implemented prmt and started working on variable precision mul inst

1 files changed, 232 insertions, 0 deletions

diff --git a/cuda-kernels/v4p_kernel.cu b/cuda-kernels/v4p_kernel.cu
new file mode 100644
index 0000000..bb9064b
--- /dev/null
+++ b/cuda-kernels/v4p_kernel.cu
@@ -0,0 +1,232 @@
+#include <stdio.h>
+#include <curand.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);
+   }
+}
+
+#define curandErrCheck(stat) { curandErrCheck_((stat), __FILE__, __LINE__); }
+void curandErrCheck_(curandStatus_t stat, const char *file, int line) {
+   if (stat != CURAND_STATUS_SUCCESS) {
+      fprintf(stderr, "cuRand Error: %d %s %d\n", 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,float *d_fp16, int M, int N, int K) {
+   //unsigned int start_time=0,end_time=0;
+   //start_time=clock();
+
+   // 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> c_frag;
+
+   // Bounds checking
+   wmma::load_matrix_sync(a_frag, a, K);
+   wmma::load_matrix_sync(b_frag, b, K);
+   wmma::load_matrix_sync(c_frag, c, N,wmma::mem_col_major);
+   wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);
+
+   wmma::store_matrix_sync(d_fp16, c_frag, N, wmma::mem_col_major);
+   //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];
+   }
+}
+__global__ void convertFp16ToFp32 (float *out, half *in, int n) {
+   int idx = blockDim.x * blockIdx.x + threadIdx.x;
+   if (idx < n) {
+      out[idx] = in[idx];
+   }
+}
+
+__global__ void convertInt32ToInt8 (int *out, int *in, int n) {
+   int idx = blockDim.x * blockIdx.x + threadIdx.x;
+   if (idx < n/4) {
+      		out[idx] =(in[4*idx]&0xff)|(in[4*idx+1]&0xff)<<8|(in[4*idx+2]&0xff)<<16|(in[4*idx+3]&0xff)<<24;
+   }
+}
+
+__global__ void convertInt8ToInt32 (int *out, int *in, int n) {
+   int idx = blockDim.x * blockIdx.x + threadIdx.x;
+   int shft_amt=8*(idx%4);
+   int shft_mask=0xff<<shft_amt;
+   if (idx < n) {
+      	out[idx]= (in[idx/4]&shft_mask)>>shft_amt;
+   }
+}
+
+int main(int argc, char* argv[]) {
+   int *a_int32;
+   int *b_int32;
+   int *c_int32;
+   int *d_int32;
+
+   int *a_int8;
+   int *b_int8;
+   
+   int  *a_host_wmma;
+   int  *b_host_wmma;
+   int  *c_host_wmma;
+   int  *d_host_wmma;
+   int  *d_cal_host_wmma;
+
+   cudaEvent_t startWMMA;
+   cudaEvent_t stopWMMA;
+   
+   
+   cudaErrCheck(cudaEventCreate(&startWMMA));
+   cudaErrCheck(cudaEventCreate(&stopWMMA));
+   
+   // Use tensor cores
+   cudaErrCheck(cudaMalloc((void**)&a_int32, MATRIX_M * MATRIX_K * sizeof(int)));
+   cudaErrCheck(cudaMalloc((void**)&b_int32, MATRIX_K * MATRIX_N * sizeof(int)));
+   cudaErrCheck(cudaMalloc((void**)&c_int32, MATRIX_K * MATRIX_N * sizeof(int)));
+   cudaErrCheck(cudaMalloc((void**)&d_int32, MATRIX_K * MATRIX_N * sizeof(int)));
+   cudaErrCheck(cudaMalloc((void**)&a_int8, MATRIX_M * MATRIX_K * sizeof(int)/4));
+   cudaErrCheck(cudaMalloc((void**)&b_int8, MATRIX_K * MATRIX_N * sizeof(int)/4));
+
+
+   a_host_wmma      = (int *)malloc(MATRIX_M * MATRIX_K * sizeof(int));
+   b_host_wmma      = (int *)malloc(MATRIX_K * MATRIX_N * sizeof(int));
+   c_host_wmma      = (int *)malloc(MATRIX_M * MATRIX_N * sizeof(int));
+   d_host_wmma      = (int *)malloc(MATRIX_M * MATRIX_N * sizeof(int));
+   d_cal_host_wmma      = (int *)malloc(MATRIX_M * MATRIX_N * sizeof(int));
+
+   printf("a_int32\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)%16;
+		printf("%d ",a_host_wmma[m*MATRIX_K+n]);
+	}
+	printf(";\n");
+   }
+  
+   printf("b_int32\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)%2;
+		printf("%d ",b_host_wmma[m*MATRIX_N+n]);
+	}
+		printf(";\n");
+   }
+   
+   printf("c_int32\n");
+   for(int m=0;m<MATRIX_M;m++){
+	for(int n=0;n<MATRIX_N;n++){
+		c_host_wmma[m*MATRIX_N+n]=(m*MATRIX_N+n)%2;
+		d_cal_host_wmma[m*MATRIX_N+n]=0;
+		printf("%d ",c_host_wmma[m*MATRIX_N+n]);
+	}
+		printf(";\n");
+   }
+   for(int m=0;m<MATRIX_M;m++){
+	for(int n=0;n<MATRIX_N;n++){
+		for(int k=0;k<MATRIX_K;k++){
+			d_cal_host_wmma[m*MATRIX_N+n]+=	a_host_wmma[m*MATRIX_K+k]*b_host_wmma[k*MATRIX_K+n];
+		}
+		d_cal_host_wmma[m*MATRIX_N+n]+=c_host_wmma[m*MATRIX_N+n];
+	}
+   }
+
+
+   cudaErrCheck(cudaMemcpy(a_int32,a_host_wmma,  MATRIX_M * MATRIX_K * sizeof(int), cudaMemcpyHostToDevice));
+   cudaErrCheck(cudaMemcpy(b_int32,b_host_wmma,  MATRIX_K * MATRIX_N * sizeof(int), cudaMemcpyHostToDevice));
+   cudaErrCheck(cudaMemcpy(c_int32,c_host_wmma,  MATRIX_M * MATRIX_N * sizeof(int), cudaMemcpyHostToDevice));
+
+   convertInt32ToInt8 <<< (MATRIX_M * MATRIX_K + 255) / 256, 256 >>> (a_int8, a_int32, MATRIX_M * MATRIX_K);
+   convertInt8ToInt32 <<< (MATRIX_M * MATRIX_K + 255) / 256, 256 >>> (d_int32, a_int8, MATRIX_M * MATRIX_K);
+   //convertFp32ToFp16 <<< (MATRIX_K * MATRIX_N + 255) / 256, 256 >>> (b_fp16, b_fp32, MATRIX_K * MATRIX_N);
+   //convertFp32ToFp16 <<< (MATRIX_M * MATRIX_N + 255) / 256, 256 >>> (c_fp16, c_fp32, MATRIX_K * MATRIX_N);
+   cudaErrCheck(cudaMemcpy(d_host_wmma, d_int32, MATRIX_M * MATRIX_N * sizeof(float), cudaMemcpyDeviceToHost));
+
+
+//AAMIR   printf("\nM = %d, N = %d, K = %d. \n", MATRIX_M, MATRIX_N, MATRIX_K);
+//AAMIR   
+//AAMIR   printf("Running with wmma...\n");
+//AAMIR   cudaErrCheck(cudaEventRecord(startWMMA));
+//AAMIR   wmma_example <<< 1, 32>>> (a_fp16, b_fp16, c_fp32, d_fp32 , MATRIX_M, MATRIX_N, MATRIX_K);
+//AAMIR   cudaErrCheck(cudaEventRecord(stopWMMA));
+//AAMIR   cudaErrCheck(cudaEventSynchronize(stopWMMA));
+//AAMIR
+//AAMIR   // Error checking
+//AAMIR   printf("\nChecking results...\n");
+//AAMIR   cudaErrCheck(cudaMemcpy(d_host_wmma, d_fp32, MATRIX_M * MATRIX_N * sizeof(float), cudaMemcpyDeviceToHost));
+//AAMIR   
+//AAMIR   printf("Results verified: cublas and WMMA agree.\n\n");
+//AAMIR   float wmmaTime;
+//AAMIR   cudaErrCheck(cudaEventElapsedTime(&wmmaTime, startWMMA, stopWMMA));
+//AAMIR   printf("wmma took %fms\n", wmmaTime);
+//AAMIR   
+//AAMIR   cudaErrCheck(cudaEventDestroy(startWMMA));
+//AAMIR   cudaErrCheck(cudaEventDestroy(stopWMMA));
+//AAMIR
+//AAMIR   int t=200000;
+//AAMIR   while(t-->0);
+//AAMIR   printf("D_CALCULATED\n");
+//AAMIR
+//AAMIR   for(int m=0;m<MATRIX_M;m++){
+//AAMIR	for(int n=0;n<MATRIX_N;n++){
+//AAMIR		printf("%.2f,",d_cal_host_wmma[m*MATRIX_N+n]);
+//AAMIR	}
+//AAMIR	printf("\n");
+//AAMIR   }
+   printf("D_WMMA\n");
+   for(int m=0;m<MATRIX_M;m++){
+	for(int n=0;n<MATRIX_N;n++){
+		printf("%d,",d_host_wmma[m*MATRIX_N+n]);
+	}
+	printf("\n");
+    }
+//AAMIR   int suc=1;
+//AAMIR   for(int m=0;m<MATRIX_M;m++){
+//AAMIR	for(int n=0;n<MATRIX_N;n++){
+//AAMIR 		if(abs(d_cal_host_wmma[m*MATRIX_N+n]-d_host_wmma[m*MATRIX_N+n])>1) 
+//AAMIR		{	
+//AAMIR			printf("ERROR:\n");
+//AAMIR			suc=0;
+//AAMIR		}
+//AAMIR	}
+//AAMIR   }
+//AAMIR   if(suc==1)
+//AAMIR	printf("COMPLETED_SUCCESSFULLY\n");
+//AAMIR   
+   
+   cudaErrCheck(cudaFree(a_int32));
+   cudaErrCheck(cudaFree(b_int32));
+   cudaErrCheck(cudaFree(c_int32));
+   cudaErrCheck(cudaFree(d_int32));
+   cudaErrCheck(cudaFree(a_int8));
+   cudaErrCheck(cudaFree(b_int8));
+
+   free(a_host_wmma);
+   free(b_host_wmma);
+   free(c_host_wmma);
+   free(d_host_wmma);
+   cudaErrCheck(cudaDeviceReset());
+   return 0;
+}
+
+