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114 lines
3.2 KiB
114 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 "NeuralNetwork.h"
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namespace paddle {
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class ParallelThread;
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enum TaskType {
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TASK_FORWARD = 0,
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TASK_BACKWARD = 1,
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TASK_END_LAYER = 2,
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TASK_THREAD_FINISH = 3,
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};
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/**
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* A ParallelNeuralNetwork is capable of calculating a neural network through
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* multiple threads in parallel.
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*/
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class ParallelNeuralNetwork : public NeuralNetwork {
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public:
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ParallelNeuralNetwork(std::string subModelName = "",
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NeuralNetwork *rootNetwork = nullptr)
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: NeuralNetwork(subModelName, rootNetwork) {}
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virtual void init(const ModelConfig &config,
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ParamInitCallback callback = nullptr,
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const std::vector<ParameterType> ¶meterTypes =
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std::vector<ParameterType>{PARAMETER_VALUE,
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PARAMETER_GRADIENT,
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PARAMETER_MOMENTUM},
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bool useGpu = FLAGS_use_gpu);
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virtual void forward(const std::vector<Argument> &inArgs,
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std::vector<Argument> *outArgs,
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PassType passType);
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virtual void backward(const UpdateCallback &callback = nullptr);
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void forwardBackward(const std::vector<Argument> &inArgs,
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std::vector<Argument> *outArgs,
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PassType passType,
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const UpdateCallback &callback = NULL);
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virtual void start();
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void addComputeThread(int deviceId);
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void dispatchByDeviceId(int deviceId, LayerPtr layer, TaskType task);
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void waitAllThread();
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// virtual void eval(Evaluator* evaluator);
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protected:
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bool useGpu_;
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/// number of gpu devices
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int numDevices_;
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std::vector<std::unique_ptr<ParallelThread>> threads_;
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};
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class ParallelThread {
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public:
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ParallelThread(int threadId, int deviceId, bool useGpu);
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~ParallelThread();
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void jobEnqueue(LayerPtr layer, TaskType task);
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void start();
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void stop();
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int getDeviceId() const { return deviceId_; }
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void setBackwardCallback(const UpdateCallback &callback) {
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backwardCallback_ = callback;
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}
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void setForwardPassType(PassType passType) { passType_ = passType; }
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protected:
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void computeThread();
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public:
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struct Job {
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LayerPtr layer_;
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TaskType task_;
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};
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typedef Queue<Job> JobQueue;
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JobQueue queue_;
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protected:
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/// from 0 to threads-1
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int threadId_;
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/// the GPU device Id which the computeThread_ used
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int deviceId_;
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bool useGpu_;
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std::unique_ptr<std::thread> computeThread_;
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/// whether the thread should stop
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bool stopping_;
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UpdateCallback backwardCallback_;
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PassType passType_;
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};
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} // namespace paddle
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