| #include <cuda_runtime.h> |
| #include <mma.h> |
|
|
| #include <math.h> |
| #include <stdint.h> |
| #include <stdio.h> |
| #include <stdlib.h> |
|
|
| #if defined(PYC_ADA_TENSOR_CORE_USE_BF16) && PYC_ADA_TENSOR_CORE_USE_BF16 |
| #include <cuda_bf16.h> |
| typedef __nv_bfloat16 pyc_tc_scalar_t; |
| #define PYC_TC_LANE_NAME "bf16" |
| static __host__ __device__ inline pyc_tc_scalar_t pyc_tc_make_scalar(float value) { |
| return __float2bfloat16(value); |
| } |
| static __host__ __device__ inline float pyc_tc_scalar_to_float(pyc_tc_scalar_t value) { |
| return __bfloat162float(value); |
| } |
| #else |
| #include <cuda_fp16.h> |
| typedef half pyc_tc_scalar_t; |
| #define PYC_TC_LANE_NAME "fp16" |
| static __host__ __device__ inline pyc_tc_scalar_t pyc_tc_make_scalar(float value) { |
| return __float2half(value); |
| } |
| static __host__ __device__ inline float pyc_tc_scalar_to_float(pyc_tc_scalar_t value) { |
| return __half2float(value); |
| } |
| #endif |
|
|
| namespace wmma = nvcuda::wmma; |
|
|
| #define PYC_TC_CTA_M 32 |
| #define PYC_TC_CTA_N 64 |
| #define PYC_TC_CTA_K 16 |
| #define PYC_TC_WARPS_PER_BLOCK 8 |
| #define PYC_TC_THREADS_PER_BLOCK 256 |
|
|
| typedef struct { |
| int m; |
| int n; |
| int k; |
| int warmup; |
| int iters; |
| } pyc_tc_config; |
|
|
| static int check_cuda(cudaError_t status, const char* what) { |
| if (status != cudaSuccess) { |
| fprintf(stderr, "%s failed: %s\n", what, cudaGetErrorString(status)); |
| return -1; |
| } |
| return 0; |
| } |
|
|
| static int parse_int_arg(const char* text, int* out_value) { |
| char* end = NULL; |
| long parsed; |
| if (!text || !out_value) { |
| return -1; |
| } |
| parsed = strtol(text, &end, 10); |
| if (end == text || *end != '\0' || parsed <= 0 || parsed > INT32_MAX) { |
| return -1; |
| } |
| *out_value = (int)parsed; |
| return 0; |
| } |
|
|
| static void fill_matrix(pyc_tc_scalar_t* data, int rows, int cols, float scale) { |
| int i; |
| for (i = 0; i < rows * cols; ++i) { |
| int pattern = (i * 19 + rows * 11 + cols * 7) % 29; |
| data[i] = pyc_tc_make_scalar(((float)pattern - 14.0f) * scale); |
| } |
| } |
|
|
| static void reference_gemm( |
| const pyc_tc_scalar_t* a, |
| const pyc_tc_scalar_t* b, |
| float* c, |
| int m, |
| int n, |
| int k) { |
| int row; |
| for (row = 0; row < m; ++row) { |
| int col; |
| for (col = 0; col < n; ++col) { |
| float acc = 0.0f; |
| int kk; |
| for (kk = 0; kk < k; ++kk) { |
| acc += pyc_tc_scalar_to_float(a[row * k + kk]) * pyc_tc_scalar_to_float(b[kk * n + col]); |
| } |
| c[row * n + col] = acc; |
| } |
| } |
| } |
|
|
| __launch_bounds__(PYC_TC_THREADS_PER_BLOCK, 2) |
| __global__ void pyc_tc_gemm_kernel( |
| const pyc_tc_scalar_t* __restrict__ a, |
| const pyc_tc_scalar_t* __restrict__ b, |
| float* __restrict__ c, |
| int m, |
| int n, |
| int k) { |
| __shared__ pyc_tc_scalar_t shared_a[PYC_TC_CTA_M][PYC_TC_CTA_K]; |
| __shared__ pyc_tc_scalar_t shared_b[PYC_TC_CTA_N][PYC_TC_CTA_K]; |
|
|
| const int warp_id = threadIdx.x / 32; |
| const int block_row = blockIdx.y * PYC_TC_CTA_M; |
| const int block_col = blockIdx.x * PYC_TC_CTA_N; |
| const int warp_row = (warp_id / 4) * 16; |
| const int warp_col = (warp_id % 4) * 16; |
| const int c_row = block_row + warp_row; |
| const int c_col = block_col + warp_col; |
|
|
| wmma::fragment<wmma::accumulator, 16, 16, 16, float> acc; |
| wmma::fill_fragment(acc, 0.0f); |
|
|
| if (warp_id >= PYC_TC_WARPS_PER_BLOCK) { |
| return; |
| } |
|
|
| for (int kk = 0; kk < k; kk += PYC_TC_CTA_K) { |
| int idx; |
|
|
| for (idx = threadIdx.x; idx < PYC_TC_CTA_M * PYC_TC_CTA_K; idx += blockDim.x) { |
| const int row = idx / PYC_TC_CTA_K; |
| const int col = idx % PYC_TC_CTA_K; |
| const int g_row = block_row + row; |
| const int g_col = kk + col; |
| if (g_row < m && g_col < k) { |
| shared_a[row][col] = a[g_row * k + g_col]; |
| } else { |
| shared_a[row][col] = pyc_tc_make_scalar(0.0f); |
| } |
| } |
|
|
| for (idx = threadIdx.x; idx < PYC_TC_CTA_N * PYC_TC_CTA_K; idx += blockDim.x) { |
| const int row = idx / PYC_TC_CTA_K; |
| const int col = idx % PYC_TC_CTA_K; |
| const int g_row = kk + col; |
| const int g_col = block_col + row; |
| if (g_row < k && g_col < n) { |
| shared_b[row][col] = b[g_row * n + g_col]; |
| } else { |
| shared_b[row][col] = pyc_tc_make_scalar(0.0f); |
| } |
| } |
|
|
| __syncthreads(); |
|
|
| { |
| wmma::fragment<wmma::matrix_a, 16, 16, 16, pyc_tc_scalar_t, wmma::row_major> a_frag; |
| wmma::fragment<wmma::matrix_b, 16, 16, 16, pyc_tc_scalar_t, wmma::col_major> b_frag; |
| wmma::load_matrix_sync(a_frag, &shared_a[warp_row][0], PYC_TC_CTA_K); |
| wmma::load_matrix_sync(b_frag, &shared_b[warp_col][0], PYC_TC_CTA_K); |
| wmma::mma_sync(acc, a_frag, b_frag, acc); |
| } |
|
|
| __syncthreads(); |
| } |
|
|
| if (c_row < m && c_col < n) { |
| wmma::store_matrix_sync(&c[c_row * n + c_col], acc, n, wmma::mem_row_major); |
| } |
| } |
|
|
| static int set_kernel_attributes(void) { |
| cudaError_t status; |
|
|
| status = cudaFuncSetAttribute( |
| pyc_tc_gemm_kernel, |
| cudaFuncAttributePreferredSharedMemoryCarveout, |
| 100); |
| if (status != cudaSuccess && status != cudaErrorNotSupported) { |
| fprintf(stderr, "cudaFuncSetAttribute failed: %s\n", cudaGetErrorString(status)); |
| return -1; |
| } |
|
|
| return 0; |
| } |
|
|
| static int parse_config(int argc, char** argv, pyc_tc_config* cfg) { |
| if (!cfg) { |
| return -1; |
| } |
|
|
| cfg->m = 1024; |
| cfg->n = 1024; |
| cfg->k = 1024; |
| cfg->warmup = 10; |
| cfg->iters = 50; |
|
|
| if (argc > 1 && parse_int_arg(argv[1], &cfg->m) != 0) return -1; |
| if (argc > 2 && parse_int_arg(argv[2], &cfg->n) != 0) return -1; |
| if (argc > 3 && parse_int_arg(argv[3], &cfg->k) != 0) return -1; |
| if (argc > 4 && parse_int_arg(argv[4], &cfg->warmup) != 0) return -1; |
| if (argc > 5 && parse_int_arg(argv[5], &cfg->iters) != 0) return -1; |
|
|
| return 0; |
| } |
|
|
| int main(int argc, char** argv) { |
| pyc_tc_config cfg; |
| cudaDeviceProp props; |
| pyc_tc_scalar_t* host_a = NULL; |
| pyc_tc_scalar_t* host_b = NULL; |
| float* host_c = NULL; |
| float* ref_c = NULL; |
| pyc_tc_scalar_t* dev_a = NULL; |
| pyc_tc_scalar_t* dev_b = NULL; |
| float* dev_c = NULL; |
| cudaEvent_t start = NULL; |
| cudaEvent_t stop = NULL; |
| size_t a_bytes; |
| size_t b_bytes; |
| size_t c_bytes; |
| dim3 block; |
| dim3 grid; |
| float elapsed_ms = 0.0f; |
| double best_ms = 0.0; |
| int iter; |
| double max_abs_diff = 0.0; |
|
|
| if (parse_config(argc, argv, &cfg) != 0) { |
| fprintf(stderr, "usage: %s [m] [n] [k] [warmup] [iters]\n", argv[0]); |
| return 2; |
| } |
|
|
| if ((cfg.m % PYC_TC_CTA_M) != 0 || (cfg.n % PYC_TC_CTA_N) != 0 || (cfg.k % PYC_TC_CTA_K) != 0) { |
| fprintf(stderr, "Tensor Core lane requires %dx%dx%d-aligned shapes\n", PYC_TC_CTA_M, PYC_TC_CTA_N, PYC_TC_CTA_K); |
| return 2; |
| } |
|
|
| if (check_cuda(cudaGetDeviceProperties(&props, 0), "cudaGetDeviceProperties") != 0) { |
| return 1; |
| } |
| if (props.major < 8 || (props.major == 8 && props.minor < 9)) { |
| fprintf(stderr, "Ada Tensor Core prototype requires sm_89-class hardware\n"); |
| return 1; |
| } |
|
|
| a_bytes = (size_t)cfg.m * (size_t)cfg.k * sizeof(pyc_tc_scalar_t); |
| b_bytes = (size_t)cfg.k * (size_t)cfg.n * sizeof(pyc_tc_scalar_t); |
| c_bytes = (size_t)cfg.m * (size_t)cfg.n * sizeof(float); |
|
|
| host_a = (pyc_tc_scalar_t*)malloc(a_bytes); |
| host_b = (pyc_tc_scalar_t*)malloc(b_bytes); |
| host_c = (float*)malloc(c_bytes); |
| ref_c = (float*)malloc(c_bytes); |
| if (!host_a || !host_b || !host_c || !ref_c) { |
| fprintf(stderr, "host allocation failed\n"); |
| return 1; |
| } |
|
|
| fill_matrix(host_a, cfg.m, cfg.k, 0.03125f); |
| fill_matrix(host_b, cfg.k, cfg.n, 0.0625f); |
| reference_gemm(host_a, host_b, ref_c, cfg.m, cfg.n, cfg.k); |
|
|
| if (check_cuda(cudaMalloc((void**)&dev_a, a_bytes), "cudaMalloc(a)") != 0) return 1; |
| if (check_cuda(cudaMalloc((void**)&dev_b, b_bytes), "cudaMalloc(b)") != 0) return 1; |
| if (check_cuda(cudaMalloc((void**)&dev_c, c_bytes), "cudaMalloc(c)") != 0) return 1; |
|
|
| if (check_cuda(cudaMemcpy(dev_a, host_a, a_bytes, cudaMemcpyHostToDevice), "cudaMemcpy(a)") != 0) return 1; |
| if (check_cuda(cudaMemcpy(dev_b, host_b, b_bytes, cudaMemcpyHostToDevice), "cudaMemcpy(b)") != 0) return 1; |
|
|
| if (set_kernel_attributes() != 0) return 1; |
|
|
| block = dim3(PYC_TC_THREADS_PER_BLOCK, 1, 1); |
| grid = dim3( |
| (unsigned int)((cfg.n + PYC_TC_CTA_N - 1) / PYC_TC_CTA_N), |
| (unsigned int)((cfg.m + PYC_TC_CTA_M - 1) / PYC_TC_CTA_M), |
| 1); |
|
|
| if (check_cuda(cudaEventCreate(&start), "cudaEventCreate(start)") != 0) return 1; |
| if (check_cuda(cudaEventCreate(&stop), "cudaEventCreate(stop)") != 0) return 1; |
|
|
| for (iter = 0; iter < cfg.warmup; ++iter) { |
| pyc_tc_gemm_kernel<<<grid, block>>>(dev_a, dev_b, dev_c, cfg.m, cfg.n, cfg.k); |
| } |
| if (check_cuda(cudaGetLastError(), "kernel launch warmup") != 0) return 1; |
| if (check_cuda(cudaDeviceSynchronize(), "cudaDeviceSynchronize warmup") != 0) return 1; |
|
|
| best_ms = 0.0; |
| for (iter = 0; iter < cfg.iters; ++iter) { |
| if (check_cuda(cudaEventRecord(start), "cudaEventRecord(start)") != 0) return 1; |
| pyc_tc_gemm_kernel<<<grid, block>>>(dev_a, dev_b, dev_c, cfg.m, cfg.n, cfg.k); |
| if (check_cuda(cudaEventRecord(stop), "cudaEventRecord(stop)") != 0) return 1; |
| if (check_cuda(cudaEventSynchronize(stop), "cudaEventSynchronize(stop)") != 0) return 1; |
| if (check_cuda(cudaEventElapsedTime(&elapsed_ms, start, stop), "cudaEventElapsedTime") != 0) return 1; |
| if (iter == 0 || elapsed_ms < (float)best_ms) { |
| best_ms = elapsed_ms; |
| } |
| } |
|
|
| if (check_cuda(cudaMemcpy(host_c, dev_c, c_bytes, cudaMemcpyDeviceToHost), "cudaMemcpy(c)") != 0) return 1; |
|
|
| for (iter = 0; iter < cfg.m * cfg.n; ++iter) { |
| double diff = fabs((double)host_c[iter] - (double)ref_c[iter]); |
| if (diff > max_abs_diff) { |
| max_abs_diff = diff; |
| } |
| } |
|
|
| printf("lane=%s\n", PYC_TC_LANE_NAME); |
| printf("shape=%dx%dx%d\n", cfg.m, cfg.n, cfg.k); |
| printf("tile=%dx%dx%d threads=%d\n", PYC_TC_CTA_M, PYC_TC_CTA_N, PYC_TC_CTA_K, PYC_TC_THREADS_PER_BLOCK); |
| printf("best_ms=%.3f\n", best_ms); |
| printf("max_abs_diff=%.6f\n", max_abs_diff); |
| if (best_ms > 0.0) { |
| double flops = 2.0 * (double)cfg.m * (double)cfg.n * (double)cfg.k; |
| double gflops = flops / (best_ms * 1.0e6); |
| printf("gflops=%.3f\n", gflops); |
| } |
|
|
| cudaEventDestroy(start); |
| cudaEventDestroy(stop); |
| cudaFree(dev_a); |
| cudaFree(dev_b); |
| cudaFree(dev_c); |
| free(host_a); |
| free(host_b); |
| free(host_c); |
| free(ref_c); |
| return max_abs_diff <= 0.2 ? 0 : 1; |
| } |
|
|
