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Added support for Tensor Cores CC >= 7.0 (V100). For FP16/32 (mixed precision) define CUDNN_HALF should be used.

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This commit is contained in:
AlexeyAB
2018-02-25 16:29:44 +03:00
parent 85eafd3d59
commit cad4d1618f
8 changed files with 123 additions and 18 deletions
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122
src/convolutional_kernels.cu
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@ -81,8 +81,8 @@ __global__ void cuda_f32_to_f16(float* input_f32, size_t size, half *output_f16)
//if (idx < size) *((unsigned short *)output_f16 + idx) = __float2half(input_f32[idx]);
}
void cuda_convert_f32_to_f16(float* input_f32, size_t size, half *output_f16) {
cuda_f32_to_f16 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> (input_f32, size, output_f16);
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);
}
__global__ void cuda_f16_to_f32(half* input_f16, size_t size, float *output_f32)
@ -92,8 +92,8 @@ __global__ void cuda_f16_to_f32(half* input_f16, size_t size, float *output_f32)
//if (idx < size) output_f32[idx] = __half2float(*((unsigned short *)input_f16 + idx));
}
void cuda_convert_f16_to_f32(half* input_f16, size_t size, float *output_f32) {
cuda_f16_to_f32 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> (input_f16, size, output_f32);
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);
}
half *cuda_make_f16_from_f32_array(float *src, size_t n)
@ -102,7 +102,7 @@ half *cuda_make_f16_from_f32_array(float *src, size_t n)
size_t size = sizeof(half)*n;
check_error(cudaMalloc((void **)&dst16, size));
if (src) {
cuda_convert_f32_to_f16(src, n, dst16);
cuda_convert_f32_to_f16(src, n, (float *)dst16);
}
if (!dst16) error("Cuda malloc failed\n");
return dst16;
@ -124,8 +124,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
}
#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.
@ -154,8 +154,9 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
output16 = cuda_make_f16_from_f32_array(NULL, max_output16_size);
}
cuda_convert_f32_to_f16(state.input, input16_size, input16);
cuda_convert_f32_to_f16(state.input, input16_size, (float *)input16);
//fill_ongpu(output16_size / 2, 0, (float *)output16, 1);
cudnnConvolutionForward(cudnn_handle(),
&alpha,
l.srcTensorDesc,
@ -170,11 +171,12 @@ 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);
cuda_convert_f16_to_f32((float *)output16, output16_size, l.output_gpu);
#else
cudnnConvolutionForward(cudnn_handle(),
&alpha,
&one,
l.srcTensorDesc,
state.input,
l.weightDesc,
@ -183,7 +185,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
l.fw_algo,
state.workspace,
l.workspace_size,
&beta,
&one,
l.dstTensorDesc,
l.output_gpu);
#endif
@ -230,7 +232,88 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
if(l.xnor) state.input = l.binary_input_gpu;
#ifdef CUDNN
float one = 1;
float one = 1;
float alpha = 1, beta = 0;
#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 (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);
}
cuda_convert_f32_to_f16(state.input, input16_size, (float *)input16);
cuda_convert_f32_to_f16(l.delta_gpu, delta16_size, (float *)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)
// 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);
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);
// 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((float *)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
// calculate conv weight updates
// if used: beta=1 then loss decreases faster
cudnnConvolutionBackwardFilter(cudnn_handle(),
&one,
l.srcTensorDesc,
@ -248,6 +331,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
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
cudnnConvolutionBackwardData(cudnn_handle(),
&one,
l.weightDesc,
@ -265,7 +349,9 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
if(l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta);
}
#else
#endif // CUDNN_HALF
#else // CUDNN
int m = l.n;
int n = l.size*l.size*l.c;
int k = l.out_w*l.out_h;
@ -318,7 +404,7 @@ 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, (half *)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);
@ -358,6 +444,14 @@ void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float
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
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);