From cfc5fedbb6df2471493b1ec162d0024485618211 Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Tue, 10 Jul 2018 20:29:15 +0000
Subject: [PATCH] Just used spaces for indents instead of Tabs
---
src/convolutional_kernels.cu | 432 +++++++++++++++++++++++++++---------------------------
1 files changed, 216 insertions(+), 216 deletions(-)
diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu
index 324fc50..5389828 100644
--- a/src/convolutional_kernels.cu
+++ b/src/convolutional_kernels.cu
@@ -76,36 +76,36 @@
__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) *((unsigned short *)output_f16 + idx) = __float2half(input_f32[idx]);
+ int idx = blockIdx.x * blockDim.x + threadIdx.x;
+ if (idx < size) output_f16[idx] = __float2half(input_f32[idx]);
+ //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 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> (input_f32, size, (half *)output_f16);
+ cuda_f32_to_f16 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> (input_f32, size, (half *)output_f16);
}
__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));
+ 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 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> ((half *)input_f16, size, output_f32);
+ cuda_f16_to_f32 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> ((half *)input_f16, size, output_f32);
}
half *cuda_make_f16_from_f32_array(float *src, size_t n)
{
- half *dst16;
- size_t size = sizeof(half)*n;
- check_error(cudaMalloc((void **)&dst16, size));
- if (src) {
- cuda_convert_f32_to_f16(src, n, (float *)dst16);
- }
- if (!dst16) error("Cuda malloc failed\n");
- return dst16;
+ half *dst16;
+ size_t size = sizeof(half)*n;
+ check_error(cudaMalloc((void **)&dst16, size));
+ if (src) {
+ 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)
@@ -124,96 +124,96 @@
}
#ifdef CUDNN
- float one = 1; // alpha[0], beta[0] is float for HALF and FLOAT
- float alpha = 1, beta = 0;
+ float one = 1; // alpha[0], beta[0] is float for HALF and FLOAT
+ float alpha = 1, beta = 0;
#ifdef CUDNN_HALF
- // 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
+ // 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;
+ 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);
- *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_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);
+ *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);
- *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size);
- }
- float *output16 = *state.net.output16_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);
+ *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size);
+ }
+ float *output16 = *state.net.output16_gpu;
- cuda_convert_f32_to_f16(state.input, input16_size, input16);
+ cuda_convert_f32_to_f16(state.input, input16_size, input16);
- //fill_ongpu(output16_size / 2, 0, (float *)output16, 1);
- cudnnConvolutionForward(cudnn_handle(),
- &alpha,
- l.srcTensorDesc,
- input16,
- l.weightDesc,
- l.weights_gpu16,
- l.convDesc,
- l.fw_algo,
- state.workspace,
- l.workspace_size,
- &beta,
- l.dstTensorDesc,
- output16);
-
+ //fill_ongpu(output16_size / 2, 0, (float *)output16, 1);
+ cudnnConvolutionForward(cudnn_handle(),
+ &alpha,
+ l.srcTensorDesc,
+ input16,
+ l.weightDesc,
+ l.weights_gpu16,
+ l.convDesc,
+ l.fw_algo,
+ state.workspace,
+ l.workspace_size,
+ &beta,
+ l.dstTensorDesc,
+ output16);
+
- if (l.batch_normalize)
- {
- if (state.train) // Training
- {
- 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;
- float zero = 0;
- // 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.
- cudnnBatchNormalizationForwardTraining(cudnn_handle(),
- CUDNN_BATCHNORM_SPATIAL,
- &one,
- &zero,
- l.normDstTensorDescF16,
- l.x_gpu, // input
- l.normDstTensorDescF16,
- output16, // output
- l.normTensorDesc,
- l.scales_gpu,
- l.biases_gpu,
- .01,
- l.rolling_mean_gpu, // output (should be FP32)
- l.rolling_variance_gpu, // output (should be FP32)
- .00001,
- l.mean_gpu, // output (should be FP32)
- l.variance_gpu); // output (should be FP32)
+ if (l.batch_normalize)
+ {
+ if (state.train) // Training
+ {
+ 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;
+ float zero = 0;
+ // 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.
+ cudnnBatchNormalizationForwardTraining(cudnn_handle(),
+ CUDNN_BATCHNORM_SPATIAL,
+ &one,
+ &zero,
+ l.normDstTensorDescF16,
+ l.x_gpu, // input
+ l.normDstTensorDescF16,
+ output16, // output
+ l.normTensorDesc,
+ l.scales_gpu,
+ l.biases_gpu,
+ .01,
+ l.rolling_mean_gpu, // output (should be FP32)
+ l.rolling_variance_gpu, // output (should be FP32)
+ .00001,
+ l.mean_gpu, // output (should be FP32)
+ l.variance_gpu); // output (should be FP32)
- 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);
- }
+ 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
@@ -230,7 +230,7 @@
&one,
l.dstTensorDesc,
l.output_gpu);
-#endif // CUDNN_HALF
+#endif // CUDNN_HALF
#else
@@ -250,16 +250,16 @@
#ifndef CUDNN_HALF
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);
- }
+ }
+ else {
+ add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h);
+ }
#endif // no CUDNN_HALF
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
+ //cudaDeviceSynchronize(); // for correct profiling of performance
}
void backward_convolutional_layer_gpu(convolutional_layer l, network_state state)
@@ -272,126 +272,126 @@
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);
+ //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;
- float alpha = 1, beta = 0;
+ float one = 1;
+ float alpha = 1, beta = 0;
#ifdef CUDNN_HALF
-
- 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);
- *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size);
- }
- float *input16 = *state.net.input16_gpu;
+
+ 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);
+ *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);
- *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size);
- }
- float *delta16 = *state.net.output16_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);
+ *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size);
+ }
+ float *delta16 = *state.net.output16_gpu;
- cuda_convert_f32_to_f16(state.input, input16_size, input16);
- cuda_convert_f32_to_f16(l.delta_gpu, delta16_size, delta16);
+ 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;
- float zero = 0;
- cudnnBatchNormalizationBackward(cudnn_handle(),
- CUDNN_BATCHNORM_SPATIAL,
- &one,
- &zero,
- &one,
- &one,
- l.normDstTensorDescF16,
- l.x_gpu, // input
- l.normDstTensorDescF16,
- delta16, // input
- l.normDstTensorDescF16,
- l.x_norm_gpu, // output
- l.normTensorDesc,
- l.scales_gpu, // output (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)
- 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);
- }
+ if (l.batch_normalize) {
+ //if (!state.train) {
+ // l.mean_gpu = l.rolling_mean_gpu;
+ // l.variance_gpu = l.rolling_variance_gpu;
+ //}
+ float one = 1;
+ float zero = 0;
+ cudnnBatchNormalizationBackward(cudnn_handle(),
+ CUDNN_BATCHNORM_SPATIAL,
+ &one,
+ &zero,
+ &one,
+ &one,
+ l.normDstTensorDescF16,
+ l.x_gpu, // input
+ l.normDstTensorDescF16,
+ delta16, // input
+ l.normDstTensorDescF16,
+ l.x_norm_gpu, // output
+ l.normTensorDesc,
+ l.scales_gpu, // output (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)
+ 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)
+ // 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
- cuda_convert_f32_to_f16(l.weight_updates_gpu, l.c*l.n*l.size*l.size, l.weight_updates_gpu16);
+ // 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
+ cuda_convert_f32_to_f16(l.weight_updates_gpu, l.c*l.n*l.size*l.size, l.weight_updates_gpu16);
- cudnnConvolutionBackwardFilter(cudnn_handle(),
- &one,
- l.srcTensorDesc,
- input16, //state.input,
- l.ddstTensorDesc,
- delta16, //l.delta_gpu,
- l.convDesc,
- l.bf_algo,
- state.workspace,
- l.workspace_size,
- &one,
- l.dweightDesc,
- l.weight_updates_gpu16); // l.weight_updates_gpu);
+ cudnnConvolutionBackwardFilter(cudnn_handle(),
+ &one,
+ l.srcTensorDesc,
+ input16, //state.input,
+ l.ddstTensorDesc,
+ delta16, //l.delta_gpu,
+ l.convDesc,
+ l.bf_algo,
+ state.workspace,
+ l.workspace_size,
+ &one,
+ l.dweightDesc,
+ l.weight_updates_gpu16); // l.weight_updates_gpu);
- cuda_convert_f16_to_f32(l.weight_updates_gpu16, l.c*l.n*l.size*l.size, l.weight_updates_gpu);
+ cuda_convert_f16_to_f32(l.weight_updates_gpu16, l.c*l.n*l.size*l.size, l.weight_updates_gpu);
- if (state.delta) {
- if (l.binary || l.xnor) swap_binary(&l);
+ 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)
- cudnnConvolutionBackwardData(cudnn_handle(),
- &alpha,
- l.weightDesc,
- l.weights_gpu16, //l.weights_gpu,
- l.ddstTensorDesc,
- delta16, //l.delta_gpu,
- l.convDesc,
- l.bd_algo,
- state.workspace,
- l.workspace_size,
- &beta,
- l.dsrcTensorDesc,
- input16); // state.delta);
+ // 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)
+ cudnnConvolutionBackwardData(cudnn_handle(),
+ &alpha,
+ l.weightDesc,
+ l.weights_gpu16, //l.weights_gpu,
+ l.ddstTensorDesc,
+ delta16, //l.delta_gpu,
+ l.convDesc,
+ l.bd_algo,
+ state.workspace,
+ l.workspace_size,
+ &beta,
+ l.dsrcTensorDesc,
+ input16); // state.delta);
- cuda_convert_f16_to_f32(input16, input16_size, 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 // CUDNN_HALF
+ 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 // CUDNN_HALF
- // calculate conv weight updates
- // if used: beta=1 then loss decreases faster
+ // calculate conv weight updates
+ // if used: beta=1 then loss decreases faster
cudnnConvolutionBackwardFilter(cudnn_handle(),
&one,
l.srcTensorDesc,
@@ -408,8 +408,8 @@
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
+ // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData
+ // calculate delta for the next layer
cudnnConvolutionBackwardData(cudnn_handle(),
&one,
l.weightDesc,
@@ -427,9 +427,9 @@
if(l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta);
}
-#endif // CUDNN_HALF
+#endif // CUDNN_HALF
-#else // CUDNN
+#else // CUDNN
int m = l.n;
int n = l.size*l.size*l.c;
int k = l.out_w*l.out_h;
@@ -482,7 +482,7 @@
{
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);
+ 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);
@@ -522,14 +522,14 @@
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{
- // update weights:
- // weights_gpu = weights_gpu*(1 - decay*lr) + weight_updates_gpu*lr / (batch*subdivision) =
- // 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
+ // update weights:
+ // weights_gpu = weights_gpu*(1 - decay*lr) + weight_updates_gpu*lr / (batch*subdivision) =
+ // 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);
scal_ongpu(size, momentum, layer.weight_updates_gpu, 1);
--
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