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

Temporary Slow implementation of XNOR on CUDA (shared_memory)

Browse Source
This commit is contained in:
AlexeyAB
2018-09-01 00:03:25 +03:00
parent c4a9e3422e
commit 007878393f
6 changed files with 187 additions and 101 deletions
Download Patch File Download Diff File Expand all files Collapse all files

96
src/convolutional_kernels.cu
View File

@ -110,7 +110,7 @@ half *cuda_make_f16_from_f32_array(float *src, size_t n)
void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
{
fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1);
//fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1);
if(l.binary){
binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu);
swap_binary(&l);
@ -123,106 +123,52 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
swap_binary(&l);
binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu);
state.input = l.binary_input_gpu;
//cudaDeviceSynchronize();
if (l.align_bit_weights_gpu && !state.train)
{
cudaError_t status;
//status = cudaMemcpy(l.align_bit_weights, l.align_bit_weights_gpu, l.align_bit_weights_size, cudaMemcpyDeviceToHost);
//check_error(status);
//float *input = (float *)calloc(l.c*l.h*l.w*l.batch, sizeof(float));
//float *workspace = (float *)calloc(l.bit_align*l.size*l.size*l.c, sizeof(float));
//float *output = (float *)calloc(l.batch*l.out_c*l.out_h*l.out_w, sizeof(float));
//status = cudaMemcpy(input, state.input, l.c*l.h*l.w*l.batch*sizeof(float), cudaMemcpyDeviceToHost);
//check_error(status);
cudaError_t status = cudaSuccess;
int m = l.n;
int k = l.size*l.size*l.c;
int n = l.out_w*l.out_h;
float * a = l.weights_gpu;
//float * b = state.workspace;
//float *b = workspace;
//float * c = l.output_gpu;
//float *c = output;
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 * n;
size_t t_bit_input_size = t_intput_size / 8;// +1;
//char *t_bit_input = (char *)calloc(t_bit_input_size, sizeof(char));
//int src_size = k * l.bit_align;
//im2col_cpu_custom_bin(input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align);
//float *align_workspace = NULL;
//int align_workspace_size = l.bit_align * k; // aligned: n*k
//status = cudaMalloc((void **)&align_workspace, align_workspace_size*sizeof(float));
//check_error(status);
int i = 0;
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);
float_to_bit_gpu(l.align_workspace_gpu, (unsigned char *)state.workspace, l.align_workspace_size);
if(1)
{
fill_int8_gpu((unsigned char *)l.align_workspace_gpu, 0, t_bit_input_size);
transpose_bin_gpu((unsigned char *)state.workspace, (unsigned char *)l.align_workspace_gpu, k, n, l.bit_align, new_ldb, 8);
//cudaDeviceSynchronize();
//int size_transposed_array = l.bit_align * new_ldb;
//status = cudaMemcpy(t_bit_input, l.align_workspace_gpu, t_bit_input_size, cudaMemcpyDeviceToHost);
//check_error(status);
}
/*
if (0) {
status = cudaMemcpy(b, state.workspace, l.align_workspace_size / 8, cudaMemcpyDeviceToHost);
check_error(status);
//float *im2 = (float *)calloc(l.align_workspace_size, sizeof(float));
//status = cudaMemcpy(im2, l.align_workspace_gpu, l.align_workspace_size * sizeof(float), cudaMemcpyDeviceToHost);
//check_error(status);
//float_to_bit(im2, (unsigned char *)b, l.align_workspace_size);
memset(t_bit_input, 0, t_bit_input_size);
// b - [bit_align, k] - [l.bit_align, l.size*l.size*l.c] = src_size
// t_input - [bit_align, k] - [n', k]
// t_bit_input - [new_ldb, n] - [k', n]
transpose_bin((char *)b, t_bit_input, k, n, l.bit_align, new_ldb, 8);
}
*/
//status = cudaMemcpy(l.align_bit_weights, l.align_bit_weights_gpu, new_ldb * m / 8, cudaMemcpyDeviceToHost);
//check_error(status);
//status = cudaMemcpy(l.mean_arr, l.mean_arr_gpu, l.n * sizeof(float), cudaMemcpyDeviceToHost);
//check_error(status);
// 5x times faster than gemm()-float32
//gemm_nn_custom_bin_mean_transposed(m, n, k, 1, (unsigned char *)l.align_bit_weights, new_ldb, (unsigned char *)t_bit_input, new_ldb, c, n, l.mean_arr);
//status = cudaMemcpy(l.output_gpu, output, l.batch*l.out_c*l.out_h*l.out_w * sizeof(float), cudaMemcpyHostToDevice);
//check_error(status);
gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, 1,
(unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.align_workspace_gpu, new_ldb, l.output_gpu, n, l.mean_arr_gpu);
//cudaDeviceSynchronize();
//free(t_bit_input);
//free(input);
//free(workspace);
//free(output);
//cudaFree(align_workspace);
// should be optimized
float_to_bit_gpu(l.align_workspace_gpu, (unsigned char *)state.workspace, l.align_workspace_size);
//cudaDeviceSynchronize();
//im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace, l.bit_align);
transpose_bin_gpu((unsigned char *)state.workspace, (unsigned char *)l.transposed_align_workspace_gpu, k, n, l.bit_align, new_ldb, 8);
//cudaDeviceSynchronize();
// should be optimized
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);
//cudaDeviceSynchronize();
//check_error(status);
add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h);
activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
if (l.binary || l.xnor) swap_binary(&l);
//cudaDeviceSynchronize();
return;
}
}
fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1);
#ifdef CUDNN
float one = 1; // alpha[0], beta[0] is float for HALF and FLOAT
float alpha = 1, beta = 0;

2
src/convolutional_layer.c
View File

@ -629,6 +629,7 @@ void binary_align_weights(convolutional_layer *l)
cudaError_t status;
l->align_workspace_size = l->bit_align * l->size * l->size * l->c;
status = cudaMalloc((void **)&l->align_workspace_gpu, l->align_workspace_size * sizeof(float));
status = cudaMalloc((void **)&l->transposed_align_workspace_gpu, l->align_workspace_size * sizeof(float));
check_error(status);
//l->align_bit_weights_gpu = cuda_make_array(l->align_bit_weights, l->align_bit_weights_size * sizeof(char)/sizeof(float));
@ -638,6 +639,7 @@ void binary_align_weights(convolutional_layer *l)
check_error(status);
l->mean_arr_gpu = cuda_make_array(l->mean_arr, l->n);
cudaDeviceSynchronize();
#endif // GPU
free(align_weights);

2
src/im2col.h
View File

@ -22,7 +22,7 @@ void transpose_bin_gpu(unsigned char *A, unsigned char *B, const int n, const in
void fill_int8_gpu(unsigned char *src, unsigned char val, size_t size);
void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, float ALPHA_UNUSED,
void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K,
unsigned char *A, int lda,
unsigned char *B, int ldb,
float *C, int ldc, float *mean_arr);

186
src/im2col_kernels.cu
View File

@ -59,8 +59,9 @@ void im2col_ongpu(float *im,
stride, height_col,
width_col, data_col);
}
// --------------------------------
#define WARP_SIZE 32
__global__ void im2col_align_gpu_kernel(const int n, const float* data_im,
const int height, const int width, const int ksize,
@ -81,6 +82,7 @@ __global__ void im2col_align_gpu_kernel(const int n, const float* data_im,
float* data_col_ptr = data_col;
//data_col_ptr += (channel_out * height_col + h_out) * width_col + w_out;
data_col_ptr += channel_out * bit_align + h_out * width_col + w_out;
float* data_col_ptr_32 = data_col + (channel_out * bit_align + h_out * width_col + w_out)/32;
const float* data_im_ptr = data_im;
data_im_ptr += (channel_in * height + h_in) * width + w_in;
for (int i = 0; i < ksize; ++i) {
@ -91,6 +93,10 @@ __global__ void im2col_align_gpu_kernel(const int n, const float* data_im,
*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?
data_im_ptr[i * width + j] : 0;
//float src_val = (h >= 0 && w >= 0 && h < height && w < width) ? data_im_ptr[i * width + j] : 0;
//unsigned int bit_mask = __ballot_sync(0xffffffff, src_val > 0);
//if (threadIdx.x % WARP_SIZE == 0) *((unsigned int*)data_col_ptr_32) = bit_mask;
//data_col_ptr_32 += bit_align / 32;
//data_col_ptr += height_col * width_col;
data_col_ptr += bit_align;
@ -113,26 +119,21 @@ void im2col_align_ongpu(float *im,
stride, height_col,
width_col, data_col, bit_align);
}
// --------------------------------
#define WARP_SIZE 32
__global__ void float_to_bit_gpu_kernel(float *src, unsigned char *dst, size_t size)
{
//size_t dst_size = size / 8 + 1;
//memset(dst, 0, dst_size);
//uint32_t bit_mask = __ballot_sync(FULL_MASK, src[i] > 0);
const int size_aligned = size + (WARP_SIZE - size % WARP_SIZE);
//const int size_aligned = size + (WARP_SIZE - size % WARP_SIZE);
int index = blockIdx.x*blockDim.x + threadIdx.x;
float src_val;
for (; index < size_aligned; index += blockDim.x*gridDim.x)
//for (; index < size_aligned; index += blockDim.x*gridDim.x)
{
if(index < size) src_val = src[index];
else src_val = 0;
src_val = src[index];
//if(index < size) src_val = src[index];
//else src_val = 0;
unsigned int bit_mask = __ballot_sync(0xffffffff, src_val > 0);
if (threadIdx.x % WARP_SIZE == 0) ((unsigned int*)dst)[index / 32] = bit_mask;
}
@ -144,10 +145,15 @@ void float_to_bit_gpu(float *src, unsigned char *dst, size_t size)
const int num_blocks = size / BLOCK + 1;
float_to_bit_gpu_kernel<<<num_blocks, BLOCK, 0, get_cuda_stream()>>>(src, dst, size);
}
// --------------------------------
__device__ __host__ static inline void remove_bit(unsigned char *const dst, size_t index) {
size_t dst_i = index / 8;
int dst_shift = index % 8;
dst[dst_i] &= ~(1 << dst_shift);
}
__device__ __host__ static inline void set_bit(unsigned char *const dst, size_t index) {
size_t dst_i = index / 8;
int dst_shift = index % 8;
@ -177,8 +183,6 @@ __device__ __host__ unsigned char reverse_byte_2(unsigned char a)
return ((a * 0x0802LU & 0x22110LU) | (a * 0x8020LU & 0x88440LU)) * 0x10101LU >> 16;
}
__device__ __host__ void transpose8rS32_reversed_diagonale(unsigned char* A, int m, int n, unsigned char* B)
{
unsigned x, y, t;
@ -223,6 +227,7 @@ __global__ void transpose_bin_gpu_kernel(unsigned char *A, unsigned char *B, con
else if (j < m) {
for (; j < m; ++j) {
if (get_bit(A, i*lda + j)) set_bit(B, j*ldb + i);
else remove_bit(B, j*ldb + i);
}
}
}
@ -237,8 +242,6 @@ void transpose_bin_gpu(unsigned char *A, unsigned char *B, const int n, const in
const int num_blocks = size / BLOCK + 1;
transpose_bin_gpu_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(A, B, n, m, lda, ldb, block_size);
}
// --------------------------------
@ -251,16 +254,17 @@ void fill_int8_gpu(unsigned char *src, unsigned char val, size_t size) {
const int num_blocks = size / BLOCK + 1;
fill_int8_gpu_kernel<<<num_blocks, BLOCK, 0, get_cuda_stream()>>>(src, val, size);
}
// --------------------------------
typedef unsigned long long int uint64_t;
typedef unsigned char uint8_t;
__device__ __host__ static inline uint64_t xnor_int64(uint64_t a, uint64_t b) {
return ~(a^b);
}
__global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int K, float ALPHA_UNUSED,
/*
__global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int K,
unsigned char *A, int lda,
unsigned char *B, int ldb,
float *C, int ldc, float *mean_arr)
@ -289,11 +293,75 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int
uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8));
uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64);
//#ifdef WIN32
// int tmp_count = __popcnt64(c_bit64);
//#else
// int tmp_count = __builtin_popcountll(c_bit64);
//#endif
int tmp_count = __popcll(c_bit64);
if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits
count += tmp_count;
//binary_int64_printf(c_bit64);
//printf(", count = %d \n\n", tmp_count);
}
C[i*ldc + j] = (2 * count - K) * mean_val;
}
}
}
}
*/
/*
// B (input) in the shared_memory
__global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int K,
unsigned char *A, int lda,
unsigned char *B, int ldb,
float *C, int ldc, float *mean_arr)
{
int index = blockIdx.x*blockDim.x + threadIdx.x;
__shared__ uint64_t B_s[4096]; // 32 KB // [ldb x N`]
int start_j = blockIdx.x*blockDim.x / M;
int end_j = (blockIdx.x*blockDim.x + blockDim.x) / M + 1;
size_t shared_size = ldb * (end_j - start_j);
int j_cur = index / M;
int local_j = j_cur - start_j;
for (int k = threadIdx.x * 64; k < shared_size; k += blockDim.x * 64) {
int x = start_j*ldb + k;
if (x < (N*ldb)) *((uint64_t *)(B_s + k / 8)) = *((uint64_t *)(B + x / 8));
}
//if (j_cur < N && (index % M == 0 || threadIdx.x == 0)) {
// for (int k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216]
// *((uint64_t *)(B_s + (local_j*ldb + k) / 8)) = *((uint64_t *)(B + (j_cur*ldb + k) / 8)); // input
//}
//}
__syncthreads();
//if (index == 0)
{
int i, j, k, h;
//#pragma omp parallel for
//for (i = 0; i < M; ++i)
i = index % M;
//if(i < M)
{ // l.n - filters [16 - 55 - 1024]
float mean_val = mean_arr[i];
//for (j = 0; j < N; ++j)
j = index / M;
if (j < N)
{ // out_h*out_w - one channel output size [169 - 173056]
int count = 0;
for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216]
uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); // weights
//uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8));
uint64_t b_bit64 = *((uint64_t *)(B_s + (local_j*ldb + k) / 8)); // input
uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64);
int tmp_count = __popcll(c_bit64);
@ -308,9 +376,70 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int
}
}
}
*/
// A (weights) in the shared_memory
__global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int K,
unsigned char *A, int lda,
unsigned char *B, int ldb,
float *C, int ldc, float *mean_arr)
{
int index = blockIdx.x*blockDim.x + threadIdx.x;
void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, float ALPHA_UNUSED,
__shared__ uint64_t A_s[6144]; // 48 KB // [lda x M`]
//__shared__ uint8_t A_s[6144*8]; // 48 KB // [lda x M`]
int start_i = blockIdx.x*blockDim.x / N;
int end_i = (blockIdx.x*blockDim.x + blockDim.x) / N + 1;
size_t shared_size = lda * (end_i - start_i);
int i_cur = index / N;
int local_i = i_cur - start_i;
for (int k = threadIdx.x * 64; k < shared_size; k += blockDim.x * 64) {
int x = start_i*lda + k;
if (x < (M*lda)) *((uint64_t *)(A_s + k / 8)) = *((uint64_t *)(A + x / 8));
}
//if (i_cur < M && (index % N == 0 || threadIdx.x == 0)) {
//for (int k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216]
//*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8)); // weights
// }
//}
__syncthreads();
int i, j, k, h;
j = index % N;
{ // out_h*out_w - one channel output size [169 - 173056]
i = index / N;
if (i < M) // l.n - filters [16 - 55 - 1024]
{
float mean_val = mean_arr[i];
int count = 0;
for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216]
//uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); // weights
uint64_t a_bit64 = *((uint64_t *)(A_s + (local_i*lda + k) / 8)); // weights
uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); // input
uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64);
int tmp_count = __popcll(c_bit64);
if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits
count += tmp_count;
}
C[i*ldc + j] = (2 * count - K) * mean_val;
}
}
}
#include <cstdio>
void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K,
unsigned char *A, int lda,
unsigned char *B, int ldb,
float *C, int ldc, float *mean_arr)
@ -318,8 +447,15 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, float ALPHA_UNU
size_t size = M*N;
const int num_blocks = size / BLOCK + 1;
/*
printf("\n gemm_bin size = %d, num_blocks = %d, M*K = %d KB, N*K = %d KB \n (w) M*K/num_blocks = %d KB, (i) N*K/num_blocks = %d KB \n",
size, num_blocks, M*K / 1024, N*K / 1024, M*lda / num_blocks / 1024, N*ldb / num_blocks / 1024);
printf(" M / 512 = %d, N / 512 = %d, M*lda / 512 = %d, N*ldb / 512 = %d \n", M / 512, N / 512, M*lda/512, N*ldb/512);
*/
//printf(" shared_memory: (w) lda*BLOCK/N = %d, (i) ldb*BLOCK/M = %d, \t lda = %d \n\n", lda*BLOCK / N, ldb*BLOCK / M, lda);
gemm_nn_custom_bin_mean_transposed_gpu_kernel<<<num_blocks, BLOCK, 0, get_cuda_stream() >>>(
M, N, K, ALPHA_UNUSED,
M, N, K,
A, lda,
B, ldb,
C, ldc,

1
src/layer.h
View File

@ -182,6 +182,7 @@ struct layer{
char *align_bit_weights_gpu;
float *mean_arr_gpu;
float *align_workspace_gpu;
float *transposed_align_workspace_gpu;
int align_workspace_size;
char *align_bit_weights;

1
src/network_kernels.cu
View File

@ -58,6 +58,7 @@ void forward_network_gpu(network net, network_state state)
if(net.wait_stream)
cudaStreamSynchronize(get_cuda_stream());
state.input = l.output_gpu;
//cudaDeviceSynchronize();
/*
cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs);
if (l.out_w >= 0 && l.out_h >= 1 && l.c >= 3) {