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XNOR uses Tensor Cores on Turing GPU CC>=7.3 (not Volta)

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This commit is contained in:
AlexeyAB
2019-02-02 00:24:34 +03:00
parent e17bd9ba8f
commit f09a9c3315
5 changed files with 158 additions and 384 deletions
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12
src/convolutional_kernels.cu
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@ -180,7 +180,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
//state.input = l.binary_input_gpu;
//cudaDeviceSynchronize();
if (l.align_bit_weights_gpu && !state.train && l.c >= 64)// && l.size > 1)
if (l.align_bit_weights_gpu && !state.train && l.c >= 32)
{
//return;
cudaError_t status = cudaSuccess;
@ -196,10 +196,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
size_t t_intput_size = new_ldb * n;
size_t t_bit_input_size = t_intput_size / 8;// +1;
//if(0)
if (l.c % 32 == 0)
//if (l.stride == 1 && l.pad == 1 && l.c % 32 == 0)
//if(1)
{
//printf("\n\n l.index = %d, l.w = %d, l.c = %d, l.n = %d, l.stride = %d, l.pad = %d - new XNOR \n", l.index, l.w, l.c, l.n, l.stride, l.pad);
//printf("l.align_workspace_size = %d, (l.c * l.w * l.h) = %d \n", l.align_workspace_size, (l.c * l.w * l.h));
@ -296,7 +293,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
//start_timer();
gemm_nn_custom_bin_mean_transposed_gpu(m, n, k,
(unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu,
new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu, l.activation);
new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu, l.activation == LEAKY);
//cudaDeviceSynchronize();
//stop_timer_and_show_name("gemm_nn_custom_bin_mean_transposed_gpu");
@ -366,7 +363,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
//start_timer();
gemm_nn_custom_bin_mean_transposed_gpu(m, n, k,
(unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu,
new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu, l.activation);
new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu, l.activation == LEAKY);
//cudaDeviceSynchronize();
//stop_timer_and_show_name("gemm_nn_custom_bin_mean_transposed_gpu");
//}
@ -391,7 +388,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
*/
//add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h);
if(l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
if (l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
//if(l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
//if (l.binary || l.xnor) swap_binary(&l);
//cudaDeviceSynchronize();
return;