generic matrix multiply kernel passed

1 files changed, 314 insertions, 0 deletions

diff --git a/cuda-kernels/genericMatrixMultiply.cu b/cuda-kernels/genericMatrixMultiply.cu
new file mode 100644
index 0000000..95cf021
--- /dev/null
+++ b/cuda-kernels/genericMatrixMultiply.cu
@@ -0,0 +1,314 @@
+/* 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 (16)
+#define MATRIX_N (16)
+#define MATRIX_K (16)
+
+
+
+// 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("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("%f ",a_host_wmma[m*MATRIX_K+n]);
+	}
+	printf("\n");
+   }
+   printf("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("%f ",b_host_wmma[m*MATRIX_K+n]);
+	}
+	printf("\n");
+   }
+   printf("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("%f ",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));
+   
+   printf("wmma:d\n"); 
+   for(int m=0;m<MATRIX_M;m++){
+	for(int n=0;n<MATRIX_N;n++){
+		printf("%f ",d_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("%f ",d_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("%f ",d_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("%f ",d_host_wmma[m*MATRIX_K+n]);
+	}
+	printf("\n");
+   }
+   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;
+}
+
+