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engine.cpp
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engine.cpp
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#include <torch/csrc/autograd/engine.h>
#include <torch/csrc/autograd/anomaly_mode.h>
#include <torch/csrc/autograd/autograd.h>
#include <torch/csrc/autograd/function.h>
#include <torch/csrc/autograd/functions/basic_ops.h>
#include <torch/csrc/autograd/grad_mode.h>
#include <torch/csrc/autograd/variable.h>
#include <torch/csrc/dynamo/compiled_autograd.h>
#include <ATen/DeviceGuard.h>
#include <ATen/ExpandUtils.h>
#include <ATen/Parallel.h>
#include <ATen/SparseCsrTensorUtils.h>
#include <ATen/detail/CUDAHooksInterface.h>
#ifndef AT_PER_OPERATOR_HEADERS
#include <ATen/Functions.h>
#else
#include <ATen/ops/isnan.h>
#endif
#include <c10/core/DeviceGuard.h>
#include <c10/core/Event.h>
#include <c10/core/Stream.h>
#include <c10/core/StreamGuard.h>
#include <c10/util/AbortHandler.h>
#include <c10/util/Exception.h>
#include <c10/util/Optional.h>
#include <c10/util/ThreadLocal.h>
#include <c10/util/irange.h>
#include <atomic>
#include <chrono>
#include <condition_variable>
#include <cstdint>
#include <functional>
#include <iostream>
#include <memory>
#include <mutex>
#include <queue>
#include <sstream>
#include <string>
#include <thread>
#include <unordered_set>
#include <utility>
namespace torch {
namespace autograd {
namespace {
static bool in_bad_autograd_fork =
false; // True for children forked after engine's thread pool init
// Called in the forked child if engine's thread pool has already been
// initialized
static void forked_autograd_child() {
in_bad_autograd_fork = true;
}
// Should be called before unsafe for forks (thread pool) calls
static void track_bad_autograd_forks() {
#if !defined(WIN32)
static c10::once_flag flag;
c10::call_once(
flag, [&] { pthread_atfork(nullptr, nullptr, forked_autograd_child); });
#endif
}
inline bool should_run_in_cpu_ready_queue(c10::DeviceType device) {
if (device == c10::kCPU || device == c10::kMeta || device == c10::kLazy) {
return true;
} else {
return false;
}
}
std::atomic<Engine::compiled_autograd_fn> the_compiled_autograd = nullptr;
#define COMPILED_AUTOGRAD_POISON \
reinterpret_cast<Engine::compiled_autograd_fn>(1)
std::atomic<int32_t> num_threads_in_backwards;
struct CompiledAutogradThreadingDebugCheck {
CompiledAutogradThreadingDebugCheck() {
num_threads_in_backwards++;
}
~CompiledAutogradThreadingDebugCheck() {
release();
}
void release() {
if (std::exchange(incremented, false)) {
num_threads_in_backwards--;
}
}
private:
bool incremented{true};
};
} // namespace
// Threads spawned by the engine are assigned a 'worker_device' specifying
// what device they process work for. This variable is initialized at:
// 1. thread creation time for CUDA, XLA device threads, as they are
// spinning threads waiting for works on their device.
// 2. before the graph task execution for CPU threads, as for each
// backward call we use the caller thread to drive engine execution.
// This is used when handling reentrant backwards calls;
// See Note [Reentrant backwards]
static thread_local int worker_device = NO_DEVICE;
// This variable is true if ALL invocations in the stack of re-entrant engine
// invocations are imperative backwards. This special variable is needed for the
// gradient checkpointing feature only.
static thread_local bool checkpoint_valid = true;
// Number of nested reentrant backwards calls currently on this thread
static thread_local int current_depth = 0;
// For all device threads (i.e. CUDA, XLA), total_depth represents the total
// nested
// reentrant backwards depths over all device threads.
// For CPU devices, it is the total depth associated with the original backward
// call.
static thread_local int total_depth = 0;
// The current GraphTask being executed by this thread. This helps
// queue_callback() to find the target GraphTask to append final callbacks.
C10_DEFINE_TLS_static(std::shared_ptr<GraphTask>, tls_current_graph_task);
#define current_graph_task (tls_current_graph_task.get())
// Every autograd worker thread is associated with a ready queue, which
// specifies the stream of work of this thread to do. This shared_ptr is a
// thread_local pointer to each thread's ready_queue, and it should be
// initialized via the Engine::init_local_ready_queue() call in each
// corresponding thread before execution.
//
// The CUDA, XLA threads are shared among all invocations of backwards via
// device_ready_queues_, while the caller thread is dedicated to processing work
// for devices returning true in should_run_in_cpu_ready_queue (most notably the
// CPU device). So any given graph task maintains its own cpu_ready_queue_ where
// you should send work for it to be done.
//
// For reentrant backward calls, if we spawn new thread from the current thread
// because we reached the maximum depth, the new thread will just reuse the same
// ReadyQueue with the parent thread for performance improvement.
// see Note [Reentrant backwards] for more details.
C10_DEFINE_TLS_static(std::shared_ptr<ReadyQueue>, tls_local_ready_queue);
#define local_ready_queue (tls_local_ready_queue.get())
// Note [Reentrant backwards]
// ~~~~~~~~~~~~~~~~~~~~~~~~~~
// To understand the reentrant backwards problem, we have to notice two
// aspects of how the autograd engine is implemented today:
//
// 1. When you call Engine::execute(), you want to block until
// differentiation finishes so that you can get the final result variables
// of the backwards pass.
//
// 2. The engine operates by having a single worker thread per work queue,
// and every work queue is pinned to a specific device where the
// operation is executed.
//
// The problem is, suppose that you call backward() inside of a worker
// thread. By property (1), we're supposed to block until the nested task
// finishes. However, by property (2), this worker thread is on the
// hook for processing the tasks assigned to it; we better not block,
// because then all of our backward executions (including the one we
// just started) will deadlock!
//
// We maintain a pool of threads waiting for work to do
// When a reentrant backwards call occurs, the current thread blocks
// and a thread from the pool is woken up to complete the blocking tasks and an
// any other tasks that would have been assigned to that worker. If there are no
// threads available, a new thread is spawned. The new thread will continue
// processing tasks from the same ReadyQueue as the parent worker
//
// When the GraphTask is finished, the parent worker thread that is waiting on
// the task is notified and the current thread returns to the pool.
// Note [Streaming backwards]
// ~~~~~~~~~~~~~~~~~~~~~~~~~~
// On CUDA devices the autograd engine's device operations are run on the
// same stream that ran them in forward. This requires automatically
// syncing the streams so that function A finishes producing its
// output before function B consumes it.
//
// This synchronization occurs when outputs are placed into input buffers.
// The functions corresponding to input buffer positions have metadata
// recording their streams from forward, and during backward this
// data is used to sync the producer's stream with the consumer's.
//
// When a CUDA function is run either all its inputs were accumulated on the
// stream used to run the function OR the inputs are on different devices
// and the function is responsible for properly acquiring them.
//
// User-facing stream semantics of a backward() (or torch.autograd.grad())
// call with respect to surrounding ops are the same as for any other call.
// See "Stream semantics of backward passes" on
// https://pytorch.org/docs/stable/notes/cuda.html
//
// Internally, backward() runs ops (including leaf nodes) on side threads.
// And streams are thread local. So GraphTask achieves the above semantics by
// 1. remembering the current streams on all active CUDA devices
// in the user-facing thread (aka, the thread that called execute() to
// launch the GraphTask)
// 2. remembering the "leaf streams" (streams each backward leaf node ran on)
// 3. during exec_post_processing, for each leaf stream, sync the remembered
// current streams (on the leaf stream's device) with that
// leaf stream.
int NodeTask::getReentrantDepth() const {
std::shared_ptr<GraphTask> graph_task = base_.lock();
if (graph_task) {
return graph_task->reentrant_depth_;
} else {
// The graph task is no longer valid indicating an error. As a result, we
// try to move this to the front of the queue to ensure the autograd
// engine threads pick up this error soon.
return std::numeric_limits<int>::max();
}
}
CheckpointValidGuard::CheckpointValidGuard(
const std::shared_ptr<const GraphTask>& graph_task)
: prev_checkpoint_valid_state(checkpoint_valid) {
checkpoint_valid =
graph_task->can_checkpoint() && prev_checkpoint_valid_state;
}
CheckpointValidGuard::~CheckpointValidGuard() {
checkpoint_valid = prev_checkpoint_valid_state;
}
auto ReadyQueue::push(NodeTask item, bool incrementOutstandingTasks) -> void {
{
// Lock mutex for writing to heap_
std::lock_guard<std::mutex> lock(mutex_);
if (incrementOutstandingTasks) {
std::shared_ptr<GraphTask> graph_task = item.base_.lock();
TORCH_INTERNAL_ASSERT(graph_task, "GraphTask is no longer valid!");
++graph_task->outstanding_tasks_;
}
heap_.push(std::move(item));
}
not_empty_.notify_one();
}
auto ReadyQueue::pushShutdownTask() -> void {
{
std::lock_guard<std::mutex> lock(mutex_);
heap_.push(NodeTask({}, nullptr, InputBuffer(0), true));
}
not_empty_.notify_one();
}
size_t ReadyQueue::size() const {
// Lock mutex for accesses to heap_
std::unique_lock<std::mutex> lock(mutex_);
return heap_.size();
}
auto ReadyQueue::pop() -> NodeTask {
// Lock mutex for accesses to heap_
std::unique_lock<std::mutex> lock(mutex_);
not_empty_.wait(lock, [this] { return !heap_.empty(); });
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
auto task = std::move(const_cast<NodeTask&>(heap_.top()));
heap_.pop();
return task;
}
bool ReadyQueue::empty() const {
// Lock mutex for accesses to heap_
std::unique_lock<std::mutex> lock(mutex_);
return heap_.empty();
}
Engine::Engine()
: max_recursion_depth_(MAX_DEPTH), non_reentrant_device_thread_count_(0) {}
Engine::~Engine() {
stop();
}
// Send shutdown tasks to all device_ready_queues_ if no backward tasks are
// running Even though readyQueue should be empty, shutdown tasks have the
// highest priority
void Engine::stop() {
if (stopped_) {
return;
}
stopped_ = true;
// Under some conditions, autograd threads can hang on shutdown
// Do not wait for them to shutdown indefinitely but rely on timeout
auto wait_duration_str = getenv("TORCH_AUTOGRAD_SHUTDOWN_WAIT_LIMIT");
auto wait_duration = wait_duration_str ? std::atof(wait_duration_str) : 10.0;
bool noBackward = true;
for (auto& queue : device_ready_queues_) {
noBackward = noBackward && queue->empty();
}
if (noBackward && wait_duration > 0.0f) {
for (auto& queue : device_ready_queues_) {
queue->pushShutdownTask();
}
// Do not wait for termination of global threads on Windows
// Because CRT terminates DLL threads before calling
// global object destructors
#if !defined(_WIN32) || defined(C10_USE_MSVC_STATIC_RUNTIME)
using namespace std::chrono_literals;
// Set a deadline for how long it is OK to wait device threads to shutdown
auto wait_deadline =
std::chrono::steady_clock::now() + wait_duration * 1.0s;
std::unique_lock<std::mutex> lk(non_reentrant_device_thread_mutex_);
while (non_reentrant_device_thread_count_.load() != 0) {
if (non_reentrant_device_thread_condvar_.wait_until(lk, wait_deadline) ==
std::cv_status::timeout) {
break;
}
}
#endif
}
// Otherwise threads are leaked
}
void Engine::release_workers() {
std::unique_lock<std::mutex> lk(non_reentrant_device_thread_mutex_);
non_reentrant_device_thread_count_.store(0);
non_reentrant_device_thread_condvar_.notify_one();
}
void Engine::increment_non_reentrant_thread_count() {
std::unique_lock<std::mutex> lk(non_reentrant_device_thread_mutex_);
non_reentrant_device_thread_count_.fetch_add(1);
non_reentrant_device_thread_condvar_.notify_one();
}
void Engine::decrement_non_reentrant_thread_count() {
std::unique_lock<std::mutex> lk(non_reentrant_device_thread_mutex_);
non_reentrant_device_thread_count_.fetch_sub(1);
non_reentrant_device_thread_condvar_.notify_one();
}
void Engine::thread_init(
int device,
const std::shared_ptr<ReadyQueue>& ready_queue,
bool should_increment) {
c10::set_terminate_handler();
if (should_increment) {
increment_non_reentrant_thread_count();
}
at::init_num_threads();
// Note [Allocating GPUs to autograd threads]
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// What's our strategy here? Originally, the autograd engine was written
// with only CUDA in mind. We allocate one thread to handle all CPU
// operations, and a thread per CUDA device.
//
// But what if we have OTHER devices? There are two plausible
// strategies:
//
// - We can allocate threads equal to max(num_cuda_devices, num_xla_devices,
// ...) and colocate cuda device 0 with xla device 0
// - We can allocate threads equal to sum(num_cuda_devices, num_xla_devices,
// ...) keeping everyone separate.
//
// We don't have any good reason to prefer one or the other, so we've
// arbitrarily picked to colocate devices. Maybe the other approach is
// better.
worker_device = device;
// initialize each device thread's thread local ready queue with the ready
// queue that is created before the thread initialization
init_local_ready_queue(ready_queue);
std::shared_ptr<GraphTask> graph_task = nullptr;
thread_main(graph_task);
if (should_increment) {
// Decrement the count during shutdown if we incremented earlier.
decrement_non_reentrant_thread_count();
}
}
GraphTaskGuard::GraphTaskGuard(std::shared_ptr<GraphTask> graph_task)
: last_graph_task_(std::move(current_graph_task)) {
current_graph_task = std::move(graph_task);
}
GraphTaskGuard::~GraphTaskGuard() {
restore_current_graph_task();
}
void GraphTaskGuard::restore_current_graph_task() {
current_graph_task = std::move(last_graph_task_);
}
// The current graph task's exec_info is being used to trim unnecessary edegs
// during node evaluation, see `Node.task_should_compute_output()` function.
const std::unordered_map<Node*, GraphTask::ExecInfo>*
get_current_graph_task_exec_info() {
return current_graph_task ? ¤t_graph_task->exec_info_ : nullptr;
}
const std::unordered_set<Node*>* get_current_graph_task_nodes_in_graph() {
return current_graph_task ? ¤t_graph_task->nodes_in_graph_ : nullptr;
}
int get_current_graph_task_id() {
return current_graph_task ? current_graph_task->id_ : -1;
}
bool get_current_graph_task_keep_graph() {
return current_graph_task ? current_graph_task->keep_graph_ : true;
}
void add_node_to_current_graph_task_exec_info(Node* fn) {
current_graph_task->exec_info_[fn].needed_ = true;
}
// NB: The engine itself does not use the outputs of this function.
std::vector<Node*> get_current_graph_task_execution_order() {
std::shared_ptr<GraphTask> task = current_graph_task;
if (!task) {
return {};
}
// We could potentially check if there is only a single device here
// but explicitly require this context doesn't seem bad either
TORCH_CHECK(
!c10::AutogradState::get_tls_state().get_multithreading_enabled(),
"get_current_graph_task_execution_order expects the current backward to be "
"executed with multithreading disabled, e.g. by running:\n\n"
">>> with torch.autograd.set_multithreading_enabled(False):\n"
"... torch.autograd.grad(...)\n");
const bool check_exec_info = !task->exec_info_.empty();
std::vector<Node*> out{};
// Do a copy since we mutate it later
std::unordered_map<Node*, int> dependencies = task->dependencies_;
auto compare_seq_nr = [](Node* n1, Node* n2) {
return n1->sequence_nr() < n2->sequence_nr();
};
std::priority_queue<Node*, std::vector<Node*>, decltype(compare_seq_nr)> heap(
compare_seq_nr);
for (Node* ptr : task->graph_roots_) {
heap.push(ptr);
}
// Implementation notes:
// - We need count dependencies even though we have sequence_nr, because
// in the accumulate_grad case we cannot assume the outputs to have higher
// sequence_nr than the inputs
// - Don't need to check topological_nr because we have exec_info
while (!heap.empty()) {
Node* fn = heap.top();
heap.pop();
out.push_back(fn);
for (const auto& edge : fn->next_edges()) {
Node* next_ptr = edge.function.get();
if (!next_ptr) {
continue;
}
if (check_exec_info) {
auto it = task->exec_info_.find(next_ptr);
if (it == task->exec_info_.end() || !it->second.should_execute()) {
continue;
}
}
auto it = dependencies.find(edge.function.get());
TORCH_INTERNAL_ASSERT(it != dependencies.end());
if (--it->second == 0) {
dependencies.erase(it);
heap.push(next_ptr);
}
}
}
return out;
}
// NOTE: graph_tasks do not necessarily form a stack. Imagine this
// case:
//
// +----> Eval1
// Root
// +----> Eval2
//
// Once Root is executed, both Eval1 and Eval2 are added to the ready queue.
// Next, Eval1 is run and this causes the worker to enter thread_main again.
// Then, it pops the next task from the queue, but at this point it is Eval2.
// It enters thread_main once again, but now with graph_task of Eval2, which is
// completely unrelated to that of Eval1 (it's not a recursive call).
// It's all ok and is handled right now, but it should be accounted for
// in case this code is to be changed.
//
// thread_main is used by:
// 1). autograd threads for devices (i.e. CUDA, XLA)
// 2). the caller/owning thread of the backward call on CPU (sync mode)
// 3). Renetrant backward that invoked by either 1) or 2)
// The exit conditions are different for the above three cases.
// For 1), we are spinning on running the thread_main on device autograd
// threads throughout the Engine lifetime, thread_main will get
// terminated during Engine destruction by pushing shutdown tasks
// For 2), the owning thread of the backward call drives the thread_main
// synchronously until the graph_task of that owning thread is
// completed and exit the thread_main to continue executing the
// result of caller's code.
// For 3), the reentrant backward that invokes
// thread_main, either from 1) or 2), will not spin and will exit as
// long as graph_task is completed and notify the owning thread as
// needed.
auto Engine::thread_main(const std::shared_ptr<GraphTask>& graph_task) -> void {
// When graph_task is nullptr, this is a long running thread that processes
// tasks (ex: device threads). When graph_task is non-null (ex: reentrant
// backwards, user thread), this function is expected to exit once that
// graph_task complete.
// local_ready_queue should already been initialized when we get into
// thread_main
TORCH_INTERNAL_ASSERT(local_ready_queue != nullptr);
while (graph_task == nullptr || !graph_task->future_result_->completed()) {
// local_graph_task represents the graph_task we retrieve from the queue.
// The outer graph_task represents the overall graph_task we need to execute
// for reentrant execution.
std::shared_ptr<GraphTask> local_graph_task;
{
// Scope this block of execution since NodeTask is not needed after this
// block and can be deallocated (release any references to grad tensors
// as part of inputs_).
NodeTask task = local_ready_queue->pop();
// This will only work if the worker is running a non backward task
// TODO Needs to be fixed this to work in all cases
if (task.isShutdownTask_) {
C10_LOG_API_USAGE_ONCE("torch.autograd.thread_shutdown");
break;
}
local_graph_task = task.base_.lock();
if (!local_graph_task) {
// GraphTask for function is no longer valid, skipping further
// execution.
continue;
}
set_device(worker_device);
if (task.fn_ && !local_graph_task->has_error_.load()) {
// Set the ThreadLocalState before calling the function.
// NB: The ThreadLocalStateGuard doesn't set the grad_mode because
// GraphTask always saves ThreadLocalState without grad_mode.
at::ThreadLocalStateGuard tls_guard(local_graph_task->thread_locals_);
c10::WarningUtils::WarningHandlerGuard warnings_guard(
&local_graph_task->warning_handler_);
try {
// The guard sets the thread_local current_graph_task on construction
// and restores it on exit. The current_graph_task variable helps
// queue_callback() to find the target GraphTask to append final
// callbacks.
GraphTaskGuard guard(local_graph_task);
NodeGuard ndguard(task.fn_);
{
RECORD_FUNCTION(
c10::str(
"autograd::engine::evaluate_function: ",
task.fn_.get()->name()),
c10::ArrayRef<const c10::IValue>());
evaluate_function(
local_graph_task,
task.fn_.get(),
task.inputs_,
local_graph_task->cpu_ready_queue_);
}
} catch (std::exception& e) {
// See Note [ Persisting PyErr state across autograd engine threads ]
thread_on_exception(local_graph_task, task.fn_, e);
}
}
}
// Decrement the outstanding tasks.
--local_graph_task->outstanding_tasks_;
// Check if we've completed execution.
if (local_graph_task->completed()) {
local_graph_task->mark_as_completed_and_run_post_processing();
auto base_owner = local_graph_task->owner_;
// The current worker thread finish the graph_task, but the owning thread
// of the graph_task might be sleeping on pop() if it does not have work.
// So we need to send a dummy function task to the owning thread just to
// ensure that it's not sleeping, so that we can exit the thread_main.
// If it has work, it might see that graph_task->outstanding_tasks_ == 0
// before it gets to the task, but it's a no-op anyway.
//
// NB: This is not necessary if the current thread is the owning thread.
if (worker_device != base_owner) {
// Synchronize outstanding_tasks_ with queue mutex
std::atomic_thread_fence(std::memory_order_release);
ready_queue_by_index(local_graph_task->cpu_ready_queue_, base_owner)
->push(NodeTask(local_graph_task, nullptr, InputBuffer(0)));
}
}
}
}
// Reentrant call will re-use the graph_task's owner thread ready_queue for
// queueing tasks (NOTE: this is not true in the async_mode of the engine).
// While we can create separate ready queue for each new reentrant
// thread, but sharing the same cpu_ready_queue with parent thread is a
// performance improvement and cuda thread still have to do the same thing.
void Engine::reentrant_thread_init() {
c10::set_terminate_handler();
at::init_num_threads();
auto tp_shared = thread_pool_shared_;
while (true) {
std::unique_lock<std::mutex> lk(tp_shared->mutex_);
++thread_pool_shared_->num_workers_;
tp_shared->work_.wait(
lk, [&tp_shared] { return !tp_shared->graphtasks_queue_.empty(); });
--thread_pool_shared_->num_workers_;
auto task = tp_shared->graphtasks_queue_.front();
tp_shared->graphtasks_queue_.pop();
lk.unlock();
std::shared_ptr<GraphTask> graph_task = task.lock();
if (!graph_task) {
LOG(INFO) << "GraphTask has expired, skipping reentrant execution";
continue;
}
set_device(graph_task->owner_);
// set the local_ready_queue to the ready queue on the graph_task->owner_
// device
local_ready_queue =
ready_queue_by_index(graph_task->cpu_ready_queue_, graph_task->owner_);
total_depth = graph_task->reentrant_depth_;
thread_main(graph_task);
}
}
void Engine::thread_on_exception(
// NOLINTNEXTLINE(performance-unnecessary-value-param)
std::shared_ptr<GraphTask> graph_task,
const std::shared_ptr<Node>& fn,
std::exception& e) {
graph_task->set_exception(std::current_exception(), fn);
}
bool GraphTask::completed() {
return outstanding_tasks_.load() == 0 ||
(exit_on_error_ && has_error_.load());
}
void GraphTask::mark_as_completed_and_run_post_processing() {
// Allow only one thread one attempt to process this logic.
if (future_completed_.exchange(true)) {
// Future is already marked complete, or being marked as such.
// In case the marking complete is only in progress, we add a
// wait() to guarantee the future is marked complete on exit.
future_result_->wait();
return;
}
try {
// Run post processing, before marking the future as complete.
// Drop lock prior to completing, to avoid holding across callbacks.
std::unique_lock<std::mutex> lock(mutex_);
exec_post_processing();
std::vector<Variable> vars = std::move(captured_vars_);
// Need to unlock before we call markCompleted to avoid holding locks
// when the callbacks are called.
lock.unlock();
future_result_->markCompleted(vars);
} catch (std::exception& e) {
future_result_->setErrorIfNeeded(std::current_exception());
}
}
void GraphTask::exec_post_processing() {
if (!not_ready_.empty()) {
throw std::runtime_error("could not compute gradients for some functions");
}
// set the thread_local current_graph_task_ as more callbacks can be installed
// by existing final callbacks.
GraphTaskGuard guard(shared_from_this());
// Lock mutex during each iteration for accessing final_callbacks.size()
// Unlocking is necessary, because the callback can register
// more callbacks (or they can be registered from other threads
// while it's waiting.
std::unique_lock<std::mutex> cb_lock(final_callbacks_lock_);
// caller_current_streams_ with nullopt entries removed
std::vector<c10::Stream> caller_current_streams_filtered;
// See Note [Streaming backwards].
// Syncs caller_current_stream with leaf streams, so final_callbacks may use
// any grad on its device's current stream.
if (!leaf_streams.empty()) {
for (const auto& leaf_stream : leaf_streams) {
// stash_current_streams() stashed streams for all device IDs that already
// had a CUDA context before the GraphTask executed. For inactive devices,
// it stashed a c10::nullopt. I don't expect GraphTask's backward pass ran
// leaf nodes on any new devices, so the stashed streams should be enough.
// If leaf_stream.device_index() happens to be for a new device,
// operator* on the c10::nullopt should throw an error.
const auto caller_current_stream =
// NOLINTNEXTLINE(bugprone-unchecked-optional-access)
*caller_current_streams_[leaf_stream.device_index()];
if (caller_current_stream != leaf_stream) {
auto event = c10::Event{c10::DeviceType::CUDA};
event.record(leaf_stream);
caller_current_stream.wait(event);
}
}
caller_current_streams_filtered.reserve(caller_current_streams_.size());
for (const auto& opt_stream : caller_current_streams_) {
if (opt_stream.has_value()) {
caller_current_streams_filtered.push_back(*opt_stream);
}
}
}
{
// final_callbacks run on the per-device caller_current_streams (the ambient
// streams surrounding the user's call to backward()). This has two
// benefits:
// 1. caller_current_streams have been synced with leaf_streams, so
// callbacks may
// safely access any grad.
// 2. The callback's results can safely be used on (user-facing)
// caller_current_streams
// after backward().
c10::MultiStreamGuard g(caller_current_streams_filtered);
// Set the ThreadLocalState before calling the function.
// NB: The ThreadLocalStateGuard doesn't set the grad_mode because GraphTask
// always saves ThreadLocalState without grad_mode.
at::ThreadLocalStateGuard tls_guard(this->thread_locals_);
// WARNING: Don't use a range-for loop here because more callbacks may be
// added in between callback calls, so iterators may become invalidated.
// NOLINTNEXTLINE(modernize-loop-convert)
for (size_t i = 0; i < final_callbacks_.size(); ++i) {
cb_lock.unlock();
final_callbacks_[i]();
cb_lock.lock();
}
}
}
void GraphTask::set_exception_without_signal(const std::shared_ptr<Node>& fn) {
if (!has_error_.exchange(true)) {
if (AnomalyMode::is_enabled() && fn) {
fn->metadata()->print_stack(fn->name());
}
}
}
void GraphTask::set_exception(
std::exception_ptr eptr,
const std::shared_ptr<Node>& fn) {
set_exception_without_signal(fn);
if (!future_completed_.exchange(true)) {
future_result_->setError(std::move(eptr));
}
}
static variable_list call_pre_hooks(Node& fn, variable_list inputs) {
for (const auto& hook : fn.pre_hooks()) {
inputs = (*hook)(inputs);
}
return inputs;
}
static variable_list call_tensor_pre_hooks(Node& fn, variable_list inputs) {
for (const auto& hook : fn.tensor_pre_hooks()) {
inputs = (*hook)(inputs);
}
for (const auto& pair : fn.retains_grad_hooks()) {
inputs = (*pair.second)(inputs);
}
return inputs;
}
static variable_list call_post_hooks(
Node& fn,
variable_list outputs,
const variable_list& inputs) {
for (const auto& hook : fn.post_hooks()) {
outputs = (*hook)(outputs, inputs);
}
return outputs;
}
void set_device(int device) {
// NB: We MUST NOT construct the guard for device CPU,
// as in some settings we compile with cuda, but
// have lazy stubs for CUDA functionality (so actually
// attempting to setup a guard(CPU_DEVICE) will cause an
// error, because it will still query GetDevice).
//
// Don't use DeviceGuard here because its destructor may be called before the
// device is reset. This is fine because the device is thread local.
if (device != CPU_DEVICE) {
for (const auto i : c10::irange(static_cast<size_t>(
c10::DeviceType::COMPILE_TIME_MAX_DEVICE_TYPES))) {
auto* impl = c10::impl::device_guard_impl_registry[i].load();
if (impl && device < impl->deviceCount()) {
impl->setDevice(at::Device(
static_cast<c10::DeviceType>(i),
static_cast<c10::DeviceIndex>(device)));
}
}
}
worker_device = device;
}
void validate_outputs(
const edge_list& edges,
variable_list& grads,
const std::function<std::string(const std::string&)>& format_error) {
if (grads.size() != edges.size()) {
std::stringstream ss;
ss << "invalid number of gradients - expected ";
ss << edges.size() << ", but got " << grads.size();
TORCH_CHECK(false, format_error(ss.str()));
}
for (const auto i : c10::irange(grads.size())) {
const auto& edge = edges[i];
if (!edge.is_valid())
continue;
const auto& metadata = edge.function->input_metadata(edge.input_nr);
auto& grad = grads[i];
if (!grad.defined()) {
// FIXME: TestJit.test_ge_optimized fails this assertion.
// std::stringstream ss;
// ss << "undefined gradient at index " << i;
// TORCH_CHECK(false, format_error(ss.str()));
continue;
}
if (!metadata.is_same_shape(grad)) {
if (metadata.is_expandable_to_shape(grad)) {
grad = metadata.reduce_grad(grad);
} else {
const auto message = metadata.incompatible_shape_error_message(i, grad);
TORCH_CHECK(false, format_error(message.str()));
}
}
bool input_is_complex =
isComplexType(c10::typeMetaToScalarType(metadata.options().dtype()));
bool grad_is_complex = isComplexType(grad.scalar_type());
TORCH_CHECK(
isFloatingType(grad.scalar_type()) ||
(input_is_complex == grad_is_complex));
if (c10::typeMetaToScalarType(metadata.options().dtype()) !=
grad.scalar_type()) {
grad = grad.to(c10::typeMetaToScalarType(metadata.options().dtype()));
}
if (grad.dtype() != metadata.dtype()) {
std::stringstream ss;
ss << "invalid gradient at index " << i << " - expected dtype ";
ss << metadata.dtype() << " but got " << grad.dtype();
TORCH_CHECK(false, format_error(ss.str()));
}
if (grad.layout() != metadata.layout()) {
// TODO: Currently we only support (*, Sparse) combination for
// (tensor.layout(), tensor.grad.layout()) In future, there will be an
// opportunity to support more combinations of layouts if they are
// composable (example., operations like addition etc., are well defined
// between tensors of different layouts.), as well as all parts of
// autograd like AccumulateGrad correctly handle this. We allow grad to be
// Strided when metadata is SparseCsr
if (!grad.is_sparse() &&
!(grad.layout() == at::kStrided &&
(at::sparse_csr::is_sparse_compressed(metadata.layout()) ||
metadata.layout() == at::kSparse))) {
std::stringstream ss;
ss << "invalid gradient at index " << i << " - expected layout ";
ss << metadata.layout() << " but got " << grad.layout();
TORCH_CHECK(false, format_error(ss.str()));
}
}
if (grad.device() != metadata.device()) {
// quick hack for: https://github.com/pytorch/pytorch/issues/65016 but
// should be eventually removed
if (!(metadata.is_tensor_subclass() ||
grad.unsafeGetTensorImpl()->is_python_dispatch())) {
if (grad.dim() == 0) {
grad = grad.to(metadata.device());
} else {
std::stringstream ss;
ss << "invalid gradient at index " << i << " - expected device ";
ss << metadata.device() << " but got " << grad.device();
TORCH_CHECK(false, format_error(ss.str()));
}
}
}
// We should not build graph for Tensors that are not differentiable
TORCH_INTERNAL_ASSERT(isDifferentiableType(grad.scalar_type()));
}
}
static variable_list call_function(
std::shared_ptr<GraphTask>& graph_task,
Node* func,
InputBuffer& inputBuffer) {
CheckpointValidGuard cpvguard(graph_task);
auto& fn = *func;
auto inputs =
call_tensor_pre_hooks(fn, InputBuffer::variables(std::move(inputBuffer)));
inputs = call_pre_hooks(fn, std::move(inputs));
if (!graph_task->keep_graph_) {
fn.will_release_variables();
}
const auto has_post_hooks = !fn.post_hooks().empty();
variable_list outputs;
if (has_post_hooks) {
// In functions/accumulate_grad.cpp, there is some logic to check the
// conditions under which the incoming gradient can be stolen directly
// (which elides a deep copy) instead of cloned. One of these conditions
// is that the incoming gradient's refcount must be 1 (nothing else is
// referencing the same data). Stashing inputs_copy here bumps the
// refcount, so if post hooks are employed, it's actually still ok for
// accumulate_grad.cpp to steal the gradient if the refcount is 2.
//
// "new_grad.use_count() <= 1 + !post_hooks().empty()" in
// accumulate_grad.cpp accounts for this, but also creates a silent
// dependency between engine.cpp (ie, this particular engine
// implementation) and accumulate_grad.cpp.
//
// If you change the logic here, make sure it's compatible with
// accumulate_grad.cpp.
auto inputs_copy = inputs;
outputs = fn(std::move(inputs_copy));
} else {
outputs = fn(std::move(inputs));
}
validate_outputs(fn.next_edges(), outputs, [&](const std::string& msg) {
std::ostringstream ss;
ss << "Function " << fn.name() << " returned an " << msg;
return ss.str();
});
if (has_post_hooks) {
// NOLINTNEXTLINE(bugprone-use-after-move)
return call_post_hooks(fn, std::move(outputs), inputs);
}
return outputs;
}
void Engine::evaluate_function(
std::shared_ptr<GraphTask>& graph_task,
Node* func,
InputBuffer& inputs,
const std::shared_ptr<ReadyQueue>& cpu_ready_queue) {
// The InputBuffer::adds that supplied incoming grads took pains to
// ensure they're safe to consume in the context of the present
// func's stream (if applicable). So we guard onto that stream
// before working with the grads in any capacity.
const auto opt_parent_stream = (*func).stream(c10::DeviceType::CUDA);
c10::OptionalStreamGuard parent_stream_guard{opt_parent_stream};
// If exec_info_ is not empty, we have to instrument the execution
auto& exec_info_ = graph_task->exec_info_;
if (!exec_info_.empty()) {
auto& fn_info = exec_info_.at(func);
variable_list new_inputs = inputs.buffer;
if (!fn_info.needed_) {
// We always want to call tensor pre-hooks, but want to avoid calling it
// twice. needed_ = True indicates that we will call tensor pre-hooks
// later.
//
// See NOTE [Hooks ordering] for more context.
new_inputs = call_tensor_pre_hooks(
*func, InputBuffer::variables(std::move(inputs)));
}
if (auto* capture_vec = fn_info.captures_.get()) {
const auto opt_parent_stream = (*func).stream(c10::DeviceType::CUDA);
// Lock mutex for writing to graph_task->captured_vars_.
std::lock_guard<std::mutex> lock(graph_task->mutex_);
for (const auto& capture : *capture_vec) {
auto& captured_grad = graph_task->captured_vars_[capture.output_idx_];
captured_grad = new_inputs[capture.input_idx_];
// NOTE [Deprecated capture hooks]
for (const auto& hook :