RNN

A primitive to compute recurrent neural network layers. More...

Classes
struct	dnnl::rnn_primitive_desc_base
	Base class for primitive descriptors for RNN primitives. More...
struct	dnnl::vanilla_rnn_forward
	Vanilla RNN forward propagation primitive. More...
struct	dnnl::vanilla_rnn_backward
	Vanilla RNN backward propagation primitive. More...
struct	dnnl::lstm_forward
	LSTM forward propagation primitive. More...
struct	dnnl::lstm_backward
	LSTM backward propagation primitive. More...
struct	dnnl::gru_forward
	GRU forward propagation primitive. More...
struct	dnnl::gru_backward
	GRU backward propagation primitive. More...
struct	dnnl::lbr_gru_forward
	LBR GRU forward propagation primitive. More...
struct	dnnl::lbr_gru_backward
	LBR GRU backward propagation primitive. More...
struct	dnnl_rnn_desc_t
	A descriptor for an RNN operation. More...

Enumerations
enum	dnnl::rnn_flags : unsigned
	RNN cell flags. More...
enum	dnnl::rnn_direction
	A direction of RNN primitive execution. More...
enum	dnnl_rnn_flags_t
	Flags for RNN cell. More...
enum	dnnl_rnn_direction_t
	A direction of RNN primitive execution. More...

Functions
dnnl_status_t DNNL_API	dnnl_vanilla_rnn_forward_desc_init (dnnl_rnn_desc_t *rnn_desc, dnnl_prop_kind_t prop_kind, const dnnl_alg_kind_t activation, const dnnl_rnn_direction_t direction, const dnnl_memory_desc_t *src_layer_desc, const dnnl_memory_desc_t *src_iter_desc, const dnnl_memory_desc_t *weights_layer_desc, const dnnl_memory_desc_t *weights_iter_desc, const dnnl_memory_desc_t *bias_desc, const dnnl_memory_desc_t *dst_layer_desc, const dnnl_memory_desc_t *dst_iter_desc, unsigned flags, float alpha, float beta)
	Initializes a descriptor for vanilla RNN forward propagation primitive. More...
dnnl_status_t DNNL_API	dnnl_vanilla_rnn_backward_desc_init (dnnl_rnn_desc_t *rnn_desc, dnnl_prop_kind_t prop_kind, const dnnl_alg_kind_t activation, const dnnl_rnn_direction_t direction, const dnnl_memory_desc_t *src_layer_desc, const dnnl_memory_desc_t *src_iter_desc, const dnnl_memory_desc_t *weights_layer_desc, const dnnl_memory_desc_t *weights_iter_desc, const dnnl_memory_desc_t *bias_desc, const dnnl_memory_desc_t *dst_layer_desc, const dnnl_memory_desc_t *dst_iter_desc, const dnnl_memory_desc_t *diff_src_layer_desc, const dnnl_memory_desc_t *diff_src_iter_desc, const dnnl_memory_desc_t *diff_weights_layer_desc, const dnnl_memory_desc_t *diff_weights_iter_desc, const dnnl_memory_desc_t *diff_bias_desc, const dnnl_memory_desc_t *diff_dst_layer_desc, const dnnl_memory_desc_t *diff_dst_iter_desc, unsigned flags, float alpha, float beta)
	Initializes a descriptor for vanilla RNN backward propagation primitive. More...
dnnl_status_t DNNL_API	dnnl_lstm_forward_desc_init (dnnl_rnn_desc_t *rnn_desc, dnnl_prop_kind_t prop_kind, dnnl_rnn_direction_t direction, const dnnl_memory_desc_t *src_layer_desc, const dnnl_memory_desc_t *src_iter_desc, const dnnl_memory_desc_t *src_iter_c_desc, const dnnl_memory_desc_t *weights_layer_desc, const dnnl_memory_desc_t *weights_iter_desc, const dnnl_memory_desc_t *bias_desc, const dnnl_memory_desc_t *dst_layer_desc, const dnnl_memory_desc_t *dst_iter_desc, const dnnl_memory_desc_t *dst_iter_c_desc, unsigned flags)
	Initializes a descriptor for LSTM forward propagation primitive. More...
dnnl_status_t DNNL_API	dnnl_lstm_forward_desc_init_v2 (dnnl_rnn_desc_t *rnn_desc, dnnl_prop_kind_t prop_kind, dnnl_rnn_direction_t direction, const dnnl_memory_desc_t *src_layer_desc, const dnnl_memory_desc_t *src_iter_desc, const dnnl_memory_desc_t *src_iter_c_desc, const dnnl_memory_desc_t *weights_layer_desc, const dnnl_memory_desc_t *weights_iter_desc, const dnnl_memory_desc_t *weights_peephole_desc, const dnnl_memory_desc_t *bias_desc, const dnnl_memory_desc_t *dst_layer_desc, const dnnl_memory_desc_t *dst_iter_desc, const dnnl_memory_desc_t *dst_iter_c_desc, unsigned flags)
	Initializes a descriptor for an LSTM (with or without peephole) forward propagation primitive. More...
dnnl_status_t DNNL_API	dnnl_lstm_backward_desc_init (dnnl_rnn_desc_t *rnn_desc, dnnl_prop_kind_t prop_kind, dnnl_rnn_direction_t direction, const dnnl_memory_desc_t *src_layer_desc, const dnnl_memory_desc_t *src_iter_desc, const dnnl_memory_desc_t *src_iter_c_desc, const dnnl_memory_desc_t *weights_layer_desc, const dnnl_memory_desc_t *weights_iter_desc, const dnnl_memory_desc_t *bias_desc, const dnnl_memory_desc_t *dst_layer_desc, const dnnl_memory_desc_t *dst_iter_desc, const dnnl_memory_desc_t *dst_iter_c_desc, const dnnl_memory_desc_t *diff_src_layer_desc, const dnnl_memory_desc_t *diff_src_iter_desc, const dnnl_memory_desc_t *diff_src_iter_c_desc, const dnnl_memory_desc_t *diff_weights_layer_desc, const dnnl_memory_desc_t *diff_weights_iter_desc, const dnnl_memory_desc_t *diff_bias_desc, const dnnl_memory_desc_t *diff_dst_layer_desc, const dnnl_memory_desc_t *diff_dst_iter_desc, const dnnl_memory_desc_t *diff_dst_iter_c_desc, unsigned flags)
	Initializes a descriptor for an LSTM backward propagation primitive. More...
dnnl_status_t DNNL_API	dnnl_lstm_backward_desc_init_v2 (dnnl_rnn_desc_t *rnn_desc, dnnl_prop_kind_t prop_kind, dnnl_rnn_direction_t direction, const dnnl_memory_desc_t *src_layer_desc, const dnnl_memory_desc_t *src_iter_desc, const dnnl_memory_desc_t *src_iter_c_desc, const dnnl_memory_desc_t *weights_layer_desc, const dnnl_memory_desc_t *weights_iter_desc, const dnnl_memory_desc_t *weights_peephole_desc, const dnnl_memory_desc_t *bias_desc, const dnnl_memory_desc_t *dst_layer_desc, const dnnl_memory_desc_t *dst_iter_desc, const dnnl_memory_desc_t *dst_iter_c_desc, const dnnl_memory_desc_t *diff_src_layer_desc, const dnnl_memory_desc_t *diff_src_iter_desc, const dnnl_memory_desc_t *diff_src_iter_c_desc, const dnnl_memory_desc_t *diff_weights_layer_desc, const dnnl_memory_desc_t *diff_weights_iter_desc, const dnnl_memory_desc_t *diff_weights_peephole_desc, const dnnl_memory_desc_t *diff_bias_desc, const dnnl_memory_desc_t *diff_dst_layer_desc, const dnnl_memory_desc_t *diff_dst_iter_desc, const dnnl_memory_desc_t *diff_dst_iter_c_desc, unsigned flags)
	Initializes a descriptor for an LSTM (with or without peephole) backward propagation primitive. More...
dnnl_status_t DNNL_API	dnnl_gru_forward_desc_init (dnnl_rnn_desc_t *rnn_desc, dnnl_prop_kind_t prop_kind, dnnl_rnn_direction_t direction, const dnnl_memory_desc_t *src_layer_desc, const dnnl_memory_desc_t *src_iter_desc, const dnnl_memory_desc_t *weights_layer_desc, const dnnl_memory_desc_t *weights_iter_desc, const dnnl_memory_desc_t *bias_desc, const dnnl_memory_desc_t *dst_layer_desc, const dnnl_memory_desc_t *dst_iter_desc, unsigned flags)
	Initializes a descriptor for GRU forward propagation primitive. More...
dnnl_status_t DNNL_API	dnnl_gru_backward_desc_init (dnnl_rnn_desc_t *rnn_desc, dnnl_prop_kind_t prop_kind, dnnl_rnn_direction_t direction, const dnnl_memory_desc_t *src_layer_desc, const dnnl_memory_desc_t *src_iter_desc, const dnnl_memory_desc_t *weights_layer_desc, const dnnl_memory_desc_t *weights_iter_desc, const dnnl_memory_desc_t *bias_desc, const dnnl_memory_desc_t *dst_layer_desc, const dnnl_memory_desc_t *dst_iter_desc, const dnnl_memory_desc_t *diff_src_layer_desc, const dnnl_memory_desc_t *diff_src_iter_desc, const dnnl_memory_desc_t *diff_weights_layer_desc, const dnnl_memory_desc_t *diff_weights_iter_desc, const dnnl_memory_desc_t *diff_bias_desc, const dnnl_memory_desc_t *diff_dst_layer_desc, const dnnl_memory_desc_t *diff_dst_iter_desc, unsigned flags)
	Initializes a descriptor for GRU backward propagation primitive. More...
dnnl_status_t DNNL_API	dnnl_lbr_gru_forward_desc_init (dnnl_rnn_desc_t *rnn_desc, dnnl_prop_kind_t prop_kind, dnnl_rnn_direction_t direction, const dnnl_memory_desc_t *src_layer_desc, const dnnl_memory_desc_t *src_iter_desc, const dnnl_memory_desc_t *weights_layer_desc, const dnnl_memory_desc_t *weights_iter_desc, const dnnl_memory_desc_t *bias_desc, const dnnl_memory_desc_t *dst_layer_desc, const dnnl_memory_desc_t *dst_iter_desc, unsigned flags)
	Initializes a descriptor for LBR GRU forward propagation primitive. More...
dnnl_status_t DNNL_API	dnnl_lbr_gru_backward_desc_init (dnnl_rnn_desc_t *rnn_desc, dnnl_prop_kind_t prop_kind, dnnl_rnn_direction_t direction, const dnnl_memory_desc_t *src_layer_desc, const dnnl_memory_desc_t *src_iter_desc, const dnnl_memory_desc_t *weights_layer_desc, const dnnl_memory_desc_t *weights_iter_desc, const dnnl_memory_desc_t *bias_desc, const dnnl_memory_desc_t *dst_layer_desc, const dnnl_memory_desc_t *dst_iter_desc, const dnnl_memory_desc_t *diff_src_layer_desc, const dnnl_memory_desc_t *diff_src_iter_desc, const dnnl_memory_desc_t *diff_weights_layer_desc, const dnnl_memory_desc_t *diff_weights_iter_desc, const dnnl_memory_desc_t *diff_bias_desc, const dnnl_memory_desc_t *diff_dst_layer_desc, const dnnl_memory_desc_t *diff_dst_iter_desc, unsigned flags)
	Initializes a descriptor for LBR GRU backward propagation primitive. More...
dnnl_rnn_flags_t	dnnl::convert_to_c (rnn_flags flags)
	Converts RNN cell flags enum value from C++ API to C API type. More...
dnnl_rnn_direction_t	dnnl::convert_to_c (rnn_direction dir)
	Converts RNN direction enum value from C++ API to C API type. More...

Detailed Description

A primitive to compute recurrent neural network layers.

See also

RNN in developer guide

Enumeration Type Documentation

rnn_flags

enum dnnl::rnn_flags : unsigned

strong

RNN cell flags.

Enumerator
undef	Undefined RNN flags.

rnn_direction

enum dnnl::rnn_direction

strong

A direction of RNN primitive execution.

Enumerator
unidirectional_left2right	Unidirectional execution of RNN primitive from left to right.
unidirectional_right2left	Unidirectional execution of RNN primitive from right to left.
bidirectional_concat	Bidirectional execution of RNN primitive with concatenation of the results.
bidirectional_sum	Bidirectional execution of RNN primitive with summation of the results.
unidirectional	Alias for dnnl::rnn_direction::unidirectional_left2right.

dnnl_rnn_flags_t

enum dnnl_rnn_flags_t

Flags for RNN cell.

Enumerator
dnnl_rnn_flags_undef	Undefined RNN flags.

dnnl_rnn_direction_t

enum dnnl_rnn_direction_t

A direction of RNN primitive execution.

Enumerator
dnnl_unidirectional_left2right	Unidirectional execution of RNN primitive from left to right.
dnnl_unidirectional_right2left	Unidirectional execution of RNN primitive from right to left.
dnnl_bidirectional_concat	Bidirectional execution of RNN primitive with concatenation of the results.
dnnl_bidirectional_sum	Bidirectional execution of RNN primitive with summation of the results.
dnnl_unidirectional	Alias for dnnl_unidirectional_left2right.

Function Documentation

dnnl_vanilla_rnn_forward_desc_init()

dnnl_status_t DNNL_API dnnl_vanilla_rnn_forward_desc_init	(	dnnl_rnn_desc_t *	rnn_desc,
		dnnl_prop_kind_t	prop_kind,
		const dnnl_alg_kind_t	activation,
		const dnnl_rnn_direction_t	direction,
		const dnnl_memory_desc_t *	src_layer_desc,
		const dnnl_memory_desc_t *	src_iter_desc,
		const dnnl_memory_desc_t *	weights_layer_desc,
		const dnnl_memory_desc_t *	weights_iter_desc,
		const dnnl_memory_desc_t *	bias_desc,
		const dnnl_memory_desc_t *	dst_layer_desc,
		const dnnl_memory_desc_t *	dst_iter_desc,
		unsigned	flags,
		float	alpha,
		float	beta
	)

Initializes a descriptor for vanilla RNN forward propagation primitive.

The src_iter_desc, bias_desc, and dst_iter_desc may either be NULL or point to a zero memory descriptor. This would then indicate that the RNN forward propagation primitive should not use them and should default to zero values instead.

Note

All memory descriptors can be initialized with dnnl_format_tag_any or with format_kind set to dnnl_format_kind_any.

Inputs:

• src_layer (dnnl_query_src_md, 0)
• src_iter (dnnl_query_src_md, 1), if used
• weights_layer (dnnl_query_weights_md, 0)
• weights_iter (dnnl_query_weights_md, 1)
• bias (dnnl_query_weights_md, 2), if used

Outputs:

• dst_layer (dnnl_query_dst_md, 0)
• dst_iter (dnnl_query_dst_md, 1), if used
• workspace (dnnl_query_workspace_md, 0), if prop_kind equals dnnl_forward_training; must be queried for using dnnl_primitive_desc_query_md() after a corresponding primitive descriptor is created

Parameters

rnn_desc	Output descriptor for vanilla RNN primitive.
prop_kind	Propagation kind. Possible values are dnnl_forward_training and dnnl_forward_inference.
activation	Activation kind. Possible values are dnnl_eltwise_relu, dnnl_eltwise_tanh or dnnl_eltwise_logistic.
direction	RNN direction. See dnnl_rnn_direction_t for more info.
src_layer_desc	Memory descriptor for the input vector.
src_iter_desc	Memory descriptor for the input recurrent hidden state vector.
weights_layer_desc	Memory descriptor for the weights applied to the layer input.
weights_iter_desc	Memory descriptor for the weights applied to the recurrent input.
bias_desc	Bias memory descriptor.
dst_layer_desc	Memory descriptor for the output vector.
dst_iter_desc	Memory descriptor for the output recurrent hidden state vector.
flags	Unused.
alpha	Negative slope if activation is dnnl_eltwise_relu.
beta	Unused.

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_vanilla_rnn_backward_desc_init()

dnnl_status_t DNNL_API dnnl_vanilla_rnn_backward_desc_init	(	dnnl_rnn_desc_t *	rnn_desc,
		dnnl_prop_kind_t	prop_kind,
		const dnnl_alg_kind_t	activation,
		const dnnl_rnn_direction_t	direction,
		const dnnl_memory_desc_t *	src_layer_desc,
		const dnnl_memory_desc_t *	src_iter_desc,
		const dnnl_memory_desc_t *	weights_layer_desc,
		const dnnl_memory_desc_t *	weights_iter_desc,
		const dnnl_memory_desc_t *	bias_desc,
		const dnnl_memory_desc_t *	dst_layer_desc,
		const dnnl_memory_desc_t *	dst_iter_desc,
		const dnnl_memory_desc_t *	diff_src_layer_desc,
		const dnnl_memory_desc_t *	diff_src_iter_desc,
		const dnnl_memory_desc_t *	diff_weights_layer_desc,
		const dnnl_memory_desc_t *	diff_weights_iter_desc,
		const dnnl_memory_desc_t *	diff_bias_desc,
		const dnnl_memory_desc_t *	diff_dst_layer_desc,
		const dnnl_memory_desc_t *	diff_dst_iter_desc,
		unsigned	flags,
		float	alpha,
		float	beta
	)

Initializes a descriptor for vanilla RNN backward propagation primitive.

The src_iter_desc together with diff_src_iter_desc, bias_desc together with diff_bias_desc, and dst_iter_desc together with diff_src_iter_desc, may either be NULL or point to a zero memory descriptor. This would then indicate that the RNN backward propagation primitive should not use the respective data and should use zero values instead.

Note

All memory descriptors can be initialized with dnnl_format_tag_any or with format_kind set to dnnl_format_kind_any.

Inputs:

• src_layer (dnnl_query_src_md, 0)
• src_iter (dnnl_query_src_md, 1), if used
• weights_layer (dnnl_query_weights_md, 0)
• weights_iter (dnnl_query_weights_md, 1)
• bias (dnnl_query_weights_md, 2), if used
• dst_layer (dnnl_query_dst_md, 0)
• dst_iter (dnnl_query_dst_md, 1), if used
• diff_dst_layer (dnnl_query_diff_dst_md, 0)
• diff_dst_iter (dnnl_query_diff_dst_md, 1), if used
• workspace (dnnl_query_workspace_md, 0)

Outputs:

• diff_src_layer (dnnl_query_diff_src_md, 0)
• diff_src_iter (dnnl_query_diff_src_md, 1), if used
• diff_weights_layer (dnnl_query_diff_weights_md, 0)
• diff_weights_iter (dnnl_query_diff_weights_md, 1)
• diff_bias (dnnl_query_diff_weights_md, 2), if used

Parameters

rnn_desc	Output descriptor for vanilla RNN primitive.
prop_kind	Propagation kind. Must be dnnl_backward.
activation	Activation kind. Possible values are dnnl_eltwise_relu, dnnl_eltwise_tanh or dnnl_eltwise_logistic.
direction	RNN direction. See dnnl_rnn_direction_t for more info.
src_layer_desc	Memory descriptor for the input vector.
src_iter_desc	Memory descriptor for the input recurrent hidden state vector.
weights_layer_desc	Memory descriptor for the weights applied to the layer input.
weights_iter_desc	Memory descriptor for the weights applied to the recurrent input.
bias_desc	Bias memory descriptor.
dst_layer_desc	Memory descriptor for the output vector.
dst_iter_desc	Memory descriptor for the output recurrent hidden state vector.
diff_src_layer_desc	Memory descriptor for the diff of input vector.
diff_src_iter_desc	Memory descriptor for the diff of input recurrent hidden state vector.
diff_weights_layer_desc	Memory descriptor for the diff of weights applied to the layer input.
diff_weights_iter_desc	Memory descriptor for the diff of weights applied to the recurrent input.
diff_bias_desc	Diff bias memory descriptor.
diff_dst_layer_desc	Memory descriptor for the diff of output vector.
diff_dst_iter_desc	Memory descriptor for the diff of output recurrent hidden state vector.
flags	Unused.
alpha	Negative slope if activation is dnnl_eltwise_relu.
beta	Unused.

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_lstm_forward_desc_init()

dnnl_status_t DNNL_API dnnl_lstm_forward_desc_init	(	dnnl_rnn_desc_t *	rnn_desc,
		dnnl_prop_kind_t	prop_kind,
		dnnl_rnn_direction_t	direction,
		const dnnl_memory_desc_t *	src_layer_desc,
		const dnnl_memory_desc_t *	src_iter_desc,
		const dnnl_memory_desc_t *	src_iter_c_desc,
		const dnnl_memory_desc_t *	weights_layer_desc,
		const dnnl_memory_desc_t *	weights_iter_desc,
		const dnnl_memory_desc_t *	bias_desc,
		const dnnl_memory_desc_t *	dst_layer_desc,
		const dnnl_memory_desc_t *	dst_iter_desc,
		const dnnl_memory_desc_t *	dst_iter_c_desc,
		unsigned	flags
	)

Initializes a descriptor for LSTM forward propagation primitive.

The src_iter_desc, src_iter_c_desc, bias_desc, dst_iter_desc, and dst_iter_c_desc may either be NULL or point to a zero memory descriptor. This would then indicate that the LSTM forward propagation primitive should not use them and should default to zero values instead.

Note

All memory descriptors can be initialized with dnnl_format_tag_any or with format_kind set to dnnl_format_kind_any.

See also

dnnl_lstm_forward_desc_init_v2 to initialize forward LSTM with and without peephole

Inputs:

• src_layer (dnnl_query_src_md, 0)
• src_iter (dnnl_query_src_md, 1), if used
• src_iter_c (dnnl_query_src_md, 2), if used
• weights_layer (dnnl_query_weights_md, 0)
• weights_iter (dnnl_query_weights_md, 1)
• bias (dnnl_query_weights_md, 2), if used

Outputs:

• dst_layer (dnnl_query_dst_md, 0)
• dst_iter (dnnl_query_dst_md, 1), if used
• dst_iter_c (dnnl_query_dst_md, 2), if used
• workspace (dnnl_query_workspace_md, 0), if prop_kind equals dnnl_forward_training; must be queried for using dnnl_primitive_desc_query_md() after a corresponding primitive descriptor is created

Parameters

rnn_desc	Output descriptor for LSTM primitive.
prop_kind	Propagation kind. Possible values are dnnl_forward_training and dnnl_forward_inference.
direction	RNN direction. See dnnl_rnn_direction_t for more info.
src_layer_desc	Memory descriptor for the input vector.
src_iter_desc	Memory descriptor for the input recurrent hidden state vector.
src_iter_c_desc	Memory descriptor for the input recurrent cell state vector.
weights_layer_desc	Memory descriptor for the weights applied to the layer input.
weights_iter_desc	Memory descriptor for the weights applied to the recurrent input.
bias_desc	Bias memory descriptor.
dst_layer_desc	Memory descriptor for the output vector.
dst_iter_desc	Memory descriptor for the output recurrent hidden state vector.
dst_iter_c_desc	Memory descriptor for the output recurrent cell state vector.
flags	Unused.

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_lstm_forward_desc_init_v2()

dnnl_status_t DNNL_API dnnl_lstm_forward_desc_init_v2	(	dnnl_rnn_desc_t *	rnn_desc,
		dnnl_prop_kind_t	prop_kind,
		dnnl_rnn_direction_t	direction,
		const dnnl_memory_desc_t *	src_layer_desc,
		const dnnl_memory_desc_t *	src_iter_desc,
		const dnnl_memory_desc_t *	src_iter_c_desc,
		const dnnl_memory_desc_t *	weights_layer_desc,
		const dnnl_memory_desc_t *	weights_iter_desc,
		const dnnl_memory_desc_t *	weights_peephole_desc,
		const dnnl_memory_desc_t *	bias_desc,
		const dnnl_memory_desc_t *	dst_layer_desc,
		const dnnl_memory_desc_t *	dst_iter_desc,
		const dnnl_memory_desc_t *	dst_iter_c_desc,
		unsigned	flags
	)

Initializes a descriptor for an LSTM (with or without peephole) forward propagation primitive.

The src_iter_desc, src_iter_c_desc, weights_peephole_desc, bias_desc, dst_iter_desc, and dst_iter_c_desc may either be NULL or point to a zero memory descriptor. This would then indicate that the LSTM forward propagation primitive should not use them and should default to zero values instead.

Note

All memory descriptors can be initialized with dnnl_format_tag_any or with format_kind set to dnnl_format_kind_any.

Inputs:

• src_layer (dnnl_query_src_md, 0)
• src_iter (dnnl_query_src_md, 1), if used
• src_iter_c (dnnl_query_src_md, 2), if used
• weights_layer (dnnl_query_weights_md, 0)
• weights_iter (dnnl_query_weights_md, 1)
• weights_peephole (dnnl_query_weights_md, 2), if used
• bias (dnnl_query_weights_md, 2), if used and LSTM is without peephole
• bias (dnnl_query_weights_md, 3), if used and LSTM is with peephole

Outputs:

• dst_layer (dnnl_query_dst_md, 0)
• dst_iter (dnnl_query_dst_md, 1), if used
• dst_iter_c (dnnl_query_dst_md, 2), if used
• workspace (dnnl_query_workspace_md, 0), if prop_kind equals dnnl_forward_training; must be queried for using dnnl_primitive_desc_query_md() after a corresponding primitive descriptor is created

Parameters

rnn_desc	Output descriptor for LSTM primitive.
prop_kind	Propagation kind. Possible values are dnnl_forward_training and dnnl_forward_inference.
direction	RNN direction. See dnnl_rnn_direction_t for more info.
src_layer_desc	Memory descriptor for the input vector.
src_iter_desc	Memory descriptor for the input recurrent hidden state vector.
src_iter_c_desc	Memory descriptor for the input recurrent cell state vector.
weights_layer_desc	Memory descriptor for the weights applied to the layer input.
weights_iter_desc	Memory descriptor for the weights applied to the recurrent input.
weights_peephole_desc	Memory descriptor for the weights applied to the cell states (according to the Peephole LSTM formula).
bias_desc	Bias memory descriptor.
dst_layer_desc	Memory descriptor for the output vector.
dst_iter_desc	Memory descriptor for the output recurrent hidden state vector.
dst_iter_c_desc	Memory descriptor for the output recurrent cell state vector.
flags	Unused.

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_lstm_backward_desc_init()

dnnl_status_t DNNL_API dnnl_lstm_backward_desc_init	(	dnnl_rnn_desc_t *	rnn_desc,
		dnnl_prop_kind_t	prop_kind,
		dnnl_rnn_direction_t	direction,
		const dnnl_memory_desc_t *	src_layer_desc,
		const dnnl_memory_desc_t *	src_iter_desc,
		const dnnl_memory_desc_t *	src_iter_c_desc,
		const dnnl_memory_desc_t *	weights_layer_desc,
		const dnnl_memory_desc_t *	weights_iter_desc,
		const dnnl_memory_desc_t *	bias_desc,
		const dnnl_memory_desc_t *	dst_layer_desc,
		const dnnl_memory_desc_t *	dst_iter_desc,
		const dnnl_memory_desc_t *	dst_iter_c_desc,
		const dnnl_memory_desc_t *	diff_src_layer_desc,
		const dnnl_memory_desc_t *	diff_src_iter_desc,
		const dnnl_memory_desc_t *	diff_src_iter_c_desc,
		const dnnl_memory_desc_t *	diff_weights_layer_desc,
		const dnnl_memory_desc_t *	diff_weights_iter_desc,
		const dnnl_memory_desc_t *	diff_bias_desc,
		const dnnl_memory_desc_t *	diff_dst_layer_desc,
		const dnnl_memory_desc_t *	diff_dst_iter_desc,
		const dnnl_memory_desc_t *	diff_dst_iter_c_desc,
		unsigned	flags
	)

Initializes a descriptor for an LSTM backward propagation primitive.

The src_iter_desc together with diff_iter_desc, src_iter_c_desc together with src_iter_c_desc, bias_desc together with diff_bias_desc, dst_iter_desc together with diff_dst_iter_desc, and dst_iter_c_desc together with diff_dst_iter_c_desc, may either be NULL or point to a zero memory descriptor. This would then indicate that the LSTM backward propagation primitive should not use them and should default to zero values instead.

Note

All memory descriptors can be initialized with dnnl_format_tag_any or with format_kind set to dnnl_format_kind_any.

See also

dnnl_lstm_backward_desc_init_v2 to initialize backward LSTM with and without peephole

Inputs:

• src_layer (dnnl_query_src_md, 0)
• src_iter (dnnl_query_src_md, 1), if used
• src_iter_c (dnnl_query_src_md, 2), if used
• weights_layer (dnnl_query_weights_md, 0)
• weights_iter (dnnl_query_weights_md, 1)
• bias (dnnl_query_weights_md, 2), if used
• dst_layer (dnnl_query_dst_md, 0)
• dst_iter (dnnl_query_dst_md, 1), if used
• dst_iter_c (dnnl_query_dst_md, 2), if used
• diff_dst_layer (dnnl_query_diff_dst_md, 0)
• diff_dst_iter (dnnl_query_diff_dst_md, 1), if used
• diff_dst_iter_c (dnnl_query_diff_dst_md, 2), if used
• workspace (dnnl_query_workspace_md, 0)

Outputs:

• diff_src_layer (dnnl_query_diff_src_md, 0)
• diff_src_iter (dnnl_query_diff_src_md, 1), if used
• diff_src_iter_c (dnnl_query_diff_src_md, 2), if used
• diff_weights_layer (dnnl_query_diff_weights_md, 0)
• diff_weights_iter (dnnl_query_diff_weights_md, 1)
• diff_bias (dnnl_query_diff_weights_md, 2), if used

Parameters

rnn_desc	Output descriptor for LSTM primitive.
prop_kind	Propagation kind. Must be dnnl_backward.
direction	RNN direction. See dnnl_rnn_direction_t for more info.
src_layer_desc	Memory descriptor for the input vector.
src_iter_desc	Memory descriptor for the input recurrent hidden state vector.
src_iter_c_desc	Memory descriptor for the input recurrent cell state vector.
weights_layer_desc	Memory descriptor for the weights applied to the layer input.
weights_iter_desc	Memory descriptor for the weights applied to the recurrent input.
bias_desc	Bias memory descriptor.
dst_layer_desc	Memory descriptor for the output vector.
dst_iter_desc	Memory descriptor for the output recurrent hidden state vector.
dst_iter_c_desc	Memory descriptor for the output recurrent cell state vector.
diff_src_layer_desc	Memory descriptor for the diff of input vector.
diff_src_iter_desc	Memory descriptor for the diff of input recurrent hidden state vector.
diff_src_iter_c_desc	Memory descriptor for the diff of input recurrent cell state vector.
diff_weights_layer_desc	Memory descriptor for the diff of weights applied to the layer input.
diff_weights_iter_desc	Memory descriptor for the diff of weights applied to the recurrent input.
diff_bias_desc	Diff bias memory descriptor.
diff_dst_layer_desc	Memory descriptor for the diff of output vector.
diff_dst_iter_desc	Memory descriptor for the diff of output recurrent hidden state vector.
diff_dst_iter_c_desc	Memory descriptor for the diff of output recurrent cell state vector.
flags	Unused.

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_lstm_backward_desc_init_v2()

dnnl_status_t DNNL_API dnnl_lstm_backward_desc_init_v2	(	dnnl_rnn_desc_t *	rnn_desc,
		dnnl_prop_kind_t	prop_kind,
		dnnl_rnn_direction_t	direction,
		const dnnl_memory_desc_t *	src_layer_desc,
		const dnnl_memory_desc_t *	src_iter_desc,
		const dnnl_memory_desc_t *	src_iter_c_desc,
		const dnnl_memory_desc_t *	weights_layer_desc,
		const dnnl_memory_desc_t *	weights_iter_desc,
		const dnnl_memory_desc_t *	weights_peephole_desc,
		const dnnl_memory_desc_t *	bias_desc,
		const dnnl_memory_desc_t *	dst_layer_desc,
		const dnnl_memory_desc_t *	dst_iter_desc,
		const dnnl_memory_desc_t *	dst_iter_c_desc,
		const dnnl_memory_desc_t *	diff_src_layer_desc,
		const dnnl_memory_desc_t *	diff_src_iter_desc,
		const dnnl_memory_desc_t *	diff_src_iter_c_desc,
		const dnnl_memory_desc_t *	diff_weights_layer_desc,
		const dnnl_memory_desc_t *	diff_weights_iter_desc,
		const dnnl_memory_desc_t *	diff_weights_peephole_desc,
		const dnnl_memory_desc_t *	diff_bias_desc,
		const dnnl_memory_desc_t *	diff_dst_layer_desc,
		const dnnl_memory_desc_t *	diff_dst_iter_desc,
		const dnnl_memory_desc_t *	diff_dst_iter_c_desc,
		unsigned	flags
	)

Initializes a descriptor for an LSTM (with or without peephole) backward propagation primitive.

The src_iter_desc together with diff_iter_desc, src_iter_c_desc together with diff_src_iter_c_desc, weights_peephole_desc together with diff_weights_peephole_desc, bias_desc together with diff_bias_desc, dst_iter_desc together with diff_dst_iter_desc, and dst_iter_c_desc together with diff_dst_iter_c_desc, may either be NULL or point to a zero memory descriptor. This would then indicate that the LSTM backward propagation primitive should not use them and should default to zero values instead.

Note

All memory descriptors can be initialized with dnnl_format_tag_any or with format_kind set to dnnl_format_kind_any.

Inputs:

• src_layer (dnnl_query_src_md, 0)
• src_iter (dnnl_query_src_md, 1), if used
• src_iter_c (dnnl_query_src_md, 2), if used
• weights_layer (dnnl_query_weights_md, 0)
• weights_iter (dnnl_query_weights_md, 1)
• weights_peephole (dnnl_query_weights_md, 2), if used
• bias (dnnl_query_weights_md, 2), if used and LSTM is without peephole
• bias (dnnl_query_weights_md, 3), if used and LSTM is with peephole
• dst_layer (dnnl_query_dst_md, 0)
• dst_iter (dnnl_query_dst_md, 1), if used
• dst_iter_c (dnnl_query_dst_md, 2), if used
• diff_dst_layer (dnnl_query_diff_dst_md, 0)
• diff_dst_iter (dnnl_query_diff_dst_md, 1), if used
• diff_dst_iter_c (dnnl_query_diff_dst_md, 2), if used
• workspace (dnnl_query_workspace_md, 0)

Outputs:

• diff_src_layer (dnnl_query_diff_src_md, 0)
• diff_src_iter (dnnl_query_diff_src_md, 1), if used
• diff_src_iter_c (dnnl_query_diff_src_md, 2), if used
• diff_weights_layer (dnnl_query_diff_weights_md, 0)
• diff_weights_iter (dnnl_query_diff_weights_md, 1)
• diff_weights_peephole (dnnl_query_weights_md, 2), if used
• diff_bias (dnnl_query_diff_weights_md, 2), if used and LSTM is without peephole
• diff_bias (dnnl_query_diff_weights_md, 3), if used and LSTM is with peephole

Parameters

rnn_desc	Output descriptor for LSTM primitive.
prop_kind	Propagation kind. Must be dnnl_backward.
direction	RNN direction. See dnnl_rnn_direction_t for more info.
src_layer_desc	Memory descriptor for the input vector.
src_iter_desc	Memory descriptor for the input recurrent hidden state vector.
src_iter_c_desc	Memory descriptor for the input recurrent cell state vector.
weights_layer_desc	Memory descriptor for the weights applied to the layer input.
weights_iter_desc	Memory descriptor for the weights applied to the recurrent input.
weights_peephole_desc	Memory descriptor for the weights applied to the cell states (according to the Peephole LSTM formula).
bias_desc	Bias memory descriptor.
dst_layer_desc	Memory descriptor for the output vector.
dst_iter_desc	Memory descriptor for the output recurrent hidden state vector.
dst_iter_c_desc	Memory descriptor for the output recurrent cell state vector.
diff_src_layer_desc	Memory descriptor for the diff of input vector.
diff_src_iter_desc	Memory descriptor for the diff of input recurrent hidden state vector.
diff_src_iter_c_desc	Memory descriptor for the diff of input recurrent cell state vector.
diff_weights_layer_desc	Memory descriptor for the diff of weights applied to the layer input.
diff_weights_iter_desc	Memory descriptor for the diff of weights applied to the recurrent input.
diff_weights_peephole_desc	Memory descriptor for the diff of weights applied to the cell states (according to the Peephole LSTM formula).
diff_bias_desc	Diff bias memory descriptor.
diff_dst_layer_desc	Memory descriptor for the diff of output vector.
diff_dst_iter_desc	Memory descriptor for the diff of output recurrent hidden state vector.
diff_dst_iter_c_desc	Memory descriptor for the diff of output recurrent cell state vector.
flags	Unused.

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_gru_forward_desc_init()

dnnl_status_t DNNL_API dnnl_gru_forward_desc_init	(	dnnl_rnn_desc_t *	rnn_desc,
		dnnl_prop_kind_t	prop_kind,
		dnnl_rnn_direction_t	direction,
		const dnnl_memory_desc_t *	src_layer_desc,
		const dnnl_memory_desc_t *	src_iter_desc,
		const dnnl_memory_desc_t *	weights_layer_desc,
		const dnnl_memory_desc_t *	weights_iter_desc,
		const dnnl_memory_desc_t *	bias_desc,
		const dnnl_memory_desc_t *	dst_layer_desc,
		const dnnl_memory_desc_t *	dst_iter_desc,
		unsigned	flags
	)

Initializes a descriptor for GRU forward propagation primitive.

The src_iter_desc, bias_desc, and dst_iter, may either be NULL or point to a zero memory descriptor. This would then indicate that the GRU forward propagation primitive should not use them and should default to zero values instead.

Note

All memory descriptors can be initialized with dnnl_format_tag_any or with format_kind set to dnnl_format_kind_any.

Inputs:

• src_layer (dnnl_query_src_md, 0)
• src_iter (dnnl_query_src_md, 1), if used
• weights_layer (dnnl_query_weights_md, 0)
• weights_iter (dnnl_query_weights_md, 1)
• bias (dnnl_query_weights_md, 2), if used

Outputs:

• dst_layer (dnnl_query_dst_md, 0)
• dst_iter (dnnl_query_dst_md, 1), if used
• workspace (dnnl_query_workspace_md, 0), if prop_kind equals dnnl_forward_training; must be queried for using dnnl_primitive_desc_query_md() after a corresponding primitive descriptor is created

Parameters

rnn_desc	Output descriptor for GRU primitive.
prop_kind	Propagation kind. Possible values are dnnl_forward_training and dnnl_forward_inference.
direction	RNN direction. See dnnl_rnn_direction_t for more info.
src_layer_desc	Memory descriptor for the input vector.
src_iter_desc	Memory descriptor for the input recurrent hidden state vector.
weights_layer_desc	Memory descriptor for the weights applied to the layer input.
weights_iter_desc	Memory descriptor for the weights applied to the recurrent input.
bias_desc	Bias memory descriptor.
dst_layer_desc	Memory descriptor for the output vector.
dst_iter_desc	Memory descriptor for the output recurrent hidden state vector.
flags	Unused.

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_gru_backward_desc_init()

dnnl_status_t DNNL_API dnnl_gru_backward_desc_init	(	dnnl_rnn_desc_t *	rnn_desc,
		dnnl_prop_kind_t	prop_kind,
		dnnl_rnn_direction_t	direction,
		const dnnl_memory_desc_t *	src_layer_desc,
		const dnnl_memory_desc_t *	src_iter_desc,
		const dnnl_memory_desc_t *	weights_layer_desc,
		const dnnl_memory_desc_t *	weights_iter_desc,
		const dnnl_memory_desc_t *	bias_desc,
		const dnnl_memory_desc_t *	dst_layer_desc,
		const dnnl_memory_desc_t *	dst_iter_desc,
		const dnnl_memory_desc_t *	diff_src_layer_desc,
		const dnnl_memory_desc_t *	diff_src_iter_desc,
		const dnnl_memory_desc_t *	diff_weights_layer_desc,
		const dnnl_memory_desc_t *	diff_weights_iter_desc,
		const dnnl_memory_desc_t *	diff_bias_desc,
		const dnnl_memory_desc_t *	diff_dst_layer_desc,
		const dnnl_memory_desc_t *	diff_dst_iter_desc,
		unsigned	flags
	)

Initializes a descriptor for GRU backward propagation primitive.

The src_iter_desc together with diff_src_iter_desc, bias_desc together with diff_bias_desc, and dst_iter together with diff_dst_iter, may either be NULL or point to a zero memory descriptor. This would then indicate that the GRU backward propagation primitive should not use them and should default to zero values instead.

Note

All memory descriptors can be initialized with dnnl_format_tag_any or with format_kind set to dnnl_format_kind_any.

Inputs:

• src_layer (dnnl_query_src_md, 0)
• src_iter (dnnl_query_src_md, 1), if used
• weights_layer (dnnl_query_weights_md, 0)
• weights_iter (dnnl_query_weights_md, 1)
• bias (dnnl_query_weights_md, 2), if used
• dst_layer (dnnl_query_dst_md, 0)
• dst_iter (dnnl_query_dst_md, 1), if used
• diff_dst_layer (dnnl_query_diff_dst_md, 0)
• diff_dst_iter (dnnl_query_diff_dst_md, 1), if used
• workspace (dnnl_query_workspace_md, 0)

Outputs:

• diff_src_layer (dnnl_query_diff_src_md, 0)
• diff_src_iter (dnnl_query_diff_src_md, 1), if used
• diff_weights_layer (dnnl_query_diff_weights_md, 0)
• diff_weights_iter (dnnl_query_diff_weights_md, 1)
• diff_bias (dnnl_query_diff_weights_md, 2), if used

Parameters

rnn_desc	Output descriptor for GRU primitive.
prop_kind	Propagation kind. Must be dnnl_backward.
direction	RNN direction. See dnnl_rnn_direction_t for more info.
src_layer_desc	Memory descriptor for the input vector.
src_iter_desc	Memory descriptor for the input recurrent hidden state vector.
weights_layer_desc	Memory descriptor for the weights applied to the layer input.
weights_iter_desc	Memory descriptor for the weights applied to the recurrent input.
bias_desc	Bias memory descriptor.
dst_layer_desc	Memory descriptor for the output vector.
dst_iter_desc	Memory descriptor for the output recurrent hidden state vector.
diff_src_layer_desc	Memory descriptor for the diff of input vector.
diff_src_iter_desc	Memory descriptor for the diff of input recurrent hidden state vector.
diff_weights_layer_desc	Memory descriptor for the diff of weights applied to the layer input.
diff_weights_iter_desc	Memory descriptor for the diff of weights applied to the recurrent input.
diff_bias_desc	Diff bias memory descriptor.
diff_dst_layer_desc	Memory descriptor for the diff of output vector.
diff_dst_iter_desc	Memory descriptor for the diff of output recurrent hidden state vector.
flags	Unused.

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_lbr_gru_forward_desc_init()

dnnl_status_t DNNL_API dnnl_lbr_gru_forward_desc_init	(	dnnl_rnn_desc_t *	rnn_desc,
		dnnl_prop_kind_t	prop_kind,
		dnnl_rnn_direction_t	direction,
		const dnnl_memory_desc_t *	src_layer_desc,
		const dnnl_memory_desc_t *	src_iter_desc,
		const dnnl_memory_desc_t *	weights_layer_desc,
		const dnnl_memory_desc_t *	weights_iter_desc,
		const dnnl_memory_desc_t *	bias_desc,
		const dnnl_memory_desc_t *	dst_layer_desc,
		const dnnl_memory_desc_t *	dst_iter_desc,
		unsigned	flags
	)

Initializes a descriptor for LBR GRU forward propagation primitive.

The src_iter_desc, bias_desc, and dst_iter, may either be NULL or point to a zero memory descriptor. This would then indicate that the LBR GRU forward propagation primitive should not use them and should default to zero values instead.

Inputs:

• src_layer (dnnl_query_src_md, 0)
• src_iter (dnnl_query_src_md, 1), if used
• weights_layer (dnnl_query_weights_md, 0)
• weights_iter (dnnl_query_weights_md, 1)
• bias (dnnl_query_weights_md, 2), if used

Outputs:

• dst_layer (dnnl_query_dst_md, 0)
• dst_iter (dnnl_query_dst_md, 1), if used
• workspace (dnnl_query_workspace_md, 0), if prop_kind equals dnnl_forward_training; must be queried for using dnnl_primitive_desc_query_md() after a corresponding primitive descriptor is created

Parameters

rnn_desc	Output descriptor for LBR GRU primitive.
prop_kind	Propagation kind. Possible values are dnnl_forward_training and dnnl_forward_inference.
direction	RNN direction. See dnnl_rnn_direction_t for more info.
src_layer_desc	Memory descriptor for the input vector.
src_iter_desc	Memory descriptor for the input recurrent hidden state vector.
weights_layer_desc	Memory descriptor for the weights applied to the layer input.
weights_iter_desc	Memory descriptor for the weights applied to the recurrent input.
bias_desc	Bias memory descriptor.
dst_layer_desc	Memory descriptor for the output vector.
dst_iter_desc	Memory descriptor for the output recurrent hidden state vector.
flags	Unused.

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_lbr_gru_backward_desc_init()

dnnl_status_t DNNL_API dnnl_lbr_gru_backward_desc_init	(	dnnl_rnn_desc_t *	rnn_desc,
		dnnl_prop_kind_t	prop_kind,
		dnnl_rnn_direction_t	direction,
		const dnnl_memory_desc_t *	src_layer_desc,
		const dnnl_memory_desc_t *	src_iter_desc,
		const dnnl_memory_desc_t *	weights_layer_desc,
		const dnnl_memory_desc_t *	weights_iter_desc,
		const dnnl_memory_desc_t *	bias_desc,
		const dnnl_memory_desc_t *	dst_layer_desc,
		const dnnl_memory_desc_t *	dst_iter_desc,
		const dnnl_memory_desc_t *	diff_src_layer_desc,
		const dnnl_memory_desc_t *	diff_src_iter_desc,
		const dnnl_memory_desc_t *	diff_weights_layer_desc,
		const dnnl_memory_desc_t *	diff_weights_iter_desc,
		const dnnl_memory_desc_t *	diff_bias_desc,
		const dnnl_memory_desc_t *	diff_dst_layer_desc,
		const dnnl_memory_desc_t *	diff_dst_iter_desc,
		unsigned	flags
	)

Initializes a descriptor for LBR GRU backward propagation primitive.

The src_iter_desc together with diff_src_iter_desc, bias_desc together with diff_bias_desc, and dst_iter together with diff_dst_iter, may either be NULL or point to a zero memory descriptor. This would then indicate that the LBR GRU backward propagation primitive should not use them and should default to zero values instead.

Note

All memory descriptors can be initialized with dnnl_format_tag_any or with format_kind set to dnnl_format_kind_any.

Inputs:

• src_layer (dnnl_query_src_md, 0)
• src_iter (dnnl_query_src_md, 1), if used
• weights_layer (dnnl_query_weights_md, 0)
• weights_iter (dnnl_query_weights_md, 1)
• bias (dnnl_query_weights_md, 2), if used
• dst_layer (dnnl_query_dst_md, 0)
• dst_iter (dnnl_query_dst_md, 1), if used
• diff_dst_layer (dnnl_query_diff_dst_md, 0)
• diff_dst_iter (dnnl_query_diff_dst_md, 1), if used
• workspace (dnnl_query_workspace_md, 0)

Outputs:

• diff_src_layer (dnnl_query_diff_src_md, 0)
• diff_src_iter (dnnl_query_diff_src_md, 1), if used
• diff_weights_layer (dnnl_query_diff_weights_md, 0)
• diff_weights_iter (dnnl_query_diff_weights_md, 1)
• diff_bias (dnnl_query_diff_weights_md, 2), if used

Parameters

rnn_desc	Output descriptor for LBR GRU primitive.
prop_kind	Propagation kind. Must be dnnl_backward.
direction	RNN direction. See dnnl_rnn_direction_t for more info.
src_layer_desc	Memory descriptor for the input vector.
src_iter_desc	Memory descriptor for the input recurrent hidden state vector.
weights_layer_desc	Memory descriptor for the weights applied to the layer input.
weights_iter_desc	Memory descriptor for the weights applied to the recurrent input.
bias_desc	Bias memory descriptor.
dst_layer_desc	Memory descriptor for the output vector.
dst_iter_desc	Memory descriptor for the output recurrent hidden state vector.
diff_src_layer_desc	Memory descriptor for the diff of input vector.
diff_src_iter_desc	Memory descriptor for the diff of input recurrent hidden state vector.
diff_weights_layer_desc	Memory descriptor for the diff of weights applied to the layer input.
diff_weights_iter_desc	Memory descriptor for the diff of weights applied to the recurrent input.
diff_bias_desc	Diff bias memory descriptor.
diff_dst_layer_desc	Memory descriptor for the diff of output vector.
diff_dst_iter_desc	Memory descriptor for the diff of output recurrent hidden state vector.
flags	Unused.

Returns

dnnl_success on success and a status describing the error otherwise.

convert_to_c() [1/2]

dnnl_rnn_flags_t dnnl::convert_to_c ( rnn_flags  flags )

inline

Converts RNN cell flags enum value from C++ API to C API type.

Parameters

flags

C++ API RNN cell flags enum value.

Returns

Corresponding C API RNN cell flags enum value.

convert_to_c() [2/2]

dnnl_rnn_direction_t dnnl::convert_to_c ( rnn_direction  dir )

inline

Converts RNN direction enum value from C++ API to C API type.

Parameters

dir	C++ API RNN direction enum value.

Returns

Corresponding C API RNN direction enum value.