marian-nmt/marian
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Merge branch 'master' of https://github.com/emjotde/Marian

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This commit is contained in:
Andre Martins 2016-09-15 17:30:14 +01:00
commit 9015577101
7 changed files with 206 additions and 80 deletions
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7
README.md
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@ -11,14 +11,15 @@ Installation
Requirements:
* g++ with C++14
* g++ with c++11
* CUDA and CuDNN
* Boost (>= 1.56)
Exporting some paths for CuDNN may be required (put it, for example, in your `.bashrc` file):
export PATH=$PATH:$HOME/.local/bin:/usr/local/cuda/bin
export LIBRARY_PATH=$LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cudnn-5/lib64
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cudnn-5/lib64
export LIBRARY_PATH=$LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:/usr/local/cudnn-5/lib64
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:/usr/local/cudnn-5/lib64
export CPATH=$CPATH:/usr/local/cudnn-5/include
Compilation with `cmake > 3.5`:

6
marian/.cproject
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@ -56,11 +56,11 @@
</tool>
</toolChain>
</folderInfo>
<fileInfo id="com.nvidia.cuda.ide.seven_five.configuration.debug.1479727693.924444438" name="train_mnist.cu" rcbsApplicability="disable" resourcePath="src/train_mnist.cu" toolsToInvoke="nvcc.compiler.base.1979453423.2078504098">
<tool id="nvcc.compiler.base.1979453423.2078504098" name="NVCC Compiler" superClass="nvcc.compiler.base.1979453423"/>
<fileInfo id="com.nvidia.cuda.ide.seven_five.configuration.debug.1479727693.843925199" name="validate_mnist_batch.cu" rcbsApplicability="disable" resourcePath="src/validate_mnist_batch.cu" toolsToInvoke="nvcc.compiler.base.1979453423.378728796">
<tool id="nvcc.compiler.base.1979453423.378728796" name="NVCC Compiler" superClass="nvcc.compiler.base.1979453423"/>
</fileInfo>
<sourceEntries>
<entry excluding="src/train_mnist.cu|src/validate_mnist.cu|src/npz_converter.cpp" flags="VALUE_WORKSPACE_PATH|RESOLVED" kind="sourcePath" name=""/>
<entry excluding="src/validate_mnist_batch.cu|src/train_mnist.cu|src/validate_mnist.cu|src/npz_converter.cpp" flags="VALUE_WORKSPACE_PATH|RESOLVED" kind="sourcePath" name=""/>
</sourceEntries>
</configuration>
</storageModule>

1
src/CMakeLists.txt
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@ -5,6 +5,7 @@ cuda_add_library(marian_lib
cnpy/cnpy.cpp
exception.cpp
expressions.cu
sgd.cu
tensor.cu
tensor_operators.cu
)

143
src/sgd.cu Normal file
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@ -0,0 +1,143 @@
#include <ctime>
#include <algorithm>
#include <vector>
#include "sgd.h"
#include "thrust_functions.h"
using namespace std;
namespace marian {
SGD::SGD(Expr& cost_func, Expr& inX, Expr& inY,
const std::vector<Expr*> params, float eta,
std::vector<float>& xData, size_t numFeatures,
std::vector<float>& yData, size_t numClasses,
size_t epochs, size_t batchSize)
: cost_function_(&cost_func),
inX_(&inX),
inY_(&inY),
params_(params),
eta_(eta),
xData_(xData),
numFeatures_(numFeatures),
yData_(yData),
numClasses_(numClasses),
epochs_(epochs),
maxBatchSize_(batchSize)
{}
void SGD::Run()
{
std::srand ( unsigned ( std::time(0) ) );
size_t numExamples = xData_.size()/ numFeatures_;
Tensor xt({(int)maxBatchSize_, (int)numExamples}, 0.0f);
Tensor yt({(int)maxBatchSize_, (int)numClasses_}, 0.0f);
vector<size_t> shuffle = CreateShuffle(numExamples);
//vector<size_t> shuffle;
for (size_t numEpoch = 0; numEpoch < epochs_; ++numEpoch) {
std::cerr << "Starting epoch #" << numEpoch << std::endl;
size_t startId = 0;
size_t endId = startId + maxBatchSize_;
while (endId < numExamples) {
PrepareBatch(startId, endId, maxBatchSize_, shuffle, xt, yt);
*inX_ = xt;
*inY_ = yt;
cost_function_->forward(maxBatchSize_);
cost_function_->backward();
UpdateModel();
startId += maxBatchSize_;
endId += maxBatchSize_;
}
}
}
std::vector<size_t> SGD::CreateShuffle(size_t numExamples) const {
vector<size_t> ret(numExamples);
std::iota(ret.begin(), ret.end(), 0);
std::random_shuffle ( ret.begin(), ret.end() );
/*
cerr << "shuffled" << endl;
for (size_t i = 0; i < ret.size(); ++i) {
cerr << ret[i] << " ";
}
*/
return ret;
}
void SGD::PrepareBatch(
size_t startId,
size_t endId,
size_t batchSize,
const std::vector<size_t> &shuffle,
Tensor& xt,
Tensor& yt) {
/*
std::vector<float> x(xData_.begin() + startId * numFeatures_,
xData_.begin() + endId * numFeatures_);
std::vector<float> y(yData_.begin() + startId * numClasses_,
yData_.begin() + endId * numClasses_);
*/
std::vector<float> x(batchSize * numFeatures_);
std::vector<float> y(batchSize * numClasses_);
/*
cerr << "startId=" << startId
<< " " << endId
<< " " << batchSize
<< endl;
cerr << "numExamples=" << shuffle.size() << endl;
cerr << "numFeatures_=" << numFeatures_ << " " << numClasses_ << endl;
cerr << "sizes=" << x.size()
<< " " << y.size()
<< " " << xData_.size()
<< " " << yData_.size()
<< endl;
*/
size_t startXId = 0;
size_t startYId = 0;
for (size_t i = startId; i < endId; ++i) {
size_t ind = shuffle[i];
size_t startXDataId = ind * numFeatures_;
size_t startYDataId = ind * numClasses_;
size_t endXDataId = startXDataId + numFeatures_;
size_t endYDataId = startYDataId + numClasses_;
/*
cerr << "i=" << i
<< " " << ind
<< " " << startXDataId << "-" << endXDataId
<< " " << startYDataId << "-" << endYDataId
<< endl;
*/
std::copy(xData_.begin() + startXDataId,
xData_.begin() + endXDataId,
x.begin() + startXId);
std::copy(yData_.begin() + startYDataId,
yData_.begin() + endYDataId,
y.begin() + startYId);
startXId += numFeatures_;
startYId += numClasses_;
}
xt.set(x);
yt.set(y);
}
void SGD::UpdateModel() {
for (auto& param : params_) {
using namespace thrust::placeholders;
Element(_1 = _1 - eta_ * _2, param->val(), param->grad());
}
}
} // namespace

77
src/sgd.h
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@ -5,6 +5,7 @@
#include "expressions.h"
#include "thrust_functions.h"
#include "tensor_operators.h"
namespace marian {
@ -14,67 +15,14 @@ class SGD {
const std::vector<Expr*> params, float eta,
std::vector<float>& xData, size_t numFeatures,
std::vector<float>& yData, size_t numClasses,
size_t epochs, size_t batchSize)
: cost_function_(&cost_func),
inX_(&inX),
inY_(&inY),
params_(params),
eta_(eta),
xData_(xData),
numFeatures_(numFeatures),
yData_(yData),
numClasses_(numClasses),
epochs_(epochs),
batchSize_(batchSize)
{}
size_t epochs, size_t batchSize);
void Run() {
size_t numExamples = xData_.size()/ numFeatures_;
Tensor xt({(int)batchSize_, (int)numExamples}, 0.0f);
Tensor yt({(int)batchSize_, (int)numClasses_}, 0.0f);
for (size_t numEpoch = 0; numEpoch < epochs_; ++numEpoch) {
std::cerr << "Starting epoch #" << numEpoch << std::endl;
size_t startId = 0;
size_t endId = startId + batchSize_;
while (endId < numExamples) {
PrepareBatch(startId, endId, xt, yt);
*inX_ = xt;
*inY_ = yt;
cost_function_->forward(batchSize_);
cost_function_->backward();
UpdateModel();
startId += batchSize_;
endId += batchSize_;
}
}
}
void PrepareBatch(size_t startId, size_t endId, Tensor& xt, Tensor& yt) {
std::vector<float> x(xData_.begin() + startId * numFeatures_,
xData_.begin() + endId * numFeatures_);
std::vector<float> y(yData_.begin() + startId * numClasses_,
yData_.begin() + endId * numClasses_);
xt.set(x);
yt.set(y);
}
void UpdateModel() {
for (auto& param : params_) {
using namespace thrust::placeholders;
Element(_1 = _1 - eta_ * _2, param->val(), param->grad());
}
}
void Run();
private:
std::shared_ptr<Expr> cost_function_;
std::shared_ptr<Expr> inX_;
std::shared_ptr<Expr> inY_;
Expr *cost_function_;
Expr *inX_;
Expr *inY_;
std::vector<Expr*> params_;
const float eta_;
std::vector<float>& xData_;
@ -82,7 +30,18 @@ class SGD {
std::vector<float>& yData_;
const size_t numClasses_;
const size_t epochs_;
const size_t batchSize_;
const size_t maxBatchSize_;
std::vector<size_t> CreateShuffle(size_t numExamples) const;
void PrepareBatch(
size_t startId,
size_t endId,
size_t batchSize,
const std::vector<size_t> &shuffle,
Tensor& xt,
Tensor& yt);
void UpdateModel();
};
} // namespace marian

2
src/validate_mnist.cu
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@ -21,7 +21,7 @@ int main(int argc, char** argv) {
std::cerr << "Done." << std::endl;
std::cerr << "Loading model params...";
NpzConverter converter("../scripts/test_model/model.npz");
NpzConverter converter("../scripts/test_model_single/model.npz");
std::vector<float> wData, bData;
Shape wShape, bShape;

50
src/validate_mnist_batch.cu
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@ -21,22 +21,39 @@ int main(int argc, char** argv) {
std::cerr << "\tDone." << std::endl;
std::cerr << "Loading model params...";
NpzConverter converter("../scripts/test_model/model.npz");
std::vector<float> wData;
Shape wShape;
converter.Load("weights", wData, wShape);
NpzConverter converter("../scripts/test_model_single/model.npz");
std::vector<float> bData;
Shape bShape;
converter.Load("bias", bData, bShape);
std::vector<float> wData1;
Shape wShape1;
converter.Load("weights1", wData1, wShape1);
std::vector<float> bData1;
Shape bShape1;
converter.Load("bias1", bData1, bShape1);
std::vector<float> wData2;
Shape wShape2;
converter.Load("weights2", wData2, wShape2);
std::vector<float> bData2;
Shape bShape2;
converter.Load("bias2", bData2, bShape2);
auto initW = [wData](Tensor t) {
t.set(wData);
auto initW1 = [wData1](Tensor t) {
t.set(wData1);
};
auto initB = [bData](Tensor t) {
t.set(bData);
auto initB1 = [bData1](Tensor t) {
t.set(bData1);
};
auto initW2 = [wData2](Tensor t) {
t.set(wData2);
};
auto initB2 = [bData2](Tensor t) {
t.set(bData2);
};
std::cerr << "\tDone." << std::endl;
@ -45,11 +62,15 @@ int main(int argc, char** argv) {
auto x = input(shape={whatevs, IMAGE_SIZE}, name="X");
auto y = input(shape={whatevs, LABEL_SIZE}, name="Y");
auto w = param(shape={IMAGE_SIZE, LABEL_SIZE}, name="W0", init=initW);
auto b = param(shape={1, LABEL_SIZE}, name="b0", init=initB);
auto w1 = param(shape={IMAGE_SIZE, 100}, name="W0", init=initW1);
auto b1 = param(shape={1, 100}, name="b0", init=initB1);
auto w2 = param(shape={100, LABEL_SIZE}, name="W1", init=initW2);
auto b2 = param(shape={1, LABEL_SIZE}, name="b1", init=initB2);
std::cerr << "Building model...";
auto predict = softmax(dot(x, w) + b, axis=1, name="pred");
auto layer1 = tanh(dot(x, w1) + b1);
auto layer2 = softmax(dot(layer1, w2) + b2, axis=1, name="layer2");
auto predict = layer2;
std::cerr << "Done." << std::endl;
@ -77,6 +98,7 @@ int main(int argc, char** argv) {
if (testLabels[startId * LABEL_SIZE + i + j]) correct = j;
if (results[i + j] > results[i + predicted]) predicted = j;
}
/*std::cerr << "CORRECT: " << correct << " PREDICTED: " << predicted << std::endl;*/
acc += (correct == predicted);
}