cuDNN Release Notes v7.4.2
- In some cases when the data is in CUDNN_DATA_HALF and NHWC, illegal memory access may occur for
cudnnBatchNormalization*functions in the cuDNN 7.4.1 library. This is now fixed.
- When the data is in CUDNN_DATA_HALF and NHWC, for
cudnnBatchNormalization*functions when (N*H*W) is large and odd number, the output may contain wrong results. This is fixed.
- When calling the
cudnnConvolutionBiasActivationForward()function with the
algoparameter set to CUDNN_CONVOLUTION_FWD_ALGO_FFT and the
activationDescparameter set to CUDNN_ACTIVATION_RELU and sufficiently large inputs, the ReLU operation is not applied and negative values are passed through to the output. This issue is now fixed. This issue was present in all previous cuDNN versions.
Performance regression was introduced in cuDNN 7.4.1 for
cudnnConvolutionBwdFilterAlgo_t()function with CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1 algorithm. This is fixed.
cudnnBatchNormMode_tis set to CUDNN_BATCHNORM_SPATIAL_PERSISTENT and the input/output tensors are in NHWC format and of CUDNN_DATA_HALF datatype, then, on Windows only, the
cudnnBatchNormalization*Exfunctions are supported only with the device in TCC mode. See Tesla Compute Cluster Mode for Windows. This issue is not present on Linux systems. This issue is present in cuDNN 7.4.1 and this current version.
In some cases the 3D convolution will have a reduced performance on Turing GPUs, compared to the previous cuDNN releases.
cudnnGetConvolutionForwardWorkspaceSize()will return CUDNN_STATUS_SUCCESS, but the execution of the convolution returns CUDNN_STATUS_NOT_SUPPORTED. This issue is present in cuDNN 7.2.2 library and later versions.