Abstract
This is the API Reference documentation for the NVIDIA cuDNN version 8.7.0 library. This API Reference lists the datatyes and functions per library. Specifically, this reference consists of a cuDNN datatype reference section that describes the types of enums and a cuDNN API reference section that describes all routines in the cuDNN library API. The cuDNN API is a contextbased API that allows for easy multithreading and (optional) interoperability with CUDA streams.
For previously released cuDNN developer documentation, refer to the NVIDIA cuDNN Archives.
NVIDIA^{®} CUDA^{®} Deep Neural Network (cuDNN) library offers a contextbased API that allows for easy multithreading and (optional) interoperability with CUDA streams. This API Reference lists the datatyes and functions per library. Specifically, this reference consists of a cuDNN datatype reference section that describes the types of enums and a cuDNN API reference section that describes all routines in the cuDNN library API. The cuDNN library as well as this API document has been split into the following libraries:
2.1. API Changes For cuDNN 8.7.0
The following tables show which API functions were added, deprecated, and removed for the cuDNN 8.7.0.
Backend descriptor types 

cudnnRngDistribution_t 
CUDNN_BACKEND_OPERATION_RNG_DESCRIPTOR 
CUDNN_BACKEND_RNG_DESCRIPTOR 
2.2. API Changes For cuDNN 8.5.0
The following tables show which API functions were added, deprecated, and removed for the cuDNN 8.5.0.
Backend descriptor types 

cudnnBackendNormFwdPhase_t 
cudnnBackendNormMode_t 
CUDNN_BACKEND_OPERATION_CONCAT_DESCRIPTOR 
CUDNN_BACKEND_OPERATION_NORM_BACKWARD_DESCRIPTOR 
CUDNN_BACKEND_OPERATION_NORM_FORWARD_DESCRIPTOR 
CUDNN_BACKEND_OPERATION_SIGNAL_DESCRIPTOR 
cudnnFraction_t 
cudnnSignalMode_t 
2.3. API Changes For cuDNN 8.4.0
The following tables show which API functions were added, deprecated, and removed for the cuDNN 8.4.0.
Backend descriptor types 

cudnnBackendBehaviorNote_t 
CUDNN_BACKEND_OPERATION_REDUCTION_DESCRIPTOR 
CUDNN_BACKEND_POINTWISE_DESCRIPTOR 
CUDNN_BACKEND_REDUCTION_DESCRIPTOR 
cudnnBackendTensorReordering_t 
cudnnBnFinalizeStatsMode_t 
cudnnPaddingMode_t 
cudnnResampleMode_t 
2.4. API Changes For cuDNN 8.3.0
The following tables show which API functions were added, deprecated, and removed for the cuDNN 8.3.0.
Backend descriptor types 

CUDNN_BACKEND_OPERATION_RESAMPLE_BWD_DESCRIPTOR 
CUDNN_BACKEND_OPERATION_RESAMPLE_FWD_DESCRIPTOR 
CUDNN_BACKEND_RESAMPLE_DESCRIPTOR 
2.5. API Changes For cuDNN 8.2.0
The following tables show which API functions were added, deprecated, and removed for the cuDNN 8.2.0.
New functions 

cudnnGetActivationDescriptorSwishBeta() 
cudnnSetActivationDescriptorSwishBeta() 
2.6. API Changes For cuDNN 8.1.0
The following tables show which API functions were added, deprecated, and removed for the cuDNN 8.1.0.
Backend descriptor types 

CUDNN_BACKEND_MATMUL_DESCRIPTOR 
CUDNN_BACKEND_OPERATION_MATMUL_DESCRIPTOR 
2.7. API Changes For cuDNN 8.0.3
The following tables show which API functions were added, deprecated, and removed for the cuDNN 8.0.3.
2.8. API Changes For cuDNN 8.0.2
The following tables show which API functions were added, deprecated, and removed for the cuDNN 8.0.2.
New functions and data types 

cudnnRNNBackwardData_v8() 
cudnnRNNBackwardWeights_v8() 
2.9. API Changes For cuDNN 8.0.0 Preview
The following tables show which API functions were added, deprecated, and removed for the cuDNN 8.0.0 Preview Release.
For our deprecation policy, refer to the Backward Compatibility And Deprecation Policy section in the cuDNN Developer Guide.
Deprecated functions and data types  Replaced with 

cudnnCopyAlgorithmDescriptor() 

cudnnCreateAlgorithmDescriptor() 

cudnnCreatePersistentRNNPlan() 
cudnnBuildRNNDynamic() 
cudnnDestroyAlgorithmDescriptor() 

cudnnDestroyPersistentRNNPlan() 

cudnnFindRNNBackwardDataAlgorithmEx() 

cudnnFindRNNBackwardWeightsAlgorithmEx() 

cudnnFindRNNForwardInferenceAlgorithmEx() 

cudnnFindRNNForwardTrainingAlgorithmEx() 

cudnnGetAlgorithmDescriptor() 

cudnnGetAlgorithmPerformance() 

cudnnGetAlgorithmSpaceSize() 

cudnnGetRNNBackwardDataAlgorithmMaxCount() 

cudnnGetRNNBackwardWeightsAlgorithmMaxCount() 


cudnnGetRNNDescriptor_v8() 
cudnnGetRNNForwardInferenceAlgorithmMaxCount() 

cudnnGetRNNForwardTrainingAlgorithmMaxCount() 


cudnnGetRNNWeightParams() 
cudnnGetRNNParamsSize() 
cudnnGetRNNWeightSpaceSize() 

cudnnGetRNNTempSpaceSizes() 
cudnnPersistentRNNPlan_t 

cudnnRestoreAlgorithm() 


cudnnRNNBackwardData_v8() 

cudnnRNNBackwardWeights_v8() 

cudnnRNNForward() 
cudnnRNNGetClip() 
cudnnRNNGetClip_v8() 
cudnnRNNSetClip() 
cudnnRNNSetClip_v8() 
cudnnSaveAlgorithm() 

cudnnSetAlgorithmDescriptor() 

cudnnSetAlgorithmPerformance() 

cudnnSetPersistentRNNPlan() 

cudnnSetRNNAlgorithmDescriptor() 


cudnnSetRNNDescriptor_v8() 
Removed functions and data types 

cudnnConvolutionBwdDataPreference_t 
cudnnConvolutionBwdFilterPreference_t 
cudnnConvolutionFwdPreference_t 
cudnnGetConvolutionBackwardDataAlgorithm() 
cudnnGetConvolutionBackwardFilterAlgorithm() 
cudnnGetConvolutionForwardAlgorithm() 
cudnnGetRNNDescriptor() 
cudnnSetRNNDescriptor() 
3.1. Data Type References
3.1.1. Pointer To Opaque Struct Types
3.1.1.1. cudnnActivationDescriptor_t
cudnnActivationDescriptor_t
is a pointer to an opaque structure holding the description of an activation operation. cudnnCreateActivationDescriptor() is used to create one instance, and cudnnSetActivationDescriptor() must be used to initialize this instance.
3.1.1.2. cudnnCTCLossDescriptor_t
cudnnCTCLossDescriptor_t
is a pointer to an opaque structure holding the description of a CTC loss operation. cudnnCreateCTCLossDescriptor() is used to create one instance, cudnnSetCTCLossDescriptor() is used to initialize this instance, and cudnnDestroyCTCLossDescriptor() is used to destroy this instance.
3.1.1.3. cudnnDropoutDescriptor_t
cudnnDropoutDescriptor_t
is a pointer to an opaque structure holding the description of a dropout operation. cudnnCreateDropoutDescriptor() is used to create one instance, cudnnSetDropoutDescriptor() is used to initialize this instance, cudnnDestroyDropoutDescriptor() is used to destroy this instance, cudnnGetDropoutDescriptor() is used to query fields of a previously initialized instance, cudnnRestoreDropoutDescriptor() is used to restore an instance to a previously saved off state.
3.1.1.4. cudnnFilterDescriptor_t
cudnnFilterDescriptor_t
is a pointer to an opaque structure holding the description of a filter dataset. cudnnCreateFilterDescriptor() is used to create one instance, and cudnnSetFilter4dDescriptor() or cudnnSetFilterNdDescriptor() must be used to initialize this instance.
3.1.1.5. cudnnHandle_t
cudnnHandle_t
is a pointer to an opaque structure holding the cuDNN library context. The cuDNN library context must be created using cudnnCreate() and the returned handle must be passed to all subsequent library function calls. The context should be destroyed at the end using cudnnDestroy(). The context is associated with only one GPU device, the current device at the time of the call to cudnnCreate(). However, multiple contexts can be created on the same GPU device.
3.1.1.6. cudnnLRNDescriptor_t
cudnnLRNDescriptor_t
is a pointer to an opaque structure holding the parameters of a local response normalization. cudnnCreateLRNDescriptor() is used to create one instance, and the routine cudnnSetLRNDescriptor() must be used to initialize this instance.
3.1.1.7. cudnnOpTensorDescriptor_t
cudnnOpTensorDescriptor_t
is a pointer to an opaque structure holding the description of a Tensor Core operation, used as a parameter to cudnnOpTensor(). cudnnCreateOpTensorDescriptor() is used to create one instance, and cudnnSetOpTensorDescriptor() must be used to initialize this instance.
3.1.1.8. cudnnPoolingDescriptor_t
cudnnPoolingDescriptor_t
is a pointer to an opaque structure holding the description of a pooling operation. cudnnCreatePoolingDescriptor() is used to create one instance, and cudnnSetPoolingNdDescriptor() or cudnnSetPooling2dDescriptor() must be used to initialize this instance.
3.1.1.9. cudnnReduceTensorDescriptor_t
cudnnReduceTensorDescriptor_t
is a pointer to an opaque structure holding the description of a tensor reduction operation, used as a parameter to cudnnReduceTensor(). cudnnCreateReduceTensorDescriptor() is used to create one instance, and cudnnSetReduceTensorDescriptor() must be used to initialize this instance.
3.1.1.10. cudnnSpatialTransformerDescriptor_t
cudnnSpatialTransformerDescriptor_t
is a pointer to an opaque structure holding the description of a spatial transformation operation. cudnnCreateSpatialTransformerDescriptor() is used to create one instance, cudnnSetSpatialTransformerNdDescriptor() is used to initialize this instance, and cudnnDestroySpatialTransformerDescriptor() is used to destroy this instance.
3.1.1.11. cudnnTensorDescriptor_t
cudnnTensorDescriptor_t
is a pointer to an opaque structure holding the description of a generic nD dataset. cudnnCreateTensorDescriptor() is used to create one instance, and one of the routines cudnnSetTensorNdDescriptor(), cudnnSetTensor4dDescriptor() or cudnnSetTensor4dDescriptorEx() must be used to initialize this instance.
3.1.1.12. cudnnTensorTransformDescriptor_t
cudnnTensorTransformDescriptor_t
is an opaque structure containing the description of the tensor transform. Use the cudnnCreateTensorTransformDescriptor() function to create an instance of this descriptor, and cudnnDestroyTensorTransformDescriptor() function to destroy a previously created instance.
3.1.2. Enumeration Types
3.1.2.1. cudnnActivationMode_t
cudnnActivationMode_t
is an enumerated type used to select the neuron activation function used in cudnnActivationForward(), cudnnActivationBackward(), and cudnnConvolutionBiasActivationForward().
Values

CUDNN_ACTIVATION_SIGMOID

Selects the sigmoid function.

CUDNN_ACTIVATION_RELU

Selects the rectified linear function.

CUDNN_ACTIVATION_TANH

Selects the hyperbolic tangent function.

CUDNN_ACTIVATION_CLIPPED_RELU

Selects the clipped rectified linear function.

CUDNN_ACTIVATION_ELU

Selects the exponential linear function.

CUDNN_ACTIVATION_IDENTITY

Selects the identity function, intended for bypassing the activation step in cudnnConvolutionBiasActivationForward(). (The cudnnConvolutionBiasActivationForward() function must use
CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM
.) Does not work with cudnnActivationForward() or cudnnActivationBackward(). 
CUDNN_ACTIVATION_SWISH
 Selects the swish function.
3.1.2.2. cudnnAlgorithm_t
This function has been deprecated in cuDNN 8.0.
3.1.2.3. cudnnBatchNormMode_t
cudnnBatchNormMode_t
is an enumerated type used to specify the mode of operation in cudnnBatchNormalizationForwardInference(), cudnnBatchNormalizationForwardTraining(), cudnnBatchNormalizationBackward() and cudnnDeriveBNTensorDescriptor() routines.
Values

CUDNN_BATCHNORM_PER_ACTIVATION

Normalization is performed peractivation. This mode is intended to be used after the nonconvolutional network layers. In this mode, the tensor dimensions of
bnBias
andbnScale
and the parameters used in thecudnnBatchNormalization*
functions are 1xCxHxW. 
CUDNN_BATCHNORM_SPATIAL

Normalization is performed over N+spatial dimensions. This mode is intended for use after convolutional layers (where spatial invariance is desired). In this mode the
bnBias
andbnScale
tensor dimensions are 1xCx1x1. 
CUDNN_BATCHNORM_SPATIAL_PERSISTENT

This mode is similar to
CUDNN_BATCHNORM_SPATIAL
but it can be faster for some tasks.An optimized path may be selected for
CUDNN_DATA_FLOAT
andCUDNN_DATA_HALF
types, compute capability 6.0 or higher for the following two batch normalization API calls: cudnnBatchNormalizationForwardTraining(), and cudnnBatchNormalizationBackward(). In the case of cudnnBatchNormalizationBackward(), thesavedMean
andsavedInvVariance
arguments should not beNULL
.The rest of this section applies to
NCHW
mode only:This mode may use a scaled atomic integer reduction that is deterministic but imposes more restrictions on the input data range. When a numerical overflow occurs, the algorithm may produce NaNs or Infs (infinity) in output buffers.
When Infs/NaNs are present in the input data, the output in this mode is the same as from a pure floatingpoint implementation.
For finite but very large input values, the algorithm may encounter overflows more frequently due to a lower dynamic range and emit Infs/NaNs while
CUDNN_BATCHNORM_SPATIAL
will produce finite results. The user can invoke cudnnQueryRuntimeError() to check if a numerical overflow occurred in this mode.
3.1.2.4. cudnnBatchNormOps_t
cudnnBatchNormOps_t
is an enumerated type used to specify the mode of operation in cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize(), cudnnBatchNormalizationForwardTrainingEx(), cudnnGetBatchNormalizationBackwardExWorkspaceSize(), cudnnBatchNormalizationBackwardEx(), and cudnnGetBatchNormalizationTrainingExReserveSpaceSize() functions.
Values

CUDNN_BATCHNORM_OPS_BN

Only batch normalization is performed, peractivation.

CUDNN_BATCHNORM_OPS_BN_ACTIVATION

First, the batch normalization is performed, and then the activation is performed.

CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION

Performs the batch normalization, then elementwise addition, followed by the activation operation.
3.1.2.5. cudnnCTCLossAlgo_t
cudnnCTCLossAlgo_t
is an enumerated type that exposes the different algorithms available to execute the CTC loss operation.
Values

CUDNN_CTC_LOSS_ALGO_DETERMINISTIC

Results are guaranteed to be reproducible.

CUDNN_CTC_LOSS_ALGO_NON_DETERMINISTIC

Results are not guaranteed to be reproducible.
3.1.2.6. cudnnDataType_t
cudnnDataType_t
is an enumerated type indicating the data type to which a tensor descriptor or filter descriptor refers.
Values

CUDNN_DATA_FLOAT

The data is a 32bit singleprecision floatingpoint (
float
). 
CUDNN_DATA_DOUBLE

The data is a 64bit doubleprecision floatingpoint (
double
). 
CUDNN_DATA_HALF

The data is a 16bit floatingpoint.

CUDNN_DATA_INT8

The data is an 8bit signed integer.

CUDNN_DATA_INT32

The data is a 32bit signed integer.

CUDNN_DATA_INT8x4

The data is 32bit elements each composed of 4 8bit signed integers. This data type is only supported with the tensor format
CUDNN_TENSOR_NCHW_VECT_C
. 
CUDNN_DATA_UINT8
 The data is an 8bit unsigned integer.

CUDNN_DATA_UINT8x4

The data is 32bit elements each composed of 4 8bit unsigned integers. This data type is only supported with the tensor format
CUDNN_TENSOR_NCHW_VECT_C
. 
CUDNN_DATA_INT8x32

The data is 32element vectors, each element being an 8bit signed integer. This data type is only supported with the tensor format
CUDNN_TENSOR_NCHW_VECT_C
. Moreover, this data type can only be used withalgo 1
, meaning,CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM
. For more information, refer to cudnnConvolutionFwdAlgo_t. 
CUDNN_DATA_BFLOAT16
 The data is a 16bit quantity, with 7 mantissa bits, 8 exponent bits, and 1 sign bit.

CUDNN_DATA_INT64
 The data is a 64bit signed integer.

CUDNN_DATA_BOOLEAN

The data is a boolean (
bool
).Note that for type
CUDNN_TYPE_BOOLEAN
, elements are expected to be “packed”: that is, one byte contains 8 elements of typeCUDNN_TYPE_BOOLEAN
. Further, within each byte, elements are indexed from the least significant bit to the most significant bit. For example, a 1 dimensional tensor of 8 elements containing 01001111 has value 1 for elements 0 through 3, 0 for elements 4 and 5, 1 for element 6 and 0 for element 7.Tensors with more than 8 elements simply use more bytes, where the order is also from least significant to most significant byte. Note, CUDA is littleendian, meaning that the least significant byte has the lower memory address address. For example, in the case of 16 elements, 01001111 11111100 has value 1 for elements 0 through 3, 0 for elements 4 and 5, 1 for element 6 and 0 for element 7, value 0 for elements 8 and 9, 1 for elements 10 through 15.

CUDNN_DATA_FP8_E4M3
 The data is an 8bit quantity, with 3 mantissa bits, 4 exponent bits, and 1 sign bit.

CUDNN_DATA_FP8_E5M2
 The data is an 8bit quantity, with 2 mantissa bits, 5 exponent bits, and 1 sign bit.

CUDNN_DATA_FAST_FLOAT_FOR_FP8

The data type is a higher throughput but lower precision compute type (compared to
CUDNN_DATA_FLOAT
) used for FP8 tensor core operations
3.1.2.7. cudnnDeterminism_t
cudnnDeterminism_t
is an enumerated type used to indicate if the computed results are deterministic (reproducible). For more information, refer to Reproducibility (determinism) in the cuDNN Developer Guide.
Values

CUDNN_NON_DETERMINISTIC

Results are not guaranteed to be reproducible.

CUDNN_DETERMINISTIC

Results are guaranteed to be reproducible.
3.1.2.8. cudnnDivNormMode_t
cudnnDivNormMode_t
is an enumerated type used to specify the mode of operation in cudnnDivisiveNormalizationForward() and cudnnDivisiveNormalizationBackward().
Values

CUDNN_DIVNORM_PRECOMPUTED_MEANS

The means tensor data pointer is expected to contain means or other kernel convolution values precomputed by the user. The means pointer can also be
NULL
, in that case, it's considered to be filled with zeroes. This is equivalent to spatial LRN.Note:In the backward pass, the means are treated as independent inputs and the gradient over means is computed independently. In this mode, to yield a net gradient over the entire LCN computational graph, the
destDiffMeans
result should be backpropagated through the user's means layer (which can be implemented using average pooling) and added to thedestDiffData
tensor produced by cudnnDivisiveNormalizationBackward().
3.1.2.9. cudnnErrQueryMode_t
cudnnErrQueryMode_t
is an enumerated type passed to cudnnQueryRuntimeError() to select the remote kernel error query mode.
Values

CUDNN_ERRQUERY_RAWCODE

Read the error storage location regardless of the kernel completion status.

CUDNN_ERRQUERY_NONBLOCKING

Report if all tasks in the user stream of the cuDNN handle were completed. If that is the case, report the remote kernel error code.

CUDNN_ERRQUERY_BLOCKING

Wait for all tasks to complete in the user stream before reporting the remote kernel error code.
3.1.2.10. cudnnFoldingDirection_t
cudnnFoldingDirection_t
is an enumerated type used to select the folding direction. For more information, refer to cudnnTensorTransformDescriptor_t.
Data Member

CUDNN_TRANSFORM_FOLD = 0U

Selects folding.

CUDNN_TRANSFORM_UNFOLD = 1U

Selects unfolding.
3.1.2.11. cudnnIndicesType_t
cudnnIndicesType_t
is an enumerated type used to indicate the data type for the indices to be computed by the cudnnReduceTensor() routine. This enumerated type is used as a field for the cudnnReduceTensorDescriptor_t descriptor.
Values

CUDNN_32BIT_INDICES

Compute unsigned int indices.

CUDNN_64BIT_INDICES

Compute unsigned long indices.

CUDNN_16BIT_INDICES

Compute unsigned short indices.

CUDNN_8BIT_INDICES

Compute unsigned char indices.
3.1.2.12. cudnnLRNMode_t
cudnnLRNMode_t
is an enumerated type used to specify the mode of operation in cudnnLRNCrossChannelForward() and cudnnLRNCrossChannelBackward().
Values

CUDNN_LRN_CROSS_CHANNEL_DIM1

LRN computation is performed across the tensor's dimension
dimA[1]
.
3.1.2.13. cudnnMathType_t
cudnnMathType_t
is an enumerated type used to indicate if the use of Tensor Core operations is permitted in a given library routine.
Values

CUDNN_DEFAULT_MATH

Tensor Core operations are not used on preNVIDIA A100 GPU devices. On A100 GPU architecture devices, Tensor Core TF32 operation is permitted.

CUDNN_TENSOR_OP_MATH

The use of Tensor Core operations is permitted but will not actively perform datatype down conversion on tensors in order to utilize Tensor Cores.

CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION

The use of Tensor Core operations is permitted and will actively perform datatype down conversion on tensors in order to utilize Tensor Cores.

CUDNN_FMA_MATH

Restricted to only kernels that use FMA instructions.
On preNVIDIA A100 GPU devices, CUDNN_DEFAULT_MATH
and CUDNN_FMA_MATH
have the same behavior: Tensor Core kernels will not be selected. With NVIDIA Ampere Architecture and CUDA toolkit 11, CUDNN_DEFAULT_MATH
permits TF32 Tensor Core operation and CUDNN_FMA_MATH
does not. The TF32 behavior for CUDNN_DEFAULT_MATH
and the other Tensor Core math types can be explicitly disabled by the environment variable NVIDIA_TF32_OVERRIDE=0
.
3.1.2.14. cudnnNanPropagation_t
cudnnNanPropagation_t
is an enumerated type used to indicate if a given routine should propagate Nan
numbers. This enumerated type is used as a field for the cudnnActivationDescriptor_t
descriptor and cudnnPoolingDescriptor_t
descriptor.
Values

CUDNN_NOT_PROPAGATE_NAN

Nan
numbers are not propagated. 
CUDNN_PROPAGATE_NAN

Nan
numbers are propagated.
3.1.2.15. cudnnNormAlgo_t
cudnnNormAlgo_t
is an enumerated type used to specify the algorithm to execute the normalization operation.
Values

CUDNN_NORM_ALGO_STANDARD

Standard normalization is performed.

CUDNN_NORM_ALGO_PERSIST

This mode is similar to
CUDNN_NORM_ALGO_STANDARD
, however it only supportsCUDNN_NORM_PER_CHANNEL
and can be faster for some tasks.An optimized path may be selected for
CUDNN_DATA_FLOAT
andCUDNN_DATA_HALF
types, compute capability 6.0 or higher for the following two normalization API calls: cudnnNormalizationForwardTraining() and cudnnNormalizationBackward(). In the case of cudnnNormalizationBackward(), thesavedMean
andsavedInvVariance
arguments should not beNULL
.The rest of this section applies to NCHW mode only: This mode may use a scaled atomic integer reduction that is deterministic but imposes more restrictions on the input data range. When a numerical overflow occurs, the algorithm may produce NaNs or Infs (infinity) in output buffers.
When Infs/NaNs are present in the input data, the output in this mode is the same as from a pure floatingpoint implementation.
For finite but very large input values, the algorithm may encounter overflows more frequently due to a lower dynamic range and emit Infs/NaNs while
CUDNN_NORM_ALGO_STANDARD
will produce finite results. The user can invoke cudnnQueryRuntimeError() to check if a numerical overflow occurred in this mode.
3.1.2.16. cudnnNormMode_t
cudnnNormMode_t
is an enumerated type used to specify the mode of operation in cudnnNormalizationForwardInference(), cudnnNormalizationForwardTraining(), cudnnBatchNormalizationBackward(), cudnnGetNormalizationForwardTrainingWorkspaceSize(), cudnnGetNormalizationBackwardWorkspaceSize(), and cudnnGetNormalizationTrainingReserveSpaceSize() routines.
Values

CUDNN_NORM_PER_ACTIVATION

Normalization is performed peractivation. This mode is intended to be used after the nonconvolutional network layers. In this mode, the tensor dimensions of
normBias
andnormScale
and the parameters used in thecudnnNormalization*
functions are 1xCxHxW. 
CUDNN_NORM_PER_CHANNEL

Normalization is performed perchannel over N+spatial dimensions. This mode is intended for use after convolutional layers (where spatial invariance is desired). In this mode, the
normBias
andnormScale
tensor dimensions are 1xCx1x1.
3.1.2.17. cudnnNormOps_t
cudnnNormOps_t
is an enumerated type used to specify the mode of operation in cudnnGetNormalizationForwardTrainingWorkspaceSize(), cudnnNormalizationForwardTraining(), cudnnGetNormalizationBackwardWorkspaceSize(), cudnnNormalizationBackward(), and cudnnGetNormalizationTrainingReserveSpaceSize() functions.
Values

CUDNN_NORM_OPS_NORM

Only normalization is performed.

CUDNN_NORM_OPS_NORM_ACTIVATION

First, the normalization is performed, then the activation is performed.

CUDNN_NORM_OPS_NORM_ADD_ACTIVATION

Performs the normalization, then elementwise addition, followed by the activation operation.
3.1.2.18. cudnnOpTensorOp_t
cudnnOpTensorOp_t
is an enumerated type used to indicate the Tensor Core operation to be used by the cudnnOpTensor() routine. This enumerated type is used as a field for the cudnnOpTensorDescriptor_t descriptor.
Values

CUDNN_OP_TENSOR_ADD

The operation to be performed is addition.

CUDNN_OP_TENSOR_MUL

The operation to be performed is multiplication.

CUDNN_OP_TENSOR_MIN

The operation to be performed is a minimum comparison.

CUDNN_OP_TENSOR_MAX

The operation to be performed is a maximum comparison.

CUDNN_OP_TENSOR_SQRT

The operation to be performed is square root, performed on only the
A
tensor. 
CUDNN_OP_TENSOR_NOT

The operation to be performed is negation, performed on only the
A
tensor.
3.1.2.19. cudnnPoolingMode_t
cudnnPoolingMode_t
is an enumerated type passed to cudnnSetPooling2dDescriptor() to select the pooling method to be used by cudnnPoolingForward() and cudnnPoolingBackward().
Values

CUDNN_POOLING_MAX

The maximum value inside the pooling window is used.

CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING

Values inside the pooling window are averaged. The number of elements used to calculate the average includes spatial locations falling in the padding region.

CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING

Values inside the pooling window are averaged. The number of elements used to calculate the average excludes spatial locations falling in the padding region.

CUDNN_POOLING_MAX_DETERMINISTIC

The maximum value inside the pooling window is used. The algorithm used is deterministic.
3.1.2.20. cudnnReduceTensorIndices_t
cudnnReduceTensorIndices_t
is an enumerated type used to indicate whether indices are to be computed by the cudnnReduceTensor() routine. This enumerated type is used as a field for the cudnnReduceTensorDescriptor_t descriptor.
Values

CUDNN_REDUCE_TENSOR_NO_INDICES

Do not compute indices.

CUDNN_REDUCE_TENSOR_FLATTENED_INDICES

Compute indices. The resulting indices are relative, and flattened.
3.1.2.21. cudnnReduceTensorOp_t
cudnnReduceTensorOp_t
is an enumerated type used to indicate the Tensor Core operation to be used by the cudnnReduceTensor() routine. This enumerated type is used as a field for the cudnnReduceTensorDescriptor_t descriptor.
Values

CUDNN_REDUCE_TENSOR_ADD

The operation to be performed is addition.

CUDNN_REDUCE_TENSOR_MUL

The operation to be performed is multiplication.

CUDNN_REDUCE_TENSOR_MIN

The operation to be performed is a minimum comparison.

CUDNN_REDUCE_TENSOR_MAX

The operation to be performed is a maximum comparison.

CUDNN_REDUCE_TENSOR_AMAX

The operation to be performed is a maximum comparison of absolute values.

CUDNN_REDUCE_TENSOR_AVG

The operation to be performed is averaging.

CUDNN_REDUCE_TENSOR_NORM1

The operation to be performed is addition of absolute values.

CUDNN_REDUCE_TENSOR_NORM2

The operation to be performed is a square root of the sum of squares.

CUDNN_REDUCE_TENSOR_MUL_NO_ZEROS

The operation to be performed is multiplication, not including elements of value zero.
3.1.2.22. cudnnRNNAlgo_t
cudnnRNNAlgo_t
is an enumerated type used to specify the algorithm used in the cudnnRNNForwardInference(), cudnnRNNForwardTraining(), cudnnRNNBackwardData() and cudnnRNNBackwardWeights() routines.
Values

CUDNN_RNN_ALGO_STANDARD
 Each RNN layer is executed as a sequence of operations. This algorithm is expected to have robust performance across a wide range of network parameters.

CUDNN_RNN_ALGO_PERSIST_STATIC

The recurrent parts of the network are executed using a persistent kernel approach. This method is expected to be fast when the first dimension of the input tensor is small (meaning, a small minibatch).
CUDNN_RNN_ALGO_PERSIST_STATIC
is only supported on devices with compute capability >= 6.0. 
CUDNN_RNN_ALGO_PERSIST_DYNAMIC

The recurrent parts of the network are executed using a persistent kernel approach. This method is expected to be fast when the first dimension of the input tensor is small (meaning, a small minibatch). When using
CUDNN_RNN_ALGO_PERSIST_DYNAMIC
persistent kernels are prepared at runtime and are able to optimize using the specific parameters of the network and active GPU. As such, when usingCUDNN_RNN_ALGO_PERSIST_DYNAMIC
a onetime plan preparation stage must be executed. These plans can then be reused in repeated calls with the same model parameters.The limits on the maximum number of hidden units supported when using
CUDNN_RNN_ALGO_PERSIST_DYNAMIC
are significantly higher than the limits when usingCUDNN_RNN_ALGO_PERSIST_STATIC
, however throughput is likely to significantly reduce when exceeding the maximums supported byCUDNN_RNN_ALGO_PERSIST_STATIC
. In this regime, this method will still outperformCUDNN_RNN_ALGO_STANDARD
for some cases.CUDNN_RNN_ALGO_PERSIST_DYNAMIC
is only supported on devices with compute capability >= 6.0 on Linux machines.
3.1.2.23. cudnnSamplerType_t
cudnnSamplerType_t
is an enumerated type passed to cudnnSetSpatialTransformerNdDescriptor() to select the sampler type to be used by cudnnSpatialTfSamplerForward() and cudnnSpatialTfSamplerBackward().
Values

CUDNN_SAMPLER_BILINEAR
 Selects the bilinear sampler.
3.1.2.24. cudnnSeverity_t
cudnnSeverity_t
is an enumerated type passed to the customized callback function for logging that users may set. This enumerate describes the severity level of the item, so the customized logging call back may react differently.
Values

CUDNN_SEV_FATAL

This value indicates a fatal error emitted by cuDNN.

CUDNN_SEV_ERROR

This value indicates a normal error emitted by cuDNN.

CUDNN_SEV_WARNING

This value indicates a warning emitted by cuDNN.

CUDNN_SEV_INFO

This value indicates a piece of information (for example, API log) emitted by cuDNN.
3.1.2.25. cudnnSoftmaxAlgorithm_t
cudnnSoftmaxAlgorithm_t
is used to select an implementation of the softmax function used in cudnnSoftmaxForward() and cudnnSoftmaxBackward().
Values

CUDNN_SOFTMAX_FAST

This implementation applies the straightforward softmax operation.

CUDNN_SOFTMAX_ACCURATE

This implementation scales each point of the softmax input domain by its maximum value to avoid potential floating point overflows in the softmax evaluation.

CUDNN_SOFTMAX_LOG

This entry performs the log softmax operation, avoiding overflows by scaling each point in the input domain as in
CUDNN_SOFTMAX_ACCURATE
.
3.1.2.26. cudnnSoftmaxMode_t
cudnnSoftmaxMode_t
is used to select over which data the cudnnSoftmaxForward() and cudnnSoftmaxBackward() are computing their results.
Values

CUDNN_SOFTMAX_MODE_INSTANCE

The softmax operation is computed per image (
N
) across the dimensionsC,H,W
. 
CUDNN_SOFTMAX_MODE_CHANNEL

The softmax operation is computed per spatial location (
H,W
) per image (N
) across dimensionC
.
3.1.2.27. cudnnStatus_t
cudnnStatus_t
is an enumerated type used for function status returns. All cuDNN library functions return their status, which can be one of the following values:
Values

CUDNN_STATUS_SUCCESS
 The operation was completed successfully.

CUDNN_STATUS_NOT_INITIALIZED
 The cuDNN library was not initialized properly. This error is usually returned when a call to cudnnCreate() fails or when cudnnCreate() has not been called prior to calling another cuDNN routine. In the former case, it is usually due to an error in the CUDA Runtime API called by cudnnCreate() or by an error in the hardware setup.

CUDNN_STATUS_ALLOC_FAILED

Resource allocation failed inside the cuDNN library. This is usually caused by an internal
cudaMalloc()
failure.To correct, prior to the function call, deallocate previously allocated memory as much as possible.

CUDNN_STATUS_BAD_PARAM

An incorrect value or parameter was passed to the function.
To correct, ensure that all the parameters being passed have valid values.

CUDNN_STATUS_ARCH_MISMATCH

The function requires a feature absent from the current GPU device. Note that cuDNN only supports devices with compute capabilities greater than or equal to 3.0.
To correct, compile and run the application on a device with appropriate compute capability.

CUDNN_STATUS_MAPPING_ERROR

An access to GPU memory space failed, which is usually caused by a failure to bind a texture.
To correct, prior to the function call, unbind any previously bound textures.
Otherwise, this may indicate an internal error/bug in the library.

CUDNN_STATUS_EXECUTION_FAILED

The GPU program failed to execute. This is usually caused by a failure to launch some cuDNN kernel on the GPU, which can occur for multiple reasons.
To correct, check that the hardware, an appropriate version of the driver, and the cuDNN library are correctly installed.
Otherwise, this may indicate an internal error/bug in the library.

CUDNN_STATUS_INTERNAL_ERROR
 An internal cuDNN operation failed.

CUDNN_STATUS_NOT_SUPPORTED
 The functionality requested is not presently supported by cuDNN.

CUDNN_STATUS_LICENSE_ERROR

The functionality requested requires some license and an error was detected when trying to check the current licensing. This error can happen if the license is not present or is expired or if the environment variable
NVIDIA_LICENSE_FILE
is not set properly. 
CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING

A runtime library required by cuDNN cannot be found in the predefined search paths. These libraries are
libcuda.so
(nvcuda.dll
) andlibnvrtc.so
(nvrtc64_<Major Release Version><Minor Release Version>_0.dll
andnvrtcbuiltins64_<Major Release Version><Minor Release Version>.dll
). 
CUDNN_STATUS_RUNTIME_IN_PROGRESS
 Some tasks in the user stream are not completed.

CUDNN_STATUS_RUNTIME_FP_OVERFLOW
 Numerical overflow occurred during the GPU kernel execution.
3.1.2.28. cudnnTensorFormat_t
cudnnTensorFormat_t
is an enumerated type used by cudnnSetTensor4dDescriptor() to create a tensor with a predefined layout. For a detailed explanation of how these tensors are arranged in memory, refer to Data Layout Formats in the cuDNN Developer Guide.
Values

CUDNN_TENSOR_NCHW

This tensor format specifies that the data is laid out in the following order: batch size, feature maps, rows, columns. The strides are implicitly defined in such a way that the data are contiguous in memory with no padding between images, feature maps, rows, and columns; the columns are the inner dimension and the images are the outermost dimension.

CUDNN_TENSOR_NHWC

This tensor format specifies that the data is laid out in the following order: batch size, rows, columns, feature maps. The strides are implicitly defined in such a way that the data are contiguous in memory with no padding between images, rows, columns, and feature maps; the feature maps are the inner dimension and the images are the outermost dimension.

CUDNN_TENSOR_NCHW_VECT_C

This tensor format specifies that the data is laid out in the following order: batch size, feature maps, rows, columns. However, each element of the tensor is a vector of multiple feature maps. The length of the vector is carried by the data type of the tensor. The strides are implicitly defined in such a way that the data are contiguous in memory with no padding between images, feature maps, rows, and columns; the columns are the inner dimension and the images are the outermost dimension. This format is only supported with tensor data types
CUDNN_DATA_INT8x4
,CUDNN_DATA_INT8x32
, andCUDNN_DATA_UINT8x4
.The
CUDNN_TENSOR_NCHW_VECT_C
can also be interpreted in the following way: The NCHW INT8x32 format is really N x (C/32) x H x W x 32 (32 Cs for every W), just as the NCHW INT8x4 format is N x (C/4) x H x W x 4 (4 Cs for every W). Hence, theVECT_C
name  each W is a vector (4 or 32) of Cs.
3.2. API Functions
3.2.1. cudnnActivationForward()
cudnnStatus_t cudnnActivationForward(
cudnnHandle_t handle,
cudnnActivationDescriptor_t activationDesc,
const void *alpha,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const void *beta,
const cudnnTensorDescriptor_t yDesc,
void *y)
This routine applies a specified neuron activation function elementwise over each input value.
 Inplace operation is allowed for this routine; meaning,
xData
andyData
pointers may be equal. However, this requiresxDesc
andyDesc
descriptors to be identical (particularly, the strides of the input and output must match for an inplace operation to be allowed).  All tensor formats are supported for 4 and 5 dimensions, however, the best performance is obtained when the strides of
xDesc
andyDesc
are equal andHWpacked
. For more than 5 dimensions the tensors must have their spatial dimensions packed.
Parameters

handle

Input. Handle to a previously created cuDNN context. For more information, refer to cudnnHandle_t.

activationDesc

Input. Activation descriptor. For more information, refer to cudnnActivationDescriptor_t.

alpha
,beta

Input. Pointers to scaling factors (in host memory) used to blend the computation result with prior value in the output layer as follows:
dstValue = alpha[0]*result + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

xDesc

Input. Handle to the previously initialized input tensor descriptor. For more information, refer to cudnnTensorDescriptor_t.

x

Input. Data pointer to GPU memory associated with the tensor descriptor
xDesc
. 
yDesc

Input. Handle to the previously initialized output tensor descriptor.

y

Output. Data pointer to GPU memory associated with the output tensor descriptor
yDesc
.
Returns

CUDNN_STATUS_SUCCESS

The function launched successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 The parameter
mode
has an invalid enumerant value.  The dimensions
n, c, h, w
of the input tensor and output tensor differ.  The
datatype
of the input tensor and output tensor differs.  The strides
nStride, cStride, hStride, wStride
of the input tensor and output tensor differ and inplace operation is used (meaning,x
andy
pointers are equal).
 The parameter

CUDNN_STATUS_EXECUTION_FAILED

The function failed to launch on the GPU.
3.2.2. cudnnAddTensor()
cudnnStatus_t cudnnAddTensor(
cudnnHandle_t handle,
const void *alpha,
const cudnnTensorDescriptor_t aDesc,
const void *A,
const void *beta,
const cudnnTensorDescriptor_t cDesc,
void *C)
This function adds the scaled values of a bias tensor to another tensor. Each dimension of the bias tensor A
must match the corresponding dimension of the destination tensor C
or must be equal to 1. In the latter case, the same value from the bias tensor for those dimensions will be used to blend into the C
tensor.
Only 4D and 5D tensors are supported. Beyond these dimensions, this routine is not supported.
Parameters

handle

Input. Handle to a previously created cuDNN context. For more information, refer to cudnnHandle_t.

alpha
,beta

Input. Pointers to scaling factors (in host memory) used to blend the source value with the prior value in the destination tensor as follows:
dstValue = alpha[0]*srcValue + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

aDesc

Input. Handle to a previously initialized tensor descriptor. For more information, refer to cudnnTensorDescriptor_t.

A

Input. Pointer to data of the tensor described by the
aDesc
descriptor. 
cDesc

Input. Handle to a previously initialized tensor descriptor.

C

Input/Output. Pointer to data of the tensor described by the
cDesc
descriptor.
Returns

CUDNN_STATUS_SUCCESS

The function executed successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

The dimensions of the bias tensor refer to an amount of data that is incompatible with the output tensor dimensions or the
dataType
of the two tensor descriptors are different. 
CUDNN_STATUS_EXECUTION_FAILED

The function failed to launch on the GPU.
3.2.3. cudnnBatchNormalizationForwardInference()
cudnnStatus_t cudnnBatchNormalizationForwardInference(
cudnnHandle_t handle,
cudnnBatchNormMode_t mode,
const void *alpha,
const void *beta,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const cudnnTensorDescriptor_t yDesc,
void *y,
const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
const void *bnScale,
const void *bnBias,
const void *estimatedMean,
const void *estimatedVariance,
double epsilon)
This function performs the forward batch normalization layer computation for the inference phase. This layer is based on the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, S. Ioffe, C. Szegedy, 2015.
 Only 4D and 5D tensors are supported.
 The input transformation performed by this function is defined as:
y = beta*y + alpha *[bnBias + (bnScale * (xestimatedMean)/sqrt(epsilon + estimatedVariance)]
 The
epsilon
value has to be the same during training, backpropagation and inference.  For the training phase, refer to cudnnBatchNormalizationForwardTraining().
 Higher performance can be obtained when HWpacked tensors are used for all of
x
anddx
.
For more information, refer to cudnnDeriveBNTensorDescriptor() for the secondary tensor descriptor generation for the parameters used in this function.
Parameters

handle

Input. Handle to a previously created cuDNN library descriptor. For more information, refer to cudnnHandle_t.

mode

Input. Mode of operation (spatial or peractivation). For more information, refer to cudnnBatchNormMode_t.

alpha
,beta

Inputs. Pointers to scaling factors (in host memory) used to blend the layer output value with prior value in the destination tensor as follows:
dstValue = alpha[0]*resultValue + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

xDesc
,yDesc

Input. Handles to the previously initialized tensor descriptors.

*x

Input. Data pointer to GPU memory associated with the tensor descriptor
xDesc
, for the layer’sx
input data. 
*y

Input/Output. Data pointer to GPU memory associated with the tensor descriptor
yDesc
, for they
output of the batch normalization layer. 
bnScaleBiasMeanVarDesc
,bnScale
,bnBias

Inputs. Tensor descriptors and pointers in device memory for the batch normalization scale and bias parameters (in the original paper bias is referred to as beta and scale as gamma).

estimatedMean
,estimatedVariance

Inputs. Mean and variance tensors (these have the same descriptor as the bias and scale). The
resultRunningMean
andresultRunningVariance
, accumulated during the training phase from the cudnnBatchNormalizationForwardTraining() call, should be passed as inputs here. 
epsilon

Input. Epsilon value used in the batch normalization formula. Its value should be equal to or greater than the value defined for
CUDNN_BN_MIN_EPSILON
incudnn.h
.
Supported configurations
This function supports the following combinations of data types for various descriptors.
Data Type Configurations  xDesc  bnScaleBiasMeanVarDesc  alpha, beta  yDesc 

INT8_CONFIG 
CUDNN_DATA_INT8 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_INT8 
PSEUDO_HALF_CONFIG 
CUDNN_DATA_HALF 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_HALF 
FLOAT_CONFIG 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
DOUBLE_CONFIG 
CUDNN_DATA_DOUBLE 
CUDNN_DATA_DOUBLE 
CUDNN_DATA_DOUBLE 
CUDNN_DATA_DOUBLE 
BFLOAT16_CONFIG 
CUDNN_DATA_BFLOAT16 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_BFLOAT16 
Returns

CUDNN_STATUS_SUCCESS

The computation was performed successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 One of the pointers
alpha, beta, x, y, bnScale, bnBias, estimatedMean, estimatedInvVariance
isNULL
.  The number of
xDesc
oryDesc
tensor descriptor dimensions is not within the range of[4,5]
(only 4D and 5D tensors are supported.) bnScaleBiasMeanVarDesc
dimensions are not 1xCx1x1 for 4D and 1xCx1x1x1 for 5D for spatial, and are not 1xCxHxW for 4D and 1xCxDxHxW for 5D for peractivation mode.epsilon
value is less thanCUDNN_BN_MIN_EPSILON
. Dimensions or data types mismatch for
xDesc
,yDesc
.
 One of the pointers
3.2.4. cudnnCopyAlgorithmDescriptor()
This function has been deprecated in cuDNN 8.0.
3.2.5. cudnnCreate()
cudnnStatus_t cudnnCreate(cudnnHandle_t *handle)
This function initializes the cuDNN library and creates a handle to an opaque structure holding the cuDNN library context. It allocates hardware resources on the host and device and must be called prior to making any other cuDNN library calls.
The cuDNN library handle is tied to the current CUDA device (context). To use the library on multiple devices, one cuDNN handle needs to be created for each device.
For a given device, multiple cuDNN handles with different configurations (for example, different current CUDA streams) may be created. Because cudnnCreate()
allocates some internal resources, the release of those resources by calling cudnnDestroy() will implicitly call cudaDeviceSynchronize; therefore, the recommended best practice is to call cudnnCreate/cudnnDestroy
outside of performancecritical code paths.
For multithreaded applications that use the same device from different threads, the recommended programming model is to create one (or a few, as is convenient) cuDNN handle(s) per thread and use that cuDNN handle for the entire life of the thread.
Parameters

handle

Output. Pointer to pointer where to store the address to the allocated cuDNN handle. For more information, refer to cudnnHandle_t.
Returns

CUDNN_STATUS_BAD_PARAM

Invalid (
NULL
) input pointer supplied. 
CUDNN_STATUS_NOT_INITIALIZED

No compatible GPU found, CUDA driver not installed or disabled, CUDA runtime API initialization failed.

CUDNN_STATUS_ARCH_MISMATCH

NVIDIA GPU architecture is too old.

CUDNN_STATUS_ALLOC_FAILED

Host memory allocation failed.

CUDNN_STATUS_INTERNAL_ERROR

CUDA resource allocation failed.

CUDNN_STATUS_LICENSE_ERROR

cuDNN license validation failed (only when the feature is enabled).

CUDNN_STATUS_SUCCESS

cuDNN handle was created successfully.
3.2.6. cudnnCreateActivationDescriptor()
cudnnStatus_t cudnnCreateActivationDescriptor(
cudnnActivationDescriptor_t *activationDesc)
This function creates an activation descriptor object by allocating the memory needed to hold its opaque structure. For more information, refer to cudnnActivationDescriptor_t.
Returns

CUDNN_STATUS_SUCCESS

The object was created successfully.

CUDNN_STATUS_ALLOC_FAILED

The resources could not be allocated.
3.2.7. cudnnCreateAlgorithmDescriptor()
This function has been deprecated in cuDNN 8.0.
cudnnStatus_t cudnnCreateAlgorithmDescriptor(
cudnnAlgorithmDescriptor_t *algoDesc)
This function creates an algorithm descriptor object by allocating the memory needed to hold its opaque structure.
Returns

CUDNN_STATUS_SUCCESS

The object was created successfully.

CUDNN_STATUS_ALLOC_FAILED

The resources could not be allocated.
3.2.8. cudnnCreateAlgorithmPerformance()
cudnnStatus_t cudnnCreateAlgorithmPerformance(
cudnnAlgorithmPerformance_t *algoPerf,
int numberToCreate)
This function creates multiple algorithm performance objects by allocating the memory needed to hold their opaque structures.
Returns

CUDNN_STATUS_SUCCESS

The object was created successfully.

CUDNN_STATUS_ALLOC_FAILED

The resources could not be allocated.
3.2.9. cudnnCreateDropoutDescriptor()
cudnnStatus_t cudnnCreateDropoutDescriptor(
cudnnDropoutDescriptor_t *dropoutDesc)
This function creates a generic dropout descriptor object by allocating the memory needed to hold its opaque structure. For more information, refer to cudnnDropoutDescriptor_t.
Returns

CUDNN_STATUS_SUCCESS

The object was created successfully.

CUDNN_STATUS_ALLOC_FAILED

The resources could not be allocated.
3.2.10. cudnnCreateFilterDescriptor()
cudnnStatus_t cudnnCreateFilterDescriptor(
cudnnFilterDescriptor_t *filterDesc)
This function creates a filter descriptor object by allocating the memory needed to hold its opaque structure. For more information, refer to cudnnFilterDescriptor_t.
Returns

CUDNN_STATUS_SUCCESS

The object was created successfully.

CUDNN_STATUS_ALLOC_FAILED

The resources could not be allocated.
3.2.11. cudnnCreateLRNDescriptor()
cudnnStatus_t cudnnCreateLRNDescriptor(
cudnnLRNDescriptor_t *poolingDesc)
This function allocates the memory needed to hold the data needed for LRN and DivisiveNormalization
layers operation and returns a descriptor used with subsequent layer forward and backward calls.
Returns

CUDNN_STATUS_SUCCESS

The object was created successfully.

CUDNN_STATUS_ALLOC_FAILED

The resources could not be allocated.
cudnnCreateOpTensorDescriptor()
cudnnStatus_t cudnnCreateOpTensorDescriptor(
cudnnOpTensorDescriptor_t* opTensorDesc)
This function creates a tensor pointwise math descriptor. For more information, refer to cudnnOpTensorDescriptor_t.
Parameters

opTensorDesc

Output. Pointer to the structure holding the description of the tensor pointwise math such as add, multiply, and more.
Returns

CUDNN_STATUS_SUCCESS

The function returned successfully.

CUDNN_STATUS_BAD_PARAM

Tensor pointwise math descriptor passed to the function is invalid.

CUDNN_STATUS_ALLOC_FAILED

Memory allocation for this tensor pointwise math descriptor failed.
3.2.13. cudnnCreatePoolingDescriptor()
cudnnStatus_t cudnnCreatePoolingDescriptor(
cudnnPoolingDescriptor_t *poolingDesc)
This function creates a pooling descriptor object by allocating the memory needed to hold its opaque structure.
Returns

CUDNN_STATUS_SUCCESS

The object was created successfully.

CUDNN_STATUS_ALLOC_FAILED

The resources could not be allocated.
3.2.14. cudnnCreateReduceTensorDescriptor()
cudnnStatus_t cudnnCreateReduceTensorDescriptor(
cudnnReduceTensorDescriptor_t* reduceTensorDesc)
This function creates a reduced tensor descriptor object by allocating the memory needed to hold its opaque structure.
Returns

CUDNN_STATUS_SUCCESS

The object was created successfully.

CUDNN_STATUS_BAD_PARAM

reduceTensorDesc
is aNULL
pointer. 
CUDNN_STATUS_ALLOC_FAILED

The resources could not be allocated.
3.2.15. cudnnCreateSpatialTransformerDescriptor()
cudnnStatus_t cudnnCreateSpatialTransformerDescriptor(
cudnnSpatialTransformerDescriptor_t *stDesc)
This function creates a generic spatial transformer descriptor object by allocating the memory needed to hold its opaque structure.
Returns

CUDNN_STATUS_SUCCESS

The object was created successfully.

CUDNN_STATUS_ALLOC_FAILED

The resources could not be allocated.
3.2.16. cudnnCreateTensorDescriptor()
cudnnStatus_t cudnnCreateTensorDescriptor(
cudnnTensorDescriptor_t *tensorDesc)
This function creates a generic tensor descriptor object by allocating the memory needed to hold its opaque structure. The data is initialized to all zeros.
Parameters

tensorDesc

Output. Pointer to pointer where the address to the allocated tensor descriptor object should be stored.
Returns

CUDNN_STATUS_BAD_PARAM

Invalid input argument.

CUDNN_STATUS_ALLOC_FAILED

The resources could not be allocated.

CUDNN_STATUS_SUCCESS

The object was created successfully.
3.2.17. cudnnCreateTensorTransformDescriptor()
cudnnStatus_t cudnnCreateTensorTransformDescriptor(
cudnnTensorTransformDescriptor_t *transformDesc);
This function creates a tensor transform descriptor object by allocating the memory needed to hold its opaque structure. The tensor data is initialized to be all zero. Use the cudnnSetTensorTransformDescriptor() function to initialize the descriptor created by this function.
Parameters

transformDesc
 Output. A pointer to an uninitialized tensor transform descriptor.
Returns

CUDNN_STATUS_SUCCESS
 The descriptor object was created successfully.

CUDNN_STATUS_BAD_PARAM

The
transformDesc
isNULL
. 
CUDNN_STATUS_ALLOC_FAILED
 The memory allocation failed.
3.2.18. cudnnDeriveBNTensorDescriptor()
cudnnStatus_t cudnnDeriveBNTensorDescriptor(
cudnnTensorDescriptor_t derivedBnDesc,
const cudnnTensorDescriptor_t xDesc,
cudnnBatchNormMode_t mode)
This function derives a secondary tensor descriptor for the batch normalization scale
, invVariance
, bnBias
, and bnScale
subtensors from the layer's x
data descriptor.
Use the tensor descriptor produced by this function as the bnScaleBiasMeanVarDesc
parameter for the cudnnBatchNormalizationForwardInference() and cudnnBatchNormalizationForwardTraining() functions, and as the bnScaleBiasDiffDesc
parameter in the cudnnBatchNormalizationBackward() function.
The resulting dimensions will be:
 1xCx1x1 for 4D and 1xCx1x1x1 for 5D for
BATCHNORM_MODE_SPATIAL
 1xCxHxW for 4D and 1xCxDxHxW for 5D for
BATCHNORM_MODE_PER_ACTIVATION
mode
For HALF
input data type the resulting tensor descriptor will have a FLOAT
type. For other data types, it will have the same type as the input data.
 Only 4D and 5D tensors are supported.
 The
derivedBnDesc
should be first created using cudnnCreateTensorDescriptor(). xDesc
is the descriptor for the layer'sx
data and has to be set up with proper dimensions prior to calling this function.
Parameters

derivedBnDesc

Output. Handle to a previously created tensor descriptor.

xDesc

Input. Handle to a previously created and initialized layer's
x
data descriptor. 
mode

Input. Batch normalization layer mode of operation.
Returns

CUDNN_STATUS_SUCCESS

The computation was performed successfully.

CUDNN_STATUS_BAD_PARAM

Invalid Batch Normalization mode.
3.2.19. cudnnDeriveNormTensorDescriptor()
cudnnStatus_t CUDNNWINAPI
cudnnDeriveNormTensorDescriptor(cudnnTensorDescriptor_t derivedNormScaleBiasDesc,
cudnnTensorDescriptor_t derivedNormMeanVarDesc,
const cudnnTensorDescriptor_t xDesc,
cudnnNormMode_t mode,
int groupCnt)
This function derives tensor descriptors for the normalization mean
, invariance
, normBias
, and normScale
subtensors from the layer's x
data descriptor and norm mode. normalization
, mean
, and invariance
share the same descriptor while bias
and scale
share the same descriptor.
Use the tensor descriptor produced by this function as the normScaleBiasDesc
or normMeanVarDesc
parameter for the cudnnNormalizationForwardInference() and cudnnNormalizationForwardTraining() functions, and as the dNormScaleBiasDesc
and normMeanVarDesc
parameters in the cudnnNormalizationBackward() function.
The resulting dimensions will be:
 1xCx1x1 for 4D and 1xCx1x1x1 for 5D for
CUDNN_NORM_PER_ACTIVATION
 1xCxHxW for 4D and 1xCxDxHxW for 5D for
CUDNN_NORM_PER_CHANNEL
mode
For HALF
input data type the resulting tensor descriptor will have a FLOAT
type. For other data types, it will have the same type as the input data.
 Only 4D and 5D tensors are supported.
 The
derivedNormScaleBiasDesc
andderivedNormMeanVarDesc
should be created first using cudnnCreateTensorDescriptor(). xDesc
is the descriptor for the layer'sx
data and has to be set up with proper dimensions prior to calling this function.
Parameters

derivedNormScaleBiasDesc

Output. Handle to a previously created tensor descriptor.

derivedNormMeanVarDesc

Output. Handle to a previously created tensor descriptor.

xDesc

Input. Handle to a previously created and initialized layer's
x
data descriptor. 
mode

Input. The normalization layer mode of operation.

groupCnt

Input. The number of grouped convolutions. Currently, only
1
is supported.
Returns

CUDNN_STATUS_SUCCESS

The computation was performed successfully.

CUDNN_STATUS_BAD_PARAM

Invalid Batch Normalization mode.
3.2.20. cudnnDestroy()
cudnnStatus_t cudnnDestroy(cudnnHandle_t handle)
This function releases the resources used by the cuDNN handle. This function is usually the last call made to cuDNN with a particular handle. Because cudnnCreate() allocates internal resources, the release of those resources by calling cudnnDestroy()
will implicitly call cudaDeviceSynchronize; therefore, the recommended best practice is to call cudnnCreate/cudnnDestroy
outside of performancecritical code paths.
Parameters

handle

Input. The cuDNN handle to be destroyed.
Returns

CUDNN_STATUS_SUCCESS

The cuDNN context destruction was successful.

CUDNN_STATUS_BAD_PARAM

Invalid (
NULL
) pointer supplied.
3.2.21. cudnnDestroyActivationDescriptor()
cudnnStatus_t cudnnDestroyActivationDescriptor(
cudnnActivationDescriptor_t activationDesc)
This function destroys a previously created activation descriptor object.
Returns

CUDNN_STATUS_SUCCESS

The object was destroyed successfully.
3.2.22. cudnnDestroyAlgorithmDescriptor()
This function has been deprecated in cuDNN 8.0.
cudnnStatus_t cudnnDestroyAlgorithmDescriptor(
cudnnActivationDescriptor_t algorithmDesc)
This function destroys a previously created algorithm descriptor object.
Returns

CUDNN_STATUS_SUCCESS

The object was destroyed successfully.
3.2.23. cudnnDestroyAlgorithmPerformance()
cudnnStatus_t cudnnDestroyAlgorithmPerformance(
cudnnAlgorithmPerformance_t algoPerf)
This function destroys a previously created algorithm descriptor object.
Returns

CUDNN_STATUS_SUCCESS

The object was destroyed successfully.
3.2.24. cudnnDestroyDropoutDescriptor()
cudnnStatus_t cudnnDestroyDropoutDescriptor(
cudnnDropoutDescriptor_t dropoutDesc)
This function destroys a previously created dropout descriptor object.
Returns

CUDNN_STATUS_SUCCESS

The object was destroyed successfully.
3.2.25. cudnnDestroyFilterDescriptor()
cudnnStatus_t cudnnDestroyFilterDescriptor(
cudnnFilterDescriptor_t filterDesc)
This function destroys a filter object.
Returns

CUDNN_STATUS_SUCCESS

The object was destroyed successfully.
3.2.26. cudnnDestroyLRNDescriptor()
cudnnStatus_t cudnnDestroyLRNDescriptor(
cudnnLRNDescriptor_t lrnDesc)
This function destroys a previously created LRN descriptor object.
Returns

CUDNN_STATUS_SUCCESS

The object was destroyed successfully.
3.2.27. cudnnDestroyOpTensorDescriptor()
cudnnStatus_t cudnnDestroyOpTensorDescriptor(
cudnnOpTensorDescriptor_t opTensorDesc)
This function deletes a tensor pointwise math descriptor object.
Parameters

opTensorDesc

Input. Pointer to the structure holding the description of the tensor pointwise math to be deleted.
Returns

CUDNN_STATUS_SUCCESS

The function returned successfully.
3.2.28. cudnnDestroyPoolingDescriptor()
cudnnStatus_t cudnnDestroyPoolingDescriptor(
cudnnPoolingDescriptor_t poolingDesc)
This function destroys a previously created pooling descriptor object.
Returns

CUDNN_STATUS_SUCCESS

The object was destroyed successfully.
3.2.29. cudnnDestroyReduceTensorDescriptor()
cudnnStatus_t cudnnDestroyReduceTensorDescriptor(
cudnnReduceTensorDescriptor_t tensorDesc)
This function destroys a previously created reduce tensor descriptor object. When the input pointer is NULL
, this function performs no destroy operation.
Parameters

tensorDesc

Input. Pointer to the reduce tensor descriptor object to be destroyed.
Returns

CUDNN_STATUS_SUCCESS

The object was destroyed successfully.
3.2.30. cudnnDestroySpatialTransformerDescriptor()
cudnnStatus_t cudnnDestroySpatialTransformerDescriptor(
cudnnSpatialTransformerDescriptor_t stDesc)
This function destroys a previously created spatial transformer descriptor object.
Returns

CUDNN_STATUS_SUCCESS

The object was destroyed successfully.
3.2.31. cudnnDestroyTensorDescriptor()
cudnnStatus_t cudnnDestroyTensorDescriptor(cudnnTensorDescriptor_t tensorDesc)
This function destroys a previously created tensor descriptor object. When the input pointer is NULL
, this function performs no destroy operation.
Parameters

tensorDesc

Input. Pointer to the tensor descriptor object to be destroyed.
Returns

CUDNN_STATUS_SUCCESS

The object was destroyed successfully.
3.2.32. cudnnDestroyTensorTransformDescriptor()
cudnnStatus_t cudnnDestroyTensorTransformDescriptor(
cudnnTensorTransformDescriptor_t transformDesc);
Destroys a previously created tensor transform descriptor.
Parameters

transformDesc
 Input. The tensor transform descriptor to be destroyed.
Returns

CUDNN_STATUS_SUCCESS
 The descriptor was destroyed successfully.
3.2.33. cudnnDivisiveNormalizationForward()
cudnnStatus_t cudnnDivisiveNormalizationForward(
cudnnHandle_t handle,
cudnnLRNDescriptor_t normDesc,
cudnnDivNormMode_t mode,
const void *alpha,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const void *means,
void *temp,
void *temp2,
const void *beta,
const cudnnTensorDescriptor_t yDesc,
void *y)
This function performs the forward spatial DivisiveNormalization
layer computation. It divides every value in a layer by the standard deviation of its spatial neighbors as described in What is the Best MultiStage Architecture for Object Recognition, Jarrett 2009, Local Contrast Normalization Layer section. Note that DivisiveNormalization
only implements the x/max(c, sigma_x)
portion of the computation, where sigma_x
is the variance over the spatial neighborhood of x
. The full LCN (Local Contrastive Normalization) computation can be implemented as a twostep process:
x_m = xmean(x);
y = x_m/max(c, sigma(x_m));
The xmean(x)
which is often referred to as "subtractive normalization" portion of the computation can be implemented using cuDNN average pooling layer followed by a call to addTensor
.
Supported tensor formats are NCHW for 4D and NCDHW for 5D with any nonoverlapping nonnegative strides. Only 4D and 5D tensors are supported.
Parameters

handle

Input. Handle to a previously created cuDNN library descriptor.

normDesc

Input. Handle to a previously initialized LRN parameter descriptor. This descriptor is used for both LRN and
DivisiveNormalization
layers. 
divNormMode

Input.
DivisiveNormalization
layer mode of operation. Currently onlyCUDNN_DIVNORM_PRECOMPUTED_MEANS
is implemented. Normalization is performed using the means input tensor that is expected to be precomputed by the user. 
alpha
,beta

Input. Pointers to scaling factors (in host memory) used to blend the layer output value with prior value in the destination tensor as follows:
dstValue = alpha[0]*resultValue + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

xDesc
,yDesc

Input. Tensor descriptor objects for the input and output tensors. Note that
xDesc
is shared betweenx
,means
,temp
, andtemp2
tensors. 
x

Input. Input tensor data pointer in device memory.

means

Input. Input means tensor data pointer in device memory. Note that this tensor can be
NULL
(in that case its values are assumed to be zero during the computation). This tensor also doesn't have to containmeans
, these can be any values, a frequently used variation is a result of convolution with a normalized positive kernel (such as Gaussian). 
temp
,temp2

Workspace. Temporary tensors in device memory. These are used for computing intermediate values during the forward pass. These tensors do not have to be preserved as inputs from forward to the backward pass. Both use
xDesc
as their descriptor. 
y

Output. Pointer in device memory to a tensor for the result of the forward
DivisiveNormalization
computation.
Returns

CUDNN_STATUS_SUCCESS

The computation was performed successfully.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 One of the tensor pointers
x, y, temp, temp2
isNULL
.  Number of input tensor or output tensor dimensions is outside of
[4,5]
range.  A mismatch in dimensions between any two of the input or output tensors.
 For inplace computation when pointers
x == y
, a mismatch in strides between the input data and output data tensors.  Alpha or beta pointer is
NULL
.  LRN descriptor parameters are outside of their valid ranges.
 Any of the tensor strides are negative.
 One of the tensor pointers

CUDNN_STATUS_UNSUPPORTED

The function does not support the provided configuration, for example, any of the input and output tensor strides mismatch (for the same dimension) is a nonsupported configuration.
3.2.34. cudnnDropoutForward()
cudnnStatus_t cudnnDropoutForward(
cudnnHandle_t handle,
const cudnnDropoutDescriptor_t dropoutDesc,
const cudnnTensorDescriptor_t xdesc,
const void *x,
const cudnnTensorDescriptor_t ydesc,
void *y,
void *reserveSpace,
size_t reserveSpaceSizeInBytes)
This function performs forward dropout operation over x
returning results in y
. If dropout
was used as a parameter to cudnnSetDropoutDescriptor(), the approximate dropout
fraction of x
values will be replaced by a 0
, and the rest will be scaled by 1/(1dropout)
. This function should not be running concurrently with another cudnnDropoutForward()
function using the same states
.
Parameters

handle

Input. Handle to a previously created cuDNN context.

dropoutDesc

Input. Previously created dropout descriptor object.

xDesc

Input. Handle to a previously initialized tensor descriptor.

x

Input. Pointer to data of the tensor described by the
xDesc
descriptor. 
yDesc

Input. Handle to a previously initialized tensor descriptor.

y

Output. Pointer to data of the tensor described by the
yDesc
descriptor. 
reserveSpace

Output. Pointer to userallocated GPU memory used by this function. It is expected that the contents of
reserveSpace
does not change betweencudnnDropoutForward()
and cudnnDropoutBackward() calls. 
reserveSpaceSizeInBytes

Input. Specifies the size in bytes of the provided memory for the reserve space.
Returns

CUDNN_STATUS_SUCCESS

The call was successful.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 The number of elements of input tensor and output tensors differ.
 The
datatype
of the input tensor and output tensors differs.  The strides of the input tensor and output tensors differ and inplace operation is used (meaning,
x
andy
pointers are equal).  The provided
reserveSpaceSizeInBytes
is less than the value returned by cudnnDropoutGetReserveSpaceSize().  cudnnSetDropoutDescriptor() has not been called on
dropoutDesc
with the nonNULL
states
argument.

CUDNN_STATUS_EXECUTION_FAILED

The function failed to launch on the GPU.
3.2.35. cudnnDropoutGetReserveSpaceSize()
cudnnStatus_t cudnnDropoutGetReserveSpaceSize(
cudnnTensorDescriptor_t xDesc,
size_t *sizeInBytes)
This function is used to query the amount of reserve needed to run dropout with the input dimensions given by xDesc
. The same reserve space is expected to be passed to cudnnDropoutForward() and cudnnDropoutBackward(), and its contents is expected to remain unchanged between cudnnDropoutForward() and cudnnDropoutBackward() calls.
Parameters

xDesc

Input. Handle to a previously initialized tensor descriptor, describing input to a dropout operation.

sizeInBytes

Output. Amount of GPU memory needed as reserve space to be able to run dropout with an input tensor descriptor specified by
xDesc
.
Returns

CUDNN_STATUS_SUCCESS

The query was successful.
3.2.36. cudnnDropoutGetStatesSize()
cudnnStatus_t cudnnDropoutGetStatesSize(
cudnnHandle_t handle,
size_t *sizeInBytes)
This function is used to query the amount of space required to store the states of the random number generators used by cudnnDropoutForward() function.
Parameters
 handle

Input. Handle to a previously created cuDNN context.
 sizeInBytes

Output. Amount of GPU memory needed to store random generator states.
Returns

CUDNN_STATUS_SUCCESS

The query was successful.
3.2.37. cudnnGetActivationDescriptor()
cudnnStatus_t cudnnGetActivationDescriptor(
const cudnnActivationDescriptor_t activationDesc,
cudnnActivationMode_t *mode,
cudnnNanPropagation_t *reluNanOpt,
double *coef)
This function queries a previously initialized generic activation descriptor object.
Parameters

activationDesc

Input. Handle to a previously created activation descriptor.

mode

Output. Enumerant to specify the activation mode.

reluNanOpt

Output. Enumerant to specify the
Nan
propagation mode. 
coef

Output. Floating point number to specify the clipping threshold when the activation mode is set to
CUDNN_ACTIVATION_CLIPPED_RELU
or to specify the alpha coefficient when the activation mode is set toCUDNN_ACTIVATION_ELU
.
Returns

CUDNN_STATUS_SUCCESS

The object was queried successfully.
3.2.38. cudnnGetActivationDescriptorSwishBeta()
cudnnStatus_t
cudnnGetActivationDescriptorSwishBeta(cudnnActivationDescriptor_t
activationDesc, double* swish_beta)
This function queries the current beta parameter set for SWISH activation.
Parameters

activationDesc

Input. Handle to a previously created activation descriptor.

swish_beta

Output. Pointer to a double value that will receive the currently configured SWISH beta parameter.
Returns

CUDNN_STATUS_SUCCESS

The beta parameter was queried successfully.

CUDNN_STATUS_BAD_PARAM

At least one of
activationDesc
orswish_beta
wereNULL
.
3.2.39. cudnnGetAlgorithmDescriptor()
This function has been deprecated in cuDNN 8.0.
cudnnStatus_t cudnnGetAlgorithmDescriptor(
const cudnnAlgorithmDescriptor_t algoDesc,
cudnnAlgorithm_t *algorithm)
This function queries a previously initialized generic algorithm descriptor object.
Parameters
 algorithmDesc

Input. Handle to a previously created algorithm descriptor.
 algorithm

Input. Struct to specify the algorithm.
Returns

CUDNN_STATUS_SUCCESS

The object was queried successfully.
3.2.40. cudnnGetAlgorithmPerformance()
This function has been deprecated in cuDNN 8.0.
cudnnStatus_t cudnnGetAlgorithmPerformance(
const cudnnAlgorithmPerformance_t algoPerf,
cudnnAlgorithmDescriptor_t* algoDesc,
cudnnStatus_t* status,
float* time,
size_t* memory)
This function queries a previously initialized generic algorithm performance object.
Parameters

algoPerf

Input/Output. Handle to a previously created algorithm performance object.

algoDesc

Output. The algorithm descriptor which the performance results describe.

status

Output. The cuDNN status returned from running the
algoDesc
algorithm. 
timecoef

Output. The GPU time spent running the
algoDesc
algorithm. 
memory

Output. The GPU memory needed to run the
algoDesc
algorithm.
Returns

CUDNN_STATUS_SUCCESS

The object was queried successfully.
3.2.41. cudnnGetAlgorithmSpaceSize()
This function has been deprecated in cuDNN 8.0.
cudnnStatus_t cudnnGetAlgorithmSpaceSize(
cudnnHandle_t handle,
cudnnAlgorithmDescriptor_t algoDesc,
size_t* algoSpaceSizeInBytes)
This function queries for the amount of host memory needed to call cudnnSaveAlgorithm(), much like the “get workspace size” function query for the amount of device memory needed.
Parameters

handle

Input. Handle to a previously created cuDNN context.

algoDesc

Input. A previously created algorithm descriptor.

algoSpaceSizeInBytes

Output. Amount of host memory needed as a workspace to be able to save the metadata from the specified
algoDesc
.
Returns

CUDNN_STATUS_SUCCESS

The function launched successfully.

CUDNN_STATUS_BAD_PARAM

At least one of the arguments is
NULL
.
3.2.42. cudnnGetCallback()
cudnnStatus_t cudnnGetCallback(
unsigned mask,
void **udata,
cudnnCallback_t fptr)
This function queries the internal states of cuDNN error reporting functionality.
Parameters

mask

Output. Pointer to the address where the current internal error reporting message bit mask will be outputted.

udata

Output. Pointer to the address where the current internally stored
udata
address will be stored. 
fptr

Output. Pointer to the address where the current internally stored
callback
function pointer will be stored. When the builtin default callback function is used,NULL
will be outputted.
Returns

CUDNN_STATUS_SUCCESS

The function launched successfully.

CUDNN_STATUS_BAD_PARAM

If any of the input parameters are
NULL
.
3.2.43. cudnnGetCudartVersion()
size_t cudnnGetCudartVersion()
The same version of a given cuDNN library can be compiled against different CUDA toolkit versions. This routine returns the CUDA toolkit version that the currently used cuDNN library has been compiled against.
3.2.44. cudnnGetDropoutDescriptor()
cudnnStatus_t cudnnGetDropoutDescriptor(
cudnnDropoutDescriptor_t dropoutDesc,
cudnnHandle_t handle,
float *dropout,
void **states,
unsigned long long *seed)
This function queries the fields of a previously initialized dropout descriptor.
Parameters

dropoutDesc

Input. Previously initialized dropout descriptor.

handle

Input. Handle to a previously created cuDNN context.

dropout

Output. The probability with which the value from input is set to 0 during the dropout layer.

states

Output. Pointer to userallocated GPU memory that holds random number generator states.

seed

Output. Seed used to initialize random number generator states.
Returns

CUDNN_STATUS_SUCCESS

The call was successful.

CUDNN_STATUS_BAD_PARAM

One or more of the arguments was an invalid pointer.
3.2.45. cudnnGetErrorString()
const char * cudnnGetErrorString(cudnnStatus_t status)
This function converts the cuDNN status code to a NULL
terminated (ASCIIZ) static string. For example, when the input argument is CUDNN_STATUS_SUCCESS
, the returned string is CUDNN_STATUS_SUCCESS
. When an invalid status value is passed to the function, the returned string is CUDNN_UNKNOWN_STATUS
.
Parameters
 status

Input. cuDNN enumerant status code.
Returns
Pointer to a static, NULL
terminated string with the status name.
3.2.46. cudnnGetFilter4dDescriptor()
cudnnStatus_t cudnnGetFilter4dDescriptor(
const cudnnFilterDescriptor_t filterDesc,
cudnnDataType_t *dataType,
cudnnTensorFormat_t *format,
int *k,
int *c,
int *h,
int *w)
This function queries the parameters of the previously initialized filter descriptor object.
Parameters

filterDesc

Input. Handle to a previously created filter descriptor.

datatype

Output. Data type.

format

Output. Type of format.

k

Output. Number of output feature maps.

c

Output. Number of input feature maps.

h

Output. Height of each filter.

w

Output. Width of each filter.
Returns

CUDNN_STATUS_SUCCESS

The object was set successfully.
3.2.47. cudnnGetFilterNdDescriptor()
cudnnStatus_t cudnnGetFilterNdDescriptor(
const cudnnFilterDescriptor_t wDesc,
int nbDimsRequested,
cudnnDataType_t *dataType,
cudnnTensorFormat_t *format,
int *nbDims,
int filterDimA[])
This function queries a previously initialized filter descriptor object.
Parameters

wDesc

Input. Handle to a previously initialized filter descriptor.

nbDimsRequested

Input. Dimension of the expected filter descriptor. It is also the minimum size of the arrays
filterDimA
in order to be able to hold the results 
datatype

Output. Data type.

format

Output. Type of format.

nbDims

Output. Actual dimension of the filter.

filterDimA

Output. Array of dimensions of at least
nbDimsRequested
that will be filled with the filter parameters from the provided filter descriptor.
Returns

CUDNN_STATUS_SUCCESS

The object was set successfully.

CUDNN_STATUS_BAD_PARAM

The parameter
nbDimsRequested
is negative.
3.2.48. cudnnGetFilterSizeInBytes()
cudnnStatus_t
cudnnGetFilterSizeInBytes(const cudnnFilterDescriptor_t filterDesc, size_t *size) ;
This function returns the size of the filter tensor in memory with respect to the given descriptor. It can be used to know the amount of GPU memory to be allocated to hold that filter tensor.
Parameters

filterDesc

Input. handle to a previously initialized filter descriptor.

size

Output. size in bytes needed to hold the tensor in GPU memory.
Returns

CUDNN_STATUS_SUCCESS

filterDesc
is valid. 
CUDNN_STATUS_BAD_PARAM

filerDesc
is invald.
3.2.49. cudnnGetLRNDescriptor()
cudnnStatus_t cudnnGetLRNDescriptor(
cudnnLRNDescriptor_t normDesc,
unsigned *lrnN,
double *lrnAlpha,
double *lrnBeta,
double *lrnK)
This function retrieves values stored in the previously initialized LRN descriptor object.
Parameters

normDesc

Input. Handle to a previously created LRN descriptor.

lrnN
,lrnAlpha
,lrnBeta
,lrnK

Output. Pointers to receive values of parameters stored in the descriptor object. For more information, refer to cudnnSetLRNDescriptor(). Any of these pointers can be
NULL
(no value is returned for the corresponding parameter).
Returns

CUDNN_STATUS_SUCCESS

Function completed successfully.
3.2.50. cudnnGetOpTensorDescriptor()
cudnnStatus_t cudnnGetOpTensorDescriptor(
const cudnnOpTensorDescriptor_t opTensorDesc,
cudnnOpTensorOp_t *opTensorOp,
cudnnDataType_t *opTensorCompType,
cudnnNanPropagation_t *opTensorNanOpt)
This function returns the configuration of the passed tensor pointwise math descriptor.
Parameters

opTensorDesc

Input. Tensor pointwise math descriptor passed to get the configuration from.

opTensorOp

Output. Pointer to the tensor pointwise math operation type, associated with this tensor pointwise math descriptor.

opTensorCompType

Output. Pointer to the cuDNN datatype associated with this tensor pointwise math descriptor.

opTensorNanOpt

Output. Pointer to the NAN propagation option associated with this tensor pointwise math descriptor.
Returns

CUDNN_STATUS_SUCCESS

The function returned successfully.

CUDNN_STATUS_BAD_PARAM

Input tensor pointwise math descriptor passed is invalid.
3.2.51. cudnnGetPooling2dDescriptor()
cudnnStatus_t cudnnGetPooling2dDescriptor(
const cudnnPoolingDescriptor_t poolingDesc,
cudnnPoolingMode_t *mode,
cudnnNanPropagation_t *maxpoolingNanOpt,
int *windowHeight,
int *windowWidth,
int *verticalPadding,
int *horizontalPadding,
int *verticalStride,
int *horizontalStride)
This function queries a previously created 2D pooling descriptor object.
Parameters

poolingDesc

Input. Handle to a previously created pooling descriptor.

mode

Output. Enumerant to specify the pooling mode.

maxpoolingNanOpt

Output. Enumerant to specify the Nan propagation mode.

windowHeight

Output. Height of the pooling window.

windowWidth

Output. Width of the pooling window.

verticalPadding

Output. Size of vertical padding.

horizontalPadding

Output. Size of horizontal padding.

verticalStride

Output. Pooling vertical stride.

horizontalStride

Output. Pooling horizontal stride.
Returns

CUDNN_STATUS_SUCCESS

The object was set successfully.
3.2.52. cudnnGetPooling2dForwardOutputDim()
cudnnStatus_t cudnnGetPooling2dForwardOutputDim(
const cudnnPoolingDescriptor_t poolingDesc,
const cudnnTensorDescriptor_t inputDesc,
int *outN,
int *outC,
int *outH,
int *outW)
This function provides the output dimensions of a tensor after 2d pooling has been applied.
Each dimension h and w
of the output images is computed as follows:
outputDim = 1 + (inputDim + 2*padding  windowDim)/poolingStride;
Parameters

poolingDesc

Input. Handle to a previously initialized pooling descriptor.

inputDesc

Input. Handle to the previously initialized input tensor descriptor.

outN

Output. Number of images in the output.

outC

Output. Number of channels in the output.

outH

Output. Height of images in the output.

outW

Output. Width of images in the output.
Returns

CUDNN_STATUS_SUCCESS

The function launched successfully.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
poolingDesc
has not been initialized.poolingDesc
orinputDesc
has an invalid number of dimensions (2 and 4 respectively are required).
3.2.53. cudnnGetPoolingNdDescriptor()
cudnnStatus_t cudnnGetPoolingNdDescriptor(
const cudnnPoolingDescriptor_t poolingDesc,
int nbDimsRequested,
cudnnPoolingMode_t *mode,
cudnnNanPropagation_t *maxpoolingNanOpt,
int *nbDims,
int windowDimA[],
int paddingA[],
int strideA[])
This function queries a previously initialized generic pooling descriptor object.
Parameters

poolingDesc

Input. Handle to a previously created pooling descriptor.

nbDimsRequested

Input. Dimension of the expected pooling descriptor. It is also the minimum size of the arrays
windowDimA
,paddingA
, andstrideA
in order to be able to hold the results. 
mode

Output. Enumerant to specify the pooling mode.

maxpoolingNanOpt

Output. Enumerant to specify the Nan propagation mode.

nbDims

Output. Actual dimension of the pooling descriptor.

windowDimA

Output. Array of dimension of at least
nbDimsRequested
that will be filled with the window parameters from the provided pooling descriptor. 
paddingA

Output. Array of dimension of at least
nbDimsRequested
that will be filled with the padding parameters from the provided pooling descriptor. 
strideA

Output. Array of dimension at least
nbDimsRequested
that will be filled with the stride parameters from the provided pooling descriptor.
Returns

CUDNN_STATUS_SUCCESS

The object was queried successfully.

CUDNN_STATUS_NOT_SUPPORTED

The parameter
nbDimsRequested
is greater thanCUDNN_DIM_MAX
.
3.2.54. cudnnGetPoolingNdForwardOutputDim()
cudnnStatus_t cudnnGetPoolingNdForwardOutputDim(
const cudnnPoolingDescriptor_t poolingDesc,
const cudnnTensorDescriptor_t inputDesc,
int nbDims,
int outDimA[])
This function provides the output dimensions of a tensor after Nd
pooling has been applied.
Each dimension of the (nbDims2)D
images of the output tensor is computed as follows:
outputDim = 1 + (inputDim + 2*padding  windowDim)/poolingStride;
Parameters

poolingDesc

Input. Handle to a previously initialized pooling descriptor.

inputDesc

Input. Handle to the previously initialized input tensor descriptor.

nbDims

Input. Number of dimensions in which pooling is to be applied.

outDimA

Output. Array of
nbDims
output dimensions.
Returns

CUDNN_STATUS_SUCCESS

The function launched successfully.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
poolingDesc
has not been initialized. The value of
nbDims
is inconsistent with the dimensionality ofpoolingDesc
andinputDesc
.
3.2.55. cudnnGetProperty()
cudnnStatus_t cudnnGetProperty(
libraryPropertyType type,
int *value)
This function writes a specific part of the cuDNN library version number into the provided host storage.
Parameters

type

Input. Enumerant type that instructs the function to report the numerical value of the cuDNN major version, minor version, or the patch level.

value

Output. Host pointer where the version information should be written.
Returns

CUDNN_STATUS_INVALID_VALUE

Invalid value of the
type
argument. 
CUDNN_STATUS_SUCCESS

Version information was stored successfully at the provided address.
3.2.56. cudnnGetReduceTensorDescriptor()
cudnnStatus_t cudnnGetReduceTensorDescriptor(
const cudnnReduceTensorDescriptor_t reduceTensorDesc,
cudnnReduceTensorOp_t *reduceTensorOp,
cudnnDataType_t *reduceTensorCompType,
cudnnNanPropagation_t *reduceTensorNanOpt,
cudnnReduceTensorIndices_t *reduceTensorIndices,
cudnnIndicesType_t *reduceTensorIndicesType)
This function queries a previously initialized reduce tensor descriptor object.
Parameters

reduceTensorDesc

Input. Pointer to a previously initialized reduce tensor descriptor object.

reduceTensorOp

Output. Enumerant to specify the reduce tensor operation.

reduceTensorCompType

Output. Enumerant to specify the computation datatype of the reduction.

reduceTensorNanOpt

Output. Enumerant to specify the Nan propagation mode.

reduceTensorIndices

Output. Enumerant to specify the reduced tensor indices.

reduceTensorIndicesType

Output. Enumerant to specify the reduce tensor indices type.
Returns

CUDNN_STATUS_SUCCESS

The object was queried successfully.

CUDNN_STATUS_BAD_PARAM

reduceTensorDesc
isNULL
.
3.2.57. cudnnGetReductionIndicesSize()
cudnnStatus_t cudnnGetReductionIndicesSize(
cudnnHandle_t handle,
const cudnnReduceTensorDescriptor_t reduceDesc,
const cudnnTensorDescriptor_t aDesc,
const cudnnTensorDescriptor_t cDesc,
size_t *sizeInBytes)
This is a helper function to return the minimum size of the index space to be passed to the reduction given the input and output tensors.
Parameters

handle

Input. Handle to a previously created cuDNN library descriptor.

reduceDesc

Input. Pointer to a previously initialized reduce tensor descriptor object.

aDesc

Input. Pointer to the input tensor descriptor.

cDesc

Input. Pointer to the output tensor descriptor.

sizeInBytes

Output. Minimum size of the index space to be passed to the reduction.
Returns

CUDNN_STATUS_SUCCESS

The index space size is returned successfully.
3.2.58. cudnnGetReductionWorkspaceSize()
cudnnStatus_t cudnnGetReductionWorkspaceSize(
cudnnHandle_t handle,
const cudnnReduceTensorDescriptor_t reduceDesc,
const cudnnTensorDescriptor_t aDesc,
const cudnnTensorDescriptor_t cDesc,
size_t *sizeInBytes)
This is a helper function to return the minimum size of the workspace to be passed to the reduction given the input and output tensors.
Parameters

handle

Input. Handle to a previously created cuDNN library descriptor.

reduceDesc

Input. Pointer to a previously initialized reduce tensor descriptor object.

aDesc

Input. Pointer to the input tensor descriptor.

cDesc

Input. Pointer to the output tensor descriptor.

sizeInBytes

Output. Minimum size of the index space to be passed to the reduction.
Returns

CUDNN_STATUS_SUCCESS

The workspace size is returned successfully.
3.2.59. cudnnGetStream()
cudnnStatus_t cudnnGetStream(
cudnnHandle_t handle,
cudaStream_t *streamId)
This function retrieves the user CUDA stream programmed in the cuDNN handle. When the user's CUDA stream is not set in the cuDNN handle, this function reports the nullstream.
Parameters

handle

Input. Pointer to the cuDNN handle.

streamID

Output. Pointer where the current CUDA stream from the cuDNN handle should be stored.
Returns

CUDNN_STATUS_BAD_PARAM

Invalid (
NULL
) handle. 
CUDNN_STATUS_SUCCESS

The stream identifier was retrieved successfully.
3.2.60. cudnnGetTensor4dDescriptor()
cudnnStatus_t cudnnGetTensor4dDescriptor(
const cudnnTensorDescriptor_t tensorDesc,
cudnnDataType_t *dataType,
int *n,
int *c,
int *h,
int *w,
int *nStride,
int *cStride,
int *hStride,
int *wStride)
This function queries the parameters of the previously initialized tensor4D descriptor object.
Parameters

tensorDesc

Input. Handle to a previously initialized tensor descriptor.

datatype

Output. Data type.

n

Output. Number of images.

c

Output. Number of feature maps per image.

h

Output. Height of each feature map.

w

Output. Width of each feature map.

nStride

Output. Stride between two consecutive images.

cStride

Output. Stride between two consecutive feature maps.

hStride

Output. Stride between two consecutive rows.

wStride

Output. Stride between two consecutive columns.
Returns

CUDNN_STATUS_SUCCESS

The operation succeeded.
3.2.61. cudnnGetTensorNdDescriptor()
cudnnStatus_t cudnnGetTensorNdDescriptor(
const cudnnTensorDescriptor_t tensorDesc,
int nbDimsRequested,
cudnnDataType_t *dataType,
int *nbDims,
int dimA[],
int strideA[])
This function retrieves values stored in a previously initialized tensor descriptor object.
Parameters

tensorDesc

Input. Handle to a previously initialized tensor descriptor.

nbDimsRequested

Input. Number of dimensions to extract from a given tensor descriptor. It is also the minimum size of the arrays
dimA
andstrideA
. If this number is greater than the resultingnbDims[0]
, onlynbDims[0]
dimensions will be returned. 
datatype

Output. Data type.

nbDims

Output. Actual number of dimensions of the tensor will be returned in
nbDims[0]
. 
dimA

Output. Array of dimensions of at least
nbDimsRequested
that will be filled with the dimensions from the provided tensor descriptor. 
strideA

Output. Array of dimensions of at least
nbDimsRequested
that will be filled with the strides from the provided tensor descriptor.
Returns

CUDNN_STATUS_SUCCESS

The results were returned successfully.

CUDNN_STATUS_BAD_PARAM

Either
tensorDesc
ornbDims
pointer isNULL
.
3.2.62. cudnnGetTensorSizeInBytes()
cudnnStatus_t cudnnGetTensorSizeInBytes(
const cudnnTensorDescriptor_t tensorDesc,
size_t *size)
This function returns the size of the tensor in memory in respect to the given descriptor. This function can be used to know the amount of GPU memory to be allocated to hold that tensor.
Parameters

tensorDesc

Input. Handle to a previously initialized tensor descriptor.

size

Output. Size in bytes needed to hold the tensor in GPU memory.
Returns

CUDNN_STATUS_SUCCESS

The results were returned successfully.
3.2.63. cudnnGetTensorTransformDescriptor()
cudnnStatus_t cudnnGetTensorTransformDescriptor(
cudnnTensorTransformDescriptor_t transformDesc,
uint32_t nbDimsRequested,
cudnnTensorFormat_t *destFormat,
int32_t padBeforeA[],
int32_t padAfterA[],
uint32_t foldA[],
cudnnFoldingDirection_t *direction);
This function returns the values stored in a previously initialized tensor transform descriptor.
Parameters

transformDesc
 Input. A previously initialized tensor transform descriptor.

nbDimsRequested
 Input. The number of dimensions to consider. For more information, refer to Tensor Descriptor in the cuDNN Developer Guide.

destFormat
 Output. The transform format that will be returned.

padBeforeA[]

Output. An array filled with the amount of padding to add before each dimension. The dimension of this
padBeforeA[]
parameter is equal tonbDimsRequested
. 
padAfterA[]

Output. An array filled with the amount of padding to add after each dimension. The dimension of this
padBeforeA[]
parameter is equal tonbDimsRequested
. 
foldA[]

Output. An array that was filled with the folding parameters for each spatial dimension. The dimension of this
foldA[]
array isnbDimsRequested2
. 
direction
 Output. The setting that selects folding or unfolding. For more information, refer to cudnnFoldingDirection_t.
Returns

CUDNN_STATUS_SUCCESS
 The results were obtained successfully.

CUDNN_STATUS_BAD_PARAM

If
transformDesc
isNULL
or ifnbDimsRequested
is less than 3 or greater thanCUDNN_DIM_MAX
.
3.2.64. cudnnGetVersion()
size_t cudnnGetVersion()
This function returns the version number of the cuDNN library. It returns the CUDNN_VERSION
defined present in the cudnn.h
header file. Starting with release R2, the routine can be used to identify dynamically the current cuDNN library used by the application. The defined CUDNN_VERSION
can be used to have the same application linked against different cuDNN versions using conditional compilation statements.
3.2.65. cudnnInitTransformDest()
cudnnStatus_t cudnnInitTransformDest(
const cudnnTensorTransformDescriptor_t transformDesc,
const cudnnTensorDescriptor_t srcDesc,
cudnnTensorDescriptor_t destDesc,
size_t *destSizeInBytes);
This function initializes and returns a destination tensor descriptor destDesc
for tensor transform operations. The initialization is done with the desired parameters described in the transform descriptor cudnnTensorDescriptor_t.
The returned tensor descriptor will be packed.
Parameters

transformDesc
 Input. Handle to a previously initialized tensor transform descriptor.

srcDesc
 Input. Handle to a previously initialized tensor descriptor.

destDesc
 Output. Handle of the tensor descriptor that will be initialized and returned.

destSizeInBytes
 Output. A pointer to hold the size, in bytes, of the new tensor.
Returns

CUDNN_STATUS_SUCCESS
 The tensor descriptor was initialized successfully.

CUDNN_STATUS_BAD_PARAM

If either
srcDesc
ordestDesc
isNULL
, or if the tensor descriptor’snbDims
is incorrect. For more information, refer to Tensor Descriptor in the cuDNN Developer Guide. 
CUDNN_STATUS_NOT_SUPPORTED
 If the provided configuration is not 4D.

CUDNN_STATUS_EXECUTION_FAILED
 Function failed to launch on the GPU.
3.2.66. cudnnLRNCrossChannelForward()
cudnnStatus_t cudnnLRNCrossChannelForward(
cudnnHandle_t handle,
cudnnLRNDescriptor_t normDesc,
cudnnLRNMode_t lrnMode,
const void *alpha,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const void *beta,
const cudnnTensorDescriptor_t yDesc,
void *y)
This function performs the forward LRN layer computation.
Supported formats are: positivestrided
, NCHW and NHWC for 4D x
and y
, and only NCDHW DHWpacked for 5D (for both x
and y
). Only nonoverlapping 4D and 5D tensors are supported. NCHW layout is preferred for performance.
Parameters

handle

Input. Handle to a previously created cuDNN library descriptor.

normDesc

Input. Handle to a previously initialized LRN parameter descriptor.

lrnMode

Input. LRN layer mode of operation. Currently only
CUDNN_LRN_CROSS_CHANNEL_DIM1
is implemented. Normalization is performed along the tensor'sdimA[1]
. 
alpha
,beta

Input. Pointers to scaling factors (in host memory) used to blend the layer output value with prior value in the destination tensor as follows:
dstValue = alpha[0]*resultValue + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

xDesc
,yDesc

Input. Tensor descriptor objects for the input and output tensors.

x

Input. Input tensor data pointer in device memory.

y

Output. Output tensor data pointer in device memory.
Returns

CUDNN_STATUS_SUCCESS

The computation was performed successfully.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 One of the tensor pointers
x
,y
isNULL
.  Number of input tensor dimensions is 2 or less.
 LRN descriptor parameters are outside of their valid ranges.
 One of the tensor parameters is 5D but is not in NCDHW DHWpacked format.
 One of the tensor pointers

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration. See the following for some examples of nonsupported configurations:
 Any of the input tensor datatypes is not the same as any of the output tensor datatype.
x
andy
tensor dimensions mismatch. Any tensor parameters strides are negative.
3.2.67. cudnnNormalizationForwardInference()
cudnnStatus_t
cudnnNormalizationForwardInference(cudnnHandle_t handle,
cudnnNormMode_t mode,
cudnnNormOps_t normOps,
cudnnNormAlgo_t algo,
const void *alpha,
const void *beta,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const cudnnTensorDescriptor_t normScaleBiasDesc,
const void *normScale,
const void *normBias,
const cudnnTensorDescriptor_t normMeanVarDesc,
const void *estimatedMean,
const void *estimatedVariance,
const cudnnTensorDescriptor_t zDesc,
const void *z,
cudnnActivationDescriptor_t activationDesc,
const cudnnTensorDescriptor_t yDesc,
void *y,
double epsilon,
int groupCnt);
This function performs the forward normalization layer computation for the inference phase. Perchannel normalization layer is based on the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, S. Ioffe, C. Szegedy, 2015.
 Only 4D and 5D tensors are supported.
 The input transformation performed by this function is defined as:
y = beta*y + alpha *[normBias + (normScale * (xestimatedMean)/sqrt(epsilon + estimatedVariance)]
 The
epsilon
value has to be the same during training, backpropagation, and inference.  For the training phase, refer to cudnnNormalizationForwardTraining().
 Higher performance can be obtained when HWpacked tensors are used for all of
x
andy
.
Parameters

handle

Input. Handle to a previously created cuDNN library descriptor. For more information, refer to cudnnHandle_t.

mode

Input. Mode of operation (perchannel or peractivation). For more information, refer to cudnnNormMode_t.

normOps

Input. Mode of postoperative. Currently,
CUDNN_NORM_OPS_NORM_ACTIVATION
andCUDNN_NORM_OPS_NORM_ADD_ACTIVATION
are not supported. 
algo

Input. Algorithm to be performed. For more information, refer to cudnnNormAlgo_t.

alpha
,beta

Inputs. Pointers to scaling factors (in host memory) used to blend the layer output value with prior value in the destination tensor as follows:
dstValue = alpha[0]*resultValue + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

xDesc
,yDesc

Input. Handles to the previously initialized tensor descriptors.

*x

Input. Data pointer to GPU memory associated with the tensor descriptor
xDesc
, for the layer’sx
input data. 
*y

Output. Data pointer to GPU memory associated with the tensor descriptor
yDesc
, for they
output of the normalization layer. 
zDesc
,*z

Input. Tensor descriptors and pointers in device memory for residual addition to the result of the normalization operation, prior to the activation.
zDesc
and*z
are optional and are only used whennormOps
isCUDNN_NORM_OPS_NORM_ADD_ACTIVATION
, otherwise users may passNULL
. When in use,z
should have exactly the same dimension asx
and the final outputy
. For more information, refer to cudnnTensorDescriptor_t.Since
normOps
is only supported forCUDNN_NORM_OPS_NORM
, we can set these toNULL
for now. 
normScaleBiasDesc
,normScale
,normBias

Inputs. Tensor descriptors and pointers in device memory for the normalization scale and bias parameters (in the original paper bias is referred to as beta and scale as gamma).

normMeanVarDesc
,estimatedMean
,estimatedVariance

Inputs. Mean and variance tensors and their tensor descriptors. The
estimatedMean
andestimatedVariance
inputs, accumulated during the training phase from the cudnnNormalizationForwardTraining() call, should be passed as inputs here. 
activationDesc

Input. Descriptor for the activation operation. When the
normOps
input is set to eitherCUDNN_NORM_OPS_NORM_ACTIVATION
orCUDNN_NORM_OPS_NORM_ADD_ACTIVATION
then this activation is used, otherwise the user may passNULL
. SincenormOps
is only supported forCUDNN_NORM_OPS_NORM
, we can set these toNULL
for now. 
epsilon

Input. Epsilon value used in the normalization formula. Its value should be equal to or greater than zero.

groupCnt

Input. The number of grouped convolutions. Currently, only
1
is supported.
Returns

CUDNN_STATUS_SUCCESS

The computation was performed successfully.

CUDNN_STATUS_NOT_SUPPORTED

A compute or data type other than what is supported was chosen, or an unknown algorithm type was chosen.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 One of the pointers
alpha
,beta
,x
,y
,normScale
,normBias
,estimatedMean
, andestimatedInvVariance
isNULL
.  The number of
xDesc
oryDesc
tensor descriptor dimensions is not within the range of [4,5] (only 4D and 5D tensors are supported). normScaleBiasDesc
andnormMeanVarDesc
dimensions are not 1xCx1x1 for 4D and 1xCx1x1x1 for 5D for perchannel, and are not 1xCxHxW for 4D and 1xCxDxHxW for 5D for peractivation mode.epsilon
value is less than zero. Dimensions or data types mismatch for
xDesc
andyDesc
.
 One of the pointers

CUDNN_STATUS_NOT_SUPPORTED

A compute or data type other than
FLOAT
was chosen, or an unknown algorithm type was chosen. 
CUDNN_STATUS_EXECUTION_FAILED

The function failed to launch on the GPU.
3.2.68. cudnnOpsInferVersionCheck()
cudnnStatus_t cudnnOpsInferVersionCheck(void)
This function is the first of a series of corresponding functions that check for consistent library versions among DLL files for different modules.
Returns

CUDNN_STATUS_SUCCESS

The version of this DLL file is consistent with cuDNN DLLs on which it depends.

CUDNN_STATUS_VERSION_MISMATCH

The version of this DLL file does not match that of a cuDNN DLLs on which it depends.
3.2.69. cudnnOpTensor()
cudnnStatus_t cudnnOpTensor(
cudnnHandle_t handle,
const cudnnOpTensorDescriptor_t opTensorDesc,
const void *alpha1,
const cudnnTensorDescriptor_t aDesc,
const void *A,
const void *alpha2,
const cudnnTensorDescriptor_t bDesc,
const void *B,
const void *beta,
const cudnnTensorDescriptor_t cDesc,
void *C)
This function implements the equation C = op(alpha1[0] * A, alpha2[0] * B) + beta[0] * C
, given the tensors A
, B
, and C
and the scaling factors alpha1
, alpha2
, and beta
. The op
to use is indicated by the descriptor cudnnOpTensorDescriptor_t, meaning, the type of opTensorDesc
. Currentlysupported ops are listed by the cudnnOpTensorOp_t enum.
The following restrictions on the input and destination tensors apply:
opTensorCompType in opTensorDesc 
A 
B 
C (destination) 

FLOAT 
FLOAT 
FLOAT 
FLOAT 
FLOAT 
INT8 
INT8 
FLOAT 
FLOAT 
HALF 
HALF 
FLOAT 
FLOAT 
BFLOAT16 
BFLOAT16 
FLOAT 
DOUBLE 
DOUBLE 
DOUBLE 
DOUBLE 
FLOAT 
FLOAT 
FLOAT 
HALF 
FLOAT 
HALF 
HALF 
HALF 
FLOAT 
INT8 
INT8 
INT8 
FLOAT 
FLOAT 
FLOAT 
INT8 
FLOAT 
FLOAT 
FLOAT 
BFLOAT16 
FLOAT 
BFLOAT16 
BFLOAT16 
BFLOAT16 
CUDNN_TENSOR_NCHW_VECT_C
is not supported as input tensor format. All tensors up to dimension five (5) are supported. This routine does not support tensor formats beyond these dimensions.
Parameters

handle

Input. Handle to a previously created cuDNN context.

opTensorDesc

Input. Handle to a previously initialized op tensor descriptor.

alpha1
,alpha2
,beta

Input. Pointers to scaling factors (in host memory) used to blend the source value with prior value in the destination tensor as follows:
dstValue = alpha[0]*resultValue + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

aDesc
,bDesc
,cDesc

Input. Handle to a previously initialized tensor descriptor.

A
,B

Input. Pointer to data of the tensors described by the
aDesc
andbDesc
descriptors, respectively. 
C

Input/Output. Pointer to data of the tensor described by the
cDesc
descriptor.
Returns

CUDNN_STATUS_SUCCESS

The function executed successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration. See the following for some examples of nonsupported configurations:
 The dimensions of the bias tensor and the output tensor dimensions are above 5.
opTensorCompType
is not set as stated above.

CUDNN_STATUS_BAD_PARAM

The data type of the destination tensor
C
is unrecognized, or the restrictions on the input and destination tensors, stated above, are not met. 
CUDNN_STATUS_EXECUTION_FAILED

The function failed to launch on the GPU.
3.2.70. cudnnPoolingForward()
cudnnStatus_t cudnnPoolingForward(
cudnnHandle_t handle,
const cudnnPoolingDescriptor_t poolingDesc,
const void *alpha,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const void *beta,
const cudnnTensorDescriptor_t yDesc,
void *y)
This function computes pooling of input values (meaning, the maximum or average of several adjacent values) to produce an output with smaller height and/or width.
 All tensor formats are supported, best performance is expected when using
HWpacked
tensors. Only 2 and 3 spatial dimensions are allowed. Vectorized tensors are only supported if they have 2 spatial dimensions.  The dimensions of the output tensor
yDesc
can be smaller or bigger than the dimensions advised by the routine cudnnGetPooling2dForwardOutputDim() or cudnnGetPoolingNdForwardOutputDim().  For average pooling, the compute type is
float
even for integer input and output data type. Output round is nearesteven and clamp to the most negative or most positive value of type if out of range.
Parameters

handle

Input. Handle to a previously created cuDNN context.

poolingDesc

Input. Handle to a previously initialized pooling descriptor.

alpha
,beta

Input. Pointers to scaling factors (in host memory) used to blend the computation result with prior value in the output layer as follows:
dstValue = alpha[0]*resultValue + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

xDesc

Input. Handle to the previously initialized input tensor descriptor. Must be of type
FLOAT
,DOUBLE
,HALF
,INT8
,INT8x4
,INT8x32
, orBFLOAT16
. For more information, refer to cudnnDataType_t. 
x

Input. Data pointer to GPU memory associated with the tensor descriptor
xDesc
. 
yDesc

Input. Handle to the previously initialized output tensor descriptor. Must be of type
FLOAT
,DOUBLE
,HALF
,INT8
,INT8x4
,INT8x32
, orBFLOAT16
. For more information, refer to cudnnDataType_t. 
y

Output. Data pointer to GPU memory associated with the output tensor descriptor
yDesc
.
Returns

CUDNN_STATUS_SUCCESS

The function launched successfully.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 The dimensions
n
,c
of the input tensor and output tensors differ.  The
datatype
of the input tensor and output tensors differs.
 The dimensions

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_EXECUTION_FAILED

The function failed to launch on the GPU.
3.2.71. cudnnQueryRuntimeError()
cudnnStatus_t cudnnQueryRuntimeError(
cudnnHandle_t handle,
cudnnStatus_t *rstatus,
cudnnErrQueryMode_t mode,
cudnnRuntimeTag_t *tag)
cuDNN library functions perform extensive input argument checking before launching GPU kernels. The last step is to verify that the GPU kernel actually started. When a kernel fails to start, CUDNN_STATUS_EXECUTION_FAILED
is returned by the corresponding API call. Typically, after a GPU kernel starts, no runtime checks are performed by the kernel itself  numerical results are simply written to output buffers.
When the CUDNN_BATCHNORM_SPATIAL_PERSISTENT
mode is selected in cudnnBatchNormalizationForwardTraining() or cudnnBatchNormalizationBackward(), the algorithm may encounter numerical overflows where CUDNN_BATCHNORM_SPATIAL
performs just fine albeit at a slower speed. The user can invoke cudnnQueryRuntimeError()
to make sure numerical overflows did not occur during the kernel execution. Those issues are reported by the kernel that performs computations.
cudnnQueryRuntimeError()
can be used in polling and blocking software control flows. There are two polling modes (CUDNN_ERRQUERY_RAWCODE
and CUDNN_ERRQUERY_NONBLOCKING
) and one blocking mode CUDNN_ERRQUERY_BLOCKING
.
CUDNN_ERRQUERY_RAWCODE
reads the error storage location regardless of the kernel completion status. The kernel might not even start and the error storage (allocated per cuDNN handle) might be used by an earlier call.
CUDNN_ERRQUERY_NONBLOCKING
checks if all tasks in the user stream are completed. The cudnnQueryRuntimeError()
function will return immediately and report CUDNN_STATUS_RUNTIME_IN_PROGRESS
in rstatus
if some tasks in the user stream are pending. Otherwise, the function will copy the remote kernel error code to rstatus
.
In the blocking mode (CUDNN_ERRQUERY_BLOCKING
), the function waits for all tasks to drain in the user stream before reporting the remote kernel error code. The blocking flavor can be further adjusted by calling cudaSetDeviceFlags
with the cudaDeviceScheduleSpin
, cudaDeviceScheduleYield
, or cudaDeviceScheduleBlockingSync
flag.
CUDNN_ERRQUERY_NONBLOCKING
and CUDNN_ERRQUERY_BLOCKING
modes should not be used when the user stream is changed in the cuDNN handle, meaning, cudnnSetStream() is invoked between functions that report runtime kernel errors and the cudnnQueryRuntimeError()
function.
The remote error status reported in rstatus
can be set to: CUDNN_STATUS_SUCCESS
, CUDNN_STATUS_RUNTIME_IN_PROGRESS
, or CUDNN_STATUS_RUNTIME_FP_OVERFLOW
. The remote kernel error is automatically cleared by cudnnQueryRuntimeError()
.
The cudnnQueryRuntimeError()
function should be used in conjunction with cudnnBatchNormalizationForwardTraining() and cudnnBatchNormalizationBackward() when the cudnnBatchNormMode_t argument is CUDNN_BATCHNORM_SPATIAL_PERSISTENT
.
Parameters

handle

Input. Handle to a previously created cuDNN context.

rstatus

Output. Pointer to the user's error code storage.

mode

Input. Remote error query mode.

tag

Input/Output. Currently, this argument should be
NULL
.
Returns

CUDNN_STATUS_SUCCESS

No errors detected (
rstatus
holds a valid value). 
CUDNN_STATUS_BAD_PARAM

Invalid input argument.

CUDNN_STATUS_INTERNAL_ERROR

A stream blocking synchronization or a nonblocking stream query failed.

CUDNN_STATUS_MAPPING_ERROR

The device cannot access zerocopy memory to report kernel errors.
3.2.72. cudnnReduceTensor()
cudnnStatus_t cudnnReduceTensor(
cudnnHandle_t handle,
const cudnnReduceTensorDescriptor_t reduceTensorDesc,
void *indices,
size_t indicesSizeInBytes,
void *workspace,
size_t workspaceSizeInBytes,
const void *alpha,
const cudnnTensorDescriptor_t aDesc,
const void *A,
const void *beta,
const cudnnTensorDescriptor_t cDesc,
void *C)
This function reduces tensor A
by implementing the equation C = alpha * reduce op ( A ) + beta * C
, given tensors A
and C
and scaling factors alpha
and beta
. The reduction op to use is indicated by the descriptor reduceTensorDesc
. Currentlysupported ops are listed by the cudnnReduceTensorOp_t enum.
Each dimension of the output tensor C
must match the corresponding dimension of the input tensor A
or must be equal to 1. The dimensions equal to 1 indicate the dimensions of A
to be reduced.
The implementation will generate indices for the min and max ops only, as indicated by the cudnnReduceTensorIndices_t enum of the reduceTensorDesc
. Requesting indices for the other reduction ops results in an error. The data type of the indices is indicated by the cudnnIndicesType_t enum; currently only the 32bit (unsigned int) type is supported.
The indices returned by the implementation are not absolute indices but relative to the dimensions being reduced. The indices are also flattened, meaning, not coordinate tuples.
The data types of the tensors A
and C
must match if of type double. In this case, alpha
and beta
and the computation enum of reduceTensorDesc
are all assumed to be of type double.
The HALF
and INT8
data types may be mixed with the FLOAT
data types. In these cases, the computation enum of reduceTensorDesc
is required to be of type FLOAT
.
Up to dimension 8, all tensor formats are supported. Beyond those dimensions, this routine is not supported.
Parameters

handle

Input. Handle to a previously created cuDNN context.

reduceTensorDesc

Input. Handle to a previously initialized reduce tensor descriptor.

indices

Output. Handle to a previously allocated space for writing indices.

indicesSizeInBytes

Input. Size of the above previously allocated space.

workspace

Input. Handle to a previously allocated space for the reduction implementation.

workspaceSizeInBytes

Input. Size of the above previously allocated space.

alpha
,beta

Input. Pointers to scaling factors (in host memory) used to blend the source value with prior value in the destination tensor as follows:
dstValue = alpha[0]*resultValue + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

aDesc
,cDesc

Input. Handle to a previously initialized tensor descriptor.

A

Input. Pointer to data of the tensor described by the
aDesc
descriptor. 
C

Input/Output. Pointer to data of the tensor described by the
cDesc
descriptor.
Returns

CUDNN_STATUS_SUCCESS

The function executed successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration. See the following for some examples of nonsupported configurations:
 The dimensions of the input tensor and the output tensor are above 8.
reduceTensorCompType
is not set as stated above.

CUDNN_STATUS_BAD_PARAM

The corresponding dimensions of the input and output tensors all match, or the conditions in the above paragraphs are unmet.

CUDNN_INVALID_VALUE

The allocations for the indices or workspace are insufficient.

CUDNN_STATUS_EXECUTION_FAILED

The function failed to launch on the GPU.
3.2.73. cudnnRestoreAlgorithm()
This function has been deprecated in cuDNN 8.0.
cudnnStatus_t cudnnRestoreAlgorithm(
cudnnHandle_t handle,
void* algoSpace,
size_t algoSpaceSizeInBytes,
cudnnAlgorithmDescriptor_t algoDesc)
This function reads algorithm metadata from the host memory space provided by the user in algoSpace
, allowing the user to use the results of RNN finds from previous cuDNN sessions.
Parameters

handle

Input. Handle to a previously created cuDNN context.

algoDesc

Input. A previously created algorithm descriptor.

algoSpace

Input. Pointer to the host memory to be read.

algoSpaceSizeInBytes

Input. Amount of host memory needed as a workspace to be able to hold the metadata from the specified
algoDesc
.
Returns

CUDNN_STATUS_SUCCESS

The function launched successfully.

CUDNN_STATUS_NOT_SUPPORTED

The metadata is from a different cuDNN version.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions is met:
 One of the arguments is
NULL
.  The metadata is corrupted.
 One of the arguments is
3.2.74. cudnnRestoreDropoutDescriptor()
cudnnStatus_t cudnnRestoreDropoutDescriptor(
cudnnDropoutDescriptor_t dropoutDesc,
cudnnHandle_t handle,
float dropout,
void *states,
size_t stateSizeInBytes,
unsigned long long seed)
This function restores a dropout descriptor to a previously savedoff state.
Parameters

dropoutDesc

Input/Output. Previously created dropout descriptor.

handle

Input. Handle to a previously created cuDNN context.

dropout

Input. Probability with which the value from an input tensor is set to
0
when performing dropout. 
states

Input. Pointer to GPU memory that holds random number generator states initialized by a prior call to cudnnSetDropoutDescriptor().

stateSizeInBytes

Input. Size in bytes of buffer holding random number generator
states
. 
seed

Input. Seed used in prior calls to cudnnSetDropoutDescriptor() that initialized
states
buffer. Using a different seed from this has no effect. A change of seed, and subsequent update to random number generator states can be achieved by calling cudnnSetDropoutDescriptor().
Returns

CUDNN_STATUS_SUCCESS

The call was successful.

CUDNN_STATUS_INVALID_VALUE

States buffer size (as indicated in
stateSizeInBytes
) is too small.
3.2.75. cudnnSaveAlgorithm()
This function has been deprecated in cuDNN 8.0.
cudnnStatus_t cudnnSaveAlgorithm(
cudnnHandle_t handle,
cudnnAlgorithmDescriptor_t algoDesc,
void* algoSpace
size_t algoSpaceSizeInBytes)
This function writes algorithm metadata into the host memory space provided by the user in algoSpace
, allowing the user to preserve the results of RNN finds after cuDNN exits.
Parameters

handle

Input. Handle to a previously created cuDNN context.

algoDesc

Input. A previously created algorithm descriptor.

algoSpace

Input. Pointer to the host memory to be written.

algoSpaceSizeInBytes

Input. Amount of host memory needed as a workspace to be able to save the metadata from the specified
algoDesc
.
Returns

CUDNN_STATUS_SUCCESS

The function launched successfully.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions is met:
 One of the arguments is
NULL
. algoSpaceSizeInBytes
is too small.
 One of the arguments is
3.2.76. cudnnScaleTensor()
cudnnStatus_t cudnnScaleTensor(
cudnnHandle_t handle,
const cudnnTensorDescriptor_t yDesc,
void *y,
const void *alpha)
This function scales all the elements of a tensor by a given factor.
Parameters

handle

Input. Handle to a previously created cuDNN context.

yDesc

Input. Handle to a previously initialized tensor descriptor.

y

Input/Output. Pointer to data of the tensor described by the
yDesc
descriptor. 
alpha

Input. Pointer in the host memory to a single value that all elements of the tensor will be scaled with. For more information, refer to Scaling Parameters in the cuDNN Developer Guide.
Returns

CUDNN_STATUS_SUCCESS

The function launched successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

One of the provided pointers is nil.

CUDNN_STATUS_EXECUTION_FAILED

The function failed to launch on the GPU.
3.2.77. cudnnSetActivationDescriptor()
cudnnStatus_t cudnnSetActivationDescriptor(
cudnnActivationDescriptor_t activationDesc,
cudnnActivationMode_t mode,
cudnnNanPropagation_t reluNanOpt,
double coef)
This function initializes a previously created generic activation descriptor object.
Parameters

activationDesc

Input/Output. Handle to a previously created activation descriptor.

mode

Input. Enumerant to specify the activation mode.

reluNanOpt

Input. Enumerant to specify the
Nan
propagation mode. 
coef

Input. Floating point number. When the activation mode (refer to cudnnActivationMode_t) is set to
CUDNN_ACTIVATION_CLIPPED_RELU
, this input specifies the clipping threshold; and when the activation mode is set toCUDNN_ACTIVATION_RELU
, this input specifies the upper bound.
Returns

CUDNN_STATUS_SUCCESS

The object was set successfully.

CUDNN_STATUS_BAD_PARAM

mode
orreluNanOpt
has an invalid enumerant value.
3.2.78. cudnnSetActivationDescriptorSwishBeta()
cudnnStatus_t cudnnSetActivationDescriptorSwishBeta(cudnnActivationDescriptor_t activationDesc, double swish_beta)
This function sets the beta parameter of the SWISH activation function to swish_beta
.
Parameters

activationDesc

Input/Output. Handle to a previously created activation descriptor.

swish_beta

Input. The value to set the SWISH activations' beta parameter to.
Returns

CUDNN_STATUS_SUCCESS

The value was set successfully.

CUDNN_STATUS_BAD_PARAM

The activation descriptor is a
NULL
pointer.
3.2.79. cudnnSetAlgorithmDescriptor()
This function has been deprecated in cuDNN 8.0.
cudnnStatus_t cudnnSetAlgorithmDescriptor(
cudnnAlgorithmDescriptor_t algorithmDesc,
cudnnAlgorithm_t algorithm)
This function initializes a previously created generic algorithm descriptor object.
Parameters

algorithmDesc

Input/Output. Handle to a previously created algorithm descriptor.

algorithm

Input. Struct to specify the algorithm.
Returns

CUDNN_STATUS_SUCCESS

The object was set successfully.
3.2.80. cudnnSetAlgorithmPerformance()
This function has been deprecated in cuDNN 8.0.
cudnnStatus_t cudnnSetAlgorithmPerformance(
cudnnAlgorithmPerformance_t algoPerf,
cudnnAlgorithmDescriptor_t algoDesc,
cudnnStatus_t status,
float time,
size_t memory)
This function initializes a previously created generic algorithm performance object.
Parameters

algoPerf

Input/Output. Handle to a previously created algorithm performance object.

algoDesc

Input. The algorithm descriptor which the performance results describe.

status

Input. The cuDNN status returned from running the
algoDesc
algorithm. 
time

Input. The GPU time spent running the
algoDesc
algorithm. 
memory

Input. The GPU memory needed to run the
algoDesc
algorithm.
Returns

CUDNN_STATUS_SUCCESS

The object was set successfully.

CUDNN_STATUS_BAD_PARAM

mode
orreluNanOpt
has an invalid enumerate value.
3.2.81. cudnnSetCallback()
cudnnStatus_t cudnnSetCallback(
unsigned mask,
void *udata,
cudnnCallback_t fptr)
This function sets the internal states of cuDNN error reporting functionality.
Parameters

mask

Input. An unsigned integer. The four least significant bits (LSBs) of this unsigned integer are used for switching on and off the different levels of error reporting messages. This applies for both the default callbacks, and for the customized callbacks. The bit position is in correspondence with the enum of
cudnnSeverity_t
. The user may utilize the predefined macrosCUDNN_SEV_ERROR_EN
,CUDNN_SEV_WARNING_EN
, andCUDNN_SEV_INFO_EN
to form the bit mask. When a bit is set to1
, the corresponding message channel is enabled.For example, when bit 3 is set to
1
, the API logging is enabled. Currently, only the log output of levelCUDNN_SEV_INFO
is functional; the others are not yet implemented. When used for turning on and off the logging with the default callback, the user may passNULL
toudata
andfptr
. In addition, the environment variableCUDNN_LOGDEST_DBG
must be set. For more information, refer to Backward Compatibility and Deprecation Policy in the cuDNN Developer Guide.CUDNN_SEV_INFO_EN
= 0b1000 (functional).CUDNN_SEV_ERROR_EN
= 0b0010 (not yet functional).CUDNN_SEV_WARNING_EN
= 0b0100 (not yet functional).
The output of
CUDNN_SEV_FATAL
is always enabled and cannot be disabled. 
udata

Input. A pointer provided by the user. This pointer will be passed to the user’s custom logging callback function. The data it points to will not be read, nor be changed by cuDNN. This pointer may be used in many ways, such as in a mutex or in a communication socket for the user’s callback function for logging. If the user is utilizing the default callback function, or doesn’t want to use this input in the customized callback function, they may pass in
NULL
. 
fptr

Input. A pointer to a usersupplied callback function. When
NULL
is passed to this pointer, then cuDNN switches back to the builtin default callback function. The usersupplied callback function prototype must be similar to the following (also defined in the header file):void customizedLoggingCallback (cudnnSeverity_t sev, void *udata, const cudnnDebug_t *dbg, const char *msg);
 The structure
cudnnDebug_t
is defined in the header file. It provides the metadata, such as time, time since start, stream ID, process and thread ID, that the user may choose to print or store in their customized callback.  The variable
msg
is the logging message generated by cuDNN. Each line of this message is terminated by\0
, and the end of the message is terminated by\0\0
. Users may select what is necessary to show in the log, and may reformat the string.
 The structure
Returns

CUDNN_STATUS_SUCCESS

The function launched successfully.
3.2.82. cudnnSetDropoutDescriptor()
cudnnStatus_t cudnnSetDropoutDescriptor(
cudnnDropoutDescriptor_t dropoutDesc,
cudnnHandle_t handle,
float dropout,
void *states,
size_t stateSizeInBytes,
unsigned long long seed)
This function initializes a previously created dropout descriptor object. If the states
argument is equal to NULL
, then the random number generator states won't be initialized, and only the dropout
value will be set. The user is expected not to change the memory pointed at by states
for the duration of the computation.
When the states
argument is not NULL
, a cuRAND initialization kernel is invoked by cudnnSetDropoutDescriptor()
. This kernel requires a substantial amount of GPU memory for the stack. Memory is released when the kernel finishes. The CUDNN_STATUS_ALLOC_FAILED
status is returned when no sufficient free memory is available for the GPU stack.
Parameters

dropoutDesc

Input/Output. Previously created dropout descriptor object.

handle

Input. Handle to a previously created cuDNN context.

dropout

Input. The probability with which the value from input is set to zero during the dropout layer.

states

Output. Pointer to userallocated GPU memory that will hold random number generator states.

stateSizeInBytes

Input. Specifies the size in bytes of the provided memory for the states.

seed

Input. Seed used to initialize random number generator states.
Returns

CUDNN_STATUS_SUCCESS

The call was successful.

CUDNN_STATUS_INVALID_VALUE

The
sizeInBytes
argument is less than the value returned by cudnnDropoutGetStatesSize(). 
CUDNN_STATUS_ALLOC_FAILED

The function failed to temporarily extend the GPU stack.

CUDNN_STATUS_EXECUTION_FAILED

The function failed to launch on the GPU.

CUDNN_STATUS_INTERNAL_ERROR

Internally used CUDA functions returned an error status.
3.2.83. cudnnSetFilter4dDescriptor()
cudnnStatus_t cudnnSetFilter4dDescriptor(
cudnnFilterDescriptor_t filterDesc,
cudnnDataType_t dataType,
cudnnTensorFormat_t format,
int k,
int c,
int h,
int w)
This function initializes a previously created filter descriptor object into a 4D filter. The layout of the filters must be contiguous in memory.
Tensor format CUDNN_TENSOR_NHWC
has limited support in cudnnConvolutionForward(), cudnnConvolutionBackwardData(), and cudnnConvolutionBackwardFilter().
Parameters

filterDesc

Input/Output. Handle to a previously created filter descriptor.

datatype

Input. Data type.

format

Input.Type of the filter layout format. If this input is set to
CUDNN_TENSOR_NCHW
, which is one of the enumerant values allowed by cudnnTensorFormat_t descriptor, then the layout of the filter is in the form ofKCRS
, where:K
represents the number of output feature mapsC
is the number of input feature mapsR
is the number of rows per filterS
is the number of columns per filter
If this input is set to
CUDNN_TENSOR_NHWC
, then the layout of the filter is in the form ofKRSC
. For more information, refer to cudnnTensorFormat_t. 
k

Input. Number of output feature maps.

c

Input. Number of input feature maps.

h

Input. Height of each filter.

w

Input. Width of each filter.
Returns

CUDNN_STATUS_SUCCESS

The object was set successfully.

CUDNN_STATUS_BAD_PARAM

At least one of the parameters
k
,c
,h
,w
is negative ordataType
orformat
has an invalid enumerant value.
3.2.84. cudnnSetFilterNdDescriptor()
cudnnStatus_t cudnnSetFilterNdDescriptor(
cudnnFilterDescriptor_t filterDesc,
cudnnDataType_t dataType,
cudnnTensorFormat_t format,
int nbDims,
const int filterDimA[])
This function initializes a previously created filter descriptor object. The layout of the filters must be contiguous in memory.
The tensor format CUDNN_TENSOR_NHWC
has limited support in cudnnConvolutionForward(), cudnnConvolutionBackwardData(), and cudnnConvolutionBackwardFilter().
Parameters

filterDesc

Input/Output. Handle to a previously created filter descriptor.

datatype

Input. Data type.

format

Input.Type of the filter layout format. If this input is set to
CUDNN_TENSOR_NCHW
, which is one of the enumerant values allowed by cudnnTensorFormat_t descriptor, then the layout of the filter is as follows: For
N=4
, a 4D filter descriptor, the filter layout is in the form ofKCRS
:K
represents the number of output feature mapsC
is the number of input feature mapsR
is the number of rows per filterS
is the number of columns per filter
 For
N=3
, a 3D filter descriptor, the numberS
(number of columns per filter) is omitted.  For
N=5
and greater, the layout of the higher dimensions immediately followsRS
.
On the other hand, if this input is set to
CUDNN_TENSOR_NHWC
, then the layout of the filter is as follows: For
N=4
, a 4D filter descriptor, the filter layout is in the form ofKRSC
.  For
N=3
, a 3D filter descriptor, the numberS
(number of columns per filter) is omitted and the layout ofC
immediately followsR
.  For
N=5
and greater, the layout of the higher dimensions are inserted betweenS
andC
. For more information, refer to cudnnTensorFormat_t.
 For

nbDims

Input. Dimension of the filter.

filterDimA

Input. Array of dimension
nbDims
containing the size of the filter for each dimension.
Returns

CUDNN_STATUS_SUCCESS

The object was set successfully.

CUDNN_STATUS_BAD_PARAM

At least one of the elements of the array
filterDimA
is negative ordataType
orformat
has an invalid enumerant value. 
CUDNN_STATUS_NOT_SUPPORTED

The parameter
nbDims
exceedsCUDNN_DIM_MAX
.
3.2.85. cudnnSetLRNDescriptor()
cudnnStatus_t cudnnSetLRNDescriptor(
cudnnLRNDescriptor_t normDesc,
unsigned lrnN,
double lrnAlpha,
double lrnBeta,
double lrnK)
This function initializes a previously created LRN descriptor object.
Parameters

normDesc

Output. Handle to a previously created LRN descriptor.

lrnN

Input. Normalization window width in elements. The LRN layer uses a window
[centerlookBehind, center+lookAhead]
, wherelookBehind = floor( (lrnN1)/2 )
,lookAhead = lrnNlookBehind1
. So forn=10
, the window is[k4...k...k+5]
with a total of 10 samples. For theDivisiveNormalization
layer, the window has the same extent as above in all spatial dimensions (dimA[2]
,dimA[3]
,dimA[4]
). By default,lrnN
is set to5
in cudnnCreateLRNDescriptor(). 
lrnAlpha

Input. Value of the alpha variance scaling parameter in the normalization formula. Inside the library code, this value is divided by the window width for LRN and by
(window width)^#spatialDimensions
forDivisiveNormalization
. By default, this value is set to1e4
in cudnnCreateLRNDescriptor(). 
lrnBeta

Input. Value of the beta power parameter in the normalization formula. By default, this value is set to
0.75
in cudnnCreateLRNDescriptor(). 
lrnK

Input. Value of the
k
parameter in the normalization formula. By default, this value is set to2.0
.
Returns

CUDNN_STATUS_SUCCESS

The object was set successfully.

CUDNN_STATUS_BAD_PARAM

One of the input parameters was out of valid range as described above.
3.2.86. cudnnSetOpTensorDescriptor()
cudnnStatus_t cudnnSetOpTensorDescriptor(
cudnnOpTensorDescriptor_t opTensorDesc,
cudnnOpTensorOp_t opTensorOp,
cudnnDataType_t opTensorCompType,
cudnnNanPropagation_t opTensorNanOpt)
This function initializes a tensor pointwise math descriptor.
Parameters

opTensorDesc

Output. Pointer to the structure holding the description of the tensor pointwise math descriptor.

opTensorOp

Input. Tensor pointwise math operation for this tensor pointwise math descriptor.

opTensorCompType

Input. Computation datatype for this tensor pointwise math descriptor.

opTensorNanOpt

Input. NAN propagation policy.
Returns

CUDNN_STATUS_SUCCESS

The function returned successfully.

CUDNN_STATUS_BAD_PARAM

At least one of the input parameters passed is invalid.
3.2.87. cudnnSetPooling2dDescriptor()
cudnnStatus_t cudnnSetPooling2dDescriptor(
cudnnPoolingDescriptor_t poolingDesc,
cudnnPoolingMode_t mode,
cudnnNanPropagation_t maxpoolingNanOpt,
int windowHeight,
int windowWidth,
int verticalPadding,
int horizontalPadding,
int verticalStride,
int horizontalStride)
This function initializes a previously created generic pooling descriptor object into a 2D description.
Parameters

poolingDesc

Input/Output. Handle to a previously created pooling descriptor.

mode

Input. Enumerant to specify the pooling mode.

maxpoolingNanOpt

Input. Enumerant to specify the Nan propagation mode.

windowHeight

Input. Height of the pooling window.

windowWidth

Input. Width of the pooling window.

verticalPadding

Input. Size of vertical padding.

horizontalPadding

Input. Size of horizontal padding

verticalStride

Input. Pooling vertical stride.

horizontalStride

Input. Pooling horizontal stride.
Returns

CUDNN_STATUS_SUCCESS

The object was set successfully.

CUDNN_STATUS_BAD_PARAM

At least one of the parameters
windowHeight
,windowWidth
,verticalStride
,horizontalStride
is negative ormode
ormaxpoolingNanOpt
has an invalid enumerate value.
3.2.88. cudnnSetPoolingNdDescriptor()
cudnnStatus_t cudnnSetPoolingNdDescriptor(
cudnnPoolingDescriptor_t poolingDesc,
const cudnnPoolingMode_t mode,
const cudnnNanPropagation_t maxpoolingNanOpt,
int nbDims,
const int windowDimA[],
const int paddingA[],
const int strideA[])
This function initializes a previously created generic pooling descriptor object.
Parameters

poolingDesc

Input/Output. Handle to a previously created pooling descriptor.

mode

Input. Enumerant to specify the pooling mode.

maxpoolingNanOpt

Input. Enumerant to specify the Nan propagation mode.

nbDims

Input. Dimension of the pooling operation. Must be greater than zero.

windowDimA

Input. Array of dimension
nbDims
containing the window size for each dimension. The value of array elements must be greater than zero. 
paddingA

Input. Array of dimension
nbDims
containing the padding size for each dimension. Negative padding is allowed. 
strideA

Input. Array of dimension
nbDims
containing the striding size for each dimension. The value of array elements must be greater than zero (meaning, negative striding size is not allowed).
Returns

CUDNN_STATUS_SUCCESS

The object was initialized successfully.

CUDNN_STATUS_NOT_SUPPORTED

If (
nbDims
>CUDNN_DIM_MAX2
). 
CUDNN_STATUS_BAD_PARAM

Either
nbDims
, or at least one of the elements of the arrayswindowDimA
orstrideA
is negative, ormode
ormaxpoolingNanOpt
has an invalid enumerate value.
3.2.89. cudnnSetReduceTensorDescriptor()
cudnnStatus_t cudnnSetReduceTensorDescriptor(
cudnnReduceTensorDescriptor_t reduceTensorDesc,
cudnnReduceTensorOp_t reduceTensorOp,
cudnnDataType_t reduceTensorCompType,
cudnnNanPropagation_t reduceTensorNanOpt,
cudnnReduceTensorIndices_t reduceTensorIndices,
cudnnIndicesType_t reduceTensorIndicesType)
This function initializes a previously created reduce tensor descriptor object.
Parameters

reduceTensorDesc

Input/Output. Handle to a previously created reduce tensor descriptor.

reduceTensorOp

Input. Enumerant to specify the reduce tensor operation.

reduceTensorCompType

Input. Enumerant to specify the computation datatype of the reduction.

reduceTensorNanOpt

Input. Enumerant to specify the Nan propagation mode.

reduceTensorIndices

Input. Enumerant to specify the reduced tensor indices.

reduceTensorIndicesType

Input. Enumerant to specify the reduce tensor indices type.
Returns

CUDNN_STATUS_SUCCESS

The object was set successfully.

CUDNN_STATUS_BAD_PARAM

reduceTensorDesc
isNULL
(reduceTensorOp
,reduceTensorCompType
,reduceTensorNanOpt
,reduceTensorIndices
orreduceTensorIndicesType
has an invalid enumerant value).
3.2.90. cudnnSetSpatialTransformerNdDescriptor()
cudnnStatus_t cudnnSetSpatialTransformerNdDescriptor(
cudnnSpatialTransformerDescriptor_t stDesc,
cudnnSamplerType_t samplerType,
cudnnDataType_t dataType,
const int nbDims,
const int dimA[])
This function initializes a previously created generic spatial transformer descriptor object.
Parameters

stDesc

Input/Output. Previously created spatial transformer descriptor object.

samplerType

Input. Enumerant to specify the sampler type.

dataType

Input. Data type.

nbDims

Input. Dimension of the transformed tensor.

dimA

Input. Array of dimension
nbDims
containing the size of the transformed tensor for every dimension.
Returns

CUDNN_STATUS_SUCCESS

The call was successful.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 Either
stDesc
ordimA
isNULL
.  Either
dataType
orsamplerType
has an invalid enumerant value
 Either
3.2.91. cudnnSetStream()
cudnnStatus_t cudnnSetStream(
cudnnHandle_t handle,
cudaStream_t streamId)
This function sets the user's CUDA stream in the cuDNN handle. The new stream will be used to launch cuDNN GPU kernels or to synchronize to this stream when cuDNN kernels are launched in the internal streams. If the cuDNN library stream is not set, all kernels use the default (NULL
) stream. Setting the user stream in the cuDNN handle guarantees the issueorder execution of cuDNN calls and other GPU kernels launched in the same stream.
With CUDA 11.x or later, internal streams have the same priority as the stream set by the last call to this function. In CUDA graph capture mode, CUDA 11.8 or later is required in order for the stream priorities to match.
Parameters

handle

Input. Pointer to the cuDNN handle.

streamID

Input. New CUDA stream to be written to the cuDNN handle.
Returns

CUDNN_STATUS_BAD_PARAM

Invalid (
NULL
) handle. 
CUDNN_STATUS_MAPPING_ERROR

Mismatch between the user stream and the cuDNN handle context.

CUDNN_STATUS_SUCCESS

The new stream was set successfully.
3.2.92. cudnnSetTensor()
cudnnStatus_t cudnnSetTensor(
cudnnHandle_t handle,
const cudnnTensorDescriptor_t yDesc,
void *y,
const void *valuePtr)
This function sets all the elements of a tensor to a given value.
Parameters

handle

Input. Handle to a previously created cuDNN context.

yDesc

Input. Handle to a previously initialized tensor descriptor.

y

Input/Output. Pointer to data of the tensor described by the
yDesc
descriptor. 
valuePtr

Input. Pointer in host memory to a single value. All elements of the
y
tensor will be set tovalue[0]
. The data type of the element invalue[0]
has to match the data type of tensory
.
Returns

CUDNN_STATUS_SUCCESS

The function launched successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

One of the provided pointers is nil.

CUDNN_STATUS_EXECUTION_FAILED

The function failed to launch on the GPU.
3.2.93. cudnnSetTensor4dDescriptor()
cudnnStatus_t cudnnSetTensor4dDescriptor(
cudnnTensorDescriptor_t tensorDesc,
cudnnTensorFormat_t format,
cudnnDataType_t dataType,
int n,
int c,
int h,
int w)
This function initializes a previously created generic tensor descriptor object into a 4D tensor. The strides of the four dimensions are inferred from the format parameter and set in such a way that the data is contiguous in memory with no padding between dimensions.
The total size of a tensor including the potential padding between dimensions is limited to 2 Gigaelements of type datatype
.
Parameters

tensorDesc

Input/Output. Handle to a previously created tensor descriptor.

format

Input. Type of format.

datatype

Input. Data type.

n

Input. Number of images.

c

Input. Number of feature maps per image.

h

Input. Height of each feature map.

w

Input. Width of each feature map.
Returns

CUDNN_STATUS_SUCCESS

The object was set successfully.

CUDNN_STATUS_BAD_PARAM

At least one of the parameters
n
,c
,h
,w
was negative orformat
has an invalid enumerant value ordataType
has an invalid enumerant value. 
CUDNN_STATUS_NOT_SUPPORTED

The total size of the tensor descriptor exceeds the maximum limit of 2 Gigaelements.
3.2.94. cudnnSetTensor4dDescriptorEx()
cudnnStatus_t cudnnSetTensor4dDescriptorEx(
cudnnTensorDescriptor_t tensorDesc,
cudnnDataType_t dataType,
int n,
int c,
int h,
int w,
int nStride,
int cStride,
int hStride,
int wStride)
This function initializes a previously created generic tensor descriptor object into a 4D tensor, similarly to cudnnSetTensor4dDescriptor()
but with the strides explicitly passed as parameters. This can be used to lay out the 4D tensor in any order or simply to define gaps between dimensions.
 At present, some cuDNN routines have limited support for strides. Those routines will return
CUDNN_STATUS_NOT_SUPPORTED
if a 4D tensor object with an unsupported stride is used. cudnnTransformTensor() can be used to convert the data to a supported layout.  The total size of a tensor including the potential padding between dimensions is limited to 2 Gigaelements of type
datatype
.
Parameters

tensorDesc

Input/Output. Handle to a previously created tensor descriptor.

datatype

Input. Data type.

n

Input. Number of images.

c

Input. Number of feature maps per image.
 h

Input. Height of each feature map.

w

Input. Width of each feature map.

nStride

Input. Stride between two consecutive images.

cStride

Input. Stride between two consecutive feature maps.

hStride

Input. Stride between two consecutive rows.

wStride

Input. Stride between two consecutive columns.
Returns

CUDNN_STATUS_SUCCESS

The object was set successfully.

CUDNN_STATUS_BAD_PARAM

At least one of the parameters
n
,c
,h
,w
ornStride
,cStride
,hStride
,wStride
is negative ordataType
has an invalid enumerant value. 
CUDNN_STATUS_NOT_SUPPORTED

The total size of the tensor descriptor exceeds the maximum limit of 2 Gigaelements.
3.2.95. cudnnSetTensorNdDescriptor()
cudnnStatus_t cudnnSetTensorNdDescriptor(
cudnnTensorDescriptor_t tensorDesc,
cudnnDataType_t dataType,
int nbDims,
const int dimA[],
const int strideA[])
This function initializes a previously created generic tensor descriptor object.
The total size of a tensor including the potential padding between dimensions is limited to 2 Gigaelements of type datatype
. Tensors are restricted to having at least 4 dimensions, and at most CUDNN_DIM_MAX
dimensions (defined in cudnn.h
). When working with lower dimensional data, it is recommended that the user create a 4D tensor, and set the size along unused dimensions to 1.
Parameters

tensorDesc

Input/Output. Handle to a previously created tensor descriptor.

datatype

Input. Data type.

nbDims

Input. Dimension of the tensor.
Note:
Do not use 2 dimensions. Due to historical reasons, the minimum number of dimensions in the filter descriptor is three. For more information, refer to cudnnGetRNNLinLayerBiasParams().

dimA

Input. Array of dimension
nbDims
that contain the size of the tensor for every dimension. The size along unused dimensions should be set to1
. By convention, the ordering of dimensions in the array follows the format  [N
,C
,D
,H
,W
], withW
occupying the smallest index in the array. 
strideA

Input. Array of dimension
nbDims
that contain the stride of the tensor for every dimension. By convention, the ordering of the strides in the array follows the format [Nstride, Cstride, Dstride, Hstride, Wstride]
, withWstride
occupying the smallest index in the array.
Returns

CUDNN_STATUS_SUCCESS

The object was set successfully.

CUDNN_STATUS_BAD_PARAM

At least one of the elements of the array
dimA
was negative or zero, ordataType
has an invalid enumerant value. 
CUDNN_STATUS_NOT_SUPPORTED

The parameter
nbDims
is outside the range[4, CUDNN_DIM_MAX]
, or the total size of the tensor descriptor exceeds the maximum limit of 2 Gigaelements.
3.2.96. cudnnSetTensorNdDescriptorEx()
cudnnStatus_t cudnnSetTensorNdDescriptorEx(
cudnnTensorDescriptor_t tensorDesc,
cudnnTensorFormat_t format,
cudnnDataType_t dataType,
int nbDims,
const int dimA[])
This function initializes an nD tensor descriptor.
Parameters

tensorDesc

Output. Pointer to the tensor descriptor struct to be initialized.

format

Input. Tensor format.

dataType

Input. Tensor data type.

nbDims

Input. Dimension of the tensor.
Note:
Do not use 2 dimensions. Due to historical reasons, the minimum number of dimensions in the filter descriptor is three. For more information, refer to cudnnGetRNNLinLayerBiasParams().

dimA

Input. Array containing the size of each dimension.
Returns

CUDNN_STATUS_SUCCESS

The function was successful.

CUDNN_STATUS_BAD_PARAM

Tensor descriptor was not allocated properly; or input parameters are not set correctly.

CUDNN_STATUS_NOT_SUPPORTED

Dimension size requested is larger than maximum dimension size supported.
3.2.97. cudnnSetTensorTransformDescriptor()
cudnnStatus_t cudnnSetTensorTransformDescriptor(
cudnnTensorTransformDescriptor_t transformDesc,
const uint32_t nbDims,
const cudnnTensorFormat_t destFormat,
const int32_t padBeforeA[],
const int32_t padAfterA[],
const uint32_t foldA[],
const cudnnFoldingDirection_t direction);
This function initializes a tensor transform descriptor that was previously created using the cudnnCreateTensorTransformDescriptor() function.
Parameters

transformDesc

Output. The tensor transform descriptor to be initialized.

nbDims

Input. The dimensionality of the transform operands. Must be greater than 2. For more information, refer to Tensor Descriptor in the cuDNN Developer Guide.

destFormat

Input. The desired destination format.

padBeforeA[]

Input. An array that contains the amount of padding that should be added before each dimension. Set to
NULL
for no padding. 
padAfterA[]

Input. An array that contains the amount of padding that should be added after each dimension. Set to
NULL
for no padding. 
foldA[]

Input. An array that contains the folding parameters for each spatial dimension (dimensions 2 and up). Set to
NULL
for no folding. 
direction

Input. Selects folding or unfolding. This input has no effect when folding parameters are all <= 1. For more information, refer to cudnnFoldingDirection_t.
Returns

CUDNN_STATUS_SUCCESS

The function was launched successfully.

CUDNN_STATUS_BAD_PARAM

The parameter
transformDesc
isNULL
, or ifdirection
is invalid, ornbDims
is <= 2. 
CUDNN_STATUS_NOT_SUPPORTED

If the dimension size requested is larger than maximum dimension size supported (meaning, one of the
nbDims
is larger thanCUDNN_DIM_MAX
), or ifdestFromat
is something other thanNCHW
orNHWC
.
3.2.98. cudnnSoftmaxForward()
cudnnStatus_t cudnnSoftmaxForward(
cudnnHandle_t handle,
cudnnSoftmaxAlgorithm_t algorithm,
cudnnSoftmaxMode_t mode,
const void *alpha,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const void *beta,
const cudnnTensorDescriptor_t yDesc,
void *y)
This routine computes the softmax function.
All tensor formats are supported for all modes and algorithms with 4 and 5D tensors. Performance is expected to be highest with NCHW fullypacked
tensors. For more than 5 dimensions tensors must be packed in their spatial dimensions.
Data Types
This function supports the following data types:
CUDNN_DATA_FLOAT
CUDNN_DATA_DOUBLE
CUDNN_DATA_HALF
CUDNN_DATA_BFLOAT16
CUDNN_DATA_INT8
Parameters

handle

Input. Handle to a previously created cuDNN context.

algorithm

Input. Enumerant to specify the softmax algorithm.

mode

Input. Enumerant to specify the softmax mode.

alpha
,beta

Input. Pointers to scaling factors (in host memory) used to blend the computation result with prior value in the output layer as follows:
dstValue = alpha[0]*result + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

xDesc

Input. Handle to the previously initialized input tensor descriptor.

x

Input. Data pointer to GPU memory associated with the tensor descriptor
xDesc
. 
yDesc

Input. Handle to the previously initialized output tensor descriptor.

y

Output. Data pointer to GPU memory associated with the output tensor descriptor
yDesc
.
Returns

CUDNN_STATUS_SUCCESS

The function launched successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 The dimensions
n
,c
,h
,w
of the input tensor and output tensors differ.  The
datatype
of the input tensor and output tensors differ.  The parameters
algorithm
ormode
have an invalid enumerant value.
 The dimensions

CUDNN_STATUS_EXECUTION_FAILED

The function failed to launch on the GPU.
3.2.99. cudnnSpatialTfGridGeneratorForward()
cudnnStatus_t cudnnSpatialTfGridGeneratorForward(
cudnnHandle_t handle,
const cudnnSpatialTransformerDescriptor_t stDesc,
const void *theta,
void *grid)
This function generates a grid of coordinates in the input tensor corresponding to each pixel from the output tensor.
Only 2d transformation is supported.
Parameters

handle

Input. Handle to a previously created cuDNN context.

stDesc

Input. Previously created spatial transformer descriptor object.

theta

Input. Affine transformation matrix. It should be of size
n*2*3
for a 2d transformation, wheren
is the number of images specified instDesc
. 
grid

Output. A grid of coordinates. It is of size
n*h*w*2
for a 2d transformation, wheren
,h
,w
is specified instDesc
. In the 4th dimension, the first coordinate isx
, and the second coordinate isy
.
Returns

CUDNN_STATUS_SUCCESS

The call was successful.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
handle
isNULL
. One of the parameters
grid
ortheta
isNULL
.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration. See the following for some examples of nonsupported configurations:
 The dimension of the transformed tensor specified in
stDesc
> 4.
 The dimension of the transformed tensor specified in

CUDNN_STATUS_EXECUTION_FAILED

The function failed to launch on the GPU.
3.2.100. cudnnSpatialTfSamplerForward()
cudnnStatus_t cudnnSpatialTfSamplerForward(
cudnnHandle_t handle,
const cudnnSpatialTransformerDescriptor_t stDesc,
const void *alpha,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const void *grid,
const void *beta,
cudnnTensorDescriptor_t yDesc,
void *y)
This function performs a sampler operation and generates the output tensor using the grid given by the grid generator.
Only 2d transformation is supported.
Parameters

handle

Input. Handle to a previously created cuDNN context.

stDesc

Input. Previously created spatial transformer descriptor object.

alpha
,beta

Input. Pointers to scaling factors (in host memory) used to blend the source value with prior value in the destination tensor as follows:
dstValue = alpha[0]*srcValue + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

xDesc

Input. Handle to the previously initialized input tensor descriptor.

x

Input. Data pointer to GPU memory associated with the tensor descriptor
xDesc
. 
grid

Input. A grid of coordinates generated by cudnnSpatialTfGridGeneratorForward().

yDesc

Input. Handle to the previously initialized output tensor descriptor.

y

Output. Data pointer to GPU memory associated with the output tensor descriptor
yDesc
.
Returns

CUDNN_STATUS_SUCCESS

The call was successful.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
handle
isNULL
. One of the parameters
x
,y
orgrid
isNULL
.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration. See the following for some examples of nonsupported configurations:
 The dimension of transformed tensor > 4.

CUDNN_STATUS_EXECUTION_FAILED

The function failed to launch on the GPU.
3.2.101. cudnnTransformFilter()
cudnnStatus_t cudnnTransformFilter(
cudnnHandle_t handle,
const cudnnTensorTransformDescriptor_t transDesc,
const void *alpha,
const cudnnFilterDescriptor_t srcDesc,
const void *srcData,
const void *beta,
const cudnnFilterDescriptor_t destDesc,
void *destData);
This function converts the filter between different formats, data types, or dimensions based on the described transformation. It can be used to convert a filter with an unsupported layout format to a filter with a supported layout format.
This function copies the scaled data from the input filter srcDesc
to the output tensor destDesc
with a different layout. If the filter descriptors of srcDesc
and destDesc
have different dimensions, they must be consistent with folding and padding amount and order specified in transDesc
.
The srcDesc
and destDesc
tensors must not overlap in any way (that is, tensors cannot be transformed in place).
When performing a folding transform or a zeropadding transform, the scaling factors (alpha
, beta
) should be set to (1, 0). However, unfolding transforms support any (alpha
, beta
) values. This function is thread safe.
Parameters

handle

Input. Handle to a previously created cuDNN context. For more information, refer to cudnnHandle_t.

transDesc

Input. A descriptor containing the details of the requested filter transformation. For more information, refer to cudnnTensorTransformDescriptor_t.

alpha
,beta

Input. Pointers, in the host memory, to the scaling factors used to scale the data in the input tensor
srcDesc
.beta
is used to scale the destination tensor, whilealpha
is used to scale the source tensor. For more information, refer to Scaling Parameters in the cuDNN Developer Guide.The beta scaling value is not honored in the folding and zeropadding cases. Unfolding supports any (
alpha
,beta
). 
srcDesc
,destDesc

Input. Handles to the previously initiated filter descriptors.
srcDesc
anddestDesc
must not overlap. For more information, refer to cudnnTensorDescriptor_t. 
srcData

Input. Pointers, in the host memory, to the data of the tensor described by
srcDesc
. 
destData

Output. Pointers, in the host memory, to the data of the tensor described by
destDesc
.
Returns

CUDNN_STATUS_SUCCESS

The function launched successfully.

CUDNN_STATUS_BAD_PARAM

A parameter is uninitialized or initialized incorrectly, or the number of dimensions is different between
srcDesc
anddestDesc
. 
CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration. Also, in the folding and padding paths, any value other than
A=1
andB=0
will result in aCUDNN_STATUS_NOT_SUPPORTED
. 
CUDNN_STATUS_EXECUTION_FAILED

The function failed to launch on the GPU.
3.2.102. cudnnTransformTensor()
cudnnStatus_t cudnnTransformTensor(
cudnnHandle_t handle,
const void *alpha,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const void *beta,
const cudnnTensorDescriptor_t yDesc,
void *y)
This function copies the scaled data from one tensor to another tensor with a different layout. Those descriptors need to have the same dimensions but not necessarily the same strides. The input and output tensors must not overlap in any way (meaning, tensors cannot be transformed in place). This function can be used to convert a tensor with an unsupported format to a supported one.
Parameters

handle

Input. Handle to a previously created cuDNN context.

alpha
,beta

Input. Pointers to scaling factors (in host memory) used to blend the source value with prior value in the destination tensor as follows:
dstValue = alpha[0]*srcValue + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

xDesc

Input. Handle to a previously initialized tensor descriptor. For more information, refer to cudnnTensorDescriptor_t.

x

Input. Pointer to data of the tensor described by the
xDesc
descriptor. 
yDesc

Input. Handle to a previously initialized tensor descriptor. For more information, refer to cudnnTensorDescriptor_t.

y

Output. Pointer to data of the tensor described by the
yDesc
descriptor.
Returns

CUDNN_STATUS_SUCCESS

The function launched successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

The dimensions
n
,c
,h
,w
or thedataType
of the two tensor descriptors are different. 
CUDNN_STATUS_EXECUTION_FAILED

The function failed to launch on the GPU.
3.2.103. cudnnTransformTensorEx()
cudnnStatus_t cudnnTransformTensorEx(
cudnnHandle_t handle,
const cudnnTensorTransformDescriptor_t transDesc,
const void *alpha,
const cudnnTensorDescriptor_t srcDesc,
const void *srcData,
const void *beta,
const cudnnTensorDescriptor_t destDesc,
void *destData);
This function converts the tensor layouts between different formats. It can be used to convert a tensor with an unsupported layout format to a tensor with a supported layout format.
This function copies the scaled data from the input tensor srcDesc
to the output tensor destDesc
with a different layout. The tensor descriptors of srcDesc
and destDesc
should have the same dimensions but need not have the same strides.
The srcDesc
and destDesc
tensors must not overlap in any way (that is, tensors cannot be transformed in place).
When performing a folding transform or a zeropadding transform, the scaling factors (alpha,beta)
should be set to (1, 0). However, unfolding transforms support any (alpha,beta)
values. This function is thread safe.
Parameters

handle

Input. Handle to a previously created cuDNN context. For more information, refer to cudnnHandle_t.

transDesc

Input. A descriptor containing the details of the requested tensor transformation. For more information, refer to cudnnTensorTransformDescriptor_t.

alpha
,beta

Input. Pointers, in the host memory, to the scaling factors used to scale the data in the input tensor
srcDesc
.beta
is used to scale the destination tensor, whilealpha
is used to scale the source tensor. For more information, refer to Scaling Parameters in the cuDNN Developer Guide.The beta scaling value is not honored in the folding and zeropadding cases. Unfolding supports any (
alpha
,beta
). 
srcDesc
,destDesc

Input. Handles to the previously initiated tensor descriptors.
srcDesc
anddestDesc
must not overlap. For more information, refer to cudnnTensorDescriptor_t. 
srcData

Input. Pointers, in the host memory, to the data of the tensor described by
srcDesc
. 
destData

Output. Pointers, in the host memory, to the data of the tensor described by
destDesc
.
Returns

CUDNN_STATUS_SUCCESS

The function was launched successfully.

CUDNN_STATUS_BAD_PARAM

A parameter is uninitialized or initialized incorrectly, or the number of dimensions is different between
srcDesc
anddestDesc
. 
CUDNN_STATUS_NOT_SUPPORTED

Function does not support the provided configuration. Also, in the folding and padding paths, any value other than
A=1
andB=0
will result in aCUDNN_STATUS_NOT_SUPPORTED
. 
CUDNN_STATUS_EXECUTION_FAILED

Function failed to launch on the GPU.
4.1. API Functions
4.1.1. cudnnActivationBackward()
cudnnStatus_t cudnnActivationBackward(
cudnnHandle_t handle,
cudnnActivationDescriptor_t activationDesc,
const void *alpha,
const cudnnTensorDescriptor_t yDesc,
const void *y,
const cudnnTensorDescriptor_t dyDesc,
const void *dy,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const void *beta,
const cudnnTensorDescriptor_t dxDesc,
void *dx)
This routine computes the gradient of a neuron activation function.
 Inplace operation is allowed for this routine; meaning
dy
anddx
pointers may be equal. However, this requires the corresponding tensor descriptors to be identical (particularly, the strides of the input and output must match for an inplace operation to be allowed).  All tensor formats are supported for 4 and 5 dimensions, however, the best performance is obtained when the strides of
yDesc
andxDesc
are equal andHWpacked
. For more than 5 dimensions the tensors must have their spatial dimensions packed.
Parameters

handle

Input. Handle to a previously created cuDNN context. For more information, refer to cudnnHandle_t.

activationDesc

Input. Activation descriptor. For more information, refer to cudnnActivationDescriptor_t.

alpha
,beta

Input. Pointers to scaling factors (in host memory) used to blend the computation result with prior value in the output layer as follows:
dstValue = alpha[0]*result + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

yDesc

Input. Handle to the previously initialized input tensor descriptor. For more information, refer to cudnnTensorDescriptor_t.

y

Input. Data pointer to GPU memory associated with the tensor descriptor
yDesc
. 
dyDesc

Input. Handle to the previously initialized input differential tensor descriptor.

dy

Input. Data pointer to GPU memory associated with the tensor descriptor
dyDesc
. 
xDesc

Input. Handle to the previously initialized output tensor descriptor.

x

Input. Data pointer to GPU memory associated with the output tensor descriptor
xDesc
. 
dxDesc

Input. Handle to the previously initialized output differential tensor descriptor.

dx

Output. Data pointer to GPU memory associated with the output tensor descriptor
dxDesc
.
Returns

CUDNN_STATUS_SUCCESS

The function launched successfully.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 The strides
nStride, cStride, hStride, wStride
of the input differential tensor and output differential tensor differ and inplace operation is used.
 The strides

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration. See the following for some examples of nonsupported configurations:
 The dimensions
n, c, h, w
of the input tensor and output tensor differ.  The
datatype
of the input tensor and output tensor differs.  The strides
nStride, cStride, hStride, wStride
of the input tensor and the input differential tensor differ.  The strides
nStride, cStride, hStride, wStride
of the output tensor and the output differential tensor differ.
 The dimensions

CUDNN_STATUS_EXECUTION_FAILED

The function failed to launch on the GPU.
4.1.2. cudnnBatchNormalizationBackward()
cudnnStatus_t cudnnBatchNormalizationBackward(
cudnnHandle_t handle,
cudnnBatchNormMode_t mode,
const void *alphaDataDiff,
const void *betaDataDiff,
const void *alphaParamDiff,
const void *betaParamDiff,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const cudnnTensorDescriptor_t dyDesc,
const void *dy,
const cudnnTensorDescriptor_t dxDesc,
void *dx,
const cudnnTensorDescriptor_t bnScaleBiasDiffDesc,
const void *bnScale,
void *resultBnScaleDiff,
void *resultBnBiasDiff,
double epsilon,
const void *savedMean,
const void *savedInvVariance)
This function performs the backward batch normalization layer computation. This layer is based on the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, S. Ioffe, C. Szegedy, 2015. .
For more information, refer to cudnnDeriveBNTensorDescriptor() for the secondary tensor descriptor generation for the parameters used in this function.
Parameters

handle

Input. Handle to a previously created cuDNN library descriptor. For more information, refer to cudnnHandle_t.

mode

Input. Mode of operation (spatial or peractivation). For more information, refer to cudnnBatchNormMode_t.

*alphaDataDiff
,*betaDataDiff

Inputs. Pointers to scaling factors (in host memory) used to blend the gradient output
dx
with a prior value in the destination tensor as follows:dstValue = alphaDataDiff[0]*resultValue + betaDataDiff[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

*alphaParamDiff
,*betaParamDiff

Inputs. Pointers to scaling factors (in host memory) used to blend the gradient outputs
resultBnScaleDiff
andresultBnBiasDiff
with prior values in the destination tensor as follows:dstValue = alphaParamDiff[0]*resultValue + betaParamDiff[0]*priorDstValue
For more information, refer to Scaling Parameters.

xDesc, dxDesc, dyDesc

Inputs. Handles to the previously initialized tensor descriptors.

*x

Inputs. Data pointer to GPU memory associated with the tensor descriptor
xDesc
, for the layer’sx
data. 
*dy

Inputs. Data pointer to GPU memory associated with the tensor descriptor
dyDesc
, for the backpropagated differentialdy
input. 
*dx

Inputs/Outputs. Data pointer to GPU memory associated with the tensor descriptor
dxDesc
, for the resulting differential output with respect tox
. 
bnScaleBiasDiffDesc

Input. Shared tensor descriptor for the following five tensors:
bnScale, resultBnScaleDiff, resultBnBiasDiff, savedMean, savedInvVariance
. The dimensions for this tensor descriptor are dependent on normalization mode. For more information, refer to cudnnDeriveBNTensorDescriptor().Note:The data type of this tensor descriptor must be
float
for FP16 and FP32 input tensors, anddouble
for FP64 input tensors.

*bnScale

Input. Pointer in the device memory for the batch normalization
scale
parameter (in the original paper the quantityscale
is referred to as gamma).Note:The
bnBias
parameter is not needed for this layer's computation. 
resultBnScaleDiff
,resultBnBiasDiff
 Outputs. Pointers in device memory for the resulting scale and bias differentials computed by this routine. Note that these scale and bias gradients are weight gradients specific to this batch normalization operation, and by definition are not backpropagated.

epsilon

Input. Epsilon value used in batch normalization formula. Its value should be equal to or greater than the value defined for
CUDNN_BN_MIN_EPSILON
incudnn.h
. The sameepsilon
value should be used in forward and backward functions. 
*savedMean
,*savedInvVariance

Inputs. Optional cache parameters containing saved intermediate results that were computed during the forward pass. For this to work correctly, the layer's
x
andbnScale
data have to remain unchanged until this backward function is called.Note:Both these parameters can be
NULL
but only at the same time. It is recommended to use this cache since the memory overhead is relatively small.
Supported configurations
This function supports the following combinations of data types for various descriptors.
Data Type Configurations  xDesc  bnScaleBiasMeanVarDesc  alpha, beta  yDesc 

PSEUDO_HALF_CONFIG 
CUDNN_DATA_HALF 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_HALF 
FLOAT_CONFIG 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
DOUBLE_CONFIG 
CUDNN_DATA_DOUBLE 
CUDNN_DATA_DOUBLE 
CUDNN_DATA_DOUBLE 
CUDNN_DATA_DOUBLE 
PSEUDO_BFLOAT16_CONFIG 
CUDNN_DATA_BFLOAT16 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_BFLOAT16 
Returns

CUDNN_STATUS_SUCCESS

The computation was performed successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 Any of the pointers
alpha, beta, x, dy, dx, bnScale, resultBnScaleDiff, resultBnBiasDiff
isNULL
.  The number of
xDesc
oryDesc
ordxDesc
tensor descriptor dimensions is not within the range of[4,5]
(only 4D and 5D tensors are supported). bnScaleBiasDiffDesc
dimensions are not 1xCx1x1 for 4D and 1xCx1x1x1 for 5D for spatial, and are not 1xCxHxW for 4D and 1xCxDxHxW for 5D for peractivation mode. Exactly one of
savedMean
,savedInvVariance
pointers isNULL
. epsilon
value is less thanCUDNN_BN_MIN_EPSILON
. Dimensions or data types mismatch for any pair of
xDesc, dyDesc, dxDesc
.
 Any of the pointers
4.1.3. cudnnBatchNormalizationBackwardEx()
cudnnStatus_t cudnnBatchNormalizationBackwardEx (
cudnnHandle_t handle,
cudnnBatchNormMode_t mode,
cudnnBatchNormOps_t bnOps,
const void *alphaDataDiff,
const void *betaDataDiff,
const void *alphaParamDiff,
const void *betaParamDiff,
const cudnnTensorDescriptor_t xDesc,
const void *xData,
const cudnnTensorDescriptor_t yDesc,
const void *yData,
const cudnnTensorDescriptor_t dyDesc,
const void *dyData,
const cudnnTensorDescriptor_t dzDesc,
void *dzData,
const cudnnTensorDescriptor_t dxDesc,
void *dxData,
const cudnnTensorDescriptor_t dBnScaleBiasDesc,
const void *bnScaleData,
const void *bnBiasData,
void *dBnScaleData,
void *dBnBiasData,
double epsilon,
const void *savedMean,
const void *savedInvVariance,
const cudnnActivationDescriptor_t activationDesc,
void *workspace,
size_t workSpaceSizeInBytes
void *reserveSpace
size_t reserveSpaceSizeInBytes);
This function is an extension of the cudnnBatchNormalizationBackward() for performing the backward batch normalization layer computation with a fast NHWC semipersistent kernel. This API will trigger the new semipersistent NHWC kernel when the following conditions are true:
 All tensors, namely,
x, y, dz, dy, dx
must be NHWCfully packed, and must be of the typeCUDNN_DATA_HALF
.  The input parameter
mode
must be set toCUDNN_BATCHNORM_SPATIAL_PERSISTENT
. workspace
is notNULL
. Before cuDNN version 8.2.0, the tensor
C
dimension should always be a multiple of 4. After 8.2.0, the tensorC
dimension should be a multiple of 4 only whenbnOps
isCUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION
. workSpaceSizeInBytes
is equal to or larger than the amount required by cudnnGetBatchNormalizationBackwardExWorkspaceSize().reserveSpaceSizeInBytes
is equal to or larger than the amount required by cudnnGetBatchNormalizationTrainingExReserveSpaceSize(). The content in
reserveSpace
stored by cudnnBatchNormalizationForwardTrainingEx() must be preserved.
If workspace
is NULL
and workSpaceSizeInBytes
of zero is passed in, this API will function exactly like the nonextended function cudnnBatchNormalizationBackward
.
This workspace
is not required to be clean. Moreover, the workspace
does not have to remain unchanged between the forward and backward pass, as it is not used for passing any information.
This extended function can accept a *workspace
pointer to the GPU workspace, and workSpaceSizeInBytes
, the size of the workspace, from the user.
The bnOps
input can be used to set this function to perform either only the batch normalization, or batch normalization followed by activation, or batch normalization followed by elementwise addition and then activation.
Only 4D and 5D tensors are supported. The epsilon
value has to be the same during the training, the backpropagation, and the inference.
When the tensor layout is NCHW, higher performance can be obtained when HWpacked tensors are used for x, dy, dx
.
Parameters

handle

Input. Handle to a previously created cuDNN library descriptor. For more information, refer to cudnnHandle_t.

mode

Input. Mode of operation (spatial or peractivation). For more information, refer to cudnnBatchNormMode_t.

bnOps

Input. Mode of operation. Currently,
CUDNN_BATCHNORM_OPS_BN_ACTIVATION
andCUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION
are only supported in the NHWC layout. For more information, refer to cudnnBatchNormOps_t. This input can be used to set this function to perform either only the batch normalization, or batch normalization followed by activation, or batch normalization followed by elementwise addition and then activation. 
*alphaDataDiff
,*betaDataDiff

Inputs. Pointers to scaling factors (in host memory) used to blend the gradient output
dx
with a prior value in the destination tensor as follows:dstValue = alpha[0]*resultValue + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

*alphaParamDiff
,*betaParamDiff

Inputs. Pointers to scaling factors (in host memory) used to blend the gradient outputs
dBnScaleData
anddBnBiasData
with prior values in the destination tensor as follows:dstValue = alpha[0]*resultValue + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

xDesc
,*x
,yDesc
,*yData
,dyDesc
,*dyData

Inputs. Tensor descriptors and pointers in the device memory for the layer's
x
data, backpropagated gradient inputdy
, the original forward outputy
data.yDesc
andyData
are not needed ifbnOps
is set toCUDNN_BATCHNORM_OPS_BN
, users may passNULL
. For more information, refer to cudnnTensorDescriptor_t. 
dzDesc
,dxDesc

Inputs. Tensor descriptors and pointers in the device memory for the computed gradient output
dz
, anddx
.dzDesc
is not needed whenbnOps
isCUDNN_BATCHNORM_OPS_BN
orCUDNN_BATCHNORM_OPS_BN_ACTIVATION
, users may passNULL
. For more information, refer to cudnnTensorDescriptor_t. 
*dzData
,*dxData

Outputs. Tensor descriptors and pointers in the device memory for the computed gradient output
dz
, anddx
.*dzData
is not needed whenbnOps
isCUDNN_BATCHNORM_OPS_BN
orCUDNN_BATCHNORM_OPS_BN_ACTIVATION
, users may passNULL
. For more information, refer to cudnnTensorDescriptor_t. 
dBnScaleBiasDesc

Input. Shared tensor descriptor for the following six tensors:
bnScaleData
,bnBiasData
,dBnScaleData
,dBnBiasData
,savedMean
, andsavedInvVariance
. For more information, refer to cudnnDeriveBNTensorDescriptor().The dimensions for this tensor descriptor are dependent on normalization mode.
Note:The data type of this tensor descriptor must be
float
for FP16 and FP32 input tensors anddouble
for FP64 input tensors.For more information, refer to cudnnTensorDescriptor_t.

*bnScaleData

Input. Pointer in the device memory for the batch normalization scale parameter (in the original paper the quantity scale is referred to as gamma).

*bnBiasData
 Input. Pointers in the device memory for the batch normalization bias parameter (in the original paper bias is referred to as beta). This parameter is used only when activation should be performed.

*dBnScaleData
,*dBnBiasData

Outputs. Pointers in the device memory for the gradients of
bnScaleData
andbnBiasData
, respectively. 
epsilon

Input. Epsilon value used in batch normalization formula. Its value should be equal to or greater than the value defined for
CUDNN_BN_MIN_EPSILON
incudnn.h
. The same epsilon value should be used in forward and backward functions. 
*savedMean
,*savedInvVariance

Inputs. Optional cache parameters containing saved intermediate results computed during the forward pass. For this to work correctly, the layer's
x
andbnScaleData
,bnBiasData
data has to remain unchanged until this backward function is called. Note that both these parameters can beNULL
but only at the same time. It is recommended to use this cache since the memory overhead is relatively small. 
activationDesc

Input. Descriptor for the activation operation. When the
bnOps
input is set to eitherCUDNN_BATCHNORM_OPS_BN_ACTIVATION
orCUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION
then this activation is used, otherwise user may passNULL
. 
workspace

Input. Pointer to the GPU workspace. If
workspace
isNULL
andworkSpaceSizeInBytes
of zero is passed in, then this API will function exactly like the nonextended function cudnnBatchNormalizationBackward(). 
workSpaceSizeInBytes
 Input. The size of the workspace. It must be large enough to trigger the fast NHWC semipersistent kernel by this function.

*reserveSpace

Input. Pointer to the GPU workspace for the
reserveSpace
. 
reserveSpaceSizeInBytes

Input. The size of the
reserveSpace
. It must be equal or larger than the amount required by cudnnGetBatchNormalizationTrainingExReserveSpaceSize().
Supported configurations
This function supports the following combinations of data types for various descriptors.
Data Type Configurations  xDesc  bnScaleBiasMeanVarDesc  alpha, beta  yDesc 

PSEUDO_HALF_CONFIG 
CUDNN_DATA_HALF 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_HALF 
FLOAT_CONFIG 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
DOUBLE_CONFIG 
CUDNN_DATA_DOUBLE 
CUDNN_DATA_DOUBLE 
CUDNN_DATA_DOUBLE 
CUDNN_DATA_DOUBLE 
PSEUDO_BFLOAT16_CONFIG 
CUDNN_DATA_BFLOAT16 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_BFLOAT16 
Returns

CUDNN_STATUS_SUCCESS

The computation was performed successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 Any of the pointers
alphaDataDiff, betaDataDiff, alphaParamDiff, betaParamDiff, x, dy, dx, bnScale, resultBnScaleDiff, resultBnBiasDiff
isNULL
.  The number of
xDesc
oryDesc
ordxDesc
tensor descriptor dimensions is not within the range of[4,5]
(only 4D and 5D tensors are supported). dBnScaleBiasDesc
dimensions not 1xCx1x1 for 4D and 1xCx1x1x1 for 5D for spatial, and are not 1xCxHxW for 4D and 1xCxDxHxW for 5D for peractivation mode. Exactly one of
savedMean
,savedInvVariance
pointers isNULL
. epsilon
value is less thanCUDNN_BN_MIN_EPSILON
. Dimensions or data types mismatch for any pair of
xDesc
,dyDesc
,dxDesc
.
 Any of the pointers
4.1.4. cudnnBatchNormalizationForwardTraining()
cudnnStatus_t cudnnBatchNormalizationForwardTraining(
cudnnHandle_t handle,
cudnnBatchNormMode_t mode,
const void *alpha,
const void *beta,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const cudnnTensorDescriptor_t yDesc,
void *y,
const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
const void *bnScale,
const void *bnBias,
double exponentialAverageFactor,
void *resultRunningMean,
void *resultRunningVariance,
double epsilon,
void *resultSaveMean,
void *resultSaveInvVariance)
This function performs the forward batch normalization layer computation for the training phase. This layer is based on the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, S. Ioffe, C. Szegedy, 2015.
 Only 4D and 5D tensors are supported.
 The epsilon value has to be the same during training, backpropagation, and inference.
 For the inference phase, use cudnnBatchNormalizationForwardInference.
 Higher performance can be obtained when HWpacked tensors are used for both x and y.
Refer to cudnnDeriveBNTensorDescriptor() for the secondary tensor descriptor generation for the parameters used in this function.
Parameters

handle

Handle to a previously created cuDNN library descriptor. For more information, refer to cudnnHandle_t.

mode

Mode of operation (spatial or peractivation). For more information, refer to cudnnBatchNormMode_t.

alpha
,beta

Inputs. Pointers to scaling factors (in host memory) used to blend the layer output value with prior value in the destination tensor as follows:
dstValue = alpha[0]*resultValue + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

xDesc
,yDesc

Tensor descriptors and pointers in device memory for the layer's
x
andy
data. For more information, refer to cudnnTensorDescriptor_t. 
*x

Input. Data pointer to GPU memory associated with the tensor descriptor
xDesc
, for the layer’sx
input data. 
*y

Input. Data pointer to GPU memory associated with the tensor descriptor
yDesc
, for they
output of the batch normalization layer. 
bnScaleBiasMeanVarDesc

Shared tensor descriptor
desc
for the secondary tensor that was derived by cudnnDeriveBNTensorDescriptor(). The dimensions for this tensor descriptor are dependent on the normalization mode. 
bnScale
,bnBias

Inputs. Pointers in device memory for the batch normalization scale and bias parameters (in the original paper bias is referred to as beta and scale as gamma). Note that
bnBias
parameter can replace the previous layer's bias parameter for improved efficiency. 
exponentialAverageFactor

Input. Factor used in the moving average computation as follows:
runningMean = runningMean*(1factor) + newMean*factor
factor=1/(1+n)
atN
th call to the function to get Cumulative Moving Average (CMA) behavior such that:CMA[n] = (x[1]+...+x[n])/n
This is proved below:
CMA[n+1] = (n*CMA[n]+x[n+1])/(n+1) = ((n+1)*CMA[n]CMA[n])/(n+1) + x[n+1]/(n+1) = CMA[n]*(11/(n+1))+x[n+1]*1/(n+1) = CMA[n]*(1factor) + x(n+1)*factor

resultRunningMean
,resultRunningVariance

Inputs/Outputs. Running mean and variance tensors (these have the same descriptor as the bias and scale). Both of these pointers can be
NULL
but only at the same time. The value stored inresultRunningVariance
(or passed as an input in inference mode) is the sample variance and is the moving average ofvariance[x]
where the variance is computed either over batch or spatial+batch dimensions depending on the mode. If these pointers are notNULL
, the tensors should be initialized to some reasonable values or to 0. 
epsilon

Input. Epsilon value used in the batch normalization formula. Its value should be equal to or greater than the value defined for
CUDNN_BN_MIN_EPSILON
incudnn.h
. The sameepsilon
value should be used in forward and backward functions. 
resultSaveMean
,resultSaveInvVariance

Outputs. Optional cache to save intermediate results computed during the forward pass. These buffers can be used to speed up the backward pass when supplied to the cudnnBatchNormalizationBackward() function. The intermediate results stored in
resultSaveMean
andresultSaveInvVariance
buffers should not be used directly by the user. Depending on the batch normalization mode, the results stored inresultSaveInvVariance
may vary. For the cache to work correctly, the input layer data must remain unchanged until the backward function is called. Note that both parameters can beNULL
but only at the same time. In such a case, intermediate statistics will not be saved, and cudnnBatchNormalizationBackward() will have to recompute them. It is recommended to use this cache as the memory overhead is relatively small because these tensors have a much lower product of dimensions than the data tensors.
Supported configurations
This function supports the following combinations of data types for various descriptors.
Data Type Configurations  xDesc  bnScaleBiasMeanVarDesc  alpha, beta  yDesc 

PSEUDO_HALF_CONFIG 
CUDNN_DATA_HALF 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_HALF 
FLOAT_CONFIG 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
DOUBLE_CONFIG 
CUDNN_DATA_DOUBLE 
CUDNN_DATA_DOUBLE 
CUDNN_DATA_DOUBLE 
CUDNN_DATA_DOUBLE 
PSEUDO_BFLOAT16_CONFIG 
CUDNN_DATA_BFLOAT16 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_BFLOAT16 
Returns

CUDNN_STATUS_SUCCESS

The computation was performed successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 One of the pointers
alpha, beta, x, y, bnScale, bnBias
isNULL
.  The number of
xDesc
oryDesc
tensor descriptor dimensions is not within the range of[4,5]
(only 4D and 5D tensors are supported). bnScaleBiasMeanVarDesc
dimensions are not 1xCx1x1 for 4D and 1xCx1x1x1 for 5D for spatial, and are not 1xCxHxW for 4D and 1xCxDxHxW for 5D for peractivation mode. Exactly one of
resultSaveMean
,resultSaveInvVariance
pointers areNULL
.  Exactly one of
resultRunningMean
,resultRunningInvVariance
pointers areNULL
. epsilon
value is less thanCUDNN_BN_MIN_EPSILON
. Dimensions or data types mismatch for
xDesc
,yDesc
.
 One of the pointers
4.1.5. cudnnBatchNormalizationForwardTrainingEx()
cudnnStatus_t cudnnBatchNormalizationForwardTrainingEx(
cudnnHandle_t handle,
cudnnBatchNormMode_t mode,
cudnnBatchNormOps_t bnOps,
const void *alpha,
const void *beta,
const cudnnTensorDescriptor_t xDesc,
const void *xData,
const cudnnTensorDescriptor_t zDesc,
const void *zData,
const cudnnTensorDescriptor_t yDesc,
void *yData,
const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
const void *bnScaleData,
const void *bnBiasData,
double exponentialAverageFactor,
void *resultRunningMeanData,
void *resultRunningVarianceData,
double epsilon,
void *saveMean,
void *saveInvVariance,
const cudnnActivationDescriptor_t activationDesc,
void *workspace,
size_t workSpaceSizeInBytes
void *reserveSpace
size_t reserveSpaceSizeInBytes);
This function is an extension of the cudnnBatchNormalizationForwardTraining() for performing the forward batch normalization layer computation. This API will trigger the new semipersistent NHWC kernel when the following conditions are true:
 All tensors, namely,
x, y, dz, dy, dx
must be NHWCfully packed and must be of the typeCUDNN_DATA_HALF
.  The input parameter
mode
must be set toCUDNN_BATCHNORM_SPATIAL_PERSISTENT
. workspace
is notNULL
. Before cuDNN version 8.2.0, the tensor
C
dimension should always be a multiple of 4. After 8.2.0, the tensorC
dimension should be a multiple of 4 only whenbnOps
isCUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION
. workSpaceSizeInBytes
is equal to or larger than the amount required by cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize().reserveSpaceSizeInBytes
is equal to or larger than the amount required by cudnnGetBatchNormalizationTrainingExReserveSpaceSize(). The content in
reserveSpace
stored by cudnnBatchNormalizationForwardTrainingEx() must be preserved.
If workspace
is NULL
and workSpaceSizeInBytes
of zero is passed in, this API will function exactly like the nonextended function cudnnBatchNormalizationForwardTraining().
This workspace is not required to be clean. Moreover, the workspace does not have to remain unchanged between the forward and backward pass, as it is not used for passing any information.
This extended function can accept a *workspace
pointer to the GPU workspace, and workSpaceSizeInBytes
, the size of the workspace, from the user.
The bnOps
input can be used to set this function to perform either only the batch normalization, or batch normalization followed by activation, or batch normalization followed by elementwise addition and then activation.
Only 4D and 5D tensors are supported. The epsilon
value has to be the same during the training, the backpropagation, and the inference.
When the tensor layout is NCHW, higher performance can be obtained when HWpacked tensors are used for x, dy, dx
.
Parameters

handle

Handle to a previously created cuDNN library descriptor. For more information, refer to cudnnHandle_t.

mode

Mode of operation (spatial or peractivation). For more information, refer to cudnnBatchNormMode_t.

bnOps
 Input. Mode of operation for the fast NHWC kernel. For more information, refer to cudnnBatchNormOps_t. This input can be used to set this function to perform either only the batch normalization, or batch normalization followed by activation, or batch normalization followed by elementwise addition and then activation.

*alpha
,*beta

Inputs. Pointers to scaling factors (in host memory) used to blend the layer output value with prior value in the destination tensor as follows:
dstValue = alpha[0]*resultValue + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

xDesc
,*xData
,zDesc
,*zData
,yDesc
,*yData

Tensor descriptors and pointers in device memory for the layer's input
x
and outputy
, and for the optionalz
tensor input for residual addition to the result of the batch normalization operation, prior to the activation. The optionalzDes
and*zData
descriptors are only used whenbnOps
isCUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION
, otherwise users may passNULL
. When in use,z
should have exactly the same dimension asx
and the final outputy
. For more information, refer to cudnnTensorDescriptor_t. 
bnScaleBiasMeanVarDesc

Shared tensor descriptor
desc
for the secondary tensor that was derived by cudnnDeriveBNTensorDescriptor(). The dimensions for this tensor descriptor are dependent on the normalization mode. 
*bnScaleData
,*bnBiasData

Inputs. Pointers in device memory for the batch normalization scale and bias parameters (in the original paper, bias is referred to as beta and scale as gamma). Note that
bnBiasData
parameter can replace the previous layer's bias parameter for improved efficiency. 
exponentialAverageFactor

Input. Factor used in the moving average computation as follows:
runningMean = runningMean*(1factor) + newMean*factor
factor=1/(1+n)
atN
th call to the function to get Cumulative Moving Average (CMA) behavior such that:CMA[n] = (x[1]+...+x[n])/n
This is proved below:
Writing
CMA[n+1] = (n*CMA[n]+x[n+1])/(n+1) = ((n+1)*CMA[n]CMA[n])/(n+1) + x[n+1]/(n+1) = CMA[n]*(11/(n+1))+x[n+1]*1/(n+1) = CMA[n]*(1factor) + x(n+1)*factor

*resultRunningMeanData
,*resultRunningVarianceData

Inputs/Outputs. Pointers to the running mean and running variance data. Both these pointers can be
NULL
but only at the same time. The value stored inresultRunningVarianceData
(or passed as an input in inference mode) is the sample variance and is the moving average ofvariance[x]
where the variance is computed either over batch or spatial+batch dimensions depending on the mode. If these pointers are notNULL
, the tensors should be initialized to some reasonable values or to0
. 
epsilon

Input. Epsilon value used in the batch normalization formula. Its value should be equal to or greater than the value defined for
CUDNN_BN_MIN_EPSILON
incudnn.h
. The sameepsilon
value should be used in forward and backward functions. 
*saveMean
,*saveInvVariance

Outputs. Optional cache parameters containing saved intermediate results computed during the forward pass. For this to work correctly, the layer's
x
andbnScaleData
,bnBiasData
data has to remain unchanged until this backward function is called. Note that both these parameters can beNULL
but only at the same time. It is recommended to use this cache since the memory overhead is relatively small. 
activationDesc

Input. The tensor descriptor for the activation operation. When the
bnOps
input is set to eitherCUDNN_BATCHNORM_OPS_BN_ACTIVATION
orCUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION
then this activation is used, otherwise user may passNULL
. 
*workspace
,workSpaceSizeInBytes

Inputs.
*workspace
is a pointer to the GPU workspace, andworkSpaceSizeInBytes
is the size of the workspace. When*workspace
is notNULL
and*workSpaceSizeInBytes
is large enough, and the tensor layout is NHWC and the data type configuration is supported, then this function will trigger a new semipersistent NHWC kernel for batch normalization. The workspace is not required to be clean. Also, the workspace does not need to remain unchanged between the forward and backward passes. 
*reserveSpace

Input. Pointer to the GPU workspace for the
reserveSpace
. 
reserveSpaceSizeInBytes

Input. The size of the
reserveSpace
. Must be equal or larger than the amount required by cudnnGetBatchNormalizationTrainingExReserveSpaceSize().
Supported configurations
This function supports the following combinations of data types for various descriptors.
Data Type Configurations  xDesc  bnScaleBiasMeanVarDesc  alpha, beta  yDesc 

PSEUDO_HALF_CONFIG 
CUDNN_DATA_HALF 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_HALF 
FLOAT_CONFIG 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
DOUBLE_CONFIG 
CUDNN_DATA_DOUBLE 
CUDNN_DATA_DOUBLE 
CUDNN_DATA_DOUBLE 
CUDNN_DATA_DOUBLE 
PSEUDO_BFLOAT16_CONFIG 
CUDNN_DATA_BFLOAT16 
CUDNN_DATA_FLOAT 
CUDNN_DATA_FLOAT 
CUDNN_DATA_BFLOAT16 
Returns

CUDNN_STATUS_SUCCESS

The computation was performed successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 One of the pointers
alpha, beta, x, y, bnScaleData, bnBiasData
isNULL
.  The number of
xDesc
oryDesc
tensor descriptor dimensions is not within the[4,5]
range (only 4D and 5D tensors are supported). bnScaleBiasMeanVarDesc
dimensions are not 1xCx1x1 for 4D and 1xCx1x1x1 for 5D for spatial, and are not 1xCxHxW for 4D and 1xCxDxHxW for 5D for peractivation mode. Exactly one of
saveMean
,saveInvVariance
pointers areNULL
.  Exactly one of
resultRunningMeanData
,resultRunningInvVarianceData
pointers areNULL
. epsilon
value is less thanCUDNN_BN_MIN_EPSILON
. Dimensions or data types mismatch for
xDesc
,yDesc
.
 One of the pointers
4.1.6. cudnnDivisiveNormalizationBackward()
cudnnStatus_t cudnnDivisiveNormalizationBackward(
cudnnHandle_t handle,
cudnnLRNDescriptor_t normDesc,
cudnnDivNormMode_t mode,
const void *alpha,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const void *means,
const void *dy,
void *temp,
void *temp2,
const void *beta,
const cudnnTensorDescriptor_t dxDesc,
void *dx,
void *dMeans)
This function performs the backward DivisiveNormalization
layer computation.
Supported tensor formats are NCHW for 4D and NCDHW for 5D with any nonoverlapping nonnegative strides. Only 4D and 5D tensors are supported.
Parameters

handle

Input. Handle to a previously created cuDNN library descriptor.

normDesc

Input. Handle to a previously initialized LRN parameter descriptor (this descriptor is used for both LRN and
DivisiveNormalization
layers). 
mode

Input.
DivisiveNormalization
layer mode of operation. Currently onlyCUDNN_DIVNORM_PRECOMPUTED_MEANS
is implemented. Normalization is performed using the means input tensor that is expected to be precomputed by the user. 
alpha
,beta

Input. Pointers to scaling factors (in host memory) used to blend the layer output value with prior value in the destination tensor as follows:
dstValue = alpha[0]*resultValue + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

xDesc
,x
,means

Input. Tensor descriptor and pointers in device memory for the layer's x and means data. Note that the
means
tensor is expected to be precomputed by the user. It can also contain any valid values (not required to be actualmeans
, and can be for instance a result of a convolution with a Gaussian kernel). 
dy

Input. Tensor pointer in device memory for the layer's
dy
cumulative loss differential data (error backpropagation). 
temp
,temp2

Workspace. Temporary tensors in device memory. These are used for computing intermediate values during the backward pass. These tensors do not have to be preserved from forward to backward pass. Both use
xDesc
as a descriptor. 
dxDesc

Input. Tensor descriptor for
dx
anddMeans
. 
dx
,dMeans

Output. Tensor pointers (in device memory) for the layers resulting in cumulative gradients
dx
anddMeans
(dLoss/dx
anddLoss/dMeans
). Both share the same descriptor.
Returns

CUDNN_STATUS_SUCCESS

The computation was performed successfully.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 One of the tensor pointers
x, dx, temp, tmep2, dy
isNULL
.  Number of any of the input or output tensor dimensions is not within the
[4,5]
range.  Either alpha or beta pointer is
NULL
.  A mismatch in dimensions between
xDesc
anddxDesc
.  LRN descriptor parameters are outside of their valid ranges.
 Any of the tensor strides is negative.
 One of the tensor pointers

CUDNN_STATUS_UNSUPPORTED

The function does not support the provided configuration, for example, any of the input and output tensor strides mismatch (for the same dimension) is a nonsupported configuration.
4.1.7. cudnnDropoutBackward()
cudnnStatus_t cudnnDropoutBackward(
cudnnHandle_t handle,
const cudnnDropoutDescriptor_t dropoutDesc,
const cudnnTensorDescriptor_t dydesc,
const void *dy,
const cudnnTensorDescriptor_t dxdesc,
void *dx,
void *reserveSpace,
size_t reserveSpaceSizeInBytes)
This function performs backward dropout operation over dy
returning results in dx
. If during forward dropout operation value from x
was propagated to y
then during backward operation value from dy
will be propagated to dx
, otherwise, dx
value will be set to 0
.
Better performance is obtained for fully packed tensors.
Parameters

handle

Input. Handle to a previously created cuDNN context.

dropoutDesc

Input. Previously created dropout descriptor object.

dyDesc

Input. Handle to a previously initialized tensor descriptor.

dy

Input. Pointer to data of the tensor described by the
dyDesc
descriptor. 
dxDesc

Input. Handle to a previously initialized tensor descriptor.

dx

Output. Pointer to data of the tensor described by the
dxDesc
descriptor. 
reserveSpace

Input. Pointer to userallocated GPU memory used by this function. It is expected that
reserveSpace
was populated during a call tocudnnDropoutForward
and has not been changed. 
reserveSpaceSizeInBytes

Input. Specifies the size in bytes of the provided memory for the reserve space
Returns

CUDNN_STATUS_SUCCESS

The call was successful.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 The number of elements of input tensor and output tensors differ.
 The
datatype
of the input tensor and output tensors differs.  The strides of the input tensor and output tensors differ and inplace operation is used (i.e.,
x
andy
pointers are equal).  The provided
reserveSpaceSizeInBytes
is less than the value returned bycudnnDropoutGetReserveSpaceSize
. cudnnSetDropoutDescriptor
has not been called ondropoutDesc
with the nonNULL
states
argument.

CUDNN_STATUS_EXECUTION_FAILED

The function failed to launch on the GPU.
cudnnGetBatchNormalizationBackwardExWorkspaceSize()
cudnnStatus_t cudnnGetBatchNormalizationBackwardExWorkspaceSize(
cudnnHandle_t handle,
cudnnBatchNormMode_t mode,
cudnnBatchNormOps_t bnOps,
const cudnnTensorDescriptor_t xDesc,
const cudnnTensorDescriptor_t yDesc,
const cudnnTensorDescriptor_t dyDesc,
const cudnnTensorDescriptor_t dzDesc,
const cudnnTensorDescriptor_t dxDesc,
const cudnnTensorDescriptor_t dBnScaleBiasDesc,
const cudnnActivationDescriptor_t activationDesc,
size_t *sizeInBytes);
This function returns the amount of GPU memory workspace the user should allocate to be able to call cudnnGetBatchNormalizationBackwardExWorkspaceSize()
function for the specified bnOps
input setting. The workspace allocated will then be passed to the function cudnnGetBatchNormalizationBackwardExWorkspaceSize()
.
Parameters

handle

Input. Handle to a previously created cuDNN library descriptor. For more information, refer to cudnnHandle_t.

mode

Input. Mode of operation (spatial or peractivation). For more information, refer to cudnnBatchNormMode_t.

bnOps

Input. Mode of operation for the fast NHWC kernel. For more information, refer to cudnnBatchNormOps_t. This input can be used to set this function to perform either only the batch normalization, or batch normalization followed by activation, or batch normalization followed by elementwise addition and then activation.

xDesc
,yDesc
,dyDesc
,dzDesc
,dxDesc

Tensor descriptors and pointers in the device memory for the layer's
x
data, back propagated differentialdy
(inputs), the optionaly
input data, the optionaldz
output, and thedx
output, which is the resulting differential with respect tox
. For more information, refer to cudnnTensorDescriptor_t. 
dBnScaleBiasDesc

Input. Shared tensor descriptor for the following six tensors:
bnScaleData, bnBiasData, dBnScaleData, dBnBiasData, savedMean,
andsavedInvVariance
. This is the shared tensor descriptor desc for the secondary tensor that was derived by cudnnDeriveBNTensorDescriptor(). The dimensions for this tensor descriptor are dependent on normalization mode. Note that the data type of this tensor descriptor must befloat
for FP16 and FP32 input tensors, anddouble
for FP64 input tensors. 
activationDesc

Input. Descriptor for the activation operation. When the
bnOps
input is set to eitherCUDNN_BATCHNORM_OPS_BN_ACTIVATION
orCUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION
, then this activation is used, otherwise user may passNULL
. 
*sizeInBytes

Output. Amount of GPU memory required for the workspace, as determined by this function, to be able to execute the cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize() function with the specified
bnOps
input setting.
Returns

CUDNN_STATUS_SUCCESS

The computation was performed successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 Number of
xDesc
,yDesc
ordxDesc
tensor descriptor dimensions is not within the range of[4,5]
(only 4D and 5D tensors are supported). dBnScaleBiasDesc
dimensions not 1xCx1x1 for 4D and 1xCx1x1x1 for 5D for spatial, and are not 1xCxHxW for 4D and 1xCxDxHxW for 5D for peractivation mode. Dimensions or data types mismatch for any pair of
xDesc, dyDesc, dxDesc
.
 Number of
cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize()
cudnnStatus_t cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize(
cudnnHandle_t handle,
cudnnBatchNormMode_t mode,
cudnnBatchNormOps_t bnOps,
const cudnnTensorDescriptor_t xDesc,
const cudnnTensorDescriptor_t zDesc,
const cudnnTensorDescriptor_t yDesc,
const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
const cudnnActivationDescriptor_t activationDesc,
size_t *sizeInBytes);
This function returns the amount of GPU memory workspace the user should allocate to be able to call cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize()
function for the specified bnOps
input setting. The workspace allocated should then be passed by the user to the function cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize()
.
Parameters

handle

Input. Handle to a previously created cuDNN library descriptor. For more information, refer to cudnnHandle_t.

mode

Input. Mode of operation (spatial or peractivation). For more information, refer to cudnnBatchNormMode_t.

bnOps
 Input. Mode of operation for the fast NHWC kernel. For more information, refer to cudnnBatchNormOps_t. This input can be used to set this function to perform either only the batch normalization, or batch normalization followed by activation, or batch normalization followed by elementwise addition and then activation.

xDesc
,zDesc
,yDesc

Tensor descriptors and pointers in the device memory for the layer's
x
data, the optionalz
input data, and they
output.zDesc
is only needed whenbnOps
isCUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION
, otherwise the user may passNULL
. For more information, refer to cudnnTensorDescriptor_t. 
bnScaleBiasMeanVarDesc

Input. Shared tensor descriptor for the following six tensors:
bnScaleData, bnBiasData, dBnScaleData, dBnBiasData, savedMean,
andsavedInvVariance
. This is the shared tensor descriptor desc for the secondary tensor that was derived by cudnnDeriveBNTensorDescriptor(). The dimensions for this tensor descriptor are dependent on normalization mode. Note that the data type of this tensor descriptor must befloat
for FP16 and FP32 input tensors, anddouble
for FP64 input tensors. 
activationDesc

Input. Descriptor for the activation operation. When the
bnOps
input is set to eitherCUDNN_BATCHNORM_OPS_BN_ACTIVATION
orCUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION
then this activation is used, otherwise the user may passNULL
. 
*sizeInBytes

Output. Amount of GPU memory required for the workspace, as determined by this function, to be able to execute the
cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize()
function with the specifiedbnOps
input setting.
Returns

CUDNN_STATUS_SUCCESS

The computation was performed successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 Number of
xDesc
,yDesc
ordxDesc
tensor descriptor dimensions is not within the range of[4,5]
(only 4D and 5D tensors are supported). dBnScaleBiasDesc
dimensions not 1xCx1x1 for 4D and 1xCx1x1x1 for 5D for spatial, and are not 1xCxHxW for 4D and 1xCxDxHxW for 5D for peractivation mode. Dimensions or data types mismatch for
xDesc, yDesc
.
 Number of
4.1.10. cudnnGetBatchNormalizationTrainingExReserveSpaceSize()
cudnnStatus_t cudnnGetBatchNormalizationTrainingExReserveSpaceSize(
cudnnHandle_t handle,
cudnnBatchNormMode_t mode,
cudnnBatchNormOps_t bnOps,
const cudnnActivationDescriptor_t activationDesc,
const cudnnTensorDescriptor_t xDesc,
size_t *sizeInBytes);
This function returns the amount of reserve GPU memory workspace the user should allocate for the batch normalization operation, for the specified bnOps
input setting. In contrast to the workspace
, the reserved space should be preserved between the forward and backward calls, and the data should not be altered.
Parameters

handle

Input. Handle to a previously created cuDNN library descriptor. For more information, refer to cudnnHandle_t.

mode

Input. Mode of operation (spatial or peractivation). For more information, refer to cudnnBatchNormMode_t.

bnOps
 Input. Mode of operation for the fast NHWC kernel. For more information, refer to cudnnBatchNormOps_t. This input can be used to set this function to perform either only the batch normalization, or batch normalization followed by activation, or batch normalization followed by elementwise addition and then activation.

xDesc

Tensor descriptors for the layer's
x
data. For more information, refer to cudnnTensorDescriptor_t. 
activationDesc

Input. Descriptor for the activation operation. When the
bnOps
input is set to eitherCUDNN_BATCHNORM_OPS_BN_ACTIVATION
orCUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION
then this activation is used, otherwise user may passNULL
. 
*sizeInBytes
 Output. Amount of GPU memory reserved.
Returns

CUDNN_STATUS_SUCCESS

The computation was performed successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 The
xDesc
tensor descriptor dimension is not within the[4,5]
range (only 4D and 5D tensors are supported).
 The
4.1.11. cudnnGetNormalizationBackwardWorkspaceSize()
cudnnStatus_t
cudnnGetNormalizationBackwardWorkspaceSize(cudnnHandle_t handle,
cudnnNormMode_t mode,
cudnnNormOps_t normOps,
cudnnNormAlgo_t algo,
const cudnnTensorDescriptor_t xDesc,
const cudnnTensorDescriptor_t yDesc,
const cudnnTensorDescriptor_t dyDesc,
const cudnnTensorDescriptor_t dzDesc,
const cudnnTensorDescriptor_t dxDesc,
const cudnnTensorDescriptor_t dNormScaleBiasDesc,
const cudnnActivationDescriptor_t activationDesc,
const cudnnTensorDescriptor_t normMeanVarDesc,
size_t *sizeInBytes,
int groupCnt);
This function returns the amount of GPU memory workspace the user should allocate to be able to call cudnnNormalizationBackward() function for the specified normOps
and algo
input setting. The workspace allocated will then be passed to the function cudnnNormalizationBackward().
Parameters

handle

Input. Handle to a previously created cuDNN library descriptor. For more information, refer to cudnnHandle_t.

mode
 Input. Mode of operation (perchannel or peractivation). For more information, refer to cudnnNormMode_t.

normOps

Input. Mode of postoperative. Currently
CUDNN_NORM_OPS_NORM_ACTIVATION
andCUDNN_NORM_OPS_NORM_ADD_ACTIVATION
are only supported in the NHWC layout. For more information, refer to cudnnNormOps_t. This input can be used to set this function to perform either only the normalization, or normalization followed by activation, or normalization followed by elementwise addition and then activation. 
algo

Input. Algorithm to be performed. For more information, refer to cudnnNormAlgo_t.

xDesc
,yDesc
,dyDesc
,dzDesc
,dxDesc

Tensor descriptors and pointers in the device memory for the layer's
x
data, back propagated differentialdy
(inputs), the optionaly
input data, the optionaldz
output, and thedx
output, which is the resulting differential with respect tox
. For more information, refer to cudnnTensorDescriptor_t. 
dNormScaleBiasDesc

Input. Shared tensor descriptor for the following four tensors:
normScaleData
,normBiasData
,dNormScaleData
,dNormBiasData
. The dimensions for this tensor descriptor are dependent on normalization mode. Note that the data type of this tensor descriptor must be float for FP16 and FP32 input tensors, and double for FP64 input tensors. 
activationDesc

Input. Descriptor for the activation operation. When the
normOps
input is set to eitherCUDNN_NORM_OPS_NORM_ACTIVATION
orCUDNN_NORM_OPS_NORM_ADD_ACTIVATION
, then this activation is used, otherwise the user may passNULL
. 
normMeanVarDesc

Input. Shared tensor descriptor for the following tensors:
savedMean
andsavedInvVariance
. The dimensions for this tensor descriptor are dependent on normalization mode. Note that the data type of this tensor descriptor must be float for FP16 and FP32 input tensors, and double for FP64 input tensors. 
*sizeInBytes

Output. Amount of GPU memory required for the workspace, as determined by this function, to be able to execute the cudnnGetNormalizationForwardTrainingWorkspaceSize() function with the specified
normOps
input setting. 
groupCnt

Input. The number of grouped convolutions. Currently, only
1
is supported.
Returns

CUDNN_STATUS_SUCCESS

The computation was performed successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 Number of
xDesc
,yDesc
ordxDesc
tensor descriptor dimensions is not within the range of [4,5] (only 4D and 5D tensors are supported). dNormScaleBiasDesc
dimensions not 1xCx1x1 for 4D and 1xCx1x1x1 for 5D for perchannel, and are not 1xCxHxW for 4D and 1xCxDxHxW for 5D for peractivation mode. Dimensions or data types mismatch for any pair of
xDesc
,dyDesc
,dxDesc
.
 Number of
4.1.12. cudnnGetNormalizationForwardTrainingWorkspaceSize()
cudnnStatus_t
cudnnGetNormalizationForwardTrainingWorkspaceSize(cudnnHandle_t handle,
cudnnNormMode_t mode,
cudnnNormOps_t normOps,
cudnnNormAlgo_t algo,
const cudnnTensorDescriptor_t xDesc,
const cudnnTensorDescriptor_t zDesc,
const cudnnTensorDescriptor_t yDesc,
const cudnnTensorDescriptor_t normScaleBiasDesc,
const cudnnActivationDescriptor_t activationDesc,
const cudnnTensorDescriptor_t normMeanVarDesc,
size_t *sizeInBytes,
int groupCnt);
This function returns the amount of GPU memory workspace the user should allocate to be able to call cudnnNormalizationForwardTraining() function for the specified normOps
and algo
input setting. The workspace allocated should then be passed by the user to the function cudnnNormalizationForwardTraining().
Parameters

handle

Input. Handle to a previously created cuDNN library descriptor. For more information, refer to cudnnHandle_t.

mode
 Input. Mode of operation (perchannel or peractivation). For more information, refer to cudnnNormMode_t.

normOps

Input. Mode of postoperative. Currently
CUDNN_NORM_OPS_NORM_ACTIVATION
andCUDNN_NORM_OPS_NORM_ADD_ACTIVATION
are only supported in the NHWC layout. For more information, refer to cudnnNormOps_t. This input can be used to set this function to perform either only the normalization, or normalization followed by activation, or normalization followed by elementwise addition and then activation. 
algo

Input. Algorithm to be performed. For more information, refer to cudnnNormAlgo_t.

xDesc
,zDesc
,yDesc

Tensor descriptors and pointers in the device memory for the layer's
x
data, the optionalz
input data, and they
output.zDesc
is only needed whennormOps
isCUDNN_NORM_OPS_NORM_ADD_ACTIVATION
, otherwise the user may passNULL
. For more information, refer to cudnnTensorDescriptor_t. 
normScaleBiasDesc

Input. Shared tensor descriptor for the following tensors:
normScaleData
andnormBiasData
. The dimensions for this tensor descriptor are dependent on normalization mode. Note that the data type of this tensor descriptor must be float for FP16 and FP32 input tensors, and double for FP64 input tensors. 
activationDesc

Input. Descriptor for the activation operation. When the
normOps
input is set to eitherCUDNN_NORM_OPS_NORM_ACTIVATION
orCUDNN_NORM_OPS_NORM_ADD_ACTIVATION
, then this activation is used, otherwise the user may passNULL
. 
normMeanVarDesc

Input. Shared tensor descriptor for the following tensors:
savedMean
andsavedInvVariance
. The dimensions for this tensor descriptor are dependent on normalization mode. Note that the data type of this tensor descriptor must be float for FP16 and FP32 input tensors, and double for FP64 input tensors. 
*sizeInBytes

Output. Amount of GPU memory required for the workspace, as determined by this function, to be able to execute the cudnnGetNormalizationForwardTrainingWorkspaceSize() function with the specified
normOps
input setting. 
groupCnt

Input. The number of grouped convolutions. Currently, only
1
is supported.
Returns

CUDNN_STATUS_SUCCESS

The computation was performed successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 Number of
xDesc
,yDesc
orzDesc
tensor descriptor dimensions is not within the range of [4,5] (only 4D and 5D tensors are supported). normScaleBiasDesc
dimensions not 1xCx1x1 for 4D and 1xCx1x1x1 for 5D for perchannel, and are not 1xCxHxW for 4D and 1xCxDxHxW for 5D for peractivation mode. Dimensions or data types mismatch for
xDesc
,yDesc
.
 Number of
4.1.13. cudnnGetNormalizationTrainingReserveSpaceSize()
cudnnStatus_t
cudnnGetNormalizationTrainingReserveSpaceSize(cudnnHandle_t handle,
cudnnNormMode_t mode,
cudnnNormOps_t normOps,
cudnnNormAlgo_t algo,
const cudnnActivationDescriptor_t activationDesc,
const cudnnTensorDescriptor_t xDesc,
size_t *sizeInBytes,
int groupCnt);
This function returns the amount of reserve GPU memory workspace the user should allocate for the normalization operation, for the specified normOps
input setting. In contrast to the workspace, the reserved space should be preserved between the forward and backward calls, and the data should not be altered.
Parameters

handle

Input. Handle to a previously created cuDNN library descriptor. For more information, refer to cudnnHandle_t.

mode
 Input. Mode of operation (perchannel or peractivation). For more information, refer to cudnnNormMode_t.

normOps

Input. Mode of postoperative. Currently
CUDNN_NORM_OPS_NORM_ACTIVATION
andCUDNN_NORM_OPS_NORM_ADD_ACTIVATION
are only supported in the NHWC layout. For more information, refer to cudnnNormOps_t . This input can be used to set this function to perform either only the normalization, or normalization followed by activation, or normalization followed by elementwise addition and then activation. 
algo

Input. Algorithm to be performed. For more information, refer to cudnnNormAlgo_t.

xDesc

Tensor descriptors for the layer's
x
data. For more information, refer to cudnnTensorDescriptor_t. 
activationDesc

Input. Descriptor for the activation operation. When the
normOps
input is set to eitherCUDNN_NORM_OPS_NORM_ACTIVATION
orCUDNN_NORM_OPS_NORM_ADD_ACTIVATION
then this activation is used, otherwise the user may passNULL
. 
*sizeInBytes

Output. Amount of GPU memory reserved.

groupCnt

Input. The number of grouped convolutions. Currently, only
1
is supported.
Returns

CUDNN_STATUS_SUCCESS

The computation was performed successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 The
xDesc
tensor descriptor dimension is not within the [4,5] range (only 4D and 5D tensors are supported).
 The
4.1.14. cudnnLRNCrossChannelBackward()
cudnnStatus_t cudnnLRNCrossChannelBackward(
cudnnHandle_t handle,
cudnnLRNDescriptor_t normDesc,
cudnnLRNMode_t lrnMode,
const void *alpha,
const cudnnTensorDescriptor_t yDesc,
const void *y,
const cudnnTensorDescriptor_t dyDesc,
const void *dy,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const void *beta,
const cudnnTensorDescriptor_t dxDesc,
void *dx)
This function performs the backward LRN layer computation.
Supported formats are: positivestrided
, NCHW and NHWC for 4D x
and y
, and only NCDHW DHWpacked for 5D (for both x
and y
). Only nonoverlapping 4D and 5D tensors are supported. NCHW layout is preferred for performance.
Parameters

handle

Input. Handle to a previously created cuDNN library descriptor.

normDesc

Input. Handle to a previously initialized LRN parameter descriptor.

lrnMode

Input. LRN layer mode of operation. Currently, only
CUDNN_LRN_CROSS_CHANNEL_DIM1
is implemented. Normalization is performed along the tensor'sdimA[1]
. 
alpha
,beta

Input. Pointers to scaling factors (in host memory) used to blend the layer output value with prior value in the destination tensor as follows:
dstValue = alpha[0]*resultValue + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

yDesc
,y

Input. Tensor descriptor and pointer in device memory for the layer's
y
data. 
dyDesc
,dy

Input. Tensor descriptor and pointer in device memory for the layer's input cumulative loss differential data
dy
(including error backpropagation). 
xDesc
,x

Input. Tensor descriptor and pointer in device memory for the layer's
x
data. Note that these values are not modified during backpropagation. 
dxDesc
,dx

Output. Tensor descriptor and pointer in device memory for the layer's resulting cumulative loss differential data
dx
(including error backpropagation).
Returns

CUDNN_STATUS_SUCCESS

The computation was performed successfully.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 One of the tensor pointers
x
,y
isNULL
.  Number of input tensor dimensions is 2 or less.
 LRN descriptor parameters are outside of their valid ranges.
 One of the tensor parameters is 5D but is not in NCDHW DHWpacked format.
 One of the tensor pointers

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration. See the following for some examples of nonsupported configurations:
 Any of the input tensor datatypes is not the same as any of the output tensor datatype.
 Any pairwise tensor dimensions mismatch for
x
,y
,dx
,dy
.  Any tensor parameters strides are negative.
4.1.15. cudnnNormalizationBackward()
cudnnStatus_t
cudnnNormalizationBackward(cudnnHandle_t handle,
cudnnNormMode_t mode,
cudnnNormOps_t normOps,
cudnnNormAlgo_t algo,
const void *alphaDataDiff,
const void *betaDataDiff,
const void *alphaParamDiff,
const void *betaParamDiff,
const cudnnTensorDescriptor_t xDesc,
const void *xData,
const cudnnTensorDescriptor_t yDesc,
const void *yData,
const cudnnTensorDescriptor_t dyDesc,
const void *dyData,
const cudnnTensorDescriptor_t dzDesc,
void *dzData,
const cudnnTensorDescriptor_t dxDesc,
void *dxData,
const cudnnTensorDescriptor_t dNormScaleBiasDesc,
const void *normScaleData,
const void *normBiasData,
void *dNormScaleData,
void *dNormBiasData,
double epsilon,
const cudnnTensorDescriptor_t normMeanVarDesc,
const void *savedMean,
const void *savedInvVariance,
cudnnActivationDescriptor_t activationDesc,
void *workSpace,
size_t workSpaceSizeInBytes,
void *reserveSpace,
size_t reserveSpaceSizeInBytes,
int groupCnt)
This function performs backward normalization layer computation that is specified by mode. Perchannel normalization layer is based on the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, S. Ioffe, C. Szegedy, 2015.
Only 4D and 5D tensors are supported.
The epsilon
value has to be the same during training, backpropagation, and inference. This workspace is not required to be clean. Moreover, the workspace does not have to remain unchanged between the forward and backward pass, as it is not used for passing any information.
This function can accept a *workspace
pointer to the GPU workspace, and workSpaceSizeInBytes
, the size of the workspace, from the user.
The normOps
input can be used to set this function to perform either only the normalization, or normalization followed by activation, or normalization followed by elementwise addition and then activation.
When the tensor layout is NCHW, higher performance can be obtained when HWpacked tensors are used for x
, dy
, dx
.
Higher performance for CUDNN_NORM_PER_CHANNEL
mode can be obtained when the following conditions are true:
 All tensors, namely,
x
,y
,dz
,dy
, anddx
must be NHWCfully packed, and must be of the typeCUDNN_DATA_HALF
.  The tensor C dimension should be a multiple of 4.
 The input parameter mode must be set to
CUDNN_NORM_PER_CHANNEL
.  The input parameter algo must be set to
CUDNN_NORM_ALGO_PERSIST
.  Workspace is not
NULL
. workSpaceSizeInBytes
is equal to or larger than the amount required by cudnnGetNormalizationBackwardWorkspaceSize().reserveSpaceSizeInBytes
is equal to or larger than the amount required by cudnnGetNormalizationTrainingReserveSpaceSize(). The content in
reserveSpace
stored by cudnnNormalizationForwardTraining() must be preserved.
Parameters

handle
 Input. Handle to a previously created cuDNN library descriptor. For more information, refer to cudnnHandle_t.

mode
 Input. Mode of operation (perchannel or peractivation). For more information, refer to cudnnNormMode_t.

normOps

Input. Mode of postoperative. Currently
CUDNN_NORM_OPS_NORM_ACTIVATION
andCUDNN_NORM_OPS_NORM_ADD_ACTIVATION
are only supported in the NHWC layout. For more information, refer to cudnnNormOps_t. This input can be used to set this function to perform either only the normalization, or normalization followed by activation, or normalization followed by elementwise addition and then activation. 
algo

Input. Algorithm to be performed. For more information, refer to cudnnNormAlgo_t.

*alphaDataDiff
,*betaDataDiff

Inputs. Pointers to scaling factors (in host memory) used to blend the gradient output
dx
with a prior value in the destination tensor as follows:dstValue = alpha[0]*resultValue + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

*alphaParamDiff
,*betaParamDiff

Inputs. Pointers to scaling factors (in host memory) used to blend the gradient outputs
dNormScaleData
anddNormBiasData
with prior values in the destination tensor as follows:dstValue = alpha[0]*resultValue + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

xDesc
,*xData
,yDesc
,*yData
,dyDesc
,*dyData

Inputs. Tensor descriptors and pointers in the device memory for the layer's x data, backpropagated gradient input
dy
, the original forward outputy
data.yDesc
andyData
are not needed ifnormOps
is set toCUDNN_NORM_OPS_NORM
, users may passNULL
. For more information, refer to cudnnTensorDescriptor_t. 
dzDesc
,dxDesc

Inputs. Tensor descriptors and pointers in the device memory for the computed gradient output
dz
anddx
.dzDesc
is not needed whennormOps
isCUDNN_NORM_OPS_NORM
orCUDNN_NORM_OPS_NORM_ACTIVATION
, users may passNULL
. For more information, refer to cudnnTensorDescriptor_t. 
*dzData
,*dxData

Outputs. Tensor descriptors and pointers in the device memory for the computed gradient output
dz
anddx
.*dzData
is not needed whennormOps
isCUDNN_NORM_OPS_NORM
orCUDNN_NORM_OPS_NORM_ACTIVATION
, users may passNULL
. For more information, refer to cudnnTensorDescriptor_t. 
dNormScaleBiasDesc

Input. Shared tensor descriptor for the following six tensors:
normScaleData
,normBiasData
,dNormScaleData
, anddNormBiasData
. The dimensions for this tensor descriptor are dependent on normalization mode.Note:The data type of this tensor descriptor must be float for FP16 and FP32 input tensors and double for FP64 input tensors.
For more information, refer to cudnnTensorDescriptor_t.

*normScaleData

Input. Pointer in the device memory for the normalization scale parameter (in the original paper the quantity scale is referred to as gamma).

*normBiasData

Input. Pointers in the device memory for the normalization bias parameter (in the original paper bias is referred to as beta). This parameter is used only when activation should be performed.

*dNormScaleData
,*dNormBiasData

Outputs. Pointers in the device memory for the gradients of
normScaleData
andnormBiasData
, respectively. 
epsilon

Input. Epsilon value used in normalization formula. Its value should be equal to or greater than zero. The same epsilon value should be used in forward and backward functions.

normMeanVarDesc

Input. Shared tensor descriptor for the following tensors:
savedMean
andsavedInvVariance
. The dimensions for this tensor descriptor are dependent on normalization mode.Note:The data type of this tensor descriptor must be float for FP16 and FP32 input tensors and double for FP64 input tensors.
For more information, refer to cudnnTensorDescriptor_t.

*savedMean
,*savedInvVariance

Inputs. Optional cache parameters containing saved intermediate results computed during the forward pass. For this to work correctly, the layer's
x
andnormScaleData
,normBiasData
data has to remain unchanged until this backward function is called. Note that both these parameters can beNULL
but only at the same time. It is recommended to use this cache since the memory overhead is relatively small. 
activationDesc

Input. Descriptor for the activation operation. When the
normOps
input is set to eitherCUDNN_NORM_OPS_NORM_ACTIVATION
orCUDNN_NORM_OPS_NORM_ADD_ACTIVATION
then this activation is used, otherwise the user may passNULL
. 
workspace

Input. Pointer to the GPU workspace.

workSpaceSizeInBytes

Input. The size of the workspace. It must be large enough to trigger the fast NHWC semipersistent kernel by this function.

*reserveSpace

Input. Pointer to the GPU workspace for the
reserveSpace
. 
reserveSpaceSizeInBytes

Input. The size of the
reserveSpace
. It must be equal or larger than the amount required by cudnnGetNormalizationTrainingReserveSpaceSize(). 
groupCnt

Input. The number of grouped convolutions. Currently, only
1
is supported.
Returns

CUDNN_STATUS_SUCCESS

The computation was performed successfully.

CUDNN_STATUS_NOT_SUPPORTED

The function does not support the provided configuration.

CUDNN_STATUS_BAD_PARAM

At least one of the following conditions are met:
 Any of the pointers
alphaDataDiff
,betaDataDiff
,alphaParamDiff
,betaParamDiff
,xData
,dyData
,dxData
,normScaleData
,dNormScaleData
, anddNormBiasData
isNULL
.  The number of
xDesc
oryDesc
ordxDesc
tensor descriptor dimensions is not within the range of [4,5] (only 4D and 5D tensors are supported). dNormScaleBiasDesc
dimensions not 1xCx1x1 for 4D and 1xCx1x1x1 for 5D for perchannel, and are not 1xCxHxW for 4D and 1xCxDxHxW for 5D for peractivation mode. Exactly one of
savedMean
,savedInvVariance
pointers isNULL
. epsilon
value is less than zero. Dimensions or data types mismatch for any pair of
xDesc
,dyDesc
,dxDesc
,dNormScaleBiasDesc
, andnormMeanVarDesc
.
 Any of the pointers
4.1.16. cudnnNormalizationForwardTraining()
cudnnStatus_t
cudnnNormalizationForwardTraining(cudnnHandle_t handle,
cudnnNormMode_t mode,
cudnnNormOps_t normOps,
cudnnNormAlgo_t algo,
const void *alpha,
const void *beta,
const cudnnTensorDescriptor_t xDesc,
const void *xData,
const cudnnTensorDescriptor_t normScaleBiasDesc,
const void *normScale,
const void *normBias,
double exponentialAverageFactor,
const cudnnTensorDescriptor_t normMeanVarDesc,
void *resultRunningMean,
void *resultRunningVariance,
double epsilon,
void *resultSaveMean,
void *resultSaveInvVariance,
cudnnActivationDescriptor_t activationDesc,
const cudnnTensorDescriptor_t zDesc,
const void *zData,
const cudnnTensorDescriptor_t yDesc,
void *yData,
void *workspace,
size_t workSpaceSizeInBytes,
void *reserveSpace,
size_t reserveSpaceSizeInBytes,
int groupCnt);
This function performs the forward normalization layer computation for the training phase. Depending on mode, different normalization operations will be performed. Perchannel layer is based on the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, S. Ioffe, C. Szegedy, 2015.
 Only 4D and 5D tensors are supported.
 The
epsilon
value has to be the same during training, back propagation, and inference.  For the inference phase, refer to cudnnNormalizationForwardInference().
 Higher performance can be obtained when HWpacked tensors are used for both
x
andy
.
This API will trigger the new semipersistent NHWC kernel when the following conditions are true:
 All tensors, namely,
xData
,yData
must be NHWCfully packed and must be of the typeCUDNN_DATA_HALF
.  The tensor C dimension should be a multiple of 4.
 The input parameter mode must be set to
CUDNN_NORM_PER_CHANNEL
.  The input parameter algo must be set to
CUDNN_NORM_ALGO_PERSIST
. workspace
is notNULL
.workSpaceSizeInBytes
is equal to or larger than the amount required by cudnnGetNormalizationForwardTrainingWorkspaceSize().reserveSpaceSizeInBytes
is equal to or larger than the amount required by cudnnGetNormalizationTrainingReserveSpaceSize(). The content in
reserveSpace
stored by cudnnNormalizationForwardTraining() must be preserved.
This workspace
is not required to be clean. Moreover, the workspace
does not have to remain unchanged between the forward and backward pass, as it is not used for passing any information. This extended function can accept a *workspace
pointer to the GPU workspace, and workSpaceSizeInBytes
, the size of the workspace, from the user.
The normOps
input can be used to set this function to perform either only the normalization, or normalization followed by activation, or normalization followed by elementwise addition and then activation.
Only 4D and 5D tensors are supported. The epsilon
value has to be the same during the training, the backpropagation, and the inference.
When the tensor layout is NCHW, higher performance can be obtained when HWpacked tensors are used for xData
, yData
.
Parameters

handle

Input. Handle to a previously created cuDNN library descriptor. For more information, refer to cudnnHandle_t.

mode
 Input. Mode of operation (perchannel or peractivation). For more information, refer to cudnnNormMode_t.

normOps

Input. Mode of postoperative. Currently
CUDNN_NORM_OPS_NORM_ACTIVATION
andCUDNN_NORM_OPS_NORM_ADD_ACTIVATION
are only supported in the NHWC layout. For more information, refer to cudnnNormOps_t. This input can be used to set this function to perform either only the normalization, or normalization followed by activation, or normalization followed by elementwise addition and then activation. 
algo

Input. Algorithm to be performed. For more information, refer to cudnnNormAlgo_t.

*alpha
,*beta

Inputs. Pointers to scaling factors (in host memory) used to blend the layer output value with prior value in the destination tensor as follows:
dstValue = alpha[0]*resultValue + beta[0]*priorDstValue
For more information, refer to Scaling Parameters in the cuDNN Developer Guide.

xDesc
,yDesc

Input. Handles to the previously initialized tensor descriptors.

*xData

Input. Data pointer to GPU memory associated with the tensor descriptor
xDesc
, for the layer’sx
input data.