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backpropagation:conv: fix misaligned address

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augustin64 2023-05-15 12:50:11 +02:00
parent 37ba3a5976
commit 3d7b641965
2 changed files with 20 additions and 20 deletions
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20
src/cnn/backpropagation.c
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@ -416,7 +416,7 @@ __global__ void backward_linearisation_kernel_2(Kernel_nn* ker, float*** input,
if (idx >= input_depth || idy >= input_width || idz >= input_width) { if (idx >= input_depth || idy >= input_width || idz >= input_width) {
return; return;
} }
int id = idx*input_width*input_width + idy*input_width + idz; int id = (idx*input_width+idy)*input_width + idz;
float tmp=0; float tmp=0;
for (int j=0; j < size_output; j++) { for (int j=0; j < size_output; j++) {
@ -498,7 +498,7 @@ void backward_linearisation(Kernel_nn* ker, float*** input, float*** input_z, fl
* Backward convolution * Backward convolution
*/ */
#ifdef __CUDACC__ #ifdef __CUDACC__
__global__ void backward_convolution_dbias_kernel(Kernel_cnn* ker, float*** output, int output_depth, int output_width) { __global__ void backward_convolution_dbias_kernel(float*** d_bias, float*** output, int output_depth, int output_width) {
int idx = threadIdx.x + blockDim.x*blockIdx.x; int idx = threadIdx.x + blockDim.x*blockIdx.x;
int idy = threadIdx.y + blockDim.y*blockIdx.y; int idy = threadIdx.y + blockDim.y*blockIdx.y;
int idz = threadIdx.z + blockDim.z*blockIdx.z; int idz = threadIdx.z + blockDim.z*blockIdx.z;
@ -506,10 +506,10 @@ __global__ void backward_convolution_dbias_kernel(Kernel_cnn* ker, float*** outp
if (idx >= output_depth || idy >= output_width || idz >= output_width) { if (idx >= output_depth || idy >= output_width || idz >= output_width) {
return; return;
} }
ker->d_bias[idx][idy][idz] += output[idx][idy][idz]; d_bias[idx][idy][idz] += output[idx][idy][idz];
} }
__global__ void backward_convolution_dweight_kernel(Kernel_cnn* ker, float*** input, float*** output, int input_depth, int output_depth, int output_width, int kernel_size) { __global__ void backward_convolution_dweight_kernel(float**** d_weights, float*** input, float*** output, int input_depth, int output_depth, int output_width, int kernel_size) {
int idx = threadIdx.x + blockDim.x*blockIdx.x; int idx = threadIdx.x + blockDim.x*blockIdx.x;
int idy = threadIdx.y + blockDim.y*blockIdx.y; int idy = threadIdx.y + blockDim.y*blockIdx.y;
int idz = threadIdx.z + blockDim.z*blockIdx.z; int idz = threadIdx.z + blockDim.z*blockIdx.z;
@ -527,10 +527,10 @@ __global__ void backward_convolution_dweight_kernel(Kernel_cnn* ker, float*** in
tmp += input[idx][l+idz1][m+idz2]*output[idy][l][m]; tmp += input[idx][l+idz1][m+idz2]*output[idy][l][m];
} }
} }
ker->d_weights[idx][idy][idz1][idz2] += tmp; d_weights[idx][idy][idz1][idz2] += tmp;
} }
__global__ void backward_convolution_propagate_kernel(Kernel_cnn* ker, float*** input, float*** input_z, float*** output, int input_depth, int input_width, int output_depth, int k_size, funcPtr d_f) { __global__ void backward_convolution_propagate_kernel(float**** weights, float*** input, float*** input_z, float*** output, int input_depth, int input_width, int output_depth, int k_size, funcPtr d_f) {
int idx = threadIdx.x + blockDim.x*blockIdx.x; int idx = threadIdx.x + blockDim.x*blockIdx.x;
int idy = threadIdx.y + blockDim.y*blockIdx.y; int idy = threadIdx.y + blockDim.y*blockIdx.y;
int idz = threadIdx.z + blockDim.z*blockIdx.z; int idz = threadIdx.z + blockDim.z*blockIdx.z;
@ -548,7 +548,7 @@ __global__ void backward_convolution_propagate_kernel(Kernel_cnn* ker, float***
max_n = min(k_size, input_width-idz); max_n = min(k_size, input_width-idz);
for (int m=min_m; m < max_m; m++) { for (int m=min_m; m < max_m; m++) {
for (int n=min_n; n < max_n; n++) { for (int n=min_n; n < max_n; n++) {
tmp += output[l][idy-k_size+m+1][idz-k_size+n+1]*ker->weights[idx][l][m][n]; tmp += output[l][idy-k_size+m+1][idz-k_size+n+1]*weights[idx][l][m][n];
} }
} }
} }
@ -560,7 +560,7 @@ void backward_convolution_device(Kernel_cnn* kernel, float*** input, float*** in
dim3 gridSize1(i_div_up(output_depth, BLOCKSIZE_x), i_div_up(output_width, BLOCKSIZE_y), i_div_up(output_width, BLOCKSIZE_y)); dim3 gridSize1(i_div_up(output_depth, BLOCKSIZE_x), i_div_up(output_width, BLOCKSIZE_y), i_div_up(output_width, BLOCKSIZE_y));
dim3 blockSize1(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z); dim3 blockSize1(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
backward_convolution_dbias_kernel<<<gridSize1, blockSize1>>>(kernel, output, output_depth, output_width); backward_convolution_dbias_kernel<<<gridSize1, blockSize1>>>(kernel->d_bias, output, output_depth, output_width);
gpuErrchk( cudaPeekAtLastError() ); gpuErrchk( cudaPeekAtLastError() );
gpuErrchk( cudaDeviceSynchronize() ); gpuErrchk( cudaDeviceSynchronize() );
@ -568,7 +568,7 @@ void backward_convolution_device(Kernel_cnn* kernel, float*** input, float*** in
dim3 gridSize2(i_div_up(input_depth, BLOCKSIZE_x), i_div_up(output_depth, BLOCKSIZE_y), i_div_up(kernel_size*kernel_size, BLOCKSIZE_y)); dim3 gridSize2(i_div_up(input_depth, BLOCKSIZE_x), i_div_up(output_depth, BLOCKSIZE_y), i_div_up(kernel_size*kernel_size, BLOCKSIZE_y));
dim3 blockSize2(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z); dim3 blockSize2(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
backward_convolution_dweight_kernel<<<gridSize2, blockSize2>>>(kernel, input, output, input_depth, output_depth, output_width, kernel_size); backward_convolution_dweight_kernel<<<gridSize2, blockSize2>>>(kernel->d_weights, input, output, input_depth, output_depth, output_width, kernel_size);
gpuErrchk( cudaPeekAtLastError() ); gpuErrchk( cudaPeekAtLastError() );
gpuErrchk( cudaDeviceSynchronize() ); gpuErrchk( cudaDeviceSynchronize() );
@ -580,7 +580,7 @@ void backward_convolution_device(Kernel_cnn* kernel, float*** input, float*** in
funcPtr d_function = get_activation_function_cuda(activation); funcPtr d_function = get_activation_function_cuda(activation);
backward_convolution_propagate_kernel<<<gridSize3, blockSize3>>>(kernel, input, input_z, output, input_depth, input_width, output_depth, kernel_size, d_function); backward_convolution_propagate_kernel<<<gridSize3, blockSize3>>>(kernel->weights, input, input_z, output, input_depth, input_width, output_depth, kernel_size, d_function);
gpuErrchk( cudaPeekAtLastError() ); gpuErrchk( cudaPeekAtLastError() );
gpuErrchk( cudaDeviceSynchronize() ); gpuErrchk( cudaDeviceSynchronize() );

20
src/cnn/backpropagation.cu
View File

@ -416,7 +416,7 @@ __global__ void backward_linearisation_kernel_2(Kernel_nn* ker, float*** input,
if (idx >= input_depth || idy >= input_width || idz >= input_width) { if (idx >= input_depth || idy >= input_width || idz >= input_width) {
return; return;
} }
int id = idx*input_width*input_width + idy*input_width + idz; int id = (idx*input_width+idy)*input_width + idz;
float tmp=0; float tmp=0;
for (int j=0; j < size_output; j++) { for (int j=0; j < size_output; j++) {
@ -498,7 +498,7 @@ void backward_linearisation(Kernel_nn* ker, float*** input, float*** input_z, fl
* Backward convolution * Backward convolution
*/ */
#ifdef __CUDACC__ #ifdef __CUDACC__
__global__ void backward_convolution_dbias_kernel(Kernel_cnn* ker, float*** output, int output_depth, int output_width) { __global__ void backward_convolution_dbias_kernel(float*** d_bias, float*** output, int output_depth, int output_width) {
int idx = threadIdx.x + blockDim.x*blockIdx.x; int idx = threadIdx.x + blockDim.x*blockIdx.x;
int idy = threadIdx.y + blockDim.y*blockIdx.y; int idy = threadIdx.y + blockDim.y*blockIdx.y;
int idz = threadIdx.z + blockDim.z*blockIdx.z; int idz = threadIdx.z + blockDim.z*blockIdx.z;
@ -506,10 +506,10 @@ __global__ void backward_convolution_dbias_kernel(Kernel_cnn* ker, float*** outp
if (idx >= output_depth || idy >= output_width || idz >= output_width) { if (idx >= output_depth || idy >= output_width || idz >= output_width) {
return; return;
} }
ker->d_bias[idx][idy][idz] += output[idx][idy][idz]; d_bias[idx][idy][idz] += output[idx][idy][idz];
} }
__global__ void backward_convolution_dweight_kernel(Kernel_cnn* ker, float*** input, float*** output, int input_depth, int output_depth, int output_width, int kernel_size) { __global__ void backward_convolution_dweight_kernel(float**** d_weights, float*** input, float*** output, int input_depth, int output_depth, int output_width, int kernel_size) {
int idx = threadIdx.x + blockDim.x*blockIdx.x; int idx = threadIdx.x + blockDim.x*blockIdx.x;
int idy = threadIdx.y + blockDim.y*blockIdx.y; int idy = threadIdx.y + blockDim.y*blockIdx.y;
int idz = threadIdx.z + blockDim.z*blockIdx.z; int idz = threadIdx.z + blockDim.z*blockIdx.z;
@ -527,10 +527,10 @@ __global__ void backward_convolution_dweight_kernel(Kernel_cnn* ker, float*** in
tmp += input[idx][l+idz1][m+idz2]*output[idy][l][m]; tmp += input[idx][l+idz1][m+idz2]*output[idy][l][m];
} }
} }
ker->d_weights[idx][idy][idz1][idz2] += tmp; d_weights[idx][idy][idz1][idz2] += tmp;
} }
__global__ void backward_convolution_propagate_kernel(Kernel_cnn* ker, float*** input, float*** input_z, float*** output, int input_depth, int input_width, int output_depth, int k_size, funcPtr d_f) { __global__ void backward_convolution_propagate_kernel(float**** weights, float*** input, float*** input_z, float*** output, int input_depth, int input_width, int output_depth, int k_size, funcPtr d_f) {
int idx = threadIdx.x + blockDim.x*blockIdx.x; int idx = threadIdx.x + blockDim.x*blockIdx.x;
int idy = threadIdx.y + blockDim.y*blockIdx.y; int idy = threadIdx.y + blockDim.y*blockIdx.y;
int idz = threadIdx.z + blockDim.z*blockIdx.z; int idz = threadIdx.z + blockDim.z*blockIdx.z;
@ -548,7 +548,7 @@ __global__ void backward_convolution_propagate_kernel(Kernel_cnn* ker, float***
max_n = min(k_size, input_width-idz); max_n = min(k_size, input_width-idz);
for (int m=min_m; m < max_m; m++) { for (int m=min_m; m < max_m; m++) {
for (int n=min_n; n < max_n; n++) { for (int n=min_n; n < max_n; n++) {
tmp += output[l][idy-k_size+m+1][idz-k_size+n+1]*ker->weights[idx][l][m][n]; tmp += output[l][idy-k_size+m+1][idz-k_size+n+1]*weights[idx][l][m][n];
} }
} }
} }
@ -560,7 +560,7 @@ void backward_convolution_device(Kernel_cnn* kernel, float*** input, float*** in
dim3 gridSize1(i_div_up(output_depth, BLOCKSIZE_x), i_div_up(output_width, BLOCKSIZE_y), i_div_up(output_width, BLOCKSIZE_y)); dim3 gridSize1(i_div_up(output_depth, BLOCKSIZE_x), i_div_up(output_width, BLOCKSIZE_y), i_div_up(output_width, BLOCKSIZE_y));
dim3 blockSize1(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z); dim3 blockSize1(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
backward_convolution_dbias_kernel<<<gridSize1, blockSize1>>>(kernel, output, output_depth, output_width); backward_convolution_dbias_kernel<<<gridSize1, blockSize1>>>(kernel->d_bias, output, output_depth, output_width);
gpuErrchk( cudaPeekAtLastError() ); gpuErrchk( cudaPeekAtLastError() );
gpuErrchk( cudaDeviceSynchronize() ); gpuErrchk( cudaDeviceSynchronize() );
@ -568,7 +568,7 @@ void backward_convolution_device(Kernel_cnn* kernel, float*** input, float*** in
dim3 gridSize2(i_div_up(input_depth, BLOCKSIZE_x), i_div_up(output_depth, BLOCKSIZE_y), i_div_up(kernel_size*kernel_size, BLOCKSIZE_y)); dim3 gridSize2(i_div_up(input_depth, BLOCKSIZE_x), i_div_up(output_depth, BLOCKSIZE_y), i_div_up(kernel_size*kernel_size, BLOCKSIZE_y));
dim3 blockSize2(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z); dim3 blockSize2(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
backward_convolution_dweight_kernel<<<gridSize2, blockSize2>>>(kernel, input, output, input_depth, output_depth, output_width, kernel_size); backward_convolution_dweight_kernel<<<gridSize2, blockSize2>>>(kernel->d_weights, input, output, input_depth, output_depth, output_width, kernel_size);
gpuErrchk( cudaPeekAtLastError() ); gpuErrchk( cudaPeekAtLastError() );
gpuErrchk( cudaDeviceSynchronize() ); gpuErrchk( cudaDeviceSynchronize() );
@ -580,7 +580,7 @@ void backward_convolution_device(Kernel_cnn* kernel, float*** input, float*** in
funcPtr d_function = get_activation_function_cuda(activation); funcPtr d_function = get_activation_function_cuda(activation);
backward_convolution_propagate_kernel<<<gridSize3, blockSize3>>>(kernel, input, input_z, output, input_depth, input_width, output_depth, kernel_size, d_function); backward_convolution_propagate_kernel<<<gridSize3, blockSize3>>>(kernel->weights, input, input_z, output, input_depth, input_width, output_depth, kernel_size, d_function);
gpuErrchk( cudaPeekAtLastError() ); gpuErrchk( cudaPeekAtLastError() );
gpuErrchk( cudaDeviceSynchronize() ); gpuErrchk( cudaDeviceSynchronize() );