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94c14cedba
| Author | SHA1 | Date | |
|---|---|---|---|
| 94c14cedba | |||
| 5d306f39ee | |||
| bed3d3123e |
@ -287,7 +287,7 @@ void backward_max_pooling(float*** input, float*** output, int input_width, int
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* Backward Dense
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*/
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#ifdef __CUDACC__
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__global__ void backward_dense_kernel_1(Kernel_nn* ker, float* input, float* output, int size_input, int size_output) {
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__global__ void backward_dense_kernel_1(float** d_weights, float* d_bias, float* input, float* output, int size_input, int size_output) {
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// Équivalents respectifs de i, j et k dans la boucle effectuée par le cpu
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int idx = threadIdx.x + blockDim.x*blockIdx.x; // < size_input
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int idy = threadIdx.y + blockDim.y*blockIdx.y; // < size_output
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@ -297,9 +297,9 @@ __global__ void backward_dense_kernel_1(Kernel_nn* ker, float* input, float* out
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}
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if (idx == 0) {
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ker->d_bias[idy] += output[idy];
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d_bias[idy] += output[idy];
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}
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ker->d_weights[idx][idy] += input[idx]*output[idy];
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d_weights[idx][idy] += input[idx]*output[idy];
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}
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__global__ void backward_dense_kernel_2(float** weights, float* input, float* input_z, float* output, int size_input, int size_output, funcPtr d_f) {
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@ -321,7 +321,7 @@ void backward_dense_device(Kernel_nn* ker, float* input, float* input_z, float*
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dim3 gridSize1(i_div_up(size_input, BLOCKSIZE_x), i_div_up(size_output, BLOCKSIZE_y));
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dim3 blockSize1(BLOCKSIZE_x, BLOCKSIZE_y);
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backward_dense_kernel_1<<<gridSize1, blockSize1>>>(ker, input, output, size_input, size_output);
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backward_dense_kernel_1<<<gridSize1, blockSize1>>>(ker->d_weights, ker->d_bias, input, output, size_input, size_output);
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gpuErrchk( cudaPeekAtLastError() );
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gpuErrchk( cudaDeviceSynchronize() );
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@ -387,7 +387,7 @@ void backward_dense(Kernel_nn* ker, float* input, float* input_z, float* output,
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* Backward linearisation
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*/
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#ifdef __CUDACC__
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__global__ void backward_linearisation_kernel_1(Kernel_nn* ker, float*** input, float* output, int input_depth, int input_width, int size_output) {
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__global__ void backward_linearisation_kernel_1(float** d_weights, float* d_bias, float*** input, float* output, int input_depth, int input_width, int size_output) {
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int idx = threadIdx.x + blockDim.x*blockIdx.x; // < input_depth
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int idy = threadIdx.y + blockDim.y*blockIdx.y; // < input_width
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int idz = threadIdx.z + blockDim.z*blockIdx.z; // < input_width
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@ -399,16 +399,16 @@ __global__ void backward_linearisation_kernel_1(Kernel_nn* ker, float*** input,
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int id = idx*input_width*input_width + idy*input_width + idz;
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for (int j=0; j < size_output; j++) {
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ker->d_weights[id][j] += input[idx][idy][idz]*output[j];
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d_weights[id][j] += input[idx][idy][idz]*output[j];
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}
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if (id == 0) {
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for (int j=0; j < size_output; j++) {
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ker->d_bias[j] += output[j];
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d_bias[j] += output[j];
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}
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}
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}
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__global__ void backward_linearisation_kernel_2(Kernel_nn* ker, float*** input, float*** input_z, float* output, int input_depth, int input_width, int size_output, funcPtr d_f) {
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__global__ void backward_linearisation_kernel_2(float** weights, float*** input, float*** input_z, float* output, int input_depth, int input_width, int size_output, funcPtr d_f) {
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int idx = threadIdx.x + blockDim.x*blockIdx.x; // < input_depth
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int idy = threadIdx.y + blockDim.y*blockIdx.y; // < input_width
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int idz = threadIdx.z + blockDim.z*blockIdx.z; // < input_width
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@ -420,7 +420,7 @@ __global__ void backward_linearisation_kernel_2(Kernel_nn* ker, float*** input,
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float tmp=0;
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for (int j=0; j < size_output; j++) {
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tmp += output[j]*ker->weights[id][j];
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tmp += output[j]*weights[id][j];
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}
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input[idx][idy][idz] = tmp*( (*d_f)(input_z[idx][idy][idz]) );
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}
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@ -430,7 +430,7 @@ void backward_linearisation_device(Kernel_nn* ker, float*** input, float*** inpu
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dim3 gridSize(i_div_up(input_depth, BLOCKSIZE_x), i_div_up(input_width, BLOCKSIZE_y), i_div_up(input_width, BLOCKSIZE_y));
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dim3 blockSize(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
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backward_linearisation_kernel_1<<<gridSize, blockSize>>>(ker, input, output, input_depth, input_width, size_output);
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backward_linearisation_kernel_1<<<gridSize, blockSize>>>(ker->d_weights, ker->d_bias, input, output, input_depth, input_width, size_output);
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gpuErrchk( cudaPeekAtLastError() );
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gpuErrchk( cudaDeviceSynchronize() );
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@ -438,7 +438,7 @@ void backward_linearisation_device(Kernel_nn* ker, float*** input, float*** inpu
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// Second kernel
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funcPtr d_function = get_activation_function_cuda(activation);
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backward_linearisation_kernel_2<<<gridSize, blockSize>>>(ker, input, input_z, output, input_depth, input_width, size_output, d_function);
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backward_linearisation_kernel_2<<<gridSize, blockSize>>>(ker->weights, input, input_z, output, input_depth, input_width, size_output, d_function);
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gpuErrchk( cudaPeekAtLastError() );
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gpuErrchk( cudaDeviceSynchronize() );
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@ -509,50 +509,64 @@ __global__ void backward_convolution_dbias_kernel(float*** d_bias, float*** outp
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d_bias[idx][idy][idz] += output[idx][idy][idz];
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}
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__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) {
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int idx = threadIdx.x + blockDim.x*blockIdx.x;
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int idy = threadIdx.y + blockDim.y*blockIdx.y;
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int idz = threadIdx.z + blockDim.z*blockIdx.z;
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__global__ void backward_convolution_dweight_kernel(float**** d_weights, float*** input, float*** output, int input_depth, int output_depth, int input_width, int output_width, int k_size, int stride, int padding) {
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/*
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* L'ordre des boucles a été changé par rapport à l'implémentation sur CPU
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* afin d'utiliser possiblement plus de coeurs à la fois (car en général, depth << width)
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* En gardant les indices des boucles sur CPU notées h,i,j,k,l,m; on fait donc l,m,i,h,j,k
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*/
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int idx = threadIdx.x + blockDim.x*blockIdx.x; // l
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int idy = threadIdx.y + blockDim.y*blockIdx.y; // m
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int idz = threadIdx.z + blockDim.z*blockIdx.z; // i
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int idz1 = idz / kernel_size;
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int idz2 = idz % kernel_size;
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if (idx >= input_depth || idy >= output_depth || idz1 >= kernel_size || idz2 >= kernel_size) {
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return;
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}
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float tmp = 0;
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for (int l=0; l < output_width; l++) {
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for (int m=0; m < output_width; m++) {
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tmp += input[idx][l+idz1][m+idz2]*output[idy][l][m];
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}
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}
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d_weights[idx][idy][idz1][idz2] += tmp;
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}
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__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) {
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int idx = threadIdx.x + blockDim.x*blockIdx.x;
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int idy = threadIdx.y + blockDim.y*blockIdx.y;
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int idz = threadIdx.z + blockDim.z*blockIdx.z;
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if (idx >= input_depth || idy >= input_width || idz >= input_width) {
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if (idx >= output_width || idy >= output_width || idz >= output_depth) {
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return;
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}
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int min_m, max_m, min_n, max_n;
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float tmp = 0;
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for (int l=0; l < output_depth; l++) {
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min_m = max(0, k_size-1-idy);
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max_m = min(k_size, input_width - idy);
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min_n = max(0, k_size-1-idz);
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max_n = min(k_size, input_width-idz);
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for (int m=min_m; m < max_m; m++) {
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for (int n=min_n; n < max_n; n++) {
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tmp += output[l][idy-k_size+m+1][idz-k_size+n+1]*weights[idx][l][m][n];
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int max_move = k_size - padding;
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for (int h=0; h < input_depth; h++) {
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for (int j=-padding; j < max_move; j++) {
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for (int k=-padding; k < max_move; k++) {
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if (not_outside(idx*stride+j, idy*stride+k, 0, input_width)) {
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atomicAdd(&d_weights[h][idz][j+padding][k+padding], input[h][idx*stride+j][idy*stride+k]*output[idz][idx][idy]);
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}
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}
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}
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}
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input[idx][idy][idz] = tmp*( (*d_f)(input_z[idx][idy][idz]) );
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}
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__global__ void backward_convolution_propagate_kernel(float**** weights, float*** input, float*** output, int input_depth, int input_width, int output_width, int output_depth, int k_size, int stride, int padding) {
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int idx = threadIdx.x + blockDim.x*blockIdx.x;
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int idy = threadIdx.y + blockDim.y*blockIdx.y;
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if (idx >= input_depth || idy >= output_depth) {
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return;
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}
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int max_move = k_size - padding;
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for (int j=-padding; j < max_move; j++) {
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for (int k=-padding; k < max_move; k++) {
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for (int l=0; l < output_width; l++) {
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for (int m=0; m < output_width; m++) {
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if (not_outside(l*stride+j, m*stride+k, 0, input_width)) {
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atomicAdd(&input[idx][l*stride+j][m*stride+k], output[idy][l][m]*weights[idx][idy][j+padding][k+padding]);
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}
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}
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}
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}
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}
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}
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__global__ void backward_convolution_apply_propagate_kernel(float*** input, float*** input_z, int input_depth, int input_width, funcPtr d_f) {
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int idx = threadIdx.x + blockDim.x*blockIdx.x;
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int idy = threadIdx.y + blockDim.y*blockIdx.y;
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if (idx >= input_depth || idy >= input_width) {
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return;
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}
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for (int k=0; k < input_width; k++) {
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input[idx][idy][k] = input[idx][idy][k]*d_f(input_z[idx][idy][k]);
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}
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}
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void backward_convolution_device(Kernel_cnn* kernel, float*** input, float*** input_z, float*** output, int input_depth, int input_width, int output_depth, int output_width, int activation, int is_first, int kernel_size, int padding, int stride) {
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@ -565,23 +579,30 @@ void backward_convolution_device(Kernel_cnn* kernel, float*** input, float*** in
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gpuErrchk( cudaPeekAtLastError() );
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gpuErrchk( cudaDeviceSynchronize() );
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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));
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dim3 gridSize2(i_div_up(output_width, BLOCKSIZE_x), i_div_up(output_width, BLOCKSIZE_y), i_div_up(output_depth, BLOCKSIZE_y));
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dim3 blockSize2(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
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backward_convolution_dweight_kernel<<<gridSize2, blockSize2>>>(kernel->d_weights, input, output, input_depth, output_depth, output_width, kernel_size);
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backward_convolution_dweight_kernel<<<gridSize2, blockSize2>>>(kernel->d_weights, input, output, input_depth, output_depth, input_width, output_width, kernel_size, stride, padding);
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gpuErrchk( cudaPeekAtLastError() );
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gpuErrchk( cudaDeviceSynchronize() );
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// input propagation Kernel
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if (is_first != 1) {
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dim3 gridSize3(i_div_up(input_depth, BLOCKSIZE_x), i_div_up(input_width, BLOCKSIZE_y), i_div_up(input_width, BLOCKSIZE_y));
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dim3 blockSize3(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
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reset_3d_array(input, input_depth, input_width, input_width);
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dim3 gridSize3(i_div_up(input_depth, BLOCKSIZE_x), i_div_up(output_depth, BLOCKSIZE_y));
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dim3 blockSize3(BLOCKSIZE_x, BLOCKSIZE_y);
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backward_convolution_propagate_kernel<<<gridSize3, blockSize3>>>(kernel->weights, input, output, input_depth, input_width, output_width, output_depth, kernel_size, stride, padding);
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dim3 gridSize4(i_div_up(input_depth, BLOCKSIZE_x), i_div_up(input_width, BLOCKSIZE_y));
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dim3 blockSize4(BLOCKSIZE_x, BLOCKSIZE_y);
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funcPtr d_function = get_activation_function_cuda(activation);
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backward_convolution_propagate_kernel<<<gridSize3, blockSize3>>>(kernel->weights, input, input_z, output, input_depth, input_width, output_depth, kernel_size, d_function);
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backward_convolution_apply_propagate_kernel<<<gridSize4, blockSize4>>>(input, input_z, input_depth, input_width, d_function);
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gpuErrchk( cudaPeekAtLastError() );
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gpuErrchk( cudaDeviceSynchronize() );
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}
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@ -616,7 +637,7 @@ void backward_convolution_cpu(Kernel_cnn* ker, float*** input, float*** input_z,
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}
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}
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}
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ker->d_weights[h][i][j][k] += tmp;
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ker->d_weights[h][i][j+padding][k+padding] += tmp;
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}
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}
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}
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@ -287,7 +287,7 @@ void backward_max_pooling(float*** input, float*** output, int input_width, int
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* Backward Dense
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*/
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#ifdef __CUDACC__
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__global__ void backward_dense_kernel_1(Kernel_nn* ker, float* input, float* output, int size_input, int size_output) {
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__global__ void backward_dense_kernel_1(float** d_weights, float* d_bias, float* input, float* output, int size_input, int size_output) {
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// Équivalents respectifs de i, j et k dans la boucle effectuée par le cpu
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int idx = threadIdx.x + blockDim.x*blockIdx.x; // < size_input
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int idy = threadIdx.y + blockDim.y*blockIdx.y; // < size_output
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@ -297,9 +297,9 @@ __global__ void backward_dense_kernel_1(Kernel_nn* ker, float* input, float* out
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}
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if (idx == 0) {
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ker->d_bias[idy] += output[idy];
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d_bias[idy] += output[idy];
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}
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ker->d_weights[idx][idy] += input[idx]*output[idy];
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d_weights[idx][idy] += input[idx]*output[idy];
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}
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__global__ void backward_dense_kernel_2(float** weights, float* input, float* input_z, float* output, int size_input, int size_output, funcPtr d_f) {
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@ -321,7 +321,7 @@ void backward_dense_device(Kernel_nn* ker, float* input, float* input_z, float*
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dim3 gridSize1(i_div_up(size_input, BLOCKSIZE_x), i_div_up(size_output, BLOCKSIZE_y));
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dim3 blockSize1(BLOCKSIZE_x, BLOCKSIZE_y);
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backward_dense_kernel_1<<<gridSize1, blockSize1>>>(ker, input, output, size_input, size_output);
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backward_dense_kernel_1<<<gridSize1, blockSize1>>>(ker->d_weights, ker->d_bias, input, output, size_input, size_output);
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gpuErrchk( cudaPeekAtLastError() );
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gpuErrchk( cudaDeviceSynchronize() );
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@ -387,7 +387,7 @@ void backward_dense(Kernel_nn* ker, float* input, float* input_z, float* output,
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* Backward linearisation
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*/
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#ifdef __CUDACC__
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__global__ void backward_linearisation_kernel_1(Kernel_nn* ker, float*** input, float* output, int input_depth, int input_width, int size_output) {
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__global__ void backward_linearisation_kernel_1(float** d_weights, float* d_bias, float*** input, float* output, int input_depth, int input_width, int size_output) {
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int idx = threadIdx.x + blockDim.x*blockIdx.x; // < input_depth
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int idy = threadIdx.y + blockDim.y*blockIdx.y; // < input_width
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int idz = threadIdx.z + blockDim.z*blockIdx.z; // < input_width
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@ -399,16 +399,16 @@ __global__ void backward_linearisation_kernel_1(Kernel_nn* ker, float*** input,
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int id = idx*input_width*input_width + idy*input_width + idz;
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for (int j=0; j < size_output; j++) {
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ker->d_weights[id][j] += input[idx][idy][idz]*output[j];
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d_weights[id][j] += input[idx][idy][idz]*output[j];
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}
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if (id == 0) {
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for (int j=0; j < size_output; j++) {
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ker->d_bias[j] += output[j];
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d_bias[j] += output[j];
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}
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}
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}
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__global__ void backward_linearisation_kernel_2(Kernel_nn* ker, float*** input, float*** input_z, float* output, int input_depth, int input_width, int size_output, funcPtr d_f) {
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__global__ void backward_linearisation_kernel_2(float** weights, float*** input, float*** input_z, float* output, int input_depth, int input_width, int size_output, funcPtr d_f) {
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int idx = threadIdx.x + blockDim.x*blockIdx.x; // < input_depth
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int idy = threadIdx.y + blockDim.y*blockIdx.y; // < input_width
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int idz = threadIdx.z + blockDim.z*blockIdx.z; // < input_width
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@ -420,7 +420,7 @@ __global__ void backward_linearisation_kernel_2(Kernel_nn* ker, float*** input,
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float tmp=0;
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for (int j=0; j < size_output; j++) {
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tmp += output[j]*ker->weights[id][j];
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tmp += output[j]*weights[id][j];
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}
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input[idx][idy][idz] = tmp*( (*d_f)(input_z[idx][idy][idz]) );
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}
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@ -430,7 +430,7 @@ void backward_linearisation_device(Kernel_nn* ker, float*** input, float*** inpu
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dim3 gridSize(i_div_up(input_depth, BLOCKSIZE_x), i_div_up(input_width, BLOCKSIZE_y), i_div_up(input_width, BLOCKSIZE_y));
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dim3 blockSize(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
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backward_linearisation_kernel_1<<<gridSize, blockSize>>>(ker, input, output, input_depth, input_width, size_output);
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backward_linearisation_kernel_1<<<gridSize, blockSize>>>(ker->d_weights, ker->d_bias, input, output, input_depth, input_width, size_output);
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gpuErrchk( cudaPeekAtLastError() );
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gpuErrchk( cudaDeviceSynchronize() );
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@ -438,7 +438,7 @@ void backward_linearisation_device(Kernel_nn* ker, float*** input, float*** inpu
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||||
// Second kernel
|
||||
funcPtr d_function = get_activation_function_cuda(activation);
|
||||
|
||||
backward_linearisation_kernel_2<<<gridSize, blockSize>>>(ker, input, input_z, output, input_depth, input_width, size_output, d_function);
|
||||
backward_linearisation_kernel_2<<<gridSize, blockSize>>>(ker->weights, input, input_z, output, input_depth, input_width, size_output, d_function);
|
||||
|
||||
gpuErrchk( cudaPeekAtLastError() );
|
||||
gpuErrchk( cudaDeviceSynchronize() );
|
||||
@ -509,50 +509,64 @@ __global__ void backward_convolution_dbias_kernel(float*** d_bias, float*** outp
|
||||
d_bias[idx][idy][idz] += output[idx][idy][idz];
|
||||
}
|
||||
|
||||
__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 idy = threadIdx.y + blockDim.y*blockIdx.y;
|
||||
int idz = threadIdx.z + blockDim.z*blockIdx.z;
|
||||
__global__ void backward_convolution_dweight_kernel(float**** d_weights, float*** input, float*** output, int input_depth, int output_depth, int input_width, int output_width, int k_size, int stride, int padding) {
|
||||
/*
|
||||
* L'ordre des boucles a été changé par rapport à l'implémentation sur CPU
|
||||
* afin d'utiliser possiblement plus de coeurs à la fois (car en général, depth << width)
|
||||
* En gardant les indices des boucles sur CPU notées h,i,j,k,l,m; on fait donc l,m,i,h,j,k
|
||||
*/
|
||||
int idx = threadIdx.x + blockDim.x*blockIdx.x; // l
|
||||
int idy = threadIdx.y + blockDim.y*blockIdx.y; // m
|
||||
int idz = threadIdx.z + blockDim.z*blockIdx.z; // i
|
||||
|
||||
int idz1 = idz / kernel_size;
|
||||
int idz2 = idz % kernel_size;
|
||||
|
||||
if (idx >= input_depth || idy >= output_depth || idz1 >= kernel_size || idz2 >= kernel_size) {
|
||||
return;
|
||||
}
|
||||
|
||||
float tmp = 0;
|
||||
for (int l=0; l < output_width; l++) {
|
||||
for (int m=0; m < output_width; m++) {
|
||||
tmp += input[idx][l+idz1][m+idz2]*output[idy][l][m];
|
||||
}
|
||||
}
|
||||
d_weights[idx][idy][idz1][idz2] += tmp;
|
||||
}
|
||||
|
||||
__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 idy = threadIdx.y + blockDim.y*blockIdx.y;
|
||||
int idz = threadIdx.z + blockDim.z*blockIdx.z;
|
||||
|
||||
if (idx >= input_depth || idy >= input_width || idz >= input_width) {
|
||||
if (idx >= output_width || idy >= output_width || idz >= output_depth) {
|
||||
return;
|
||||
}
|
||||
|
||||
int min_m, max_m, min_n, max_n;
|
||||
float tmp = 0;
|
||||
for (int l=0; l < output_depth; l++) {
|
||||
min_m = max(0, k_size-1-idy);
|
||||
max_m = min(k_size, input_width - idy);
|
||||
min_n = max(0, k_size-1-idz);
|
||||
max_n = min(k_size, input_width-idz);
|
||||
for (int m=min_m; m < max_m; m++) {
|
||||
for (int n=min_n; n < max_n; n++) {
|
||||
tmp += output[l][idy-k_size+m+1][idz-k_size+n+1]*weights[idx][l][m][n];
|
||||
int max_move = k_size - padding;
|
||||
for (int h=0; h < input_depth; h++) {
|
||||
for (int j=-padding; j < max_move; j++) {
|
||||
for (int k=-padding; k < max_move; k++) {
|
||||
if (not_outside(idx*stride+j, idy*stride+k, 0, input_width)) {
|
||||
atomicAdd(&d_weights[h][idz][j+padding][k+padding], input[h][idx*stride+j][idy*stride+k]*output[idz][idx][idy]);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
input[idx][idy][idz] = tmp*( (*d_f)(input_z[idx][idy][idz]) );
|
||||
}
|
||||
|
||||
__global__ void backward_convolution_propagate_kernel(float**** weights, float*** input, float*** output, int input_depth, int input_width, int output_width, int output_depth, int k_size, int stride, int padding) {
|
||||
int idx = threadIdx.x + blockDim.x*blockIdx.x;
|
||||
int idy = threadIdx.y + blockDim.y*blockIdx.y;
|
||||
|
||||
if (idx >= input_depth || idy >= output_depth) {
|
||||
return;
|
||||
}
|
||||
int max_move = k_size - padding;
|
||||
for (int j=-padding; j < max_move; j++) {
|
||||
for (int k=-padding; k < max_move; k++) {
|
||||
for (int l=0; l < output_width; l++) {
|
||||
for (int m=0; m < output_width; m++) {
|
||||
if (not_outside(l*stride+j, m*stride+k, 0, input_width)) {
|
||||
atomicAdd(&input[idx][l*stride+j][m*stride+k], output[idy][l][m]*weights[idx][idy][j+padding][k+padding]);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
__global__ void backward_convolution_apply_propagate_kernel(float*** input, float*** input_z, int input_depth, int input_width, funcPtr d_f) {
|
||||
int idx = threadIdx.x + blockDim.x*blockIdx.x;
|
||||
int idy = threadIdx.y + blockDim.y*blockIdx.y;
|
||||
|
||||
if (idx >= input_depth || idy >= input_width) {
|
||||
return;
|
||||
}
|
||||
|
||||
for (int k=0; k < input_width; k++) {
|
||||
input[idx][idy][k] = input[idx][idy][k]*d_f(input_z[idx][idy][k]);
|
||||
}
|
||||
}
|
||||
|
||||
void backward_convolution_device(Kernel_cnn* kernel, float*** input, float*** input_z, float*** output, int input_depth, int input_width, int output_depth, int output_width, int activation, int is_first, int kernel_size, int padding, int stride) {
|
||||
@ -565,23 +579,30 @@ void backward_convolution_device(Kernel_cnn* kernel, float*** input, float*** in
|
||||
gpuErrchk( cudaPeekAtLastError() );
|
||||
gpuErrchk( cudaDeviceSynchronize() );
|
||||
|
||||
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(output_width, BLOCKSIZE_x), i_div_up(output_width, BLOCKSIZE_y), i_div_up(output_depth, BLOCKSIZE_y));
|
||||
dim3 blockSize2(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
|
||||
|
||||
backward_convolution_dweight_kernel<<<gridSize2, blockSize2>>>(kernel->d_weights, 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, input_width, output_width, kernel_size, stride, padding);
|
||||
|
||||
gpuErrchk( cudaPeekAtLastError() );
|
||||
gpuErrchk( cudaDeviceSynchronize() );
|
||||
|
||||
// input propagation Kernel
|
||||
if (is_first != 1) {
|
||||
dim3 gridSize3(i_div_up(input_depth, BLOCKSIZE_x), i_div_up(input_width, BLOCKSIZE_y), i_div_up(input_width, BLOCKSIZE_y));
|
||||
dim3 blockSize3(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
|
||||
reset_3d_array(input, input_depth, input_width, input_width);
|
||||
|
||||
dim3 gridSize3(i_div_up(input_depth, BLOCKSIZE_x), i_div_up(output_depth, BLOCKSIZE_y));
|
||||
dim3 blockSize3(BLOCKSIZE_x, BLOCKSIZE_y);
|
||||
|
||||
backward_convolution_propagate_kernel<<<gridSize3, blockSize3>>>(kernel->weights, input, output, input_depth, input_width, output_width, output_depth, kernel_size, stride, padding);
|
||||
|
||||
dim3 gridSize4(i_div_up(input_depth, BLOCKSIZE_x), i_div_up(input_width, BLOCKSIZE_y));
|
||||
dim3 blockSize4(BLOCKSIZE_x, BLOCKSIZE_y);
|
||||
|
||||
funcPtr d_function = get_activation_function_cuda(activation);
|
||||
|
||||
backward_convolution_propagate_kernel<<<gridSize3, blockSize3>>>(kernel->weights, input, input_z, output, input_depth, input_width, output_depth, kernel_size, d_function);
|
||||
|
||||
backward_convolution_apply_propagate_kernel<<<gridSize4, blockSize4>>>(input, input_z, input_depth, input_width, d_function);
|
||||
|
||||
gpuErrchk( cudaPeekAtLastError() );
|
||||
gpuErrchk( cudaDeviceSynchronize() );
|
||||
}
|
||||
@ -616,7 +637,7 @@ void backward_convolution_cpu(Kernel_cnn* ker, float*** input, float*** input_z,
|
||||
}
|
||||
}
|
||||
}
|
||||
ker->d_weights[h][i][j][k] += tmp;
|
||||
ker->d_weights[h][i][j+padding][k+padding] += tmp;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -174,7 +174,7 @@ void free_network_creation(Network* network) {
|
||||
}
|
||||
|
||||
void free_network(Network* network) {
|
||||
#if defined(USE_CUDA) || defined(TEST_MEMORY_MANAGEMENT)
|
||||
#if (defined(USE_CUDA) || defined(TEST_MEMORY_MANAGEMENT)) && defined(FREE_ALL_OPT)
|
||||
// Supprimer toute la mémoire allouée avec nalloc directement
|
||||
// Il n'y a alors plus besoin de parcourir tout le réseau,
|
||||
// mais il faut que TOUTE la mémoire du réseau ait été allouée de cette manière
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
#define DEF_CONFIG_H
|
||||
|
||||
|
||||
//* Paramètres d'entraînement
|
||||
//** Paramètres d'entraînement
|
||||
#define EPOCHS 10 // Nombre d'époques par défaut (itérations sur toutes les images)
|
||||
#define BATCHES 32 // Nombre d'images à voir avant de mettre le réseau à jour
|
||||
#define LEARNING_RATE 3e-4 // Taux d'apprentissage
|
||||
@ -25,14 +25,6 @@
|
||||
//#define ADAM_CNN_BIAS
|
||||
|
||||
|
||||
//* Paramètre d'optimisation pour un dataset Jpeg
|
||||
// keep images in ram e.g re-read and decompress each time
|
||||
// Enabling this will lead to a large amount of ram used while economizing not that
|
||||
// much computing power
|
||||
// Note: 50States10K dataset is 90Go once decompressed, use with caution
|
||||
//#define STORE_IMAGES_TO_RAM
|
||||
|
||||
|
||||
//* Limite du réseau
|
||||
// Des valeurs trop grandes dans le réseau risqueraient de provoquer des overflows notamment.
|
||||
// On utilise donc la méthode gradient_clipping,
|
||||
@ -40,11 +32,29 @@
|
||||
// https://arxiv.org/pdf/1905.11881.pdf
|
||||
#define NETWORK_CLIP_VALUE 300
|
||||
|
||||
//* Paramètres CUDA
|
||||
|
||||
//** Paramètres CUDA
|
||||
// Le produit des 3 dimensions doit être au maximum 1024 (atteignable avec 8*8*16)
|
||||
// Le réduire permet d'éviter des erreurs "Out of memory" ou "too many resources requested" au lancement des Kernel
|
||||
#define BLOCKSIZE_x 8
|
||||
#define BLOCKSIZE_y 8
|
||||
#define BLOCKSIZE_z 8
|
||||
|
||||
|
||||
//** Paramètres d'optimisation
|
||||
//* Paramètre d'optimisation pour un dataset Jpeg
|
||||
// keep images in ram e.g re-read and decompress each time
|
||||
// Enabling this will lead to a large amount of ram used while economizing not that
|
||||
// much computing power
|
||||
// Note: 50States10K dataset is 90Go once decompressed, use with caution
|
||||
//#define STORE_IMAGES_TO_RAM
|
||||
|
||||
//* Optimisation de libération de la mémoire pour de larges réseaux
|
||||
// En utilisant CUDA, de larges réseaux créés dans src/common/memory_management.cu
|
||||
// peuvent prendre jusqu'à plusieurs heures pour être libérés
|
||||
// Une optimisation consiste alors à considérer que seul le réseau est dans cet emplacement de mémoire.
|
||||
// La libération d'un réseau entraîne alors la libération de toute la mémoire, ce qui peut poser problème
|
||||
// dans certaines situations.
|
||||
#define FREE_ALL_OPT
|
||||
|
||||
#endif
|
||||
@ -69,6 +69,7 @@ void free_all_memory() {
|
||||
tail = NULL;
|
||||
#endif
|
||||
|
||||
memory = NULL;
|
||||
pthread_mutex_unlock(&memory_lock);
|
||||
}
|
||||
|
||||
|
||||
@ -69,6 +69,7 @@ void free_all_memory() {
|
||||
tail = NULL;
|
||||
#endif
|
||||
|
||||
memory = NULL;
|
||||
pthread_mutex_unlock(&memory_lock);
|
||||
}
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user