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https://github.com/marian-nmt/marian.git
synced 2024-09-11 06:15:56 +03:00
sgd variants
This commit is contained in:
parent
7c63606b75
commit
15429dd88f
@ -5,7 +5,6 @@ cuda_add_library(marian_lib
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cnpy/cnpy.cpp
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exception.cpp
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expression_graph.cu
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sgd.cu
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tensor.cu
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tensor_operators.cu
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expression_operators.cu
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@ -39,12 +39,12 @@ std::string Expr::Debug() const
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}
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///////////////////////////////////////////////////////
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ExpressionGraph::ExpressionGraph(int cudaDevice)
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: stack_(new ChainableStack)
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{
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std::srand (time(NULL));
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cudaSetDevice(0);
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}
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//ExpressionGraph::ExpressionGraph(int cudaDevice)
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//: stack_(new ChainableStack)
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//{
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// std::srand (time(NULL));
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// cudaSetDevice(0);
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//
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//}
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}
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@ -38,9 +38,14 @@ class Expr {
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class ExpressionGraph {
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public:
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ExpressionGraph(int cudaDevice);
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ExpressionGraph() : stack_(new ChainableStack) {}
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void forward(size_t batchSize) {
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void backprop(int batchSize) {
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forward(batchSize);
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backward();
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}
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void forward(int batchSize) {
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for(auto&& v : *stack_) {
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v->allocate(batchSize);
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}
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@ -48,6 +53,16 @@ class ExpressionGraph {
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v->forward();
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}
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void backward() {
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for(auto&& v : *stack_)
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v->set_zero_adjoint();
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typedef typename ChainableStack::reverse_iterator It;
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stack_->back()->init_dependent();
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for(It it = stack_->rbegin(); it != stack_->rend(); ++it)
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(*it)->backward();
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}
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std::string graphviz() {
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std::stringstream ss;
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ss << "digraph ExpressionGraph {" << std::endl;
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@ -60,19 +75,13 @@ class ExpressionGraph {
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return ss.str();
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}
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void backward() {
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for(auto&& v : *stack_)
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v->set_zero_adjoint();
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typedef typename ChainableStack::reverse_iterator It;
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stack_->back()->init_dependent();
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for(It it = stack_->rbegin(); it != stack_->rend(); ++it)
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(*it)->backward();
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}
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/*********************************************************/
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template <typename ...Args>
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inline Expr input(Args ...args) {
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return Expr(this, new InputNode(args...));
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Expr e(this, new InputNode(args...));
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inputs_.emplace_back(e);
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return e;
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}
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template <typename ...Args>
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@ -117,14 +126,20 @@ class ExpressionGraph {
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named_.emplace(name, e);
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}
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std::vector<Expr>& inputs() {
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return inputs_;
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}
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std::vector<Expr>& params() {
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return params_;
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}
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private:
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ChainableStackPtr stack_;
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std::map<std::string, Expr> named_;
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std::vector<Expr> params_;
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std::vector<Expr> inputs_;
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};
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}
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140
src/sgd.cu
140
src/sgd.cu
@ -1,140 +0,0 @@
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#include <ctime>
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#include <algorithm>
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#include <vector>
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#include "sgd.h"
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#include "thrust_functions.h"
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using namespace std;
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namespace marian {
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SGD::SGD(ExpressionGraph& g, float eta,
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std::vector<float>& xData, size_t numFeatures,
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std::vector<float>& yData, size_t numClasses,
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size_t epochs, size_t batchSize)
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: graph_(g),
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eta_(eta),
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xData_(xData),
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numFeatures_(numFeatures),
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yData_(yData),
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numClasses_(numClasses),
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epochs_(epochs),
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maxBatchSize_(batchSize)
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{}
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void SGD::Run()
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{
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size_t numExamples = xData_.size()/ numFeatures_;
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Tensor xt({(int)maxBatchSize_, (int)numExamples}, 0.0f);
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Tensor yt({(int)maxBatchSize_, (int)numClasses_}, 0.0f);
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vector<size_t> shuffle = CreateShuffle(numExamples);
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//vector<size_t> shuffle;
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for (size_t numEpoch = 0; numEpoch < epochs_; ++numEpoch) {
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std::cerr << "Starting epoch #" << numEpoch << std::endl;
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size_t startId = 0;
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while (startId < numExamples) {
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size_t batchSize = std::min(maxBatchSize_, numExamples - startId);
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size_t endId = startId + batchSize;
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PrepareBatch(startId, endId, batchSize, shuffle, xt, yt);
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graph_["x"] = xt;
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graph_["y"] = yt;
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graph_.forward(maxBatchSize_);
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graph_.backward();
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UpdateModel();
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startId += maxBatchSize_;
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}
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}
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}
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std::vector<size_t> SGD::CreateShuffle(size_t numExamples) const {
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vector<size_t> ret(numExamples);
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std::iota(ret.begin(), ret.end(), 0);
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std::random_shuffle ( ret.begin(), ret.end() );
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/*
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cerr << "shuffled" << endl;
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for (size_t i = 0; i < ret.size(); ++i) {
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cerr << ret[i] << " ";
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}
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*/
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return ret;
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}
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void SGD::PrepareBatch(
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size_t startId,
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size_t endId,
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size_t batchSize,
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const std::vector<size_t> &shuffle,
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Tensor& xt,
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Tensor& yt) {
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/*
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std::vector<float> x(xData_.begin() + startId * numFeatures_,
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xData_.begin() + endId * numFeatures_);
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std::vector<float> y(yData_.begin() + startId * numClasses_,
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yData_.begin() + endId * numClasses_);
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*/
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std::vector<float> x(batchSize * numFeatures_);
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std::vector<float> y(batchSize * numClasses_);
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//cerr << "batchSize=" << batchSize << endl;
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/*
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cerr << "startId=" << startId
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<< " " << endId
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<< " " << batchSize
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<< endl;
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cerr << "numExamples=" << shuffle.size() << endl;
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cerr << "numFeatures_=" << numFeatures_ << " " << numClasses_ << endl;
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cerr << "sizes=" << x.size()
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<< " " << y.size()
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<< " " << xData_.size()
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<< " " << yData_.size()
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<< endl;
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*/
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size_t startXId = 0;
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size_t startYId = 0;
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for (size_t i = startId; i < endId; ++i) {
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size_t ind = shuffle[i];
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size_t startXDataId = ind * numFeatures_;
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size_t startYDataId = ind * numClasses_;
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size_t endXDataId = startXDataId + numFeatures_;
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size_t endYDataId = startYDataId + numClasses_;
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/*
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cerr << "i=" << i
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<< " " << ind
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<< " " << startXDataId << "-" << endXDataId
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<< " " << startYDataId << "-" << endYDataId
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<< endl;
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*/
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std::copy(xData_.begin() + startXDataId,
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xData_.begin() + endXDataId,
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x.begin() + startXId);
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std::copy(yData_.begin() + startYDataId,
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yData_.begin() + endYDataId,
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y.begin() + startYId);
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startXId += numFeatures_;
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startYId += numClasses_;
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}
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xt.set(x);
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yt.set(y);
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}
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void SGD::UpdateModel() {
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for (auto& param : graph_.params()) {
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using namespace thrust::placeholders;
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Element(_1 -= eta_ * _2, param.val(), param.grad());
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}
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}
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} // namespace
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71
src/sgd.h
71
src/sgd.h
@ -1,43 +1,48 @@
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#pragma once
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#include <memory>
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#include <iostream>
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#include "expression_graph.h"
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#include "thrust_functions.h"
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#include <map>
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#include <boost/any.hpp>
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#include "tensor_operators.h"
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namespace marian {
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class SGD {
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class Sgd {
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public:
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SGD(ExpressionGraph& g, float eta,
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std::vector<float>& xData, size_t numFeatures,
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std::vector<float>& yData, size_t numClasses,
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size_t epochs, size_t batchSize);
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void Run();
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Sgd(float eta=0.1) : eta_(eta) {}
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void operator()(ExpressionGraph& graph, int batchSize) {
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graph.backprop(batchSize);
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for(auto& param : graph.params())
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Element(_1 -= eta_ * _2, param.val(), param.grad());
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}
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private:
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ExpressionGraph& graph_;
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const float eta_;
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std::vector<float>& xData_;
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const size_t numFeatures_;
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std::vector<float>& yData_;
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const size_t numClasses_;
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const size_t epochs_;
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const size_t maxBatchSize_;
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std::vector<size_t> CreateShuffle(size_t numExamples) const;
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void PrepareBatch(
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size_t startId,
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size_t endId,
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size_t batchSize,
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const std::vector<size_t> &shuffle,
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Tensor& xt,
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Tensor& yt);
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void UpdateModel();
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float eta_;
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};
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} // namespace marian
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class Adagrad {
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public:
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Adagrad(float eta=0.1) : eta_(eta) {}
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void operator()(ExpressionGraph& graph, int batchSize) {
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if(history_.size() < graph.params().size())
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for(auto& param : graph.params())
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history_.emplace_back(Tensor(param.grad().shape(), 0));
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graph.backprop(batchSize);
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auto it = history_.begin();
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for(auto& param : graph.params()) {
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Element(_1 -= eta_ / Sqrt(_2) * _3, param.val(), *it, param.grad());
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Element(_1 += _2 * _2, *it, param.grad());
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it++;
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}
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}
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private:
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float eta_;
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std::vector<Tensor> history_;
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};
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}
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#include <fstream>
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#include "tensor.h"
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using namespace std;
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namespace marian {
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void Tensor::set(const std::vector<float>& data)
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@ -21,7 +21,7 @@ int main(int argc, char** argv) {
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std::vector<Expr> Y;
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std::vector<Expr> H;
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ExpressionGraph g(0);
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ExpressionGraph g;
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for (int t = 0; t < num_inputs; ++t) {
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X.emplace_back(g.input(shape={batch_size, input_size}));
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@ -39,10 +39,9 @@ int main(int argc, char** argv) {
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string sourceLine, targetLine;
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while (getline(sourceFile, sourceLine)) {
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getline(targetFile, targetLine);
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std::vector<size_t> sourceIds = sourceVocab.ProcessSentence(sourceLine);
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std::vector<size_t> targetIds = sourceVocab.ProcessSentence(targetLine);
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getline(targetFile, targetLine);
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std::vector<size_t> sourceIds = sourceVocab.ProcessSentence(sourceLine);
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std::vector<size_t> targetIds = sourceVocab.ProcessSentence(targetLine);
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}
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std::cerr << "Building RNN..." << std::endl;
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@ -85,6 +85,19 @@ namespace thrust
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return compose(unary_operator<unary_tanh>(), _1);
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}
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template<typename T>
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struct unary_sqrt : public thrust::unary_function<T,T> {
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__host__ __device__
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T operator()(const T &x) const { return sqrtf(x); }
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};
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template<typename Eval>
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__host__ __device__
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actor<composite<unary_operator<unary_sqrt>, actor<Eval>>>
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Sqrt(const actor<Eval> &_1) {
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return compose(unary_operator<unary_sqrt>(), _1);
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}
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template<typename T1, typename T2>
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__host__ __device__
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actor<composite<binary_operator<thrust::maximum>, actor<T1>, actor<T2>>>
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@ -11,12 +11,12 @@ int main(int argc, char** argv) {
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int numofdata;
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vector<float> trainImages = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", numofdata, IMAGE_SIZE);
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vector<float>trainLabels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", numofdata, LABEL_SIZE);
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vector<float> trainLabels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", numofdata, LABEL_SIZE);
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using namespace marian;
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using namespace keywords;
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ExpressionGraph g(0);
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ExpressionGraph g;
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Expr x = named(g.input(shape={whatevs, IMAGE_SIZE}), "x");
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Expr y = named(g.input(shape={whatevs, LABEL_SIZE}), "y");
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@ -24,16 +24,13 @@ int main(int argc, char** argv) {
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Expr w = named(g.param(shape={IMAGE_SIZE, LABEL_SIZE}), "w");
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Expr b = named(g.param(shape={1, LABEL_SIZE}), "b");
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std::vector<Expr*> params;
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params.push_back(&w);
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params.push_back(&b);
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auto scores = dot(x, w) + b;
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auto lr = softmax_fast(scores);
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auto cost = named(-mean(sum(y * log(lr), axis=1), axis=0), "cost");
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cerr << "lr=" << lr.Debug() << endl;
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SGD opt(g, 0.9, trainImages, IMAGE_SIZE, trainLabels, LABEL_SIZE, 3, 24);
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opt.Run();
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Adagrad opt;
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opt(g, 300);
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return 0;
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}
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@ -32,7 +32,7 @@ int main(int argc, char** argv) {
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int num_inputs = 8;
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int num_outputs = 6;
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ExpressionGraph g(0);
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ExpressionGraph g;
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std::vector<Expr*> X(num_inputs+1); // For the stop symbol.
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std::vector<Expr*> Y(num_outputs);
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std::vector<Expr*> H(num_inputs+1); // For the stop symbol.
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@ -10,7 +10,7 @@ const size_t IMAGE_SIZE = 784;
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const size_t LABEL_SIZE = 10;
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int BATCH_SIZE = 10000;
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ExpressionGraph build_graph(int cudaDevice) {
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ExpressionGraph build_graph() {
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std::cerr << "Loading model params...";
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NpzConverter converter("../scripts/test_model_single/model.npz");
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@ -22,7 +22,7 @@ ExpressionGraph build_graph(int cudaDevice) {
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std::cerr << "Building model...";
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ExpressionGraph g(cudaDevice);
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ExpressionGraph g;
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auto x = named(g.input(shape={whatevs, IMAGE_SIZE}), "x");
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auto y = named(g.input(shape={whatevs, LABEL_SIZE}), "y");
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@ -52,7 +52,7 @@ int main(int argc, char** argv) {
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std::vector<float> testLabels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", BATCH_SIZE, LABEL_SIZE);
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std::cerr << "Done." << std::endl;
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ExpressionGraph g = build_graph(0);
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ExpressionGraph g = build_graph();
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Tensor xt({BATCH_SIZE, IMAGE_SIZE});
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Tensor yt({BATCH_SIZE, LABEL_SIZE});
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@ -56,8 +56,7 @@ int main(int argc, char** argv) {
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std::cerr << "\tDone." << std::endl;
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ExpressionGraph g(0);
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ExpressionGraph g;
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auto x = g.input(shape={whatevs, IMAGE_SIZE}, name="X");
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auto y = g.input(shape={whatevs, LABEL_SIZE}, name="Y");
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