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screwdriver/cuda/include/cudnn_ops_infer.h
JEFF eca964d725 上傳gitea
2025-02-06 16:10:58 +08:00

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/*
* Copyright 1993-2020 NVIDIA Corporation. All rights reserved.
*
* NOTICE TO LICENSEE:
*
* This source code and/or documentation ("Licensed Deliverables") are
* subject to NVIDIA intellectual property rights under U.S. and
* international Copyright laws.
*
* These Licensed Deliverables contained herein is PROPRIETARY and
* CONFIDENTIAL to NVIDIA and is being provided under the terms and
* conditions of a form of NVIDIA software license agreement by and
* between NVIDIA and Licensee ("License Agreement") or electronically
* accepted by Licensee. Notwithstanding any terms or conditions to
* the contrary in the License Agreement, reproduction or disclosure
* of the Licensed Deliverables to any third party without the express
* written consent of NVIDIA is prohibited.
*
* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
* OF THESE LICENSED DELIVERABLES.
*
* U.S. Government End Users. These Licensed Deliverables are a
* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
* 1995), consisting of "commercial computer software" and "commercial
* computer software documentation" as such terms are used in 48
* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
* U.S. Government End Users acquire the Licensed Deliverables with
* only those rights set forth herein.
*
* Any use of the Licensed Deliverables in individual and commercial
* software must include, in the user documentation and internal
* comments to the code, the above Disclaimer and U.S. Government End
* Users Notice.
*/
/*
* cudnn_ops_infer : cuDNN's basic definitions and inference operations.
*/
#if !defined(CUDNN_OPS_INFER_H_)
#define CUDNN_OPS_INFER_H_
#include <cuda_runtime.h>
#include <stdint.h>
#include "cudnn_version.h"
/* These version numbers are autogenerated, do not edit manually. */
#define CUDNN_OPS_INFER_MAJOR 8
#define CUDNN_OPS_INFER_MINOR 1
#define CUDNN_OPS_INFER_PATCH 0
#if (CUDNN_OPS_INFER_MAJOR != CUDNN_MAJOR) || (CUDNN_OPS_INFER_MINOR != CUDNN_MINOR) || \
(CUDNN_OPS_INFER_PATCH != CUDNN_PATCHLEVEL)
#error Version mismatch in cuDNN OPS INFER!!!
#endif
#ifndef CUDNNWINAPI
#ifdef _WIN32
#define CUDNNWINAPI __stdcall
#else
#define CUDNNWINAPI
#endif
#endif
/* Warnings for deprecated API-s are enabled using the CUDNN_WARN_DEPRECATED macro */
#if defined(CUDNN_WARN_DEPRECATED) && (defined(__GNUC__) || defined(__clang__))
/* GCC, Intel C/C++, Cray C/C++, CLANG, IBM XL C/C++ little endian */
#define CUDNN_DEPRECATED __attribute__((deprecated))
#elif defined(CUDNN_WARN_DEPRECATED) && defined(_MSC_VER)
/* Microsoft Visual C++ */
#define CUDNN_DEPRECATED __declspec(deprecated)
#elif defined(CUDNN_WARN_DEPRECATED) && (__cplusplus >= 201402L)
/* C++14 compilers */
#define CUDNN_DEPRECATED [[deprecated]]
#else
/* No support for the deprecated attribute */
#define CUDNN_DEPRECATED
#endif
#if defined(__cplusplus)
extern "C" {
#endif
struct cudnnContext;
typedef struct cudnnContext *cudnnHandle_t;
size_t CUDNNWINAPI
cudnnGetVersion(void);
/* Returns CUDA Runtime version statically linked against cudnn */
size_t CUDNNWINAPI
cudnnGetCudartVersion(void);
/*
* CUDNN return codes
*/
typedef enum {
CUDNN_STATUS_SUCCESS = 0,
CUDNN_STATUS_NOT_INITIALIZED = 1,
CUDNN_STATUS_ALLOC_FAILED = 2,
CUDNN_STATUS_BAD_PARAM = 3,
CUDNN_STATUS_INTERNAL_ERROR = 4,
CUDNN_STATUS_INVALID_VALUE = 5,
CUDNN_STATUS_ARCH_MISMATCH = 6,
CUDNN_STATUS_MAPPING_ERROR = 7,
CUDNN_STATUS_EXECUTION_FAILED = 8,
CUDNN_STATUS_NOT_SUPPORTED = 9,
CUDNN_STATUS_LICENSE_ERROR = 10,
CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING = 11,
CUDNN_STATUS_RUNTIME_IN_PROGRESS = 12,
CUDNN_STATUS_RUNTIME_FP_OVERFLOW = 13,
CUDNN_STATUS_VERSION_MISMATCH = 14,
} cudnnStatus_t;
/* human-readable error messages */
const char *CUDNNWINAPI
cudnnGetErrorString(cudnnStatus_t status);
/* Forward definition in this version only */
typedef struct cudnnRuntimeTag_t cudnnRuntimeTag_t;
typedef enum {
CUDNN_ERRQUERY_RAWCODE = 0,
CUDNN_ERRQUERY_NONBLOCKING = 1,
CUDNN_ERRQUERY_BLOCKING = 2,
} cudnnErrQueryMode_t;
cudnnStatus_t CUDNNWINAPI
cudnnQueryRuntimeError(cudnnHandle_t handle, cudnnStatus_t *rstatus, cudnnErrQueryMode_t mode, cudnnRuntimeTag_t *tag);
#ifndef __LIBRARY_TYPES_H__
typedef enum libraryPropertyType_t { MAJOR_VERSION, MINOR_VERSION, PATCH_LEVEL } libraryPropertyType;
#endif
cudnnStatus_t CUDNNWINAPI
cudnnGetProperty(libraryPropertyType type, int *value);
cudnnStatus_t CUDNNWINAPI
cudnnCreate(cudnnHandle_t *handle);
cudnnStatus_t CUDNNWINAPI
cudnnDestroy(cudnnHandle_t handle);
cudnnStatus_t CUDNNWINAPI
cudnnSetStream(cudnnHandle_t handle, cudaStream_t streamId);
cudnnStatus_t CUDNNWINAPI
cudnnGetStream(cudnnHandle_t handle, cudaStream_t *streamId);
/* Data structures to represent Image/Filter and the Neural Network Layer */
typedef struct cudnnTensorStruct *cudnnTensorDescriptor_t;
typedef struct cudnnPoolingStruct *cudnnPoolingDescriptor_t;
typedef struct cudnnFilterStruct *cudnnFilterDescriptor_t;
typedef struct cudnnLRNStruct *cudnnLRNDescriptor_t;
typedef struct cudnnActivationStruct *cudnnActivationDescriptor_t;
typedef struct cudnnSpatialTransformerStruct *cudnnSpatialTransformerDescriptor_t;
typedef struct cudnnOpTensorStruct *cudnnOpTensorDescriptor_t;
typedef struct cudnnReduceTensorStruct *cudnnReduceTensorDescriptor_t;
typedef struct cudnnCTCLossStruct *cudnnCTCLossDescriptor_t;
typedef struct cudnnTensorTransformStruct *cudnnTensorTransformDescriptor_t;
/*
* CUDNN data type
*/
typedef enum {
CUDNN_DATA_FLOAT = 0,
CUDNN_DATA_DOUBLE = 1,
CUDNN_DATA_HALF = 2,
CUDNN_DATA_INT8 = 3,
CUDNN_DATA_INT32 = 4,
CUDNN_DATA_INT8x4 = 5,
CUDNN_DATA_UINT8 = 6,
CUDNN_DATA_UINT8x4 = 7,
CUDNN_DATA_INT8x32 = 8,
CUDNN_DATA_BFLOAT16 = 9,
CUDNN_DATA_INT64 = 10,
} cudnnDataType_t;
/*
* CUDNN math type
*/
typedef enum {
CUDNN_DEFAULT_MATH = 0,
CUDNN_TENSOR_OP_MATH = 1,
CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION = 2,
CUDNN_FMA_MATH = 3,
} cudnnMathType_t;
/*
* CUDNN propagate Nan
*/
typedef enum {
CUDNN_NOT_PROPAGATE_NAN = 0,
CUDNN_PROPAGATE_NAN = 1,
} cudnnNanPropagation_t;
/*
* CUDNN Determinism
*/
typedef enum {
CUDNN_NON_DETERMINISTIC = 0,
CUDNN_DETERMINISTIC = 1,
} cudnnDeterminism_t;
/* Maximum supported number of tensor dimensions */
#define CUDNN_DIM_MAX 8
/* Create an instance of a generic Tensor descriptor */
cudnnStatus_t CUDNNWINAPI
cudnnCreateTensorDescriptor(cudnnTensorDescriptor_t *tensorDesc);
typedef enum {
CUDNN_TENSOR_NCHW = 0, /* row major (wStride = 1, hStride = w) */
CUDNN_TENSOR_NHWC = 1, /* feature maps interleaved ( cStride = 1 )*/
CUDNN_TENSOR_NCHW_VECT_C = 2, /* each image point is vector of element of C, vector length in data type */
} cudnnTensorFormat_t;
cudnnStatus_t CUDNNWINAPI
cudnnSetTensor4dDescriptor(cudnnTensorDescriptor_t tensorDesc,
cudnnTensorFormat_t format,
cudnnDataType_t dataType, /* image data type */
int n, /* number of inputs (batch size) */
int c, /* number of input feature maps */
int h, /* height of input section */
int w); /* width of input section */
cudnnStatus_t CUDNNWINAPI
cudnnSetTensor4dDescriptorEx(cudnnTensorDescriptor_t tensorDesc,
cudnnDataType_t dataType, /* image data type */
int n, /* number of inputs (batch size) */
int c, /* number of input feature maps */
int h, /* height of input section */
int w, /* width of input section */
int nStride,
int cStride,
int hStride,
int wStride);
cudnnStatus_t CUDNNWINAPI
cudnnGetTensor4dDescriptor(const cudnnTensorDescriptor_t tensorDesc,
cudnnDataType_t *dataType, /* image data type */
int *n, /* number of inputs (batch size) */
int *c, /* number of input feature maps */
int *h, /* height of input section */
int *w, /* width of input section */
int *nStride,
int *cStride,
int *hStride,
int *wStride);
cudnnStatus_t CUDNNWINAPI
cudnnSetTensorNdDescriptor(cudnnTensorDescriptor_t tensorDesc,
cudnnDataType_t dataType,
int nbDims,
const int dimA[],
const int strideA[]);
cudnnStatus_t CUDNNWINAPI
cudnnSetTensorNdDescriptorEx(cudnnTensorDescriptor_t tensorDesc,
cudnnTensorFormat_t format,
cudnnDataType_t dataType,
int nbDims,
const int dimA[]);
cudnnStatus_t CUDNNWINAPI
cudnnGetTensorNdDescriptor(const cudnnTensorDescriptor_t tensorDesc,
int nbDimsRequested,
cudnnDataType_t *dataType,
int *nbDims,
int dimA[],
int strideA[]);
cudnnStatus_t CUDNNWINAPI
cudnnGetTensorSizeInBytes(const cudnnTensorDescriptor_t tensorDesc, size_t *size);
/* PixelOffset( n, c, h, w ) = n *input_stride + c * feature_stride + h * h_stride + w * w_stride
1)Example of all images in row major order one batch of features after the other (with an optional padding on row)
input_stride : c x h x h_stride
feature_stride : h x h_stride
h_stride : >= w ( h_stride = w if no padding)
w_stride : 1
2)Example of all images in row major with features maps interleaved
input_stride : c x h x h_stride
feature_stride : 1
h_stride : w x c
w_stride : c
3)Example of all images in column major order one batch of features after the other (with optional padding on column)
input_stride : c x w x w_stride
feature_stride : w x w_stride
h_stride : 1
w_stride : >= h
*/
/* Destroy an instance of Tensor4d descriptor */
cudnnStatus_t CUDNNWINAPI
cudnnDestroyTensorDescriptor(cudnnTensorDescriptor_t tensorDesc);
/* Fold/unfold transforms */
typedef enum {
CUDNN_TRANSFORM_FOLD = 0U,
CUDNN_TRANSFORM_UNFOLD = 1U,
} cudnnFoldingDirection_t;
/** Create a destination descriptor for cudnnTransformTensor */
cudnnStatus_t CUDNNWINAPI
cudnnInitTransformDest(const cudnnTensorTransformDescriptor_t transformDesc,
const cudnnTensorDescriptor_t srcDesc,
cudnnTensorDescriptor_t destDesc,
size_t *destSizeInBytes);
/** Create an empty tensor transform descriptor */
cudnnStatus_t CUDNNWINAPI
cudnnCreateTensorTransformDescriptor(cudnnTensorTransformDescriptor_t *transformDesc);
/** Initialize a previously created tensor transform descriptor. */
cudnnStatus_t CUDNNWINAPI
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);
/**
* Retrieves the values stored in a previously initialized tensor transform
* descriptor.
*/
cudnnStatus_t CUDNNWINAPI
cudnnGetTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc,
uint32_t nbDimsRequested,
cudnnTensorFormat_t *destFormat,
int32_t padBeforeA[],
int32_t padAfterA[],
uint32_t foldA[],
cudnnFoldingDirection_t *direction);
/**
* Destroys a previously created tensor transform descriptor.
*/
cudnnStatus_t CUDNNWINAPI
cudnnDestroyTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc);
/* Tensor layout conversion helper (y = alpha * x + beta * y) */
cudnnStatus_t CUDNNWINAPI
cudnnTransformTensor(cudnnHandle_t handle,
const void *alpha,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const void *beta,
const cudnnTensorDescriptor_t yDesc,
void *y);
cudnnStatus_t CUDNNWINAPI
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);
/* Tensor Bias addition : C = alpha * A + beta * C */
cudnnStatus_t CUDNNWINAPI
cudnnAddTensor(cudnnHandle_t handle,
const void *alpha,
const cudnnTensorDescriptor_t aDesc,
const void *A,
const void *beta,
const cudnnTensorDescriptor_t cDesc,
void *C);
/*
* CUDNN OpTensor op type
*/
typedef enum {
CUDNN_OP_TENSOR_ADD = 0,
CUDNN_OP_TENSOR_MUL = 1,
CUDNN_OP_TENSOR_MIN = 2,
CUDNN_OP_TENSOR_MAX = 3,
CUDNN_OP_TENSOR_SQRT = 4,
CUDNN_OP_TENSOR_NOT = 5,
} cudnnOpTensorOp_t;
cudnnStatus_t CUDNNWINAPI
cudnnCreateOpTensorDescriptor(cudnnOpTensorDescriptor_t *opTensorDesc);
cudnnStatus_t CUDNNWINAPI
cudnnSetOpTensorDescriptor(cudnnOpTensorDescriptor_t opTensorDesc,
cudnnOpTensorOp_t opTensorOp,
cudnnDataType_t opTensorCompType,
cudnnNanPropagation_t opTensorNanOpt);
cudnnStatus_t CUDNNWINAPI
cudnnGetOpTensorDescriptor(const cudnnOpTensorDescriptor_t opTensorDesc,
cudnnOpTensorOp_t *opTensorOp,
cudnnDataType_t *opTensorCompType,
cudnnNanPropagation_t *opTensorNanOpt);
cudnnStatus_t CUDNNWINAPI
cudnnDestroyOpTensorDescriptor(cudnnOpTensorDescriptor_t opTensorDesc);
/* Tensor operation : C = op( alpha1 * A, alpha2 * B ) + beta * C */
/* B tensor is ignored for CUDNN_OP_TENSOR_SQRT, CUDNN_OP_TENSOR_NOT. */
cudnnStatus_t CUDNNWINAPI
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);
/*
* CUDNN ReduceTensor op type
*/
typedef enum {
CUDNN_REDUCE_TENSOR_ADD = 0,
CUDNN_REDUCE_TENSOR_MUL = 1,
CUDNN_REDUCE_TENSOR_MIN = 2,
CUDNN_REDUCE_TENSOR_MAX = 3,
CUDNN_REDUCE_TENSOR_AMAX = 4,
CUDNN_REDUCE_TENSOR_AVG = 5,
CUDNN_REDUCE_TENSOR_NORM1 = 6,
CUDNN_REDUCE_TENSOR_NORM2 = 7,
CUDNN_REDUCE_TENSOR_MUL_NO_ZEROS = 8,
} cudnnReduceTensorOp_t;
/*
* CUDNN ReduceTensor indices type
*/
typedef enum {
CUDNN_REDUCE_TENSOR_NO_INDICES = 0,
CUDNN_REDUCE_TENSOR_FLATTENED_INDICES = 1,
} cudnnReduceTensorIndices_t;
/*
* CUDNN tensor indices type size (all unsigned)
* Currently not supported, default is 32 bit unsigned.
*/
typedef enum {
CUDNN_32BIT_INDICES = 0,
CUDNN_64BIT_INDICES = 1,
CUDNN_16BIT_INDICES = 2,
CUDNN_8BIT_INDICES = 3,
} cudnnIndicesType_t;
cudnnStatus_t CUDNNWINAPI
cudnnCreateReduceTensorDescriptor(cudnnReduceTensorDescriptor_t *reduceTensorDesc);
cudnnStatus_t CUDNNWINAPI
cudnnSetReduceTensorDescriptor(cudnnReduceTensorDescriptor_t reduceTensorDesc,
cudnnReduceTensorOp_t reduceTensorOp,
cudnnDataType_t reduceTensorCompType,
cudnnNanPropagation_t reduceTensorNanOpt,
cudnnReduceTensorIndices_t reduceTensorIndices,
cudnnIndicesType_t reduceTensorIndicesType);
cudnnStatus_t CUDNNWINAPI
cudnnGetReduceTensorDescriptor(const cudnnReduceTensorDescriptor_t reduceTensorDesc,
cudnnReduceTensorOp_t *reduceTensorOp,
cudnnDataType_t *reduceTensorCompType,
cudnnNanPropagation_t *reduceTensorNanOpt,
cudnnReduceTensorIndices_t *reduceTensorIndices,
cudnnIndicesType_t *reduceTensorIndicesType);
cudnnStatus_t CUDNNWINAPI
cudnnDestroyReduceTensorDescriptor(cudnnReduceTensorDescriptor_t reduceTensorDesc);
/* Helper function to return the minimum size of the index space to be passed to the reduction given the input and
* output tensors */
cudnnStatus_t CUDNNWINAPI
cudnnGetReductionIndicesSize(cudnnHandle_t handle,
const cudnnReduceTensorDescriptor_t reduceTensorDesc,
const cudnnTensorDescriptor_t aDesc,
const cudnnTensorDescriptor_t cDesc,
size_t *sizeInBytes);
/* Helper function to return the minimum size of the workspace to be passed to the reduction given the input and output
* tensors */
cudnnStatus_t CUDNNWINAPI
cudnnGetReductionWorkspaceSize(cudnnHandle_t handle,
const cudnnReduceTensorDescriptor_t reduceTensorDesc,
const cudnnTensorDescriptor_t aDesc,
const cudnnTensorDescriptor_t cDesc,
size_t *sizeInBytes);
/* Tensor operation : C = reduce op( alpha * A ) + beta * C */
/* The NaN propagation enum applies to only the min and max reduce ops; the other reduce ops propagate NaN as usual. */
/* The indices space is ignored for reduce ops other than min or max. */
cudnnStatus_t CUDNNWINAPI
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);
/* Set all values of a tensor to a given value : y[i] = value[0] */
cudnnStatus_t CUDNNWINAPI
cudnnSetTensor(cudnnHandle_t handle, const cudnnTensorDescriptor_t yDesc, void *y, const void *valuePtr);
/* Scale all values of a tensor by a given factor : y[i] = alpha * y[i] */
cudnnStatus_t CUDNNWINAPI
cudnnScaleTensor(cudnnHandle_t handle, const cudnnTensorDescriptor_t yDesc, void *y, const void *alpha);
/* Create an instance of FilterStruct */
cudnnStatus_t CUDNNWINAPI
cudnnCreateFilterDescriptor(cudnnFilterDescriptor_t *filterDesc);
cudnnStatus_t CUDNNWINAPI
cudnnSetFilter4dDescriptor(cudnnFilterDescriptor_t filterDesc,
cudnnDataType_t dataType, /* image data type */
cudnnTensorFormat_t format,
int k, /* number of output feature maps */
int c, /* number of input feature maps */
int h, /* height of each input filter */
int w); /* width of each input filter */
cudnnStatus_t CUDNNWINAPI
cudnnGetFilter4dDescriptor(const cudnnFilterDescriptor_t filterDesc,
cudnnDataType_t *dataType, /* image data type */
cudnnTensorFormat_t *format,
int *k, /* number of output feature maps */
int *c, /* number of input feature maps */
int *h, /* height of each input filter */
int *w); /* width of each input filter */
cudnnStatus_t CUDNNWINAPI
cudnnSetFilterNdDescriptor(cudnnFilterDescriptor_t filterDesc,
cudnnDataType_t dataType, /* image data type */
cudnnTensorFormat_t format,
int nbDims,
const int filterDimA[]);
cudnnStatus_t CUDNNWINAPI
cudnnGetFilterNdDescriptor(const cudnnFilterDescriptor_t filterDesc,
int nbDimsRequested,
cudnnDataType_t *dataType, /* image data type */
cudnnTensorFormat_t *format,
int *nbDims,
int filterDimA[]);
cudnnStatus_t CUDNNWINAPI
cudnnGetFilterSizeInBytes(const cudnnFilterDescriptor_t filterDesc, size_t *size);
cudnnStatus_t CUDNNWINAPI
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);
cudnnStatus_t CUDNNWINAPI
cudnnDestroyFilterDescriptor(cudnnFilterDescriptor_t filterDesc);
/*
* softmax algorithm
*/
typedef enum {
CUDNN_SOFTMAX_FAST = 0, /* straightforward implementation */
CUDNN_SOFTMAX_ACCURATE = 1, /* subtract max from every point to avoid overflow */
CUDNN_SOFTMAX_LOG = 2
} cudnnSoftmaxAlgorithm_t;
typedef enum {
CUDNN_SOFTMAX_MODE_INSTANCE = 0, /* compute the softmax over all C, H, W for each N */
CUDNN_SOFTMAX_MODE_CHANNEL = 1 /* compute the softmax over all C for each H, W, N */
} cudnnSoftmaxMode_t;
/* Softmax functions: All of the form "output = alpha * Op(inputs) + beta * output" */
/* Function to perform forward softmax */
cudnnStatus_t CUDNNWINAPI
cudnnSoftmaxForward(cudnnHandle_t handle,
cudnnSoftmaxAlgorithm_t algo,
cudnnSoftmaxMode_t mode,
const void *alpha,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const void *beta,
const cudnnTensorDescriptor_t yDesc,
void *y);
/*
* pooling mode
*/
typedef enum {
CUDNN_POOLING_MAX = 0,
CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING = 1, /* count for average includes padded values */
CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING = 2, /* count for average does not include padded values */
CUDNN_POOLING_MAX_DETERMINISTIC = 3
} cudnnPoolingMode_t;
/* Create an instance of pooling descriptor */
cudnnStatus_t CUDNNWINAPI
cudnnCreatePoolingDescriptor(cudnnPoolingDescriptor_t *poolingDesc);
cudnnStatus_t CUDNNWINAPI
cudnnSetPooling2dDescriptor(cudnnPoolingDescriptor_t poolingDesc,
cudnnPoolingMode_t mode,
cudnnNanPropagation_t maxpoolingNanOpt,
int windowHeight,
int windowWidth,
int verticalPadding,
int horizontalPadding,
int verticalStride,
int horizontalStride);
cudnnStatus_t CUDNNWINAPI
cudnnGetPooling2dDescriptor(const cudnnPoolingDescriptor_t poolingDesc,
cudnnPoolingMode_t *mode,
cudnnNanPropagation_t *maxpoolingNanOpt,
int *windowHeight,
int *windowWidth,
int *verticalPadding,
int *horizontalPadding,
int *verticalStride,
int *horizontalStride);
cudnnStatus_t CUDNNWINAPI
cudnnSetPoolingNdDescriptor(cudnnPoolingDescriptor_t poolingDesc,
const cudnnPoolingMode_t mode,
const cudnnNanPropagation_t maxpoolingNanOpt,
int nbDims,
const int windowDimA[],
const int paddingA[],
const int strideA[]);
cudnnStatus_t CUDNNWINAPI
cudnnGetPoolingNdDescriptor(const cudnnPoolingDescriptor_t poolingDesc,
int nbDimsRequested,
cudnnPoolingMode_t *mode,
cudnnNanPropagation_t *maxpoolingNanOpt,
int *nbDims,
int windowDimA[],
int paddingA[],
int strideA[]);
cudnnStatus_t CUDNNWINAPI
cudnnGetPoolingNdForwardOutputDim(const cudnnPoolingDescriptor_t poolingDesc,
const cudnnTensorDescriptor_t inputTensorDesc,
int nbDims,
int outputTensorDimA[]);
cudnnStatus_t CUDNNWINAPI
cudnnGetPooling2dForwardOutputDim(const cudnnPoolingDescriptor_t poolingDesc,
const cudnnTensorDescriptor_t inputTensorDesc,
int *n,
int *c,
int *h,
int *w);
/* Destroy an instance of pooling descriptor */
cudnnStatus_t CUDNNWINAPI
cudnnDestroyPoolingDescriptor(cudnnPoolingDescriptor_t poolingDesc);
/* Pooling functions: All of the form "output = alpha * Op(inputs) + beta * output" */
/* Function to perform forward pooling */
cudnnStatus_t CUDNNWINAPI
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);
/*
* activation mode
*/
typedef enum {
CUDNN_ACTIVATION_SIGMOID = 0,
CUDNN_ACTIVATION_RELU = 1,
CUDNN_ACTIVATION_TANH = 2,
CUDNN_ACTIVATION_CLIPPED_RELU = 3,
CUDNN_ACTIVATION_ELU = 4,
CUDNN_ACTIVATION_IDENTITY = 5
} cudnnActivationMode_t;
/* Activation functions: All of the form "output = alpha * Op(inputs) + beta * output" */
cudnnStatus_t CUDNNWINAPI
cudnnCreateActivationDescriptor(cudnnActivationDescriptor_t *activationDesc);
cudnnStatus_t CUDNNWINAPI
cudnnSetActivationDescriptor(cudnnActivationDescriptor_t activationDesc,
cudnnActivationMode_t mode,
cudnnNanPropagation_t reluNanOpt,
double coef); /* ceiling for clipped RELU, alpha for ELU */
cudnnStatus_t CUDNNWINAPI
cudnnGetActivationDescriptor(const cudnnActivationDescriptor_t activationDesc,
cudnnActivationMode_t *mode,
cudnnNanPropagation_t *reluNanOpt,
double *coef); /* ceiling for clipped RELU, alpha for ELU */
cudnnStatus_t CUDNNWINAPI
cudnnDestroyActivationDescriptor(cudnnActivationDescriptor_t activationDesc);
/* Function to perform forward activation */
cudnnStatus_t CUDNNWINAPI
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);
/*
* Create an instance of LRN (Local Response Normalization) descriptor
* Uses lrnN=5, lrnAlpha=1e-4, lrnBeta=0.75, lrnK=2.0 as defaults from Krizhevsky'12 ImageNet paper
*/
cudnnStatus_t CUDNNWINAPI
cudnnCreateLRNDescriptor(cudnnLRNDescriptor_t *normDesc);
#define CUDNN_LRN_MIN_N 1 /* minimum allowed lrnN */
#define CUDNN_LRN_MAX_N 16 /* maximum allowed lrnN */
#define CUDNN_LRN_MIN_K 1e-5 /* minimum allowed lrnK */
#define CUDNN_LRN_MIN_BETA 0.01 /* minimum allowed lrnBeta */
/* LRN layer mode */
typedef enum {
CUDNN_LRN_CROSS_CHANNEL_DIM1 = 0, /* Normalize across tensor's dimA[1] dimension */
} cudnnLRNMode_t;
/*
* Uses a window [center-lookBehind, center+lookAhead], where
* lookBehind = floor( (lrnN-1)/2 ), lookAhead = lrnN-lookBehind-1.
* Values of double parameters cast to tensor data type.
*/
cudnnStatus_t CUDNNWINAPI
cudnnSetLRNDescriptor(cudnnLRNDescriptor_t normDesc, unsigned lrnN, double lrnAlpha, double lrnBeta, double lrnK);
/*
* Retrieve the settings currently stored in an LRN layer descriptor
* Any of the provided pointers can be NULL (no corresponding value will be returned)
*/
cudnnStatus_t CUDNNWINAPI
cudnnGetLRNDescriptor(cudnnLRNDescriptor_t normDesc, unsigned *lrnN, double *lrnAlpha, double *lrnBeta, double *lrnK);
/* Destroy an instance of LRN descriptor */
cudnnStatus_t CUDNNWINAPI
cudnnDestroyLRNDescriptor(cudnnLRNDescriptor_t lrnDesc);
/* LRN functions: output = alpha * normalize(x) + beta * old_y */
/* LRN cross-channel forward computation. Double parameters cast to tensor data type */
cudnnStatus_t CUDNNWINAPI
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);
typedef enum {
CUDNN_DIVNORM_PRECOMPUTED_MEANS = 0,
} cudnnDivNormMode_t;
/* LCN/divisive normalization functions: y = alpha * normalize(x) + beta * y */
cudnnStatus_t CUDNNWINAPI
cudnnDivisiveNormalizationForward(cudnnHandle_t handle,
cudnnLRNDescriptor_t normDesc,
cudnnDivNormMode_t mode,
const void *alpha,
const cudnnTensorDescriptor_t xDesc, /* same desc for means, temp, temp2 */
const void *x,
const void *means, /* if NULL, means are assumed to be zero */
void *temp,
void *temp2,
const void *beta,
const cudnnTensorDescriptor_t yDesc,
void *y);
typedef enum {
/* bnScale, bnBias tensor dims are 1xCxHxWx.. (one value per CHW...-slice, normalized over N slice) */
CUDNN_BATCHNORM_PER_ACTIVATION = 0,
/* bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors) */
CUDNN_BATCHNORM_SPATIAL = 1,
/*
* bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors).
* May be faster than CUDNN_BATCHNORM_SPATIAL but imposes some limits on the range of values
*/
CUDNN_BATCHNORM_SPATIAL_PERSISTENT = 2,
} cudnnBatchNormMode_t;
#define CUDNN_BN_MIN_EPSILON 0.0 /* Minimum epsilon allowed to be used in the Batch Normalization formula */
/*
* Derives a tensor descriptor from layer data descriptor for BatchNormalization
* scale, invVariance, bnBias, bnScale tensors. Use this tensor desc for
* bnScaleBiasMeanVarDesc and bnScaleBiasDiffDesc in Batch Normalization forward and backward functions.
*/
cudnnStatus_t CUDNNWINAPI
cudnnDeriveBNTensorDescriptor(cudnnTensorDescriptor_t derivedBnDesc,
const cudnnTensorDescriptor_t xDesc,
cudnnBatchNormMode_t mode);
typedef enum {
CUDNN_BATCHNORM_OPS_BN = 0, /* do batch normalization only */
CUDNN_BATCHNORM_OPS_BN_ACTIVATION = 1, /* do batchNorm, then activation */
CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION = 2, /* do batchNorm, then elemWiseAdd, then activation */
} cudnnBatchNormOps_t;
/*
* Performs Batch Normalization during Inference:
* y[i] = bnScale[k]*(x[i]-estimatedMean[k])/sqrt(epsilon+estimatedVariance[k]) + bnBias[k]
* with bnScale, bnBias, runningMean, runningInvVariance tensors indexed
* according to spatial or per-activation mode. Refer to cudnnBatchNormalizationForwardTraining
* above for notes on function arguments.
*/
cudnnStatus_t CUDNNWINAPI
cudnnBatchNormalizationForwardInference(cudnnHandle_t handle,
cudnnBatchNormMode_t mode,
const void *alpha, /* alpha[0] = result blend factor */
const void *beta, /* beta[0] = dest layer blend factor */
const cudnnTensorDescriptor_t xDesc,
const void *x, /* NxCxHxW */
const cudnnTensorDescriptor_t yDesc,
void *y, /* NxCxHxW */
const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
const void *bnScale,
const void *bnBias,
const void *estimatedMean,
const void *estimatedVariance,
double epsilon);
typedef enum {
/* bnScale, bnBias tensor dims are 1xCxHxWx.. (one value per CHW...-slice, normalized over N slice) */
CUDNN_NORM_PER_ACTIVATION = 0,
/* bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors) */
CUDNN_NORM_PER_CHANNEL = 1,
} cudnnNormMode_t;
typedef enum { CUDNN_NORM_ALGO_STANDARD = 0, CUDNN_NORM_ALGO_PERSIST = 1 } cudnnNormAlgo_t;
/*
* Derives a tensor descriptor from layer data descriptor for Normalization
* scale, invVariance, bnBias, bnScale tensors. Use this tensor desc for
* normScaleBiasMeanVarDesc and normScaleBiasDiffDesc in Normalization forward and backward functions.
*/
cudnnStatus_t CUDNNWINAPI
cudnnDeriveNormTensorDescriptor(cudnnTensorDescriptor_t derivedNormScaleBiasDesc,
cudnnTensorDescriptor_t derivedNormMeanVarDesc,
const cudnnTensorDescriptor_t xDesc,
cudnnNormMode_t mode,
int groupCnt); /* Place hold for future work, should be set to 1 now*/
typedef enum {
CUDNN_NORM_OPS_NORM = 0, /* do normalization only */
CUDNN_NORM_OPS_NORM_ACTIVATION = 1, /* do Norm, then activation */
CUDNN_NORM_OPS_NORM_ADD_ACTIVATION = 2, /* do Norm, then elemWiseAdd, then activation */
} cudnnNormOps_t;
/*
* Performs Normalization during Inference:
* y[i] = normScale[k]*(x[i]-estimatedMean[k])/sqrt(epsilon+estimatedVariance[k]) + normBias[k]
* with normScale, normBias, runningMean, runningInvVariance tensors indexed
* according to per-channel or per-activation mode. Refer to cudnnNormalizationForwardTraining
* above for notes on function arguments.
*/
cudnnStatus_t CUDNNWINAPI
cudnnNormalizationForwardInference(cudnnHandle_t handle,
cudnnNormMode_t mode,
cudnnNormOps_t normOps,
cudnnNormAlgo_t algo,
const void *alpha, /* alpha[0] = result blend factor */
const void *beta, /* beta[0] = dest layer blend factor */
const cudnnTensorDescriptor_t xDesc,
const void *x, /* NxCxHxW */
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, /* NxCxHxW */
double epsilon,
int groupCnt); /* Place hold for future work*/
/* APIs for spatial transformer network*/
typedef enum {
CUDNN_SAMPLER_BILINEAR = 0,
} cudnnSamplerType_t;
cudnnStatus_t CUDNNWINAPI
cudnnCreateSpatialTransformerDescriptor(cudnnSpatialTransformerDescriptor_t *stDesc);
cudnnStatus_t CUDNNWINAPI
cudnnSetSpatialTransformerNdDescriptor(cudnnSpatialTransformerDescriptor_t stDesc,
cudnnSamplerType_t samplerType,
cudnnDataType_t dataType,
const int nbDims,
const int dimA[]);
cudnnStatus_t CUDNNWINAPI
cudnnDestroySpatialTransformerDescriptor(cudnnSpatialTransformerDescriptor_t stDesc);
cudnnStatus_t CUDNNWINAPI
cudnnSpatialTfGridGeneratorForward(cudnnHandle_t handle,
const cudnnSpatialTransformerDescriptor_t stDesc,
const void *theta,
void *grid);
cudnnStatus_t CUDNNWINAPI
cudnnSpatialTfSamplerForward(cudnnHandle_t handle,
cudnnSpatialTransformerDescriptor_t stDesc,
const void *alpha,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const void *grid,
const void *beta,
cudnnTensorDescriptor_t yDesc,
void *y);
typedef struct cudnnDropoutStruct *cudnnDropoutDescriptor_t;
cudnnStatus_t CUDNNWINAPI
cudnnCreateDropoutDescriptor(cudnnDropoutDescriptor_t *dropoutDesc);
cudnnStatus_t CUDNNWINAPI
cudnnDestroyDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc);
/*helper function to determine size of the states to be passed to cudnnSetDropoutDescriptor */
cudnnStatus_t CUDNNWINAPI
cudnnDropoutGetStatesSize(cudnnHandle_t handle, size_t *sizeInBytes);
/*helper function to determine size of the reserve space to be passed to dropout forward/backward calls */
cudnnStatus_t CUDNNWINAPI
cudnnDropoutGetReserveSpaceSize(cudnnTensorDescriptor_t xdesc, size_t *sizeInBytes);
cudnnStatus_t CUDNNWINAPI
cudnnSetDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
cudnnHandle_t handle,
float dropout,
void *states,
size_t stateSizeInBytes,
unsigned long long seed);
/* Restores the dropout descriptor to a previously saved-off state */
cudnnStatus_t CUDNNWINAPI
cudnnRestoreDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
cudnnHandle_t handle,
float dropout,
void *states,
size_t stateSizeInBytes,
unsigned long long seed);
cudnnStatus_t CUDNNWINAPI
cudnnGetDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
cudnnHandle_t handle,
float *dropout,
void **states,
unsigned long long *seed);
cudnnStatus_t CUDNNWINAPI
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);
/* TODO: remove */
typedef struct cudnnAlgorithmStruct *cudnnAlgorithmDescriptor_t;
typedef struct cudnnAlgorithmPerformanceStruct *cudnnAlgorithmPerformance_t;
/* TODO: move these enums out to the appropriate submodule */
typedef enum {
CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM = 0,
CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM = 1,
CUDNN_CONVOLUTION_FWD_ALGO_GEMM = 2,
CUDNN_CONVOLUTION_FWD_ALGO_DIRECT = 3,
CUDNN_CONVOLUTION_FWD_ALGO_FFT = 4,
CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING = 5,
CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD = 6,
CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED = 7,
CUDNN_CONVOLUTION_FWD_ALGO_COUNT = 8
} cudnnConvolutionFwdAlgo_t;
typedef enum {
CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0 = 0, /* non-deterministic */
CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1 = 1,
CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT = 2,
CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3 = 3, /* non-deterministic */
CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD = 4, /* not implemented */
CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED = 5,
CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING = 6,
CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT = 7
} cudnnConvolutionBwdFilterAlgo_t;
typedef enum {
CUDNN_CONVOLUTION_BWD_DATA_ALGO_0 = 0, /* non-deterministic */
CUDNN_CONVOLUTION_BWD_DATA_ALGO_1 = 1,
CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT = 2,
CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING = 3,
CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD = 4,
CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED = 5,
CUDNN_CONVOLUTION_BWD_DATA_ALGO_COUNT = 6
} cudnnConvolutionBwdDataAlgo_t;
typedef enum {
CUDNN_RNN_ALGO_STANDARD = 0,
CUDNN_RNN_ALGO_PERSIST_STATIC = 1,
CUDNN_RNN_ALGO_PERSIST_DYNAMIC = 2,
CUDNN_RNN_ALGO_PERSIST_STATIC_SMALL_H = 3,
CUDNN_RNN_ALGO_COUNT = 4,
} cudnnRNNAlgo_t;
typedef enum { CUDNN_CTC_LOSS_ALGO_DETERMINISTIC = 0, CUDNN_CTC_LOSS_ALGO_NON_DETERMINISTIC = 1 } cudnnCTCLossAlgo_t;
/* TODO: remove */
typedef struct {
union Algorithm {
cudnnConvolutionFwdAlgo_t convFwdAlgo;
cudnnConvolutionBwdFilterAlgo_t convBwdFilterAlgo;
cudnnConvolutionBwdDataAlgo_t convBwdDataAlgo;
cudnnRNNAlgo_t RNNAlgo;
cudnnCTCLossAlgo_t CTCLossAlgo;
} algo;
} cudnnAlgorithm_t;
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnCreateAlgorithmDescriptor(cudnnAlgorithmDescriptor_t *algoDesc);
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnSetAlgorithmDescriptor(cudnnAlgorithmDescriptor_t algoDesc, cudnnAlgorithm_t algorithm);
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetAlgorithmDescriptor(const cudnnAlgorithmDescriptor_t algoDesc, cudnnAlgorithm_t *algorithm);
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnCopyAlgorithmDescriptor(const cudnnAlgorithmDescriptor_t src, cudnnAlgorithmDescriptor_t dest);
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnDestroyAlgorithmDescriptor(cudnnAlgorithmDescriptor_t algoDesc);
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnCreateAlgorithmPerformance(cudnnAlgorithmPerformance_t *algoPerf, int numberToCreate);
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnSetAlgorithmPerformance(cudnnAlgorithmPerformance_t algoPerf,
cudnnAlgorithmDescriptor_t algoDesc,
cudnnStatus_t status,
float time,
size_t memory);
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetAlgorithmPerformance(const cudnnAlgorithmPerformance_t algoPerf,
cudnnAlgorithmDescriptor_t *algoDesc,
cudnnStatus_t *status,
float *time,
size_t *memory);
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnDestroyAlgorithmPerformance(cudnnAlgorithmPerformance_t *algoPerf, int numberToDestroy);
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetAlgorithmSpaceSize(cudnnHandle_t handle, cudnnAlgorithmDescriptor_t algoDesc, size_t *algoSpaceSizeInBytes);
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnSaveAlgorithm(cudnnHandle_t handle,
cudnnAlgorithmDescriptor_t algoDesc,
void *algoSpace,
size_t algoSpaceSizeInBytes);
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnRestoreAlgorithm(cudnnHandle_t handle,
void *algoSpace,
size_t algoSpaceSizeInBytes,
cudnnAlgorithmDescriptor_t algoDesc);
typedef enum {
CUDNN_SEV_FATAL = 0,
CUDNN_SEV_ERROR = 1,
CUDNN_SEV_WARNING = 2,
CUDNN_SEV_INFO = 3,
} cudnnSeverity_t;
/* Message masks to be used with cudnnSetCallback() */
#define CUDNN_SEV_ERROR_EN (1U << CUDNN_SEV_ERROR)
#define CUDNN_SEV_WARNING_EN (1U << CUDNN_SEV_WARNING)
#define CUDNN_SEV_INFO_EN (1U << CUDNN_SEV_INFO)
/* struct containing useful informaiton for each API call */
typedef struct {
unsigned cudnn_version;
cudnnStatus_t cudnnStatus;
unsigned time_sec; /* epoch time in seconds */
unsigned time_usec; /* microseconds part of epoch time */
unsigned time_delta; /* time since start in seconds */
cudnnHandle_t handle; /* cudnn handle */
cudaStream_t stream; /* cuda stream ID */
unsigned long long pid; /* process ID */
unsigned long long tid; /* thread ID */
int cudaDeviceId; /* CUDA device ID */
int reserved[15]; /* reserved for future use */
} cudnnDebug_t;
typedef void (*cudnnCallback_t)(cudnnSeverity_t sev, void *udata, const cudnnDebug_t *dbg, const char *msg);
cudnnStatus_t CUDNNWINAPI
cudnnSetCallback(unsigned mask, void *udata, cudnnCallback_t fptr);
cudnnStatus_t CUDNNWINAPI
cudnnGetCallback(unsigned *mask, void **udata, cudnnCallback_t *fptr);
/*
* \brief Cross-library version checker.
* This function is implemented differently in each sub-library. Each sublib
* checks whether its own version matches that of its dependencies.
* \returns CUDNN_STATUS_SUCCESS if the version check passes,
* CUDNN_STATUS_VERSION_MISMATCH if the versions are inconsistent.
*/
cudnnStatus_t CUDNNWINAPI
cudnnOpsInferVersionCheck(void);
#if defined(__cplusplus)
}
#endif
#endif /* CUDNN_OPS_INFER_H_ */
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