一般来说A,B是half,即半精度,而C是float,里面具体的数据类型转换本人还没有搞清楚,在cublas里面也有一个类似的函数叫cublasGemmEx,这个函数的参数和cublasSgemm大致类似,使用方法也是如此,后面我们将会利用cublasGemmEx和tensor core做数值比较。
RuntimeError: CUDA error: CUBLAS_STATUS_NOT_SUPPORTED when callingcublasGemmEx( handle, opa, opb, m, n, k, &falpha, a, CUDA_R_16F, lda, b, CUDA_R_16F, ldb, &fbeta, c, CUDA_R_16F, ldc, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP)#125 ...
Gemm是一个经典的计算kernel,TensorCore自从Volta架构推出以来也是广为熟知的加速硬件。近几年也有不少工作实现各种高性能Gemm Kernel,比如CUTLASS, TensorIR, Triton。但如果让一个人自己写CUDA Kernel去取得不错的性能,并非一件简单的事情。 已有的高性能Gemm公开材料比较零碎,对Pascal之前的架构,实现Gemm的算法和...
intstart_algo=CUBLAS_GEMM_DEFAULT; intend_algo=CUBLAS_GEMM_ALGO23; intstart_algo_t_op=CUBLAS_GEMM_DEFAULT_TENSOR_OP; intend_algo_t_op=CUBLAS_GEMM_ALGO15_TENSOR_OP; intiteration=10; float*fA,*fB,*fC; __half*hA,*hB,*hC; int8_t*iA,*iB;int32_t*iC; floatf_alpha=1,f_beta=0...
RuntimeError:CUDAerror:CUBLAS_STATUS_EXECUTION_FAILEDwhencalling`cublasGemmEx(handle,opa,opb,m,n,k,&falpha,a,CUDA_R_16BF,lda,b,CUDA_R_16BF,ldb,&fbeta,c,CUDA_R_16BF,ldc,CUDA_R_32F,CUBLAS_GEMM_DEFAULT_TENSOR_OP) The following code should be quite easy to reproduce. All you need ...
CUBLAS_GEMM_DEFAULT_TENSOR_OP); } // TN: A row major MxK, B col major NxK, C row major MxN voidcublas_tensor_op_tn(half*A,half*B,half*C,size_tM,size_tN,size_tK){ statichalfalpha=1.0; statichalfbeta=0.0; if(g_handle==nullptr){ ...
(%d, %d)\n", m, k, k, n); int start_algo = CUBLAS_GEMM_DEFAULT; int end_algo = CUBLAS_GEMM_ALGO23; int start_algo_t_op = CUBLAS_GEMM_DEFAULT_TENSOR_OP; int end_algo_t_op = CUBLAS_GEMM_ALGO15_TENSOR_OP; int iteration = 10; float *fA, *fB, *fC; __half *hA, *...
@文心快码runtimeerror: cuda error: cublas_status_invalid_value when calling `cublasgemmex( handle, opa, opb, m, n, k, &falpha, a, cuda_r_16f, lda, b, cuda_r_16f, ldb, &fbeta, c, cuda_r_16f, ldc, cuda_r_32f, cublas_gemm_dfalt_tensor_op)`...
CUBLAS_GEMM_DEFAULT_TENSOR_OP[DEPRECATED] This mode is deprecated and will be removed in a future release. Apply Heuristics to select the GEMM algorithm, while allowing use of reduced precision CUBLAS_COMPUTE_32F_FAST_16F kernels (for backward compatibility). cuBLAS Library DU-06702-001_v11.4...
问cublas批处理gemm抛出不受支持错误，批处理大EN在批处理中，for是最为强大的命令语句，它的出现，使得解析文本内容、遍历文件路径、数值递增/递减等操作成为可能；配合if、call、 goto等流程控制语句，更是可以实现脚本复杂的自动化、智能化操作；合理使用for语句，还能使代码大为简化，免除各位编写大量重复语句之苦...