Convolutions

Classes | Enumerations | Functions

Convolutions

Classes
struct	miopenConvAlgoPerf_t
	Perf struct for forward, backward filter, or backward data algorithms. More...
struct	miopenConvSolution_t
	Performance struct for forward, backward filter, or backward data algorithms in immediate mode. More...

Enumerations
enum	miopenConvFwdAlgorithm_t {   miopenConvolutionFwdAlgoGEMM = 0 ,   miopenConvolutionFwdAlgoDirect = 1 ,   miopenConvolutionFwdAlgoFFT = 2 ,   miopenConvolutionFwdAlgoWinograd = 3 ,   miopenConvolutionFwdAlgoImplicitGEMM = 5 }
enum	miopenConvBwdWeightsAlgorithm_t {   miopenConvolutionBwdWeightsAlgoGEMM = 0 ,   miopenConvolutionBwdWeightsAlgoDirect = 1 ,   miopenConvolutionBwdWeightsAlgoWinograd = 3 ,   miopenConvolutionBwdWeightsAlgoImplicitGEMM = 5 }
enum	miopenConvBwdDataAlgorithm_t {   miopenConvolutionBwdDataAlgoGEMM = 0 ,   miopenConvolutionBwdDataAlgoDirect = 1 ,   miopenConvolutionBwdDataAlgoFFT = 2 ,   miopenConvolutionBwdDataAlgoWinograd = 3 ,   miopenTransposeBwdDataAlgoGEMM ,   miopenConvolutionBwdDataAlgoImplicitGEMM = 5 }
enum	miopenConvAlgorithm_t {   miopenConvolutionAlgoGEMM = 0 ,   miopenConvolutionAlgoDirect = 1 ,   miopenConvolutionAlgoFFT = 2 ,   miopenConvolutionAlgoWinograd = 3 ,   miopenConvolutionAlgoImplicitGEMM = 5 }
enum	miopenConvolutionMode_t {   miopenConvolution = 0 ,   miopenTranspose = 1 ,   miopenGroupConv = 2 ,   miopenDepthwise = 3 }
enum	miopenConvolutionAttrib_t {   MIOPEN_CONVOLUTION_ATTRIB_FP16_ALT_IMPL ,   MIOPEN_CONVOLUTION_ATTRIB_DETERMINISTIC ,   MIOPEN_CONVOLUTION_ATTRIB_FP8_ROUNDING_MODE }

Functions
	MIOPEN_DECLARE_OBJECT (miopenConvolutionDescriptor)
	Creates the miopenConvolutionDescriptor_t type. More...
miopenStatus_t	miopenCreateConvolutionDescriptor (miopenConvolutionDescriptor_t *convDesc)
	Creates a convolution layer descriptor. More...
miopenStatus_t	miopenInitConvolutionDescriptor (miopenConvolutionDescriptor_t convDesc, miopenConvolutionMode_t c_mode, int pad_h, int pad_w, int stride_h, int stride_w, int dilation_h, int dilation_w)
	Creates a 2-D convolution layer descriptor. More...
miopenStatus_t	miopenInitConvolutionNdDescriptor (miopenConvolutionDescriptor_t convDesc, int spatialDim, const int *padA, const int *strideA, const int *dilationA, miopenConvolutionMode_t c_mode)
	Creates a N-dimensional convolution layer descriptor. More...
miopenStatus_t	miopenGetConvolutionSpatialDim (miopenConvolutionDescriptor_t convDesc, int *spatialDim)
	Retrieves the spatial dimension of a convolution layer descriptor. More...
miopenStatus_t	miopenGetConvolutionDescriptor (miopenConvolutionDescriptor_t convDesc, miopenConvolutionMode_t *c_mode, int *pad_h, int *pad_w, int *stride_h, int *stride_w, int *dilation_h, int *dilation_w)
	Retrieves a 2-D convolution layer descriptor's details. More...
miopenStatus_t	miopenGetConvolutionNdDescriptor (miopenConvolutionDescriptor_t convDesc, int requestedSpatialDim, int *spatialDim, int *padA, int *strideA, int *dilationA, miopenConvolutionMode_t *c_mode)
	Retrieves a N-dimensional convolution layer descriptor's details. More...
miopenStatus_t	miopenGetConvolutionGroupCount (miopenConvolutionDescriptor_t convDesc, int *groupCount)
	Get the number of groups to be used in Group/Depthwise convolution. More...
miopenStatus_t	miopenSetConvolutionGroupCount (miopenConvolutionDescriptor_t convDesc, int groupCount)
	Set the number of groups to be used in Group/Depthwise convolution. More...
miopenStatus_t	miopenSetTransposeConvOutputPadding (miopenConvolutionDescriptor_t convDesc, int adj_h, int adj_w)
	Set the output padding to be used in 2-D Transpose convolution. More...
miopenStatus_t	miopenSetTransposeConvNdOutputPadding (miopenConvolutionDescriptor_t convDesc, int spatialDim, const int *adjA)
	Set the output padding to be used in N-dimensional Transpose convolution. More...
miopenStatus_t	miopenGetConvolutionForwardOutputDim (miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t inputTensorDesc, const miopenTensorDescriptor_t filterDesc, int *n, int *c, int *h, int *w)
	Get the shape of a resulting 4-D tensor from a 2-D convolution. More...
miopenStatus_t	miopenGetConvolutionNdForwardOutputDim (miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t inputTensorDesc, const miopenTensorDescriptor_t filterDesc, int *nDim, int *outputTensorDimA)
	Get the shape of a resulting N-dimensional tensor from a (N-2)-dimensional convolution. More...
miopenStatus_t	miopenDestroyConvolutionDescriptor (miopenConvolutionDescriptor_t convDesc)
	Destroys the tensor descriptor object. More...
miopenStatus_t	miopenSetConvolutionAttribute (miopenConvolutionDescriptor_t convDesc, const miopenConvolutionAttrib_t attr, int value)
	Set the attribute of the convolution descriptor. More...
miopenStatus_t	miopenGetConvolutionAttribute (miopenConvolutionDescriptor_t convDesc, const miopenConvolutionAttrib_t attr, int *value)
	Get the attribute of the convolution descriptor. More...
miopenStatus_t	miopenConvolutionForwardGetSolutionCount (miopenHandle_t handle, const miopenTensorDescriptor_t wDesc, const miopenTensorDescriptor_t xDesc, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t yDesc, size_t *solutionCount)
	Query the maximum number of solutions applicable for the given input/output and weights tensor descriptor for Convolution in the Forward direction. More...
miopenStatus_t	miopenConvolutionForwardGetSolution (miopenHandle_t handle, const miopenTensorDescriptor_t wDesc, const miopenTensorDescriptor_t xDesc, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t yDesc, const size_t maxSolutionCount, size_t *solutionCount, miopenConvSolution_t *solutions)
	Query the applicable solutions for a convolution configuration described by input, output and convolution descriptors. More...
miopenStatus_t	miopenConvolutionForwardGetSolutionWorkspaceSize (miopenHandle_t handle, const miopenTensorDescriptor_t wDesc, const miopenTensorDescriptor_t xDesc, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t yDesc, const uint64_t solution_id, size_t *workSpaceSize)
	Returns the workspace size required for a particular solution id. More...
miopenStatus_t	miopenConvolutionForwardCompileSolution (miopenHandle_t handle, const miopenTensorDescriptor_t wDesc, const miopenTensorDescriptor_t xDesc, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t yDesc, const uint64_t solution_id)
	Compiles the solution provided by the user, this solution may be acquired by the miopenConvolutionForwardGetSolution API call above. Compiling the solution ensures that the first API call to miopenConvolutionForwardImmediate does not cause a compile. More...
miopenStatus_t	miopenConvolutionForwardImmediate (miopenHandle_t handle, const miopenTensorDescriptor_t wDesc, const void *w, const miopenTensorDescriptor_t xDesc, const void *x, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t yDesc, void *y, void *workSpace, size_t workSpaceSize, const uint64_t solution_id)
	Executes the Forward convolution operation based on the provided solution ID. More...
miopenStatus_t	miopenConvolutionBackwardDataGetSolutionCount (miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t wDesc, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t dxDesc, size_t *solutionCount)
	Query the maximum number of solutions applicable for the given input/output and weights tensor descriptor for backward Convolution w-r-t Data. More...
miopenStatus_t	miopenConvolutionBackwardDataGetSolution (miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t wDesc, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t dxDesc, const size_t maxSolutionCount, size_t *solutionCount, miopenConvSolution_t *solutions)
	Query the applicable solutions for a backward convolution w-r-t data as described by input, output and convolution descriptors. More...
miopenStatus_t	miopenConvolutionBackwardDataGetSolutionWorkspaceSize (miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t wDesc, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t dxDesc, const uint64_t solution_id, size_t *workSpaceSize)
	Returns the workspace size required for a particular solution id. More...
miopenStatus_t	miopenConvolutionBackwardDataCompileSolution (miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t wDesc, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t dxDesc, const uint64_t solution_id)
	Compiles the solution provided by the user, this solution may be acquired by the miopenConvolutionBackwardDataGetSolution API call above. Compiling the solution ensures that the first API call to miopenConvolutionBackwardDataImmediate does not cause a compile. More...
miopenStatus_t	miopenConvolutionBackwardDataImmediate (miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const void *dy, const miopenTensorDescriptor_t wDesc, const void *w, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t dxDesc, void *dx, void *workSpace, size_t workSpaceSize, const uint64_t solution_id)
	Executes the Backward convolution w-r-t data operation based on the provided solution ID. More...
miopenStatus_t	miopenConvolutionBackwardWeightsGetSolutionCount (miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t xDesc, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t dwDesc, size_t *solutionCount)
	Query the maximum number of solutions applicable for the given input/output and weights tensor descriptor for backward Convolution w-r-t Weights. More...
miopenStatus_t	miopenConvolutionBackwardWeightsGetSolution (miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t xDesc, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t dwDesc, const size_t maxSolutionCount, size_t *solutionCount, miopenConvSolution_t *solutions)
	Query the applicable solutions for a backward convolution w-r-t weights as described by input, output and convolution descriptors. More...
miopenStatus_t	miopenConvolutionBackwardWeightsGetSolutionWorkspaceSize (miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t xDesc, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t dwDesc, const uint64_t solution_id, size_t *workSpaceSize)
	Returns the workspace size required for a particular solution id. More...
miopenStatus_t	miopenConvolutionBackwardWeightsCompileSolution (miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t xDesc, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t dwDesc, const uint64_t solution_id)
	Compiles the solution provided by the user, this solution may be acquired by the miopenConvolutionBackwardWeightsGetSolution API call above. Compiling the solution ensures that the first API call to miopenConvolutionBackwardWeightsImmediate does not cause a compile. More...
miopenStatus_t	miopenConvolutionBackwardWeightsImmediate (miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const void *dy, const miopenTensorDescriptor_t xDesc, const void *x, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t dwDesc, void *dw, void *workSpace, size_t workSpaceSize, const uint64_t solution_id)
	Executes the Backward convolution w-r-t weights operation based on the provided solution ID. More...
miopenStatus_t	miopenConvolutionForwardGetWorkSpaceSize (miopenHandle_t handle, const miopenTensorDescriptor_t wDesc, const miopenTensorDescriptor_t xDesc, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t yDesc, size_t *workSpaceSize)
	Query the workspace size required for a forward convolution algorithm. More...
miopenStatus_t	miopenFindConvolutionForwardAlgorithm (miopenHandle_t handle, const miopenTensorDescriptor_t xDesc, const void *x, const miopenTensorDescriptor_t wDesc, const void *w, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t yDesc, void *y, const int requestAlgoCount, int *returnedAlgoCount, miopenConvAlgoPerf_t *perfResults, void *workSpace, size_t workSpaceSize, bool exhaustiveSearch)
	Search and run the forward convolutional algorithms and return a list of kernel times. More...
miopenStatus_t	miopenConvolutionForward (miopenHandle_t handle, const void *alpha, const miopenTensorDescriptor_t xDesc, const void *x, const miopenTensorDescriptor_t wDesc, const void *w, const miopenConvolutionDescriptor_t convDesc, miopenConvFwdAlgorithm_t algo, const void *beta, const miopenTensorDescriptor_t yDesc, void *y, void *workSpace, size_t workSpaceSize)
	Execute a forward convolution layer. More...
miopenStatus_t	miopenConvolutionForwardBias (miopenHandle_t handle, const void *alpha, const miopenTensorDescriptor_t bDesc, const void *b, const void *beta, const miopenTensorDescriptor_t yDesc, void *y)
	Calculate element-wise scale and shift of a tensor via a bias tensor. More...
miopenStatus_t	miopenConvolutionBackwardDataGetWorkSpaceSize (miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t wDesc, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t dxDesc, size_t *workSpaceSize)
	Query the workspace size required for a backward data convolution algorithm. More...
miopenStatus_t	miopenFindConvolutionBackwardDataAlgorithm (miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const void *dy, const miopenTensorDescriptor_t wDesc, const void *w, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t dxDesc, void *dx, const int requestAlgoCount, int *returnedAlgoCount, miopenConvAlgoPerf_t *perfResults, void *workSpace, size_t workSpaceSize, bool exhaustiveSearch)
	Search and run the backwards data convolution algorithms and return a list of kernel times. More...
miopenStatus_t	miopenConvolutionBackwardData (miopenHandle_t handle, const void *alpha, const miopenTensorDescriptor_t dyDesc, const void *dy, const miopenTensorDescriptor_t wDesc, const void *w, const miopenConvolutionDescriptor_t convDesc, miopenConvBwdDataAlgorithm_t algo, const void *beta, const miopenTensorDescriptor_t dxDesc, void *dx, void *workSpace, size_t workSpaceSize)
	Execute a backward data convolution layer. More...
miopenStatus_t	miopenConvolutionBackwardWeightsGetWorkSpaceSize (miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t xDesc, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t dwDesc, size_t *workSpaceSize)
	Get the GPU memory required for the backward weights convolution algorithm. More...
miopenStatus_t	miopenFindConvolutionBackwardWeightsAlgorithm (miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const void *dy, const miopenTensorDescriptor_t xDesc, const void *x, const miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t dwDesc, void *dw, const int requestAlgoCount, int *returnedAlgoCount, miopenConvAlgoPerf_t *perfResults, void *workSpace, size_t workSpaceSize, bool exhaustiveSearch)
	Search and run the backwards weights convolutional algorithms and return a list of kernel times. More...
miopenStatus_t	miopenConvolutionBackwardWeights (miopenHandle_t handle, const void *alpha, const miopenTensorDescriptor_t dyDesc, const void *dy, const miopenTensorDescriptor_t xDesc, const void *x, const miopenConvolutionDescriptor_t convDesc, miopenConvBwdWeightsAlgorithm_t algo, const void *beta, const miopenTensorDescriptor_t dwDesc, void *dw, void *workSpace, size_t workSpaceSize)
	Execute a backward weights convolution layer. More...
miopenStatus_t	miopenConvolutionBackwardBias (miopenHandle_t handle, const void *alpha, const miopenTensorDescriptor_t dyDesc, const void *dy, const void *beta, const miopenTensorDescriptor_t dbDesc, void *db)
	Calculates the gradient with respect to the bias. More...

Detailed Description

Enumeration Type Documentation

◆ miopenConvAlgorithm_t

enum miopenConvAlgorithm_t

Top-level convolutional algorithm mode

Enumerator
miopenConvolutionAlgoGEMM	GEMM variant
miopenConvolutionAlgoDirect	Direct convolutions
miopenConvolutionAlgoFFT	Fast Fourier Transform indirect convolutions
miopenConvolutionAlgoWinograd	Winograd indirect convolutions
miopenConvolutionAlgoImplicitGEMM	Implicit GEMM convolutions

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvBwdDataAlgorithm_t

enum miopenConvBwdDataAlgorithm_t

Convolutional algorithm mode for back propagation on data.

Enumerator
miopenConvolutionBwdDataAlgoGEMM	GEMM variant
miopenConvolutionBwdDataAlgoDirect	Direct convolutions
miopenConvolutionBwdDataAlgoFFT	Fast Fourier Transform indirect convolutions
miopenConvolutionBwdDataAlgoWinograd	Winograd indirect convolutions
miopenTransposeBwdDataAlgoGEMM	Deprecated Transpose GEMM variant legacy, ToBe Removed
miopenConvolutionBwdDataAlgoImplicitGEMM	Implicit GEMM convolutions

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvBwdWeightsAlgorithm_t

enum miopenConvBwdWeightsAlgorithm_t

Convolutional algorithm mode for back propagation on weights.

Enumerator
miopenConvolutionBwdWeightsAlgoGEMM	GEMM variant
miopenConvolutionBwdWeightsAlgoDirect	Direct convolution algorithm
miopenConvolutionBwdWeightsAlgoWinograd	Winograd convolutions
miopenConvolutionBwdWeightsAlgoImplicitGEMM	Implicit GEMM convolutions

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvFwdAlgorithm_t

enum miopenConvFwdAlgorithm_t

Convolutional algorithm mode for forward propagation. MIOpen use cross-correlation for its convolution implementation.

Enumerator
miopenConvolutionFwdAlgoGEMM	GEMM variant
miopenConvolutionFwdAlgoDirect	Direct convolutions
miopenConvolutionFwdAlgoFFT	Fast Fourier Transform indirect convolutions
miopenConvolutionFwdAlgoWinograd	Winograd indirect convolutions
miopenConvolutionFwdAlgoImplicitGEMM	Implicit GEMM convolutions

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionAttrib_t

enum miopenConvolutionAttrib_t

Attribute for convolution descriptor, used for alternating the convolution behavior

Enumerator
MIOPEN_CONVOLUTION_ATTRIB_FP16_ALT_IMPL	Use alternative fp16 implementation. Only supported for gfx90a; has no effect for other targets. 0 - disabled, 1 - enabled, -1 or unset - default (F0B1W1) >
MIOPEN_CONVOLUTION_ATTRIB_DETERMINISTIC	Restrict MIOpen convolutions to kernels which produce numerically deterministic results. 0 - disabled (default), 1 - enabled >
MIOPEN_CONVOLUTION_ATTRIB_FP8_ROUNDING_MODE	Specifies the rounding mode for the 8-bit floating data types. Currently, two rounding modes are supported miopenF8RoundingModeStandard and miopenF8RoundingModeStochastic. These are listed as part of the miopenF8RoundingMode_t enum.>

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionMode_t

enum miopenConvolutionMode_t

Convolution mode selection for convolution layer preference.

Enumerator
miopenConvolution	Cross-Correlation convolution
miopenTranspose	Transpose convolutions – deconvolution
miopenGroupConv	Deprecated Group convolution legacy, ToBe Removed
miopenDepthwise	Deprecated Depthwise convolution legacy, ToBe Removed

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

Function Documentation

◆ MIOPEN_DECLARE_OBJECT()

MIOPEN_DECLARE_OBJECT

(

miopenConvolutionDescriptor

)

Creates the miopenConvolutionDescriptor_t type.

Convolution descriptor is an object that allows the user to specify a layer's padding, stride, and dilation of the convolutional filter. Parameters must all be non-negative.

◆ miopenConvolutionBackwardBias()

miopenStatus_t miopenConvolutionBackwardBias	(	miopenHandle_t	handle,
		const void *	alpha,
		const miopenTensorDescriptor_t	dyDesc,
		const void *	dy,
		const void *	beta,
		const miopenTensorDescriptor_t	dbDesc,
		void *	db
	)

Calculates the gradient with respect to the bias.

Compute the convolution backwards gradient with respect to the bias tensor. The scaling parameter alpha (float) and shift parameter beta (float) are only supported for alpha = 1 and beta = 0.

Parameters

handle	MIOpen handle (input)
alpha	Floating point scaling factor, allocated on the host (input)
dyDesc	Tensor descriptor for data input tensor dy (input)
dy	Data delta tensor dy (input)
beta	Floating point shift factor, allocated on the host (input)
dbDesc	Tensor descriptor for input bias tensor db (input)
db	Bias delta tensor db (output)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionBackwardData()

miopenStatus_t miopenConvolutionBackwardData	(	miopenHandle_t	handle,
		const void *	alpha,
		const miopenTensorDescriptor_t	dyDesc,
		const void *	dy,
		const miopenTensorDescriptor_t	wDesc,
		const void *	w,
		const miopenConvolutionDescriptor_t	convDesc,
		miopenConvBwdDataAlgorithm_t	algo,
		const void *	beta,
		const miopenTensorDescriptor_t	dxDesc,
		void *	dx,
		void *	workSpace,
		size_t	workSpaceSize
	)

Execute a backward data convolution layer.

Runs the backward data convolution layer based on the selected algorithm. The function miopenFindConvolutionBackwardDataAlgorithm() must have been executed previously to determine the required memory needed for the workspace and the best convolutional algorithm.

The backward data convolution is designed to accommodate both packed and non-packed tensor strides for multiple data types and dimensions across various platforms. This flexibility ensures optimal performance in handling diverse computational scenarios. To configure tensor parameters, including strides, users can utilize the APIs miopenSetTensorDescriptor() and miopenGetTensorDescriptor(). These APIs empower developers to seamlessly set and retrieve tensor information, facilitating a more intuitive and efficient workflow. The tensor strides are non-packed by default.

If using Group/Depthwise convolution mode, call miopenSetConvolutionGroupCount() before running this.

Parameters

handle	MIOpen handle (input)
alpha	Floating point scaling factor, allocated on the host (input)
dyDesc	Tensor descriptor for data input tensor dy (input)
dy	Data delta tensor dy (input)
wDesc	Tensor descriptor for weight tensor w (input)
w	Weights tensor w (input)
convDesc	Convolution layer descriptor (input)
algo	Algorithm selected (input)
beta	Floating point shift factor, allocated on the host (input)
dxDesc	Tensor descriptor for output data tensor dx (input)
dx	Data delta tensor dx (output)
workSpace	Pointer to workspace required for the search (input)
workSpaceSize	Size in bytes of the memory needed for find (input)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionBackwardDataCompileSolution()

miopenStatus_t miopenConvolutionBackwardDataCompileSolution	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	dyDesc,
		const miopenTensorDescriptor_t	wDesc,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	dxDesc,
		const uint64_t	solution_id
	)

Compiles the solution provided by the user, this solution may be acquired by the miopenConvolutionBackwardDataGetSolution API call above. Compiling the solution ensures that the first API call to miopenConvolutionBackwardDataImmediate does not cause a compile.

This is an optional step and may be skipped if a slow first miopenConvolutionBackwardDataImmediate invocation is acceptable.

Parameters

handle	MIOpen handle (input)
dyDesc	Tensor descriptor for data input tensor dy (input)
wDesc	Tensor descriptor for weight tensor w (input)
convDesc	Convolution layer descriptor (input)
dxDesc	Tensor descriptor for output data tensor dx (input)
solution_id	ID of the solution to be compiled, as chosen by the user

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionBackwardDataGetSolution()

miopenStatus_t miopenConvolutionBackwardDataGetSolution	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	dyDesc,
		const miopenTensorDescriptor_t	wDesc,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	dxDesc,
		const size_t	maxSolutionCount,
		size_t *	solutionCount,
		miopenConvSolution_t *	solutions
	)

Query the applicable solutions for a backward convolution w-r-t data as described by input, output and convolution descriptors.

The returned solutions array is sorted in the order of decreasing performance. The returned solutions ns might be based on heuristics and for more consistent performance results the user the advised to run the Find step. The maximum length of the solutions array may be queried using miopenConvolutionBackwardDataGetSolutionCount

Parameters

handle	MIOpen handle (input)
dyDesc	Tensor descriptor for data input tensor dy (input)
wDesc	Tensor descriptor for weight tensor w (input)
convDesc	Convolution layer descriptor (input)
dxDesc	Tensor descriptor for output data tensor dx (input)
maxSolutionCount	The size of the solutions array passed in below (input)
solutionCount	The size of the solutions array returned (output)
solutions	A pointer to an array of type miopenConvSolution_t allocated by the user, filled in by MIOpen with applicable solutions. (output)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionBackwardDataGetSolutionCount()

miopenStatus_t miopenConvolutionBackwardDataGetSolutionCount	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	dyDesc,
		const miopenTensorDescriptor_t	wDesc,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	dxDesc,
		size_t *	solutionCount
	)

Query the maximum number of solutions applicable for the given input/output and weights tensor descriptor for backward Convolution w-r-t Data.

This call returns the maximum number of applicable solutions for a the convolution problem, the number returned may be used to allocate the memory required for the miopenConvAlgoPert2_t which is required by miopenConvolutionBackwardDataGetSolution API calls.

Parameters

handle	MIOpen handle (input)
dyDesc	Tensor descriptor for data input tensor dy (input)
wDesc	Tensor descriptor for weight tensor w (input)
convDesc	Convolution layer descriptor (input)
dxDesc	Tensor descriptor for output data tensor dx (input)
solutionCount	Pointer to memory to return number of applicable solutions (output)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionBackwardDataGetSolutionWorkspaceSize()

miopenStatus_t miopenConvolutionBackwardDataGetSolutionWorkspaceSize	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	dyDesc,
		const miopenTensorDescriptor_t	wDesc,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	dxDesc,
		const uint64_t	solution_id,
		size_t *	workSpaceSize
	)

Returns the workspace size required for a particular solution id.

This is an optional call for users who may have serialized the solution id and just need the workspace size for it. The same information is returned by the miopenConvolutionBackwardDataGetSolution as part of the miopenConvSolution_t struct.

Parameters

handle	MIOpen handle (input)
dyDesc	Tensor descriptor for data input tensor dy (input)
wDesc	Tensor descriptor for weight tensor w (input)
convDesc	Convolution layer descriptor (input)
dxDesc	Tensor descriptor for output data tensor dx (input)
solution_id	ID of the solution for which workspace size is required (input)
workSpaceSize	The size of the workspace (output)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionBackwardDataGetWorkSpaceSize()

miopenStatus_t miopenConvolutionBackwardDataGetWorkSpaceSize	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	dyDesc,
		const miopenTensorDescriptor_t	wDesc,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	dxDesc,
		size_t *	workSpaceSize
	)

Query the workspace size required for a backward data convolution algorithm.

For given tensor and convolution descriptors, this function calculates and returns the minimum size of the workspace that must be provided to miopenFindConvolutionBackwardDataAlgorithm() in order for the latter to find the best candidate from the available backward data convolution algorithms.

WARNING: Providing smaller workspace may result in the selection of a slow convolution algorithm, and therefore affect library performance.

It should be assumed that the required workspace size is different for each convolution configuration. Therefore, typically this function should be called at least once for each convolution configuration used.

Since the convolution configuration is determined by tensor and convolution descriptors, the user should ensure that all descriptors contain complete information. For example, if Group/Depthwise convolution mode is used, then miopenSetConvolutionGroupCount() should be called before running this, and so on.

Parameters

handle	MIOpen handle (input)
dyDesc	Tensor descriptor for data input tensor dy (input)
wDesc	Tensor descriptor for weight tensor w (input)
convDesc	Convolution layer descriptor (input)
dxDesc	Tensor descriptor for output data tensor dx (input)
workSpaceSize	Size in bytes of the memory required (output)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionBackwardDataImmediate()

miopenStatus_t miopenConvolutionBackwardDataImmediate	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	dyDesc,
		const void *	dy,
		const miopenTensorDescriptor_t	wDesc,
		const void *	w,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	dxDesc,
		void *	dx,
		void *	workSpace,
		size_t	workSpaceSize,
		const uint64_t	solution_id
	)

Executes the Backward convolution w-r-t data operation based on the provided solution ID.

Parameters

handle	MIOpen handle (input)
dyDesc	Tensor descriptor for data input tensor dy (input)
dy	Data delta tensor dy (input)
wDesc	Tensor descriptor for weight tensor w (input)
w	Weights tensor w (input)
convDesc	Convolution layer descriptor (input)
dxDesc	Tensor descriptor for output data tensor dx (input)
dx	Data delta tensor dx (output)
workSpace	Workspace tensor (input)
workSpaceSize	Size in bytes of the workspace memory pointed to by workSpace
solution_id	ID of the solution to be compiled, as chosen by the user

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionBackwardWeights()

miopenStatus_t miopenConvolutionBackwardWeights	(	miopenHandle_t	handle,
		const void *	alpha,
		const miopenTensorDescriptor_t	dyDesc,
		const void *	dy,
		const miopenTensorDescriptor_t	xDesc,
		const void *	x,
		const miopenConvolutionDescriptor_t	convDesc,
		miopenConvBwdWeightsAlgorithm_t	algo,
		const void *	beta,
		const miopenTensorDescriptor_t	dwDesc,
		void *	dw,
		void *	workSpace,
		size_t	workSpaceSize
	)

Execute a backward weights convolution layer.

Runs the backward weights convolution layer based on the selected algorithm. The function miopenFindConvolutionBackwardWeightsAlgorithm() must have been executed previously to determine the required memory needed for the workspace and the best convolutional algorithm.

The backward weights convolution is designed to accommodate both packed and non-packed tensor strides for multiple data types and dimensions across various platforms. This flexibility ensures optimal performance in handling diverse computational scenarios. To configure tensor parameters, including strides, users can utilize the APIs miopenSetTensorDescriptor() and miopenGetTensorDescriptor(). These APIs empower developers to seamlessly set and retrieve tensor information, facilitating a more intuitive and efficient workflow. The tensor strides are non-packed by default.

If using Group/Depthwise convolution mode, call miopenSetConvolutionGroupCount() before running this.

Parameters

handle	MIOpen handle (input)
alpha	Floating point scaling factor, allocated on the host (input)
dyDesc	Tensor descriptor for data tensor dy (input)
dy	Data delta tensor dy (input)
xDesc	Tensor descriptor for data tensor x (input)
x	Data tensor x (input)
convDesc	Convolution layer descriptor (input)
algo	Algorithm selected (input)
beta	Floating point shift factor, allocated on the host (input)
dwDesc	Tensor descriptor for weight tensor dw (input)
dw	Weights delta tensor dw (output)
workSpace	Pointer to workspace required for the search (input)
workSpaceSize	Size in bytes of the memory needed for find (input)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionBackwardWeightsCompileSolution()

miopenStatus_t miopenConvolutionBackwardWeightsCompileSolution	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	dyDesc,
		const miopenTensorDescriptor_t	xDesc,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	dwDesc,
		const uint64_t	solution_id
	)

Compiles the solution provided by the user, this solution may be acquired by the miopenConvolutionBackwardWeightsGetSolution API call above. Compiling the solution ensures that the first API call to miopenConvolutionBackwardWeightsImmediate does not cause a compile.

This is an optional step and may be skipped if a slow first miopenConvolutionBackwardWeightsImmediate invocation is acceptable.

Parameters

handle	MIOpen handle (input)
dyDesc	Tensor descriptor for data tensor dy (input)
xDesc	Tensor descriptor for data tensor x (input)
convDesc	Convolution layer descriptor (input)
dwDesc	Tensor descriptor for weight tensor dw (input)
solution_id	ID of the solution to be compiled, as chosen by the user

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionBackwardWeightsGetSolution()

miopenStatus_t miopenConvolutionBackwardWeightsGetSolution	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	dyDesc,
		const miopenTensorDescriptor_t	xDesc,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	dwDesc,
		const size_t	maxSolutionCount,
		size_t *	solutionCount,
		miopenConvSolution_t *	solutions
	)

Query the applicable solutions for a backward convolution w-r-t weights as described by input, output and convolution descriptors.

The returned solutions array is sorted in the order of decreasing performance. The returned solutions might be based on heuristics and for more consistent performance results the user the advised to run the Find step. The maximum length of the solutions array may be queried using miopenConvolutionBackwardWeightsGetSolutionCount

Parameters

handle	MIOpen handle (input)
dyDesc	Tensor descriptor for data tensor dy (input)
xDesc	Tensor descriptor for data tensor x (input)
convDesc	Convolution layer descriptor (input)
dwDesc	Tensor descriptor for weight tensor dw (input)
maxSolutionCount	The size of the solutions array passed in below (input)
solutionCount	The size of the solutions array returned (output)
solutions	A pointer to an array of type miopenConvSolution_t allocated by the user, filled in by MIOpen with applicable solutions. (output)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionBackwardWeightsGetSolutionCount()

miopenStatus_t miopenConvolutionBackwardWeightsGetSolutionCount	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	dyDesc,
		const miopenTensorDescriptor_t	xDesc,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	dwDesc,
		size_t *	solutionCount
	)

Query the maximum number of solutions applicable for the given input/output and weights tensor descriptor for backward Convolution w-r-t Weights.

This call returns the maximum number of applicable solutions for a the convolution problem, the number returned may be used to allocate the memory required for the miopenConvAlgoPert2_t which is required by miopenConvolutionBackwardWeightsGetSolution API calls.

Parameters

handle	MIOpen handle (input)
dyDesc	Tensor descriptor for data tensor dy (input)
xDesc	Tensor descriptor for data tensor x (input)
convDesc	Convolution layer descriptor (input)
dwDesc	Tensor descriptor for weight tensor dw (input)
solutionCount	Pointer to memory to return number of applicable solutions (output)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionBackwardWeightsGetSolutionWorkspaceSize()

miopenStatus_t miopenConvolutionBackwardWeightsGetSolutionWorkspaceSize	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	dyDesc,
		const miopenTensorDescriptor_t	xDesc,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	dwDesc,
		const uint64_t	solution_id,
		size_t *	workSpaceSize
	)

Returns the workspace size required for a particular solution id.

This is an optional call for users who may have serialized the solution id and just need the workspace size for it. The same information is returned by the miopenConvolutionBackwardWeightsGetSolution as part of the miopenConvSolution_t struct.

Parameters

handle	MIOpen handle (input)
dyDesc	Tensor descriptor for data tensor dy (input)
xDesc	Tensor descriptor for data tensor x (input)
convDesc	Convolution layer descriptor (input)
dwDesc	Tensor descriptor for weight tensor dw (input)
solution_id	ID of the solution for which workspace size is required (input)
workSpaceSize	The size of the workspace (output)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionBackwardWeightsGetWorkSpaceSize()

miopenStatus_t miopenConvolutionBackwardWeightsGetWorkSpaceSize	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	dyDesc,
		const miopenTensorDescriptor_t	xDesc,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	dwDesc,
		size_t *	workSpaceSize
	)

Get the GPU memory required for the backward weights convolution algorithm.

For given tensor and convolution descriptors, this function calculates and returns the minimum size of the workspace that must be provided to miopenFindConvolutionBackwardWeightsAlgorithm() in order for the latter to find the best candidate from the available backward weights convolution algorithms.

WARNING: Providing smaller workspace may result in the selection of a slow convolution algorithm, and therefore affect library performance.

It should be assumed that the required workspace size is different for each convolution configuration. Therefore, typically this function should be called at least once for each convolution configuration used.

Since the convolution configuration is determined by tensor and convolution descriptors, the user should ensure that all descriptors contain complete information. For example, if Group/Depthwise convolution mode is used, then miopenSetConvolutionGroupCount() should be called before running this, and so on.

Parameters

handle	MIOpen handle (input)
dyDesc	Tensor descriptor for data input tensor dy (input)
xDesc	Tensor descriptor for data tensor x (input)
convDesc	Convolution layer descriptor (input)
dwDesc	Tensor descriptor for output weights tensor dw (input)
workSpaceSize	Size in bytes of the memory required (output)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionBackwardWeightsImmediate()

miopenStatus_t miopenConvolutionBackwardWeightsImmediate	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	dyDesc,
		const void *	dy,
		const miopenTensorDescriptor_t	xDesc,
		const void *	x,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	dwDesc,
		void *	dw,
		void *	workSpace,
		size_t	workSpaceSize,
		const uint64_t	solution_id
	)

Executes the Backward convolution w-r-t weights operation based on the provided solution ID.

Parameters

handle	MIOpen handle (input)
dyDesc	Tensor descriptor for data tensor dy (input)
dy	Data delta tensor dy (input)
xDesc	Tensor descriptor for data tensor x (input)
x	Data tensor x (input)
convDesc	Convolution layer descriptor (input)
dwDesc	Tensor descriptor for weight tensor dw (input)
dw	Weights delta tensor dw (output)
workSpace	Workspace tensor (input)
workSpaceSize	Size in bytes of the memory passed in, pointed to by workSpace pointer above
solution_id	ID of the solution to be compiled, as chosen by the user

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionForward()

miopenStatus_t miopenConvolutionForward	(	miopenHandle_t	handle,
		const void *	alpha,
		const miopenTensorDescriptor_t	xDesc,
		const void *	x,
		const miopenTensorDescriptor_t	wDesc,
		const void *	w,
		const miopenConvolutionDescriptor_t	convDesc,
		miopenConvFwdAlgorithm_t	algo,
		const void *	beta,
		const miopenTensorDescriptor_t	yDesc,
		void *	y,
		void *	workSpace,
		size_t	workSpaceSize
	)

Execute a forward convolution layer.

Runs the forward convolution layer based on the selected algorithm. The function miopenFindConvolutionForwardAlgorithm() must have been executed previously to determine the required memory needed for the workspace and the best convolutional algorithm. The scaling parameter alpha (float) and shift parameter beta (float) are only supported for alpha = 1 and beta = 0 in 2D. In 3D, these parameters can take other values.

The forward convolution is designed to accommodate both packed and non-packed tensor strides for multiple data types and dimensions across various platforms. This flexibility ensures optimal performance in handling diverse computational scenarios. To configure tensor parameters, including strides, users can utilize the APIs miopenSetTensorDescriptor() and miopenGetTensorDescriptor(). These APIs empower developers to seamlessly set and retrieve tensor information, facilitating a more intuitive and efficient workflow. The tensor strides are non-packed by default.

If using Group/Depthwise convolution mode, call miopenSetConvolutionGroupCount() before running this.

Parameters

handle	MIOpen handle (input)
alpha	Floating point scaling factor, allocated on the host (input)
xDesc	Tensor descriptor for data input tensor x (input)
x	Data tensor x (input)
wDesc	Tensor descriptor for weight tensor w (input)
w	Weights tensor w (inputs)
convDesc	Convolution layer descriptor (inputs)
algo	Algorithm selected (inputs)
beta	Floating point shift factor, allocated on the host (input)
yDesc	Tensor descriptor for output data tensor y (input)
y	Data tensor y (output)
workSpace	Pointer to workspace required (input)
workSpaceSize	Size in bytes of the memory determined by the find step (input)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionForwardBias()

miopenStatus_t miopenConvolutionForwardBias	(	miopenHandle_t	handle,
		const void *	alpha,
		const miopenTensorDescriptor_t	bDesc,
		const void *	b,
		const void *	beta,
		const miopenTensorDescriptor_t	yDesc,
		void *	y
	)

Calculate element-wise scale and shift of a tensor via a bias tensor.

This function applies an element-wise bias to a data tensor from an input bias tensor. The scaling parameter alpha (float) and shift parameter beta (float) are only supported for alpha = 1 and beta = 0.

Parameters

handle	MIOpen handle (input)
alpha	Floating point scaling factor, allocated on the host (input)
bDesc	Tensor descriptor for bias tensor b (input)
b	Bias tensor b (input)
beta	Floating point shift factor, allocated on the host (input)
yDesc	Tensor descriptor for data tensor y (input)
y	Data tensor y (input and output)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionForwardCompileSolution()

miopenStatus_t miopenConvolutionForwardCompileSolution	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	wDesc,
		const miopenTensorDescriptor_t	xDesc,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	yDesc,
		const uint64_t	solution_id
	)

Compiles the solution provided by the user, this solution may be acquired by the miopenConvolutionForwardGetSolution API call above. Compiling the solution ensures that the first API call to miopenConvolutionForwardImmediate does not cause a compile.

This is an optional step and may be skipped if a slow first miopenConvolutionForwardImmediate invocation is acceptable.

Parameters

handle	MIOpen handle (input)
wDesc	Tensor descriptor for weight tensor w (input)
xDesc	Tensor descriptor for input data tensor x (input)
convDesc	Convolution layer descriptor (input)
yDesc	Tensor descriptor for output data tensor y (input)
solution_id	ID of the solution to be compiled, as chosen by the user

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionForwardGetSolution()

miopenStatus_t miopenConvolutionForwardGetSolution	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	wDesc,
		const miopenTensorDescriptor_t	xDesc,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	yDesc,
		const size_t	maxSolutionCount,
		size_t *	solutionCount,
		miopenConvSolution_t *	solutions
	)

Query the applicable solutions for a convolution configuration described by input, output and convolution descriptors.

The returned solutions array is sorted in the order of decreasing performance. The returned solutions might be based on heuristics and for more consistent performance results the user the advised to run the Find step. The maximum length of the solutions array may be queried using miopenConvolutionForwardGetSolutionCount

Parameters

handle	MIOpen handle (input)
wDesc	Tensor descriptor for weight tensor w (input)
xDesc	Tensor descriptor for input data tensor x (input)
convDesc	Convolution layer descriptor (input)
yDesc	Tensor descriptor for output data tensor y (input)
maxSolutionCount	The size of the solutions array passed in below (input)
solutionCount	The size of the solutions array returned (output)
solutions	A pointer to an array of type miopenConvSolution_t allocated by the user, filled in by MIOpen with applicable solutions. (output)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionForwardGetSolutionCount()

miopenStatus_t miopenConvolutionForwardGetSolutionCount	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	wDesc,
		const miopenTensorDescriptor_t	xDesc,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	yDesc,
		size_t *	solutionCount
	)

Query the maximum number of solutions applicable for the given input/output and weights tensor descriptor for Convolution in the Forward direction.

This call returns the maximum number of applicable solutions for a forward convolution problem. The solutionCount returned may be used to allocate the memory required for the miopenConvAlgoPerf_t which is required by miopenConvolutionGetSolution API calls.

Parameters

handle	MIOpen handle (input)
wDesc	Tensor descriptor for weight tensor w (input)
xDesc	Tensor descriptor for input data tensor x (input)
convDesc	Convolution layer descriptor (input)
yDesc	Tensor descriptor for output data tensor y (input)
solutionCount	Pointer to memory to return number of applicable solutions (output)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionForwardGetSolutionWorkspaceSize()

miopenStatus_t miopenConvolutionForwardGetSolutionWorkspaceSize	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	wDesc,
		const miopenTensorDescriptor_t	xDesc,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	yDesc,
		const uint64_t	solution_id,
		size_t *	workSpaceSize
	)

Returns the workspace size required for a particular solution id.

This is an optional call for users who may have serialized the solution id and just need the workspace size for it. The same information is returned by the miopenConvolutionForwardGetSolution as part of the miopenConvSolution_t struct.

Parameters

handle	MIOpen handle (input)
wDesc	Tensor descriptor for weight tensor w (input)
xDesc	Tensor descriptor for input data tensor x (input)
convDesc	Convolution layer descriptor (input)
yDesc	Tensor descriptor for output data tensor y (input)
solution_id	ID of the solution for which workspace size is required (input)
workSpaceSize	The size of the workspace (output)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionForwardGetWorkSpaceSize()

miopenStatus_t miopenConvolutionForwardGetWorkSpaceSize	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	wDesc,
		const miopenTensorDescriptor_t	xDesc,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	yDesc,
		size_t *	workSpaceSize
	)

Query the workspace size required for a forward convolution algorithm.

For given tensor and convolution descriptors, this function calculates and returns the minimum size of the workspace that must be provided to miopenFindConvolutionForwardAlgorithm() in order for the latter to find the best candidate from the available forward data convolution algorithms.

WARNING: Providing smaller workspace may result in the selection of a slow convolution algorithm, and therefore affect library performance.

It should be assumed that the required workspace size is different for each convolution configuration. Therefore, typically this function should be called at least once for each convolution configuration used.

Since the convolution configuration is determined by tensor and convolution descriptors, the user should ensure that all descriptors contain complete information. For example, if Group/Depthwise convolution mode is used, then miopenSetConvolutionGroupCount() should be called before running this, and so on.

Parameters

handle	MIOpen handle (input)
wDesc	Tensor descriptor for weight tensor w (input)
xDesc	Tensor descriptor for input data tensor x (input)
convDesc	Convolution layer descriptor (input)
yDesc	Tensor descriptor for output data tensor y (input)
workSpaceSize	Pointer to memory to return size in bytes (output)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenConvolutionForwardImmediate()

miopenStatus_t miopenConvolutionForwardImmediate	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	wDesc,
		const void *	w,
		const miopenTensorDescriptor_t	xDesc,
		const void *	x,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	yDesc,
		void *	y,
		void *	workSpace,
		size_t	workSpaceSize,
		const uint64_t	solution_id
	)

Executes the Forward convolution operation based on the provided solution ID.

Supported datatypes are fp32, fp16, bfp16, and int8

Parameters

handle	MIOpen handle (input)
wDesc	Tensor descriptor for weight tensor w (input)
w	Weights tensor w (input)
xDesc	Tensor descriptor for input data tensor x (input)
x	Data tensor x (input)
convDesc	Convolution layer descriptor (input)
yDesc	Tensor descriptor for output data tensor y (input)
y	Data tensor y (output)
workSpace	Workspace tensor (input)
workSpaceSize	Size of the memory in bytes pointed to by workSpace above
solution_id	ID of the solution to be compiled, as chosen by the user

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenCreateConvolutionDescriptor()

miopenStatus_t miopenCreateConvolutionDescriptor

(

miopenConvolutionDescriptor_t *

convDesc

)

Creates a convolution layer descriptor.

Parameters

convDesc

Convolution layer descriptor

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenDestroyConvolutionDescriptor()

miopenStatus_t miopenDestroyConvolutionDescriptor

(

miopenConvolutionDescriptor_t

convDesc

)

Destroys the tensor descriptor object.

Parameters

convDesc

Convolution tensor descriptor type (input)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenFindConvolutionBackwardDataAlgorithm()

miopenStatus_t miopenFindConvolutionBackwardDataAlgorithm	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	dyDesc,
		const void *	dy,
		const miopenTensorDescriptor_t	wDesc,
		const void *	w,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	dxDesc,
		void *	dx,
		const int	requestAlgoCount,
		int *	returnedAlgoCount,
		miopenConvAlgoPerf_t *	perfResults,
		void *	workSpace,
		size_t	workSpaceSize,
		bool	exhaustiveSearch
	)

Search and run the backwards data convolution algorithms and return a list of kernel times.

This function attempts all MIOpen backward data convolution algorithms, and outputs the performance metrics to a user-allocated array of type miopenConvAlgoPerf_t. These metrics are written in sorted fashion where the first element has the lowest compute time. This function is mandatory before using backwards convolutions. Users can chose the top-most algorithm if they only care about the fastest algorithm.

This function is mandatory before using miopenConvolutionBackwardData(). In order to execute this function, miopenConvolutionBackwardsDataGetWorkSpaceSize() must be run to determine the required memory for this search.

• If exhaustiveSearch == 0, MIOpen will look for the first kernel with a configuration match. If a configuration match is not found, a default configuration will be returned.
• If exhaustiveSearch == 1, MIOpen will look for the best kernel for the provided configuration. If a match is not found, an exhaustive search is performed by running individual algorithms.

If using Group/Depthwise convolution mode, call miopenSetConvolutionGroupCount() before running this.

Parameters

handle	MIOpen handle (input)
dyDesc	Tensor descriptor for data input tensor dy (input)
dy	Data delta tensor dy (input)
wDesc	Tensor descriptor for weight tensor w (input)
w	Weights tensor w (input)
convDesc	Convolution layer descriptor (input)
dxDesc	Tensor descriptor for output data tensor dx (input)
dx	Data delta tensor dx (input)
requestAlgoCount	Number of algorithms to return kernel times (input)
returnedAlgoCount	Pointer to number of algorithms returned (output)
perfResults	Pointer to union of best algorithm for forward and backwards (output)
workSpace	Pointer to workspace buffer (input).
workSpaceSize	Size in bytes of the workspace buffer (input). The buffer must be allocated on the device by the caller. The size of the buffer should be determined by calling miopenConvolutionBackwardDataGetWorkSpaceSize(), see its documentation for details.
exhaustiveSearch	A boolean to toggle a full search of all algorithms and configurations (input)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenFindConvolutionBackwardWeightsAlgorithm()

miopenStatus_t miopenFindConvolutionBackwardWeightsAlgorithm	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	dyDesc,
		const void *	dy,
		const miopenTensorDescriptor_t	xDesc,
		const void *	x,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	dwDesc,
		void *	dw,
		const int	requestAlgoCount,
		int *	returnedAlgoCount,
		miopenConvAlgoPerf_t *	perfResults,
		void *	workSpace,
		size_t	workSpaceSize,
		bool	exhaustiveSearch
	)

Search and run the backwards weights convolutional algorithms and return a list of kernel times.

This function attempts all MIOpen backward weights convolution algorithms, and outputs the performance metrics to a user-allocated array of type miopenConvAlgoPerf_t. These metrics are written in sorted fashion where the first element has the lowest compute time. This function is mandatory before using backwards weight convolutions. Users can chose the top-most algorithm if they only care about the fastest algorithm.

This function is mandatory before using miopenConvolutionBackwardWeights(). In order to execute this function, miopenConvolutionBackwardsWeightsGetWorkSpaceSize() must be run to determine the required memory for this search.

• If exhaustiveSearch == 0, MIOpen will look for the first kernel with a configuration match. If a configuration match is not found, a default configuration will be returned.
• If exhaustiveSearch == 1, MIOpen will look for the best kernel for the provided configuration. If a match is not found, an exhaustive search is performed by running individual algorithms.

If using Group/Depthwise convolution mode, call miopenSetConvolutionGroupCount() before running this.

Parameters

handle	MIOpen handle (input)
dyDesc	Tensor descriptor for data input tensor dy (input)
dy	Data delta tensor dy (input)
xDesc	Tensor descriptor for output data tensor x (input)
x	Data delta tensor dx (input)
convDesc	Convolution layer descriptor (input)
dwDesc	Tensor descriptor for weight tensor dw (input)
dw	Weights delta tensor dw (input)
requestAlgoCount	Number of algorithms to return kernel times (input)
returnedAlgoCount	Pointer to number of algorithms returned (output)
perfResults	Pointer to union of best algorithm for forward and backwards (output)
workSpace	Pointer to workspace buffer (input).
workSpaceSize	Size in bytes of the workspace buffer (input). The buffer must be allocated on the device by the caller. The size of the buffer should be determined by calling miopenConvolutionBackwardWeightsGetWorkSpaceSize(), see its documentation for details.
exhaustiveSearch	A boolean to toggle a full search of all algorithms and configurations (input)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenFindConvolutionForwardAlgorithm()

miopenStatus_t miopenFindConvolutionForwardAlgorithm	(	miopenHandle_t	handle,
		const miopenTensorDescriptor_t	xDesc,
		const void *	x,
		const miopenTensorDescriptor_t	wDesc,
		const void *	w,
		const miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	yDesc,
		void *	y,
		const int	requestAlgoCount,
		int *	returnedAlgoCount,
		miopenConvAlgoPerf_t *	perfResults,
		void *	workSpace,
		size_t	workSpaceSize,
		bool	exhaustiveSearch
	)

Search and run the forward convolutional algorithms and return a list of kernel times.

This function attempts all MIOpen forward convolution algorithms based on the input configuration, and outputs performance metrics to a user-allocated array of type miopenConvAlgoPerf_t. These metrics are written in a sorted fashion where the first element has the lowest compute time. Users can chose the top-most algorithm if they only care about the fastest algorithm.

This function is mandatory before using miopenConvolutionForward(). In order to execute this function, miopenConvolutionForwardGetWorkSpaceSize() must be run to determine the required memory for this search.

• If exhaustiveSearch == 0, MIOpen will look for the first kernel with a configuration match. If a configuration match is not found, a default configuration will be returned.
• If exhaustiveSearch == 1, MIOpen will look for the best kernel for the provided configuration. If a match is not found, an exhaustive search is performed by running individual algorithms.

If using Group/Depthwise convolution mode, call miopenSetConvolutionGroupCount() before running this.

Parameters

handle	MIOpen handle (input)
xDesc	Tensor descriptor for data input tensor x (input)
x	Data tensor x (input)
wDesc	Tensor descriptor for weight tensor w (input)
w	Weights tensor w (input)
convDesc	Convolution layer descriptor (input)
yDesc	Tensor descriptor for output data tensor y (input)
y	Data tensor y (output)
requestAlgoCount	Number of algorithms to return kernel times (input)
returnedAlgoCount	Pointer to number of algorithms returned (output)
perfResults	Pointer to union of best algorithm for forward and backwards (input)
workSpace	Pointer to workspace buffer (input).
workSpaceSize	Size in bytes of the workspace buffer (input). The buffer must be allocated on the device by the caller. The size of the buffer should be determined by calling miopenConvolutionForwardGetWorkSpaceSize(), see its documentation for details.
exhaustiveSearch	A boolean to toggle a full search of all algorithms and configurations (input)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenGetConvolutionAttribute()

miopenStatus_t miopenGetConvolutionAttribute	(	miopenConvolutionDescriptor_t	convDesc,
		const miopenConvolutionAttrib_t	attr,
		int *	value
	)

Get the attribute of the convolution descriptor.

Parameters

convDesc	Convolution layer descriptor (input)
attr	Attribute of this convolution to get (input)
value	Value of this attribute (output)

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenGetConvolutionDescriptor()

miopenStatus_t miopenGetConvolutionDescriptor	(	miopenConvolutionDescriptor_t	convDesc,
		miopenConvolutionMode_t *	c_mode,
		int *	pad_h,
		int *	pad_w,
		int *	stride_h,
		int *	stride_w,
		int *	dilation_h,
		int *	dilation_w
	)

Retrieves a 2-D convolution layer descriptor's details.

For group/depthwise convolution dilation height and width, only a dilation value of 1 is supported.

Parameters

convDesc	Convolution layer descriptor (input)
c_mode	Convolutional mode (output)
pad_h	Height input data padding (output)
pad_w	Width input data padding (output)
stride_h	Stride for the height of input data (output)
stride_w	Stride for the width of input data (output)
dilation_h	Dilation height (output)
dilation_w	Dilation width (output)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenGetConvolutionForwardOutputDim()

miopenStatus_t miopenGetConvolutionForwardOutputDim	(	miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	inputTensorDesc,
		const miopenTensorDescriptor_t	filterDesc,
		int *	n,
		int *	c,
		int *	h,
		int *	w
	)

Get the shape of a resulting 4-D tensor from a 2-D convolution.

This function returns the dimensions of the resulting 4D tensor of a 2D convolution, given the convolution descriptor, the input tensor descriptor and the filter descriptor. This function can help to setup the output tensor and allocate the proper amount of memory prior to launch the actual convolution.

Parameters

convDesc	Convolution layer descriptor (input)
inputTensorDesc	Input data tensor descriptor (input)
filterDesc	Weight descriptor (input)
n	Mini-batch size (output)
c	Number of channels (output)
h	Data height dimension size (output)
w	Data width dimension size (output)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenGetConvolutionGroupCount()

miopenStatus_t miopenGetConvolutionGroupCount	(	miopenConvolutionDescriptor_t	convDesc,
		int *	groupCount
	)

Get the number of groups to be used in Group/Depthwise convolution.

Parameters

convDesc	Convolution layer descriptor (input)
groupCount	Pointer to number of groups in group/depthwise convolution (output)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenGetConvolutionNdDescriptor()

miopenStatus_t miopenGetConvolutionNdDescriptor	(	miopenConvolutionDescriptor_t	convDesc,
		int	requestedSpatialDim,
		int *	spatialDim,
		int *	padA,
		int *	strideA,
		int *	dilationA,
		miopenConvolutionMode_t *	c_mode
	)

Retrieves a N-dimensional convolution layer descriptor's details.

Parameters

convDesc	Convolution layer descriptor (input)
requestedSpatialDim	Expected convolution spatial dimension (intput)
spatialDim	Convolutional spatial dimension (output)
padA	Array of input data padding (output)
strideA	Array of convolution stride (output)
dilationA	Array of convolution dilation (output)
c_mode	Convolutional mode (output)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenGetConvolutionNdForwardOutputDim()

miopenStatus_t miopenGetConvolutionNdForwardOutputDim	(	miopenConvolutionDescriptor_t	convDesc,
		const miopenTensorDescriptor_t	inputTensorDesc,
		const miopenTensorDescriptor_t	filterDesc,
		int *	nDim,
		int *	outputTensorDimA
	)

Get the shape of a resulting N-dimensional tensor from a (N-2)-dimensional convolution.

This function returns the dimensions of the resulting N-dimensional tensor of a (N-2)-dimensional convolution, given the convolution descriptor, the input tensor descriptor and the filter descriptor. It is used to setup the output tensor descriptor prior to executing the convolution layer.

Parameters

convDesc	Convolution layer descriptor (input)
inputTensorDesc	Input data tensor descriptor (input)
filterDesc	Weight descriptor (input)
nDim	Pointer to Output data tensor dimension (output)
outputTensorDimA	Array of Output data tensor length (output)

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenGetConvolutionSpatialDim()

miopenStatus_t miopenGetConvolutionSpatialDim	(	miopenConvolutionDescriptor_t	convDesc,
		int *	spatialDim
	)

Retrieves the spatial dimension of a convolution layer descriptor.

Parameters

convDesc	Convolution layer descriptor (input)
spatialDim	Spatial dimension of convolution descriptor (output)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenInitConvolutionDescriptor()

miopenStatus_t miopenInitConvolutionDescriptor	(	miopenConvolutionDescriptor_t	convDesc,
		miopenConvolutionMode_t	c_mode,
		int	pad_h,
		int	pad_w,
		int	stride_h,
		int	stride_w,
		int	dilation_h,
		int	dilation_w
	)

Creates a 2-D convolution layer descriptor.

For group/depthwise convolution dilation height and width, only a dilation value of 1 is supported.

Parameters

convDesc	Convolution layer descriptor (output)
c_mode	Convolutional mode (input)
pad_h	Height input data padding (input)
pad_w	Width input data padding (input)
stride_h	Stride for the height of input data (input)
stride_w	Stride for the width of input data (input)
dilation_h	Dilation height (input)
dilation_w	Dilation width (input)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenInitConvolutionNdDescriptor()

miopenStatus_t miopenInitConvolutionNdDescriptor	(	miopenConvolutionDescriptor_t	convDesc,
		int	spatialDim,
		const int *	padA,
		const int *	strideA,
		const int *	dilationA,
		miopenConvolutionMode_t	c_mode
	)

Creates a N-dimensional convolution layer descriptor.

Parameters

convDesc	Convolution layer descriptor (output)
spatialDim	Convolutional spatial dimension (input)
padA	Array of input data padding (input)
strideA	Array of convolution stride (input)
dilationA	Array of convolution dilation (input)
c_mode	Convolutional mode (input)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenSetConvolutionAttribute()

miopenStatus_t miopenSetConvolutionAttribute	(	miopenConvolutionDescriptor_t	convDesc,
		const miopenConvolutionAttrib_t	attr,
		int	value
	)

Set the attribute of the convolution descriptor.

Parameters

convDesc	Convolution layer descriptor (input)
attr	Attribute of this convolution to set (input)
value	Value of this attribute (input)

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenSetConvolutionGroupCount()

miopenStatus_t miopenSetConvolutionGroupCount	(	miopenConvolutionDescriptor_t	convDesc,
		int	groupCount
	)

Set the number of groups to be used in Group/Depthwise convolution.

Must be called before all computational APIs of group/depthwise convolution, it is preferable to call miopenInitConvolutionDescriptor() first, then miopenSetConvolutionGroupCount() to fully initialize group convolutions. Both Convolution Mode and Transpose Convolution Mode support group/depthwise convolution. To run depthwise convolution, set groupCount value equal to number of channels.

Parameters

convDesc	Convolution layer descriptor (output)
groupCount	number of groups, in depthwise conv using filter_number/channel_multiplier (input)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenSetTransposeConvNdOutputPadding()

miopenStatus_t miopenSetTransposeConvNdOutputPadding	(	miopenConvolutionDescriptor_t	convDesc,
		int	spatialDim,
		const int *	adjA
	)

Set the output padding to be used in N-dimensional Transpose convolution.

This function is optional for initialization of Transpose convolution. If applicable, it must be called before all computational APIs of Transpose convolution. It is preferable to call miopenInitConvolutionNdDescriptor() first, then miopenSetTransposeConvNdOutputPadding() to fully initialize transpose convolutions. Currently, 2-D and 3-D convolutions are supported.

Parameters

convDesc	Convolution layer descriptor (output)
spatialDim	Convolutional spatial dimension (input)
adjA	array of output padding for output data (input)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.

◆ miopenSetTransposeConvOutputPadding()

miopenStatus_t miopenSetTransposeConvOutputPadding	(	miopenConvolutionDescriptor_t	convDesc,
		int	adj_h,
		int	adj_w
	)

Set the output padding to be used in 2-D Transpose convolution.

This function is optional for initialization of Transpose convolution. If applicable, it must be called before all computational APIs of Transpose convolution. It is preferable to call miopenInitConvolutionDescriptor() first, then miopenSetTransposeConvOutputPadding() to fully initialize transpose convolutions.

Parameters

convDesc	Convolution layer descriptor (output)
adj_h	output padding for the height of output data (input)
adj_w	output padding for the width of output data (input)

Returns

miopenStatus_t

Examples

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.3.0/include/miopen/miopen.h.