GradientMachine.cpp

/* 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. */

#include "PaddleAPI.h"
#include "PaddleAPIPrivate.h"

#include "Internal.h"
#include "paddle/gserver/gradientmachines/NeuralNetwork.h"

std::vector<int> GradientMachine::defaultParamTypes = {
    PARAMETER_VALUE, PARAMETER_GRADIENT, PARAMETER_MOMENTUM};

GradientMachine::GradientMachine() : m(new GradientMachinePrivate()) {}

GradientMachine::~GradientMachine() { delete m; }

GradientMachine* GradientMachine::createFromPaddleModelPtr(
    const void* confPtr,
    GradientMatchineCreateMode mode,
    const std::vector<int>& types) {
  auto& conf = *(const paddle::ModelConfig*)(confPtr);
  std::vector<ParameterType> realTypes;
  staticCastVector(&realTypes, types);
  auto machineRawPtr = paddle::GradientMachine::create(conf, mode, realTypes);
  auto machinePtr = std::shared_ptr<paddle::GradientMachine>(machineRawPtr);
  if (machinePtr != nullptr) {
    auto machine = new GradientMachine();
    machine->m->machine = machinePtr;
    return machine;
  } else {
    return nullptr;
  }
}

GradientMachine* GradientMachine::createByConfigProtoStr(
    const std::string& protoStr,
    GradientMatchineCreateMode mode,
    const std::vector<int>& types) {
  paddle::ModelConfig conf;
  conf.ParseFromString(protoStr);
  if (conf.IsInitialized()) {
    return GradientMachine::createFromPaddleModelPtr(&conf, mode, types);
  } else {
    return nullptr;
  }
}

GradientMachine* GradientMachine::createByModelConfig(
    ModelConfig* conf,
    GradientMatchineCreateMode mode,
    const std::vector<int>& types) {
  auto confPtr = &conf->m->conf->getModelConfig();
  return GradientMachine::createFromPaddleModelPtr(confPtr, mode, types);
}

void GradientMachine::start() { m->machine->start(); }

void GradientMachine::finish() { m->machine->finish(); }

void GradientMachine::onPassEnd() { m->machine->onPassEnd(); }

void GradientMachine::prefetch(const Arguments& inArgs) {
  auto& in =
      m->cast<std::vector<paddle::Argument>>(inArgs.getInternalArgumentsPtr());
  m->machine->prefetch(in);
}

void GradientMachine::forward(const Arguments& inArgs,
                              Arguments* outArgs,
                              PassType passType) {
  auto& in =
      m->cast<std::vector<paddle::Argument>>(inArgs.getInternalArgumentsPtr());
  auto& out = m->cast<std::vector<paddle::Argument>>(
      outArgs->getInternalArgumentsPtr());
  paddle::PassType pt = (paddle::PassType)(passType);
  m->machine->forward(in, &out, pt);
}

UpdateCallback::~UpdateCallback() {}

void UpdateCallback::apply(Parameter* p) {
  // UNUSED(p);
}

class UpdateCallbackWrapper {
public:
  explicit UpdateCallbackWrapper(const UpdateCallback& callback)
      : callback(const_cast<UpdateCallback&>(callback)) {}

  void operator()(paddle::Parameter* param) {
    auto p = Parameter::createFromRawPtr(&param);
    // @TODO Use Stack variable instead.
    callback.apply(p);
    delete p;
  }

private:
  UpdateCallback& callback;
};

void GradientMachine::backward(const UpdateCallback& callback) {
  m->machine->backward(UpdateCallbackWrapper(callback));
}

void GradientMachine::forwardBackward(const Arguments& inArgs,
                                      Arguments* outArgs,
                                      PassType passType,
                                      const UpdateCallback& callback) {
  auto& in =
      m->cast<std::vector<paddle::Argument>>(inArgs.getInternalArgumentsPtr());
  auto& out = m->cast<std::vector<paddle::Argument>>(
      outArgs->getInternalArgumentsPtr());
  paddle::PassType pt = (paddle::PassType)(passType);
  m->machine->forwardBackward(in, &out, pt, UpdateCallbackWrapper(callback));
}

void GradientMachine::loadParameters(const std::string& path) {
  m->machine->loadParameters(path);
}

size_t GradientMachine::getParameterSize() const {
  return m->machine->getParameters().size();
}

Parameter* GradientMachine::getParameter(size_t i) throw(RangeError) {
  auto params = m->machine->getParameters();
  if (i < params.size()) {
    return Parameter::createFromSharedPtr(&m->machine->getParameters()[i]);
  } else {
    throw RangeError();
  }
}

void GradientMachine::randParameters() { m->machine->randParameters(); }

Matrix* GradientMachine::getLayerOutput(const std::string& layerName) const
    throw(UnsupportError) {
  auto nn = std::dynamic_pointer_cast<paddle::NeuralNetwork>(m->machine);
  if (nn) {
    auto mat = nn->getLayerOutput(layerName);
    return Matrix::createByPaddleMatrixPtr(&mat);
  } else {
    throw UnsupportError();
  }
}

SequenceGenerator* GradientMachine::asSequenceGenerator(
    const std::vector<std::string>& dict,
    size_t begin_id,
    size_t end_id,
    size_t max_length,
    size_t beam_size) {
  SequenceGenerator* r =
      SequenceGenerator::createByGradientMachineSharedPtr(&m->machine);
  r->setDict(dict);
  r->setBos(begin_id);
  r->setEos(end_id);
  r->setMaxLength(max_length);
  r->setBeamSize(beam_size);
  return r;
}

Evaluator* GradientMachine::makeEvaluator() {
  auto ev = new Evaluator();
  ev->m->rawPtr = m->machine->makeEvaluator();
  return ev;
}

void GradientMachine::eval(Evaluator* evaluator) {
  m->machine->eval(evaluator->m->rawPtr);
}