502 lines
25 KiB
C
502 lines
25 KiB
C
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/*
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* Copyright 1993-2020 NVIDIA Corporation. All rights reserved.
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*
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* NOTICE TO LICENSEE:
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*
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* This source code and/or documentation ("Licensed Deliverables") are
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* subject to NVIDIA intellectual property rights under U.S. and
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* international Copyright laws.
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*
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* These Licensed Deliverables contained herein is PROPRIETARY and
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* CONFIDENTIAL to NVIDIA and is being provided under the terms and
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* conditions of a form of NVIDIA software license agreement by and
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* between NVIDIA and Licensee ("License Agreement") or electronically
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* accepted by Licensee. Notwithstanding any terms or conditions to
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* the contrary in the License Agreement, reproduction or disclosure
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* of the Licensed Deliverables to any third party without the express
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* written consent of NVIDIA is prohibited.
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*
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* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
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* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
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* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
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* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
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* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
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* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
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* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
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* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
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* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
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* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
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* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
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* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
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* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
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* OF THESE LICENSED DELIVERABLES.
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*
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* U.S. Government End Users. These Licensed Deliverables are a
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* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
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* 1995), consisting of "commercial computer software" and "commercial
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* computer software documentation" as such terms are used in 48
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* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
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* only as a commercial end item. Consistent with 48 C.F.R.12.212 and
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* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
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* U.S. Government End Users acquire the Licensed Deliverables with
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* only those rights set forth herein.
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*
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* Any use of the Licensed Deliverables in individual and commercial
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* software must include, in the user documentation and internal
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* comments to the code, the above Disclaimer and U.S. Government End
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* Users Notice.
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*/
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/*
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* cudnn_ops_train : cuDNN's basic training operations and algorithms.
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*/
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#if !defined(CUDNN_OPS_TRAIN_H_)
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#define CUDNN_OPS_TRAIN_H_
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#include <cuda_runtime.h>
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#include <stdint.h>
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#include "cudnn_version.h"
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#include "cudnn_ops_infer.h"
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/* These version numbers are autogenerated, do not edit manually. */
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#define CUDNN_OPS_TRAIN_MAJOR 8
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#define CUDNN_OPS_TRAIN_MINOR 1
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#define CUDNN_OPS_TRAIN_PATCH 0
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#if (CUDNN_OPS_TRAIN_MAJOR != CUDNN_MAJOR) || (CUDNN_OPS_TRAIN_MINOR != CUDNN_MINOR) || \
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(CUDNN_OPS_TRAIN_PATCH != CUDNN_PATCHLEVEL)
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#error Version mismatch in cuDNN OPS TRAIN!!!
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#endif
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#if defined(__cplusplus)
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extern "C" {
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#endif
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/* Function to perform backward softmax */
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cudnnStatus_t CUDNNWINAPI
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cudnnSoftmaxBackward(cudnnHandle_t handle,
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cudnnSoftmaxAlgorithm_t algo,
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cudnnSoftmaxMode_t mode,
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const void *alpha,
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const cudnnTensorDescriptor_t yDesc,
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const void *y,
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const cudnnTensorDescriptor_t dyDesc,
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const void *dy,
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const void *beta,
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const cudnnTensorDescriptor_t dxDesc,
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void *dx);
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/* Function to perform backward pooling */
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cudnnStatus_t CUDNNWINAPI
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cudnnPoolingBackward(cudnnHandle_t handle,
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const cudnnPoolingDescriptor_t poolingDesc,
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const void *alpha,
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const cudnnTensorDescriptor_t yDesc,
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const void *y,
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const cudnnTensorDescriptor_t dyDesc,
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const void *dy,
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const cudnnTensorDescriptor_t xDesc,
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const void *x,
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const void *beta,
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const cudnnTensorDescriptor_t dxDesc,
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void *dx);
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/* Function to perform backward activation */
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cudnnStatus_t CUDNNWINAPI
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cudnnActivationBackward(cudnnHandle_t handle,
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cudnnActivationDescriptor_t activationDesc,
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const void *alpha,
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const cudnnTensorDescriptor_t yDesc,
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const void *y,
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const cudnnTensorDescriptor_t dyDesc,
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const void *dy,
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const cudnnTensorDescriptor_t xDesc,
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const void *x,
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const void *beta,
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const cudnnTensorDescriptor_t dxDesc,
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void *dx);
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/* LRN cross-channel backward computation. Double parameters cast to tensor data type */
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cudnnStatus_t CUDNNWINAPI
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cudnnLRNCrossChannelBackward(cudnnHandle_t handle,
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cudnnLRNDescriptor_t normDesc,
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cudnnLRNMode_t lrnMode,
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const void *alpha,
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const cudnnTensorDescriptor_t yDesc,
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const void *y,
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const cudnnTensorDescriptor_t dyDesc,
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const void *dy,
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const cudnnTensorDescriptor_t xDesc,
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const void *x,
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const void *beta,
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const cudnnTensorDescriptor_t dxDesc,
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void *dx);
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cudnnStatus_t CUDNNWINAPI
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cudnnDivisiveNormalizationBackward(cudnnHandle_t handle,
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cudnnLRNDescriptor_t normDesc,
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cudnnDivNormMode_t mode,
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const void *alpha,
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const cudnnTensorDescriptor_t xDesc, /* same desc for x, means, dy, temp, temp2 */
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const void *x,
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const void *means, /* if NULL, means are assumed to be zero */
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const void *dy,
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void *temp,
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void *temp2,
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const void *beta,
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const cudnnTensorDescriptor_t dXdMeansDesc, /* same desc for dx, dMeans */
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void *dx, /* output x differential */
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void *dMeans); /* output means differential, can be NULL */
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cudnnStatus_t CUDNNWINAPI
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cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize(cudnnHandle_t handle,
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cudnnBatchNormMode_t mode,
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cudnnBatchNormOps_t bnOps,
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const cudnnTensorDescriptor_t xDesc,
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const cudnnTensorDescriptor_t zDesc,
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const cudnnTensorDescriptor_t yDesc,
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const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
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const cudnnActivationDescriptor_t activationDesc,
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size_t *sizeInBytes);
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cudnnStatus_t CUDNNWINAPI
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cudnnGetBatchNormalizationBackwardExWorkspaceSize(cudnnHandle_t handle,
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cudnnBatchNormMode_t mode,
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cudnnBatchNormOps_t bnOps,
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const cudnnTensorDescriptor_t xDesc,
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const cudnnTensorDescriptor_t yDesc,
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const cudnnTensorDescriptor_t dyDesc,
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const cudnnTensorDescriptor_t dzDesc,
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const cudnnTensorDescriptor_t dxDesc,
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const cudnnTensorDescriptor_t dBnScaleBiasDesc,
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const cudnnActivationDescriptor_t activationDesc,
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size_t *sizeInBytes);
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cudnnStatus_t CUDNNWINAPI
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cudnnGetBatchNormalizationTrainingExReserveSpaceSize(cudnnHandle_t handle,
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cudnnBatchNormMode_t mode,
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cudnnBatchNormOps_t bnOps,
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const cudnnActivationDescriptor_t activationDesc,
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const cudnnTensorDescriptor_t xDesc,
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size_t *sizeInBytes);
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/* Computes y = BN(x). Also accumulates moving averages of mean and inverse variances */
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cudnnStatus_t CUDNNWINAPI
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cudnnBatchNormalizationForwardTraining(
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cudnnHandle_t handle,
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cudnnBatchNormMode_t mode,
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const void *alpha, /* alpha[0] = result blend factor */
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const void *beta, /* beta[0] = dest layer blend factor */
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const cudnnTensorDescriptor_t xDesc,
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const void *x, /* NxCxHxW */
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const cudnnTensorDescriptor_t yDesc,
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void *y, /* NxCxHxW */
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/* Shared desc for the next 6 tensors in the argument list.
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Data type to be set as follows:
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type = (typeOf(x) == double) ? double : float
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Dimensions for this descriptor depend on normalization mode
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- Spatial Normalization : tensors are expected to have dims 1xCx1x1
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(normalization is performed across NxHxW)
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- Per-Activation Normalization : tensors are expected to have dims of 1xCxHxW
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(normalization is performed across N) */
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const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
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/* 'Gamma' and 'Beta' respectively in Ioffe and Szegedy's paper's notation */
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const void *bnScale,
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const void *bnBias,
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/* MUST use factor=1 in the very first call of a complete training cycle.
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Use a factor=1/(1+n) at N-th call to the function to get
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Cumulative Moving Average (CMA) behavior
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CMA[n] = (x[1]+...+x[n])/n
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Since CMA[n+1] = (n*CMA[n]+x[n+1])/(n+1) =
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((n+1)*CMA[n]-CMA[n])/(n+1) + x[n+1]/(n+1) =
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CMA[n]*(1-1/(n+1)) + x[n+1]*1/(n+1) */
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double exponentialAverageFactor,
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/* Used in Training phase only.
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runningMean = newMean*factor + runningMean*(1-factor) */
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void *resultRunningMean,
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/* Output in training mode, input in inference. Is the moving average
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of variance[x] (factor is applied in the same way as for runningMean) */
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void *resultRunningVariance,
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/* Has to be >= CUDNN_BN_MIN_EPSILON. Should be the same in forward and backward functions. */
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double epsilon,
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/* Optionally save intermediate results from the forward pass here
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- can be reused to speed up backward pass. NULL if unused */
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void *resultSaveMean,
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void *resultSaveInvVariance);
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/* Computes y = relu(BN(x) + z). Also accumulates moving averages of mean and inverse variances */
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cudnnStatus_t CUDNNWINAPI
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cudnnBatchNormalizationForwardTrainingEx(
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cudnnHandle_t handle,
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cudnnBatchNormMode_t mode,
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cudnnBatchNormOps_t bnOps,
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const void *alpha, /* alpha[0] = result blend factor */
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const void *beta, /* beta[0] = dest layer blend factor */
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const cudnnTensorDescriptor_t xDesc,
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const void *xData,
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const cudnnTensorDescriptor_t zDesc,
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const void *zData,
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const cudnnTensorDescriptor_t yDesc,
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void *yData,
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const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
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const void *bnScale,
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const void *bnBias,
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double exponentialAverageFactor,
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void *resultRunningMean,
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void *resultRunningVariance,
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/* Has to be >= CUDNN_BN_MIN_EPSILON. Should be the same in forward and backward functions. */
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double epsilon,
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/* Optionally save intermediate results from the forward pass here
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- can be reused to speed up backward pass. NULL if unused */
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void *resultSaveMean,
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void *resultSaveInvVariance,
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cudnnActivationDescriptor_t activationDesc,
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void *workspace,
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size_t workSpaceSizeInBytes,
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void *reserveSpace,
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size_t reserveSpaceSizeInBytes);
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/* Performs backward pass of Batch Normalization layer. Returns x gradient,
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* bnScale gradient and bnBias gradient */
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cudnnStatus_t CUDNNWINAPI
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cudnnBatchNormalizationBackward(cudnnHandle_t handle,
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cudnnBatchNormMode_t mode,
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const void *alphaDataDiff,
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const void *betaDataDiff,
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const void *alphaParamDiff,
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const void *betaParamDiff,
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const cudnnTensorDescriptor_t xDesc, /* same desc for x, dx, dy */
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const void *x,
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const cudnnTensorDescriptor_t dyDesc,
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const void *dy,
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const cudnnTensorDescriptor_t dxDesc,
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void *dx,
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/* Shared tensor desc for the 4 tensors below */
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const cudnnTensorDescriptor_t dBnScaleBiasDesc,
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const void *bnScale, /* bnBias doesn't affect backpropagation */
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/* scale and bias diff are not backpropagated below this layer */
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void *dBnScaleResult,
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void *dBnBiasResult,
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/* Same epsilon as forward pass */
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double epsilon,
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/* Optionally cached intermediate results from
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forward pass */
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const void *savedMean,
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const void *savedInvVariance);
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cudnnStatus_t CUDNNWINAPI
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cudnnBatchNormalizationBackwardEx(cudnnHandle_t handle,
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cudnnBatchNormMode_t mode,
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cudnnBatchNormOps_t bnOps,
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const void *alphaDataDiff,
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const void *betaDataDiff,
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const void *alphaParamDiff,
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const void *betaParamDiff,
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const cudnnTensorDescriptor_t xDesc,
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const void *xData,
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const cudnnTensorDescriptor_t yDesc,
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const void *yData,
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const cudnnTensorDescriptor_t dyDesc,
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const void *dyData,
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const cudnnTensorDescriptor_t dzDesc,
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void *dzData,
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const cudnnTensorDescriptor_t dxDesc,
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void *dxData,
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/* Shared tensor desc for the 4 tensors below */
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const cudnnTensorDescriptor_t dBnScaleBiasDesc,
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const void *bnScaleData,
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const void *bnBiasData, /* needed if there is activation */
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void *dBnScaleData,
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void *dBnBiasData,
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double epsilon, /* Same epsilon as forward pass */
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/* Optionally cached intermediate results from
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forward pass */
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const void *savedMean,
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const void *savedInvVariance,
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cudnnActivationDescriptor_t activationDesc,
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void *workSpace,
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size_t workSpaceSizeInBytes,
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void *reserveSpace,
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size_t reserveSpaceSizeInBytes);
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cudnnStatus_t CUDNNWINAPI
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cudnnGetNormalizationForwardTrainingWorkspaceSize(cudnnHandle_t handle,
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cudnnNormMode_t mode,
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cudnnNormOps_t normOps,
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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_ */
|