mmr/tkDNN
Archived
1
0
Fork 0
Code Issues 46 Pull Requests 12 Releases 4 Activity

structure ok, result wrong

Browse Source
This commit is contained in:
Francesco Gatti
2020-02-16 17:08:19 +01:00
parent 0ac292ea48
commit 4b48a2f38c
3 changed files with 55 additions and 61 deletions
Download Patch File Download Diff File Expand all files Collapse all files
include/tkDNN/Layer.h
+8 -1
View File
@@ -207,7 +207,9 @@ protected:
/**
Bidirectional LSTM layer
ONLY BIDIRECTIONAL (TODO: more configurable)
currently implemented as 2 inferences: forward and backward (TODO: only 1 cudnn inference)
implementation info:
https://github.com/jiangnanhugo/seq2seq_cuda/blob/e4dbdcfa0517c972bfd4beea9f11a5233954093c/src/rnn.cpp
https://github.com/Jeffery-Song/mxnet-test/blob/aab666faad44011f7a67b527b5f6c960367d0422/src/operator/cudnn_rnn-inl.h
@@ -261,6 +263,11 @@ protected:
dnnType *w_ptr;
dnnType *w_h;
dnnType *wf_ptr, *wb_ptr; // params pointer forward and backward layer
// used during inference
dataDim_t one_output_dim; // output dim of as single inference
dnnType *srcF, *srcB; // input of single inference
dnnType *dstF, *dstB_NR, *dstB; // output of single inference, dstB_NR = dstB not reversed
};
src/LSTM.cpp
+44 -59
View File
@@ -13,8 +13,6 @@ LSTM::LSTM( Network *net, int hiddensize, bool returnSeq, std::string fname_weig
seqLen = input_dim.w;
stateSize = hiddensize;
std::cout<<"LSTM seqLen: "<<seqLen<<"\n";
// init Tensor Descriptors
std::vector<cudnnTensorDescriptor_t> x_vec(seqLen);
std::vector<cudnnTensorDescriptor_t> y_vec(seqLen);
@@ -110,7 +108,7 @@ LSTM::LSTM( Network *net, int hiddensize, bool returnSeq, std::string fname_weig
checkCUDNN(cudnnGetRNNParamsSize(net->cudnnHandle,
rnnDesc,x_desc_vec_[0], &cudnn_param_size, net->dataType));
int cudnn_params = cudnn_param_size/sizeof(dnnType);
std::cout<<"LSTM params size: "<<cudnn_params << ", bytes: "<<cudnn_param_size<<"\n";
//std::cout<<"LSTM params size: "<<cudnn_params << ", bytes: "<<cudnn_param_size<<"\n";
// Set param descriptors
checkCUDNN(cudnnCreateFilterDescriptor(&w_desc_));
@@ -129,15 +127,25 @@ LSTM::LSTM( Network *net, int hiddensize, bool returnSeq, std::string fname_weig
// set output dim
output_dim = input_dim;
output_dim.c = stateSize*(bidirectional ? 2 : 1);
//allocate data for infer result
checkCuda( cudaMalloc(&dstData, output_dim.tot()*sizeof(dnnType)) );
// if retunseq is disabled only the last timestep is returned
if(!returnSeq) {
output_dim.h = 1;
output_dim.w = 1;
}
//allocate data for infer result
checkCuda( cudaMalloc(&dstData, output_dim.tot()*sizeof(dnnType)) );
// used during inference
dataDim_t one_output_dim = input_dim;
one_output_dim.c = stateSize;
checkCuda( cudaMalloc(&srcF, input_dim.tot()*sizeof(dnnType)) );
checkCuda( cudaMalloc(&srcB, input_dim.tot()*sizeof(dnnType)) );
checkCuda( cudaMalloc(&dstF, one_output_dim.tot()*sizeof(dnnType)) );
checkCuda( cudaMalloc(&dstB_NR, one_output_dim.tot()*sizeof(dnnType)) );
checkCuda( cudaMalloc(&dstB, one_output_dim.tot()*sizeof(dnnType)) );
/*
// Query weight layout
@@ -147,7 +155,7 @@ LSTM::LSTM( Network *net, int hiddensize, bool returnSeq, std::string fname_weig
int n = 8; // lstm layers
printCenteredTitle("WEIGHTS", '=', 20);
for (int i = 0; i < numLayers*(bidirectional?2:1); ++i) {
for (int i = 0; i < numLayers; ++i) {
for (int j = 0; j < n; ++j) {
checkCUDNN(cudnnGetRNNLinLayerMatrixParams(net->cudnnHandle, rnnDesc,
@@ -172,7 +180,7 @@ LSTM::LSTM( Network *net, int hiddensize, bool returnSeq, std::string fname_weig
}
printCenteredTitle("BIAS", '=', 20);
for (int i = 0; i < numLayers*(bidirectional?2:1); ++i) {
for (int i = 0; i < numLayers; ++i) {
for (int j = 0; j < n; ++j) {
checkCUDNN(cudnnGetRNNLinLayerBiasParams(net->cudnnHandle, rnnDesc,
i, x_desc_vec_[0], w_desc_, 0, j, m_desc, (void**)&p));
@@ -209,37 +217,27 @@ LSTM::~LSTM() {
checkCuda(cudaFree(work_space_ ));
checkCuda(cudaFree(dropout_states_));
checkCuda(cudaFree(srcF));
checkCuda(cudaFree(srcB));
checkCuda(cudaFree(dstF));
checkCuda(cudaFree(dstB_NR));
checkCuda(cudaFree(dstB));
checkCuda(cudaFree(dstData));
}
dnnType* LSTM::infer(dataDim_t &dim, dnnType* srcData) {
std::cout<<"LSTM infer\n";
// transpose input
matrixTranspose(net->cublasHandle, srcData, srcF, dim.c, dim.h*dim.w*dim.l);
dnnType *trans;
checkCuda( cudaMalloc(&trans, dim.tot()*sizeof(dnnType)));
matrixTranspose(net->cublasHandle, srcData, trans, dim.c, dim.h*dim.w*dim.l);
srcData = trans;
// reposition in invered order
dnnType *srcBack;
checkCuda( cudaMalloc(&srcBack, dim.tot()*sizeof(dnnType)));
// build srcB as reversed srcF
for(int i=0; i<input_dim.w; i++) {
int off_0 = i*(input_dim.c);
int off_1 = (i+1)*(input_dim.c);
std::cout<<off_0<<" "<<off_1<<"\n";
checkCuda( cudaMemcpy(srcBack + dim.tot() - off_1, srcData + off_0, input_dim.c*sizeof(dnnType), cudaMemcpyDeviceToDevice));
checkCuda( cudaMemcpy(srcB + dim.tot() - off_1, srcF + off_0,
input_dim.c*sizeof(dnnType), cudaMemcpyDeviceToDevice));
}
dataDim_t singleOutput = input_dim;
singleOutput.c = stateSize;
dnnType *dstF = dstData;
dnnType *dstB = dstData + singleOutput.tot();
std::cout<<"INPUT:\n";
printDeviceVector(input_dim.tot(), srcData);
// forward
{
// reset states
@@ -251,7 +249,7 @@ dnnType* LSTM::infer(dataDim_t &dim, dnnType* srcData) {
rnnDesc,
seqLen, // number of time steps (nT)
x_desc_vec_.data(), // input array of desc (nT*nC_in)
srcData, // input pointer
srcF, // input pointer
hx_desc_, // initial hidden state desc
hx_ptr, // initial hidden state pointer
cx_desc_, // initial cell state desc
@@ -267,11 +265,6 @@ dnnType* LSTM::infer(dataDim_t &dim, dnnType* srcData) {
work_space_, // workspace pointer
workspace_byte_)); // workspace size
}
std::cout<<"OUTPUT F:\n";
printDeviceVector(singleOutput.tot(), dstF);
std::cout<<"INPUT:\n";
printDeviceVector(input_dim.tot(), srcBack);
// backward
{
@@ -283,7 +276,7 @@ dnnType* LSTM::infer(dataDim_t &dim, dnnType* srcData) {
rnnDesc,
seqLen, // number of time steps (nT)
x_desc_vec_.data(), // input array of desc (nT*nC_in)
srcBack, // input pointer
srcB, // input pointer
hx_desc_, // initial hidden state desc
hx_ptr, // initial hidden state pointer
cx_desc_, // initial cell state desc
@@ -291,7 +284,7 @@ dnnType* LSTM::infer(dataDim_t &dim, dnnType* srcData) {
w_desc_, // weights desc
wb_ptr, // weights pointer
y_desc_vec_.data(), // output desc (nT*nC_out)
dstB, // output pointer
dstB_NR, // output pointer
hy_desc_, // final hidden state desc
hy_ptr, // final hidden state pointer
cy_desc_, // final cell state desc
@@ -301,39 +294,31 @@ dnnType* LSTM::infer(dataDim_t &dim, dnnType* srcData) {
}
// reposition in invered order
dnnType *dstBack;
checkCuda( cudaMalloc(&dstBack, singleOutput.tot()*sizeof(dnnType)));
for(int i=0; i<singleOutput.w; i++) {
int off_0 = i*(singleOutput.c);
int off_1 = (i+1)*(singleOutput.c);
std::cout<<off_0<<" "<<off_1<<"\n";
checkCuda( cudaMemcpy(dstBack + singleOutput.tot() - off_1, dstB + off_0, singleOutput.c*sizeof(dnnType), cudaMemcpyDeviceToDevice));
// reverse order of dstB
for(int i=0; i<one_output_dim.w; i++) {
int off_0 = i*(one_output_dim.c);
int off_1 = (i+1)*(one_output_dim.c);
checkCuda( cudaMemcpy(dstB + one_output_dim.tot() - off_1, dstB_NR + off_0,
one_output_dim.c*sizeof(dnnType), cudaMemcpyDeviceToDevice));
}
dstB = dstBack;
std::cout<<"OUTPUT B:\n";
printDeviceVector(singleOutput.tot(), dstB);
checkCuda( cudaMalloc(&trans, singleOutput.tot()*2*sizeof(dnnType)));
// if retunseq is disabled only the last timestep is returned
if(returnSeq) {
// forward transpose
matrixTranspose(net->cublasHandle, dstF, trans,
singleOutput.h*singleOutput.w*singleOutput.l, singleOutput.c);
matrixTranspose(net->cublasHandle, dstF, dstData,
one_output_dim.h* one_output_dim.w*one_output_dim.l, one_output_dim.c);
// backward transpose
matrixTranspose(net->cublasHandle, dstB, trans + singleOutput.tot(),
singleOutput.h*singleOutput.w*singleOutput.l, singleOutput.c);
dstData = trans;
matrixTranspose(net->cublasHandle, dstB, dstData + one_output_dim.tot(),
one_output_dim.h* one_output_dim.w*one_output_dim.l, one_output_dim.c);
} else {
// copy last of forward
checkCuda( cudaMemcpy(trans, dstF + singleOutput.tot() - singleOutput.c, singleOutput.c*sizeof(dnnType), cudaMemcpyDeviceToDevice));
checkCuda( cudaMemcpy(dstData, dstF + one_output_dim.tot() - one_output_dim.c,
one_output_dim.c*sizeof(dnnType), cudaMemcpyDeviceToDevice));
// copy first of backward
checkCuda( cudaMemcpy(trans + singleOutput.c, dstB, singleOutput.c*sizeof(dnnType), cudaMemcpyDeviceToDevice));
dstData = trans;
checkCuda( cudaMemcpy(dstData + one_output_dim.c, dstB,
one_output_dim.c*sizeof(dnnType), cudaMemcpyDeviceToDevice));
}
dim = output_dim;
return dstData;
}
tests/imuodom/imuodom.cpp
+3 -1
View File
@@ -66,7 +66,7 @@ int main() {
TIMER_START
// Inference
data = net.infer(dim, data); dim.print();
data = net.infer(dim, data);
TIMER_STOP
// Print real test
@@ -75,7 +75,9 @@ int main() {
dnnType *out0_h, *out1_h;
readBinaryFile(o0_bin, d0.output_dim.tot(), &out0_h, &out0);
readBinaryFile(o1_bin, d1.output_dim.tot(), &out1_h, &out1);
d0.output_dim.print();
checkResult(d0.output_dim.tot(), d0.dstData, out0);
d1.output_dim.print();
checkResult(d1.output_dim.tot(), d1.dstData, out1);
return 0;
}