cuDNN Release 7.x.x
cuDNN 7.6.5
This is the cuDNN 7.6.5 release notes. This release includes fixes from the previous cuDNN v7.x.x releases as well as the following additional changes. These release notes are applicable to both cuDNN and JetPack users unless appended specifically with (not applicable for Jetson platforms).
For previous cuDNN release notes, see the cuDNN Archived Documentation.
Key Features and Enhancements
The following features and enhancements have been added to this release:
Compatibility
For the latest compatibility software versions of the OS, CUDA, the CUDA driver, and the NVIDIA hardware, see the cuDNN Support Matrix for v7.6.5.
Limitations

RNN and multihead attention API calls may exhibit nondeterministic behavior when the cuDNN 7.6.5 library is built with CUDA Toolkit 10.2 or higher. This is the result of a new buffer management and heuristics in the cuBLAS library. As described in the Results Reproducibility section in the cuBLAS Library User Guide, numerical results may not be deterministic when cuBLAS APIs are launched in more than one CUDA stream via the same cuBLAS handle. This is caused by two buffer sizes (16 KB and 4 MB) used in the default configuration.
When a larger buffer size is not available at runtime, instead of waiting for a buffer of that size to be released, a smaller buffer may be used with a different GPU kernel. The kernel selection may affect numerical results. The user can eliminate the nondeterministic behavior of cuDNN RNN and multihead attention APIs, by setting a single buffer size in the CUBLAS_WORKSPACE_CONFIG environmental variable, for example, :16:8 or :4096:2.
The first configuration instructs cuBLAS to allocate eight buffers of 16 KB each in GPU memory while the second setting creates two buffers of 4 MB each. The default buffer configuration in cuBLAS 10.2 and 11.0 is :16:8:4096:2, i.e., we have two buffer sizes. In earlier cuBLAS libraries, such as cuBLAS 10.0, it used the :16:8 nonadjustable configuration. When buffers of only one size are available, the behavior of cuBLAS calls is deterministic in multistream setups.
Known Issues
 Updated: August 24, 2020
Two dimensional forward convolutions using algo1 may segfault when the filter size is large. For example, we’ve observed this issue when the filter width and height are more than or equal to 363.
 Updated: September 28,
2020
cudnnConvolutionForward(), cudnnConvolutionBackwardData(), and cudnnConvolutionBackwardFilter() calls with algo0 or algo1 can result in a illegal memory access for PSEUDO_HALF_CONFIG data configuration when the number of elements in the output tensor is odd. This can be mitigated by allocating one extra element in the output buffer.
cuDNN 7.6.4
This is the cuDNN 7.6.4 release notes. This release includes fixes from the previous cuDNN v7.x.x releases as well as the following additional changes.
For previous cuDNN release notes, see the cuDNN Archived Documentation.
Key Features and Enhancements
The following features and enhancements have been added to this release:
Compatibility
For the latest compatibility software versions of the OS, CUDA, the CUDA driver, and the NVIDIA hardware, see the cuDNN Support Matrix for v7.6.4.
cuDNN 7.6.3
This is the cuDNN 7.6.3 release notes. This release includes fixes from the previous cuDNN v7.x.x releases as well as the following additional changes. These release notes are applicable to both cuDNN and JetPack users unless appended specifically with (not applicable for Jetson platforms).
For previous cuDNN release notes, see the cuDNN Archived Documentation.
cuDNN 7.6.2
This is the cuDNN 7.6.2 release notes. This release includes fixes from the previous cuDNN v7.x.x releases as well as the following additional changes.
For previous cuDNN release notes, see the cuDNN Archived Documentation.
cuDNN 7.6.1
cuDNN 7.6.0
cuDNN 7.5.1
cuDNN 7.5.0
cuDNN 7.4.2
This is the cuDNN 7.4.2 release notes. This release includes fixes from the previous cuDNN v7.x.x releases as well as the following additional changes.
Fixed Issues
The following issues have been fixed in this release:
 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 algo parameter set to CUDNN_CONVOLUTION_FWD_ALGO_FFT and the activationDesc parameter 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.
Known Issues
The following issues and limitations exist in this release:
 When cudnnBatchNormMode_t is 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*Ex functions 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.

The functions cudnnGetConvolutionForwardAlgorithm_v7() and 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.
cuDNN 7.4.1
This is the cuDNN 7.4.1 release notes. This release includes fixes from the previous cuDNN v7.x.x releases as well as the following additional changes.
Key Features and Enhancements
The following enhancements have been added to this release:

Added a new family of fast NHWC batch normalization functions. See the following five new functions and one new type descriptor:

cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize() function

cudnnBatchNormalizationForwardTrainingEx function

cudnnGetBatchNormalizationBackwardExWorkspaceSize() function

cudnnBatchNormalizationBackwardEx() function

cudnnGetBatchNormalizationTrainingExReserveSpaceSize() function

cudnnBatchNormOps_t type descriptor

 For API Logging, a conversion specifier for the process id is added. With this, the process id can be included in the log file name. See API Logging.
 Performance of cudnnPoolingBackward() is enhanced for the average pooling when using NHWC data formatfor both the CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING and CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING cases of cudnnPoolingMode_t.
 Performance of the strided convolution in cudnnConvolutionBackwardData() is enhanced when the filter is in NHWC format and the data type is TRUE_HALF_CONFIG or PSEUDO_HALF_CONFIG or FLOAT_CONFIG. For strides u,v < r,s the performance is further enhanced.
 Significantly improved the performance of cudnnConvolutionForward(), cudnnConvolutionBackwardData() and cudnnConvolutionBackwardFilter() functions on RCNN models such as Fast RCNN, Faster RCNN, & Mask RCNN.
Fixed Issues
The following issues have been fixed in this release:
 The following set up was giving “Misaligned Address” error in cuDNN 7.3.x. This is fixed in cuDNN 7.4.1: For the cudnnConvolutionForward() function with the CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM algorithm, in the data type configuration of PSEUDO_HALF_CONFIG, when the input and output tensors are in in NHWC and the filter is 1x1 and NCHW, and Tensor Op is enabled.
 For a few convolution sizes for ALGO_0 and ALGO_1, the performance of the function cudnnConvolutionBackwardFilter() was degraded in cuDNN 7.3.1. This is now fixed.
 Fixed. In cuDNN 7.3.1 the function cudnnAddTensor was computing incorrect results when run on GPUs with the compute capability < 6.0 (prior to Pascal).
Known Issues
The following issues and limitations exist in this release:
 When calling the cudnnConvolutionBiasActivationForward() function with the algo parameter set to CUDNN_CONVOLUTION_FWD_ALGO_FFT and the activationDesc parameter 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 present in all previous cuDNN versions.
cuDNN 7.3.1
This is the cuDNN 7.3.1 release notes. This release includes fixes from the previous cuDNN v7.x.x releases as well as the following additional changes.
Key Features and Enhancements
The following enhancements have been added to this release:
 The FFT tiling algorithms for convolution have been enhanced to support strided convolution. In specific, for the algorithms CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING and CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING, the convDesc's vertical and horizontal filter stride can be 2 when neither the filter width nor the filter height is 1.
 The CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD algorithm for cudnnConvolutionForward() and cudnnConvolutionBackwardData() now give superior performance for Volta architecture. In addition, the mobile version of this algorithm in the same functions gives superior performance for Maxwell and Pascal architectures.
 Dilated convolutions now give superior performance for cudnnConvolutionForward(), cudnnConvolutionBackwardData(), and cudnnConvolutionBackwardFilter() on Volta architecture, in some cases.
Known Issues and Limitations
The following issues and limitations exist in this release:
 For the cudnnConvolutionForward(), when using a 1x1 filter with input and output tensors of NHWC format and of CUDNN_DATA_HALF (half precision) type, and the filter format is NCHW, with compute type of float, cuDNN will generate incorrect results.
 On Quadro P4000, when calling cudnnConvolutionForward() function with CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED algorithm, there may be a small chance of seeing intermittent inaccurate results.
 When using cudnnConvolutionBackwardFilter() with CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0 in mixed precision computation, with input/output in CUDNN_DATA_HALF (half precision) and compute type of float, when the number of batches (N) is larger than 1 the results might include INF due to an intermediate downconvert to half float. In other words, with an accumulation of float for all intermediate values (such as in CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1) the result will be a finite half precision float. This limitation also exists in all previous cuDNN versions.
Fixed Issues
The following issues have been fixed in this release:
 Fixed a pointer arithmetic integer overflow issue in RNN forward and backward functions, when sequence length and minibatch size are sufficiently large.
 When tensor cores are enabled in cuDNN 7.3.0, the cudnnConvolutionBackwardFilter() calculations were performing an illegal memory access when K and C values are both nonintegral multiples of 8. This issue is fixed.
 For the CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1 algorithm in cudnnConvolutionBackwardFilter(), on Volta, the tensor operations were occasionally failing when the filter spatial size (filter h * filter w) was greater than 64. This issue is fixed.
 While running cuDNN 7.3.0 on Turing with CUDA 10.0, r400 driver, the functions cudnnRNNForwardTraining(Ex) and cudnnRNNForwardInference(Ex) errored out returning CUDNN_STATUS_NOT_SUPPORTED. This issue is fixed.
 In cuDNN 7.3.0, when using CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1 with tensor data or filter data in NHWC format, the function might have resulted in a silent failure. This is now fixed.
cuDNN 7.3.0
This is the cuDNN 7.3.0release notes. This release includes fixes from the previous cuDNN v7.x.x releases as well as the following additional changes.
Key Features and Enhancements
The following enhancements have been added to this release:
 Support is added to the following for the dilated convolution, for
NCHW and NHWC filter formats:
 cudnnConvolutionForward() for 2D, CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM,
 cudnnConvolutionBackwardData() for 2D, CUDNN_CONVOLUTION_BWD_DATA_ALGO_1, and
 cudnnConvolutionBackwardFilter() for 2D, CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1
For these supported cases, the dilated convolution is expected to offer superior speed, compared to the existing dilated convolution with algo 0.
 Grouped convolutions for depthwise separable convolutions are optimized for the following NHWC formats: HHH (input: Half, compute: Half, output: Half), HSH, and SSS.
 While using CUDNN_TENSOR_OP_MATH or CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION, with the tensor cores, the c and k dimensions of the tensors are now padded to multiples of 8 (as needed), to allow a tensor core kernel to run.
 The CUDNN_BATCHNORM_SPATIAL_PERSISTENT algo is enhanced in cudnnBatchNormalizationForwardTraining() and cudnnBatchNormalizationBackward() to propagate NaNs or Infs as in a pure floating point implementation (the "persistent" flavor of the batch normalization is optimized for speed and it uses integer atomics for inter threadblock reductions). In earlier versions of cuDNN we recommended invoking cudnnQueryRuntimeError() to ensure no overflow was encountered. When it happened, the best practice was to discard the results, and use CUDNN_BATCHNORM_SPATIAL instead, as some results generated by CUDNN_BATCHNORM_SPATIAL_PERSISTENT could be finite but invalid. This behavior is now corrected: NaNs and/or Infs are consistently output when intermediate results are out of range. The refined implementation simulates math operations on special floating point values, for example, +InfInf=NaN.
Known Issues and Limitations
Following issues and limitations exist in this release:
 When tensor cores are enabled in cuDNN 7.3.0, the wgrad calculations will perform an illegal memory access when K and C values are both nonintegral multiples of 8. This will not likely produce incorrect results, but may corrupt other memory depending on the user buffer locations. This issue is present on Volta & Turing architectures.
 Using cudnnGetConvolution*_v7 routines with cudnnConvolutionDescriptor_t set to CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION leads to incorrect outputs. These incorrect outputs will consist only of CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION cases, instead of also returning the performance results for both DEFAULT_MATH and CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION cases.
Fixed Issues
The following issues have been fixed in this release:
 Using cudnnConvolutionBackwardData() with CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD algorithm produced incorrect results due to an incorrect filter transform. This issue was present in cuDNN 7.2.1.
 For INT8 type, with xDesc and yDesc of NHWC format, the cudnnGetConvolutionForwardAlgorithm_v7 function was incorrectly returning CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM as a valid algorithm. This is fixed.
 cudnnConvolutionForward() using CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD intermittently produced incorrect results in cuDNN 7.2, due to a race condition. This issue is fixed.
 When running cudnnConvolutionBackwardFilter() with NHWC filter format, when n, c, and k are all multiple of 8, and when the workSpace input is exactly as indicated by cudnnGetConvolutionBackwardFilterWorkspaceSize(), leads to error in cuDNN 7.2. This is fixed.
 When the user runs cudnnRNNForward* or cudnnRNNBackward* with FP32 input/output on sm_70 or sm_72, with RNN descriptor's algo field set to CUDNN_RNN_ALGO_PERSIST_STATIC, and cudnnMathType_t type set to CUDNN_TENSOR_OP_MATH via cudnnSetRNNMatrixMathType, then the results were incorrect. This is fixed.
 When the user runs cudnnRNNForward* or cudnnRNNBackward* with FP32 input/output on sm_70 or sm_72, with RNN descriptor's algo field set to CUDNN_RNN_ALGO_PERSIST_STATIC, and cudnnMathType_t type set to CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION via cudnnSetRNNMatrixMathType, then the resulting performance was suboptimal. This is fixed.
 Convolution routines with filter format as NHWC require both input and output formats to be NHWC. However, in cuDNN 7.2 and earlier, this condition was not being checked for, as a result of which silent failures may have occurred. This is fixed in 7.3.0 to correctly return CUDNN_STATUS_NOT_SUPPORTED.
cuDNN 7.2.1
This is the cuDNN 7.2.1 release notes. This release includes fixes from the previous cuDNN v7.x.x releases as well as the following additional changes.
Key Features and Enhancements
The following enhancements have been added to this release:
 The following new functions are added to provide support for the padding
mask for the cudnnRNN* family of functions:
 cudnnSetRNNPaddingMode(): Enables/disables the padded RNN input/output.
 cudnnGetRNNPaddingMode(): Reads the padding mode status.
 cudnnCreateRNNDataDescriptor() and cudnnDestroyRNNDataDescriptor(): Creates and destroys, respectively, cudnnRNNDataDescriptor_t, an RNN data descriptor.
 cudnnSetRNNDataDescriptor() and cudnnGetRNNDataDescriptor(): Initializes and reads, respectively, the RNN data descriptor.
 cudnnRNNForwardTrainingEx(): An extended version of the cudnnRNNForwardTraining() to allow for the padded (unpacked) layout for the input/output.
 cudnnRNNForwardInferenceEx(): An extended version of the cudnnRNNForwardInference() to allow for the padded (unpacked) layout for the input/output.
 cudnnRNNBackwardDataEx(): An extended version of the cudnnRNNBackwardData() to allow for the padded (unpacked) layout for the input/output.
 cudnnRNNBackwardWeightsEx(): An extended version of the cudnnRNNBackwardWeights() to allow for the padded (unpacked) layout for the input/output.

Added support for cell clipping in cuDNN LSTM. The following new functions are added:
 cudnnRNNSetClip() and cudnnRNNGetClip(): Sets and retrieves, respectively, the LSTM cell clipping mode.
 Accelerate your convolution computation with this new feature: When the
input channel size c is a multiple of 32, you can use the
new data type CUDNN_DATA_INT8x32 to accelerate your convolution computation.
Note: This new data type CUDNN_DATA_INT8x32 is only supported by sm_72.
 Enhanced the family of cudnnFindRNN* functions. The findIntensity input to these functions now enable the user to control the overall runtime of the RNN find algorithms, by selecting a percentage of a large Cartesian product space to be searched.
 A new mode CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION is added to cudnnMathType_t. The computation time for FP32 tensors can be reduced by selecting this mode.
 The functions cudnnRNNForwardInference(), cudnnRNNForwardTraining(), cudnnRNNBackwardData(), and cudnnRNNBackwardWeights() will now perform down conversion of FP32 input/output only when CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION is set.
 Improved the heuristics for cudnnGet*Algorithm() functions.
Known Issues and Limitations
Following issues and limitations exist in this release:
 For FP16 inputs, the functions cudnnGetConvolutionForwardAlgorithm(), cudnnGetConvolutionBackwardDataAlgorithm(), and cudnnGetConvolutionBackwardFilterAlgorithm() will obtain a slower algorithm.
 For cases where beta is not equal to zero, and when the input
channel size is greater than 65535, then the below
cudnnConvolutionBackwardFilter() algorithms may return
EXECUTION_FAILED error:
 CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0,
 CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1, and
 CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3
 This is a rare occurrence: When beta is not equal to zero, the function cudnnFindConvolutionBackwardFilterAlgorithm() may not return the fastest algorithm available for cudnnConvolutionBackwardFilter().
 Grouped convolutions are not supported in the TRUE_HALF_CONFIG (convDesc is CUDNN_DATA_HALF) data type configuration. As a workaround, the PSEUDO_HALF_CONFIG (convDesc is CUDNN_DATA_FLOAT) data type configuration can be used without losing any precision.
 For the cudnnConvolutionBiasActivationForward() function, if the input cudnnActivationMode_t is set to enum value CUDNN_ACTIVATION_IDENTITY, then the input cudnnConvolutionFwdAlgo_t must be set to the enum value CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM.
 When the user runs cudnnRNNForward* or cudnnRNNBackward* with FP32 input/output, on sm_70 or sm_72, with RNN descriptor's algo field set to CUDNN_RNN_ALGO_PERSIST_STATIC, and math type set to CUDNN_TENSOR_OP_MATH via cudnnSetRNNMatrixMathType(), then the results are incorrect.
 When the user runs cudnnRNNForward* or cudnnRNNBackward* with FP32 input/output, on sm_70 or sm_72, with RNN descriptor's algo field set to CUDNN_RNN_ALGO_PERSIST_STATIC, and math type set to CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION via cudnnSetRNNMatrixMathType(), then the resulting performance is suboptimal.
Fixed Issues
The following issues have been fixed in this release:
 The cudnnConvolutionBackwardData() function produced incorrect
result under these conditions:
 The algo input is set to CUDNN_CONVOLUTION_BWD_DATA_ALGO_1 in cudnnConvolutionBwdDataAlgo_t, and
 CUDNN_TENSOR_OP_MATH is selected.
Under above conditions, the dgrad computation was giving incorrect results when the data is not packed and the data format is NCHW. This is fixed.

When the cudnnConvolutionFwdAlgo_t() was set to CONVOLUTION_FWD_ALGO_FFT_TILING then the function cudnnConvolutionForward() was leading to illegal memory access. This is now fixed.
 cudnnPoolingBackward() was failing when using a large kernel size used for 'global_pooling' with NHWC I/O layout. This is fixed.
 The below two items are fixed: If you set RNN mathtype to CUDNN_TENSOR_OP_MATH,
and run RNN on sm6x or earlier hardware:
 a. You may have received CUDNN_STATUS_NOT_SUPPORTED when algo selected is CUDNN_RNN_ALGO_STANDARD or CUDNN_RNN_ALGO_PERSIST_STATIC.
 b. You may have received incorrect results when algo selected is CUDNN_RNN_ALGO_PERSIST_DYNAMIC.
 If you passed in variable sequence length input tensor to cudnnRNNForwardInference(), cudnnRNNForwardTraining(), cudnnRNNBackwardData(), and used CUDNN_RNN_ALGO_PERSIST_STATIC or CUDNN_RNN_ALGO_PERSIST_DYNAMIC, then you may have received incorrect results. Now this is being checked, and CUDNN_STATUS_NOT_SUPPORTED will be returned.
cuDNN 7.1.4
This is the cuDNN 7.1.4 release notes. This release includes fixes from the previous cuDNN v7.x.x releases as well as the following additional changes.
Key Features and Enhancements
The following enhancements have been added to this release:
 Improved performance for some cases of datagradient convolutions and maxpooling. This is expected to improve performance of ResNet50 like networks.
 The runtime of the RNN Find algorithm suite is improved in v7.1.4 resulting in slightly improved runtime of cudnnFindRNN***AlgorithmEx.
Known Issues
Following are known issues in this release:
 cudnnGet picks a slow algorithm that does not use Tensor Cores on Volta when inputs are FP16 and it is possible to do so.
 The cudnnConvolutionBackwardFilter() function may output incorrect results for CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING when the convolution mode is CUDNN_CONVOLUTION. This function should not be used in this mode.
Fixed Issues
The following issues have been fixed in this release:
 cudnnAddTensorNd might cause a segmentation fault if called with bad arguments (e.g. null pointer), this issue is in 7.1.3 only and fixed in 7.1.4.
 cudnnRNNBackwardData LSTM cell with fp16 (half) inputs might generate wrong values (silently), this issue exists in cudnn 7.1.3 binaries compiled with cuda toolkit 9.0 and toolkit cuda 9.2, and does not exist in cudnn 7.1.3 binaries compiled with toolkit 9.1.
 cudnnGetRNNLinLayerMatrixParams wrongly returns CUDNN_STATUS_BAD_PARAM when cudnnSetRNNDescriptor is called with dataType == CUDNN_DATA_FLOAT. This is an issue in 7.1.3 only and will be fixed in 7.1.4. The dataType argument as of today supports only CUDNN_DATA_FLOAT and we plan to support additional compute types in the future.
 There is a small memory leak issue when calling cudnnRNNBackwardData with CUDNN_RNN_ALGO_STANDARD. This issue also affects previous cuDNN v7 releases. This is fixed in 7.1.4.
 RNN with half precision returns CUDNN_EXECUTION_FAILED on Kepler gpu in 7.1.3. This is fixed in 7.1.4 to use pseudofp16 computation
 The RNN Find algorithm suite mistakenly did not test CUDNN_RNN_ALGO_PERSIST_STATIC and CUDNN_RNN_ALGO_PERSIST_DYNAMIC kernels with tensor operations enabled when it was possible to do so. This is fixed in v7.1.4.
cuDNN 7.1.3
This is the cuDNN 7.1.3 release notes. This release includes fixes from the previous cuDNN v7.x.x releases as well as the following additional changes.
Known Issues
Following are known issues in this release:
 cudnnGet picks a slow algorithm that does not use Tensor Cores on Volta when inputs are FP16 and it is possible to do so.
 The cudnnConvolutionBackwardFilter() function may output incorrect results for CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING when the convolution mode is CUDNN_CONVOLUTION and the product "n*k" (n  batch size, k  number of output feature maps) is large, i.e., several thousand or more. It appears that the CUDNN_CROSS_CORRELATION mode is not affected by this bug.
 There is a small memory leak issue when calling cudnnRNNBackwardData with CUDNN_RNN_ALGO_STANDARD. This issue also affects previous cuDNN v7 releases.
 RNN with half precision will not work on Kepler GPUs and will return CUDNN_EXECUTION_FAILED. This will be fixed in future releases to return CUDNN_STATUS_UNSUPPORTED.
Fixed Issues
The following issues have been fixed in this release:
 cudnnRNNbackwardData for LSTM with recurrent projection in half precision may fail in rare cases with misaligned memory access on Pascal and Maxwell.
 cudnnRNNbackwardData for bidirectional LSTM with recurrent projection may produce inaccurate results, or CUDNN_STATUS_UNSUPPORTED.
 Algo 1 for forward convolution and dgrad may produce erroneous results when the filter size is greater than the input size. This issue is fixed in 7.1.3.
 For very large RNN networks, the function cudnnGetRNNWorkspaceSize and cudnnGetRNNTrainingReserveSize may internally overflow and give incorrect results.
 The small performance regression on multilayer RNNs using the STANDARD algorithm and Tensor Core math in 7.1.2, as compared to 7.0.5, is fixed in this release.
 Fixed an issue with Persistent LSTM backward pass with a hidden state size in the range 257 to 512 on GPUs with number of SMs between 22 and 31 might hang. This issue also exists in 7.1.1. This is fixed in 7.1.3.
 Fixed an issue Persistent GRU backward pass with a hidden state size in the range 513>720 on GPUs with exactly 30 SMs would hang. This issue also exists in 7.1.1. This is fixed in 7.1.3.
cuDNN 7.1.2
This is the cuDNN 7.1.2 release notes. This release includes fixes from the previous cuDNN v7.x.x releases as well as the following additional changes.
Key Features and Enhancements
The following enhancements have been added to this release:
 RNN search API extended to support all RNN algorithms.
 Newly added projection Layer supported for inference bidirectional RNN cells and for backward data and gradient.
 Support IDENTITY Activation for all cudnnConvolutionBiasActivationForward data types for CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM.
 Added documentation to clarify RNN/LSTM weight formats.
Known Issues
Following are known issues in this release:
 cudnnGet picks a slow algorithm that does not use Tensor Cores on Volta when inputs are FP16 and it is possible to do so.
 There may be a small performance regression on multilayer RNNs using the STANDARD algorithm with Tensor Core math in this release compared to v7.0.5.
 LSTM projection dgrad half precision may fail in rare cases with misaligned memory access on Pascal and Maxwell.
 Dgrad for bidirectional LSTM with projection should not be used, may produce inaccurate results, or CUDNN_STATUS_UNSUPPORTED.
 The cudnnConvolutionBackwardFilter() function may output incorrect results for CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING when the convolution mode is CUDNN_CONVOLUTION and the product "n*k" (n  batch size, k  number of output feature maps) is large, i.e., several thousand or more. It appears that the CUDNN_CROSS_CORRELATION mode is not affected by this.
 Persistent LSTM backward pass with a hidden state size in the range 257 to 512 on GPUs with number of SMs between 22 and 31 might hang. This issue also exists in 7.1.1 and will be fixed in 7.1.3.
 Persistent GRU backward pass with a hidden state size in the range 513 to 720 on GPUs with exactly 30 SMs would hang. This issue also exists in 7.1.1 and will be fixed in 7.1.3.
 Algo 1 for forward convolution and dgrad may produce erroneous results when the filter size is greater than the input size.
Fixed Issues
The following issues have been fixed in this release:
 The uint8 input for convolution is restricted to Volta and later. We added support for older architectures, for algo: CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM.
 In some cases when algorithm CUDNN_CONVOLUTION_BWD_FILTER_ALGO1 was selected, the routine cudnnConvolutionBackwardFilter could fail at runtime and return CUDNN_STATUS_EXECUTION_FAILED. It now returns CUDNN_STATUS_NOT_SUPPORTED.
 cudnnSetRNNDescriptor no longer needs valid Dropout Descriptor in inference mode, user can pass NULL for Dropout Descriptor in inference mode.
cuDNN 7.1.1
This is the cuDNN 7.1.1 release notes. This release includes fixes from the previous cuDNN v7.x.x releases as well as the following additional changes.
Key Features and Enhancements
The following enhancements have been added to this release:
 Added new API cudnnSetRNNProjectionLayers and cudnnGetRNNProjectionLayers to support Projection Layer for the RNN LSTM cell. In this release only the inference use case will be supported. The bidirectional and the training forward and backward for training is not supported in 7.1.1 but will be supported in the upcoming 7.1.2 release without API changes. For all the unsupported cases in this release, CUDNN_NOT_SUPPORTED is returned when projection layer is set and the RNN is called.
 The cudnnGetRNNLinLayerMatrixParams() function was enhanced and
a bug was fixed without modifying its prototype. Specifically:
 The cudnnGetRNNLinLayerMatrixParams() function was updated to support the RNN projection feature. An extra linLayerID value of 8 can be used to retrieve the address and the size of the “recurrent” projection weight matrix when "mode" in cudnnSetRNNDescriptor() is configured to CUDNN_LSTM and the recurrent projection is enabled via cudnnSetRNNProjectionLayers().
 Instead of reporting the total number of elements in each weight matrix in the “linLayerMatDesc” filter descriptor, the cudnnGetRNNLinLayerMatrixParams() function returns the matrix size as two dimensions: rows and columns. This allows the user to easily print and initialize RNN weight matrices. Elements in each weight matrix are arranged in the rowmajor order. Due to historical reasons, the minimum number of dimensions in the filter descriptor is three. In previous versions of the cuDNN library, cudnnGetRNNLinLayerMatrixParams() returned the total number of weights as follows: filterDimA[0]=total_size, filterDimA[1]=1, filterDimA[2]=1. In v7.1.1, the format was changed to: filterDimA[0]=1, filterDimA[1]=rows, filterDimA[2]=columns. In both cases, the "format" field of the filter descriptor should be ignored when retrieved by cudnnGetFilterNdDescriptor().
 A bug in cudnnGetRNNLinLayerMatrixParams() was fixed to return a zeroed filter descriptor when the corresponding weight matrix does not exist. This occurs, for example, for linLayerID values of 03 when the first RNN layer is configured to exclude matrix multiplications applied to RNN input data (inputMode=CUDNN_SKIP_INPUT in cudnnSetRNNDescriptor() specifies implicit, fixed identity weight matrices for RNN input). Such cases in previous versions of the cuDNN library caused cudnnGetRNNLinLayerMatrixParams() to return corrupted filter descriptors with some entries from the previous call. A workaround was to create a new filter descriptor for every invocation of cudnnGetRNNLinLayerMatrixParams().
 The cudnnGetRNNLinLayerBiasParams() function was updated to report the bias column vectors in "linLayerBiasDesc" in the same format as cudnnGetRNNLinLayerMatrixParams(). In previous versions of the cuDNN library, cudnnGetRNNLinLayerBiasParams() returned the total number of adjustable bias parameters as follows: filterDimA[0]=total_size, filterDimA[1]=1, filterDimA[2]=1. In v7.1.1, the format was changed to: filterDimA[0]=1, filterDimA[1]=rows, filterDimA[2]=1 (number of columns). In both cases, the "format" field of the filter descriptor should be ignored when retrieved by cudnnGetFilterNdDescriptor(). The recurrent projection GEMM does not have a bias so the range of valid inputs for the "linLayerID" argument remains the same.
 Added support for use of Tensor Core for the CUDNN_RNN_ALGO_PERSIST_STATIC. This required cuda cuDNN v7.1 build with CUDA 9.1 and 387 or higher driver. It will not work with CUDA 9.0 and 384 driver.
 Added RNN search API that allows the application to provide an RNN descriptor and get a list of possible algorithm choices with performance and memory usage, to allow applications to choose between different implementations. For more information, refer to the documentation of: cudnnFindRNNForwardInferenceAlgorithmEx, cudnnFindRNNForwardTrainingAlgorithmEx, cudnnFindRNNBackwardDataAlgorithmEx, and cudnnFindRNNBackwardWeightsAlgorithmEx. In this release, the search will operate on STANDARD algorithm and will not support PERSISTENT algorithms of RNN.
 Added uint8 for support for the input data for cudnnConvolutionBiasActivationForward and cudnnConvolutionForward. Currently the support is on Volta (sm 70 ) and later architectures. Support for older architectures will be gradually added in the upcoming releases.
 Suport for CUDNN_ACTIVATION_IDENTITY is added to cudnnConvolutionBiasActivationForward. This allows users to perform Convolution and Bias without Activation.
 All API functions now support logging. User can trigger logging by setting environment variable “CUDNN_LOGINFO_DBG=1” and “CUDNN_LOGDEST_DBG= <option>” where <option> (i.e., the output destination of the log) can be chosen from “stdout”, “stderr”, or a file path. User may also use the new Set/GetCallBack functions to install their customized callback function. Log files can be added to the reported bugs or shared with us for analysis and future optimizations through partners.nvidia.com.
 Improved performance of 3D convolution on Volta architecture.
 The following algorelated functions have been added for this release: cudnnGetAlgorithmSpaceSize, cudnnSaveAlgorithm, cudnnRestoreAlgorithm, cudnnCreateAlgorithmDescriptor, cudnnSetAlgorithmDescriptor, cudnnGetAlgorithmDescriptor, cudnnDestroyAlgorithmDescriptor, cudnnCreateAlgorithmPerformance, cudnnSetAlgorithmPerformance, cudnnGetAlgorithmPerformance, cudnnDestroyAlgorithmPerformance.
 All algorithms for convolutions now support groupCount > 1. This includes cudnConvolutionForward(), cudnnConvolutionBackwardData(), and cudnnConvolutionBackwardFilter().
Known Issues
Following are known issues in this release:
 RNN search Algorithm is restricted to STANDARD algorithm.
 Newly added projection Layer supported for inference and one directional RNN cells.
 uint8 input for convolution is restricted to Volta and later.
 cudnnGet picks a slow algorithm that doesn't use Tensor Cores on Volta when inputs are FP16 and it is possible to do so.
 There may be a small performance regression on multilayer RNNs using the STANDARD algorithm with Tensor Core math in this release compared to 7.0.5.
Fixed Issues
The following issues have been fixed in this release:
 3D convolution performance improvements for Volta.
 Added support for Algorithm 0 data gradients to cover cases previously not supported.
 Removed the requirement for dropout Descriptor in RNN inference. Before application had to set a non point for the dropout Descriptor which was not used.
 Use of CUDNN_TENSOR_NCHW_VECT_C with nonzero padding resulted in a return status of CUDNN_STATUS_INTERNAL_ERROR. This issue is now fixed.
cuDNN 7.0.5
This is the cuDNN 7.0.5 release notes. This release includes fixes from the previous cuDNN v7.x.x releases as well as the following additional changes.
Key Features and Enhancements
The following enhancements have been added to this release:
 None.
Known Issues
Following are known issues in this release:
 cuDNN library may trigger a CPU floating point exception when FP exceptions are enabled by user. This issue exists for all 7.0.x releases.
 There are heavy use cases of RNN layers that might hit a memory allocation issue in the CUDA driver when using cuDNN v7 with CUDA 8.0 and R375 driver on prePascal architectures (Kepler and Maxwell). In these cases, subsequent CUDA kernels may fail to launch with an Error Code 30. To resolve the issue, it is recommended to use the latest R384 driver (from NVIDIA driver downloads) or to ensure that the persistence daemon is started. This behavior is observed on all 7.0.x releases.
 When using TENSOR_OP_MATH mode with cudnnConvolutionBiasActivationForward, the pointer to the bias must be aligned to 16 bytes and the size of allocated memory must be multiples of 256 elements. This behavior exists for all 7.0.x releases.
Fixed Issues
The following issues have been fixed in this release:
 Corrected the algorithm fallback behavior in RNN when user set to use CUDNN_TENSOR_OP_MATH when using compute card without Tensor Cores. Instead of returning CUDNN_STATUS_NOT_SUPPORTED, the RNN algorithm will now continue to run using CUDNN_DEFAULT_MATH. The correct behavior is to fall back to using default math when Tensor Core is not supported. Fixed to the expected behavior.
 On Volta hardware, BWD_FILTER_ALGO_1 and BWD_DATA_ALGO_1 convolutions using a number of filter elements greater than 512 were causing CUDA_ERROR_ILLEGAL_ADDRESS and CUDNN_STATUS_INTERNAL_ERROR errors. Logic was added to fall back to a generic kernel for these filter sizes.
 cuDNN v7 with CUDA 8.0 produced erroneous results on Volta for some common cases of Algo 1. Logic was added to fall back to a generic kernel when cudnn v7 with CUDA 8.0 is used on Volta.
cuDNN 7.0.4
This is the cuDNN 7.0.4 release notes. This release includes fixes from the previous cuDNN v7.x.x releases as well as the following additional changes.
Key Features and Enhancements
Performance improvements for grouped convolutions when input channels and output channels per group are 1, 2, or 4 for the following algorithms:
 CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM
 CUDNN_CONVOLUTION_BWD_DATA_ALGO0
 CUDNN_CONVOLUTION_BWD_DATA_ALGO_1
 CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0
 CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1
Known Issues
Following are known issues in this release:
 The CUDA 8.0 build of cuDNN may produce incorrect computations when run on Volta.
 cuDNN library triggers CPU floating point exception when FP exceptions are enabled by user. This issue exists for all 7.0.x releases.
 There are heavy use cases of RNN layers that might hit a memory allocation issue in the CUDA driver when using cuDNN v7 with CUDA 8.0 and R375 driver on prePascal architectures (Kepler and Maxwell). In these cases, subsequent CUDA kernels may fail to launch with an Error Code 30. To resolve the issue, it is recommended to use the latest R384 driver (from NVIDIA driver downloads) or to ensure that the persistence daemon is started. This behavior is observed on all 7.0.x releases.
 When using TENSOR_OP_MATH mode with cudnnConvolutionBiasActivationForward, the pointer to the bias must be aligned to 16 bytes and the size of allocated memory must be multiples of 256 elements. This behavior exists for all 7.0.x releases.
Fixed Issues
The following issues have been fixed in this release:
 Fixed outofband global memory accesses in the 256point 1D FFT kernel. The problem affected convolutions with 1x1 filters and tall but narrow images, e.g., 1x500 (WxH). In those cases, the workspace size for the FFT_TILING algo was computed incorrectly. There was no error in the FFT kernel.
 Eliminated a source of floating point exceptions in the CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED algorithm. The host code to generate a negative infinity floating point value was substituted with a different logic. By default, FP exceptions are disabled. However, a user program enabled them by invoking feenableexcept(). There are at least two other sources of FP exceptions in the cuDNN library, affecting for example BATCHNORM_SPATIAL_PERSISTENT. Those sources of FP exceptions will be eliminated in future releases of the cuDNN library.
cuDNN 7.0.3
This is the cuDNN 7.0.3 release notes. This release includes fixes from the previous cuDNN v7.x.x releases as well as the following additional changes.
Key Features and Enhancements
Known Issues
The following are known issues in this release:
 CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING may cause CUDA_ERROR_ILLEGAL_ADDRESS. This issue affects input images of just one 1 pixel in width and certain n, c, k, h combinations.
Fixed Issues
The following issues have been fixed in this release:
 AddTensor and TensorOp produce incorrect results for half and INT8 inputs for various use cases.
 cudnnPoolingBackward() can produce incorrect values for rare cases of nondeterministic MAX pooling with window_width > 256. These rare cases are when the maximum element in a window is duplicated horizontally (along width) by a stride of 256*k for some k. The behavior is now fixed to accumulate derivatives for the duplicate that is leftmost.
 cudnnGetConvolutionForwardWorkspaceSize() produces incorrect workspace size for algorithm FFT_TILING for 1d convolutions. This only occurs for large sized convolutions where intermediate calculations produce values greater than 2^31 (2 to the power of 31).
 CUDNN_STATUS_NOT_SUPPORTED returned by cudnnPooling*() functions for small x image (channels * height * width < 4).
cuDNN 7.0.2
This is the cuDNN 7.0.2 release notes. This release includes fixes from the previous cuDNN v7.x.x releases as well as the following additional changes.
Key Features and Enhancements
This is a patch release of cuDNN 7.0 and includes bug fixes and performance improvements mainly on Volta.
 Algo 1 Convolutions Performance Improvements
 Performance improvements were made to CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM, CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1, and CUDNN_CONVOLUTION_BWD_DATA_ALGO_1. These improvements consist of new SASS kernels and improved heuristics. The new kernels implement convolutions over various data sizes and tile sizes. The improved heuristics take advantage of these new kernels.
Known Issues
The following are known issues in this release:
 cudnnGetConvolutionForwardWorkspaceSize() returns overflowed size_t value for certain input shape for CUDNN_CONVOLUTION_*_ALGO_FFT_TILING.
 cudnnPoolingBackward() fails for pooling window size > 256.
Fixed Issues
The following issues have been fixed in this release:
 Batch Norm CUDNN_BATCHNORM_SPATIAL_PERSISTENT might get into race conditions in certain scenarios.
 cuDNN convolution layers using TENSOR_OP_MATH with fp16 inputs and outputs and fp32 compute will use “round to nearest” mode instead of “round to zero” mode as in 7.0.1. This rounding mode has proven to achieve better results in training.
 Fixed synchronization logic in the CUDNN_CTC_LOSS_ALGO_DETERMINISTIC algo for CTC. The original code would hang in rare cases.
 Convolution algorithms using TENSOR_OP_MATH returned a workspace size from *GetWorkspaceSize() smaller than actually necessary.
 The results of int8 are inaccurate in certain cases when calling cudnnConvolutionForward() in convolution layer.
 cudnnConvolutionForward() called with xDesc’s channel = yDesc’s channel = groupCount could compute incorrect values when vertical padding > 0.
cuDNN 7.0.1
This is the cuDNN 7.0.1 release notes. This release includes the following changes.
cuDNN v7.0.1 is the first release to support the Volta GPU architecture. In addition, cuDNN v7.0.1 brings new layers, grouped convolutions, and improved convolution find as error query mechanism.
Key Features and Enhancements
This cuDNN release includes the following key features and enhancements.
 Tensor Cores
 Version 7.0.1 of cuDNN is the first to support the Tensor Core operations in its implementation. Tensor Cores provide highly optimized matrix multiplication building blocks that do not have an equivalent numerical behavior in the traditional instructions, therefore, its numerical behavior is slightly different.
 cudnnSetConvolutionMathType, cudnnSetRNNMatrixMathType, and cudnnMathType_t
 The cudnnSetConvolutionMathType and
cudnnSetRNNMatrixMathType functions enable you to
choose whether or not to use Tensor Core operations in the convolution
and RNN layers respectively by setting the math mode to either
CUDNN_TENSOR_OP_MATH or
CUDNN_DEFAULT_MATH.
Tensor Core operations perform parallel floating point accumulation of multiple floating point products.
Setting the math mode to CUDNN_TENSOR_OP_MATH indicates that the library will use Tensor Core operations.
The default is CUDNN_DEFAULT_MATH. This default indicates that the Tensor Core operations will be avoided by the library. The default mode is a serialized operation whereas, the Tensor Core is a parallelized operation, therefore, the two might result in slightly different numerical results due to the different sequencing of operations.Note: The library falls back to the default math mode when Tensor Core operations are not supported or not permitted.  cudnnSetConvolutionGroupCount
 A new interface that allows applications to perform convolution groups in the convolution layers in a single API call.
 cudnnCTCLoss
 cudnnCTCLoss provides a GPU implementation of the Connectionist Temporal Classification (CTC) loss function for RNNs. The CTC loss function is used for phoneme recognition in speech and handwriting recognition.
 CUDNN_BATCHNORM_SPATIAL_PERSISTENT
 The CUDNN_BATCHNORM_SPATIAL_PERSISTENT function is a new batch normalization mode for cudnnBatchNormalizationForwardTraining and cudnnBatchNormalizationBackward. This mode is similar to CUDNN_BATCHNORM_SPATIAL, however, it can be faster for some tasks.
 cudnnQueryRuntimeError
 The cudnnQueryRuntimeError function reports error codes written by GPU kernels when executing cudnnBatchNormalizationForwardTraining and cudnnBatchNormalizationBackward with the CUDNN_BATCHNORM_SPATIAL_PERSISTENT mode.
 cudnnGetConvolutionForwardAlgorithm_v7
 This new API returns all algorithms sorted by expected performance (using internal heuristics). These algorithms are output similarly to cudnnFindConvolutionForwardAlgorithm.
 cudnnGetConvolutionBackwardDataAlgorithm_v7
 This new API returns all algorithms sorted by expected performance (using internal heuristics). These algorithms are output similarly to cudnnFindConvolutionBackwardAlgorithm.
 cudnnGetConvolutionBackwardFilterAlgorithm_v7
 This new API returns all algorithms sorted by expected performance (using internal heuristics). These algorithms are output similarly to cudnnFindConvolutionBackwardFilterAlgorithm.
 CUDNN_REDUCE_TENSOR_MUL_NO_ZEROS
 The MUL_NO_ZEROS function is a multiplication reduction that ignores zeros in the data.
 CUDNN_OP_TENSOR_NOT
 The OP_TENSOR_NOT function is a unary operation that takes the negative of (alpha*A).
 cudnnGetDropoutDescriptor
 The cudnnGetDropoutDescriptor function allows applications to get dropout values.
Using cuDNN v7.0.1
 Multithreading behavior has been modified. Multithreading is allowed only when using different cuDNN handles in different threads.
 In cudnnConvolutionBackwardFilter, dilated convolution did not support cases where the product of all filter dimensions was odd for half precision floating point. These are now supported by CUDNN_CONVOLUTION_BWD_FILTER_ALGO1.
 Fixed bug that produced a silent computation error for when a batch size was larger than 65536 for CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM.
 In getConvolutionForwardAlgorithm, an error was not correctly reported in v5 when the output size was larger than expected. In v6 the CUDNN_STATUS_NOT_SUPPORTED, error message displayed. In v7, this error is modified to CUDNN_STATUS_BAD_PARAM.
 In cudnnConvolutionBackwardFilter, cuDNN now runs some exceptional cases correctly where it previously erroneously returned CUDNN_STATUS_NOT_SUPPORTED. This impacted the algorithms CUDNN_CONVOLUTION_BWD_FILTER_ALGO0 and CUDNN_CONVOLUTION_BWD_FILTER_ALGO3.
Deprecated Features
 cudnnSetConvolution2dDescriptor_v4
 cudnnSetConvolution2dDescriptor_v5
 cudnnGetConvolution2dDescriptor_v4
 cudnnGetConvolution2dDescriptor_v5
 cudnnSetRNNDescriptor_v5  The nonsuffixed version of the
routines in cuDNN v7.0.1 are now mapped to their _v6
equivalent.
Attention: It is strongly advised to use the nonsuffixed version as the _v5 and _v6 routines will be removed in the next cuDNN release.
 cudnnGetConvolutionForwardAlgorithm, cudnnGetConvolutionBackwardDataAlgorithm, and cudnnGetConvolutionBackwardFilterAlgorithm  A _v7 version of this routine has been created. For more information, see the Backward compatibility and deprecation policy chapter of the cuDNN documentation for details.