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https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
Added support for Tensor Cores CC >= 7.0 (V100). For FP16/32 (mixed precision) define CUDNN_HALF should be used.
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@ -81,8 +81,8 @@ __global__ void cuda_f32_to_f16(float* input_f32, size_t size, half *output_f16)
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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, half *output_f16) {
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cuda_f32_to_f16 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> (input_f32, size, output_f16);
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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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@ -92,8 +92,8 @@ __global__ void cuda_f16_to_f32(half* input_f16, size_t size, float *output_f32)
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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(half* input_f16, size_t size, float *output_f32) {
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cuda_f16_to_f32 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> (input_f16, size, output_f32);
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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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@ -102,7 +102,7 @@ half *cuda_make_f16_from_f32_array(float *src, size_t n)
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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, dst16);
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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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@ -124,8 +124,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
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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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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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@ -154,8 +154,9 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
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output16 = cuda_make_f16_from_f32_array(NULL, max_output16_size);
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}
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cuda_convert_f32_to_f16(state.input, input16_size, input16);
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cuda_convert_f32_to_f16(state.input, input16_size, (float *)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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@ -170,11 +171,12 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
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l.dstTensorDesc,
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output16);
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cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu);
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cuda_convert_f16_to_f32((float *)output16, output16_size, l.output_gpu);
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#else
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cudnnConvolutionForward(cudnn_handle(),
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&alpha,
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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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@ -183,7 +185,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
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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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&one,
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l.dstTensorDesc,
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l.output_gpu);
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#endif
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@ -230,7 +232,88 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
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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 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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static size_t max_input16_size = input16_size;
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static half* input16 = cuda_make_f16_from_f32_array(NULL, max_input16_size);
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const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h;
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static size_t max_delta16_size = delta16_size;
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static half* delta16 = cuda_make_f16_from_f32_array(NULL, max_delta16_size);
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if (max_input16_size < input16_size) {
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max_input16_size = input16_size;
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cuda_free((float *)input16);
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input16 = cuda_make_f16_from_f32_array(state.input, max_input16_size);
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}
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if (max_delta16_size < delta16_size) {
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max_delta16_size = delta16_size;
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cuda_free((float *)delta16);
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delta16 = cuda_make_f16_from_f32_array(NULL, max_delta16_size);
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}
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cuda_convert_f32_to_f16(state.input, input16_size, (float *)input16);
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cuda_convert_f32_to_f16(l.delta_gpu, delta16_size, (float *)delta16);
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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((float *)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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@ -248,6 +331,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
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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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@ -265,7 +349,9 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
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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
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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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@ -318,7 +404,7 @@ void push_convolutional_layer(convolutional_layer layer)
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{
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cuda_push_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size);
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#ifdef CUDNN_HALF
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cuda_convert_f32_to_f16(layer.weights_gpu, layer.c*layer.n*layer.size*layer.size, (half *)layer.weights_gpu16);
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cuda_convert_f32_to_f16(layer.weights_gpu, layer.c*layer.n*layer.size*layer.size, layer.weights_gpu16);
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#endif
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cuda_push_array(layer.biases_gpu, layer.biases, layer.n);
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cuda_push_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size);
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@ -358,6 +444,14 @@ void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float
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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{
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// 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) =
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// weights_gpu * 0.999 999 5 + weight_updates_gpu * 0.000 001 953125
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//
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// weight_updates_gpu = (weight_updates_gpu - weights_gpu*decay*batch*subdivision)*momentum =
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// (weight_updates_gpu - weights_gpu * 0.0005 * 64 * 8) * 0.9 =
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// weight_updates_gpu*0.9 - weights_gpu*0.2304
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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, 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);
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