mirror/darknet
1
0
Fork 0
mirror of https://github.com/pjreddie/darknet.git synced 2023-08-10 21:13:14 +03:00
Code Issues Projects Releases Wiki Activity

Accelerated by another 5% using FP16/32 Batch-norm for Tensor Cores.

Browse Source
This commit is contained in:
AlexeyAB
2018-04-17 02:51:11 +03:00
parent 701f4fab63
commit 9bae70b225
5 changed files with 105 additions and 20 deletions
Download Patch File Download Diff File Expand all files Collapse all files

91
src/convolutional_kernels.cu
View File

@ -169,7 +169,51 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
l.dstTensorDesc,
output16);
cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu);
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<69>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);
}
#else
@ -186,7 +230,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
&one,
l.dstTensorDesc,
l.output_gpu);
#endif
#endif // CUDNN_HALF
#else
@ -203,12 +247,14 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
}
#endif
#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);
}
#endif // no CUDNN_HALF
activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
//if(l.dot > 0) dot_error_gpu(l);
@ -222,12 +268,13 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
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);
//axpy_ongpu(l.outputs*l.batch, -state.net.decay, l.x_gpu, 1, l.delta_gpu, 1);
} else {
//axpy_ongpu(l.outputs*l.batch, -state.net.decay, l.output_gpu, 1, l.delta_gpu, 1);
//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;
@ -256,7 +303,41 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
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);
}
// 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)