mmr/tkDNN
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removed unused var

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This commit is contained in:
Francesco Gatti
2020-02-13 23:10:48 +01:00
parent 03d39d991c
commit c1c2173e4d
2 changed files with 17 additions and 35 deletions
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include/tkDNN/Layer.h
+4 -6
View File
@@ -246,18 +246,16 @@ protected:
cudnnDropoutDescriptor_t dropoutDesc;
dnnType *dropout_states_, *work_space_;
size_t workspace_byte_, reserve_space_byte_, dropout_byte_;
size_t workspace_byte_, dropout_byte_;
int workspace_size_, dropout_size_;
std::vector<cudnnTensorDescriptor_t> x_desc_vec_, y_desc_vec_, dx_desc_vec_, dy_desc_vec_;
std::vector<cudnnTensorDescriptor_t> x_desc_vec_, y_desc_vec_;
cudnnTensorDescriptor_t hx_desc_, cx_desc_;
cudnnTensorDescriptor_t hy_desc_, cy_desc_;
cudnnTensorDescriptor_t dhx_desc_, dcx_desc_;
cudnnTensorDescriptor_t dhy_desc_, dcy_desc_;
dnnType *hx_ptr, *cx_ptr, *hy_ptr, *cy_ptr;
cudnnFilterDescriptor_t w_desc_, dw_desc_;
dnnType *w_ptr, *dw_ptr;
cudnnFilterDescriptor_t w_desc_;
dnnType *w_ptr;
};
src/LSTM.cpp
+13 -29
View File
@@ -17,16 +17,12 @@ LSTM::LSTM( Network *net, int hiddensize, std::string fname_weights) :
// init Tensor Descriptors
std::vector<cudnnTensorDescriptor_t> x_vec(seqLen);
std::vector<cudnnTensorDescriptor_t> y_vec(seqLen);
std::vector<cudnnTensorDescriptor_t> dx_vec(seqLen);
std::vector<cudnnTensorDescriptor_t> dy_vec(seqLen);
int dimA[3];
int strideA[3];
for (int i = 0; i < seqLen; i++) {
checkCUDNN(cudnnCreateTensorDescriptor(&x_vec[i]));
checkCUDNN(cudnnCreateTensorDescriptor(&y_vec[i]));
checkCUDNN(cudnnCreateTensorDescriptor(&dx_vec[i]));
checkCUDNN(cudnnCreateTensorDescriptor(&dy_vec[i]));
dimA[0] = batchSize;
dimA[1] = inputSize;
@@ -36,29 +32,21 @@ LSTM::LSTM( Network *net, int hiddensize, std::string fname_weights) :
strideA[0] = dimA[2] * dimA[1];
strideA[1] = dimA[2];
strideA[2] = 1;
checkCUDNN(cudnnSetTensorNdDescriptor(x_vec[i],
net->dataType, 3, dimA, strideA));
checkCUDNN(cudnnSetTensorNdDescriptor(dx_vec[i],
net->dataType, 3, dimA, strideA));
dimA[0] = batchSize;
dimA[1] = bidirectional ? stateSize*2 : stateSize;
dimA[2] = 1;
strideA[0] = dimA[2] * dimA[1];
strideA[1] = dimA[2];
strideA[2] = 1;
checkCUDNN(cudnnSetTensorNdDescriptor(y_vec[i],
net->dataType, 3, dimA, strideA));
checkCUDNN(cudnnSetTensorNdDescriptor(dy_vec[i],
net->dataType, 3, dimA, strideA));
}
// apply tensordesc
x_desc_vec_ = x_vec;
y_desc_vec_ = y_vec;
dx_desc_vec_ = dx_vec;
dy_desc_vec_ = dy_vec;
// set the state tensors
@@ -72,18 +60,10 @@ LSTM::LSTM( Network *net, int hiddensize, std::string fname_weights) :
checkCUDNN(cudnnCreateTensorDescriptor(&cx_desc_));
checkCUDNN(cudnnCreateTensorDescriptor(&hy_desc_));
checkCUDNN(cudnnCreateTensorDescriptor(&cy_desc_));
checkCUDNN(cudnnCreateTensorDescriptor(&dhx_desc_));
checkCUDNN(cudnnCreateTensorDescriptor(&dcx_desc_));
checkCUDNN(cudnnCreateTensorDescriptor(&dhy_desc_));
checkCUDNN(cudnnCreateTensorDescriptor(&dcy_desc_));
checkCUDNN(cudnnSetTensorNdDescriptor(hx_desc_, net->dataType, 3, dimA, strideA));
checkCUDNN(cudnnSetTensorNdDescriptor(cx_desc_, net->dataType, 3, dimA, strideA));
checkCUDNN(cudnnSetTensorNdDescriptor(hy_desc_, net->dataType, 3, dimA, strideA));
checkCUDNN(cudnnSetTensorNdDescriptor(cy_desc_, net->dataType, 3, dimA, strideA));
checkCUDNN(cudnnSetTensorNdDescriptor(dhx_desc_, net->dataType, 3, dimA, strideA));
checkCUDNN(cudnnSetTensorNdDescriptor(dcx_desc_, net->dataType, 3, dimA, strideA));
checkCUDNN(cudnnSetTensorNdDescriptor(dhy_desc_, net->dataType, 3, dimA, strideA));
checkCUDNN(cudnnSetTensorNdDescriptor(dcy_desc_, net->dataType, 3, dimA, strideA));
// allocate dnnType *hx_ptr, *cx_ptr, *hy_ptr, *cy_ptr;
checkCuda( cudaMalloc(&hx_ptr, dimA[0]*dimA[1]*dimA[2]*sizeof(dnnType)) );
checkCuda( cudaMalloc(&cx_ptr, dimA[0]*dimA[1]*dimA[2]*sizeof(dnnType)) );
@@ -130,28 +110,32 @@ LSTM::LSTM( Network *net, int hiddensize, std::string fname_weights) :
// Set param descriptors
checkCUDNN(cudnnCreateFilterDescriptor(&w_desc_));
checkCUDNN(cudnnCreateFilterDescriptor(&dw_desc_));
int dim_w[3] = {1, 1, 1};
dim_w[0] = cudnn_params;
checkCUDNN(cudnnSetFilterNdDescriptor(w_desc_,
net->dataType, net->tensorFormat, 3, dim_w));
checkCUDNN(cudnnSetFilterNdDescriptor(dw_desc_,
net->dataType, net->tensorFormat, 3, dim_w));
// allocate params dnnType *w_ptr, *dw_ptr;
// allocate params dnnType *w_ptr;
checkCuda( cudaMalloc(&w_ptr, cudnn_params*sizeof(dnnType)) );
checkCuda( cudaMalloc(&dw_ptr, cudnn_params*sizeof(dnnType)) );
// set output dim
output_dim = input_dim;
output_dim.c = stateSize*2;
//allocate data for infer result
checkCuda( cudaMalloc(&dstData, output_dim.tot()*sizeof(dnnType)) );
}
LSTM::~LSTM() {
checkCuda(cudaFree(hx_ptr));
checkCuda(cudaFree(cx_ptr));
checkCuda(cudaFree(hy_ptr));
checkCuda(cudaFree(cy_ptr));
checkCuda(cudaFree(w_ptr ));
checkCuda( cudaFree(dstData) );
checkCuda(cudaFree(work_space_ ));
checkCuda(cudaFree(dropout_states_));
checkCuda(cudaFree(dstData));
}
dnnType* LSTM::infer(dataDim_t &dim, dnnType* srcData) {