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115 lines
3.2 KiB
115 lines
3.2 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include <forward_list>
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#include <list>
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#include <mutex>
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#include <vector>
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#include "paddle/platform/device_context.h"
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namespace paddle {
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namespace platform {
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enum EventKind { kMark, kPushRange, kPopRange };
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class Event {
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public:
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// The DeviceContext is used to get the cuda stream.
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// If CPU profiling mode, can pass nullptr.
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Event(EventKind kind, std::string name, uint32_t thread_id,
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DeviceContext* dev_ctx);
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std::string kind() const;
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std::string name() const { return name_; }
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bool has_cuda() const { return has_cuda_; }
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#ifdef PADDLE_WITH_CUDA
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cudaEvent_t event() const { return event_; }
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int device() const { return device_; }
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#endif
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double CpuElapsedUs(const Event& e) const;
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double CudaElapsedUs(const Event& e) const;
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private:
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EventKind kind_;
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std::string name_;
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uint32_t thread_id_;
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int64_t cpu_ns_;
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bool has_cuda_;
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#ifdef PADDLE_WITH_CUDA
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cudaEvent_t event_ = nullptr;
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int device_ = -1;
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#endif
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};
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struct EventList {
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constexpr static size_t kMB = 1024 * 1024;
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constexpr static size_t kEventBlockSize = 16 * kMB;
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constexpr static size_t kEventSize = sizeof(Event);
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constexpr static size_t kEventAlign = alignof(Event);
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constexpr static size_t kNumBlock =
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kEventBlockSize /
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((kEventSize + kEventAlign - 1) / kEventAlign * kEventAlign);
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template <typename... Args>
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void Record(Args&&... args) {
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if (event_blocks.empty() || event_blocks.front().size() == kNumBlock) {
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event_blocks.emplace_front();
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event_blocks.front().reserve(kNumBlock);
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}
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event_blocks.front().emplace_back(std::forward<Args>(args)...);
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}
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std::vector<Event> Reduce() {
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std::vector<Event> result;
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for (auto& block : event_blocks) {
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result.insert(result.begin(), std::make_move_iterator(block.begin()),
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std::make_move_iterator(block.end()));
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}
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event_blocks.clear();
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return result;
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}
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std::forward_list<std::vector<Event>> event_blocks;
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};
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enum ProfilerState {
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kDisabled, // disabled state
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kCPU, // CPU profiling state
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kCUDA, // GPU profiling state
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};
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void Mark(const std::string& name, DeviceContext* dev_ctx);
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struct RecordEvent {
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explicit RecordEvent(const std::string& name, DeviceContext* dev_ctx);
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~RecordEvent();
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// The device context is used by Event to get the current cuda stream.
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DeviceContext* dev_ctx_;
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};
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// Enable the profiling function.
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void EnableProfiler(ProfilerState state);
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// Return the event list of all threads. Asummed the returned value calls
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// event_lists, event_lists[i][j] represents the j-th Event of i-th thread.
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std::vector<std::vector<Event>> DisableProfiler();
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} // namespace platform
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} // namespace paddle
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