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-rw-r--r--cuda-kernels/genericMatrixMultiply.cu288
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diff --git a/cuda-kernels/genericMatrixMultiply.cu b/cuda-kernels/genericMatrixMultiply.cu
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-/* Copyright (c) 1993-2017, NVIDIA CORPORATION. All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- * * Redistributions of source code must retain the above copyright
- * notice, this list of conditions and the following disclaimer.
- * * Redistributions in binary form must reproduce the above copyright
- * notice, this list of conditions and the following disclaimer in the
- * documentation and/or other materials provided with the distribution.
- * * Neither the name of NVIDIA CORPORATION nor the names of its
- * contributors may be used to endorse or promote products derived
- * from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
- * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
- * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
- * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
- * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
- * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
- * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
- * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- */
-
-#include <stdio.h>
-#include <stdlib.h>
-// Define some error checking macros.
-#define cudaErrCheck(stat) { cudaErrCheck_((stat), __FILE__, __LINE__); }
-void cudaErrCheck_(cudaError_t stat, const char *file, int line) {
- if (stat != cudaSuccess) {
- fprintf(stderr, "CUDA Error: %s %s %d\n", cudaGetErrorString(stat), file, line);
- }
-}
-
-
-#include <mma.h>
-using namespace nvcuda;
-
-// Must be multiples of 16 for wmma code to work
-#define MATRIX_M (64)
-#define MATRIX_N (64)
-#define MATRIX_K (64)
-
-
-
-// The only dimensions currently supported by WMMA
-const int WMMA_M = 16;
-const int WMMA_N = 16;
-const int WMMA_K = 16;
-
-__global__ void wmma_example(half *a, half *b, float *c, int M, int N, int K, float alpha, float beta) {
- // Leading dimensions. Packed with no transpositions.
- int lda = M;
- int ldb = K;
- int ldc = M;
-
- // Tile using a 2D grid
- int warpM = (blockIdx.x * blockDim.x + threadIdx.x) / warpSize;
- int warpN = (blockIdx.y * blockDim.y + threadIdx.y);
-
- // Declare the fragments
- wmma::fragment<wmma::matrix_a, WMMA_M, WMMA_N, WMMA_K, half, wmma::col_major> a_frag;
- wmma::fragment<wmma::matrix_b, WMMA_M, WMMA_N, WMMA_K, half, wmma::col_major> b_frag;
- wmma::fragment<wmma::accumulator, WMMA_M, WMMA_N, WMMA_K, float> acc_frag;
- wmma::fragment<wmma::accumulator, WMMA_M, WMMA_N, WMMA_K, float> c_frag;
-
- wmma::fill_fragment(acc_frag, 0.0f);
-
- // Loop over k
- for (int i = 0; i < K; i += WMMA_K) {
- int aRow = warpM * WMMA_M;
- int aCol = i;
-
- int bRow = i;
- int bCol = warpN * WMMA_N;
-
- // Bounds checking
- if (aRow < M && aCol < K && bRow < K && bCol < N) {
- // Load the inputs
- wmma::load_matrix_sync(a_frag, a + aRow + aCol * lda, lda);
- wmma::load_matrix_sync(b_frag, b + bRow + bCol * ldb, ldb);
-
- // Perform the matrix multiplication
- wmma::mma_sync(acc_frag, a_frag, b_frag, acc_frag);
-
- }
- }
-
- // Load in the current value of c, scale it by beta, and add this our result scaled by alpha
- int cRow = warpM * WMMA_M;
- int cCol = warpN * WMMA_N;
-
- if (cRow < M && cCol < N) {
- wmma::load_matrix_sync(c_frag, c + cRow + cCol * ldc, ldc, wmma::mem_col_major);
-
-
- for(int i=0; i < c_frag.num_elements; i++) {
- c_frag.x[i] = alpha * acc_frag.x[i] + beta * c_frag.x[i];
- }
-
- // Store the output
- wmma::store_matrix_sync(c + cRow + cCol * ldc, c_frag, ldc, wmma::mem_col_major);
- }
-}
-
-__global__ void convertFp32ToFp16 (half *out, float *in, int n) {
- int idx = blockDim.x * blockIdx.x + threadIdx.x;
- if (idx < n) {
- out[idx] = in[idx];
- }
-}
-
-int main(int argc, char* argv[]) {
- float *a_fp32;
- float *b_fp32;
- half *a_fp16;
- half *b_fp16;
-
- float *c;
- float *c_wmma;
-
- float *d_host_wmma;
- float *d_cal_host_wmma;
- float *a_host_wmma;
- float *b_host_wmma;
- float *c_host_wmma;
-
-
- cudaEvent_t startWMMA;
- cudaEvent_t stopWMMA;
-
-
- cudaErrCheck(cudaEventCreate(&startWMMA));
- cudaErrCheck(cudaEventCreate(&stopWMMA));
-
- // Use tensor cores
- cudaErrCheck(cudaMalloc((void**)&a_fp32, MATRIX_M * MATRIX_K * sizeof(float)));
- cudaErrCheck(cudaMalloc((void**)&b_fp32, MATRIX_K * MATRIX_N * sizeof(float)));
- cudaErrCheck(cudaMalloc((void**)&a_fp16, MATRIX_M * MATRIX_K * sizeof(half)));
- cudaErrCheck(cudaMalloc((void**)&b_fp16, MATRIX_K * MATRIX_N * sizeof(half)));
-
- cudaErrCheck(cudaMalloc((void**)&c, MATRIX_M * MATRIX_N * sizeof(float)));
- cudaErrCheck(cudaMalloc((void**)&c_wmma, MATRIX_M * MATRIX_N * sizeof(float)));
-
- d_host_wmma = (float*)malloc(MATRIX_M * MATRIX_N * sizeof(float));
- d_cal_host_wmma = (float*)malloc(MATRIX_M * MATRIX_N * sizeof(float));
- c_host_wmma = (float*)malloc(MATRIX_M * MATRIX_N * sizeof(float));
- a_host_wmma = (float*)malloc(MATRIX_M * MATRIX_K * sizeof(float));
- b_host_wmma = (float*)malloc(MATRIX_K * MATRIX_N * sizeof(float));
-
-
- printf("INITIAL_MATRIX_A\n");
- for(int m=0;m<MATRIX_M;m++){
- for(int n=0;n<MATRIX_K;n++){
- a_host_wmma[m*MATRIX_K+n]= (rand()%3);///3.0;
- printf("%.2f ",a_host_wmma[m*MATRIX_K+n]);
- }
- printf("\n");
- }
- printf("INITIAL_MATRIX_B\n");
- for(int m=0;m<MATRIX_K;m++){
- for(int n=0;n<MATRIX_N;n++){
- b_host_wmma[m*MATRIX_N+n]=(rand()%3);///3.0;
- printf("%.2f ",b_host_wmma[m*MATRIX_K+n]);
- }
- printf("\n");
- }
- printf("INITIAL_MATRIX_C\n");
- for(int m=0;m<MATRIX_M;m++){
- for(int n=0;n<MATRIX_N;n++){
- c_host_wmma[m*MATRIX_N+n]= (rand()%3);///3.0;
- printf("%.2f ",c_host_wmma[m*MATRIX_K+n]);
- }
- printf("\n");
- }
-
- cudaErrCheck(cudaMemcpy(a_fp32,a_host_wmma, MATRIX_M * MATRIX_K * sizeof(float), cudaMemcpyHostToDevice));
- cudaErrCheck(cudaMemcpy(b_fp32,b_host_wmma, MATRIX_K * MATRIX_N * sizeof(float), cudaMemcpyHostToDevice));
-
- convertFp32ToFp16 <<< (MATRIX_M * MATRIX_K + 255) / 256, 256 >>> (a_fp16, a_fp32, MATRIX_M * MATRIX_K);
- convertFp32ToFp16 <<< (MATRIX_K * MATRIX_N + 255) / 256, 256 >>> (b_fp16, b_fp32, MATRIX_K * MATRIX_N);
-
- cudaErrCheck(cudaMemcpy(c, c_host_wmma, MATRIX_M * MATRIX_N * sizeof(float), cudaMemcpyHostToDevice));
- cudaErrCheck(cudaMemcpy(c_wmma, c, MATRIX_M * MATRIX_N * sizeof(float), cudaMemcpyDeviceToDevice));
-
- float alpha = 1.0f;
- float beta = 1.0f;
-
-
- printf("\nM = %d, N = %d, K = %d. alpha = %f, beta = %f\n\n", MATRIX_M, MATRIX_N, MATRIX_K, alpha, beta);
-
- // First: using WMMA
- dim3 gridDim;
- dim3 blockDim;
-
- // blockDim.x must be a multple of warpSize
- // 128x4 means we have 16 warps and a block computes a 64x64 output tile
- blockDim.x = 64;
- blockDim.y = 2;
-
- gridDim.x = (MATRIX_M + (WMMA_M * blockDim.x / 32 - 1)) / (WMMA_M * blockDim.x / 32);
- gridDim.y = (MATRIX_N + WMMA_N * blockDim.y - 1) / (WMMA_N * blockDim.y);
- printf("GRID:X=%d,Y=%d\n",gridDim.x,gridDim.y);
- printf("BLOCK:X=%d,Y=%d\n",blockDim.x,blockDim.y);
-
- printf("Running with wmma...\n");
- cudaErrCheck(cudaEventRecord(startWMMA));
- wmma_example <<< gridDim, blockDim >>> (a_fp16, b_fp16, c_wmma, MATRIX_M, MATRIX_N, MATRIX_K, alpha, beta);
- cudaErrCheck(cudaEventRecord(stopWMMA));
- cudaErrCheck(cudaEventSynchronize(stopWMMA));
-
- printf("\nChecking results...\n");
- cudaErrCheck(cudaMemcpy(d_host_wmma, c_wmma, MATRIX_M * MATRIX_N * sizeof(float), cudaMemcpyDeviceToHost));
-
- int t=200000000;
- while(t-->0);
- for(int m=0;m<MATRIX_M;m++){
- for(int n=0;n<MATRIX_N;n++){
- d_cal_host_wmma[n*MATRIX_N+m]=0;
- for(int k=0;k<MATRIX_K;k++){
- d_cal_host_wmma[n*MATRIX_N+m]+= a_host_wmma[k*MATRIX_K+m]*b_host_wmma[n*MATRIX_K+k];
- }
- d_cal_host_wmma[n*MATRIX_N+m]+=c_host_wmma[n*MATRIX_N+m];
- }
- }
- printf("cal:d\n");
- for(int m=0;m<MATRIX_M;m++){
- for(int n=0;n<MATRIX_N;n++){
- printf("%.2f ",d_cal_host_wmma[m*MATRIX_K+n]);
- }
- printf("\n");
- }
- printf("wmma:d\n");
- for(int m=0;m<MATRIX_M;m++){
- for(int n=0;n<MATRIX_N;n++){
- printf("%.2f ",d_host_wmma[m*MATRIX_K+n]);
- }
- printf("\n");
- }
- int suc=1;
- float relative_error;
- for(int m=0;m<MATRIX_M;m++){
- for(int n=0;n<MATRIX_N;n++){
- relative_error=100*abs(d_cal_host_wmma[m*MATRIX_N+n]-d_host_wmma[m*MATRIX_N+n])/d_host_wmma[m*MATRIX_N+n];
- printf("relative_error=%f\n",relative_error);
- if((int)relative_error>1)
- {
- printf("ERROR:\n");
- suc=0;
- printf("ROW=%d,COL=%d:cpu=%f,gpgpusim=%f\n",m,n,d_cal_host_wmma[m*MATRIX_N+n],d_host_wmma[m*MATRIX_N+n]);
- }
- }
- }
- if(suc==1)
- printf("COMPLETED_SUCCESSFULLY\n");
-
- //int errors = 0;
- //for (int i = 0; i < MATRIX_M * MATRIX_N; i++) {
- // float v1 = c_host_wmma[i];
- // float v2 = c_host_cublas[i];
- // if (v1 / v2 > 1.0001 || v2 / v1 > 1.0001 || abs(v1 - v2) > 1e-5) {
- // errors++;
- // if (errors < 10) printf("%f %f\n", v1, v2);
- // }
- //}
-
- float wmmaTime;
- cudaErrCheck(cudaEventElapsedTime(&wmmaTime, startWMMA, stopWMMA));
- printf("wmma took %fms\n", wmmaTime);
-
- cudaErrCheck(cudaEventDestroy(startWMMA));
- cudaErrCheck(cudaEventDestroy(stopWMMA));
- cudaErrCheck(cudaFree(a_fp32));
- cudaErrCheck(cudaFree(b_fp32));
- cudaErrCheck(cudaFree(a_fp16));
- cudaErrCheck(cudaFree(b_fp16));
- cudaErrCheck(cudaFree(c));
- cudaErrCheck(cudaFree(c_wmma));
- free(d_host_wmma);
- free(c_host_wmma);
- cudaErrCheck(cudaDeviceReset());
- return 0;
-}
-
-
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