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Temporary experimental XNOR on GPU (repack channels)

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This commit is contained in:
AlexeyAB
2019-01-16 02:43:44 +03:00
parent 920d792a0c
commit 4c05166215
6 changed files with 406 additions and 7 deletions
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134
src/convolutional_kernels.cu
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@ -190,7 +190,7 @@ 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 >= 256 && l.size > 1)
if (l.align_bit_weights_gpu && !state.train && l.c >= 64)// && l.size > 1)
{
//return;
cudaError_t status = cudaSuccess;
@ -207,10 +207,135 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
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));
//float *intput_cpu = (float *)calloc(l.inputs, sizeof(float));
// state.input
//cudaMemcpy(intput_cpu, state.input, l.inputs * sizeof(float), cudaMemcpyDefault);
int ldb_align = l.lda_align;
size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8;
size_t t_intput_size = new_ldb * l.bit_align;// n;
size_t t_bit_input_size = t_intput_size / 8;// +1;
const int new_c = l.c / 32;
//float *re_packed_input = (float *)calloc(l.c * l.w * l.h, sizeof(float));
//uint32_t *bin_re_packed_input = (uint32_t *)calloc(new_c * l.w * l.h + 1, sizeof(uint32_t));
// float32x4 by channel (as in cuDNN)
//repack_input(intput_cpu, re_packed_input, l.w, l.h, l.c);
// 32 x floats -> 1 x uint32_t
//float_to_bit(re_packed_input, (uint8_t *)bin_re_packed_input, l.c * l.w * l.h);
//cudaDeviceSynchronize();
//start_timer();
repack_input_gpu_bin(state.input, (uint32_t *)l.align_workspace_gpu, l.w, l.h, l.c);
//repack_input_gpu(state.input, state.workspace, l.w, l.h, l.c);
// 32 x floats -> 1 x uint32_t
//float_to_bit_gpu(state.workspace, (unsigned char *)l.align_workspace_gpu, l.c * l.w * l.h);// l.align_workspace_size);
//cudaDeviceSynchronize();
//stop_timer_and_show_name("repack_input_gpu + float_to_bit_gpu");
//free(re_packed_input);
// slow - convolution the packed inputs and weights: float x 32 by channel (as in cuDNN)
//convolution_repacked((uint32_t *)bin_re_packed_input, (uint32_t *)l.align_bit_weights, l.output,
// l.w, l.h, l.c, l.n, l.size, l.pad, l.new_lda, l.mean_arr);
// // then exit from if()
//float *b = state.workspace;
//float *b = (float *)calloc(100 * 1024 * 1024, sizeof(float));
//float *c = l.output;
//memset(c, 0, l.outputs * sizeof(float));
//im2col_cpu_custom((float *)bin_re_packed_input, new_c, l.h, l.w, l.size, l.stride, l.pad, b);
//cudaMemcpy(l.align_workspace_gpu, bin_re_packed_input, (new_c * l.w * l.h + 1) * sizeof(uint32_t), cudaMemcpyDefault);
//start_timer();
im2col_ongpu(l.align_workspace_gpu, new_c, l.h, l.w, l.size, l.stride, l.pad, state.workspace);
//cudaDeviceSynchronize();
//stop_timer_and_show_name("im2col_ongpu");
//free(bin_re_packed_input);
int new_k = l.size*l.size*l.c / 32;
// good for (l.c == 64)
//gemm_nn_bin_32bit_packed(m, n, new_k, 1,
// l.align_bit_weights, l.new_lda/32,
// b, n,
// c, n, l.mean_arr);
// // then exit from if()
//size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8;
//size_t t_intput_size = new_ldb * l.bit_align;// n;
//size_t t_bit_input_size = t_intput_size / 8;// +1;
//char *t_bit_input = (char *)calloc(t_bit_input_size, sizeof(char));
//transpose_uint32((uint32_t *)b, (uint32_t *)t_bit_input, new_k, n, n, new_ldb);
//cudaMemcpy(l.transposed_align_workspace_gpu, t_bit_input, t_bit_input_size * sizeof(char), cudaMemcpyDefault);
//cudaMemcpy(state.workspace, b, t_bit_input_size * sizeof(char), cudaMemcpyDefault);
//printf("\n n = %d, n % 32 = %d, new_ldb = %d, new_ldb % 32 = %d \n", n, n % 32, new_ldb, new_ldb % 32);
//start_timer();
transpose_uint32_gpu_2((uint32_t *)state.workspace, (uint32_t *)l.transposed_align_workspace_gpu, new_k, n, n, new_ldb);
//cudaDeviceSynchronize();
//stop_timer_and_show_name("transpose_uint32_gpu");
//cudaDeviceSynchronize();
//stop_timer_and_show_name("repack_input_gpu_bin + im2col_ongpu + transpose_uint32_gpu_2");
//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);
//cudaDeviceSynchronize();
//stop_timer_and_show_name("gemm_nn_custom_bin_mean_transposed_gpu");
// the main GEMM function
//gemm_nn_custom_bin_mean_transposed(m, n, k, 1, (uint8_t *)l.align_bit_weights, new_ldb, (uint8_t *)t_bit_input, new_ldb, c, n, l.mean_arr);
//add_bias(l.output, l.biases, l.batch, l.n, l.out_h*l.out_w);
//cudaMemcpy(l.output_gpu, l.output, l.outputs * sizeof(float), cudaMemcpyDefault);
// // alternative GEMM
//gemm_nn_bin_transposed_32bit_packed(m, n, new_k, 1,
// l.align_bit_weights, l.new_lda/32,
// t_bit_input, new_ldb / 32,
// c, n, l.mean_arr);
//free(t_bit_input);
//free(b);
}
else
{
//printf("\n\n l.index = %d, l.w = %d, l.c = %d, l.n = %d, l.stride = %d, l.pad = %d - old XNOR \n", l.index, l.w, l.c, l.n, l.stride, l.pad);
//cudaDeviceSynchronize();
int i = 0;
/*
// if (l.stride == 1 && l.c >= 256 && l.size > 1)
if (l.stride == 1 && l.c >= 1024 && l.size > 1 && 0)// && l.w >= 13) // disabled
{
@ -220,9 +345,9 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
//cudaDeviceSynchronize();
//stop_timer_and_show_name("im2col_align_bin_ongpu");
}
else
else*/
{
//start_timer();
start_timer();
im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.align_workspace_gpu, l.bit_align);
//cudaDeviceSynchronize();
//stop_timer_and_show_name("im2col_align_ongpu");
@ -239,6 +364,9 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
//cudaDeviceSynchronize();
//stop_timer_and_show_name("transpose_bin_gpu");
//cudaDeviceSynchronize();
//stop_timer_and_show_name("im2col_align_ongpu + float_to_bit_gpu + transpose_bin_gpu");
// should be optimized
//if(0) {//if (k > 1000) { // sequentially input-shared - BAD
// gemm_nn_custom_bin_mean_transposed_sequentially_gpu(m, n, k,