1、cudnnDataType_t 定义数据类型 cudnn_ops_infer.h中定义的支持的数据如下: /* * CUDNN data type */ typedef enum { CUDNN_DATA_FLOAT = 0, CUDNN_DATA_DOUBLE = 1, CUDNN_DATA_HALF = 2, CUDNN_DATA_INT8 = 3, CUDNN_DATA_INT32 = 4, CUDNN_DATA_INT8x4 = 5, CUDNN_DATA_UINT8 ...
dataType,Attention的输入、权重、输出的数据格式,可选为CUDNN_DATA_HALF/CUDNN_DATA_FLOAT/CUDNN_DATA_DOUBLEcomputePrec,Attention的做计算时的使用的数据格式,可选为CUDNN_DATA_HALF/CUDNN_DATA_FLOAT/CUDNN_DATA_DOUBLE,其精度要小于等于dataType mathType,做矩阵乘法时的Tensor Core选项(mma寄存器的数据类型)。
输入,过滤和输出描述符(如适用,xDesc,yDesc,wDesc,dxDesc,dyDesc和dwDesc)具有dataType = CUDNN_DATA_HALF。 输入和输出特征映射的数量是8的倍数。 过滤器类型为CUDNN_TENSOR_NCHW或CUDNN_TENSOR_NHWC。 使用CUDNN_TENSOR_NHWC类型的滤波器时,需要将输入,滤波器和输出数据指针(X,Y,W,dX,dY和dW,如适用)...
NVIDIA cuDNN PR-09702-001_v8.9.2 | 14 cudnn_ops_infer.so Library CUDNN_DATA_DOUBLE The data is a 64-bit double-precision floating-point (double). CUDNN_DATA_HALF The data is a 16-bit floating-point. CUDNN_DATA_INT8 The data is an 8-bit signed integer. CU...
For ConvolutionFwdCUDNN_DATA_HALF,CUDNN_DATA_INT32, andCUDNN_DATA_FLOAT For ConvolutionBwDataand ConvolutionBwFilter Only CUDNN_DATA_FLOAT CUDNN_ATTR_CONVOLUTION_SPATIAL_DIMS 2 or 3 CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_ALPHA 1.0f CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_BETA 0.0f Tab...
输入,过滤和输出描述符(如适用,xDesc,yDesc,wDesc,dxDesc,dyDesc和dwDesc)具有dataType = CUDNN_DATA_HALF。 输入和输出特征映射的数量是8的倍数。 过滤器类型为CUDNN_TENSOR_NCHW或CUDNN_TENSOR_NHWC。 使用CUDNN_TENSOR_NHWC类型的滤波器时,需要将输入,滤波器和输出数据指针(X,Y,W,dX,dY和dW,如适用)...
algoPerf->algo = search::DEFAULT_ALGO;if(args.params.dataType == CUDNN_DATA_HALF) { algoPerf->mathType = CUDNN_TENSOR_OP_MATH; }else{ algoPerf->mathType = CUDNN_DEFAULT_MATH; } search::getWorkspaceSize(args, algoPerf->algo, &(algoPerf->memory));return; ...
if (args.params.dataType == CUDNN_DATA_HALF) { algoPerf->mathType = CUDNN_TENSOR_OP_MATH; } else { algoPerf->mathType = CUDNN_DEFAULT_MATH; } search::getWorkspaceSize(args, algoPerf->algo, &(algoPerf->memory)); return;
if (args.params.dataType == CUDNN_DATA_HALF) { algoPerf->mathType = CUDNN_TENSOR_OP_MATH; } else { algoPerf->mathType = CUDNN_DEFAULT_MATH; } search::getWorkspaceSize(args, algoPerf->algo, &(algoPerf->memory)); return;
model = model.half() model.eval()# Do warmup roundforiinrange(opts['num_warmup_batches']): model(data)# Do benchmark roundbatch_times = np.zeros(opts['num_batches'])foriinrange(opts['num_batches']): start_time = timeit.default_timer() ...