/*
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*
* NOTICE TO LICENSEE:
*
* This source code and/or documentation ("Licensed Deliverables") are
* subject to NVIDIA intellectual property rights under U.S. and
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*
* These Licensed Deliverables contained herein is PROPRIETARY and
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* of the Licensed Deliverables to any third party without the express
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* Any use of the Licensed Deliverables in individual and commercial
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* comments to the code, the above Disclaimer and U.S. Government End
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*/
/*
* cudnn_ops_train : cuDNN's basic training operations and algorithms.
*/
#if !defined(CUDNN_OPS_TRAIN_H_)
#define CUDNN_OPS_TRAIN_H_
#include <cuda_runtime.h>
#include <stdint.h>
#include "cudnn_version.h"
#include "cudnn_ops_infer.h"
/* These version numbers are autogenerated, do not edit manually. */
#define CUDNN_OPS_TRAIN_MAJOR 8
#define CUDNN_OPS_TRAIN_MINOR 9
#define CUDNN_OPS_TRAIN_PATCH 2
#if (CUDNN_OPS_TRAIN_MAJOR != CUDNN_MAJOR) || (CUDNN_OPS_TRAIN_MINOR != CUDNN_MINOR) || \
(CUDNN_OPS_TRAIN_PATCH != CUDNN_PATCHLEVEL)
#error Version mismatch in cuDNN OPS TRAIN!!!
#endif
#if defined(__cplusplus)
extern "C" {
#endif
/* Function to perform backward softmax */
cudnnStatus_t CUDNNWINAPI
cudnnSoftmaxBackward(cudnnHandle_t handle,
cudnnSoftmaxAlgorithm_t algo,
cudnnSoftmaxMode_t mode,
const void *alpha,
const cudnnTensorDescriptor_t yDesc,
const void *y,
const cudnnTensorDescriptor_t dyDesc,
const void *dy,
const void *beta,
const cudnnTensorDescriptor_t dxDesc,
void *dx);
/* Function to perform backward pooling */
cudnnStatus_t CUDNNWINAPI
cudnnPoolingBackward(cudnnHandle_t handle,
const cudnnPoolingDescriptor_t poolingDesc,
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);
/* Function to perform backward activation */
cudnnStatus_t CUDNNWINAPI
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);
/* LRN cross-channel backward computation. Double parameters cast to tensor data type */
cudnnStatus_t CUDNNWINAPI
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);
cudnnStatus_t CUDNNWINAPI
cudnnDivisiveNormalizationBackward(cudnnHandle_t handle,
cudnnLRNDescriptor_t normDesc,
cudnnDivNormMode_t mode,
const void *alpha,
const cudnnTensorDescriptor_t xDesc, /* same desc for x, means, dy, temp, temp2 */
const void *x,
const void *means, /* if NULL, means are assumed to be zero */
const void *dy,
void *temp,
void *temp2,
const void *beta,
const cudnnTensorDescriptor_t dXdMeansDesc, /* same desc for dx, dMeans */
void *dx, /* output x differential */
void *dMeans); /* output means differential, can be NULL */
cudnnStatus_t CUDNNWINAPI
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);
cudnnStatus_t CUDNNWINAPI
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);
cudnnStatus_t CUDNNWINAPI
cudnnGetBatchNormalizationTrainingExReserveSpaceSize(cudnnHandle_t handle,
cudnnBatchNormMode_t mode,
cudnnBatchNormOps_t bnOps,
const cudnnActivationDescriptor_t activationDesc,
const cudnnTensorDescriptor_t xDesc,
size_t *sizeInBytes);
/* Computes y = BN(x). Also accumulates moving averages of mean and inverse variances */
cudnnStatus_t CUDNNWINAPI
cudnnBatchNormalizationForwardTraining(
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 */
/* Shared desc for the next 6 tensors in the argument list.
Data type to be set as follows:
type = (typeOf(x) == double) ? double : float
Dimensions for this descriptor depend on normalization mode
- Spatial Normalization : tensors are expected to have dims 1xCx1x1
(normalization is performed across NxHxW)
- Per-Activation Normalization : tensors are expected to have dims of 1xCxHxW
(normalization is performed across N) */
const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
/* 'Gamma' and 'Beta' respectively in Ioffe and Szegedy's paper's notation */
const void *bnScale,
const void *bnBias,
/* MUST use factor=1 in the very first call of a complete training cycle.
Use a factor=1/(1+n) at N-th call to the function to get
Cumulative Moving Average (CMA) behavior
CMA[n] = (x[1]+...+x[n])/n
Since 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]*(1-1/(n+1)) + x[n+1]*1/(n+1) */
double exponentialAverageFactor,
/* Used in Training phase only.
runningMean = newMean*factor + runningMean*(1-factor) */
void *resultRunningMean,
/* Output in training mode, input in inference. Is the moving average
of variance[x] (factor is applied in the same way as for runningMean) */
void *resultRunningVariance,
/* Has to be >= CUDNN_BN_MIN_EPSILON. Should be the same in forward and backward functions. */
double epsilon,
/* Optionally save intermediate results from the forward pass here
- can be reused to speed up backward pass. NULL if unused */
void *resultSaveMean,
void *resultSaveInvVariance);
/* Computes y = relu(BN(x) + z). Also accumulates moving averages of mean and inverse variances */
cudnnStatus_t CUDNNWINAPI
cudnnBatchNormalizationForwardTrainingEx(
cudnnHandle_t handle,
cudnnBatchNormMode_t mode,
cudnnBatchNormOps_t bnOps,
const void *alpha, /* alpha[0] = result blend factor */
const void *beta, /* beta[0] = dest layer blend factor */
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 *bnScale,
const void *bnBias,
double exponentialAverageFactor,
void *resultRunningMean,
void *resultRunningVariance,
/* Has to be >= CUDNN_BN_MIN_EPSILON. Should be the same in forward and backward functions. */
double epsilon,
/* Optionally save intermediate results from the forward pass here
- can be reused to speed up backward pass. NULL if unused */
void *resultSaveMean,
void *resultSaveInvVariance,
cudnnActivationDescriptor_t activationDesc,
void *workspace,
size_t workSpaceSizeInBytes,
void *reserveSpace,
size_t reserveSpaceSizeInBytes);
/* Performs backward pass of Batch Normalization layer. Returns x gradient,
* bnScale gradient and bnBias gradient */
cudnnStatus_t CUDNNWINAPI
cudnnBatchNormalizationBackward(cudnnHandle_t handle,
cudnnBatchNormMode_t mode,
const void *alphaDataDiff,
const void *betaDataDiff,
const void *alphaParamDiff,
const void *betaParamDiff,
const cudnnTensorDescriptor_t xDesc, /* same desc for x, dx, dy */
const void *x,
const cudnnTensorDescriptor_t dyDesc,
const void *dy,
const cudnnTensorDescriptor_t dxDesc,
void *dx,
/* Shared tensor desc for the 4 tensors below */
const cudnnTensorDescriptor_t dBnScaleBiasDesc,
const void *bnScale, /* bnBias doesn't affect backpropagation */
/* scale and bias diff are not backpropagated below this layer */
void *dBnScaleResult,
void *dBnBiasResult,
/* Same epsilon as forward pass */
double epsilon,
/* Optionally cached intermediate results from
forward pass */
const void *savedMean,
const void *savedInvVariance);
cudnnStatus_t CUDNNWINAPI
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,
/* Shared tensor desc for the 4 tensors below */
const cudnnTensorDescriptor_t dBnScaleBiasDesc,
const void *bnScaleData,
const void *bnBiasData, /* needed if there is activation */
void *dBnScaleData,
void *dBnBiasData,
double epsilon, /* Same epsilon as forward pass */
/* Optionally cached intermediate results from
forward pass */
const void *savedMean,
const void *savedInvVariance,
cudnnActivationDescriptor_t activationDesc,
void *workSpace,
size_t workSpaceSizeInBytes,
void *reserveSpace,
size_t reserveSpaceSizeInBytes);
cudnnStatus_t CUDNNWINAPI
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); /* Place hold for future work, should be set to 1 now*/
cudnnStatus_t CUDNNWINAPI
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); /* Place hold for future work, should be set to 1 now*/
cudnnStatus_t CUDNNWINAPI
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); /* Place hold for future work, should be set to 1 now*/
/* Computes y = relu(Norm(x) + z). Also accumulates moving averages of mean and inverse variances */
cudnnStatus_t CUDNNWINAPI
cudnnNormalizationForwardTraining(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 *xData,
const cudnnTensorDescriptor_t normScaleBiasDesc,
const void *normScale,
const void *normBias,
double exponentialAverageFactor,
const cudnnTensorDescriptor_t normMeanVarDesc,
void *resultRunningMean,
void *resultRunningVariance,
/* Has to be >= 0. Should be the same in forward and backward functions. */
double epsilon,
/* Optionally save intermediate results from the forward pass here
- can be reused to speed up backward pass. NULL if unused */
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); /* Place hold for future work, should be set to 1 now*/
cudnnStatus_t CUDNNWINAPI
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,
/* Shared tensor desc for the 4 tensors below */
const cudnnTensorDescriptor_t dNormScaleBiasDesc,
const void *normScaleData,
const void *normBiasData, /* needed if there is activation */
void *dNormScaleData,
void *dNormBiasData,
double epsilon, /* Same epsilon as forward pass */
const cudnnTensorDescriptor_t normMeanVarDesc,
/* Optionally cached intermediate results from
forward pass */
const void *savedMean,
const void *savedInvVariance,
cudnnActivationDescriptor_t activationDesc,
void *workSpace,
size_t workSpaceSizeInBytes,
void *reserveSpace,
size_t reserveSpaceSizeInBytes,
int groupCnt); /* Place hold for future work, should be set to 1 now*/
cudnnStatus_t CUDNNWINAPI
cudnnSpatialTfGridGeneratorBackward(cudnnHandle_t handle,
const cudnnSpatialTransformerDescriptor_t stDesc,
const void *dgrid,
void *dtheta);
cudnnStatus_t CUDNNWINAPI
cudnnSpatialTfSamplerBackward(cudnnHandle_t handle,
cudnnSpatialTransformerDescriptor_t stDesc,
const void *alpha,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const void *beta,
const cudnnTensorDescriptor_t dxDesc,
void *dx,
const void *alphaDgrid,
const cudnnTensorDescriptor_t dyDesc,
const void *dy,
const void *grid,
const void *betaDgrid,
void *dgrid);
cudnnStatus_t CUDNNWINAPI
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);
/*
* \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
cudnnOpsTrainVersionCheck(void);
#if defined(__cplusplus)
}
#endif
#endif /* CUDNN_OPS_TRAIN_H_ */