RNN

RNN#

MIOpen: RNN

Enumerations
enum	miopenRNNMode_t { miopenRNNRELU = 0 , miopenRNNTANH = 1 , miopenLSTM = 2 , miopenGRU = 3 }

enum	miopenRNNInputMode_t { miopenRNNlinear = 0 , miopenRNNskip = 1 }

enum	miopenRNNAlgo_t { miopenRNNdefault = 0 , miopenRNNfundamental }

enum	miopenRNNDirectionMode_t { miopenRNNunidirection = 0 , miopenRNNbidirection = 1 }

enum	miopenRNNBiasMode_t { miopenRNNNoBias = 0 , miopenRNNwithBias = 1 }

enum	miopenRNNGEMMalgoMode_t { miopenRNNAlgoGEMM = 0 }

enum	miopenRNNPaddingMode_t { miopenRNNIONotPadded = 0 , miopenRNNIOWithPadding = 1 }

enum	miopenRNNFWDMode_t { miopenRNNTraining = 0 , miopenRNNInference = 1 }

enum	miopenRNNBaseLayout_t { miopenRNNDataUnknownLayout = 0 , miopenRNNDataSeqMajorNotPadded = 1 , miopenRNNDataSeqMajorPadded = 2 , miopenRNNDataBatchMajorPadded = 3 }

Functions
	MIOPEN_DECLARE_OBJECT (miopenRNNDescriptor)
	Creates the miopenRNNDescriptor_t type. More...

miopenStatus_t	miopenCreateRNNDescriptor (miopenRNNDescriptor_t *rnnDesc)
	Create a RNN layer Descriptor. More...

miopenStatus_t	miopenGetRNNDescriptor (miopenRNNDescriptor_t rnnDesc, miopenRNNMode_t rnnMode, miopenRNNAlgo_t algoMode, miopenRNNInputMode_t inputMode, miopenRNNDirectionMode_t dirMode, miopenRNNBiasMode_t biasMode, int hiddenSize, int *layer)
	Retrieves a RNN layer descriptor's details. More...

miopenStatus_t	miopenGetRNNDescriptor_V2 (miopenRNNDescriptor_t rnnDesc, int hiddenSize, int layer, miopenDropoutDescriptor_t dropoutDesc, miopenRNNInputMode_t inputMode, miopenRNNDirectionMode_t dirMode, miopenRNNMode_t rnnMode, miopenRNNBiasMode_t biasMode, miopenRNNAlgo_t algoMode, miopenDataType_t *dataType)
	Retrieves a RNN layer descriptor's details version 2. This version enables retrieving information of the dropout descriptor of the rnn descriptor. More...

miopenStatus_t	miopenDestroyRNNDescriptor (miopenRNNDescriptor_t rnnDesc)
	Destroys the tensor descriptor object. More...

miopenStatus_t	miopenSetRNNDescriptor (miopenRNNDescriptor_t rnnDesc, const int hsize, const int nlayers, miopenRNNInputMode_t inMode, miopenRNNDirectionMode_t direction, miopenRNNMode_t rnnMode, miopenRNNBiasMode_t biasMode, miopenRNNAlgo_t algo, miopenDataType_t dataType)
	Set the details of the RNN descriptor. More...

miopenStatus_t	miopenSetRNNDescriptor_V2 (miopenRNNDescriptor_t rnnDesc, const int hsize, const int nlayers, miopenDropoutDescriptor_t dropoutDesc, miopenRNNInputMode_t inMode, miopenRNNDirectionMode_t direction, miopenRNNMode_t rnnMode, miopenRNNBiasMode_t biasMode, miopenRNNAlgo_t algo, miopenDataType_t dataType)
	Set the details of the RNN descriptor version 2. This version enables the use of dropout in rnn. More...

miopenStatus_t	miopenSetRNNDataSeqTensorDescriptor (miopenSeqTensorDescriptor_t seqTensorDesc, miopenDataType_t dataType, miopenRNNBaseLayout_t layout, int maxSequenceLen, int batchSize, int vectorSize, const int sequenceLenArray, void paddingMarker)
	Set shape of RNN seqData tensor. More...

miopenStatus_t	miopenGetRNNDataSeqTensorDescriptor (miopenSeqTensorDescriptor_t seqTensorDesc, miopenDataType_t dataType, miopenRNNBaseLayout_t layout, int maxSequenceLen, int batchSize, int vectorSize, int sequenceLenArrayLimit, int sequenceLenArray, void *paddingMarker)
	Get shape of RNN seqData tensor. More...

miopenStatus_t	miopenGetRNNWorkspaceSize (miopenHandle_t handle, const miopenRNNDescriptor_t rnnDesc, const int sequenceLen, const miopenTensorDescriptor_t xDesc, size_t numBytes)
	Query the amount of memory required to execute the RNN layer. More...

miopenStatus_t	miopenGetRNNTrainingReserveSize (miopenHandle_t handle, miopenRNNDescriptor_t rnnDesc, const int sequenceLen, const miopenTensorDescriptor_t xDesc, size_t numBytes)
	Query the amount of memory required for RNN training. More...

miopenStatus_t	miopenGetRNNTempSpaceSizes (miopenHandle_t handle, miopenRNNDescriptor_t rnnDesc, miopenSeqTensorDescriptor_t xDesc, miopenRNNFWDMode_t fwdMode, size_t workSpaceSize, size_t reserveSpaceSize)
	Query the amount of additional memory required for this RNN layer execution. More...

miopenStatus_t	miopenGetRNNParamsSize (miopenHandle_t handle, miopenRNNDescriptor_t rnnDesc, miopenTensorDescriptor_t xDesc, size_t *numBytes, miopenDataType_t dtype)
	Query the amount of parameter memory required for RNN training. More...

miopenStatus_t	miopenGetRNNParamsDescriptor (miopenHandle_t handle, miopenRNNDescriptor_t rnnDesc, miopenTensorDescriptor_t xDesc, miopenTensorDescriptor_t wDesc, miopenDataType_t dtype)
	Obtain a weight tensor descriptor for RNNs. More...

miopenStatus_t	miopenGetRNNInputTensorSize (miopenHandle_t handle, miopenRNNDescriptor_t rnnDesc, const int seqLen, miopenTensorDescriptor_t xDesc, size_t numBytes)
	Obtain a the size in bytes of the RNN input tensor. More...

miopenStatus_t	miopenGetRNNHiddenTensorSize (miopenHandle_t handle, miopenRNNDescriptor_t rnnDesc, const int seqLen, miopenTensorDescriptor_t xDesc, size_t numBytes)
	Obtain a the size in bytes of the RNN hidden tensor. More...

miopenStatus_t	miopenGetRNNLayerParamSize (miopenHandle_t handle, miopenRNNDescriptor_t rnnDesc, const int layer, miopenTensorDescriptor_t xDesc, const int paramID, size_t *numBytes)
	Gets the number of bytes of a parameter matrix. More...

miopenStatus_t	miopenGetRNNLayerBiasSize (miopenHandle_t handle, miopenRNNDescriptor_t rnnDesc, const int layer, const int biasID, size_t *numBytes)
	Gets the number of bytes of a bias. More...

miopenStatus_t	miopenGetRNNLayerParam (miopenHandle_t handle, miopenRNNDescriptor_t rnnDesc, const int layer, miopenTensorDescriptor_t xDesc, miopenTensorDescriptor_t wDesc, const void w, const int paramID, miopenTensorDescriptor_t paramDesc, void layerParam)
	Gets a weight matrix for a specific layer in an RNN stack. More...

miopenStatus_t	miopenGetRNNLayerBias (miopenHandle_t handle, miopenRNNDescriptor_t rnnDesc, const int layer, miopenTensorDescriptor_t xDesc, miopenTensorDescriptor_t wDesc, const void w, const int biasID, miopenTensorDescriptor_t biasDesc, void layerBias)
	Gets a bias for a specific layer in an RNN stack. More...

miopenStatus_t	miopenGetRNNLayerParamOffset (miopenRNNDescriptor_t rnnDesc, const int layer, miopenTensorDescriptor_t xDesc, const int paramID, miopenTensorDescriptor_t paramDesc, size_t *layerParamOffset)
	Gets an index offset for a specific weight matrix for a layer in the RNN stack. More...

miopenStatus_t	miopenGetRNNLayerBiasOffset (miopenRNNDescriptor_t rnnDesc, const int layer, miopenTensorDescriptor_t xDesc, const int biasID, miopenTensorDescriptor_t biasDesc, size_t *layerBiasOffset)
	Gets a bias index offset for a specific layer in an RNN stack. More...

miopenStatus_t	miopenSetRNNLayerParam (miopenHandle_t handle, miopenRNNDescriptor_t rnnDesc, const int layer, miopenTensorDescriptor_t xDesc, miopenTensorDescriptor_t wDesc, void w, const int paramID, miopenTensorDescriptor_t paramDesc, const void layerParam)
	Sets a weight matrix for a specific layer in an RNN stack. More...

miopenStatus_t	miopenSetRNNLayerBias (miopenHandle_t handle, miopenRNNDescriptor_t rnnDesc, const int layer, miopenTensorDescriptor_t xDesc, miopenTensorDescriptor_t wDesc, void w, const int biasID, miopenTensorDescriptor_t biasDesc, const void layerBias)
	Sets a bias for a specific layer in an RNN stack. More...

miopenStatus_t	miopenSetRNNPaddingMode (miopenRNNDescriptor_t rnnDesc, miopenRNNPaddingMode_t paddingMode)
	Sets a bias for a specific layer in an RNN stack. More...

miopenStatus_t	miopenGetRNNPaddingMode (miopenRNNDescriptor_t rnnDesc, miopenRNNPaddingMode_t *paddingMode)
	This function retrieves the RNN padding mode from the RNN descriptor. More...

miopenStatus_t	miopenRNNForward (miopenHandle_t handle, const miopenRNNDescriptor_t rnnDesc, miopenRNNFWDMode_t fwdMode, const miopenSeqTensorDescriptor_t xDesc, const void x, const miopenTensorDescriptor_t hDesc, const void hx, void hy, const miopenTensorDescriptor_t cDesc, const void cx, void cy, const miopenSeqTensorDescriptor_t yDesc, void y, const void w, size_t weightSpaceSize, void workSpace, size_t workSpaceNumBytes, void *reserveSpace, size_t reserveSpaceNumBytes)
	Execute forward training for recurrent layer. More...

miopenStatus_t	miopenRNNBackwardSeqData (miopenHandle_t handle, const miopenRNNDescriptor_t rnnDesc, const miopenSeqTensorDescriptor_t yDesc, const void y, const void dy, const miopenTensorDescriptor_t hDesc, const void hx, const void dhy, void dhx, const miopenTensorDescriptor_t cDesc, const void cx, const void dcy, void dcx, const miopenSeqTensorDescriptor_t xDesc, void dx, const void w, size_t weightSpaceSize, void workSpace, size_t workSpaceNumBytes, void reserveSpace, size_t reserveSpaceNumBytes)
	Execute backward data for recurrent layer. More...

miopenStatus_t	miopenRNNBackwardWeightsSeqTensor (miopenHandle_t handle, const miopenRNNDescriptor_t rnnDesc, const miopenSeqTensorDescriptor_t xDesc, const void x, const miopenTensorDescriptor_t hDesc, const void hx, const miopenSeqTensorDescriptor_t yDesc, const void y, void dw, size_t weightSpaceSize, void workSpace, size_t workSpaceNumBytes, const void reserveSpace, size_t reserveSpaceNumBytes)
	Execute backward weights for recurrent layer. More...

miopenStatus_t	miopenRNNForwardTraining (miopenHandle_t handle, const miopenRNNDescriptor_t rnnDesc, const int sequenceLen, const miopenTensorDescriptor_t xDesc, const void x, const miopenTensorDescriptor_t hxDesc, const void hx, const miopenTensorDescriptor_t cxDesc, const void cx, const miopenTensorDescriptor_t wDesc, const void w, const miopenTensorDescriptor_t yDesc, void y, const miopenTensorDescriptor_t hyDesc, void hy, const miopenTensorDescriptor_t cyDesc, void cy, void workSpace, size_t workSpaceNumBytes, void *reserveSpace, size_t reserveSpaceNumBytes)
	Execute forward training for recurrent layer. More...

miopenStatus_t	miopenRNNBackwardData (miopenHandle_t handle, const miopenRNNDescriptor_t rnnDesc, const int sequenceLen, const miopenTensorDescriptor_t yDesc, const void y, const miopenTensorDescriptor_t dyDesc, const void dy, const miopenTensorDescriptor_t dhyDesc, const void dhy, const miopenTensorDescriptor_t dcyDesc, const void dcy, const miopenTensorDescriptor_t wDesc, const void w, const miopenTensorDescriptor_t hxDesc, const void hx, const miopenTensorDescriptor_t cxDesc, const void cx, const miopenTensorDescriptor_t dxDesc, void dx, const miopenTensorDescriptor_t dhxDesc, void dhx, const miopenTensorDescriptor_t dcxDesc, void dcx, void workSpace, size_t workSpaceNumBytes, void *reserveSpace, size_t reserveSpaceNumBytes)
	Execute backward data for recurrent layer. More...

miopenStatus_t	miopenRNNBackwardWeights (miopenHandle_t handle, const miopenRNNDescriptor_t rnnDesc, const int sequenceLen, const miopenTensorDescriptor_t xDesc, const void x, const miopenTensorDescriptor_t hxDesc, const void hx, const miopenTensorDescriptor_t yDesc, const void y, const miopenTensorDescriptor_t dwDesc, void dw, void workSpace, size_t workSpaceNumBytes, const void reserveSpace, size_t reserveSpaceNumBytes)
	Execute backward weights for recurrent layer. More...

miopenStatus_t	miopenRNNForwardInference (miopenHandle_t handle, miopenRNNDescriptor_t rnnDesc, const int sequenceLen, const miopenTensorDescriptor_t xDesc, const void x, const miopenTensorDescriptor_t hxDesc, const void hx, const miopenTensorDescriptor_t cxDesc, const void cx, const miopenTensorDescriptor_t wDesc, const void w, const miopenTensorDescriptor_t yDesc, void y, const miopenTensorDescriptor_t hyDesc, void hy, const miopenTensorDescriptor_t cyDesc, void cy, void workSpace, size_t workSpaceNumBytes)
	Execute forward inference for RNN layer. More...

Detailed Description

Enumeration Type Documentation

◆ miopenRNNAlgo_t

enum miopenRNNAlgo_t

Recurrent Neural Network algorithm mode

Enumerator
miopenRNNdefault	Use dedicated gate-operation kernel for LSTM and fundamental algorithm for vanilla RNN & GRU
miopenRNNfundamental	Function by basic tesnsor operations, supported for vanilla RNN, LSTM, GRU

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenRNNBaseLayout_t

enum miopenRNNBaseLayout_t

Data layouts for RNN operations

Enumerator
miopenRNNDataUnknownLayout
miopenRNNDataSeqMajorNotPadded
miopenRNNDataSeqMajorPadded
miopenRNNDataBatchMajorPadded

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenRNNBiasMode_t

enum miopenRNNBiasMode_t

Recurrent Neural Network add on bias

Enumerator
miopenRNNNoBias	No Biases will be applied to GEMM operations
miopenRNNwithBias	Biases will be applied to GEMM operations

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenRNNDirectionMode_t

enum miopenRNNDirectionMode_t

Recurrent Neural Network bi-directional behavior

Enumerator
miopenRNNunidirection	Forward in time only.
miopenRNNbidirection	Forward and backwards in time.

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenRNNFWDMode_t

enum miopenRNNFWDMode_t

Recurrent Neural Network Training/Inference mode

Enumerator
miopenRNNTraining	FWD, BWD, WRW
miopenRNNInference	Only FWD-inference no back-propagation

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenRNNGEMMalgoMode_t

enum miopenRNNGEMMalgoMode_t

Recurrent Neural Network add on bias

Enumerator
miopenRNNAlgoGEMM

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenRNNInputMode_t

enum miopenRNNInputMode_t

Recurrent Neural Network layer initial input mode

Enumerator
miopenRNNlinear	Matrix multiplication at the input of the first layer
miopenRNNskip	No operation is performed at the input of the first layer.

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenRNNMode_t

enum miopenRNNMode_t

RNN mode selection for rnn layer preference

Enumerator
miopenRNNRELU	RNN with ReLU activation
miopenRNNTANH	RNN with tanh activation
miopenLSTM	LSTM
miopenGRU	GRU

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenRNNPaddingMode_t

enum miopenRNNPaddingMode_t

Recurrent Neural Network input/output data padding mode

Enumerator
miopenRNNIONotPadded	Not padded data at RNN input/output
miopenRNNIOWithPadding	Padded data at RNN input/output

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

Function Documentation

◆ MIOPEN_DECLARE_OBJECT()

MIOPEN_DECLARE_OBJECT ( miopenRNNDescriptor )

Creates the miopenRNNDescriptor_t type.

◆ miopenCreateRNNDescriptor()

miopenStatus_t miopenCreateRNNDescriptor ( miopenRNNDescriptor_t * rnnDesc )

Create a RNN layer Descriptor.

API for creating an uninitialized RNN layer descriptor.

Parameters

rnnDesc Pointer to a tensor descriptor type

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenDestroyRNNDescriptor()

miopenStatus_t miopenDestroyRNNDescriptor ( miopenRNNDescriptor_t rnnDesc )

Destroys the tensor descriptor object.

Parameters

rnnDesc RNN tensor descriptor type (input)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenGetRNNDataSeqTensorDescriptor()

miopenStatus_t miopenGetRNNDataSeqTensorDescriptor	(	miopenSeqTensorDescriptor_t	seqTensorDesc,
		miopenDataType_t *	dataType,
		miopenRNNBaseLayout_t *	layout,
		int *	maxSequenceLen,
		int *	batchSize,
		int *	vectorSize,
		int	sequenceLenArrayLimit,
		int *	sequenceLenArray,
		void *	paddingMarker
	)

Get shape of RNN seqData tensor.

Interface for setting tensor shape to be used as RNN input data

Parameters

seqTensorDesc	Tensor descriptor (input)
dataType	MIOpen datatype (output)
layout	One of the main supported layouts for RNN data(output)
maxSequenceLen	Sequence length limit within this SeqTensor(output)
batchSize	Number of sequences within this SeqTensor (output)
vectorSize	Vector size (output)
sequenceLenArrayLimit	Limit for number of elements that can be returned to user by sequenceLenArray (input)
sequenceLenArray	Array containing the length of each sequence in the SeqTensor. This is allowed to be a NULL pointer if sequenceLenArrayLimit is 0 (output)
paddingMarker	Not used, should be NULL (input)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenGetRNNDescriptor()

miopenStatus_t miopenGetRNNDescriptor	(	miopenRNNDescriptor_t	rnnDesc,
		miopenRNNMode_t *	rnnMode,
		miopenRNNAlgo_t *	algoMode,
		miopenRNNInputMode_t *	inputMode,
		miopenRNNDirectionMode_t *	dirMode,
		miopenRNNBiasMode_t *	biasMode,
		int *	hiddenSize,
		int *	layer
	)

Retrieves a RNN layer descriptor's details.

Parameters

rnnDesc	RNN layer descriptor (input)
rnnMode	RNN mode (output)
algoMode	RNN algorithm mode (output)
inputMode	RNN data input mode (output)
dirMode	Uni or bi direction mode (output)
biasMode	Bias used (output)
hiddenSize	Size of hidden state (output)
layer	Number of stacked layers (output)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenGetRNNDescriptor_V2()

miopenStatus_t miopenGetRNNDescriptor_V2	(	miopenRNNDescriptor_t	rnnDesc,
		int *	hiddenSize,
		int *	layer,
		miopenDropoutDescriptor_t *	dropoutDesc,
		miopenRNNInputMode_t *	inputMode,
		miopenRNNDirectionMode_t *	dirMode,
		miopenRNNMode_t *	rnnMode,
		miopenRNNBiasMode_t *	biasMode,
		miopenRNNAlgo_t *	algoMode,
		miopenDataType_t *	dataType
	)

Retrieves a RNN layer descriptor's details version 2. This version enables retrieving information of the dropout descriptor of the rnn descriptor.

Parameters

rnnDesc	RNN layer descriptor (input)
hiddenSize	Size of hidden state (output)
layer	Number of stacked layers (output)
dropoutDesc	Pre-configured dropout descriptor for dropout layer in between RNN layers (output)
inputMode	RNN data input mode (output)
dirMode	Uni or bi direction mode (output)
rnnMode	RNN mode (output)
biasMode	Bias used (output)
algoMode	RNN algorithm mode (output)
dataType	Data type of RNN (output)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenGetRNNHiddenTensorSize()

miopenStatus_t miopenGetRNNHiddenTensorSize	(	miopenHandle_t	handle,
		miopenRNNDescriptor_t	rnnDesc,
		const int	seqLen,
		miopenTensorDescriptor_t *	xDesc,
		size_t *	numBytes
	)

Obtain a the size in bytes of the RNN hidden tensor.

This function determines the size in bytes of the allocation needed for the hidden tensor over all layers

Parameters

handle	MIOpen handle (input)
rnnDesc	Fully populated RNN layer descriptor type (input)
seqLen	Number of iteration unrolls (input)
xDesc	An array of previously populated tensor descriptors (input)
numBytes	Number of bytes required for input tensor (output)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenGetRNNInputTensorSize()

miopenStatus_t miopenGetRNNInputTensorSize	(	miopenHandle_t	handle,
		miopenRNNDescriptor_t	rnnDesc,
		const int	seqLen,
		miopenTensorDescriptor_t *	xDesc,
		size_t *	numBytes
	)

Obtain a the size in bytes of the RNN input tensor.

This function determines the size in bytes of the allocation needed for the input data tensor for an RNN layer. The number of bytes is derived from the array of tensor descriptors.

Parameters

handle	MIOpen handle (input)
rnnDesc	Fully populated RNN layer descriptor (input)
seqLen	Number of iteration unrolls (input)
xDesc	An array of tensor descriptors. These are the input descriptors to each time step. The first dimension of each descriptor is the batch size and may decrease from element n to element n+1 and not increase in size. The second dimension is the same for all descriptors in the array and is the input vector length. (input)
numBytes	Number of bytes required for input tensor (output)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenGetRNNLayerBias()

miopenStatus_t miopenGetRNNLayerBias	(	miopenHandle_t	handle,
		miopenRNNDescriptor_t	rnnDesc,
		const int	layer,
		miopenTensorDescriptor_t	xDesc,
		miopenTensorDescriptor_t	wDesc,
		const void *	w,
		const int	biasID,
		miopenTensorDescriptor_t	biasDesc,
		void *	layerBias
	)

Gets a bias for a specific layer in an RNN stack.

This function retrieves the bias data for a specific layer and bias ID and copies the data into previously allocated device memory.

For RNN vanilla miopenRNNRELU and miopenRNNTANH, biasID == 0 retrieves the bias associated with the in input GEMM, while biasID == 1 retrieves the bias associated with the hidden state GEMM.

For miopenLSTM biasID 0 to 3 refer to the biases associated with the input GEMM, 4-7 are associated with biases associated with the hidden state GEMM.

biasID 0 and 4 are for the input gate.
biasID 1 and 5 are for the forget gate.
biasID 2 and 6 are for the output gate.
biasID 3 and 7 are for the new memory gate.

For miopenGRU biasID 0 to 2 refer to the biases associated with the input GEMM, while 3 through 5 are associated with the hidden state GEMM.

biasID 0 and 3 are for the update gate.
biasID 1 and 4 are for the reset gate.
biasID 2 and 5 are for the new memory gate.

For bi-directional RNNs the backwards in time direction is numbered as the layer directly after the forward in time direction.

The output argument biasDesc is a previously created tensor descriptor that is populated to describe the memory layout of the bias. It is full packed and is used when calling to miopenSetRNNLayerBias()

The argument layerBias should either be nullptr, or have device memory allocated to allow copying of the entire layer bias into it. If layerBias is nullptr then only the biasDesc is populated and returned. The size in bytes of the layer bias can be determined by using miopenGetRNNLayerBiasSize().

Note: When inputSkip mode is selected there is no input layer matrix operation, and therefore no associated memory. In this case miopenGetRNNLayerBias() will return a error status miopenStatusBadParm for input biasID associated with the input GEMM.

Parameters

handle	MIOpen handle (input)
rnnDesc	RNN layer descriptor type (input)
layer	The layer number in the RNN stack (input)
xDesc	A tensor descriptor to input (input)
wDesc	A tensor descriptor to the parameter tensor (input)
w	Pointer to memory containing parameter tensor (input)
biasID	ID of the internal parameter tensor (input)
biasDesc	Descriptor of the parameter tensor (output)
layerBias	Pointer to the memory location of the bias tensor (output)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenGetRNNLayerBiasOffset()

miopenStatus_t miopenGetRNNLayerBiasOffset	(	miopenRNNDescriptor_t	rnnDesc,
		const int	layer,
		miopenTensorDescriptor_t	xDesc,
		const int	biasID,
		miopenTensorDescriptor_t	biasDesc,
		size_t *	layerBiasOffset
	)

Gets a bias index offset for a specific layer in an RNN stack.

This function retrieves the bias index offset for a specific layer and bias ID.

For RNN vanilla miopenRNNRELU and miopenRNNTANH, biasID == 0 retrieves the bias associated with the in input GEMM, while biasID == 1 retrieves the weight matrix associated with the hidden state GEMM.

For miopenLSTM biasID 0 to 3 refer to the bias offset associated with the input GEMM, 4-7 are the bias offsets associated with the hidden state GEMM.

biasID 0 and 4 are for the input gate.
biasID 1 and 5 are for the forget gate.
biasID 2 and 6 are for the output gate.
biasID 3 and 7 are for the new memory gate.

For miopenGRU biasID 0 to 2 refer to the biases associated with the input GEMM, while 3 through 5 are associated with the hidden state GEMM.

biasID 0 and 3 are for the update gate.
biasID 1 and 4 are for the reset gate.
biasID 2 and 5 are for the new memory gate.

For bi-directional RNNs the backwards in time direction is numbered as the layer directly after the forward in time direction.

The output argument biasDesc is a previously created tensor descriptor that is populated to describe the memory layout of the bias. It is full packed and is used when calling to miopenSetRNNLayerBias()

The argument layerBiasOffset should either be nullptr, or point to an output address. If layerBias is nullptr then only the biasDesc is populated and returned.

Note: When inputSkip mode is selected there is no input layer matrix operation, and therefore no associated memory. In this case miopenGetRNNLayerBiasOffset() will return a error status miopenStatusBadParm for input biasID associated with the input GEMM.

Parameters

rnnDesc	RNN layer descriptor type (input)
layer	The layer number in the RNN stack (input)
xDesc	A tensor descriptor to input (input)
biasID	ID of the internal parameter tensor (input)
biasDesc	Descriptor of the parameter tensor (output)
layerBiasOffset	Pointer to the memory location of the bias tensor (output)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenGetRNNLayerBiasSize()

miopenStatus_t miopenGetRNNLayerBiasSize	(	miopenHandle_t	handle,
		miopenRNNDescriptor_t	rnnDesc,
		const int	layer,
		const int	biasID,
		size_t *	numBytes
	)

Gets the number of bytes of a bias.

For RNN vanilla miopenRNNRELU and miopenRNNTANH, biasID == 0 retrieves the weight matrix associated with the in input GEMM, while biasID == 1 retrieves the bias associated with the hidden state GEMM.

For miopenLSTM biasID 0 to 3 refer to the biases associated with the input GEMM, 4-7 are associated with biases associated with the hidden state GEMM.

biasID 0 and 4 are for the input gate.
biasID 1 and 5 are for the forget gate.
biasID 2 and 6 are for the output gate.
biasID 3 and 7 are for the new memory gate.

For miopenGRU biasID 0 to 2 refer to the biases associated with the input GEMM, while 3 through 5 are associated with the hidden state GEMM.

biasID 0 and 3 are for the update gate.
biasID 1 and 4 are for the reset gate.
biasID 2 and 5 are for the new memory gate.

For bi-directional RNNs the backwards in time direction is numbered as the layer directly after the forward in time direction.

Parameters

handle	MIOpen handle (input)
rnnDesc	RNN layer descriptor type (input)
layer	The layer number in the RNN stack (input)
biasID	ID of the internal parameter tensor (input)
numBytes	The number of bytes of the layer's bias (output)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenGetRNNLayerParam()

miopenStatus_t miopenGetRNNLayerParam	(	miopenHandle_t	handle,
		miopenRNNDescriptor_t	rnnDesc,
		const int	layer,
		miopenTensorDescriptor_t	xDesc,
		miopenTensorDescriptor_t	wDesc,
		const void *	w,
		const int	paramID,
		miopenTensorDescriptor_t	paramDesc,
		void *	layerParam
	)

Gets a weight matrix for a specific layer in an RNN stack.

This function retrieves the weight matrix data for a specific layer and parameter ID and copies the data into previously allocated device memory.

For RNN vanilla miopenRNNRELU and miopenRNNTANH, paramID == 0 retrieves the weight matrix associated with the in input GEMM, while paramID == 1 retrieves the weight matrix associated with the hidden state GEMM.

For miopenLSTM paramID 0 to 3 refer to the weight matrices associated with the input GEMM, 4-7 are associated with matrices associated with the hidden state GEMM.

paramID 0 and 4 are for the input gate.
paramID 1 and 5 are for the forget gate.
paramID 2 and 6 are for the output gate.
paramID 3 and 7 are for the new memory gate.

For miopenGRU paramID 0 to 2 refer to the weight matrix offset associated with the input GEMM, while 3 through 5 are associated with the hidden state GEMM.

paramID 0 and 3 are for the update gate.
paramID 1 and 4 are for the reset gate.
paramID 2 and 5 are for the new memory gate.

For bi-directional RNNs the backwards in time direction is numbered as the layer directly after the forward in time direction.

The output argument paramDesc is a previously created tensor descriptor that is populated to describe the memory layout of the parameter matrix. It is full packed and is used when calling to miopenSetRNNLayerParam()

The argument layerParam should either be nullptr, or have device memory allocated to allow copying of the entire layer parameter matrix into it. If layerParam is nullptr then only the paramDesc is populated and returned. The size in bytes of the layer parameter matrix can be determined by using miopenGetRNNLayerParamSize().

Note: When inputSkip mode is selected there is no input layer matrix operation, and therefore no associated memory. In this case miopenGetRNNLayerParam() will return a error status miopenStatusBadParm for input paramID associated with the input GEMM.

Parameters

handle	MIOpen handle (input)
rnnDesc	RNN layer descriptor type (input)
layer	The layer number in the RNN stack (input)
xDesc	A tensor descriptor to input (input)
wDesc	A tensor descriptor to the parameter tensor (input)
w	Pointer to memory containing parameter tensor (input)
paramID	ID of the internal parameter tensor (input)
paramDesc	Tensor descriptor for the fully packed output parameter tensor (output)
layerParam	Pointer to the memory location of the parameter tensor (output)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenGetRNNLayerParamOffset()

miopenStatus_t miopenGetRNNLayerParamOffset	(	miopenRNNDescriptor_t	rnnDesc,
		const int	layer,
		miopenTensorDescriptor_t	xDesc,
		const int	paramID,
		miopenTensorDescriptor_t	paramDesc,
		size_t *	layerParamOffset
	)

Gets an index offset for a specific weight matrix for a layer in the RNN stack.

This function retrieves the index offset for a weight matrix in a layer.

For RNN vanilla miopenRNNRELU and miopenRNNTANH, paramID == 0 retrieves the weight matrix offset associated with the in input GEMM, while paramID == 1 retrieves the weight matrix offset associated with the hidden state GEMM.

For miopenLSTM paramID 0 to 3 refer to the weight matrix offsets associated with the input GEMM, 4-7 are associated with matrix offset associated with the hidden state GEMM.

paramID 0 and 4 are for the input gate.
paramID 1 and 5 are for the forget gate.
paramID 2 and 6 are for the output gate.
paramID 3 and 7 are for the new memory gate.

For miopenGRU paramID 0 to 2 refer to the weight matrix offset associated with the input GEMM, while 3 through 5 are associated with the hidden state GEMM.

paramID 0 and 3 are for the update gate.
paramID 1 and 4 are for the reset gate.
paramID 2 and 5 are for the new memory gate.

For bi-directional RNNs the backwards in time direction is numbered as the layer directly after the forward in time direction.

The output argument paramDesc is a previously created tensor descriptor that is populated to describe the memory layout of the parameter matrix. It is full packed and is used when calling to miopenSetRNNLayerParam().

The argument layerParamOffset should either be nullptr, or an address to place the offset. If layerParamOffset is nullptr then only the paramDesc is populated and returned.

Note: When inputSkip mode is selected there is no input layer matrix operation, and therefore no associated memory. In this case miopenGetRNNLayerParamOffset() will return a error status miopenStatusBadParm for input paramID associated with the input GEMM.

Parameters

rnnDesc	RNN layer descriptor type (input)
layer	The layer number in the RNN stack (input)
xDesc	A tensor descriptor to input (input)
paramID	ID of the internal parameter tensor (input)
paramDesc	Tensor descriptor for the fully packed output parameter tensor (output)
layerParamOffset	Location for the parameter offset (output)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenGetRNNLayerParamSize()

miopenStatus_t miopenGetRNNLayerParamSize	(	miopenHandle_t	handle,
		miopenRNNDescriptor_t	rnnDesc,
		const int	layer,
		miopenTensorDescriptor_t	xDesc,
		const int	paramID,
		size_t *	numBytes
	)

Gets the number of bytes of a parameter matrix.

For RNN vanilla miopenRNNRELU and miopenRNNTANH, paramID == 0 retrieves the weight matrix associated with the in input GEMM, while paramID == 1 retrieves the weight matrix associated with the hidden state GEMM.

For miopenLSTM paramID 0 to 3 refer to the weight matrices associated with the input GEMM, 4-7 are associated with matrices associated with the hidden state GEMM.

paramID 0 and 4 are for the input gate.
paramID 1 and 5 are for the forget gate.
paramID 2 and 6 are for the output gate.
paramID 3 and 7 are for the new memory gate.

For miopenGRU paramID 0 to 2 refer to the weight matrix offset associated with the input GEMM, while 3 through 5 are associated with the hidden state GEMM.

paramID 0 and 3 are for the update gate.
paramID 1 and 4 are for the reset gate.
paramID 2 and 5 are for the new memory gate.

For bi-directional RNNs the backwards in time direction is numbered as the layer directly after the forward in time direction.

Parameters

handle	MIOpen handle (input)
rnnDesc	RNN layer descriptor type (input)
layer	The layer number in the RNN stack (input)
xDesc	A tensor descriptor to input (input)
paramID	ID of the internal parameter tensor (input)
numBytes	The number of bytes of the layer's parameter matrix (output)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenGetRNNPaddingMode()

miopenStatus_t miopenGetRNNPaddingMode	(	miopenRNNDescriptor_t	rnnDesc,
		miopenRNNPaddingMode_t *	paddingMode
	)

This function retrieves the RNN padding mode from the RNN descriptor.

Parameters

rnnDesc	RNN layer descriptor type (input)
paddingMode	Pointer to the RNN padding mode (output)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenGetRNNParamsDescriptor()

miopenStatus_t miopenGetRNNParamsDescriptor	(	miopenHandle_t	handle,
		miopenRNNDescriptor_t	rnnDesc,
		miopenTensorDescriptor_t	xDesc,
		miopenTensorDescriptor_t	wDesc,
		miopenDataType_t	dtype
	)

Obtain a weight tensor descriptor for RNNs.

This function populates a weight descriptor that describes the memory layout of the weight matrix.

Parameters

handle	MIOpen handle (input)
rnnDesc	Fully populated RNN layer descriptor type (input)
xDesc	A previously populated tensor descriptor (input)
wDesc	A previously allocated tensor descriptor (output)
dtype	MIOpen data type enum (input)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenGetRNNParamsSize()

miopenStatus_t miopenGetRNNParamsSize	(	miopenHandle_t	handle,
		miopenRNNDescriptor_t	rnnDesc,
		miopenTensorDescriptor_t	xDesc,
		size_t *	numBytes,
		miopenDataType_t	dtype
	)

Query the amount of parameter memory required for RNN training.

This function calculates the amount of parameter memory required to train the RNN layer given an RNN descriptor and a tensor descriptor.

Parameters

handle	MIOpen handle (input)
rnnDesc	RNN layer descriptor type (input)
xDesc	A tensor descriptor (input)
numBytes	Number of bytes required for RNN layer execution (output)
dtype	MIOpen data type enum (input)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenGetRNNTempSpaceSizes()

miopenStatus_t miopenGetRNNTempSpaceSizes	(	miopenHandle_t	handle,
		miopenRNNDescriptor_t	rnnDesc,
		miopenSeqTensorDescriptor_t	xDesc,
		miopenRNNFWDMode_t	fwdMode,
		size_t *	workSpaceSize,
		size_t *	reserveSpaceSize
	)

Query the amount of additional memory required for this RNN layer execution.

This function calculates the size of extra buffers, depending on the layer configuration, which is determined by: RNN descriptor, isInference, and data descriptor. If isInference is True, reserve_space_size is always zero, because the reserve_space buffer is not used in Inference computation.

Parameters

handle	MIOpen handle (input)
rnnDesc	RNN layer descriptor type (input)
xDesc	Sequence data tensor descriptor (input)
fwdMode	Specifies in which mode the buffers will be used.
workSpaceSize	Minimum WorkSpace buffer size required for RNN layer execution (output)
reserveSpaceSize	Minimum ReserveSpaceSize buffer size required for RNN layer execution (output)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenGetRNNTrainingReserveSize()

miopenStatus_t miopenGetRNNTrainingReserveSize	(	miopenHandle_t	handle,
		miopenRNNDescriptor_t	rnnDesc,
		const int	sequenceLen,
		const miopenTensorDescriptor_t *	xDesc,
		size_t *	numBytes
	)

Query the amount of memory required for RNN training.

This function calculates the amount of memory required to train the RNN layer given an RNN descriptor and a tensor descriptor.

Parameters

handle	MIOpen handle (input)
rnnDesc	RNN layer descriptor type (input)
sequenceLen	Number of iteration unrolls (input)
xDesc	An array of tensor descriptors. These are the input descriptors to each time step. The first dimension of each descriptor is the batch size and may decrease from element n to element n+1 and not increase in size. The second dimension is the same for all descriptors in the array and is the input vector length. (input)
numBytes	Number of bytes required for RNN layer execution (output)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenGetRNNWorkspaceSize()

miopenStatus_t miopenGetRNNWorkspaceSize	(	miopenHandle_t	handle,
		const miopenRNNDescriptor_t	rnnDesc,
		const int	sequenceLen,
		const miopenTensorDescriptor_t *	xDesc,
		size_t *	numBytes
	)

Query the amount of memory required to execute the RNN layer.

This function calculates the amount of memory required to run the RNN layer given an RNN descriptor and a tensor descriptor.

Parameters

handle	MIOpen handle (input)
rnnDesc	RNN layer descriptor type (input)
sequenceLen	Number of iteration unrolls (input)
xDesc	An array of tensor descriptors. These are the input descriptors to each time step. The first dimension of each descriptor is the batch size and may decrease from element n to element n+1 and not increase in size. The second dimension is the same for all descriptors in the array and is the input vector length. (input)
numBytes	Number of bytes required for RNN layer execution (output)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenRNNBackwardData()

miopenStatus_t miopenRNNBackwardData	(	miopenHandle_t	handle,
		const miopenRNNDescriptor_t	rnnDesc,
		const int	sequenceLen,
		const miopenTensorDescriptor_t *	yDesc,
		const void *	y,
		const miopenTensorDescriptor_t *	dyDesc,
		const void *	dy,
		const miopenTensorDescriptor_t	dhyDesc,
		const void *	dhy,
		const miopenTensorDescriptor_t	dcyDesc,
		const void *	dcy,
		const miopenTensorDescriptor_t	wDesc,
		const void *	w,
		const miopenTensorDescriptor_t	hxDesc,
		const void *	hx,
		const miopenTensorDescriptor_t	cxDesc,
		const void *	cx,
		const miopenTensorDescriptor_t *	dxDesc,
		void *	dx,
		const miopenTensorDescriptor_t	dhxDesc,
		void *	dhx,
		const miopenTensorDescriptor_t	dcxDesc,
		void *	dcx,
		void *	workSpace,
		size_t	workSpaceNumBytes,
		void *	reserveSpace,
		size_t	reserveSpaceNumBytes
	)

Execute backward data for recurrent layer.

Interface for executing the backward data pass on a RNN.

Parameters

handle	MIOpen handle (input)
rnnDesc	RNN layer descriptor type (input)
sequenceLen	Temporal iterations to unroll (input)
yDesc	An array of tensor descriptors (input)
y	Pointer to input tensor (input)
dyDesc	An array of fully packed tensor descriptors associated with the output from each time step. The first dimension of the tensor descriptors must equal the first dimension of the first descriptor (batch size) in the xDesc tensor array. The second dimension of the element of the descriptor array depends on the direction mode selected. If the direction mode is unidirectional, the second dimension is the hiddenSize. If direction mode is bidirectional the second dimension is twice the hiddenSize. (input)
dy	Pointer to the hidden layer input tensor (input)
dhyDesc	A hidden tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
dhy	Pointer to the cell layer input tensor (input)
dcyDesc	A cell tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
dcy	Pointer to the cell layer input tensor. If dcy is NULL, then the initial delta cell state will be zero initialized. (input)
wDesc	A weights tensor descriptor (input)
w	Pointer to input weights tensor (input)
hxDesc	An input hidden tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
hx	Pointer to the hidden layer input tensor. If hx is NULL, then the initial hidden state will be zero initialized. (input)
cxDesc	A input cell tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
cx	Pointer to the hidden layer input tensor. If cx is NULL, then the initial cell state will be zero initialized. (input)
dxDesc	An array of tensor descriptors. These are the input descriptors to each time step. The first dimension of each descriptor is the batch size and may decrease from element n to element n+1 and not increase in size. The second dimension is the same for all descriptors in the array and is the input vector length. (input)
dx	Pointer to the cell layer output tensor (output)
dhxDesc	A hidden tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
dhx	Pointer to the delta hidden layer output tensor. If dhx is NULL the hidden gradient will not ouput. (output)
dcxDesc	A tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
dcx	Pointer to the cell layer output tensor. If dcx is NULL the cell gradient will not ouput. (output)
workSpace	Pointer to memory allocated for forward training (input)
workSpaceNumBytes	Number of allocated bytes in memory for the workspace (input)
reserveSpace	Pointer to memory allocated for random states (input / output)
reserveSpaceNumBytes	Number of allocated bytes in memory for use in the forward (input)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenRNNBackwardSeqData()

miopenStatus_t miopenRNNBackwardSeqData	(	miopenHandle_t	handle,
		const miopenRNNDescriptor_t	rnnDesc,
		const miopenSeqTensorDescriptor_t	yDesc,
		const void *	y,
		const void *	dy,
		const miopenTensorDescriptor_t	hDesc,
		const void *	hx,
		const void *	dhy,
		void *	dhx,
		const miopenTensorDescriptor_t	cDesc,
		const void *	cx,
		const void *	dcy,
		void *	dcx,
		const miopenSeqTensorDescriptor_t	xDesc,
		void *	dx,
		const void *	w,
		size_t	weightSpaceSize,
		void *	workSpace,
		size_t	workSpaceNumBytes,
		void *	reserveSpace,
		size_t	reserveSpaceNumBytes
	)

Execute backward data for recurrent layer.

Interface for executing the backward data pass on a RNN.

Parameters

handle	MIOpen handle (input)
rnnDesc	RNN layer descriptor type (input)
yDesc	An output tensor descriptor for sequenced RNN data. This miopenSeqTensorDescriptor_t should be initialyzed by `miopenSetRNNDataSeqTensorDescriptor` function.(input)
y	Pointer to input tensor (input)
dy	Pointer to the hidden layer input tensor (input)
hDesc	An input hidden tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
hx	Pointer to the hidden layer input tensor. If hx is NULL, then the initial hidden state will be zero initialized. (input)
dhy	Pointer to the cell layer input tensor (input)
dhx	Pointer to the delta hidden layer output tensor. If dhx is NULL the hidden gradient will not ouput. (output)
cDesc	A input cell tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
cx	Pointer to the hidden layer input tensor. If cx is NULL, then the initial cell state will be zero initialized. (input)
dcy	Pointer to the cell layer input tensor. If dcy is NULL, then the initial delta cell state will be zero initialized. (input)
dcx	Pointer to the cell layer output tensor. If dcx is NULL the cell gradient will not ouput. (output)
xDesc	An input tensor descriptor for sequenced RNN data. This miopenSeqTensorDescriptor_t should be initialyzed by `miopenSetRNNDataSeqTensorDescriptor` function.(input)
dx	Pointer to the cell layer output tensor (output)
w	Pointer to input weights tensor (input)
weightSpaceSize	Number of allocated bytes in memory for the weights tensor
workSpace	Pointer to memory allocated for forward training (input)
workSpaceNumBytes	Number of allocated bytes in memory for the workspace (input)
reserveSpace	Pointer to memory allocated for random states (input / output)
reserveSpaceNumBytes	Number of allocated bytes in memory for use in the forward (input)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenRNNBackwardWeights()

miopenStatus_t miopenRNNBackwardWeights	(	miopenHandle_t	handle,
		const miopenRNNDescriptor_t	rnnDesc,
		const int	sequenceLen,
		const miopenTensorDescriptor_t *	xDesc,
		const void *	x,
		const miopenTensorDescriptor_t	hxDesc,
		const void *	hx,
		const miopenTensorDescriptor_t *	yDesc,
		const void *	y,
		const miopenTensorDescriptor_t	dwDesc,
		void *	dw,
		void *	workSpace,
		size_t	workSpaceNumBytes,
		const void *	reserveSpace,
		size_t	reserveSpaceNumBytes
	)

Execute backward weights for recurrent layer.

Interface for executing the backward weights pass on a RNN.

Parameters

handle	MIOpen handle (input)
rnnDesc	RNN layer descriptor type (input)
sequenceLen	Temporal iterations to unroll (input)
xDesc	An array of tensor descriptors. These are the input descriptors to each time step. The first dimension of each descriptor is the batch size and may decrease from element n to element n+1 and not increase in size. The second dimension is the same for all descriptors in the array and is the input vector length. (input)
x	Pointer to input tensor (input)
hxDesc	A hidden tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
hx	Pointer to the hidden layer input tensor. If hx is NULL, then the initial hidden state will be zero initialized. (input)
yDesc	An array of fully packed tensor descriptors associated with the output from each time step. The first dimension of the tensor descriptors must equal the first dimension of the first descriptor (batch size) in the xDesc tensor array. The second dimension of the element of the descriptor array depends on the direction mode selected. If the direction mode is unidirectional, the second dimension is the hiddenSize. If direction mode is bidirectional the second dimension is twice the hiddenSize. (input)
y	Pointer to the output tensor (input)
dwDesc	A weights tensor descriptor (input)
dw	Pointer to input weights tensor (input / output)
workSpace	Pointer to memory allocated for forward training (input)
workSpaceNumBytes	Number of allocated bytes in memory for the workspace (input)
reserveSpace	Pointer to memory allocated for random states (input)
reserveSpaceNumBytes	Number of allocated bytes in memory for use in the forward (input)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenRNNBackwardWeightsSeqTensor()

miopenStatus_t miopenRNNBackwardWeightsSeqTensor	(	miopenHandle_t	handle,
		const miopenRNNDescriptor_t	rnnDesc,
		const miopenSeqTensorDescriptor_t	xDesc,
		const void *	x,
		const miopenTensorDescriptor_t	hDesc,
		const void *	hx,
		const miopenSeqTensorDescriptor_t	yDesc,
		const void *	y,
		void *	dw,
		size_t	weightSpaceSize,
		void *	workSpace,
		size_t	workSpaceNumBytes,
		const void *	reserveSpace,
		size_t	reserveSpaceNumBytes
	)

Execute backward weights for recurrent layer.

Interface for executing the backward weights pass on a RNN.

Parameters

handle	MIOpen handle (input)
rnnDesc	RNN layer descriptor type (input)
xDesc	An input tensor descriptor for sequenced RNN data. This miopenSeqTensorDescriptor_t should be initialyzed by `miopenSetRNNDataSeqTensorDescriptor` function.(input)
x	Pointer to input tensor (input)
hDesc	A hidden tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
hx	Pointer to the hidden layer input tensor. If hx is NULL, then the initial hidden state will be zero initialized. (input)
yDesc	An output tensor descriptor for sequenced RNN data. This miopenSeqTensorDescriptor_t should be initialyzed by `miopenSetRNNDataSeqTensorDescriptor` function.(input)
y	Pointer to the output tensor (input)
dw	Pointer to input weights tensor (input / output)
weightSpaceSize	Number of allocated bytes in memory for the weights tensor
workSpace	Pointer to memory allocated for forward training (input)
workSpaceNumBytes	Number of allocated bytes in memory for the workspace (input)
reserveSpace	Pointer to memory allocated for random states (input)
reserveSpaceNumBytes	Number of allocated bytes in memory for use in the forward (input)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenRNNForward()

miopenStatus_t miopenRNNForward	(	miopenHandle_t	handle,
		const miopenRNNDescriptor_t	rnnDesc,
		miopenRNNFWDMode_t	fwdMode,
		const miopenSeqTensorDescriptor_t	xDesc,
		const void *	x,
		const miopenTensorDescriptor_t	hDesc,
		const void *	hx,
		void *	hy,
		const miopenTensorDescriptor_t	cDesc,
		const void *	cx,
		void *	cy,
		const miopenSeqTensorDescriptor_t	yDesc,
		void *	y,
		const void *	w,
		size_t	weightSpaceSize,
		void *	workSpace,
		size_t	workSpaceNumBytes,
		void *	reserveSpace,
		size_t	reserveSpaceNumBytes
	)

Execute forward training for recurrent layer.

Interface for executing the forward training / inference pass on a RNN.

Parameters

handle	MIOpen handle (input)
rnnDesc	RNN layer descriptor type (input)
fwdMode	Specifies in which mode the buffers will be used.
xDesc	An input tensor descriptor for sequenced RNN data. This miopenSeqTensorDescriptor_t should be initialyzed by `miopenSetRNNDataSeqTensorDescriptor` function.(input)
x	Pointer to input tensor (input)
hDesc	A hidden tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
hx	Pointer to the hidden layer input tensor. If hx is NULL, then the initial hidden state will be zero initialized. (input)
hy	Pointer to the hidden layer output tensor. If hy is NULL, then the final hidden state will not be saved. (output)
cDesc	A cell tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
cx	Pointer to the cell layer input tensor. If cx is NULL, then the initial cell state will be zero initialized. (input)
cy	Pointer to the cell layer output tensor. If hy is NULL, then the final cell state will not be saved. (output)
yDesc	An array of fully packed tensor descriptors associated with the output from each time step. The first dimension of the tensor descriptors must equal the first dimension of the first descriptor (batch size) in the xDesc tensor array. The second dimension of the element of the descriptor array depends on the direction mode selected. If the direction mode is unidirectional, the second dimension is the hiddenSize. If direction mode is bidirectional the second dimension is twice the hiddenSize. (input)
y	Pointer to output tensor (output)
w	Pointer to input weights tensor (input)
weightSpaceSize	Number of allocated bytes in memory for the weights tensor
workSpace	Pointer to memory allocated for forward (input / output)
workSpaceNumBytes	Number of allocated bytes in memory for the workspace (input)
reserveSpace	Pointer to memory allocated for hidden states used durning training (input / output)
reserveSpaceNumBytes	Number of allocated bytes in memory for use in the forward (input)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenRNNForwardInference()

miopenStatus_t miopenRNNForwardInference	(	miopenHandle_t	handle,
		miopenRNNDescriptor_t	rnnDesc,
		const int	sequenceLen,
		const miopenTensorDescriptor_t *	xDesc,
		const void *	x,
		const miopenTensorDescriptor_t	hxDesc,
		const void *	hx,
		const miopenTensorDescriptor_t	cxDesc,
		const void *	cx,
		const miopenTensorDescriptor_t	wDesc,
		const void *	w,
		const miopenTensorDescriptor_t *	yDesc,
		void *	y,
		const miopenTensorDescriptor_t	hyDesc,
		void *	hy,
		const miopenTensorDescriptor_t	cyDesc,
		void *	cy,
		void *	workSpace,
		size_t	workSpaceNumBytes
	)

Execute forward inference for RNN layer.

Interface for executing the forward inference pass on a RNN.

Parameters

handle	MIOpen handle (input)
rnnDesc	RNN layer descriptor type (input)
sequenceLen	Temporal iterations to unroll (input)
xDesc	An array of tensor descriptors. These are the input descriptors to each time step. The first dimension of each descriptor is the batch size and may decrease from element n to element n+1 and not increase in size. The second dimension is the same for all descriptors in the array and is the input vector length. (input)
x	Pointer to input tensor (input)
hxDesc	A hidden tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
hx	Pointer to the hidden layer input tensor. If hx is NULL, then the initial hidden state will be zero initialized. (input)
cxDesc	A cell tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
cx	Pointer to the cell layer input tensor. If cx is NULL, then the initial cell state will be zero initialized. (input)
wDesc	A weights tensor descriptor (input)
w	Pointer to input weights tensor (input)
yDesc	An array of fully packed tensor descriptors associated with the output from each time step. The first dimension of the tensor descriptors must equal the first dimension of the first descriptor (batch size) in the xDesc tensor array. The second dimension of the element of the descriptor array depends on the direction mode selected. If the direction mode is unidirectional, the second dimension is the hiddenSize. If direction mode is bidirectional the second dimension is twice the hiddenSize. (input)
y	Pointer to output tensor (output)
hyDesc	A hidden tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
hy	Pointer to the hidden layer output tensor. If hy is NULL, then the final hidden state will not be saved. (output)
cyDesc	A output cell tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
cy	Pointer to the cell layer output tensor. If cy is NULL, then the final cell state will not be saved. (output)
workSpace	Pointer to memory allocated for forward training (input)
workSpaceNumBytes	Number of allocated bytes in memory for the workspace (input)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenRNNForwardTraining()

miopenStatus_t miopenRNNForwardTraining	(	miopenHandle_t	handle,
		const miopenRNNDescriptor_t	rnnDesc,
		const int	sequenceLen,
		const miopenTensorDescriptor_t *	xDesc,
		const void *	x,
		const miopenTensorDescriptor_t	hxDesc,
		const void *	hx,
		const miopenTensorDescriptor_t	cxDesc,
		const void *	cx,
		const miopenTensorDescriptor_t	wDesc,
		const void *	w,
		const miopenTensorDescriptor_t *	yDesc,
		void *	y,
		const miopenTensorDescriptor_t	hyDesc,
		void *	hy,
		const miopenTensorDescriptor_t	cyDesc,
		void *	cy,
		void *	workSpace,
		size_t	workSpaceNumBytes,
		void *	reserveSpace,
		size_t	reserveSpaceNumBytes
	)

Execute forward training for recurrent layer.

Interface for executing the forward training pass on a RNN.

Parameters

handle	MIOpen handle (input)
rnnDesc	RNN layer descriptor type (input)
sequenceLen	Temporal iterations to unroll (input)
xDesc	An array of tensor descriptors. These are the input descriptors to each time step. The first dimension of each descriptor is the batch size and may decrease from element n to element n+1 and not increase in size. The second dimension is the same for all descriptors in the array and is the input vector length. (input)
x	Pointer to input tensor (input)
hxDesc	A hidden tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
hx	Pointer to the hidden layer input tensor. If hx is NULL, then the initial hidden state will be zero initialized. (input)
cxDesc	A cell tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
cx	Pointer to the cell layer input tensor. If cx is NULL, then the initial cell state will be zero initialized. (input)
wDesc	A weights tensor descriptor (input)
w	Pointer to input weights tensor (input)
yDesc	An array of fully packed tensor descriptors associated with the output from each time step. The first dimension of the tensor descriptors must equal the first dimension of the first descriptor (batch size) in the xDesc tensor array. The second dimension of the element of the descriptor array depends on the direction mode selected. If the direction mode is unidirectional, the second dimension is the hiddenSize. If direction mode is bidirectional the second dimension is twice the hiddenSize. (input)
y	Pointer to output tensor (output)
hyDesc	A hidden tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
hy	Pointer to the hidden layer output tensor. If hy is NULL, then the final hidden state will not be saved. (output)
cyDesc	A cell tensor descriptor that has as its first dimension of the number of layers if the direction mode is unidirectional and twice the number of layers if the direction mode is bidirectional. The second dimension of the descriptor must equal the largest first dimension of the xDesc tensor descriptor array. The third dimension equals the hiddenSize. (input)
cy	Pointer to the cell layer output tensor. If hy is NULL, then the final cell state will not be saved. (output)
workSpace	Pointer to memory allocated for forward training (input)
workSpaceNumBytes	Number of allocated bytes in memory for the workspace (input)
reserveSpace	Pointer to memory allocated for random states (input / output)
reserveSpaceNumBytes	Number of allocated bytes in memory for use in the forward (input)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenSetRNNDataSeqTensorDescriptor()

miopenStatus_t miopenSetRNNDataSeqTensorDescriptor	(	miopenSeqTensorDescriptor_t	seqTensorDesc,
		miopenDataType_t	dataType,
		miopenRNNBaseLayout_t	layout,
		int	maxSequenceLen,
		int	batchSize,
		int	vectorSize,
		const int *	sequenceLenArray,
		void *	paddingMarker
	)

Set shape of RNN seqData tensor.

Interface for setting tensor shape to be used as RNN input data

Parameters

seqTensorDesc	Tensor descriptor (input/output)
dataType	MIOpen datatype (input)
layout	One of the main supported layouts for RNN data(input)
maxSequenceLen	Sequence length limit within this SeqTensor(input)
batchSize	Number of sequences within this SeqTensor (input)
vectorSize	Vector size (input)
sequenceLenArray	Array containing the length of each sequence in the SeqTensor(input)
paddingMarker	Not used, should be NULL (input)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenSetRNNDescriptor()

miopenStatus_t miopenSetRNNDescriptor	(	miopenRNNDescriptor_t	rnnDesc,
		const int	hsize,
		const int	nlayers,
		miopenRNNInputMode_t	inMode,
		miopenRNNDirectionMode_t	direction,
		miopenRNNMode_t	rnnMode,
		miopenRNNBiasMode_t	biasMode,
		miopenRNNAlgo_t	algo,
		miopenDataType_t	dataType
	)

Set the details of the RNN descriptor.

Interface for setting the values of the RNN descriptor object. This function requires specific algorithm selection.

Parameters

rnnDesc	RNN layer descriptor type (input)
hsize	Hidden layer size (input)
nlayers	Number of layers (input)
inMode	RNN first layer input mode (input)
direction	RNN direction (input)
rnnMode	RNN model type (input)
biasMode	RNN bias included (input)
algo	RNN algorithm selected (input)
dataType	MIOpen datatype (input)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenSetRNNDescriptor_V2()

miopenStatus_t miopenSetRNNDescriptor_V2	(	miopenRNNDescriptor_t	rnnDesc,
		const int	hsize,
		const int	nlayers,
		miopenDropoutDescriptor_t	dropoutDesc,
		miopenRNNInputMode_t	inMode,
		miopenRNNDirectionMode_t	direction,
		miopenRNNMode_t	rnnMode,
		miopenRNNBiasMode_t	biasMode,
		miopenRNNAlgo_t	algo,
		miopenDataType_t	dataType
	)

Set the details of the RNN descriptor version 2. This version enables the use of dropout in rnn.

Interface for setting the values of the RNN descriptor object. This function requires specific algorithm selection.

Parameters

rnnDesc	RNN layer descriptor type (input/output)
hsize	Hidden layer size (input)
nlayers	Number of layers (input)
dropoutDesc	Pre-initialized dropout descriptor for dropout layer in between RNN layers (input)
inMode	RNN first layer input mode (input)
direction	RNN direction (input)
rnnMode	RNN model type (input)
biasMode	RNN bias included (input)
algo	RNN algorithm selected (input)
dataType	MIOpen datatype (input)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenSetRNNLayerBias()

miopenStatus_t miopenSetRNNLayerBias	(	miopenHandle_t	handle,
		miopenRNNDescriptor_t	rnnDesc,
		const int	layer,
		miopenTensorDescriptor_t	xDesc,
		miopenTensorDescriptor_t	wDesc,
		void *	w,
		const int	biasID,
		miopenTensorDescriptor_t	biasDesc,
		const void *	layerBias
	)

Sets a bias for a specific layer in an RNN stack.

This function sets the bias data for a specific layer and bias ID.

For RNN vanilla miopenRNNRELU and miopenRNNTANH, biasID == 0 retrieves the weight matrix associated with the in input GEMM, while biasID == 1 retrieves the bias associated with the hidden state GEMM.

For miopenLSTM biasID 0 to 3 refer to the biases associated with the input GEMM, 4-7 are associated with the biases associated with the hidden state GEMM.

biasID 0 and 4 are for the input gate.
biasID 1 and 5 are for the forget gate.
biasID 2 and 6 are for the output gate.
biasID 3 and 7 are for the new memory gate.

For miopenGRU biasID 0 to 2 refer to the biases associated with the input GEMM, while 3 through 5 are associated with the hidden state GEMM.

biasID 0 and 3 are for the update gate.
biasID 1 and 4 are for the reset gate.
biasID 2 and 5 are for the new new memory gate.

For bi-directional RNNs the backwards in time direction is numbered as the layer directly after the forward in time direction.

The input argument biasDesc is a previously populated tensor descriptor typically by first calling miopenGetRNNLayeBias().

Note: When inputSkip mode is selected there is no input layer matrix operation, and therefore no associated memory. In this case miopenSetRNNLayerBias will return a error status miopenStatusBadParm for input biasID associated with the input GEMM.

Parameters

handle	MIOpen handle (input)
rnnDesc	RNN layer descriptor type (input)
layer	The layer number in the RNN stack (input)
xDesc	A tensor descriptor to input (input)
wDesc	A tensor descriptor to the bias tensor (input)
w	Pointer to memory containing bias tensor (input)
biasID	ID of the internal bias tensor (input)
biasDesc	Descriptor of the bias tensor (output)
layerBias	Pointer to the memory location of the bias tensor (output)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenSetRNNLayerParam()

miopenStatus_t miopenSetRNNLayerParam	(	miopenHandle_t	handle,
		miopenRNNDescriptor_t	rnnDesc,
		const int	layer,
		miopenTensorDescriptor_t	xDesc,
		miopenTensorDescriptor_t	wDesc,
		void *	w,
		const int	paramID,
		miopenTensorDescriptor_t	paramDesc,
		const void *	layerParam
	)

Sets a weight matrix for a specific layer in an RNN stack.

This function sets the weight matrix data for a specific layer and parameter ID.

For RNN vanilla miopenRNNRELU and miopenRNNTANH, paramID == 0 sets the weight matrix associated with the in input GEMM, while paramID == 1 sets the weight matrix associated with the hidden state GEMM.

For miopenLSTM paramID 0 to 3 refer to the weight matrices associated with the input GEMM, 4-7 are associated with matrices associated with the hidden state GEMM.

paramID 0 and 4 are for the input gate.
paramID 1 and 5 are for the forget gate.
paramID 2 and 6 are for the output gate.
paramID 3 and 7 are for the new memory gate.

For miopenGRU paramID 0 to 2 refer to the weight matrix offset associated with the input GEMM, while 3 through 5 are associated with the hidden state GEMM.

paramID 0 and 3 are for the update gate.
paramID 1 and 4 are for the reset gate.
paramID 2 and 5 are for the new memory gate.

For bi-directional RNNs the backwards in time direction is numbered as the layer directly after the forward in time direction.

The input argument paramDesc is a previously populated tensor descriptor typically by first calling miopenGetRNNLayerParam().

Note: When inputSkip mode is selected there is no input layer matrix operation, and therefore no associated memory. In this case miopenSetRNNLayerParam() will return a error status miopenStatusBadParm for input paramID associated with the input GEMM.

Parameters

handle	MIOpen handle (input)
rnnDesc	RNN layer descriptor type (input)
layer	The layer number in the RNN stack (input)
xDesc	A tensor descriptor to input (input)
wDesc	A tensor descriptor to the parameter tensor (input)
w	Pointer to memory containing parameter tensor (input)
paramID	ID of the internal parameter tensor (input)
paramDesc	Descriptor of the parameter tensor (input)
layerParam	Pointer to the memory location of the parameter tensor (input)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

◆ miopenSetRNNPaddingMode()

miopenStatus_t miopenSetRNNPaddingMode	(	miopenRNNDescriptor_t	rnnDesc,
		miopenRNNPaddingMode_t	paddingMode
	)

Sets a bias for a specific layer in an RNN stack.

This function changes padidng mode at previously created and initialized RNN descriptor. This function must be called before using miopenGetRNNWorkspaceSize() and miopenGetRNNTrainingReserveSize() functions. By default, not padded data is expected at the RNN input/output.

Parameters

rnnDesc	RNN layer descriptor type (input/output)
paddingMode	RNN input/output data padding mode (input)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/docs-6.2.2/include/miopen/miopen.h.

RNN

RNN#

Enumerations

Functions

Detailed Description

Enumeration Type Documentation

◆ miopenRNNAlgo_t

◆ miopenRNNBaseLayout_t

◆ miopenRNNBiasMode_t

◆ miopenRNNDirectionMode_t

◆ miopenRNNFWDMode_t

◆ miopenRNNGEMMalgoMode_t

◆ miopenRNNInputMode_t

◆ miopenRNNMode_t

◆ miopenRNNPaddingMode_t

Function Documentation

◆ MIOPEN_DECLARE_OBJECT()

◆ miopenCreateRNNDescriptor()

◆ miopenDestroyRNNDescriptor()

◆ miopenGetRNNDataSeqTensorDescriptor()

◆ miopenGetRNNDescriptor()

◆ miopenGetRNNDescriptor_V2()

◆ miopenGetRNNHiddenTensorSize()

◆ miopenGetRNNInputTensorSize()

◆ miopenGetRNNLayerBias()

◆ miopenGetRNNLayerBiasOffset()

◆ miopenGetRNNLayerBiasSize()

◆ miopenGetRNNLayerParam()

◆ miopenGetRNNLayerParamOffset()

◆ miopenGetRNNLayerParamSize()

◆ miopenGetRNNPaddingMode()

◆ miopenGetRNNParamsDescriptor()

◆ miopenGetRNNParamsSize()

◆ miopenGetRNNTempSpaceSizes()

◆ miopenGetRNNTrainingReserveSize()

◆ miopenGetRNNWorkspaceSize()

◆ miopenRNNBackwardData()

◆ miopenRNNBackwardSeqData()

◆ miopenRNNBackwardWeights()

◆ miopenRNNBackwardWeightsSeqTensor()

◆ miopenRNNForward()

◆ miopenRNNForwardInference()

◆ miopenRNNForwardTraining()

◆ miopenSetRNNDataSeqTensorDescriptor()

◆ miopenSetRNNDescriptor()

◆ miopenSetRNNDescriptor_V2()

◆ miopenSetRNNLayerBias()

◆ miopenSetRNNLayerParam()

◆ miopenSetRNNPaddingMode()