派生自 Algorithm/baseDetector
lib/detecter_tools/darknet/convolutional_kernels.cu
@@ -1,1362 +1,1436 @@ #include <cuda_runtime.h> #include <curand.h> #include <cublas_v2.h> #include "convolutional_layer.h" #include "batchnorm_layer.h" #include "gemm.h" #include "blas.h" #include "im2col.h" #include "col2im.h" #include "utils.h" #include "dark_cuda.h" #include "box.h" __global__ void binarize_kernel(float *x, int n, float *binary) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i >= n) return; binary[i] = (x[i] >= 0) ? 1 : -1; } void binarize_gpu(float *x, int n, float *binary) { binarize_kernel<<<cuda_gridsize(n), BLOCK, 0, get_cuda_stream() >>>(x, n, binary); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void binarize_input_kernel(float *input, int n, int size, float *binary) { int s = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (s >= size) return; int i = 0; float mean = 0; for(i = 0; i < n; ++i){ mean += fabs(input[i*size + s]); } mean = mean / n; for(i = 0; i < n; ++i){ binary[i*size + s] = (input[i*size + s] > 0) ? mean : -mean; } } void binarize_input_gpu(float *input, int n, int size, float *binary) { binarize_input_kernel<<<cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >>>(input, n, size, binary); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void binarize_weights_kernel(float *weights, int n, int size, float *binary) { int f = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (f >= n) return; int i = 0; float mean = 0; for (i = 0; i < size; ++i) { mean += fabs(weights[f*size + i]); } mean = mean / size; for (i = 0; i < size; ++i) { binary[f*size + i] = (weights[f*size + i] > 0) ? mean : -mean; //binary[f*size + i] = weights[f*size + i]; } } void binarize_weights_gpu(float *weights, int n, int size, float *binary) { binarize_weights_kernel << <cuda_gridsize(n), BLOCK, 0, get_cuda_stream() >> >(weights, n, size, binary); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void set_zero_kernel(float *src, int size) { int i = blockIdx.x * blockDim.x + threadIdx.x; if (i < size) src[i] = 0; } __inline__ __device__ float warpAllReduceSum(float val) { for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2) #if CUDART_VERSION >= 9000 val += __shfl_xor_sync(0xffffffff, val, mask); #else val += __shfl_xor(val, mask); #endif return val; } // only if (size % 32 == 0) __global__ void reduce_kernel(float *weights, int n, int size, float *mean_arr_gpu) { int i = blockIdx.x * blockDim.x + threadIdx.x; int f = i / size; if (f >= n) return; float warp_mean = warpAllReduceSum(fabs(weights[i])); if(i % 32 == 0) atomicAdd(&mean_arr_gpu[f], warp_mean / size); } __global__ void binarize_weights_mean_kernel(float *weights, int n, int size, float *binary, float *mean_arr_gpu) { int i = blockIdx.x * blockDim.x + threadIdx.x; int f = i / size; if (f >= n) return; float mean = mean_arr_gpu[f]; binary[i] = (weights[i] > 0) ? mean : -mean; } void fast_binarize_weights_gpu(float *weights, int n, int size, float *binary, float *mean_arr_gpu) { if (size % 32 == 0) { size_t gridsize = n * size; const int num_blocks = get_number_of_blocks(gridsize, BLOCK);// gridsize / BLOCK + 1; set_zero_kernel << <(n/BLOCK + 1), BLOCK, 0, get_cuda_stream() >> > (mean_arr_gpu, n); reduce_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> > (weights, n, size, mean_arr_gpu); binarize_weights_mean_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> > (weights, n, size, binary, mean_arr_gpu); CHECK_CUDA(cudaPeekAtLastError()); } else { binarize_weights_gpu(weights, n, size, binary); } } __global__ void cuda_f32_to_f16(float* input_f32, size_t size, half *output_f16) { int idx = blockIdx.x * blockDim.x + threadIdx.x; if (idx < size) output_f16[idx] = __float2half(input_f32[idx]); //if (idx < size) output_f16[idx] = __float2half_rn(input_f32[idx]); // can't be compiled on Linux without casting // __float2half_ru, __float2half_rd, __float2half_rz, __float2half_rn //if (idx < size) *((unsigned short *)output_f16 + idx) = __float2half(input_f32[idx]); } void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16) { cuda_f32_to_f16 <<< get_number_of_blocks(size, BLOCK), BLOCK, 0, get_cuda_stream() >>> (input_f32, size, (half *)output_f16); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void cuda_f16_to_f32(half* input_f16, size_t size, float *output_f32) { int idx = blockIdx.x * blockDim.x + threadIdx.x; if (idx < size) output_f32[idx] = __half2float(input_f16[idx]); //if (idx < size) output_f32[idx] = __half2float(*((unsigned short *)input_f16 + idx)); } void cuda_convert_f16_to_f32(float* input_f16, size_t size, float *output_f32) { cuda_f16_to_f32 <<< get_number_of_blocks(size, BLOCK), BLOCK, 0, get_cuda_stream() >>> ((half *)input_f16, size, output_f32); CHECK_CUDA(cudaPeekAtLastError()); } half *cuda_make_f16_from_f32_array(float *src, size_t n) { half *dst16; size_t size = sizeof(half)*n; CHECK_CUDA(cudaMalloc((void **)&dst16, size)); if (src) { assert(n > 0); cuda_convert_f32_to_f16(src, n, (float *)dst16); } if (!dst16) error("Cuda malloc failed\n"); return dst16; } void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) { //fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); if(l.binary){ binarize_weights_gpu(l.weights_gpu, l.n, (l.c / l.groups)*l.size*l.size, l.binary_weights_gpu); swap_binary(&l); } if(l.xnor){ if (!l.align_bit_weights_gpu || state.train) { //binarize_weights_gpu(l.weights_gpu, l.n, (l.c / l.groups)*l.size*l.size, l.binary_weights_gpu); fast_binarize_weights_gpu(l.weights_gpu, l.n, (l.c / l.groups)*l.size*l.size, l.binary_weights_gpu, l.mean_arr_gpu); } if (l.align_bit_weights_gpu && !state.train && l.c >= 32 && l.stride_x == l.stride_y) { //return; //cudaError_t status = cudaSuccess; //int input_size = l.c*l.h*l.w*l.batch; int m = l.n / l.groups; int k = l.size*l.size*l.c / l.groups; int n = l.out_w*l.out_h; //float * a = l.weights_gpu; // int i, j; // for(i = 0; i < l.batch; ++i){ // for (j = 0; j < l.groups; ++j) { int ldb_align = l.lda_align; size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; //size_t t_intput_size = new_ldb * n; //size_t t_bit_input_size = t_intput_size / 8;// +1; if (l.c % 32 == 0) { //printf("\n\n l.index = %d, l.w = %d, l.c = %d, l.n = %d, l.stride = %d, l.pad = %d - new XNOR \n", l.index, l.w, l.c, l.n, l.stride, l.pad); //printf("l.align_workspace_size = %d, (l.c * l.w * l.h) = %d \n", l.align_workspace_size, (l.c * l.w * l.h)); //float *intput_cpu = (float *)calloc(l.inputs, sizeof(float)); // state.input //cudaMemcpy(intput_cpu, state.input, l.inputs * sizeof(float), cudaMemcpyDefault); int ldb_align = l.lda_align; size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; //size_t t_intput_size = new_ldb * l.bit_align;// n; //size_t t_bit_input_size = t_intput_size / 8;// +1; const int new_c = l.c / 32; //float *re_packed_input = (float *)calloc(l.c * l.w * l.h, sizeof(float)); //uint32_t *bin_re_packed_input = (uint32_t *)calloc(new_c * l.w * l.h + 1, sizeof(uint32_t)); // float32x4 by channel (as in cuDNN) //repack_input(intput_cpu, re_packed_input, l.w, l.h, l.c); // 32 x floats -> 1 x uint32_t //float_to_bit(re_packed_input, (uint8_t *)bin_re_packed_input, l.c * l.w * l.h); //cudaDeviceSynchronize(); //start_timer(); repack_input_gpu_bin(state.input, (uint32_t *)l.align_workspace_gpu, l.w, l.h, l.c); //repack_input_gpu(state.input, state.workspace, l.w, l.h, l.c); // 32 x floats -> 1 x uint32_t //float_to_bit_gpu(state.workspace, (unsigned char *)l.align_workspace_gpu, l.c * l.w * l.h);// l.align_workspace_size); //cudaDeviceSynchronize(); //stop_timer_and_show_name("repack_input_gpu + float_to_bit_gpu"); //free(re_packed_input); // slow - convolution the packed inputs and weights: float x 32 by channel (as in cuDNN) //convolution_repacked((uint32_t *)bin_re_packed_input, (uint32_t *)l.align_bit_weights, l.output, // l.w, l.h, l.c, l.n, l.size, l.pad, l.new_lda, l.mean_arr); // // then exit from if() //float *b = state.workspace; //float *b = (float *)calloc(100 * 1024 * 1024, sizeof(float)); //float *c = l.output; //memset(c, 0, l.outputs * sizeof(float)); //im2col_cpu_custom((float *)bin_re_packed_input, new_c, l.h, l.w, l.size, l.stride, l.pad, b); //cudaMemcpy(l.align_workspace_gpu, bin_re_packed_input, (new_c * l.w * l.h + 1) * sizeof(uint32_t), cudaMemcpyDefault); //start_timer(); im2col_ongpu(l.align_workspace_gpu, new_c, l.h, l.w, l.size, l.stride, l.pad, state.workspace); //cudaDeviceSynchronize(); //stop_timer_and_show_name("im2col_ongpu"); //free(bin_re_packed_input); int new_k = l.size*l.size*l.c / 32; // good for (l.c == 64) //gemm_nn_bin_32bit_packed(m, n, new_k, 1, // l.align_bit_weights, l.new_lda/32, // b, n, // c, n, l.mean_arr); // // then exit from if() //size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; //size_t t_intput_size = new_ldb * l.bit_align;// n; //size_t t_bit_input_size = t_intput_size / 8;// +1; //char *t_bit_input = (char *)calloc(t_bit_input_size, sizeof(char)); //transpose_uint32((uint32_t *)b, (uint32_t *)t_bit_input, new_k, n, n, new_ldb); //cudaMemcpy(l.transposed_align_workspace_gpu, t_bit_input, t_bit_input_size * sizeof(char), cudaMemcpyDefault); //cudaMemcpy(state.workspace, b, t_bit_input_size * sizeof(char), cudaMemcpyDefault); //printf("\n n = %d, n % 32 = %d, new_ldb = %d, new_ldb % 32 = %d \n", n, n % 32, new_ldb, new_ldb % 32); //start_timer(); transpose_uint32_gpu((uint32_t *)state.workspace, (uint32_t *)l.transposed_align_workspace_gpu, new_k, n, n, new_ldb); //cudaDeviceSynchronize(); //stop_timer_and_show_name("transpose_uint32_gpu"); //cudaDeviceSynchronize(); //stop_timer_and_show_name("repack_input_gpu_bin + im2col_ongpu + transpose_uint32_gpu_2"); //start_timer(); gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu, l.activation == LEAKY, l.bin_conv_shortcut_in_gpu, l.bin_conv_shortcut_out_gpu); //cudaDeviceSynchronize(); //stop_timer_and_show_name("gemm_nn_custom_bin_mean_transposed_gpu"); // the main GEMM function //gemm_nn_custom_bin_mean_transposed(m, n, k, 1, (uint8_t *)l.align_bit_weights, new_ldb, (uint8_t *)t_bit_input, new_ldb, c, n, l.mean_arr); //add_bias(l.output, l.biases, l.batch, l.n, l.out_h*l.out_w); //cudaMemcpy(l.output_gpu, l.output, l.outputs * sizeof(float), cudaMemcpyDefault); // // alternative GEMM //gemm_nn_bin_transposed_32bit_packed(m, n, new_k, 1, // l.align_bit_weights, l.new_lda/32, // t_bit_input, new_ldb / 32, // c, n, l.mean_arr); //free(t_bit_input); //free(b); } else { //printf("\n\n l.index = %d, l.w = %d, l.c = %d, l.n = %d, l.stride = %d, l.pad = %d - old XNOR \n", l.index, l.w, l.c, l.n, l.stride, l.pad); //cudaDeviceSynchronize(); int i = 0; /* // if (l.stride == 1 && l.c >= 256 && l.size > 1) if (l.stride == 1 && l.c >= 1024 && l.size > 1 && 0)// && l.w >= 13) // disabled { // stride=1 only //start_timer(); im2col_align_bin_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace, l.bit_align); //cudaDeviceSynchronize(); //stop_timer_and_show_name("im2col_align_bin_ongpu"); } else*/ { //start_timer(); im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.align_workspace_gpu, l.bit_align); //cudaDeviceSynchronize(); //stop_timer_and_show_name("im2col_align_ongpu"); //getchar(); // should be optimized //start_timer(); float_to_bit_gpu(l.align_workspace_gpu, (unsigned char *)state.workspace, l.align_workspace_size); //cudaDeviceSynchronize(); //stop_timer_and_show_name("float_to_bit_gpu"); } //start_timer(); transpose_bin_gpu((unsigned char *)state.workspace, (unsigned char *)l.transposed_align_workspace_gpu, k, n, l.bit_align, new_ldb, 8); //cudaDeviceSynchronize(); //stop_timer_and_show_name("transpose_bin_gpu"); //cudaDeviceSynchronize(); //stop_timer_and_show_name("im2col_align_ongpu + float_to_bit_gpu + transpose_bin_gpu"); // should be optimized //if(0) {//if (k > 1000) { // sequentially input-shared - BAD // gemm_nn_custom_bin_mean_transposed_sequentially_gpu(m, n, k, // (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, new_ldb, l.output_gpu, n, l.mean_arr_gpu); //} //else { // coalescing & weights-shared-memory - GOOD //start_timer(); gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu, l.activation == LEAKY, l.bin_conv_shortcut_in_gpu, l.bin_conv_shortcut_out_gpu); //cudaDeviceSynchronize(); //stop_timer_and_show_name("gemm_nn_custom_bin_mean_transposed_gpu"); //} //cudaDeviceSynchronize(); //check_error(status); //getchar(); } /* { float_to_bit_gpu(state.input, (unsigned char *)l.align_workspace_gpu, input_size); convolve_bin_gpu(l.align_workspace_gpu, (float *)l.align_bit_weights_gpu, l.output_gpu, l.w, l.h, l.c, l.n, l.size, l.pad, l.new_lda, l.mean_arr_gpu); //convolve_gpu(state.input, l.weights_gpu, l.output_gpu, l.w, l.h, l.c, l.n, l.size, l.pad); //cudaDeviceSynchronize(); //check_error(status); add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); } */ //add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == MISH) activate_array_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == NORM_CHAN) activate_array_normalize_channels_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu); else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu, 0); else if (l.activation == NORM_CHAN_SOFTMAX_MAXVAL) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu, 1); else if (l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if (l.binary || l.xnor) swap_binary(&l); //cudaDeviceSynchronize(); return; } } if (l.xnor) { swap_binary(&l); binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu); state.input = l.binary_input_gpu; } //fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); #ifdef CUDNN //float one = 1; // alpha[0], beta[0] is float for HALF and FLOAT float alpha = 1, beta = 0; //#ifdef CUDNN_HALF //if (state.use_mixed_precision) { int iteration_num = get_current_iteration(state.net); // (*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || (iteration_num > 3 * state.net.burn_in) && state.net.loss_scale != 1) && (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && l.groups <= 1 && l.size > 1) { //printf("\n CUDNN_HALF!!! state.index = %d \n", state.index); // Note: For improved performance it is advised to use beta[0] = 0.0. // For Tensor Core: cudnnSetConvolutionMathType() where cudnnMathType_t mathType = CUDNN_TENSOR_OP_MATH; // 1. or CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM and use CUDNN_DATA_HALF // 2. or CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED // More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops const size_t input16_size = l.batch*l.c*l.w*l.h; const size_t output16_size = l.batch*l.out_c*l.out_h*l.out_w; if (*state.net.max_input16_size < input16_size) { //printf("\n input16_size: cur = %zu \t max = %zu \n", input16_size, *state.net.max_input16_size); *state.net.max_input16_size = input16_size; if (*state.net.input16_gpu) cuda_free(*state.net.input16_gpu); assert(*state.net.max_input16_size > 0); *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size); } float *input16 = *state.net.input16_gpu; if (*state.net.max_output16_size < output16_size) { *state.net.max_output16_size = output16_size; if (*state.net.output16_gpu) cuda_free(*state.net.output16_gpu); assert(*state.net.max_output16_size > 0); *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size); } float *output16 = *state.net.output16_gpu; assert(input16_size > 0); cuda_convert_f32_to_f16(state.input, input16_size, input16); //fill_ongpu(output16_size / 2, 0, (float *)output16, 1); CHECK_CUDNN(cudnnConvolutionForward(cudnn_handle(), &alpha, l.srcTensorDesc16, input16, l.weightDesc16, l.weights_gpu16, l.convDesc, l.fw_algo16, state.workspace, l.workspace_size, &beta, l.dstTensorDesc16, output16)); if (l.batch_normalize) { if (state.train && !state.net.adversarial) // Training { simple_copy_ongpu(l.outputs*l.batch / 2, output16, l.x_gpu); //copy_ongpu(l.outputs*l.batch / 2, output16, 1, l.x_gpu, 1); //cudaMemcpyAsync(l.x_gpu, output16, l.outputs*l.batch*sizeof(half), cudaMemcpyDefault, get_cuda_stream()); float one = 1.0f; float zero = 0.0f; // Batch-normalization can still take FP16 inputs and outputs, saving half the bandwidth // compared to FP32, it's just that the statistics and value adjustment should be done in FP32. CHECK_CUDNN(cudnnBatchNormalizationForwardTraining(cudnn_handle(), CUDNN_BATCHNORM_SPATIAL, &one, &zero, l.normDstTensorDescF16, l.x_gpu, // input l.normDstTensorDescF16, output16, // output l.normTensorDesc, l.scales_gpu, // input l.biases_gpu, // input .01, l.rolling_mean_gpu, // input/output (should be FP32) l.rolling_variance_gpu, // input/output (should be FP32) .00001, l.mean_gpu, // output (should be FP32) - optional cache to speedup cudnnBatchNormalizationBackward() l.variance_gpu)); // output (should be FP32) - optional cache to speedup cudnnBatchNormalizationBackward() cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); //forward_batchnorm_layer_gpu(l, state); } else // Detection { cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); } } else // BIAS only { cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); } } else { //#else /* int input_nan_inf = is_nan_or_inf(state.input, l.inputs * l.batch); printf("\n is_nan_or_inf(state.input) = %d \n", input_nan_inf); if (input_nan_inf) getchar(); int weights_nan_inf = is_nan_or_inf(l.weights_gpu, l.nweights); printf("\n is_nan_or_inf(l.weights_gpu) = %d \n", weights_nan_inf); if (weights_nan_inf) getchar(); */ CHECK_CUDNN(cudnnConvolutionForward(cudnn_handle(), &alpha, //&one, l.srcTensorDesc, state.input, l.weightDesc, l.weights_gpu, l.convDesc, l.fw_algo, state.workspace, l.workspace_size, &beta, //&one, l.dstTensorDesc, l.output_gpu)); //cudaDeviceSynchronize(); if (l.batch_normalize) { forward_batchnorm_layer_gpu(l, state); } else { add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); } //#endif // CUDNN_HALF } #else fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); int i, j; int m = l.n / l.groups; int k = l.size*l.size*l.c / l.groups; int n = l.out_w*l.out_h; for(i = 0; i < l.batch; ++i){ for (j = 0; j < l.groups; ++j) { //float *im = state.input + i*l.c*l.h*l.w; float *im = state.input + (i*l.groups + j)*l.c / l.groups*l.h*l.w; float *a = l.weights_gpu + j*l.nweights / l.groups; float *b = state.workspace; float *c = l.output_gpu + (i*l.groups + j)*n*m; if (l.size == 1) { b = im; } else { //im2col_ongpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, state.workspace); im2col_gpu_ext(im, // input l.c / l.groups, // input channels l.h, l.w, // input size (h, w) l.size, l.size, // kernel size (h, w) l.pad * l.dilation, l.pad * l.dilation, // padding (h, w) l.stride_y, l.stride_x, // stride (h, w) l.dilation, l.dilation, // dilation (h, w) state.workspace); // output } //gemm_ongpu(0, 0, m, n, k, 1., a, k, b, n, 1., c + i*m*n, n); gemm_ongpu(0, 0, m, n, k, 1, a, k, b, n, 1, c, n); } } if (l.batch_normalize) { forward_batchnorm_layer_gpu(l, state); } else { add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); } #endif //#ifndef CUDNN_HALF //#endif // no CUDNN_HALF if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == MISH) activate_array_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == NORM_CHAN) activate_array_normalize_channels_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu); else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu, 0); else if (l.activation == NORM_CHAN_SOFTMAX_MAXVAL) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu, 1); else if (l.activation != LINEAR) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.dot > 0) dot_error_gpu(l); if(l.binary || l.xnor) swap_binary(&l); //cudaDeviceSynchronize(); // for correct profiling of performance if (state.net.try_fix_nan) { fix_nan_and_inf(l.output_gpu, l.outputs*l.batch); } if(l.assisted_excitation && state.train) assisted_excitation_forward_gpu(l, state); if (l.antialiasing) { network_state s = { 0 }; s.train = state.train; s.workspace = state.workspace; s.net = state.net; if (!state.train) s.index = state.index; // don't use TC for training (especially without cuda_convert_f32_to_f16() ) s.input = l.output_gpu; forward_convolutional_layer_gpu(*(l.input_layer), s); simple_copy_ongpu(l.outputs*l.batch, l.output_gpu, l.input_antialiasing_gpu); simple_copy_ongpu(l.input_layer->outputs*l.input_layer->batch, l.input_layer->output_gpu, l.output_gpu); } } void backward_convolutional_layer_gpu(convolutional_layer l, network_state state) { if (l.antialiasing) { network_state s = { 0 }; s.train = state.train; s.workspace = state.workspace; s.net = state.net; s.delta = l.delta_gpu; // s.delta will be returned to l.delta_gpu s.input = l.input_antialiasing_gpu; //if (!state.train) s.index = state.index; // don't use TC for training (especially without cuda_convert_f32_to_f16() ) simple_copy_ongpu(l.input_layer->outputs*l.input_layer->batch, l.delta_gpu, l.input_layer->delta_gpu); backward_convolutional_layer_gpu(*(l.input_layer), s); simple_copy_ongpu(l.outputs*l.batch, l.input_antialiasing_gpu, l.output_gpu); } if(state.net.try_fix_nan) constrain_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1); if (l.activation == SWISH) gradient_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); else if (l.activation == MISH) gradient_array_mish_ongpu(l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); else if (l.activation == NORM_CHAN_SOFTMAX || l.activation == NORM_CHAN_SOFTMAX_MAXVAL) gradient_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); else if (l.activation == NORM_CHAN) gradient_array_normalize_channels_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); else gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); if (!l.batch_normalize) backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); //#ifndef CUDNN_HALF //if(l.batch_normalize){ // backward_batchnorm_layer_gpu(l, state); //} else { // //backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); //} //#endif // no CUDNN_HALF float *original_input = state.input; if(l.xnor) state.input = l.binary_input_gpu; #ifdef CUDNN float one = 1.f; float alpha = 1, beta = 0; //#ifdef CUDNN_HALF int iteration_num = get_current_iteration(state.net); //(*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || (iteration_num > 3 * state.net.burn_in) && state.net.loss_scale != 1) && (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && l.groups <= 1 && l.size > 1) { const size_t input16_size = l.batch*l.c*l.w*l.h; const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h; if (*state.net.max_input16_size < input16_size) { *state.net.max_input16_size = input16_size; if (*state.net.input16_gpu) cuda_free(*state.net.input16_gpu); assert(*state.net.max_input16_size > 0); *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size); } float *input16 = *state.net.input16_gpu; if (*state.net.max_output16_size < delta16_size) { *state.net.max_output16_size = delta16_size; if (*state.net.output16_gpu) cuda_free(*state.net.output16_gpu); assert(*state.net.max_output16_size > 0); *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size); } float *delta16 = *state.net.output16_gpu; assert(input16_size > 0); assert(delta16_size > 0); cuda_convert_f32_to_f16(state.input, input16_size, input16); cuda_convert_f32_to_f16(l.delta_gpu, delta16_size, delta16); if (l.batch_normalize) { //if (!state.train) { // l.mean_gpu = l.rolling_mean_gpu; // l.variance_gpu = l.rolling_variance_gpu; //} float one = 1.0f; float zero = 0.0f; CHECK_CUDNN(cudnnBatchNormalizationBackward(cudnn_handle(), CUDNN_BATCHNORM_SPATIAL, &one, &zero, &one, &one, l.normDstTensorDescF16, l.x_gpu, // input (input in BN-forward-inference) l.normDstTensorDescF16, delta16, // input l.normDstTensorDescF16, l.output_gpu, //l.x_norm_gpu, // output (new delta) l.normTensorDesc, l.scales_gpu, // input (should be FP32) l.scale_updates_gpu, // output (should be FP32) l.bias_updates_gpu, // output (should be FP32) .00001, l.mean_gpu, // input (should be FP32) l.variance_gpu)); // input (should be FP32) simple_copy_ongpu(l.outputs*l.batch / 2, l.output_gpu, delta16); //copy_ongpu(l.outputs*l.batch / 2, l.x_norm_gpu, 1, delta16, 1); //cudaMemcpyAsync(delta16, l.x_norm_gpu, l.outputs*l.batch * sizeof(half), cudaMemcpyDefault, get_cuda_stream()); } else { //backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); } // convert input: state.input (x), l.delta_gpu (y) from fp32 to fp16 // get output: l.weight_updates_gpu (dw) and convert it to fp32 (ONLY if it is fp16) // calculate conv weight updates // Already: l.weight_updates_gpu = (l.weight_updates_gpu - l.weight*decay*batch*subdivision)*momentum // so we should copy f32 to f16, or compute: f16=(w_up - w*d*b*s)*m assert((l.nweights) > 0); cuda_convert_f32_to_f16(l.weight_updates_gpu, l.nweights, l.weight_updates_gpu16); if (!state.net.adversarial && !l.train_only_bn) { CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(), &one, l.srcTensorDesc16, input16, //state.input, l.ddstTensorDesc16, delta16, //l.delta_gpu, l.convDesc, l.bf_algo16, state.workspace, l.workspace_size, &one, l.dweightDesc16, l.weight_updates_gpu16)); // l.weight_updates_gpu); cuda_convert_f16_to_f32(l.weight_updates_gpu16, l.nweights, l.weight_updates_gpu); } if (state.delta) { if (l.binary || l.xnor) swap_binary(&l); // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData // calculate delta for the next layer // convert input: l.weights_gpu (w), l.delta_gpu (dy) from fp32 to fp16 // get output: state.delta (dx) and convert it to fp32 (ONLY if it is fp16) CHECK_CUDNN(cudnnConvolutionBackwardData(cudnn_handle(), &alpha, l.weightDesc16, l.weights_gpu16, //l.weights_gpu, l.ddstTensorDesc16, delta16, //l.delta_gpu, l.convDesc, l.bd_algo16, state.workspace, l.workspace_size, &beta, l.dsrcTensorDesc16, input16)); // state.delta); cuda_convert_f16_to_f32(input16, input16_size, state.delta); if (l.binary || l.xnor) swap_binary(&l); if (l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta); } } else { //#else // CUDNN_HALF if(l.batch_normalize){ backward_batchnorm_layer_gpu(l, state); } if (!state.net.adversarial && !l.train_only_bn) { // calculate conv weight updates // if used: beta=1 then loss decreases faster CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(), &one, l.srcTensorDesc, state.input, l.ddstTensorDesc, l.delta_gpu, l.convDesc, l.bf_algo, state.workspace, l.workspace_size, &one, l.dweightDesc, l.weight_updates_gpu)); } if (state.delta) { if (l.binary || l.xnor) swap_binary(&l); // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData // calculate delta for the next layer CHECK_CUDNN(cudnnConvolutionBackwardData(cudnn_handle(), &one, l.weightDesc, l.weights_gpu, l.ddstTensorDesc, l.delta_gpu, l.convDesc, l.bd_algo, state.workspace, l.workspace_size, &one, l.dsrcTensorDesc, state.delta)); if (l.binary || l.xnor) swap_binary(&l); if (l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta); } } //#endif // CUDNN_HALF #else // CUDNN if (l.batch_normalize) { backward_batchnorm_layer_gpu(l, state); } int m = l.n / l.groups; int n = l.size*l.size*l.c / l.groups; int k = l.out_w*l.out_h; int i, j; for(i = 0; i < l.batch; ++i){ for (j = 0; j < l.groups; ++j) { float * a = l.delta_gpu + (i*l.groups + j)*m*k; float * b = state.workspace; float * c = l.weight_updates_gpu + j*l.nweights / l.groups; float *im = state.input + (i*l.groups + j)*l.c / l.groups*l.h*l.w; if (!state.net.adversarial && !l.train_only_bn) { //im2col_ongpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, state.workspace); im2col_gpu_ext(im, // input l.c / l.groups, // input channels l.h, l.w, // input size (h, w) l.size, l.size, // kernel size (h, w) l.pad * l.dilation, l.pad * l.dilation, // padding (h, w) l.stride_y, l.stride_x, // stride (h, w) l.dilation, l.dilation, // dilation (h, w) state.workspace); // output //gemm_ongpu(0, 1, m, n, k, 1, a + i*m*k, k, b, k, 1, c, n); gemm_ongpu(0, 1, m, n, k, 1, a, k, b, k, 1, c, n); } if (state.delta) { if (l.binary || l.xnor) swap_binary(&l); float * a = l.weights_gpu + j*l.nweights / l.groups; float * b = l.delta_gpu + (i*l.groups + j)*m*k; float * c = state.workspace; //gemm_ongpu(1, 0, n, k, m, 1, a, n, b + i*k*m, k, 0, c, k); gemm_ongpu(1, 0, n, k, m, 1, a, n, b, k, 0, c, k); float *delta = state.delta + (i*l.groups + j)*l.c / l.groups*l.h*l.w; //col2im_ongpu(state.workspace, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, delta); col2im_gpu_ext( state.workspace, // input l.c / l.groups, // input channels l.h, l.w, // input size (h, w) l.size, l.size, // kernel size (h, w) l.pad * l.dilation, l.pad * l.dilation, // padding size (h, w) l.stride_y, l.stride_x, // stride size (h, w) l.dilation, l.dilation, // dilation size (h, w) delta); // output (delta) if (l.binary || l.xnor) { swap_binary(&l); } if (l.xnor) gradient_array_ongpu(original_input + i*l.c*l.h*l.w, l.c*l.h*l.w, HARDTAN, state.delta + i*l.c*l.h*l.w); } } } #endif if (state.net.try_fix_nan) { if (state.delta) { reset_nan_and_inf(state.delta, l.inputs * l.batch); } int size = l.nweights; reset_nan_and_inf(l.weight_updates_gpu, size); fix_nan_and_inf(l.weights_gpu, size); } } __global__ void calc_avg_activation_kernel(float *src, float *dst, int size, int channels, int batches) { int i = blockIdx.x * blockDim.x + threadIdx.x; int xy = i % size; int b = i / size; if (i < size*batches) { dst[i] = 0; for (int c = 0; c < channels; ++c) { dst[i] += src[xy + size*(c + channels*b)]; } dst[i] = dst[i] / channels; } } void calc_avg_activation_gpu(float *src, float *dst, int size, int channels, int batches) { const int num_blocks = get_number_of_blocks(size*batches, BLOCK); calc_avg_activation_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> > (src, dst, size, channels, batches); } __global__ void assisted_activation_kernel(float alpha, float *output, float *gt_gpu, float *a_avg_gpu, int size, int channels, int batches) { int i = blockIdx.x * blockDim.x + threadIdx.x; int xy = i % size; int b = i / size; if (b < batches) { for (int c = 0; c < channels; ++c) { output[xy + size*(c + channels*b)] += alpha * gt_gpu[i] * a_avg_gpu[i]; //output[xy + size*(c + channels*b)] += gt_gpu[i] * a_avg_gpu[i]; //output[xy + size*(c + channels*b)] += gt_gpu[i] * output[xy + size*(c + channels*b)]; //output[xy + size*(c + channels*b)] = a_avg_gpu[i]; } } } void assisted_activation_gpu(float alpha, float *output, float *gt_gpu, float *a_avg_gpu, int size, int channels, int batches) { const int num_blocks = get_number_of_blocks(size*batches, BLOCK); assisted_activation_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> > (alpha, output, gt_gpu, a_avg_gpu, size, channels, batches); } __global__ void assisted_activation2_kernel(float alpha, float *output, float *gt_gpu, float *a_avg_gpu, int size, int channels, int batches) { int i = blockIdx.x * blockDim.x + threadIdx.x; int xy = i % size; int b = i / size; float beta = 1 - alpha; if (b < batches) { for (int c = 0; c < channels; ++c) { if(gt_gpu[i] == 0) output[xy + size*(c + channels*b)] *= beta; } } } void assisted_activation2_gpu(float alpha, float *output, float *gt_gpu, float *a_avg_gpu, int size, int channels, int batches) { const int num_blocks = get_number_of_blocks(size*batches, BLOCK); assisted_activation2_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> > (alpha, output, gt_gpu, a_avg_gpu, size, channels, batches); } void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) { const int iteration_num = get_current_iteration(state.net); //(*state.net.seen) / (state.net.batch*state.net.subdivisions); // epoch //const float epoch = (float)(*state.net.seen) / state.net.train_images_num; // calculate alpha //const float alpha = (1 + cos(3.141592 * iteration_num)) / (2 * state.net.max_batches); //const float alpha = (1 + cos(3.141592 * epoch)) / (2 * state.net.max_batches); float alpha = (1 + cos(3.141592 * iteration_num / state.net.max_batches)) / 2; //float alpha = (1 + cos(3.141592 * iteration_num / state.net.max_batches)); if (l.assisted_excitation == 1) { if (iteration_num > state.net.max_batches / 2) return; } else { if (iteration_num < state.net.burn_in) return; else if (iteration_num > l.assisted_excitation) return; else alpha = (1 + cos(3.141592 * iteration_num / (state.net.burn_in + l.assisted_excitation))) / 2; // from 1 to 0 } //printf("\n epoch = %f, alpha = %f, seen = %d, max_batches = %d, train_images_num = %d \n", // epoch, alpha, (*state.net.seen), state.net.max_batches, state.net.train_images_num); //const int size = l.outputs * l.batch; float *a_avg = (float *)calloc(l.out_w * l.out_h * l.batch, sizeof(float)); float *gt = (float *)calloc(l.out_w * l.out_h * l.batch, sizeof(float)); int b; int w, h; l.max_boxes = state.net.num_boxes; l.truths = l.max_boxes*(4 + 1); int num_truth = l.batch*l.truths; float *truth_cpu = (float *)calloc(num_truth, sizeof(float)); cuda_pull_array(state.truth, truth_cpu, num_truth); //cudaStreamSynchronize(get_cuda_stream()); //CHECK_CUDA(cudaPeekAtLastError()); for (b = 0; b < l.batch; ++b) { // calculate G int t; for (t = 0; t < state.net.num_boxes; ++t) { box truth = float_to_box_stride(truth_cpu + t*(4 + 1) + b*l.truths, 1); if (!truth.x) break; // continue; float beta = 0; //float beta = 1 - alpha; // from 0 to 1 float dw = (1 - truth.w) * beta; float dh = (1 - truth.h) * beta; //printf(" alpha = %f, beta = %f, truth.w = %f, dw = %f, tw+dw = %f, l.out_w = %d \n", alpha, beta, truth.w, dw, truth.w+dw, l.out_w); int left = floor((truth.x - (dw + truth.w) / 2) * l.out_w); int right = ceil((truth.x + (dw + truth.w) / 2) * l.out_w); int top = floor((truth.y - (dh + truth.h) / 2) * l.out_h); int bottom = ceil((truth.y + (dh + truth.h) / 2) * l.out_h); if (left < 0) left = 0; if (top < 0) top = 0; if (right > l.out_w) right = l.out_w; if (bottom > l.out_h) bottom = l.out_h; for (w = left; w <= right; w++) { for (h = top; h < bottom; h++) { gt[w + l.out_w * h + l.out_w*l.out_h*b] = 1; } } } } cuda_push_array(l.gt_gpu, gt, l.out_w * l.out_h * l.batch); //cudaStreamSynchronize(get_cuda_stream()); //CHECK_CUDA(cudaPeekAtLastError()); // calc avg_output on GPU - for whole batch calc_avg_activation_gpu(l.output_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); //cudaStreamSynchronize(get_cuda_stream()); //CHECK_CUDA(cudaPeekAtLastError()); // calc new output //assisted_activation2_gpu(1, l.output_gpu, l.gt_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); // AE3: gt increases (beta = 1 - alpha = 0) //assisted_activation2_gpu(alpha, l.output_gpu, l.gt_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); assisted_activation_gpu(alpha, l.output_gpu, l.gt_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); //cudaStreamSynchronize(get_cuda_stream()); //CHECK_CUDA(cudaPeekAtLastError()); /* for (b = 0; b < l.batch; ++b) { // calculate average A for (w = 0; w < l.out_w; w++) { for (h = 0; h < l.out_h; h++) { for (c = 0; c < l.out_c; c++) { a_avg[w + l.out_w*(h + l.out_h*b)] += l.output[w + l.out_w*(h + l.out_h*(c + l.out_c*b))]; } a_avg[w + l.out_w*(h + l.out_h*b)] /= l.out_c; // a_avg / d } } } // change activation for (b = 0; b < l.batch; ++b) { for (w = 0; w < l.out_w; w++) { for (h = 0; h < l.out_h; h++) { for (c = 0; c < l.out_c; c++) { // a = a + alpha(t) + e(c,i,j) = a + alpha(t) + g(i,j) * avg_a(i,j) / channels l.output[w + l.out_w*(h + l.out_h*(c + l.out_c*b))] += alpha * g[w + l.out_w*(h + l.out_h*b)] * a_avg[w + l.out_w*(h + l.out_h*b)]; //l.output[w + l.out_w*(h + l.out_h*(c + l.out_c*b))] = // alpha * g[w + l.out_w*(h + l.out_h*b)] * a_avg[w + l.out_w*(h + l.out_h*b)]; } } } } */ if (0) // visualize ground truth { #ifdef OPENCV cuda_pull_array(l.output_gpu, l.output, l.outputs * l.batch); cudaStreamSynchronize(get_cuda_stream()); CHECK_CUDA(cudaPeekAtLastError()); for (b = 0; b < l.batch; ++b) { printf(" Assisted Excitation alpha = %f \n", alpha); image img = float_to_image(l.out_w, l.out_h, 1, >[l.out_w*l.out_h*b]); char buff[100]; sprintf(buff, "a_excitation_gt_%d", b); show_image_cv(img, buff); //image img2 = float_to_image(l.out_w, l.out_h, 1, &l.output[l.out_w*l.out_h*l.out_c*b]); image img2 = float_to_image_scaled(l.out_w, l.out_h, 1, &l.output[l.out_w*l.out_h*l.out_c*b]); char buff2[100]; sprintf(buff2, "a_excitation_output_%d", b); show_image_cv(img2, buff2); /* int c = l.out_c; if (c > 4) c = 4; image img3 = float_to_image(l.out_w, l.out_h, c, &l.output[l.out_w*l.out_h*l.out_c*b]); image dc = collapse_image_layers(img3, 1); char buff3[100]; sprintf(buff3, "a_excitation_act_collapsed_%d", b); show_image_cv(dc, buff3); */ wait_key_cv(5); } wait_until_press_key_cv(); #endif // OPENCV } free(truth_cpu); free(gt); free(a_avg); } void pull_convolutional_layer(convolutional_layer l) { cuda_pull_array_async(l.weights_gpu, l.weights, l.nweights); cuda_pull_array_async(l.biases_gpu, l.biases, l.n); cuda_pull_array_async(l.weight_updates_gpu, l.weight_updates, l.nweights); cuda_pull_array_async(l.bias_updates_gpu, l.bias_updates, l.n); if (l.batch_normalize){ cuda_pull_array_async(l.scales_gpu, l.scales, l.n); cuda_pull_array_async(l.rolling_mean_gpu, l.rolling_mean, l.n); cuda_pull_array_async(l.rolling_variance_gpu, l.rolling_variance, l.n); } if (l.adam){ cuda_pull_array_async(l.m_gpu, l.m, l.nweights); cuda_pull_array_async(l.v_gpu, l.v, l.nweights); } CHECK_CUDA(cudaPeekAtLastError()); cudaStreamSynchronize(get_cuda_stream()); } void push_convolutional_layer(convolutional_layer l) { cuda_push_array(l.weights_gpu, l.weights, l.nweights); #ifdef CUDNN_HALF assert(l.nweights > 0); cuda_convert_f32_to_f16(l.weights_gpu, l.nweights, l.weights_gpu16); #endif cuda_push_array(l.biases_gpu, l.biases, l.n); if (l.train) { cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.nweights); cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n); } if (l.batch_normalize){ cuda_push_array(l.scales_gpu, l.scales, l.n); cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.n); cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.n); } if (l.adam){ cuda_push_array(l.m_gpu, l.m, l.nweights); cuda_push_array(l.v_gpu, l.v, l.nweights); } CHECK_CUDA(cudaPeekAtLastError()); } void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay, float loss_scale) { /* for (int angle = 0; angle < 360; angle++) { printf(" angle = %d \n", angle); smooth_rotate_weights_kernel(l.weights_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, angle, 0); cuda_pull_array(l.weight_deform_gpu, l.weights, l.nweights); visualize_convolutional_layer(l, "weights", NULL); wait_key_cv(10); } */ if (l.deform) { //for (l.angle = 0; l.angle < 360; l.angle += 1) //{ //stretch_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, l.angle/180, 1); //else simple_copy_ongpu(l.nweights, l.weight_updates_gpu, l.weight_deform_gpu); if (l.rotate) rotate_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, 1); else if (l.sway) sway_and_flip_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, l.angle, 1); else if (l.stretch) stretch_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, 0, 1); else if (l.stretch_sway) stretch_sway_flip_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, l.angle, 1); //simple_copy_ongpu(l.nweights, l.weight_updates_gpu, l.weight_deform_gpu); reduce_and_expand_array_gpu(l.weight_deform_gpu, l.weight_updates_gpu, l.nweights, 4); //printf(" angle = %f \n", l.angle); //cuda_pull_array(l.weight_deform_gpu, l.weights, l.nweights); //visualize_convolutional_layer(l, "weights", NULL); //wait_key_cv(10); //} } float learning_rate = learning_rate_init*l.learning_rate_scale; //float momentum = a.momentum; //float decay = a.decay; //int batch = a.batch; // Loss scale for Mixed-Precision on Tensor-Cores if (loss_scale != 1.0) { if (l.weight_updates_gpu && l.nweights > 0) scal_ongpu(l.nweights, 1.0 / loss_scale, l.weight_updates_gpu, 1); if (l.bias_updates_gpu && l.n > 0) scal_ongpu(l.n, 1.0 / loss_scale, l.bias_updates_gpu, 1); if (l.scale_updates_gpu && l.n > 0) scal_ongpu(l.n, 1.0 / loss_scale, l.scale_updates_gpu, 1); } reset_nan_and_inf(l.weight_updates_gpu, l.nweights); fix_nan_and_inf(l.weights_gpu, l.nweights); // Gradient Centralization if (l.grad_centr && l.batch_normalize) { // weights[filters][channels][height][width] // for(filters) w[f] = w[f] - mean(w[c][h][w]) gradient_centralization_gpu(l.size, l.size, l.c / l.groups, l.n, l.weight_updates_gpu); } if (l.adam) { //adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.nweights, batch, a.t); adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.nweights, batch, l.t); adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.n, batch, l.t); if (l.scales_gpu) { adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.n, batch, l.t); } } else { //axpy_ongpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); //axpy_ongpu(l.nweights, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); //scal_ongpu(l.nweights, momentum, l.weight_updates_gpu, 1); axpy_ongpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); axpy_ongpu(l.nweights, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); scal_ongpu(l.nweights, momentum, l.weight_updates_gpu, 1); axpy_ongpu(l.n, learning_rate / batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); scal_ongpu(l.n, momentum, l.bias_updates_gpu, 1); if (l.scales_gpu) { axpy_ongpu(l.n, learning_rate / batch, l.scale_updates_gpu, 1, l.scales_gpu, 1); scal_ongpu(l.n, momentum, l.scale_updates_gpu, 1); } } if (l.deform) { //for (l.angle = 0; l.angle < 360; l.angle += 4) //{ expand_array_gpu(l.weights_gpu, l.weight_deform_gpu, l.nweights, 4); //simple_copy_ongpu(l.nweights, l.weight_deform_gpu, l.weights_gpu); if (l.rotate) rotate_weights_gpu(l.weight_deform_gpu, l.weights_gpu, l.nweights, l.n, l.size, 0); else if (l.sway) sway_and_flip_weights_gpu(l.weight_deform_gpu, l.weights_gpu, l.nweights, l.n, l.size, l.angle, 0); else if (l.stretch) stretch_weights_gpu(l.weight_deform_gpu, l.weights_gpu, l.nweights, l.n, l.size, 0, 0); else if (l.stretch_sway) stretch_sway_flip_weights_gpu(l.weight_deform_gpu, l.weights_gpu, l.nweights, l.n, l.size, l.angle, 0); //printf(" angle = %f, reverse = %d \n", l.angle, 0); //cuda_pull_array(l.weights_gpu, l.weights, l.nweights); //visualize_convolutional_layer(l, "weights", NULL); //wait_key_cv(10); //} } if (l.clip) { constrain_ongpu(l.nweights, l.clip, l.weights_gpu, 1); } } /* void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay) { int size = layer.size*layer.size*layer.c*layer.n; axpy_ongpu(layer.n, learning_rate/batch, layer.bias_updates_gpu, 1, layer.biases_gpu, 1); scal_ongpu(layer.n, momentum, layer.bias_updates_gpu, 1); if(layer.scales_gpu){ axpy_ongpu(layer.n, learning_rate/batch, layer.scale_updates_gpu, 1, layer.scales_gpu, 1); scal_ongpu(layer.n, momentum, layer.scale_updates_gpu, 1); } if(layer.adam){ scal_ongpu(size, layer.B1, layer.m_gpu, 1); scal_ongpu(size, layer.B2, layer.v_gpu, 1); axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1); axpy_ongpu(size, -(1-layer.B1), layer.weight_updates_gpu, 1, layer.m_gpu, 1); mul_ongpu(size, layer.weight_updates_gpu, 1, layer.weight_updates_gpu, 1); axpy_ongpu(size, (1-layer.B2), layer.weight_updates_gpu, 1, layer.v_gpu, 1); adam_gpu(size, layer.weights_gpu, layer.m_gpu, layer.v_gpu, layer.B1, layer.B2, learning_rate/batch, layer.eps, layer.t+1); fill_ongpu(size, 0, layer.weight_updates_gpu, 1); }else{ axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1); // wu = wu - w*decay*batch axpy_ongpu(size, learning_rate/batch, layer.weight_updates_gpu, 1, layer.weights_gpu, 1); // w = w + wu*lr/batch scal_ongpu(size, momentum, layer.weight_updates_gpu, 1); // wu = wu*momentum // wu = (wu - w*decay*batch)*momentum // w = w + (wu - w*decay*batch)*lr/batch = w + wu*lr/batch - w*decay*lr = w*(1-decay*lr) + wu*lr/batch //wu_prev = (wu_old - w_old*decay*batch)*momentum //weights_update = weights_update_new + (weights_update_old - weights_old*decay*batch)*momentum - weights_new*decay*batch = // = weights_update_new + weights_update_old*momentum - weights_old*decay*batch*momentum - weights_new*decay*batch // = weights_update_new + weights_update_old*momentum - (weights_old*momentum + weights_new)*decay*batch //------------- RESULT -------------- // weights_update = weights_update_new + weights_update_old*momentum - (weights_old*momentum + weights_new)*decay*batch //----------------------------------- // weights_newest = weights_new + (weights_update_new + weights_update_old*momentum - (weights_old*momentum + weights_new)*decay*batch)*lr/batch // = weights_new + weights_update_new*lr/batch + weights_update_old*momentum*lr/batch - weights_old*momentum*decay*batch*lr/batch - weights_new*decay*batch*lr/batch // = weights_new + weights_update_new*lr/batch + weights_update_old*momentum*lr/batch - weights_old*momentum*decay*lr - weights_new*decay*lr // = weights_new*(1 - decay*lr) - weights_old*momentum*decay*lr + (weights_update_new + weights_update_old*momentum)*lr/batch //------------- RESULT -------------- // weights_newest = weights_new*(1 - decay*lr) - weights_old*momentum*(decay*lr) + (weights_update_new + weights_update_old*momentum)*lr/batch = // = weights_new - (weights_new + weights_old*momentum)*decay*lr + (weights_update_new + weights_update_old*momentum)*lr / batch //----------------------------------- } } */ #include <cuda_runtime.h> #include <curand.h> #include <cublas_v2.h> #include "convolutional_layer.h" #include "batchnorm_layer.h" #include "gemm.h" #include "blas.h" #include "im2col.h" #include "col2im.h" #include "utils.h" #include "dark_cuda.h" #include "box.h" __global__ void binarize_kernel(float *x, int n, float *binary) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i >= n) return; binary[i] = (x[i] >= 0) ? 1 : -1; } void binarize_gpu(float *x, int n, float *binary) { binarize_kernel<<<cuda_gridsize(n), BLOCK, 0, get_cuda_stream() >>>(x, n, binary); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void binarize_input_kernel(float *input, int n, int size, float *binary) { int s = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (s >= size) return; int i = 0; float mean = 0; for(i = 0; i < n; ++i){ mean += fabs(input[i*size + s]); } mean = mean / n; for(i = 0; i < n; ++i){ binary[i*size + s] = (input[i*size + s] > 0) ? mean : -mean; } } void binarize_input_gpu(float *input, int n, int size, float *binary) { binarize_input_kernel<<<cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >>>(input, n, size, binary); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void binarize_weights_kernel(float *weights, int n, int size, float *binary) { int f = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (f >= n) return; int i = 0; float mean = 0; for (i = 0; i < size; ++i) { mean += fabs(weights[f*size + i]); } mean = mean / size; for (i = 0; i < size; ++i) { binary[f*size + i] = (weights[f*size + i] > 0) ? mean : -mean; //binary[f*size + i] = weights[f*size + i]; } } void binarize_weights_gpu(float *weights, int n, int size, float *binary) { binarize_weights_kernel << <cuda_gridsize(n), BLOCK, 0, get_cuda_stream() >> >(weights, n, size, binary); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void set_zero_kernel(float *src, int size) { int i = blockIdx.x * blockDim.x + threadIdx.x; if (i < size) src[i] = 0; } __inline__ __device__ float warpAllReduceSum(float val) { for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2) #if CUDART_VERSION >= 9000 val += __shfl_xor_sync(0xffffffff, val, mask); #else val += __shfl_xor(val, mask); #endif return val; } // only if (size % 32 == 0) __global__ void reduce_kernel(float *weights, int n, int size, float *mean_arr_gpu) { int i = blockIdx.x * blockDim.x + threadIdx.x; int f = i / size; if (f >= n) return; float warp_mean = warpAllReduceSum(fabs(weights[i])); if(i % 32 == 0) atomicAdd(&mean_arr_gpu[f], warp_mean / size); } __global__ void binarize_weights_mean_kernel(float *weights, int n, int size, float *binary, float *mean_arr_gpu) { int i = blockIdx.x * blockDim.x + threadIdx.x; int f = i / size; if (f >= n) return; float mean = mean_arr_gpu[f]; binary[i] = (weights[i] > 0) ? mean : -mean; } void fast_binarize_weights_gpu(float *weights, int n, int size, float *binary, float *mean_arr_gpu) { if (size % 32 == 0) { size_t gridsize = n * size; const int num_blocks = get_number_of_blocks(gridsize, BLOCK);// gridsize / BLOCK + 1; set_zero_kernel << <(n/BLOCK + 1), BLOCK, 0, get_cuda_stream() >> > (mean_arr_gpu, n); reduce_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> > (weights, n, size, mean_arr_gpu); binarize_weights_mean_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> > (weights, n, size, binary, mean_arr_gpu); CHECK_CUDA(cudaPeekAtLastError()); } else { binarize_weights_gpu(weights, n, size, binary); } } __global__ void cuda_f32_to_f16(float* input_f32, size_t size, half *output_f16) { int idx = blockIdx.x * blockDim.x + threadIdx.x; if (idx < size) output_f16[idx] = __float2half(input_f32[idx]); //if (idx < size) output_f16[idx] = __float2half_rn(input_f32[idx]); // can't be compiled on Linux without casting // __float2half_ru, __float2half_rd, __float2half_rz, __float2half_rn //if (idx < size) *((unsigned short *)output_f16 + idx) = __float2half(input_f32[idx]); } void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16) { cuda_f32_to_f16 <<< get_number_of_blocks(size, BLOCK), BLOCK, 0, get_cuda_stream() >>> (input_f32, size, (half *)output_f16); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void cuda_f16_to_f32(half* input_f16, size_t size, float *output_f32) { int idx = blockIdx.x * blockDim.x + threadIdx.x; if (idx < size) output_f32[idx] = __half2float(input_f16[idx]); //if (idx < size) output_f32[idx] = __half2float(*((unsigned short *)input_f16 + idx)); } void cuda_convert_f16_to_f32(float* input_f16, size_t size, float *output_f32) { cuda_f16_to_f32 <<< get_number_of_blocks(size, BLOCK), BLOCK, 0, get_cuda_stream() >>> ((half *)input_f16, size, output_f32); CHECK_CUDA(cudaPeekAtLastError()); } half *cuda_make_f16_from_f32_array(float *src, size_t n) { half *dst16; size_t size = sizeof(half)*n; CHECK_CUDA(cudaMalloc((void **)&dst16, size)); if (src) { assert(n > 0); cuda_convert_f32_to_f16(src, n, (float *)dst16); } if (!dst16) error("Cuda malloc failed\n"); return dst16; } void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) { if (l.stream >= 0) { switch_stream(l.stream); } if (l.wait_stream_id >= 0) { wait_stream(l.wait_stream_id); } //fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); if(l.binary){ binarize_weights_gpu(l.weights_gpu, l.n, (l.c / l.groups)*l.size*l.size, l.binary_weights_gpu); swap_binary(&l); } if(l.xnor){ if (!l.align_bit_weights_gpu || state.train) { //binarize_weights_gpu(l.weights_gpu, l.n, (l.c / l.groups)*l.size*l.size, l.binary_weights_gpu); fast_binarize_weights_gpu(l.weights_gpu, l.n, (l.c / l.groups)*l.size*l.size, l.binary_weights_gpu, l.mean_arr_gpu); } if (l.align_bit_weights_gpu && !state.train && l.c >= 32 && l.stride_x == l.stride_y) { //return; //cudaError_t status = cudaSuccess; //int input_size = l.c*l.h*l.w*l.batch; int m = l.n / l.groups; int k = l.size*l.size*l.c / l.groups; int n = l.out_w*l.out_h; //float * a = l.weights_gpu; // int i, j; // for(i = 0; i < l.batch; ++i){ // for (j = 0; j < l.groups; ++j) { int ldb_align = l.lda_align; size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; //size_t t_intput_size = new_ldb * n; //size_t t_bit_input_size = t_intput_size / 8;// +1; if (l.c % 32 == 0) { //printf("\n\n l.index = %d, l.w = %d, l.c = %d, l.n = %d, l.stride = %d, l.pad = %d - new XNOR \n", l.index, l.w, l.c, l.n, l.stride, l.pad); //printf("l.align_workspace_size = %d, (l.c * l.w * l.h) = %d \n", l.align_workspace_size, (l.c * l.w * l.h)); //float *intput_cpu = (float *)calloc(l.inputs, sizeof(float)); // state.input //cudaMemcpy(intput_cpu, state.input, l.inputs * sizeof(float), cudaMemcpyDefault); int ldb_align = l.lda_align; size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; //size_t t_intput_size = new_ldb * l.bit_align;// n; //size_t t_bit_input_size = t_intput_size / 8;// +1; const int new_c = l.c / 32; //float *re_packed_input = (float *)calloc(l.c * l.w * l.h, sizeof(float)); //uint32_t *bin_re_packed_input = (uint32_t *)calloc(new_c * l.w * l.h + 1, sizeof(uint32_t)); // float32x4 by channel (as in cuDNN) //repack_input(intput_cpu, re_packed_input, l.w, l.h, l.c); // 32 x floats -> 1 x uint32_t //float_to_bit(re_packed_input, (uint8_t *)bin_re_packed_input, l.c * l.w * l.h); //cudaDeviceSynchronize(); //start_timer(); repack_input_gpu_bin(state.input, (uint32_t *)l.align_workspace_gpu, l.w, l.h, l.c); //repack_input_gpu(state.input, state.workspace, l.w, l.h, l.c); // 32 x floats -> 1 x uint32_t //float_to_bit_gpu(state.workspace, (unsigned char *)l.align_workspace_gpu, l.c * l.w * l.h);// l.align_workspace_size); //cudaDeviceSynchronize(); //stop_timer_and_show_name("repack_input_gpu + float_to_bit_gpu"); //free(re_packed_input); // slow - convolution the packed inputs and weights: float x 32 by channel (as in cuDNN) //convolution_repacked((uint32_t *)bin_re_packed_input, (uint32_t *)l.align_bit_weights, l.output, // l.w, l.h, l.c, l.n, l.size, l.pad, l.new_lda, l.mean_arr); // // then exit from if() //float *b = state.workspace; //float *b = (float *)calloc(100 * 1024 * 1024, sizeof(float)); //float *c = l.output; //memset(c, 0, l.outputs * sizeof(float)); //im2col_cpu_custom((float *)bin_re_packed_input, new_c, l.h, l.w, l.size, l.stride, l.pad, b); //cudaMemcpy(l.align_workspace_gpu, bin_re_packed_input, (new_c * l.w * l.h + 1) * sizeof(uint32_t), cudaMemcpyDefault); //start_timer(); im2col_ongpu(l.align_workspace_gpu, new_c, l.h, l.w, l.size, l.stride, l.pad, state.workspace); //cudaDeviceSynchronize(); //stop_timer_and_show_name("im2col_ongpu"); //free(bin_re_packed_input); int new_k = l.size*l.size*l.c / 32; // good for (l.c == 64) //gemm_nn_bin_32bit_packed(m, n, new_k, 1, // l.align_bit_weights, l.new_lda/32, // b, n, // c, n, l.mean_arr); // // then exit from if() //size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; //size_t t_intput_size = new_ldb * l.bit_align;// n; //size_t t_bit_input_size = t_intput_size / 8;// +1; //char *t_bit_input = (char *)calloc(t_bit_input_size, sizeof(char)); //transpose_uint32((uint32_t *)b, (uint32_t *)t_bit_input, new_k, n, n, new_ldb); //cudaMemcpy(l.transposed_align_workspace_gpu, t_bit_input, t_bit_input_size * sizeof(char), cudaMemcpyDefault); //cudaMemcpy(state.workspace, b, t_bit_input_size * sizeof(char), cudaMemcpyDefault); //printf("\n n = %d, n % 32 = %d, new_ldb = %d, new_ldb % 32 = %d \n", n, n % 32, new_ldb, new_ldb % 32); //start_timer(); transpose_uint32_gpu((uint32_t *)state.workspace, (uint32_t *)l.transposed_align_workspace_gpu, new_k, n, n, new_ldb); //cudaDeviceSynchronize(); //stop_timer_and_show_name("transpose_uint32_gpu"); //cudaDeviceSynchronize(); //stop_timer_and_show_name("repack_input_gpu_bin + im2col_ongpu + transpose_uint32_gpu_2"); //start_timer(); gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu, l.activation == LEAKY, l.bin_conv_shortcut_in_gpu, l.bin_conv_shortcut_out_gpu); //cudaDeviceSynchronize(); //stop_timer_and_show_name("gemm_nn_custom_bin_mean_transposed_gpu"); // the main GEMM function //gemm_nn_custom_bin_mean_transposed(m, n, k, 1, (uint8_t *)l.align_bit_weights, new_ldb, (uint8_t *)t_bit_input, new_ldb, c, n, l.mean_arr); //add_bias(l.output, l.biases, l.batch, l.n, l.out_h*l.out_w); //cudaMemcpy(l.output_gpu, l.output, l.outputs * sizeof(float), cudaMemcpyDefault); // // alternative GEMM //gemm_nn_bin_transposed_32bit_packed(m, n, new_k, 1, // l.align_bit_weights, l.new_lda/32, // t_bit_input, new_ldb / 32, // c, n, l.mean_arr); //free(t_bit_input); //free(b); } else { //printf("\n\n l.index = %d, l.w = %d, l.c = %d, l.n = %d, l.stride = %d, l.pad = %d - old XNOR \n", l.index, l.w, l.c, l.n, l.stride, l.pad); //cudaDeviceSynchronize(); int i = 0; /* // if (l.stride == 1 && l.c >= 256 && l.size > 1) if (l.stride == 1 && l.c >= 1024 && l.size > 1 && 0)// && l.w >= 13) // disabled { // stride=1 only //start_timer(); im2col_align_bin_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace, l.bit_align); //cudaDeviceSynchronize(); //stop_timer_and_show_name("im2col_align_bin_ongpu"); } else*/ { //start_timer(); im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.align_workspace_gpu, l.bit_align); //cudaDeviceSynchronize(); //stop_timer_and_show_name("im2col_align_ongpu"); //getchar(); // should be optimized //start_timer(); float_to_bit_gpu(l.align_workspace_gpu, (unsigned char *)state.workspace, l.align_workspace_size); //cudaDeviceSynchronize(); //stop_timer_and_show_name("float_to_bit_gpu"); } //start_timer(); transpose_bin_gpu((unsigned char *)state.workspace, (unsigned char *)l.transposed_align_workspace_gpu, k, n, l.bit_align, new_ldb, 8); //cudaDeviceSynchronize(); //stop_timer_and_show_name("transpose_bin_gpu"); //cudaDeviceSynchronize(); //stop_timer_and_show_name("im2col_align_ongpu + float_to_bit_gpu + transpose_bin_gpu"); // should be optimized //if(0) {//if (k > 1000) { // sequentially input-shared - BAD // gemm_nn_custom_bin_mean_transposed_sequentially_gpu(m, n, k, // (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, new_ldb, l.output_gpu, n, l.mean_arr_gpu); //} //else { // coalescing & weights-shared-memory - GOOD //start_timer(); gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu, l.activation == LEAKY, l.bin_conv_shortcut_in_gpu, l.bin_conv_shortcut_out_gpu); //cudaDeviceSynchronize(); //stop_timer_and_show_name("gemm_nn_custom_bin_mean_transposed_gpu"); //} //cudaDeviceSynchronize(); //check_error(status); //getchar(); } /* { float_to_bit_gpu(state.input, (unsigned char *)l.align_workspace_gpu, input_size); convolve_bin_gpu(l.align_workspace_gpu, (float *)l.align_bit_weights_gpu, l.output_gpu, l.w, l.h, l.c, l.n, l.size, l.pad, l.new_lda, l.mean_arr_gpu); //convolve_gpu(state.input, l.weights_gpu, l.output_gpu, l.w, l.h, l.c, l.n, l.size, l.pad); //cudaDeviceSynchronize(); //check_error(status); add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); } */ //add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == MISH) activate_array_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == HARD_MISH) activate_array_hard_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == NORM_CHAN) activate_array_normalize_channels_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu); else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu, 0); else if (l.activation == NORM_CHAN_SOFTMAX_MAXVAL) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu, 1); else if (l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if (l.binary || l.xnor) swap_binary(&l); //cudaDeviceSynchronize(); return; } } if (l.xnor) { swap_binary(&l); binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu); state.input = l.binary_input_gpu; } //fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); #ifdef CUDNN //float one = 1; // alpha[0], beta[0] is float for HALF and FLOAT float alpha = 1, beta = 0; //#ifdef CUDNN_HALF //if (state.use_mixed_precision) { int iteration_num = get_current_iteration(state.net); // (*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || (iteration_num > 3 * state.net.burn_in) && state.net.loss_scale != 1) && (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && l.groups <= 1 && l.size > 1) { //printf("\n CUDNN_HALF!!! state.index = %d \n", state.index); // Note: For improved performance it is advised to use beta[0] = 0.0. // For Tensor Core: cudnnSetConvolutionMathType() where cudnnMathType_t mathType = CUDNN_TENSOR_OP_MATH; // 1. or CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM and use CUDNN_DATA_HALF // 2. or CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED // More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops const size_t input16_size = l.batch*l.c*l.w*l.h; const size_t output16_size = l.batch*l.out_c*l.out_h*l.out_w; if (*state.net.max_input16_size < input16_size) { //printf("\n input16_size: cur = %zu \t max = %zu \n", input16_size, *state.net.max_input16_size); *state.net.max_input16_size = input16_size; if (*state.net.input16_gpu) cuda_free(*state.net.input16_gpu); assert(*state.net.max_input16_size > 0); *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size); } float *input16 = *state.net.input16_gpu; if (*state.net.max_output16_size < output16_size) { *state.net.max_output16_size = output16_size; if (*state.net.output16_gpu) cuda_free(*state.net.output16_gpu); assert(*state.net.max_output16_size > 0); *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size); } float *output16 = *state.net.output16_gpu; assert(input16_size > 0); cuda_convert_f32_to_f16(state.input, input16_size, input16); //fill_ongpu(output16_size / 2, 0, (float *)output16, 1); CHECK_CUDNN(cudnnConvolutionForward(cudnn_handle(), &alpha, l.srcTensorDesc16, input16, l.weightDesc16, l.weights_gpu16, l.convDesc, l.fw_algo16, state.workspace, l.workspace_size, &beta, l.dstTensorDesc16, output16)); if (l.batch_normalize) { if (state.train && !state.net.adversarial) // Training { simple_copy_ongpu(l.outputs*l.batch / 2, output16, l.x_gpu); //copy_ongpu(l.outputs*l.batch / 2, output16, 1, l.x_gpu, 1); //cudaMemcpyAsync(l.x_gpu, output16, l.outputs*l.batch*sizeof(half), cudaMemcpyDefault, get_cuda_stream()); float one = 1.0f; float zero = 0.0f; // Batch-normalization can still take FP16 inputs and outputs, saving half the bandwidth // compared to FP32, it's just that the statistics and value adjustment should be done in FP32. CHECK_CUDNN(cudnnBatchNormalizationForwardTraining(cudnn_handle(), CUDNN_BATCHNORM_SPATIAL, &one, &zero, l.normDstTensorDescF16, l.x_gpu, // input l.normDstTensorDescF16, output16, // output l.normTensorDesc, l.scales_gpu, // input l.biases_gpu, // input .01, l.rolling_mean_gpu, // input/output (should be FP32) l.rolling_variance_gpu, // input/output (should be FP32) .00001, l.mean_gpu, // output (should be FP32) - optional cache to speedup cudnnBatchNormalizationBackward() l.variance_gpu)); // output (should be FP32) - optional cache to speedup cudnnBatchNormalizationBackward() cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); //forward_batchnorm_layer_gpu(l, state); } else // Detection { cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); } } else // BIAS only { cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); } } else { //#else /* int input_nan_inf = is_nan_or_inf(state.input, l.inputs * l.batch); printf("\n is_nan_or_inf(state.input) = %d \n", input_nan_inf); if (input_nan_inf) getchar(); int weights_nan_inf = is_nan_or_inf(l.weights_gpu, l.nweights); printf("\n is_nan_or_inf(l.weights_gpu) = %d \n", weights_nan_inf); if (weights_nan_inf) getchar(); */ CHECK_CUDNN(cudnnConvolutionForward(cudnn_handle(), &alpha, //&one, l.srcTensorDesc, state.input, l.weightDesc, l.weights_gpu, l.convDesc, l.fw_algo, state.workspace, l.workspace_size, &beta, //&one, l.dstTensorDesc, l.output_gpu)); //cudaDeviceSynchronize(); if (l.batch_normalize) { forward_batchnorm_layer_gpu(l, state); } else { add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); } //#endif // CUDNN_HALF } #else fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); int i, j; int m = l.n / l.groups; int k = l.size*l.size*l.c / l.groups; int n = l.out_w*l.out_h; for(i = 0; i < l.batch; ++i){ for (j = 0; j < l.groups; ++j) { //float *im = state.input + i*l.c*l.h*l.w; float *im = state.input + (i*l.groups + j)*l.c / l.groups*l.h*l.w; float *a = l.weights_gpu + j*l.nweights / l.groups; float *b = state.workspace; float *c = l.output_gpu + (i*l.groups + j)*n*m; if (l.size == 1 && l.stride == 1 && l.dilation == 1) { b = im; } else { //im2col_ongpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, state.workspace); im2col_gpu_ext(im, // input l.c / l.groups, // input channels l.h, l.w, // input size (h, w) l.size, l.size, // kernel size (h, w) l.pad * l.dilation, l.pad * l.dilation, // padding (h, w) l.stride_y, l.stride_x, // stride (h, w) l.dilation, l.dilation, // dilation (h, w) state.workspace); // output } //gemm_ongpu(0, 0, m, n, k, 1., a, k, b, n, 1., c + i*m*n, n); gemm_ongpu(0, 0, m, n, k, 1, a, k, b, n, 1, c, n); } } if (l.batch_normalize) { forward_batchnorm_layer_gpu(l, state); } else { add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); } #endif //#ifndef CUDNN_HALF //#endif // no CUDNN_HALF if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == MISH) activate_array_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == HARD_MISH) activate_array_hard_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == NORM_CHAN) activate_array_normalize_channels_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu); else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu, 0); else if (l.activation == NORM_CHAN_SOFTMAX_MAXVAL) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu, 1); else if (l.activation != LINEAR) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.dot > 0) dot_error_gpu(l); if(l.binary || l.xnor) swap_binary(&l); //cudaDeviceSynchronize(); // for correct profiling of performance if (state.net.try_fix_nan) { fix_nan_and_inf(l.output_gpu, l.outputs*l.batch); } if(l.assisted_excitation && state.train) assisted_excitation_forward_gpu(l, state); if (l.antialiasing) { network_state s = { 0 }; s.train = state.train; s.workspace = state.workspace; s.net = state.net; if (!state.train) s.index = state.index; // don't use TC for training (especially without cuda_convert_f32_to_f16() ) s.input = l.output_gpu; forward_convolutional_layer_gpu(*(l.input_layer), s); simple_copy_ongpu(l.outputs*l.batch, l.output_gpu, l.input_antialiasing_gpu); simple_copy_ongpu(l.input_layer->outputs*l.input_layer->batch, l.input_layer->output_gpu, l.output_gpu); } if (l.coordconv) { coord_conv_gpu(l.output_gpu, l.outputs*l.batch, l.out_w, l.out_h, l.out_c, l.batch, 0); } } void backward_convolutional_layer_gpu(convolutional_layer l, network_state state) { if (l.coordconv) { coord_conv_gpu(l.delta_gpu, l.outputs*l.batch, l.out_w, l.out_h, l.out_c, l.batch, 1); } if (l.antialiasing) { network_state s = { 0 }; s.train = state.train; s.workspace = state.workspace; s.net = state.net; s.delta = l.delta_gpu; // s.delta will be returned to l.delta_gpu s.input = l.input_antialiasing_gpu; //if (!state.train) s.index = state.index; // don't use TC for training (especially without cuda_convert_f32_to_f16() ) simple_copy_ongpu(l.input_layer->outputs*l.input_layer->batch, l.delta_gpu, l.input_layer->delta_gpu); backward_convolutional_layer_gpu(*(l.input_layer), s); simple_copy_ongpu(l.outputs*l.batch, l.input_antialiasing_gpu, l.output_gpu); } if(state.net.try_fix_nan) constrain_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1); if (l.activation == SWISH) gradient_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); else if (l.activation == MISH) gradient_array_mish_ongpu(l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); else if (l.activation == HARD_MISH) gradient_array_hard_mish_ongpu(l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); else if (l.activation == NORM_CHAN_SOFTMAX || l.activation == NORM_CHAN_SOFTMAX_MAXVAL) gradient_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); else if (l.activation == NORM_CHAN) gradient_array_normalize_channels_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); else gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); if (!l.batch_normalize) backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); //#ifndef CUDNN_HALF //if(l.batch_normalize){ // backward_batchnorm_layer_gpu(l, state); //} else { // //backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); //} //#endif // no CUDNN_HALF float *original_input = state.input; if(l.xnor) state.input = l.binary_input_gpu; #ifdef CUDNN float one = 1.f; float alpha = 1, beta = 0; //#ifdef CUDNN_HALF int iteration_num = get_current_iteration(state.net); //(*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || (iteration_num > 3 * state.net.burn_in) && state.net.loss_scale != 1) && (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && l.groups <= 1 && l.size > 1) { const size_t input16_size = l.batch*l.c*l.w*l.h; const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h; if (*state.net.max_input16_size < input16_size) { *state.net.max_input16_size = input16_size; if (*state.net.input16_gpu) cuda_free(*state.net.input16_gpu); assert(*state.net.max_input16_size > 0); *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size); } float *input16 = *state.net.input16_gpu; if (*state.net.max_output16_size < delta16_size) { *state.net.max_output16_size = delta16_size; if (*state.net.output16_gpu) cuda_free(*state.net.output16_gpu); assert(*state.net.max_output16_size > 0); *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size); } float *delta16 = *state.net.output16_gpu; assert(input16_size > 0); assert(delta16_size > 0); cuda_convert_f32_to_f16(state.input, input16_size, input16); cuda_convert_f32_to_f16(l.delta_gpu, delta16_size, delta16); if (l.batch_normalize) { //if (!state.train) { // l.mean_gpu = l.rolling_mean_gpu; // l.variance_gpu = l.rolling_variance_gpu; //} float one = 1.0f; float zero = 0.0f; CHECK_CUDNN(cudnnBatchNormalizationBackward(cudnn_handle(), CUDNN_BATCHNORM_SPATIAL, &one, &zero, &one, &one, l.normDstTensorDescF16, l.x_gpu, // input (input in BN-forward-inference) l.normDstTensorDescF16, delta16, // input l.normDstTensorDescF16, l.output_gpu, //l.x_norm_gpu, // output (new delta) l.normTensorDesc, l.scales_gpu, // input (should be FP32) l.scale_updates_gpu, // output (should be FP32) l.bias_updates_gpu, // output (should be FP32) .00001, l.mean_gpu, // input (should be FP32) l.variance_gpu)); // input (should be FP32) simple_copy_ongpu(l.outputs*l.batch / 2, l.output_gpu, delta16); //copy_ongpu(l.outputs*l.batch / 2, l.x_norm_gpu, 1, delta16, 1); //cudaMemcpyAsync(delta16, l.x_norm_gpu, l.outputs*l.batch * sizeof(half), cudaMemcpyDefault, get_cuda_stream()); } else { //backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); } // convert input: state.input (x), l.delta_gpu (y) from fp32 to fp16 // get output: l.weight_updates_gpu (dw) and convert it to fp32 (ONLY if it is fp16) // calculate conv weight updates // Already: l.weight_updates_gpu = (l.weight_updates_gpu - l.weight*decay*batch*subdivision)*momentum // so we should copy f32 to f16, or compute: f16=(w_up - w*d*b*s)*m assert((l.nweights) > 0); cuda_convert_f32_to_f16(l.weight_updates_gpu, l.nweights, l.weight_updates_gpu16); if (!state.net.adversarial && !l.train_only_bn) { CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(), &one, l.srcTensorDesc16, input16, //state.input, l.ddstTensorDesc16, delta16, //l.delta_gpu, l.convDesc, l.bf_algo16, state.workspace, l.workspace_size, &one, l.dweightDesc16, l.weight_updates_gpu16)); // l.weight_updates_gpu); cuda_convert_f16_to_f32(l.weight_updates_gpu16, l.nweights, l.weight_updates_gpu); } if (state.delta) { if (l.binary || l.xnor) swap_binary(&l); // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData // calculate delta for the next layer // convert input: l.weights_gpu (w), l.delta_gpu (dy) from fp32 to fp16 // get output: state.delta (dx) and convert it to fp32 (ONLY if it is fp16) CHECK_CUDNN(cudnnConvolutionBackwardData(cudnn_handle(), &alpha, l.weightDesc16, l.weights_gpu16, //l.weights_gpu, l.ddstTensorDesc16, delta16, //l.delta_gpu, l.convDesc, l.bd_algo16, state.workspace, l.workspace_size, &beta, l.dsrcTensorDesc16, input16)); // state.delta); cuda_convert_f16_to_f32(input16, input16_size, state.delta); if (l.binary || l.xnor) swap_binary(&l); if (l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta); } } else { //#else // CUDNN_HALF if(l.batch_normalize){ backward_batchnorm_layer_gpu(l, state); } if (!state.net.adversarial && !l.train_only_bn) { float *old_input = state.input; /* if (l.reverse) { if (*state.net.max_output16_size < l.inputs*l.batch) { *state.net.max_output16_size = l.inputs*l.batch; if (*state.net.output16_gpu) cuda_free(*state.net.output16_gpu); assert(*state.net.max_output16_size > 0); *state.net.output16_gpu = cuda_make_array(NULL, *state.net.max_output16_size); } float clip = 0.0; float divider = 1.0; float abs_add = 1.0; mult_inverse_array_gpu(state.input, *state.net.output16_gpu, l.inputs*l.batch, l.reverse, divider, clip, abs_add); state.input = *state.net.output16_gpu; } */ // calculate conv weight updates // if used: beta=1 then loss decreases faster CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(), &one, l.srcTensorDesc, state.input, l.ddstTensorDesc, l.delta_gpu, l.convDesc, l.bf_algo, state.workspace, l.workspace_size, &one, l.dweightDesc, l.weight_updates_gpu)); state.input = old_input; } if (state.delta) { if (l.binary || l.xnor) swap_binary(&l); float *old_weights = l.weights_gpu; /* if (l.reverse) { if (*state.net.max_output16_size < l.nweights) { *state.net.max_output16_size = l.nweights; if (*state.net.output16_gpu && *state.net.max_output16_size > 0) cuda_free(*state.net.output16_gpu); assert(*state.net.max_output16_size > 0); *state.net.output16_gpu = cuda_make_array(NULL, l.nweights); } float clip = 0.0; float divider = 1.0; float abs_add = 1.0; mult_inverse_array_gpu(l.weights_gpu, *state.net.output16_gpu, l.nweights, l.reverse, divider, clip, abs_add); l.weights_gpu = *state.net.output16_gpu; } */ // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData // calculate delta for the next layer CHECK_CUDNN(cudnnConvolutionBackwardData(cudnn_handle(), &one, l.weightDesc, l.weights_gpu, l.ddstTensorDesc, l.delta_gpu, l.convDesc, l.bd_algo, state.workspace, l.workspace_size, &one, l.dsrcTensorDesc, state.delta)); l.weights_gpu = old_weights; if (l.binary || l.xnor) swap_binary(&l); if (l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta); } } //#endif // CUDNN_HALF #else // CUDNN if (l.batch_normalize) { backward_batchnorm_layer_gpu(l, state); } int m = l.n / l.groups; int n = l.size*l.size*l.c / l.groups; int k = l.out_w*l.out_h; int i, j; for(i = 0; i < l.batch; ++i){ for (j = 0; j < l.groups; ++j) { float * a = l.delta_gpu + (i*l.groups + j)*m*k; float * b = state.workspace; float * c = l.weight_updates_gpu + j*l.nweights / l.groups; float *im = state.input + (i*l.groups + j)*l.c / l.groups*l.h*l.w; if (!state.net.adversarial && !l.train_only_bn) { //im2col_ongpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, state.workspace); im2col_gpu_ext(im, // input l.c / l.groups, // input channels l.h, l.w, // input size (h, w) l.size, l.size, // kernel size (h, w) l.pad * l.dilation, l.pad * l.dilation, // padding (h, w) l.stride_y, l.stride_x, // stride (h, w) l.dilation, l.dilation, // dilation (h, w) state.workspace); // output //gemm_ongpu(0, 1, m, n, k, 1, a + i*m*k, k, b, k, 1, c, n); gemm_ongpu(0, 1, m, n, k, 1, a, k, b, k, 1, c, n); } if (state.delta) { if (l.binary || l.xnor) swap_binary(&l); float * a = l.weights_gpu + j*l.nweights / l.groups; float * b = l.delta_gpu + (i*l.groups + j)*m*k; float * c = state.workspace; //gemm_ongpu(1, 0, n, k, m, 1, a, n, b + i*k*m, k, 0, c, k); gemm_ongpu(1, 0, n, k, m, 1, a, n, b, k, 0, c, k); float *delta = state.delta + (i*l.groups + j)*l.c / l.groups*l.h*l.w; //col2im_ongpu(state.workspace, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, delta); col2im_gpu_ext( state.workspace, // input l.c / l.groups, // input channels l.h, l.w, // input size (h, w) l.size, l.size, // kernel size (h, w) l.pad * l.dilation, l.pad * l.dilation, // padding size (h, w) l.stride_y, l.stride_x, // stride size (h, w) l.dilation, l.dilation, // dilation size (h, w) delta); // output (delta) if (l.binary || l.xnor) { swap_binary(&l); } if (l.xnor) gradient_array_ongpu(original_input + i*l.c*l.h*l.w, l.c*l.h*l.w, HARDTAN, state.delta + i*l.c*l.h*l.w); } } } #endif if (state.net.try_fix_nan) { if (state.delta) { reset_nan_and_inf(state.delta, l.inputs * l.batch); } int size = l.nweights; reset_nan_and_inf(l.weight_updates_gpu, size); fix_nan_and_inf(l.weights_gpu, size); } } __global__ void calc_avg_activation_kernel(float *src, float *dst, int size, int channels, int batches) { int i = blockIdx.x * blockDim.x + threadIdx.x; int xy = i % size; int b = i / size; if (i < size*batches) { dst[i] = 0; for (int c = 0; c < channels; ++c) { dst[i] += src[xy + size*(c + channels*b)]; } dst[i] = dst[i] / channels; } } void calc_avg_activation_gpu(float *src, float *dst, int size, int channels, int batches) { const int num_blocks = get_number_of_blocks(size*batches, BLOCK); calc_avg_activation_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> > (src, dst, size, channels, batches); } __global__ void assisted_activation_kernel(float alpha, float *output, float *gt_gpu, float *a_avg_gpu, int size, int channels, int batches) { int i = blockIdx.x * blockDim.x + threadIdx.x; int xy = i % size; int b = i / size; if (b < batches) { for (int c = 0; c < channels; ++c) { output[xy + size*(c + channels*b)] += alpha * gt_gpu[i] * a_avg_gpu[i]; //output[xy + size*(c + channels*b)] += gt_gpu[i] * a_avg_gpu[i]; //output[xy + size*(c + channels*b)] += gt_gpu[i] * output[xy + size*(c + channels*b)]; //output[xy + size*(c + channels*b)] = a_avg_gpu[i]; } } } void assisted_activation_gpu(float alpha, float *output, float *gt_gpu, float *a_avg_gpu, int size, int channels, int batches) { const int num_blocks = get_number_of_blocks(size*batches, BLOCK); assisted_activation_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> > (alpha, output, gt_gpu, a_avg_gpu, size, channels, batches); } __global__ void assisted_activation2_kernel(float alpha, float *output, float *gt_gpu, float *a_avg_gpu, int size, int channels, int batches) { int i = blockIdx.x * blockDim.x + threadIdx.x; int xy = i % size; int b = i / size; float beta = 1 - alpha; if (b < batches) { for (int c = 0; c < channels; ++c) { if(gt_gpu[i] == 0) output[xy + size*(c + channels*b)] *= beta; } } } void assisted_activation2_gpu(float alpha, float *output, float *gt_gpu, float *a_avg_gpu, int size, int channels, int batches) { const int num_blocks = get_number_of_blocks(size*batches, BLOCK); assisted_activation2_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> > (alpha, output, gt_gpu, a_avg_gpu, size, channels, batches); } void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) { const int iteration_num = get_current_iteration(state.net); //(*state.net.seen) / (state.net.batch*state.net.subdivisions); // epoch //const float epoch = (float)(*state.net.seen) / state.net.train_images_num; // calculate alpha //const float alpha = (1 + cos(3.141592 * iteration_num)) / (2 * state.net.max_batches); //const float alpha = (1 + cos(3.141592 * epoch)) / (2 * state.net.max_batches); float alpha = (1 + cos(3.141592 * iteration_num / state.net.max_batches)) / 2; //float alpha = (1 + cos(3.141592 * iteration_num / state.net.max_batches)); if (l.assisted_excitation == 1) { if (iteration_num > state.net.max_batches / 2) return; } else { if (iteration_num < state.net.burn_in) return; else if (iteration_num > l.assisted_excitation) return; else alpha = (1 + cos(3.141592 * iteration_num / (state.net.burn_in + l.assisted_excitation))) / 2; // from 1 to 0 } //printf("\n epoch = %f, alpha = %f, seen = %d, max_batches = %d, train_images_num = %d \n", // epoch, alpha, (*state.net.seen), state.net.max_batches, state.net.train_images_num); //const int size = l.outputs * l.batch; float *a_avg = (float *)calloc(l.out_w * l.out_h * l.batch, sizeof(float)); float *gt = (float *)calloc(l.out_w * l.out_h * l.batch, sizeof(float)); int b; int w, h; l.max_boxes = state.net.num_boxes; l.truths = l.max_boxes*(4 + 1); int num_truth = l.batch*l.truths; float *truth_cpu = (float *)calloc(num_truth, sizeof(float)); cuda_pull_array(state.truth, truth_cpu, num_truth); //cudaStreamSynchronize(get_cuda_stream()); //CHECK_CUDA(cudaPeekAtLastError()); for (b = 0; b < l.batch; ++b) { // calculate G int t; for (t = 0; t < state.net.num_boxes; ++t) { box truth = float_to_box_stride(truth_cpu + t*(4 + 1) + b*l.truths, 1); if (!truth.x) break; // continue; float beta = 0; //float beta = 1 - alpha; // from 0 to 1 float dw = (1 - truth.w) * beta; float dh = (1 - truth.h) * beta; //printf(" alpha = %f, beta = %f, truth.w = %f, dw = %f, tw+dw = %f, l.out_w = %d \n", alpha, beta, truth.w, dw, truth.w+dw, l.out_w); int left = floorf((truth.x - (dw + truth.w) / 2) * l.out_w); int right = ceilf((truth.x + (dw + truth.w) / 2) * l.out_w); int top = floorf((truth.y - (dh + truth.h) / 2) * l.out_h); int bottom = ceilf((truth.y + (dh + truth.h) / 2) * l.out_h); if (left < 0) left = 0; if (top < 0) top = 0; if (right > l.out_w) right = l.out_w; if (bottom > l.out_h) bottom = l.out_h; for (w = left; w <= right; w++) { for (h = top; h < bottom; h++) { gt[w + l.out_w * h + l.out_w*l.out_h*b] = 1; } } } } cuda_push_array(l.gt_gpu, gt, l.out_w * l.out_h * l.batch); //cudaStreamSynchronize(get_cuda_stream()); //CHECK_CUDA(cudaPeekAtLastError()); // calc avg_output on GPU - for whole batch calc_avg_activation_gpu(l.output_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); //cudaStreamSynchronize(get_cuda_stream()); //CHECK_CUDA(cudaPeekAtLastError()); // calc new output //assisted_activation2_gpu(1, l.output_gpu, l.gt_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); // AE3: gt increases (beta = 1 - alpha = 0) //assisted_activation2_gpu(alpha, l.output_gpu, l.gt_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); assisted_activation_gpu(alpha, l.output_gpu, l.gt_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); //cudaStreamSynchronize(get_cuda_stream()); //CHECK_CUDA(cudaPeekAtLastError()); /* for (b = 0; b < l.batch; ++b) { // calculate average A for (w = 0; w < l.out_w; w++) { for (h = 0; h < l.out_h; h++) { for (c = 0; c < l.out_c; c++) { a_avg[w + l.out_w*(h + l.out_h*b)] += l.output[w + l.out_w*(h + l.out_h*(c + l.out_c*b))]; } a_avg[w + l.out_w*(h + l.out_h*b)] /= l.out_c; // a_avg / d } } } // change activation for (b = 0; b < l.batch; ++b) { for (w = 0; w < l.out_w; w++) { for (h = 0; h < l.out_h; h++) { for (c = 0; c < l.out_c; c++) { // a = a + alpha(t) + e(c,i,j) = a + alpha(t) + g(i,j) * avg_a(i,j) / channels l.output[w + l.out_w*(h + l.out_h*(c + l.out_c*b))] += alpha * g[w + l.out_w*(h + l.out_h*b)] * a_avg[w + l.out_w*(h + l.out_h*b)]; //l.output[w + l.out_w*(h + l.out_h*(c + l.out_c*b))] = // alpha * g[w + l.out_w*(h + l.out_h*b)] * a_avg[w + l.out_w*(h + l.out_h*b)]; } } } } */ if (0) // visualize ground truth { #ifdef OPENCV cuda_pull_array(l.output_gpu, l.output, l.outputs * l.batch); cudaStreamSynchronize(get_cuda_stream()); CHECK_CUDA(cudaPeekAtLastError()); for (b = 0; b < l.batch; ++b) { printf(" Assisted Excitation alpha = %f \n", alpha); image img = float_to_image(l.out_w, l.out_h, 1, >[l.out_w*l.out_h*b]); char buff[100]; sprintf(buff, "a_excitation_gt_%d", b); show_image_cv(img, buff); //image img2 = float_to_image(l.out_w, l.out_h, 1, &l.output[l.out_w*l.out_h*l.out_c*b]); image img2 = float_to_image_scaled(l.out_w, l.out_h, 1, &l.output[l.out_w*l.out_h*l.out_c*b]); char buff2[100]; sprintf(buff2, "a_excitation_output_%d", b); show_image_cv(img2, buff2); /* int c = l.out_c; if (c > 4) c = 4; image img3 = float_to_image(l.out_w, l.out_h, c, &l.output[l.out_w*l.out_h*l.out_c*b]); image dc = collapse_image_layers(img3, 1); char buff3[100]; sprintf(buff3, "a_excitation_act_collapsed_%d", b); show_image_cv(dc, buff3); */ wait_key_cv(5); } wait_until_press_key_cv(); #endif // OPENCV } free(truth_cpu); free(gt); free(a_avg); } void pull_convolutional_layer(convolutional_layer l) { cuda_pull_array_async(l.weights_gpu, l.weights, l.nweights); cuda_pull_array_async(l.biases_gpu, l.biases, l.n); cuda_pull_array_async(l.weight_updates_gpu, l.weight_updates, l.nweights); cuda_pull_array_async(l.bias_updates_gpu, l.bias_updates, l.n); if (l.batch_normalize){ cuda_pull_array_async(l.scales_gpu, l.scales, l.n); cuda_pull_array_async(l.rolling_mean_gpu, l.rolling_mean, l.n); cuda_pull_array_async(l.rolling_variance_gpu, l.rolling_variance, l.n); } if (l.adam){ cuda_pull_array_async(l.m_gpu, l.m, l.nweights); cuda_pull_array_async(l.v_gpu, l.v, l.nweights); } CHECK_CUDA(cudaPeekAtLastError()); cudaStreamSynchronize(get_cuda_stream()); } void push_convolutional_layer(convolutional_layer l) { cuda_push_array(l.weights_gpu, l.weights, l.nweights); #ifdef CUDNN_HALF assert(l.nweights > 0); cuda_convert_f32_to_f16(l.weights_gpu, l.nweights, l.weights_gpu16); #endif cuda_push_array(l.biases_gpu, l.biases, l.n); if (l.train) { cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.nweights); cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n); } if (l.batch_normalize){ cuda_push_array(l.scales_gpu, l.scales, l.n); cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.n); cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.n); } if (l.adam){ cuda_push_array(l.m_gpu, l.m, l.nweights); cuda_push_array(l.v_gpu, l.v, l.nweights); } CHECK_CUDA(cudaPeekAtLastError()); } void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay, float loss_scale) { /* for (int angle = 0; angle < 360; angle++) { printf(" angle = %d \n", angle); smooth_rotate_weights_kernel(l.weights_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, angle, 0); cuda_pull_array(l.weight_deform_gpu, l.weights, l.nweights); visualize_convolutional_layer(l, "weights", NULL); wait_key_cv(10); } */ if (l.deform) { //for (l.angle = 0; l.angle < 360; l.angle += 1) //{ //stretch_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, l.angle/180, 1); //else simple_copy_ongpu(l.nweights, l.weight_updates_gpu, l.weight_deform_gpu); if (l.rotate) rotate_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, 1); else if (l.sway) sway_and_flip_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, l.angle, 1); else if (l.stretch) stretch_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, 0, 1); else if (l.stretch_sway) stretch_sway_flip_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, l.angle, 1); //simple_copy_ongpu(l.nweights, l.weight_updates_gpu, l.weight_deform_gpu); reduce_and_expand_array_gpu(l.weight_deform_gpu, l.weight_updates_gpu, l.nweights, 4); //printf(" angle = %f \n", l.angle); //cuda_pull_array(l.weight_deform_gpu, l.weights, l.nweights); //visualize_convolutional_layer(l, "weights", NULL); //wait_key_cv(10); //} } // Loss scale for Mixed-Precision on Tensor-Cores float learning_rate = learning_rate_init*l.learning_rate_scale / loss_scale; //float momentum = a.momentum; //float decay = a.decay; //int batch = a.batch; reset_nan_and_inf(l.weight_updates_gpu, l.nweights); fix_nan_and_inf(l.weights_gpu, l.nweights); // Gradient Centralization if (l.grad_centr && l.batch_normalize) { // weights[filters][channels][height][width] // for(filters) w[f] = w[f] - mean(w[c][h][w]) gradient_centralization_gpu(l.size, l.size, l.c / l.groups, l.n, l.weight_updates_gpu); } if (l.adam) { //adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.nweights, batch, a.t); adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.nweights, batch, l.t); adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.n, batch, l.t); if (l.scales_gpu) { adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.n, batch, l.t); } } else { //axpy_ongpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); //axpy_ongpu(l.nweights, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); //scal_ongpu(l.nweights, momentum, l.weight_updates_gpu, 1); float *old_weight_updates_gpu = l.weight_updates_gpu; if (l.reverse) { float clip = 0.0; float divider = 1.0; float abs_add = 1.0; mult_inverse_array_gpu(l.weight_updates_gpu, l.output_gpu, l.inputs*l.batch, l.reverse, divider, clip, abs_add); l.weight_updates_gpu = l.output_gpu; } axpy_ongpu(l.nweights, -decay*batch*loss_scale, l.weights_gpu, 1, l.weight_updates_gpu, 1); axpy_ongpu(l.nweights, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); l.weight_updates_gpu = old_weight_updates_gpu; scal_ongpu(l.nweights, momentum, l.weight_updates_gpu, 1); axpy_ongpu(l.n, learning_rate / batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); scal_ongpu(l.n, momentum, l.bias_updates_gpu, 1); if (l.scales_gpu) { axpy_ongpu(l.n, learning_rate / batch, l.scale_updates_gpu, 1, l.scales_gpu, 1); scal_ongpu(l.n, momentum, l.scale_updates_gpu, 1); } } if (l.deform) { //for (l.angle = 0; l.angle < 360; l.angle += 4) //{ expand_array_gpu(l.weights_gpu, l.weight_deform_gpu, l.nweights, 4); //simple_copy_ongpu(l.nweights, l.weight_deform_gpu, l.weights_gpu); if (l.rotate) rotate_weights_gpu(l.weight_deform_gpu, l.weights_gpu, l.nweights, l.n, l.size, 0); else if (l.sway) sway_and_flip_weights_gpu(l.weight_deform_gpu, l.weights_gpu, l.nweights, l.n, l.size, l.angle, 0); else if (l.stretch) stretch_weights_gpu(l.weight_deform_gpu, l.weights_gpu, l.nweights, l.n, l.size, 0, 0); else if (l.stretch_sway) stretch_sway_flip_weights_gpu(l.weight_deform_gpu, l.weights_gpu, l.nweights, l.n, l.size, l.angle, 0); //printf(" angle = %f, reverse = %d \n", l.angle, 0); //cuda_pull_array(l.weights_gpu, l.weights, l.nweights); //visualize_convolutional_layer(l, "weights", NULL); //wait_key_cv(10); //} } if (l.clip) { constrain_ongpu(l.nweights, l.clip, l.weights_gpu, 1); } } /* void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay) { int size = layer.size*layer.size*layer.c*layer.n; axpy_ongpu(layer.n, learning_rate/batch, layer.bias_updates_gpu, 1, layer.biases_gpu, 1); scal_ongpu(layer.n, momentum, layer.bias_updates_gpu, 1); if(layer.scales_gpu){ axpy_ongpu(layer.n, learning_rate/batch, layer.scale_updates_gpu, 1, layer.scales_gpu, 1); scal_ongpu(layer.n, momentum, layer.scale_updates_gpu, 1); } if(layer.adam){ scal_ongpu(size, layer.B1, layer.m_gpu, 1); scal_ongpu(size, layer.B2, layer.v_gpu, 1); axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1); axpy_ongpu(size, -(1-layer.B1), layer.weight_updates_gpu, 1, layer.m_gpu, 1); mul_ongpu(size, layer.weight_updates_gpu, 1, layer.weight_updates_gpu, 1); axpy_ongpu(size, (1-layer.B2), layer.weight_updates_gpu, 1, layer.v_gpu, 1); adam_gpu(size, layer.weights_gpu, layer.m_gpu, layer.v_gpu, layer.B1, layer.B2, learning_rate/batch, layer.eps, layer.t+1); fill_ongpu(size, 0, layer.weight_updates_gpu, 1); }else{ axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1); // wu = wu - w*decay*batch axpy_ongpu(size, learning_rate/batch, layer.weight_updates_gpu, 1, layer.weights_gpu, 1); // w = w + wu*lr/batch scal_ongpu(size, momentum, layer.weight_updates_gpu, 1); // wu = wu*momentum // wu = (wu - w*decay*batch)*momentum // w = w + (wu - w*decay*batch)*lr/batch = w + wu*lr/batch - w*decay*lr = w*(1-decay*lr) + wu*lr/batch //wu_prev = (wu_old - w_old*decay*batch)*momentum //weights_update = weights_update_new + (weights_update_old - weights_old*decay*batch)*momentum - weights_new*decay*batch = // = weights_update_new + weights_update_old*momentum - weights_old*decay*batch*momentum - weights_new*decay*batch // = weights_update_new + weights_update_old*momentum - (weights_old*momentum + weights_new)*decay*batch //------------- RESULT -------------- // weights_update = weights_update_new + weights_update_old*momentum - (weights_old*momentum + weights_new)*decay*batch //----------------------------------- // weights_newest = weights_new + (weights_update_new + weights_update_old*momentum - (weights_old*momentum + weights_new)*decay*batch)*lr/batch // = weights_new + weights_update_new*lr/batch + weights_update_old*momentum*lr/batch - weights_old*momentum*decay*batch*lr/batch - weights_new*decay*batch*lr/batch // = weights_new + weights_update_new*lr/batch + weights_update_old*momentum*lr/batch - weights_old*momentum*decay*lr - weights_new*decay*lr // = weights_new*(1 - decay*lr) - weights_old*momentum*decay*lr + (weights_update_new + weights_update_old*momentum)*lr/batch //------------- RESULT -------------- // weights_newest = weights_new*(1 - decay*lr) - weights_old*momentum*(decay*lr) + (weights_update_new + weights_update_old*momentum)*lr/batch = // = weights_new - (weights_new + weights_old*momentum)*decay*lr + (weights_update_new + weights_update_old*momentum)*lr / batch //----------------------------------- } } */
