You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
239 lines
6.9 KiB
239 lines
6.9 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License. */
|
|
|
|
#pragma once
|
|
|
|
#include <iostream>
|
|
#include <vector>
|
|
|
|
#include "ModelConfig.pb.h"
|
|
#include "TrainerConfig.pb.h"
|
|
#include "paddle/gserver/dataproviders/DataProvider.h"
|
|
#include "paddle/gserver/evaluators/Evaluator.h"
|
|
#include "paddle/gserver/layers/Layer.h"
|
|
#include "paddle/math/Matrix.h"
|
|
#include "paddle/parameter/Parameter.h"
|
|
#include "paddle/parameter/ParameterUpdaterBase.h"
|
|
#include "paddle/utils/Thread.h"
|
|
|
|
namespace paddle {
|
|
/**
|
|
* @brief A gradient machine is capable of calculating some outputs given
|
|
* some inputs and performing gradient calculation based on the
|
|
* derivative from the outputs.
|
|
*
|
|
* A gradient machine can be either a full neural network or part of a neural
|
|
* network.
|
|
*
|
|
* Usage for training:
|
|
*
|
|
* 1. Prepare inArgs. Put your input data into inArgs[i].value.
|
|
*
|
|
* 2. Call forward(inArgs, &outArgs)
|
|
*
|
|
* 3. Calculate gradient with respect to outArgs[i]->value
|
|
* and fill them into outArgs[i]->grad.
|
|
* This step can be skipped if your the outputs are from cost layers.
|
|
*
|
|
* 4. Call backward(). After backward, gradient of each parameter is
|
|
* accumulated to getParameters()[i]->getBuf(PARAMETER_GRADIENT)
|
|
*
|
|
* 5. Update parameter value getParameters()[i]->getBuf(PARAMETER_VALUE) using
|
|
* gradients.
|
|
*
|
|
* 6. Clear gradients to zero.
|
|
*
|
|
* Usage for prediction:
|
|
*
|
|
* 1. Prepare inArgs. Put your input data into inArgs[i].value.
|
|
*
|
|
* 2. Call forward(inArgs, &outArgs)
|
|
*
|
|
* 3. Obtain the prediction result from outArgs[i]
|
|
*/
|
|
|
|
typedef std::vector<LayerStatePtr> MachineState;
|
|
|
|
class GradientMachine;
|
|
|
|
typedef std::shared_ptr<GradientMachine> GradientMachinePtr;
|
|
|
|
class GradientMachine {
|
|
public:
|
|
enum CreateMode {
|
|
kNormal = 0,
|
|
kSgdSparseCpuTraining = 3,
|
|
kTesting = 4,
|
|
kCustom = 10
|
|
};
|
|
|
|
/**
|
|
* Create a gradient machine from ModelConfig
|
|
* Parameter will have parameterTypes
|
|
*/
|
|
static GradientMachine* create(
|
|
const ModelConfig& config,
|
|
int mode = kNormal,
|
|
const std::vector<ParameterType>& parameterTypes =
|
|
std::vector<ParameterType>{
|
|
PARAMETER_VALUE, PARAMETER_GRADIENT, PARAMETER_MOMENTUM});
|
|
|
|
virtual ~GradientMachine() {}
|
|
|
|
/**
|
|
* Prefetch row ids of sparse parameter.
|
|
*/
|
|
virtual void prefetch(const std::vector<Argument>& inArgs) { (void)inArgs; }
|
|
|
|
/**
|
|
* @brief Forward propagation.
|
|
*
|
|
* Calculate outputs (outArgs) based the inputs (inArgs)
|
|
*
|
|
* @note: if passType==PASS_TEST, then backward() should not be called
|
|
*/
|
|
virtual void forward(const std::vector<Argument>& inArgs,
|
|
std::vector<Argument>* outArgs,
|
|
PassType passType) = 0;
|
|
|
|
/**
|
|
* @brief Backward propagation.
|
|
*
|
|
* Calculate the gradient of inArgs and parameter.
|
|
*
|
|
* This function should only be called after a corresponding forward() call.
|
|
* The caller is responsible for filling the correct grad for the outArgs
|
|
* obtained using forward().
|
|
*
|
|
* It may also change the grad field for the inArgs supplied at forward()
|
|
*/
|
|
virtual void backward(const UpdateCallback& callback = nullptr) = 0;
|
|
|
|
/**
|
|
* Combine forward() and backward(). For multithread training, this
|
|
* may be faster.
|
|
*
|
|
* @note: passType PASS_TEST is not allowed for forwardBackward().
|
|
*/
|
|
virtual void forwardBackward(const std::vector<Argument>& inArgs,
|
|
std::vector<Argument>* outArgs,
|
|
PassType passType,
|
|
const UpdateCallback& callback = nullptr) {
|
|
forward(inArgs, outArgs, passType);
|
|
backward(callback);
|
|
}
|
|
|
|
virtual Argument getLayerOutput(const std::string& layerName) = 0;
|
|
|
|
// see comment in Layer.h for the function with the same name
|
|
virtual void resetState() {}
|
|
|
|
// set machine state
|
|
virtual void setState(const MachineState& machineState) {}
|
|
|
|
// save machine state
|
|
virtual void getState(MachineState& machineState) {}
|
|
|
|
virtual void onPassEnd() = 0;
|
|
|
|
/**
|
|
* Create an evaluator which can be used for eval()
|
|
*/
|
|
virtual Evaluator* makeEvaluator() const = 0;
|
|
|
|
/**
|
|
* evaluate using the given evaluator
|
|
*/
|
|
virtual void eval(Evaluator* evaluator) const = 0;
|
|
|
|
std::vector<ParameterPtr>& getParameters() { return parameters_; }
|
|
|
|
std::vector<ParameterPtr>& getNonStaticParameters() {
|
|
if (nonStaticParameters_.empty()) {
|
|
for (auto para : parameters_) {
|
|
if (!para->isStatic()) {
|
|
nonStaticParameters_.push_back(para);
|
|
}
|
|
}
|
|
}
|
|
return nonStaticParameters_;
|
|
}
|
|
|
|
inline bool hasStaticParameters() {
|
|
return parameters_.size() != getNonStaticParameters().size();
|
|
}
|
|
|
|
/**
|
|
* @brief Used before formal training, start work-threads and set
|
|
* trainer Parameters;
|
|
*
|
|
* @note This function will only been implemented and used in a
|
|
* multithreaded environment.
|
|
*/
|
|
virtual void start() {}
|
|
|
|
/**
|
|
* @brief check each work-thread whether is failed/error/finish,
|
|
* if not, return ture, and yes return false.
|
|
*
|
|
* @note This function will only been implemented and used in a
|
|
* multithreaded environment.
|
|
*/
|
|
virtual void finish() {}
|
|
|
|
/**
|
|
* @brief set the training status a "finished" value, the sub_work_threads
|
|
* will option the change, and then exit.
|
|
*
|
|
* @note This function will only been implemented and used in a
|
|
* multithreaded environment.
|
|
*/
|
|
virtual bool trainIsOn() { return true; }
|
|
|
|
/**
|
|
* @brief when all or some of the sub-workThreads are suspended to waiting
|
|
* controller's instructions, and after some processing done in the
|
|
* controller, it will call this function to wake up all the pending
|
|
* thread.
|
|
*
|
|
* @note This function will only been implemented and used in a
|
|
* multithreaded environment.
|
|
*/
|
|
virtual void restart() {}
|
|
|
|
/// Set the gradient of the output from outside.
|
|
virtual void setOutputGrad(const std::vector<Argument>& args) {
|
|
LOG(FATAL) << "Not implemented!";
|
|
}
|
|
|
|
void saveParameters(const std::string& dir) const;
|
|
|
|
void loadParameters(const std::string& dir);
|
|
|
|
void randParameters();
|
|
|
|
virtual void getStats(real& cost, int64_t& numProcessed) {
|
|
(void)cost;
|
|
(void)numProcessed;
|
|
}
|
|
|
|
protected:
|
|
virtual void onLoadParameter() {}
|
|
|
|
std::vector<ParameterPtr> parameters_;
|
|
std::vector<ParameterPtr> nonStaticParameters_;
|
|
};
|
|
|
|
} // namespace paddle
|