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_rnn_desc_t;
struct dnnl::gru_backward;
struct dnnl::gru_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_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
    );

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
    );

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
    );

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
    );

dnnl_status_t DNNL_API dnnl_lstm_forward_desc_init_v3(
    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* 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
    );

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
    );

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
    );

dnnl_status_t DNNL_API dnnl_lstm_backward_desc_init_v3(
    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* 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
    );

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
    );

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
    );

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
    );

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
    );

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_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 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:

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_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 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:

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

src_iter_desc together with src_iter_c_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.

Note

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

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.

See also:

dnnl_lstm_forward_desc_init_v2 to initialize forward LSTM with and without peephole

dnnl_lstm_forward_desc_init_v3 to initialize forward LSTM with and without peephole / recurrent projection layer

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 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.

Note

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

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.

See also:

dnnl_lstm_forward_desc_init_v3 to initialize forward LSTM with and without peephole / recurrent projection layer

dnnl_status_t DNNL_API dnnl_lstm_forward_desc_init_v3(
    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* 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
    )

Initializes a descriptor for an LSTM (with or without peephole and with or without recurrent projection layer) 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:

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).
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.

Returns:

dnnl_success on success and a status describing the error otherwise.

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 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,
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.

Note

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

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.

See also:

dnnl_lstm_backward_desc_init_v2 to initialize backward LSTM with and without peephole

dnnl_lstm_backward_desc_init_v3 to initialize backward LSTM with and without peephole / recurrent projection layer

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 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.

Note

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

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.

See also:

dnnl_lstm_backward_desc_init_v3 to initialize backward LSTM with and without peephole / recurrent projection layer

dnnl_status_t DNNL_API dnnl_lstm_backward_desc_init_v3(
    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* 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
    )

Initializes a descriptor for an LSTM (with or without peephole and with or with out recurrent projection layer) 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:

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).
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.

Returns:

dnnl_success on success and a status describing the error otherwise.

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 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:

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_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 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:

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_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 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:

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_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 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:

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.

struct dnnl::inner_product_forward::primitive_desc enum dnnl_rnn_direction_t

oneDNN v2.4.4 documentation

RNN¶

Overview¶

Detailed Documentation¶

Global Functions¶