mirror of
https://github.com/marian-nmt/marian.git
synced 2024-09-11 06:15:56 +03:00
faster set/get
This commit is contained in:
Marcin Junczys-Dowmunt
parent
5439e52abf
commit
0be1b07308
@ -60,8 +60,8 @@ class SGD {
|
||||
std::vector<float> y(yData_.begin() + startId * numClasses_,
|
||||
yData_.begin() + endId * numClasses_);
|
||||
|
||||
xt.Load(x);
|
||||
yt.Load(y);
|
||||
xt.set(x);
|
||||
yt.set(y);
|
||||
}
|
||||
|
||||
void UpdateModel() {
|
||||
|
||||
@ -5,91 +5,12 @@ using namespace std;
|
||||
|
||||
namespace marian {
|
||||
|
||||
inline std::vector<std::string> Tokenize(const std::string& str,
|
||||
const std::string& delimiters = " \t")
|
||||
{
|
||||
std::vector<std::string> tokens;
|
||||
// Skip delimiters at beginning.
|
||||
std::string::size_type lastPos = str.find_first_not_of(delimiters, 0);
|
||||
// Find first "non-delimiter".
|
||||
std::string::size_type pos = str.find_first_of(delimiters, lastPos);
|
||||
|
||||
while (std::string::npos != pos || std::string::npos != lastPos) {
|
||||
// Found a token, add it to the vector.
|
||||
tokens.push_back(str.substr(lastPos, pos - lastPos));
|
||||
// Skip delimiters. Note the "not_of"
|
||||
lastPos = str.find_first_not_of(delimiters, pos);
|
||||
// Find next "non-delimiter"
|
||||
pos = str.find_first_of(delimiters, lastPos);
|
||||
}
|
||||
|
||||
return tokens;
|
||||
}
|
||||
|
||||
//! convert string to variable of type T. Used to reading floats, int etc from files
|
||||
template<typename T>
|
||||
T Scan(const std::string &input)
|
||||
{
|
||||
std::stringstream stream(input);
|
||||
T ret;
|
||||
stream >> ret;
|
||||
return ret;
|
||||
}
|
||||
|
||||
//! convert vectors of string to vectors of type T variables
|
||||
template<typename T>
|
||||
inline std::vector<T> Scan(const std::vector< std::string > &input)
|
||||
{
|
||||
std::vector<T> output(input.size());
|
||||
for (size_t i = 0 ; i < input.size() ; i++) {
|
||||
output[i] = Scan<T>( input[i] );
|
||||
}
|
||||
return output;
|
||||
}
|
||||
|
||||
//! tokenise input string to vector of type T
|
||||
template<typename T>
|
||||
inline std::vector<T> Tokenize( const std::string &input
|
||||
, const std::string& delimiters = " \t")
|
||||
{
|
||||
std::vector<std::string> stringVector = Tokenize(input, delimiters);
|
||||
return Scan<T>( stringVector );
|
||||
}
|
||||
|
||||
|
||||
void Tensor::Load(const std::string &path)
|
||||
{
|
||||
size_t totSize = GetTotalSize(pimpl_->shape());
|
||||
cerr << "totSize=" << totSize << endl;
|
||||
std::vector<float> hostData(totSize);
|
||||
|
||||
fstream strm;
|
||||
strm.open(path.c_str());
|
||||
|
||||
string line;
|
||||
size_t ind = 0;
|
||||
while ( getline (strm, line) )
|
||||
{
|
||||
cerr << line << '\n';
|
||||
vector<Float> toks = Tokenize<Float>(line);
|
||||
for (size_t i = 0; i < toks.size(); ++i) {
|
||||
hostData[ind] = toks[i];
|
||||
}
|
||||
|
||||
++ind;
|
||||
}
|
||||
strm.close();
|
||||
|
||||
Load(hostData.begin(), hostData.begin());
|
||||
}
|
||||
|
||||
void Tensor::Load(const std::vector<float>& data)
|
||||
void Tensor::set(const std::vector<float>& data)
|
||||
{
|
||||
pimpl_->set(data.begin(), data.end());
|
||||
}
|
||||
|
||||
|
||||
void Tensor::Load(const std::vector<float>::const_iterator &begin, const std::vector<float>::const_iterator &end)
|
||||
void Tensor::set(const std::vector<float>::const_iterator &begin, const std::vector<float>::const_iterator &end)
|
||||
{
|
||||
pimpl_->set(begin, end);
|
||||
}
|
||||
|
||||
80
src/tensor.h
80
src/tensor.h
@ -12,30 +12,6 @@
|
||||
|
||||
namespace marian {
|
||||
|
||||
//struct Handles {
|
||||
// //cudnnHandle_t cudnnHandle;
|
||||
// //cublasHandle_t cublasHandle;
|
||||
//
|
||||
// //cudnnOpTensorDescriptor_t add;
|
||||
//
|
||||
// Handles() {
|
||||
// cudnnCreate(&cudnnHandle);
|
||||
// cublasCreate(&cublasHandle);
|
||||
// cudnnCreateOpTensorDescriptor(&add);
|
||||
// cudnnSetOpTensorDescriptor(add, CUDNN_OP_TENSOR_ADD, CUDNN_DATA_FLOAT, CUDNN_NOT_PROPAGATE_NAN);
|
||||
// }
|
||||
//
|
||||
// ~Handles() {
|
||||
// cudnnDestroy(cudnnHandle);
|
||||
// cublasDestroy(cublasHandle);
|
||||
// cudnnDestroyOpTensorDescriptor(add);
|
||||
// }
|
||||
//};
|
||||
//
|
||||
//const Handles handles;
|
||||
|
||||
// typedef std::vector<int> Shape;
|
||||
|
||||
inline std::string Debug(const Shape &shape)
|
||||
{
|
||||
std::stringstream strm;
|
||||
@ -59,18 +35,9 @@ class TensorImpl {
|
||||
private:
|
||||
Shape shape_;
|
||||
thrust::device_vector<Float> data_;
|
||||
//cudnnTensorDescriptor_t desc_;
|
||||
size_t tno_;
|
||||
static size_t tensorCounter;
|
||||
|
||||
//cudnnDataType_t dataType() {
|
||||
// switch(sizeof(Float)) {
|
||||
// case 2: return CUDNN_DATA_HALF;
|
||||
// case 8: return CUDNN_DATA_DOUBLE;
|
||||
// default: return CUDNN_DATA_FLOAT;
|
||||
// }
|
||||
//}
|
||||
|
||||
public:
|
||||
typedef Float value_type;
|
||||
|
||||
@ -89,30 +56,11 @@ class TensorImpl {
|
||||
|
||||
int size = GetTotalSize(shape_);
|
||||
data_.resize(size, value);
|
||||
//cudnnCreateTensorDescriptor(&desc_);
|
||||
//switch (shape_.size()) {
|
||||
// case 1:
|
||||
// cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
|
||||
// shape_[0], 1, 1, 1); break;
|
||||
// case 2:
|
||||
// cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
|
||||
// shape_[0], shape_[1], 1, 1); break;
|
||||
// case 3:
|
||||
// cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
|
||||
// shape_[0], shape_[1], shape_[2], 1); break;
|
||||
// case 4:
|
||||
// cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
|
||||
// shape_[0], shape_[1], shape_[2], shape_[3]); break;
|
||||
//}
|
||||
}
|
||||
|
||||
TensorImpl(const TensorImpl&) = delete;
|
||||
TensorImpl(TensorImpl&&) = delete;
|
||||
|
||||
~TensorImpl() {
|
||||
//cudnnDestroyTensorDescriptor(desc_);
|
||||
}
|
||||
|
||||
value_type operator[](size_t i) const {
|
||||
return data_[i];
|
||||
}
|
||||
@ -145,10 +93,6 @@ class TensorImpl {
|
||||
return thrust::raw_pointer_cast(data_.data());
|
||||
}
|
||||
|
||||
//cudnnTensorDescriptor_t desc() const {
|
||||
// return desc_;
|
||||
//}
|
||||
|
||||
size_t id() const {
|
||||
return tno_;
|
||||
}
|
||||
@ -158,12 +102,13 @@ class TensorImpl {
|
||||
}
|
||||
|
||||
void set(const std::vector<float>::const_iterator &begin, const std::vector<float>::const_iterator &end) {
|
||||
size_t totSize = GetTotalSize(shape());
|
||||
//std::cerr << "tensor size=" << totSize << " vector size=" << values.size() << std::endl;
|
||||
//assert(totSize == values.size());
|
||||
thrust::copy(begin, end, data_.begin());
|
||||
}
|
||||
|
||||
void get(std::vector<float>::iterator out) {
|
||||
thrust::copy(data_.begin(), data_.end(), out);
|
||||
}
|
||||
|
||||
std::string Debug() const
|
||||
{
|
||||
std::stringstream strm;
|
||||
@ -245,10 +190,6 @@ class Tensor {
|
||||
return pimpl_->shape();
|
||||
}
|
||||
|
||||
//cudnnTensorDescriptor_t desc() const {
|
||||
// return pimpl_->desc();
|
||||
//}
|
||||
|
||||
void set(value_type value) {
|
||||
pimpl_->set(value);
|
||||
}
|
||||
@ -273,10 +214,17 @@ class Tensor {
|
||||
std::cerr << std::endl;
|
||||
}
|
||||
|
||||
void Load(const std::string &path);
|
||||
void Load(const std::vector<float>& data);
|
||||
void Load(const std::vector<float>::const_iterator &begin, const std::vector<float>::const_iterator &end);
|
||||
//void Load(const std::string &path);
|
||||
void set(const std::vector<float>& data);
|
||||
void set(const std::vector<float>::const_iterator &begin, const std::vector<float>::const_iterator &end);
|
||||
|
||||
void get(std::vector<float>::iterator out) {
|
||||
pimpl_->get(out);
|
||||
}
|
||||
|
||||
void get(std::vector<float> &vout) {
|
||||
pimpl_->get(vout.begin());
|
||||
}
|
||||
};
|
||||
|
||||
}
|
||||
|
||||
@ -31,21 +31,21 @@ int main(int argc, char** argv) {
|
||||
converter.Load("bias", bData, bShape);
|
||||
|
||||
auto initW = [wData](Tensor t) {
|
||||
thrust::copy(wData.begin(), wData.end(), t.begin());
|
||||
t.set(wData.begin(), wData.end());
|
||||
};
|
||||
|
||||
auto initB = [bData](Tensor t) {
|
||||
thrust::copy(bData.begin(), bData.end(), t.begin());
|
||||
t.set(bData.begin(), bData.end());
|
||||
};
|
||||
|
||||
std::cerr << "\tDone." << std::endl;
|
||||
|
||||
|
||||
Expr x = input(shape={whatevs, IMAGE_SIZE}, name="X");
|
||||
Expr y = input(shape={whatevs, LABEL_SIZE}, name="Y");
|
||||
auto x = input(shape={whatevs, IMAGE_SIZE}, name="X");
|
||||
auto y = input(shape={whatevs, LABEL_SIZE}, name="Y");
|
||||
|
||||
Expr w = param(shape={IMAGE_SIZE, LABEL_SIZE}, name="W0", init=initW);
|
||||
Expr b = param(shape={1, LABEL_SIZE}, name="b0", init=initB);
|
||||
auto w = param(shape={IMAGE_SIZE, LABEL_SIZE}, name="W0", init=initW);
|
||||
auto b = param(shape={1, LABEL_SIZE}, name="b0", init=initB);
|
||||
|
||||
std::cerr << "Building model...";
|
||||
auto predict = softmax(dot(x, w) + b,
|
||||
@ -53,13 +53,13 @@ int main(int argc, char** argv) {
|
||||
auto graph = -mean(sum(y * log(predict), axis=1),
|
||||
axis=0, name="cost");
|
||||
|
||||
std::cerr << "\tDone." << std::endl;
|
||||
std::cerr << "Done." << std::endl;
|
||||
|
||||
Tensor xt({numofdata, IMAGE_SIZE});
|
||||
xt.Load(testImages);
|
||||
xt.set(testImages);
|
||||
|
||||
Tensor yt({numofdata, LABEL_SIZE});
|
||||
yt.Load(testLabels);
|
||||
yt.set(testLabels);
|
||||
|
||||
x = xt;
|
||||
y = yt;
|
||||
@ -68,6 +68,9 @@ int main(int argc, char** argv) {
|
||||
auto results = predict.val();
|
||||
graph.backward();
|
||||
|
||||
std::vector<float> resultsv(results.size());
|
||||
results.get(resultsv);
|
||||
|
||||
std::cerr << b.grad().Debug() << std::endl;
|
||||
|
||||
size_t acc = 0;
|
||||
@ -76,14 +79,14 @@ int main(int argc, char** argv) {
|
||||
size_t predicted = 0;
|
||||
for (size_t j = 0; j < LABEL_SIZE; ++j) {
|
||||
if (testLabels[i+j]) correct = j;
|
||||
if (results[i + j] > results[i + predicted]) predicted = j;
|
||||
if (resultsv[i + j] > resultsv[i + predicted]) predicted = j;
|
||||
}
|
||||
acc += (correct == predicted);
|
||||
//std::cerr << "corect: " << correct << " | " << predicted << "(";
|
||||
//std::cerr << correct << " | " << predicted << " ( ";
|
||||
//for (size_t j = 0; j < LABEL_SIZE; ++j) {
|
||||
// std::cerr << results[i+j] << " ";
|
||||
// std::cerr << resultsv[i+j] << " ";
|
||||
//}
|
||||
//std::cerr << std::endl;
|
||||
//std::cerr << ")" << std::endl;
|
||||
}
|
||||
std::cerr << "ACC: " << float(acc)/numofdata << std::endl;
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user