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https://github.com/pytorch/pytorch.git
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6c008e2fb5
Summary: `ParallelGraphExecutor` inherits from `GraphExecutorBase` and executes all nodes in the graph in a parallel manner Test Plan: CI Rollback Plan: Differential Revision: D77088996 Pull Request resolved: https://github.com/pytorch/pytorch/pull/156751 Approved by: https://github.com/zhxchen17, https://github.com/dolpm
95 lines
2.6 KiB
C++
95 lines
2.6 KiB
C++
#pragma once
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#include <c10/util/Semaphore.h>
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#include <torch/nativert/executor/GraphExecutorBase.h> // @manual
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#include <torch/nativert/executor/SessionState.h> // @manual
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#include <thread>
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namespace moodycamel {
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struct ProducerToken;
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struct ConsumerToken;
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struct ConcurrentQueueDefaultTraits;
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template <typename T, typename Traits>
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class ConcurrentQueue;
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} // namespace moodycamel
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namespace torch::nativert {
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class ThreadPoolExecutor;
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typedef std::function<void()> Work;
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struct WorkUnit {
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const Node* node;
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OpKernel* kernel;
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std::vector<WorkUnit*> users;
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void run(ThreadPoolExecutor* executor, SessionState* sessionState);
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};
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class ThreadPoolExecutor {
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public:
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explicit ThreadPoolExecutor();
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~ThreadPoolExecutor();
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ThreadPoolExecutor(const ThreadPoolExecutor&) = delete;
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ThreadPoolExecutor& operator=(ThreadPoolExecutor const&) = delete;
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ThreadPoolExecutor(ThreadPoolExecutor&&) = delete;
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ThreadPoolExecutor& operator=(ThreadPoolExecutor&&) = delete;
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void run(SessionState& session, const std::vector<WorkUnit*>& roots);
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void start(int32_t numThreads);
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void stop();
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// execute unit on the current thread
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// NOTE: children can still be offloaded to other threads
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C10_ALWAYS_INLINE void execute_inline(SessionState* session, WorkUnit* unit);
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void add(SessionState* session, WorkUnit* unit);
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void add(
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SessionState* session,
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std::vector<WorkUnit*>::const_iterator&& begin,
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const std::vector<WorkUnit*>::const_iterator&& end);
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C10_ALWAYS_INLINE moodycamel::ProducerToken& ptok();
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C10_ALWAYS_INLINE moodycamel::ConsumerToken& ctok();
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private:
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void loop();
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std::atomic_bool stopped_{false};
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std::unique_ptr<c10::Semaphore> sem_{std::make_unique<c10::Semaphore>()};
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std::unique_ptr<moodycamel::ConcurrentQueue<
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Work,
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moodycamel::ConcurrentQueueDefaultTraits>>
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work_;
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std::vector<std::thread> threads_;
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};
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class ParallelGraphExecutor : public GraphExecutorBase {
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public:
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ParallelGraphExecutor(
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const Graph& graph,
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std::vector<std::unique_ptr<OpKernel>> nodeKernels,
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const torch::nativert::ExecutorConfig& executorConfig);
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std::vector<c10::IValue> execute(
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ExecutionFrame& frame,
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std::vector<c10::IValue> inputs) override;
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std::vector<c10::IValue> executeWithPrefilledFrame(
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ExecutionFrame& frame) override;
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private:
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ThreadPoolExecutor executor_;
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std::vector<WorkUnit*> inputWorkUnits_;
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c10::FastMap<const Node*, WorkUnit*> nodeToWorkUnit_;
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std::vector<WorkUnit> workUnits_;
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const Graph& graph_;
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c10::FastMap<const Node*, copyable_atomic<std::uint_fast32_t>> producers_;
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};
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} // namespace torch::nativert
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