profilerEnabled(), "Profiler is already enabled on this thread");
if (config.state == ProfilerState::NVTX) {
torch::profiler::impl::pushNVTXCallbacks(config, scopes);
return;
}
TORCH_CHECK(
config.state == ProfilerState::KINETO ||
config.state == ProfilerState::KINETO_GPU_FALLBACK ||
config.state == ProfilerState::KINETO_ONDEMAND);
TORCH_CHECK(
!activities.empty(), "No activities specified for Kineto profiler");
if (config.state == ProfilerState::KINETO ||
config.state == ProfilerState::KINETO_GPU_FALLBACK) {
auto state = std::make_shared<KinetoThreadLocalState>(config, activities);
c10::ThreadLocalDebugInfo::_push(c10::DebugInfoKind::PROFILER_STATE, state);
if (state->tracePython()) {
python_tracer::call(python_tracer::Command::kStartOne);
}
if (activities.count(ActivityType::CPU)) {
pushProfilingCallbacks<false>(scopes);
}
torch::profiler::impl::kineto::startTrace();
}
if (config.state == ProfilerState::KINETO_ONDEMAND) {
initGlobalState(config, activities);
TORCH_INTERNAL_ASSERT(activities.count(ActivityType::CPU), "Ondemand profiling must enable CPU tracing");
pushProfilingCallbacks<true>(scopes);
}
}
std::unique_ptr<ProfilerResult> disableProfiler() {
auto state_ptr = static_cast<ProfilerThreadLocalStateBase*>(
(globalStatePtr == nullptr) ? getStatePtr<false>() : getStatePtr<true>());
const auto& config = state_ptr->config();
TORCH_CHECK(
state_ptr &&
(config.state == ProfilerState::KINETO ||
config.state == ProfilerState::KINETO_GPU_FALLBACK ||
config.state == ProfilerState::KINETO_ONDEMAND ||
config.state == ProfilerState::NVTX),
"Can't disable Kineto profiler when it's not running");
if (state_ptr->hasCallbackHandle()) {
at::removeCallback(state_ptr->callbackHandle());
}
// Traces are converged via libkineto automatically for ondemand flow
if (state_ptr->config().state == ProfilerState::KINETO_ONDEMAND) {
auto kineto_state_ptr = static_cast<KinetoThreadLocalState*>(state_ptr);
auto trace = kineto_state_ptr->finalizeTrace();
resetGlobalState();
return std::make_unique<ProfilerResult>();
}
// Shared among NVTX, KINETO, KINETO_GPU_FALLBACK
std::unique_ptr<ProfilerResult> result;
if (state_ptr->config().state == ProfilerState::NVTX) {
result = std::make_unique<ProfilerResult>();
}
if (config.state == ProfilerState::KINETO ||
config.state == ProfilerState::KINETO_GPU_FALLBACK) {
auto kineto_state_ptr = static_cast<KinetoThreadLocalState*>(state_ptr);
if (kineto_state_ptr->tracePython()) {
python_tracer::call(python_tracer::Command::kStop);
}
auto trace = kineto_state_ptr->finalizeTrace();
if (kineto_state_ptr->tracePython()) {
python_tracer::call(python_tracer::Command::kClear);
}
result = std::make_unique<ProfilerResult>(
kineto_state_ptr->start_time_,
std::move(kineto_state_ptr->kineto_events_),
std::move(trace));
}
// Disable thread-local profiler. We can't pop until the very end as it would invalidate
// the `state_ptr` reference which we need to process the traces.
(void)c10::ThreadLocalDebugInfo::_pop(c10::DebugInfoKind::PROFILER_STATE);
return result;
}
int64_t KinetoEvent::cudaElapsedUs() const {
if (!cuda_event_start_ || !cuda_event_end_) {
return -1;
}
try {
return (int64_t)torch::profiler::impl::cudaStubs()->elapsed(&cuda_event_start_, &cuda_event_end_);
} catch (std::exception& e) {
LOG(WARNING) << "Failed to measure time between two CUDA events. "
<< e.what();
}
return -1;
}
ProfilerResult::ProfilerResult(
uint64_t start_time,
std::vector<KinetoEvent> events,
torch::profiler::impl::kineto::ActivityTraceWrapper trace)
: trace_start_us_(start_time),
events_(std::move(events)),
trace_(std::move(trace)) {}
ProfilerResult::ProfilerResult() = default;
ProfilerResult::~ProfilerResult() = default;
void ProfilerResult::save(const std::string& path) {
trace_.save(path);
}
} // namespace profiler
} // namespace autograd
} // namespace torch