added wmma parsing but execution getting aborted

1 files changed, 250 insertions, 0 deletions

diff --git a/cuda-kernels/tensor_core.cu b/cuda-kernels/tensor_core.cu
new file mode 100644
index 0000000..483a42b
--- /dev/null
+++ b/cuda-kernels/tensor_core.cu
@@ -0,0 +1,250 @@
+/* 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>
+
+// 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;
+
+
+// Performs an MxNxK GEMM (C=alpha*A*B + beta*C) assuming:
+//  1) Matrices are packed in memory.
+//  2) M, N and K are multiples of 16. 
+//  3) Neither A nor B are transposed.
+// Note: This is NOT a high performance example but is for demonstration purposes only
+//       For a high performance code please use the GEMM provided in cuBLAS.
+__global__ void wmma_example(half *a, half *b, float *c, int M, int N, int K, float alpha, float beta) {
+   unsigned int start_time=0,end_time=0;
+   // Leading dimensions. Packed with no transpositions.
+   start_time=clock();
+   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::row_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(c_frag, 0.0f);
+   
+   int i=0;
+   int aRow = warpM * WMMA_M;
+   int bCol = warpN * WMMA_N;
+   int aCol = i;
+   int bRow = i;
+
+
+   // Bounds checking
+   if (aRow < M && aCol < K && bRow < K && bCol < N) {
+      wmma::load_matrix_sync(a_frag, a+aRow+aCol*lda, lda);
+      wmma::load_matrix_sync(b_frag, b+bRow*ldb+bCol, ldb);
+      wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);
+      //wmma::mma_sync(acc_frag, a_frag, b_frag, acc_frag);
+   }
+   int cRow = warpM * WMMA_M;
+   int cCol = warpN * WMMA_N;
+   wmma::store_matrix_sync(c + cRow + cCol * ldc, c_frag, ldc, wmma::mem_col_major);
+   end_time=clock();
+   printf("clock=%d",end_time-start_time);
+}
+
+__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_cublas;
+   float *c_wmma;
+
+   float *c_host_cublas;
+   float *c_host_wmma;
+   float *a_host_wmma;
+   float *b_host_wmma;
+   float *c_init_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)));
+
+   c_host_wmma      = (float*)malloc(MATRIX_M * MATRIX_N * sizeof(float));
+   c_init_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_fp32\n");
+   for(int m=0;m<MATRIX_M;m++){
+	for(int n=0;n<MATRIX_K;n++){
+		a_host_wmma[m*MATRIX_K+n]=m*MATRIX_K+n;
+	}
+	//printf(";\n");
+   }
+  // printf("b_fp32\n");
+   for(int m=0;m<MATRIX_K;m++){
+	for(int n=0;n<MATRIX_N;n++){
+		b_host_wmma[m*MATRIX_N+n]=m*MATRIX_N+n;
+//		printf("%f ",b_host_wmma[m*MATRIX_N+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));
+
+   // curand doesn't currently support fp16 so we generate in fp32 and convert to fp16.
+   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);
+
+   for(int m=0;m<MATRIX_M;m++){
+	for(int n=0;n<MATRIX_N;n++){
+		c_init_host_wmma[m*MATRIX_N+n]=0;
+	}
+   }
+   cudaErrCheck(cudaMemcpy(c, c_init_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 = 128;
+   blockDim.y = 4;
+
+   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("Running with wmma...\n");
+   cudaErrCheck(cudaEventRecord(startWMMA));
+   wmma_example <<< 1, 32>>> (a_fp16, b_fp16, c_wmma, MATRIX_M, MATRIX_N, MATRIX_K, alpha, beta);
+  // wmma_example <<< gridDim, blockDim >>> (a_fp16, b_fp16, c_wmma, MATRIX_M, MATRIX_N, MATRIX_K, alpha, beta);
+   cudaErrCheck(cudaEventRecord(stopWMMA));
+
+
+   
+
+   // Error checking
+   printf("\nChecking results...\n");
+   cudaErrCheck(cudaMemcpy(c_host_wmma, c_wmma, MATRIX_M * MATRIX_N * sizeof(float), cudaMemcpyDeviceToHost));
+  // printf("c_host\n");
+  // for(int m=0;m<MATRIX_M;m++){
+//	for(int n=0;n<MATRIX_N;n++){
+//	printf("%f ",c_host_wmma[m*MATRIX_N+n]);
+//	}
+//	printf(";\n");
+  // }
+   
+      float wmmaTime;
+      cudaErrCheck(cudaEventSynchronize(stopWMMA));
+      cudaErrCheck(cudaEventElapsedTime(&wmmaTime, startWMMA, stopWMMA));
+      printf("wmma took %fms\n", wmmaTime);
+      //printf("Clock=%d",stopWMMA-startWMMA);
+      printf("\nFor a faster code using wmma you should check out the cudaTensorCoreGemm sample in the CUDA Toolkit.\nThis code was written as a demo only!\n\n");
+   
+   
+   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(c_host_wmma);
+
+   cudaErrCheck(cudaDeviceReset());
+   return 0;
+}
+
+