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

Fixed multi-GPU training for Tensor Cores

Browse Source
This commit is contained in:
AlexeyAB
2018-03-09 19:44:46 +03:00
parent a6c51e3b75
commit 880cf187d8
5 changed files with 48 additions and 35 deletions
Download Patch File Download Diff File Expand all files Collapse all files

59
src/convolutional_kernels.cu
View File

@ -135,26 +135,24 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
// 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;
static size_t max_input16_size = input16_size;
static half* input16 = cuda_make_f16_from_f32_array(NULL, max_input16_size);
const size_t output16_size = l.batch*l.out_c*l.out_h*l.out_w;
static size_t max_output16_size = output16_size;
static half* output16 = cuda_make_f16_from_f32_array(NULL, max_output16_size);
if (max_input16_size < input16_size) {
max_input16_size = input16_size;
cuda_free((float *)input16);
input16 = cuda_make_f16_from_f32_array(state.input, max_input16_size);
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 (max_output16_size < output16_size) {
max_output16_size = output16_size;
cuda_free((float *)output16);
output16 = cuda_make_f16_from_f32_array(NULL, max_output16_size);
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, (float *)input16);
cuda_convert_f32_to_f16(state.input, input16_size, input16);
//fill_ongpu(output16_size / 2, 0, (float *)output16, 1);
cudnnConvolutionForward(cudnn_handle(),
@ -171,7 +169,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
l.dstTensorDesc,
output16);
cuda_convert_f16_to_f32((float *)output16, output16_size, l.output_gpu);
cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu);
#else
@ -238,27 +236,24 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
#ifdef CUDNN_HALF
const size_t input16_size = l.batch*l.c*l.w*l.h;
static size_t max_input16_size = input16_size;
static half* input16 = cuda_make_f16_from_f32_array(NULL, max_input16_size);
const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h;
static size_t max_delta16_size = delta16_size;
static half* delta16 = cuda_make_f16_from_f32_array(NULL, max_delta16_size);
if (max_input16_size < input16_size) {
max_input16_size = input16_size;
cuda_free((float *)input16);
input16 = cuda_make_f16_from_f32_array(state.input, max_input16_size);
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 (max_delta16_size < delta16_size) {
max_delta16_size = delta16_size;
cuda_free((float *)delta16);
delta16 = cuda_make_f16_from_f32_array(NULL, max_delta16_size);
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, (float *)input16);
cuda_convert_f32_to_f16(l.delta_gpu, delta16_size, (float *)delta16);
cuda_convert_f32_to_f16(state.input, input16_size, input16);
cuda_convert_f32_to_f16(l.delta_gpu, delta16_size, delta16);
// 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)
@ -305,7 +300,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
l.dsrcTensorDesc,
input16); // state.delta);
cuda_convert_f16_to_f32((float *)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);