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Look at wmma::bmma_sync(), bmmaBitOpXOR, bmmaAccumulateOpPOPC

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pull/2347/head
AlexeyAB 6 years ago
parent b47db904ee
commit 2d3220cef5
2 changed files with 285 additions and 148 deletions
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  1. 10
      src/convolutional_layer.c
  2. 409
      src/im2col_kernels.cu

10
src/convolutional_layer.c
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@ -719,6 +719,16 @@ void binary_align_weights(convolutional_layer *l)
float_to_bit(align_weights, l->align_bit_weights, align_weights_size);
/*
if ((l->n % 8) == 0 && ((l->out_w*l->out_h) % 8) == 0 && l->c >= 64 && l->n == 128) {
int M = l->n;
int N = l->out_w*l->out_h;
//printf("\n M = %d, N = %d, M %% 8 = %d, N %% 8 = %d - weights \n", M, N, M % 8, N % 8);
for (i = 0; i < align_weights_size / 8; ++i) l->align_bit_weights[i] = ~(l->align_bit_weights[i]);
}
*/
get_mean_array(l->binary_weights, m*k, l->n, l->mean_arr);
//get_mean_array(l->binary_weights, m*new_lda, l->n, l->mean_arr);
}

409
src/im2col_kernels.cu
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@ -1192,6 +1192,38 @@ __device__ __host__ static inline ulonglong4 xnor_int256(ulonglong4 a, ulonglong
return res;
}
//-------
__device__ __host__ static inline uint8_t xor_bit1(uint8_t a, uint8_t b) {
return (a^b) & 0b1;
}
__device__ __host__ static inline uint32_t xor_int32(uint32_t a, uint32_t b) {
return (a^b);
}
__device__ __host__ static inline uint64_t xor_int64(uint64_t a, uint64_t b) {
return (a^b);
}
__device__ __host__ static inline uint4 xor_int128(uint4 a, uint4 b) {
uint4 res;
res.w = (a.w^b.w);
res.x = (a.x^b.x);
res.y = (a.y^b.y);
res.z = (a.z^b.z);
return res;
}
__device__ __host__ static inline ulonglong4 xor_int256(ulonglong4 a, ulonglong4 b) {
ulonglong4 res;
res.w = (a.w^b.w);
res.x = (a.x^b.x);
res.y = (a.y^b.y);
res.z = (a.z^b.z);
return res;
}
__device__ static inline int popcnt_256(ulonglong4 a) {
return __popcll(a.w) + __popcll(a.x) + __popcll(a.y) + __popcll(a.z);
@ -1398,6 +1430,222 @@ int warpAllReduceSum(int val) {
return val;
}
// Tensor Cores binary (CC >= 7.3 && CUDA >= 10.0) - __CUDA_SUBBYTE_IMMA__
#if CUDART_VERSION >= 10000
#include <mma.h>
using namespace nvcuda;
#endif
// Coalescing
// A (weights) in the shared_memory - GOOD
__global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel(int M, int N, int K,
unsigned char *A, int lda,
unsigned char *B, int ldb,
float *C, int ldc, float *mean_arr, float *bias_arr)
{
// total 57%
int index = blockIdx.x*blockDim.x + threadIdx.x;
__shared__ uint8_t A_s[6144 * 8 / 4];
//__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;
// ~10%
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));
}
__syncthreads();
int i, j, k, h;
// 47% = 29 + 10 + 8
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]
{
int count = 0;
k = 0;
if (i < M)
{
float mean_val = mean_arr[i];
float bias_val = bias_arr[i];
for (; k < K; k += 128) { // l.size*l.size*l.c - one filter size [27 - 144 - 9216]
//uint4 a_bit128 = *((uint4 *)(A + (i*lda + k) / 8)); // weights
uint4 a_bit128 = *((uint4 *)(A_s + (local_i*lda + k) / 8)); // weights
uint4 b_bit128 = *((uint4 *)(B + (j*ldb + k) / 8)); // input
uint4 c_bit128 = xor_int128(a_bit128, b_bit128);
count += __popc(c_bit128.w) + __popc(c_bit128.x) +
__popc(c_bit128.y) + __popc(c_bit128.z);
}
const int bit_step = 128;// 256;
int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step));
count = count - f1; // remove extra bits (from empty space for align only)
C[i*ldc + j] = (2 * count - K) *mean_val + bias_val;
}
}
}
}
#if CUDART_VERSION >= 10000
// Coalescing
// A (weights) in the shared_memory - GOOD
__global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel_old(int M, int N, int K,
unsigned char *A, int lda,
unsigned char *B, int ldb,
float *C, int ldc, float *mean_arr, float *bias_arr)
{
// total 57%
int index = blockIdx.x*blockDim.x + threadIdx.x;
__shared__ int C_s[8*8 * 32]; // BIN GEMM WMMA
const int lane_id = threadIdx.x % 32;
const int warp_id = threadIdx.x / 32;
const int global_warp_id = index / 32;
int i, j, k, h;
// 47% = 29 + 10 + 8
j = global_warp_id % (N / 8);
j = j * 8;
{ // out_h*out_w - one channel output size [169 - 173056]
i = global_warp_id / (N / 8);
i = i * 8;
if (i == 0 && j == 0 && lane_id == 0) {
// printf(" i = %d, j = %d, global_warp_id = %d, index = %d \n ", i, j, global_warp_id, index);
}
//if (i < M) // l.n - filters [16 - 55 - 1024]
{
int count = 0;
k = 0;
if (i < M)
{
// Tensor Cores binary (CC >= 7.3 && CUDA >= 10.0) - __CUDA_SUBBYTE_IMMA__
//#if __CUDA_ARCH__ >= 730 && CUDART_VERSION >= 10000
#define WMMA_M 8
#define WMMA_N 8
#define WMMA_K 128
#define WMMA_K32 (WMMA_K/32)
wmma::fragment<wmma::matrix_a, WMMA_M, WMMA_N, WMMA_K, wmma::experimental::precision::b1, wmma::row_major> a_frag;
wmma::fragment<wmma::matrix_b, WMMA_M, WMMA_N, WMMA_K, wmma::experimental::precision::b1, wmma::col_major> b_frag;
wmma::fragment<wmma::accumulator, WMMA_M, WMMA_N, WMMA_K, int> c_frag;
wmma::fill_fragment(c_frag, 0); // !!!! XOR isn't XNOR !!!!!!!!!!
// lda, ldb - are in bits, should be divided by /8 or /32
// 8 x 8 x 4 (uint32_t, 4 * 32 = 128 bit)
for (; k < K; k += 128)
{ // l.size*l.size*l.c - one filter size [27 - 144 - 9216]
int64_t A_cur_index = (i*lda + k) / 8;
//int64_t A_cur_index = (local_i*lda + k) / 8;
int64_t B_cur_index = (j*ldb + k) / 8;
wmma::load_matrix_sync(a_frag, (uint32_t *)(A + A_cur_index), lda); // lda = M
wmma::load_matrix_sync(b_frag, (uint32_t *)(B + B_cur_index), ldb); // ldb = K
/*
if (i == 0 && j == 0) {
printf(" %d - %u, ", lane_id, a_frag.x[0]);
}
if (i == 0 && j == 0 && lane_id == 1) {
printf("\n\n now raw mem \n");
for (int i_d = 0; i_d < WMMA_M; ++i_d) { //8
for (int k_d = 0; k_d < WMMA_K; k_d += 32) { //4
uint32_t a_bit32 = *((uint32_t *)(A + ((i + i_d)*lda + (k + k_d)) / 8)); // weights
//uint32_t a_bit32 = *((uint32_t *)(A + A_cur_index + i_d*lda/8 + k_d/ 8)); // weights
printf(" %d - %u, ", i_d*WMMA_K32 + k_d/32, a_bit32);
}
printf("\n");
}
printf("\n\n");
}
*/
wmma::bmma_sync(c_frag, a_frag, b_frag, c_frag);
// C[i*ldc + j]
wmma::store_matrix_sync(&C_s[warp_id*WMMA_M*WMMA_N], c_frag, WMMA_N, wmma::mem_row_major);
}
/*
for (; k < K; k += 128) { // l.size*l.size*l.c - one filter size [27 - 144 - 9216]
uint4 a_bit128 = *((uint4 *)(A + (i*lda + k) / 8)); // weights
//uint4 a_bit128 = *((uint4 *)(A_s + (local_i*lda + k) / 8)); // weights
uint4 b_bit128 = *((uint4 *)(B + (j*ldb + k) / 8)); // input
uint4 c_bit128 = xnor_int128(a_bit128, b_bit128);
count += __popc(c_bit128.w) + __popc(c_bit128.x) +
__popc(c_bit128.y) + __popc(c_bit128.z);
}
*/
//#endif
#pragma UNROLL
for (int i_d = 0; i_d < WMMA_M; ++i_d) {
for (int j_d = 0; j_d < WMMA_N; ++j_d)
{
int count = C_s[warp_id*WMMA_M*WMMA_N + i_d*WMMA_N + j_d];
if (i == 0 && j == 0 && lane_id == 0) {
//printf(" %d -", count);
}
const int bit_step = 128;
int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step));
count = count - f1; // remove extra bits (from empty space for align only)
count = (2 * count - K);
if (i == 0 && j == 0 && lane_id == 0) {
//printf(" %d,", count);
}
float mean_val = mean_arr[i + i_d];
float bias_val = bias_arr[i + i_d];
C[(i + i_d)*ldc + (j + j_d)] = count *mean_val + bias_val;
//C[(i + i_d)*ldc + (j + j_d)] = (2 * count - K) *mean_val + bias_val;
}
if (i == 0 && j == 0 && lane_id == 0) {
//printf(" i = %d, j = %d, i_d = %d \n ", i, j, i_d);
}
}
}
}
}
}
#endif // CUDART_VERSION >= 10000
// Coalescing
// A (weights) in the shared_memory - GOOD
@ -1434,6 +1682,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int
i = index / N;
//if (i < M) // l.n - filters [16 - 55 - 1024]
{
int bit_step = 256;
int count = 0;
k = 0;
@ -1447,7 +1696,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int
int64_t B_cur_index = (j*ldb + k) / 8;
if (i >= M) A_cur_index = 0;
#pragma unroll
#pragma unroll
for (int t = 0; t < WARP_SIZE; ++t) {
const int lane_id = threadIdx.x % WARP_SIZE;
@ -1469,6 +1718,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int
}
#endif
//#ifdef NOT_USED
// 32 thread X 64 bit = 2048 bit // 29%
for (; k < (K - 2048); k += 2048) { // l.size*l.size*l.c - one filter size [27 - 9216]
@ -1640,7 +1890,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel_leaky(int M, int N
}
#endif
//#ifdef NOT_USED
//#ifdef NOT_USED
// 32 thread X 64 bit = 2048 bit // 29%
for (; k < (K - 2048); k += 2048) { // l.size*l.size*l.c - one filter size [27 - 9216]
uint64_t c_bit64;
@ -1669,7 +1919,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel_leaky(int M, int N
}
}
}
//#endif
//#endif
//#ifdef NOT_USED
// 32 thread X 32 bit = 1024 bit // 10%
@ -1742,144 +1992,6 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel_leaky(int M, int N
}
}
/*
// Coalescing
// B (input) in the shared_memory - GOOD
__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, float *bias_arr)
{
int index = blockIdx.x*blockDim.x + threadIdx.x;
__shared__ uint8_t B_s[4096*8]; // 32 KB // [ldb x N`] // max = 262 144 bits
//__shared__ uint64_t B_s[4096]; // 32 KB // [ldb x N`] // max = 262 144 bits
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 * 256; k < shared_size; k += blockDim.x * 256) {
int x = start_j*ldb + k;
if (x < (N*ldb)) *((ulonglong4 *)(B_s + k / 8)) = *((ulonglong4 *)(B + x / 8));
}
__syncthreads();
int i, j, k;
i = index % M; // l.n - filters [16 - 55 - 1024]
{
j = index / M; // out_h*out_w - one channel output size [169 - 173056]
if (j < N)
{
int count = 0;
k = 0;
//#ifdef NOT_USED
// 32 thread X 64 bit = 2048 bit
for (; k < (K - 2048); k += 2048) { // l.size*l.size*l.c - one filter size [27 - 9216]
uint64_t c_bit64;
int64_t A_cur_index = (i*lda + k) / 8;
//int64_t B_cur_index = (j*ldb + k) / 8;
int64_t B_cur_index = (local_j*ldb + k) / 8;
if (i >= M) A_cur_index = 0;
#pragma unroll
for (int t = 0; t < WARP_SIZE; ++t) {
const int lane_id = threadIdx.x % WARP_SIZE;
const int64_t A_i = __shfl(A_cur_index, t) + 8 * lane_id;
const int64_t B_i = __shfl(B_cur_index, t) + 8 * lane_id;
{
uint64_t a_bit64 = *((uint64_t *)(A + A_i)); // weights
//uint64_t b_bit64 = *((uint64_t *)(B + B_i)); // input
uint64_t b_bit64 = *((uint64_t *)(B_s + B_i)); // input
c_bit64 = xnor_int64(a_bit64, b_bit64);
int tmp_count = __popcll(c_bit64);
int sum_count = warpAllReduceSum(tmp_count);
if (lane_id == t) count += sum_count;
}
}
}
//#endif
//#ifdef NOT_USED
// 32 thread X 32 bit = 1024 bit
for (; k < (K - 1024); k += 1024) { // l.size*l.size*l.c - one filter size [27 - 9216]
int64_t A_cur_index = (i*lda + k) / 8;
//int64_t B_cur_index = (j*ldb + k) / 8;
int64_t B_cur_index = (local_j*ldb + k) / 8;
if (i >= M) A_cur_index = 0;
#pragma unroll
for (int t = 0; t < WARP_SIZE; ++t) {
const int lane_id = threadIdx.x % WARP_SIZE;
const int64_t A_i = __shfl(A_cur_index, t) + 4 * lane_id;
const int64_t B_i = __shfl(B_cur_index, t) + 4 * lane_id;
{
uint32_t a_bit32 = *((uint32_t *)(A + A_i)); // weights
//uint32_t b_bit32 = *((uint32_t *)(B + B_i)); // input
uint32_t b_bit32 = *((uint32_t *)(B_s + B_i)); // input
uint32_t c_bit32 = xnor_int32(a_bit32, b_bit32);
int tmp_count = __popc(c_bit32);
int sum_count = warpAllReduceSum(tmp_count);
if (lane_id == t) count += sum_count;
}
}
}
//#endif
if (i < M)
{
float mean_val = mean_arr[i];
float bias_val = bias_arr[i];
//#ifdef NOT_USED
for (; k < K; k += 256) { // l.size*l.size*l.c - one filter size [27 - 144 - 9216]
ulonglong4 a_bit256 = *((ulonglong4 *)(A + (i*lda + k) / 8)); // weights
//ulonglong4 b_bit256 = *((ulonglong4 *)(B + (j*ldb + k) / 8)); // input
ulonglong4 b_bit256 = *((ulonglong4 *)(B_s + (local_j*ldb + k) / 8)); // input
ulonglong4 c_bit256 = xnor_int256(a_bit256, b_bit256);
count += __popcll(c_bit256.w) + __popcll(c_bit256.x) +
__popcll(c_bit256.y) + __popcll(c_bit256.z);
}
//#endif
#ifdef NOT_USED
for (; 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)); // input
uint64_t b_bit64 = *((uint64_t *)(B_s + (local_j*ldb + k) / 8)); // input
uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64);
count += __popcll(c_bit64);
}
#endif
const int bit_step = 256;
int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step));
count = count - f1; // remove extra bits (from empty space for align only)
C[i*ldc + j] = (2 * count - K) * mean_val + bias_val;
}
}
}
}
*/
// further optimization - use WMMA GEMM for using Tensor Cores
// https://github.com/NVIDIA-developer-blog/code-samples/blob/master/posts/tensor-cores/simpleTensorCoreGEMM.cu
// https://github.com/NVIDIA/cuda-samples/blob/master/Samples/cudaTensorCoreGemm/cudaTensorCoreGemm.cu
@ -1904,6 +2016,8 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K,
size_t size = M*N;
const int num_blocks = get_number_of_blocks(size, BLOCK);
//printf("\n M = %d, N = %d, M %% 8 = %d, N %% 8 = %d \n", M, N, M % 8, N % 8);
/*
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);
@ -1920,6 +2034,18 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K,
mean_arr, bias);
}
else {
/*
if (M % 8 == 0 && N % 8 == 0 && M == 128) {
//printf(" lda = %d, ldb = %d, ldc = %d, lda/32 = %d, ldb/32 = %d, ldc/32 = %d \n", lda, ldb, ldc, lda / 32, ldb / 32, ldc / 32);
gemm_nn_custom_bin_mean_transposed_tensor_kernel_old << <num_blocks, BLOCK, 0, get_cuda_stream() >> > (
M, N, K,
A, lda,
B, ldb,
C, ldc,
mean_arr, bias);
}
else*/
{
gemm_nn_custom_bin_mean_transposed_gpu_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> > (
M, N, K,
A, lda,
@ -1927,6 +2053,7 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K,
C, ldc,
mean_arr, bias);
}
}
}
// --------------------------------

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