repository

Contents

RNN

Overview

A primitive to compute recurrent neural network layers. More…

// enums

enum dnnl_rnn_direction_t;
enum dnnl_rnn_flags_t;
enum dnnl::rnn_direction;
enum dnnl::rnn_flags;

// structs

struct dnnl::augru_backward;
struct dnnl::augru_forward;
struct dnnl::gru_backward;
struct dnnl::gru_forward;
struct dnnl::lbr_augru_backward;
struct dnnl::lbr_augru_forward;
struct dnnl::lbr_gru_backward;
struct dnnl::lbr_gru_forward;
struct dnnl::lstm_backward;
struct dnnl::lstm_forward;
struct dnnl::rnn_primitive_desc_base;
struct dnnl::vanilla_rnn_backward;
struct dnnl::vanilla_rnn_forward;

// global functions

dnnl_rnn_flags_t dnnl::convert_to_c(rnn_flags flags);
dnnl_rnn_direction_t dnnl::convert_to_c(rnn_direction dir);

dnnl_status_t DNNL_API dnnl_vanilla_rnn_forward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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,
    const_dnnl_primitive_attr_t attr
    );

dnnl_status_t DNNL_API dnnl_vanilla_rnn_backward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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,
    const_dnnl_primitive_desc_t hint_fwd_pd,
    const_dnnl_primitive_attr_t attr
    );

dnnl_status_t DNNL_API dnnl_lstm_forward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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 weights_projection_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,
    const_dnnl_primitive_attr_t attr
    );

dnnl_status_t DNNL_API dnnl_lstm_backward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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 weights_projection_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_weights_projection_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,
    const_dnnl_primitive_desc_t hint_fwd_pd,
    const_dnnl_primitive_attr_t attr
    );

dnnl_status_t DNNL_API dnnl_gru_forward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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,
    const_dnnl_primitive_attr_t attr
    );

dnnl_status_t DNNL_API dnnl_gru_backward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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,
    const_dnnl_primitive_desc_t hint_fwd_pd,
    const_dnnl_primitive_attr_t attr
    );

dnnl_status_t DNNL_API dnnl_lbr_gru_forward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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,
    const_dnnl_primitive_attr_t attr
    );

dnnl_status_t DNNL_API dnnl_lbr_gru_backward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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,
    const_dnnl_primitive_desc_t hint_fwd_pd,
    const_dnnl_primitive_attr_t attr
    );

dnnl_status_t DNNL_API dnnl_augru_forward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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 attention_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,
    const_dnnl_primitive_attr_t attr
    );

dnnl_status_t DNNL_API dnnl_augru_backward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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 attention_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_attention_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,
    const_dnnl_primitive_desc_t hint_fwd_pd,
    const_dnnl_primitive_attr_t attr
    );

dnnl_status_t DNNL_API dnnl_lbr_augru_forward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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 attention_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,
    const_dnnl_primitive_attr_t attr
    );

dnnl_status_t DNNL_API dnnl_lbr_augru_backward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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 attention_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_attention_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,
    const_dnnl_primitive_desc_t hint_fwd_pd,
    const_dnnl_primitive_attr_t attr
    );

Detailed Documentation

A primitive to compute recurrent neural network layers.

See also:

RNN in developer guide

Global Functions

dnnl_rnn_flags_t dnnl::convert_to_c(rnn_flags flags)

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.

dnnl_rnn_direction_t dnnl::convert_to_c(rnn_direction dir)

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.

dnnl_status_t DNNL_API dnnl_vanilla_rnn_forward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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,
    const_dnnl_primitive_attr_t attr
    )

Creates a primitive descriptor for vanilla RNN forward propagation primitive.

The following arguments may either be NULL or point to a zero memory descriptor:

• src_iter_desc,

• bias_desc,

• dst_iter_desc.

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.

Parameters:

primitive_desc	Output primitive descriptor.
engine	Engine to use.
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.
attr	Primitive attributes (can be NULL).

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_vanilla_rnn_backward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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,
    const_dnnl_primitive_desc_t hint_fwd_pd,
    const_dnnl_primitive_attr_t attr
    )

Creates a primitive descriptor for vanilla RNN backward propagation primitive.

The following arguments may either be NULL or point to a zero memory descriptor:

• src_iter_desc together with diff_src_iter_desc,

• bias_desc together with diff_bias_desc,

• dst_iter_desc together with diff_dst_iter_desc.

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.

Parameters:

primitive_desc	Output primitive descriptor.
engine	Engine to use.
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.
hint_fwd_pd	Primitive descriptor for a respective forward propagation primitive.
attr	Primitive attributes (can be NULL).

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_lstm_forward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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 weights_projection_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,
    const_dnnl_primitive_attr_t attr
    )

Creates a primitive descriptor for an LSTM forward propagation primitive.

The following arguments may either be NULL or point to a zero memory descriptor:

• src_iter_desc together with src_iter_c_desc,

• weights_peephole_desc,

• bias_desc,

• dst_iter_desc together with dst_iter_c_desc.

This would then indicate that the LSTM forward propagation primitive should not use them and should default to zero values instead.

The weights_projection_desc could either be NULL or point to a zero memory descriptor. This would then indicate that the LSTM doesn’t have recurrent projection layer.

Note

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

Parameters:

primitive_desc	Output primitive descriptor.
engine	Engine to use.
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).
weights_projection_desc	Memory descriptor for the weights applied to the hidden states to get the recurrent projection (according to the Projection 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.
attr	Primitive attributes (can be NULL).

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_lstm_backward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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 weights_projection_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_weights_projection_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,
    const_dnnl_primitive_desc_t hint_fwd_pd,
    const_dnnl_primitive_attr_t attr
    )

Creates a primitive descriptor for an LSTM backward propagation primitive.

The following arguments may either be NULL or point to a zero memory descriptor:

• src_iter_desc together with src_iter_c_desc, diff_src_iter_desc, and 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 dst_iter_c_desc, diff_dst_iter_desc, and diff_dst_iter_c_desc.

This would then indicate that the LSTM backward propagation primitive should not use them and should default to zero values instead.

The weights_projection_desc together with diff_weights_projection_desc could either be NULL or point to a zero memory descriptor. This would then indicate that the LSTM doesn’t have recurrent projection layer.

Note

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

Parameters:

primitive_desc	Output primitive descriptor.
engine	Engine to use.
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).
weights_projection_desc	Memory descriptor for the weights applied to the hidden states to get the recurrent projection (according to the Projection 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_weights_projection_desc	Memory descriptor for the diff of weights applied to the hidden states to get the recurrent projection (according to the Projection 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.
hint_fwd_pd	Primitive descriptor for a respective forward propagation primitive.
attr	Primitive attributes (can be NULL).

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_gru_forward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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,
    const_dnnl_primitive_attr_t attr
    )

Creates a primitive descriptor for GRU forward propagation primitive.

The following arguments may either be NULL or point to a zero memory descriptor:

• src_iter_desc,

• bias_desc,

• dst_iter_desc.

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.

Parameters:

primitive_desc	Output primitive descriptor.
engine	Engine to use.
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.
attr	Primitive attributes (can be NULL).

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_gru_backward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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,
    const_dnnl_primitive_desc_t hint_fwd_pd,
    const_dnnl_primitive_attr_t attr
    )

Creates a primitive descriptor for GRU backward propagation primitive.

The following arguments may either be NULL or point to a zero memory descriptor:

• src_iter_desc together with diff_src_iter_desc,

• bias_desc together with diff_bias_desc,

• dst_iter_desc together with diff_dst_iter_desc.

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.

Parameters:

primitive_desc	Output primitive descriptor.
engine	Engine to use.
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.
hint_fwd_pd	Primitive descriptor for a respective forward propagation primitive.
attr	Primitive attributes (can be NULL).

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_lbr_gru_forward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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,
    const_dnnl_primitive_attr_t attr
    )

Creates a descriptor for LBR GRU forward propagation primitive.

The following arguments may either be NULL or point to a zero memory descriptor:

• src_iter_desc,

• bias_desc,

• dst_iter_desc.

This would then indicate that the LBR GRU forward propagation primitive should not use them and should default to zero values instead.

Parameters:

primitive_desc	Output primitive descriptor.
engine	Engine to use.
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.
attr	Primitive attributes (can be NULL).

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_lbr_gru_backward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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,
    const_dnnl_primitive_desc_t hint_fwd_pd,
    const_dnnl_primitive_attr_t attr
    )

Creates a primitive descriptor for LBR GRU backward propagation primitive.

The following arguments may either be NULL or point to a zero memory descriptor:

• src_iter_desc together with diff_src_iter_desc,

• bias_desc together with diff_bias_desc,

• dst_iter_desc together with diff_dst_iter_desc.

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.

Parameters:

primitive_desc	Output primitive descriptor.
engine	Engine to use.
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.
hint_fwd_pd	Primitive descriptor for a respective forward propagation primitive.
attr	Primitive attributes (can be NULL).

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_augru_forward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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 attention_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,
    const_dnnl_primitive_attr_t attr
    )

Creates a primitive descriptor for AUGRU forward propagation primitive.

The following arguments may either be NULL or point to a zero memory descriptor:

• src_iter_desc,

• bias_desc,

• dst_iter_desc.

This would then indicate that the AUGRU 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.

Parameters:

primitive_desc	Output primitive descriptor.
engine	Engine to use.
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.
attention_desc	Memory descriptor for the attention 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.
attr	Primitive attributes (can be NULL).

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_augru_backward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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 attention_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_attention_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,
    const_dnnl_primitive_desc_t hint_fwd_pd,
    const_dnnl_primitive_attr_t attr
    )

Creates a primitive descriptor for AUGRU backward propagation primitive.

The following arguments may either be NULL or point to a zero memory descriptor:

• src_iter_desc together with diff_src_iter_desc,

• bias_desc together with diff_bias_desc,

• dst_iter_desc together with diff_dst_iter_desc.

This would then indicate that the AUGRU 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.

Parameters:

primitive_desc	Output primitive descriptor.
engine	Engine to use.
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.
attention_desc	Memory descriptor for the attention 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_attention_desc	Memory descriptor for the diff of attention 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.
hint_fwd_pd	Primitive descriptor for a respective forward propagation primitive.
attr	Primitive attributes (can be NULL).

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_lbr_augru_forward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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 attention_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,
    const_dnnl_primitive_attr_t attr
    )

Creates a primitive descriptor for LBR AUGRU forward propagation primitive.

The following arguments may either be NULL or point to a zero memory descriptor:

• src_iter_desc,

• bias_desc,

• dst_iter_desc.

This would then indicate that the LBR AUGRU forward propagation primitive should not use them and should default to zero values instead.

Parameters:

primitive_desc	Output primitive descriptor.
engine	Engine to use.
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.
attention_desc	Memory descriptor for the attention 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.
attr	Primitive attributes (can be NULL).

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_lbr_augru_backward_primitive_desc_create(
    dnnl_primitive_desc_t* primitive_desc,
    dnnl_engine_t engine,
    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 attention_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_attention_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,
    const_dnnl_primitive_desc_t hint_fwd_pd,
    const_dnnl_primitive_attr_t attr
    )

Creates a primitive descriptor for LBR AUGRU backward propagation primitive.

The following arguments may either be NULL or point to a zero memory descriptor:

• src_iter_desc together with diff_src_iter_desc,

• bias_desc together with diff_bias_desc,

• dst_iter_desc together with diff_dst_iter_desc.

This would then indicate that the LBR AUGRU 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.

Parameters:

primitive_desc	Output primitive descriptor.
engine	Engine to use.
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.
attention_desc	Memory descriptor for the attention 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_attention_desc	Memory descriptor for the diff of attention 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.
hint_fwd_pd	Primitive descriptor for a respective forward propagation primitive.
attr	Primitive attributes (can be NULL).

Returns:

dnnl_success on success and a status describing the error otherwise.

struct dnnl::inner_product_forward::primitive_desc enum dnnl_rnn_direction_t