#include "cuda_runtime.h"
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#include "curand.h"
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#include "cublas_v2.h"
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#ifdef CUDNN
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#pragma comment(lib, "cudnn.lib")
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#endif
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extern "C" {
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#include "convolutional_layer.h"
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#include "batchnorm_layer.h"
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#include "gemm.h"
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#include "blas.h"
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#include "im2col.h"
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#include "col2im.h"
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#include "utils.h"
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#include "cuda.h"
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}
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__global__ void binarize_kernel(float *x, int n, float *binary)
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{
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int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if (i >= n) return;
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binary[i] = (x[i] >= 0) ? 1 : -1;
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}
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void binarize_gpu(float *x, int n, float *binary)
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{
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binarize_kernel<<<cuda_gridsize(n), BLOCK>>>(x, n, binary);
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check_error(cudaPeekAtLastError());
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}
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__global__ void binarize_input_kernel(float *input, int n, int size, float *binary)
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{
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int s = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if (s >= size) return;
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int i = 0;
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float mean = 0;
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for(i = 0; i < n; ++i){
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mean += fabs(input[i*size + s]);
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}
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mean = mean / n;
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for(i = 0; i < n; ++i){
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binary[i*size + s] = (input[i*size + s] > 0) ? mean : -mean;
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}
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}
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void binarize_input_gpu(float *input, int n, int size, float *binary)
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{
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binarize_input_kernel<<<cuda_gridsize(size), BLOCK>>>(input, n, size, binary);
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check_error(cudaPeekAtLastError());
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}
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__global__ void binarize_weights_kernel(float *weights, int n, int size, float *binary)
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{
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int f = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if (f >= n) return;
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int i = 0;
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float mean = 0;
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for(i = 0; i < size; ++i){
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mean += fabs(weights[f*size + i]);
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}
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mean = mean / size;
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for(i = 0; i < size; ++i){
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binary[f*size + i] = (weights[f*size + i] > 0) ? mean : -mean;
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//binary[f*size + i] = weights[f*size + i];
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}
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}
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void binarize_weights_gpu(float *weights, int n, int size, float *binary)
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{
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binarize_weights_kernel<<<cuda_gridsize(n), BLOCK>>>(weights, n, size, binary);
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check_error(cudaPeekAtLastError());
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}
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__global__ void cuda_f32_to_f16(float* input_f32, size_t size, half *output_f16)
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{
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int idx = blockIdx.x * blockDim.x + threadIdx.x;
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if (idx < size) output_f16[idx] = __float2half(input_f32[idx]);
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//if (idx < size) *((unsigned short *)output_f16 + idx) = __float2half(input_f32[idx]);
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}
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void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16) {
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cuda_f32_to_f16 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> (input_f32, size, (half *)output_f16);
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}
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__global__ void cuda_f16_to_f32(half* input_f16, size_t size, float *output_f32)
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{
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int idx = blockIdx.x * blockDim.x + threadIdx.x;
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if (idx < size) output_f32[idx] = __half2float(input_f16[idx]);
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//if (idx < size) output_f32[idx] = __half2float(*((unsigned short *)input_f16 + idx));
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}
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void cuda_convert_f16_to_f32(float* input_f16, size_t size, float *output_f32) {
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cuda_f16_to_f32 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> ((half *)input_f16, size, output_f32);
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}
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half *cuda_make_f16_from_f32_array(float *src, size_t n)
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{
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half *dst16;
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size_t size = sizeof(half)*n;
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check_error(cudaMalloc((void **)&dst16, size));
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if (src) {
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cuda_convert_f32_to_f16(src, n, (float *)dst16);
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}
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if (!dst16) error("Cuda malloc failed\n");
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return dst16;
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}
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void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
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{
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fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1);
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if(l.binary){
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binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu);
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swap_binary(&l);
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}
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if(l.xnor){
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binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu);
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swap_binary(&l);
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binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu);
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state.input = l.binary_input_gpu;
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}
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#ifdef CUDNN
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float one = 1; // alpha[0], beta[0] is float for HALF and FLOAT
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float alpha = 1, beta = 0;
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#ifdef CUDNN_HALF
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// Note: For improved performance it is advised to use beta[0] = 0.0.
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// For Tensor Core: cudnnSetConvolutionMathType() where cudnnMathType_t mathType = CUDNN_TENSOR_OP_MATH;
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// 1. or CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM and use CUDNN_DATA_HALF
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// 2. or CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED
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// More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops
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const size_t input16_size = l.batch*l.c*l.w*l.h;
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const size_t output16_size = l.batch*l.out_c*l.out_h*l.out_w;
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if (*state.net.max_input16_size < input16_size) {
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//printf("\n input16_size: cur = %zu \t max = %zu \n", input16_size, *state.net.max_input16_size);
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*state.net.max_input16_size = input16_size;
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if (*state.net.input16_gpu) cuda_free(*state.net.input16_gpu);
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*state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size);
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}
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float *input16 = *state.net.input16_gpu;
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if (*state.net.max_output16_size < output16_size) {
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*state.net.max_output16_size = output16_size;
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if (*state.net.output16_gpu) cuda_free(*state.net.output16_gpu);
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*state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size);
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}
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float *output16 = *state.net.output16_gpu;
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cuda_convert_f32_to_f16(state.input, input16_size, input16);
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//fill_ongpu(output16_size / 2, 0, (float *)output16, 1);
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cudnnConvolutionForward(cudnn_handle(),
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&alpha,
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l.srcTensorDesc,
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input16,
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l.weightDesc,
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l.weights_gpu16,
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l.convDesc,
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l.fw_algo,
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state.workspace,
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l.workspace_size,
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&beta,
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l.dstTensorDesc,
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output16);
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if (l.batch_normalize)
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{
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if (state.train) // Training
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{
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copy_ongpu(l.outputs*l.batch / 2, output16, 1, l.x_gpu, 1);
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//cudaMemcpyAsync(l.x_gpu, output16, l.outputs*l.batch*sizeof(half), cudaMemcpyDefault, get_cuda_stream());
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float one = 1;
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float zero = 0;
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// Batch-normalization can still take FP16 inputs and outputs, saving half the bandwidth
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// compared to FP32, its just that the statistics and value adjustment should be done in FP32.
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cudnnBatchNormalizationForwardTraining(cudnn_handle(),
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CUDNN_BATCHNORM_SPATIAL,
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&one,
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&zero,
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l.normDstTensorDescF16,
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l.x_gpu, // input
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l.normDstTensorDescF16,
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output16, // output
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l.normTensorDesc,
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l.scales_gpu,
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l.biases_gpu,
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.01,
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l.rolling_mean_gpu, // output (should be FP32)
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l.rolling_variance_gpu, // output (should be FP32)
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.00001,
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l.mean_gpu, // output (should be FP32)
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l.variance_gpu); // output (should be FP32)
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cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu);
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//forward_batchnorm_layer_gpu(l, state);
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}
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else // Detection
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{
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cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu);
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normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w);
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scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w);
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add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h);
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}
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}
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else // BIAS only
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{
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cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu);
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add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h);
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}
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#else
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cudnnConvolutionForward(cudnn_handle(),
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&one,
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l.srcTensorDesc,
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state.input,
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l.weightDesc,
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l.weights_gpu,
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l.convDesc,
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l.fw_algo,
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state.workspace,
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l.workspace_size,
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&one,
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l.dstTensorDesc,
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l.output_gpu);
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#endif // CUDNN_HALF
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#else
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int i;
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int m = l.n;
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int k = l.size*l.size*l.c;
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int n = l.out_w*l.out_h;
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for(i = 0; i < l.batch; ++i){
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im2col_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace);
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float * a = l.weights_gpu;
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float * b = state.workspace;
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float * c = l.output_gpu;
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gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n);
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}
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#endif
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#ifndef CUDNN_HALF
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if (l.batch_normalize) {
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forward_batchnorm_layer_gpu(l, state);
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}
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else {
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add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h);
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}
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#endif // no CUDNN_HALF
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activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
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//if(l.dot > 0) dot_error_gpu(l);
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if(l.binary || l.xnor) swap_binary(&l);
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//cudaDeviceSynchronize(); // for correct profiling of performance
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}
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void backward_convolutional_layer_gpu(convolutional_layer l, network_state state)
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{
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gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
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backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h);
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#ifndef CUDNN_HALF
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if(l.batch_normalize){
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backward_batchnorm_layer_gpu(l, state);
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} else {
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//backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h);
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}
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#endif // no CUDNN_HALF
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float *original_input = state.input;
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if(l.xnor) state.input = l.binary_input_gpu;
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#ifdef CUDNN
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float one = 1;
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float alpha = 1, beta = 0;
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#ifdef CUDNN_HALF
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const size_t input16_size = l.batch*l.c*l.w*l.h;
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const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h;
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if (*state.net.max_input16_size < input16_size) {
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*state.net.max_input16_size = input16_size;
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if(*state.net.input16_gpu) cuda_free(*state.net.input16_gpu);
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*state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size);
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}
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float *input16 = *state.net.input16_gpu;
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if (*state.net.max_output16_size < delta16_size) {
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*state.net.max_output16_size = delta16_size;
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if(*state.net.output16_gpu) cuda_free(*state.net.output16_gpu);
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*state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size);
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}
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float *delta16 = *state.net.output16_gpu;
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cuda_convert_f32_to_f16(state.input, input16_size, input16);
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cuda_convert_f32_to_f16(l.delta_gpu, delta16_size, delta16);
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if (l.batch_normalize) {
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//if (!state.train) {
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// l.mean_gpu = l.rolling_mean_gpu;
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// l.variance_gpu = l.rolling_variance_gpu;
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//}
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float one = 1;
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float zero = 0;
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cudnnBatchNormalizationBackward(cudnn_handle(),
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CUDNN_BATCHNORM_SPATIAL,
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&one,
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&zero,
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&one,
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&one,
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l.normDstTensorDescF16,
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l.x_gpu, // input
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l.normDstTensorDescF16,
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delta16, // input
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l.normDstTensorDescF16,
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l.x_norm_gpu, // output
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l.normTensorDesc,
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l.scales_gpu, // output (should be FP32)
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l.scale_updates_gpu, // output (should be FP32)
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l.bias_updates_gpu, // output (should be FP32)
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.00001,
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l.mean_gpu, // input (should be FP32)
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l.variance_gpu); // input (should be FP32)
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copy_ongpu(l.outputs*l.batch / 2, l.x_norm_gpu, 1, delta16, 1);
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//cudaMemcpyAsync(delta16, l.x_norm_gpu, l.outputs*l.batch * sizeof(half), cudaMemcpyDefault, get_cuda_stream());
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}
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else
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{
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//backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h);
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}
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// convert input: state.input (x), l.delta_gpu (y) from fp32 to fp16
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// get output: l.weight_updates_gpu (dw) and convert it to fp32 (ONLY if it is fp16)
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// calculate conv weight updates
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// Already: l.weight_updates_gpu = (l.weight_updates_gpu - l.weight*decay*batch*subdivision)*momentum
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// so we should copy f32 to f16, or compute: f16=(w_up - w*d*b*s)*m
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cuda_convert_f32_to_f16(l.weight_updates_gpu, l.c*l.n*l.size*l.size, l.weight_updates_gpu16);
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cudnnConvolutionBackwardFilter(cudnn_handle(),
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&one,
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l.srcTensorDesc,
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input16, //state.input,
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l.ddstTensorDesc,
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delta16, //l.delta_gpu,
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l.convDesc,
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l.bf_algo,
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state.workspace,
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l.workspace_size,
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&one,
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l.dweightDesc,
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l.weight_updates_gpu16); // l.weight_updates_gpu);
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cuda_convert_f16_to_f32(l.weight_updates_gpu16, l.c*l.n*l.size*l.size, l.weight_updates_gpu);
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if (state.delta) {
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if (l.binary || l.xnor) swap_binary(&l);
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// http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData
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// calculate delta for the next layer
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// convert input: l.weights_gpu (w), l.delta_gpu (dy) from fp32 to fp16
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// get output: state.delta (dx) and convert it to fp32 (ONLY if it is fp16)
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cudnnConvolutionBackwardData(cudnn_handle(),
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&alpha,
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l.weightDesc,
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l.weights_gpu16, //l.weights_gpu,
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l.ddstTensorDesc,
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delta16, //l.delta_gpu,
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l.convDesc,
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l.bd_algo,
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state.workspace,
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l.workspace_size,
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&beta,
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l.dsrcTensorDesc,
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input16); // state.delta);
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cuda_convert_f16_to_f32(input16, input16_size, state.delta);
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if (l.binary || l.xnor) swap_binary(&l);
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if (l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta);
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}
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#else // CUDNN_HALF
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// calculate conv weight updates
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// if used: beta=1 then loss decreases faster
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cudnnConvolutionBackwardFilter(cudnn_handle(),
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&one,
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l.srcTensorDesc,
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state.input,
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l.ddstTensorDesc,
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l.delta_gpu,
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l.convDesc,
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l.bf_algo,
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state.workspace,
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l.workspace_size,
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&one,
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l.dweightDesc,
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l.weight_updates_gpu);
|
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if(state.delta){
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if(l.binary || l.xnor) swap_binary(&l);
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// http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData
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// calculate delta for the next layer
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cudnnConvolutionBackwardData(cudnn_handle(),
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&one,
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l.weightDesc,
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l.weights_gpu,
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l.ddstTensorDesc,
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l.delta_gpu,
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l.convDesc,
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l.bd_algo,
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state.workspace,
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l.workspace_size,
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&one,
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l.dsrcTensorDesc,
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state.delta);
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if(l.binary || l.xnor) swap_binary(&l);
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if(l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta);
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}
|
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#endif // CUDNN_HALF
|
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#else // CUDNN
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int m = l.n;
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int n = l.size*l.size*l.c;
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int k = l.out_w*l.out_h;
|
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int i;
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for(i = 0; i < l.batch; ++i){
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float * a = l.delta_gpu;
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float * b = state.workspace;
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float * c = l.weight_updates_gpu;
|
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im2col_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace);
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gemm_ongpu(0,1,m,n,k,1,a + i*m*k,k,b,k,1,c,n);
|
|
if(state.delta){
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if(l.binary || l.xnor) swap_binary(&l);
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float * a = l.weights_gpu;
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float * b = l.delta_gpu;
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float * c = state.workspace;
|
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gemm_ongpu(1,0,n,k,m,1,a,n,b + i*k*m,k,0,c,k);
|
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col2im_ongpu(state.workspace, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta + i*l.c*l.h*l.w);
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if(l.binary || l.xnor) {
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swap_binary(&l);
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}
|
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);
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}
|
}
|
#endif
|
}
|
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void pull_convolutional_layer(convolutional_layer layer)
|
{
|
cuda_pull_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size);
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cuda_pull_array(layer.biases_gpu, layer.biases, layer.n);
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cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size);
|
cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
|
if (layer.batch_normalize){
|
cuda_pull_array(layer.scales_gpu, layer.scales, layer.n);
|
cuda_pull_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n);
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cuda_pull_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n);
|
}
|
if (layer.adam){
|
cuda_pull_array(layer.m_gpu, layer.m, layer.c*layer.n*layer.size*layer.size);
|
cuda_pull_array(layer.v_gpu, layer.v, layer.c*layer.n*layer.size*layer.size);
|
}
|
}
|
|
void push_convolutional_layer(convolutional_layer layer)
|
{
|
cuda_push_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size);
|
#ifdef CUDNN_HALF
|
cuda_convert_f32_to_f16(layer.weights_gpu, layer.c*layer.n*layer.size*layer.size, layer.weights_gpu16);
|
#endif
|
cuda_push_array(layer.biases_gpu, layer.biases, layer.n);
|
cuda_push_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size);
|
cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
|
if (layer.batch_normalize){
|
cuda_push_array(layer.scales_gpu, layer.scales, layer.n);
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cuda_push_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n);
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cuda_push_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n);
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}
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if (layer.adam){
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cuda_push_array(layer.m_gpu, layer.m, layer.c*layer.n*layer.size*layer.size);
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cuda_push_array(layer.v_gpu, layer.v, layer.c*layer.n*layer.size*layer.size);
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}
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}
|
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void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay)
|
{
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int size = layer.size*layer.size*layer.c*layer.n;
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axpy_ongpu(layer.n, learning_rate/batch, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
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scal_ongpu(layer.n, momentum, layer.bias_updates_gpu, 1);
|
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if(layer.scales_gpu){
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axpy_ongpu(layer.n, learning_rate/batch, layer.scale_updates_gpu, 1, layer.scales_gpu, 1);
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scal_ongpu(layer.n, momentum, layer.scale_updates_gpu, 1);
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}
|
|
if(layer.adam){
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scal_ongpu(size, layer.B1, layer.m_gpu, 1);
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scal_ongpu(size, layer.B2, layer.v_gpu, 1);
|
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axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1);
|
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axpy_ongpu(size, -(1-layer.B1), layer.weight_updates_gpu, 1, layer.m_gpu, 1);
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mul_ongpu(size, layer.weight_updates_gpu, 1, layer.weight_updates_gpu, 1);
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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);
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fill_ongpu(size, 0, layer.weight_updates_gpu, 1);
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}else{
|
// update weights:
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// weights_gpu = weights_gpu*(1 - decay*lr) + weight_updates_gpu*lr / (batch*subdivision) =
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// weights_gpu*(1 - 0.0005*0.001) + weight_updates_gpu*0.001/(64*8) =
|
// weights_gpu * 0.999 999 5 + weight_updates_gpu * 0.000 001 953125
|
//
|
// weight_updates_gpu = (weight_updates_gpu - weights_gpu*decay*batch*subdivision)*momentum =
|
// (weight_updates_gpu - weights_gpu * 0.0005 * 64 * 8) * 0.9 =
|
// weight_updates_gpu*0.9 - weights_gpu*0.2304
|
axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1);
|
axpy_ongpu(size, learning_rate/batch, layer.weight_updates_gpu, 1, layer.weights_gpu, 1);
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scal_ongpu(size, momentum, layer.weight_updates_gpu, 1);
|
}
|
}
|
