Merge branch 'master' of github.com:emjotde/marian

This commit is contained in:
Marcin Junczys-Dowmunt 2016-09-16 18:34:45 +02:00
commit 3f74962df7
6 changed files with 2398 additions and 1 deletions

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@ -20,3 +20,14 @@ endif(Boost_FOUND)
include_directories(${marian_SOURCE_DIR}/src)
add_subdirectory(src)
# add a target to generate API documentation with Doxygen
find_package(Doxygen)
if(DOXYGEN_FOUND)
configure_file(${CMAKE_CURRENT_SOURCE_DIR}/Doxyfile.in ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile @ONLY)
add_custom_target(doc ALL
${DOXYGEN_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
COMMENT "Generating API documentation with Doxygen" VERBATIM
)
endif(DOXYGEN_FOUND)

2303
Doxyfile.in Normal file

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@ -29,3 +29,6 @@ Compilation with `cmake > 3.5`:
cmake ..
make -j
To compile API documentation using Doxygen, first cd to the build directory, and then:
make doc

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@ -1,5 +1,7 @@
#include "tensor_operators.h"
using namespace std;
namespace marian {
__global__ void gSubtractMean(float* out, float* weights,
@ -53,6 +55,7 @@ void SubtractMean(Tensor* Out, Tensor &Weights) {
cudaStreamSynchronize(0);
}
///////////////////////////////////////////////////////
__global__ void gSoftMax(float* softMaxP, size_t rows, size_t cols) {
for(int bid = 0; bid < rows; bid += gridDim.x) {
int j = bid + blockIdx.x;
@ -97,6 +100,35 @@ void Softmax(Tensor* Out) {
gSoftMax<<<blocks, threads, shared>>>(Out->data(), m, k);
cudaStreamSynchronize(0);
}
///////////////////////////////////////////////////////
__global__ void gArgMax(float *out, const float *data, size_t rows, size_t cols) {
size_t row = blockIdx.x;
size_t startInd = row * cols;
float maxScore = -99999;
size_t maxInd;
for (size_t col = 0; col < cols; ++col) {
size_t ind = startInd + col;
float score = data[ind];
if (score > maxScore) {
maxScore = score;
maxInd = col;
}
}
out[row] = maxInd;
}
void Argmax(Tensor* Out, const Tensor* In) {
size_t m = In->shape()[0];
size_t k = In->shape()[1];
int blocks = m; //std::min(MAX_BLOCKS, (int) m);
int threads = k; //std::min(MAX_THREADS, (int) k);
//int shared = sizeof(float) * threads * 2;
gArgMax<<<blocks, threads>>>(Out->data(), In->data(), m, k);
cudaStreamSynchronize(0);
}
///////////////////////////////////////////////////////
Tensor Prod(cublasHandle_t handle, Tensor C, const Tensor A, const Tensor B,
bool transA, bool transB, Float beta) {
@ -137,4 +169,4 @@ Tensor Prod(Tensor C, const Tensor A, const Tensor B,
return temp;
}
}
}

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@ -151,6 +151,10 @@ __global__ void gSoftMax(float* softMaxP, size_t rows, size_t cols);
void Softmax(Tensor* Out);
__global__ void gArgMax(float *out, const float *data, size_t rows, size_t cols);
void Argmax(Tensor* Out, const Tensor* In);
Tensor Prod(cublasHandle_t handle, Tensor C, const Tensor A, const Tensor B,
bool transA, bool transB, Float beta);

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@ -3,7 +3,51 @@
#include "mnist.h"
#include "vocab.h"
#include "tensor_operators.h"
using namespace std;
///////////////////////////////////////////////////////
string output(const std::vector<float> &vec)
{
stringstream strm;
for (size_t i = 0; i < vec.size(); ++i) {
strm << vec[i] << " ";
}
return strm.str();
}
void testArgMax()
{
using namespace std;
using namespace marian;
std::vector<float> hVec({29,19, 49,39, 79,99, 79,39});
cerr << "hVec =" << output(hVec) << endl;
thrust::device_vector<float> dVec(8);
thrust::copy(hVec.begin(), hVec.end(), dVec.begin());
float *data = thrust::raw_pointer_cast(dVec.data());
thrust::device_vector<float> dLabel(4);
float *labelPtr = thrust::raw_pointer_cast(dLabel.data());
gArgMax<<<4, 1, sizeof(float)>>>(labelPtr, data, 4, 2);
std::vector<float> hVec2(8);
thrust::copy(dVec.begin(), dVec.end(), hVec2.begin());
cerr << "hVec2=" << output(hVec2) << endl;
std::vector<float> hLabel(4);
thrust::copy(dLabel.begin(), dLabel.end(), hLabel.begin());
cerr << "hLabel=" << output(hLabel) << endl;
exit(0);
}
///////////////////////////////////////////////////////
int main(int argc, char** argv) {
//testArgMax();
using namespace std;
using namespace marian;