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LSTM, RNN, GRU - use connected_layer that uses cuDNN. Fixed CRNN for conv-layer with cuDNN.

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This commit is contained in:
AlexeyAB
2019-01-28 23:50:51 +03:00
parent 0e1f3eaf35
commit 640bdbc063
13 changed files with 292 additions and 101 deletions
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138
src/convolutional_layer.c
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@ -105,29 +105,29 @@ size_t get_workspace_size(layer l){
if(gpu_index >= 0){
size_t most = 0;
size_t s = 0;
cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(),
CHECK_CUDNN(cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(),
l.srcTensorDesc,
l.weightDesc,
l.convDesc,
l.dstTensorDesc,
l.fw_algo,
&s);
&s));
if (s > most) most = s;
cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(),
CHECK_CUDNN(cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(),
l.srcTensorDesc,
l.ddstTensorDesc,
l.convDesc,
l.dweightDesc,
l.bf_algo,
&s);
&s));
if (s > most) most = s;
cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(),
CHECK_CUDNN(cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(),
l.weightDesc,
l.ddstTensorDesc,
l.convDesc,
l.dsrcTensorDesc,
l.bd_algo,
&s);
&s));
if (s > most) most = s;
return most;
}
@ -141,29 +141,29 @@ size_t get_workspace_size16(layer l) {
if (gpu_index >= 0) {
size_t most = 0;
size_t s = 0;
cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(),
CHECK_CUDNN(cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(),
l.srcTensorDesc16,
l.weightDesc16,
l.convDesc,
l.dstTensorDesc16,
l.fw_algo16,
&s);
&s));
if (s > most) most = s;
cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(),
CHECK_CUDNN(cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(),
l.srcTensorDesc16,
l.ddstTensorDesc16,
l.convDesc,
l.dweightDesc16,
l.bf_algo16,
&s);
&s));
if (s > most) most = s;
cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(),
CHECK_CUDNN(cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(),
l.weightDesc16,
l.ddstTensorDesc16,
l.convDesc,
l.dsrcTensorDesc16,
l.bd_algo16,
&s);
&s));
if (s > most) most = s;
return most;
}
@ -175,6 +175,29 @@ size_t get_workspace_size16(layer l) {
#ifdef GPU
#ifdef CUDNN
void create_convolutional_cudnn_tensors(layer *l)
{
CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->normTensorDesc));
CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->normDstTensorDesc));
CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->srcTensorDesc));
CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->dstTensorDesc));
CHECK_CUDNN(cudnnCreateFilterDescriptor(&l->weightDesc));
CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->dsrcTensorDesc));
CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->ddstTensorDesc));
CHECK_CUDNN(cudnnCreateFilterDescriptor(&l->dweightDesc));
CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->normDstTensorDescF16));
CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->srcTensorDesc16));
CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->dstTensorDesc16));
CHECK_CUDNN(cudnnCreateFilterDescriptor(&l->weightDesc16));
CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->dsrcTensorDesc16));
CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->ddstTensorDesc16));
CHECK_CUDNN(cudnnCreateFilterDescriptor(&l->dweightDesc16));
CHECK_CUDNN(cudnnCreateConvolutionDescriptor(&l->convDesc));
}
void cudnn_convolutional_setup(layer *l, int cudnn_preference)
{
@ -194,9 +217,9 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference)
// 2. Loss Scaling - required only for: activation gradients. We do not use.
// 3. FP32 Master Copy of Weights
// More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops
cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH);
CHECK_CUDNN(cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH));
#if((CUDNN_MAJOR*10 + CUDNN_MINOR) >= 72) // cuDNN >= 7.2
cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION);
CHECK_CUDNN(cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION));
#endif
#endif
@ -205,38 +228,38 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference)
//cudnnDataType_t data_type = CUDNN_DATA_INT8;
// backward delta
cudnnSetTensor4dDescriptor(l->dsrcTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->c, l->h, l->w);
cudnnSetTensor4dDescriptor(l->ddstTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w);
cudnnSetFilter4dDescriptor(l->dweightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size);
CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dsrcTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->c, l->h, l->w));
CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->ddstTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w));
CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->dweightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size));
// forward
cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->c, l->h, l->w);
cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w);
cudnnSetFilter4dDescriptor(l->weightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size);
CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->c, l->h, l->w));
CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w));
CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->weightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size));
//#ifdef CUDNN_HALF
// backward delta
cudnnSetTensor4dDescriptor(l->dsrcTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->c, l->h, l->w);
cudnnSetTensor4dDescriptor(l->ddstTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w);
cudnnSetFilter4dDescriptor(l->dweightDesc16, CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size);
CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dsrcTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->c, l->h, l->w));
CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->ddstTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w));
CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->dweightDesc16, CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size));
// forward
cudnnSetTensor4dDescriptor(l->srcTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->c, l->h, l->w);
cudnnSetTensor4dDescriptor(l->dstTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w);
cudnnSetFilter4dDescriptor(l->weightDesc16, CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size);
CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->srcTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->c, l->h, l->w));
CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dstTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w));
CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->weightDesc16, CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size));
// batch norm
cudnnSetTensor4dDescriptor(l->normDstTensorDescF16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w);
CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->normDstTensorDescF16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w));
//#endif
// batch norm
cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1);
cudnnSetTensor4dDescriptor(l->normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w);
CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1));
CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w));
#if(CUDNN_MAJOR >= 6)
cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT); // cudnn >= 6.0
CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); // cudnn >= 6.0
#else
cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION); // cudnn 5.1
CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION)); // cudnn 5.1
#endif
int forward_algo = CUDNN_CONVOLUTION_FWD_PREFER_FASTEST;
int backward_algo = CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST;
@ -249,30 +272,30 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference)
printf(" CUDNN-slow ");
}
cudnnGetConvolutionForwardAlgorithm(cudnn_handle(),
CHECK_CUDNN(cudnnGetConvolutionForwardAlgorithm(cudnn_handle(),
l->srcTensorDesc,
l->weightDesc,
l->convDesc,
l->dstTensorDesc,
forward_algo,
0,
&l->fw_algo);
cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(),
&l->fw_algo));
CHECK_CUDNN(cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(),
l->weightDesc,
l->ddstTensorDesc,
l->convDesc,
l->dsrcTensorDesc,
backward_algo,
0,
&l->bd_algo);
cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(),
&l->bd_algo));
CHECK_CUDNN(cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(),
l->srcTensorDesc,
l->ddstTensorDesc,
l->convDesc,
l->dweightDesc,
backward_filter,
0,
&l->bf_algo);
&l->bf_algo));
//if (data_type == CUDNN_DATA_HALF)
{
@ -290,8 +313,9 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference)
#endif
#endif
convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index)
convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index)
{
int total_batch = batch*steps;
int i;
convolutional_layer l = {0};
l.type = CONVOLUTIONAL;
@ -327,8 +351,8 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
l.outputs = l.out_h * l.out_w * l.out_c;
l.inputs = l.w * l.h * l.c;
l.output = calloc(l.batch*l.outputs, sizeof(float));
l.delta = calloc(l.batch*l.outputs, sizeof(float));
l.output = calloc(total_batch*l.outputs, sizeof(float));
l.delta = calloc(total_batch*l.outputs, sizeof(float));
l.forward = forward_convolutional_layer;
l.backward = backward_convolutional_layer;
@ -364,8 +388,8 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
l.rolling_mean = calloc(n, sizeof(float));
l.rolling_variance = calloc(n, sizeof(float));
l.x = calloc(l.batch*l.outputs, sizeof(float));
l.x_norm = calloc(l.batch*l.outputs, sizeof(float));
l.x = calloc(total_batch*l.outputs, sizeof(float));
l.x_norm = calloc(total_batch*l.outputs, sizeof(float));
}
if(adam){
l.adam = 1;
@ -402,8 +426,8 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
l.biases_gpu = cuda_make_array(l.biases, n);
l.bias_updates_gpu = cuda_make_array(l.bias_updates, n);
l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n);
l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
l.delta_gpu = cuda_make_array(l.delta, total_batch*out_h*out_w*n);
l.output_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n);
if(binary){
l.binary_weights_gpu = cuda_make_array(l.weights, c*n*size*size);
@ -427,29 +451,11 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
l.scales_gpu = cuda_make_array(l.scales, n);
l.scale_updates_gpu = cuda_make_array(l.scale_updates, n);
l.x_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
l.x_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n);
l.x_norm_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n);
}
#ifdef CUDNN
cudnnCreateTensorDescriptor(&l.normTensorDesc);
cudnnCreateTensorDescriptor(&l.normDstTensorDesc);
cudnnCreateTensorDescriptor(&l.srcTensorDesc);
cudnnCreateTensorDescriptor(&l.dstTensorDesc);
cudnnCreateFilterDescriptor(&l.weightDesc);
cudnnCreateTensorDescriptor(&l.dsrcTensorDesc);
cudnnCreateTensorDescriptor(&l.ddstTensorDesc);
cudnnCreateFilterDescriptor(&l.dweightDesc);
cudnnCreateTensorDescriptor(&l.normDstTensorDescF16);
cudnnCreateTensorDescriptor(&l.srcTensorDesc16);
cudnnCreateTensorDescriptor(&l.dstTensorDesc16);
cudnnCreateFilterDescriptor(&l.weightDesc16);
cudnnCreateTensorDescriptor(&l.dsrcTensorDesc16);
cudnnCreateTensorDescriptor(&l.ddstTensorDesc16);
cudnnCreateFilterDescriptor(&l.dweightDesc16);
cudnnCreateConvolutionDescriptor(&l.convDesc);
create_convolutional_cudnn_tensors(&l);
cudnn_convolutional_setup(&l, cudnn_fastest);
#endif
}
@ -486,7 +492,7 @@ void denormalize_convolutional_layer(convolutional_layer l)
void test_convolutional_layer()
{
convolutional_layer l = make_convolutional_layer(1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 0, 0, 0, 0, 0);
convolutional_layer l = make_convolutional_layer(1, 1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 0, 0, 0, 0, 0);
l.batch_normalize = 1;
float data[] = {1,1,1,1,1,
1,1,1,1,1,