struct dnnl::vanilla_rnn_backward::primitive

Intel® oneAPI Deep Neural Network Developer Guide and Reference

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ID 768875

Date 12/16/2022

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struct dnnl::vanilla_rnn_backward::primitive_desc

Overview

Primitive descriptor for an RNN backward propagation primitive. More…

#include <dnnl.hpp>

struct primitive_desc: public dnnl::rnn_primitive_desc_base
{
    // construction

    primitive_desc();

    primitive_desc(
        const engine& aengine,
        prop_kind aprop_kind,
        algorithm activation,
        rnn_direction direction,
        const memory::desc& src_layer_desc,
        const memory::desc& src_iter_desc,
        const memory::desc& weights_layer_desc,
        const memory::desc& weights_iter_desc,
        const memory::desc& bias_desc,
        const memory::desc& dst_layer_desc,
        const memory::desc& dst_iter_desc,
        const memory::desc& diff_src_layer_desc,
        const memory::desc& diff_src_iter_desc,
        const memory::desc& diff_weights_layer_desc,
        const memory::desc& diff_weights_iter_desc,
        const memory::desc& diff_bias_desc,
        const memory::desc& diff_dst_layer_desc,
        const memory::desc& diff_dst_iter_desc,
        const vanilla_rnn_forward::primitive_desc& hint_fwd_pd,
        const primitive_attr& attr = default_attr(),
        bool allow_empty = false
        );

    primitive_desc(
        const engine& aengine,
        prop_kind aprop_kind,
        algorithm activation,
        rnn_direction direction,
        const memory::desc& src_layer_desc,
        const memory::desc& src_iter_desc,
        const memory::desc& weights_layer_desc,
        const memory::desc& weights_iter_desc,
        const memory::desc& bias_desc,
        const memory::desc& dst_layer_desc,
        const memory::desc& dst_iter_desc,
        const memory::desc& diff_src_layer_desc,
        const memory::desc& diff_src_iter_desc,
        const memory::desc& diff_weights_layer_desc,
        const memory::desc& diff_weights_iter_desc,
        const memory::desc& diff_bias_desc,
        const memory::desc& diff_dst_layer_desc,
        const memory::desc& diff_dst_iter_desc,
        float alpha,
        const vanilla_rnn_forward::primitive_desc& hint_fwd_pd,
        const primitive_attr& attr = default_attr(),
        bool allow_empty = false
        );

    primitive_desc(dnnl_primitive_desc_t pd);

    // methods

    memory::desc src_layer_desc() const;
    memory::desc src_iter_desc() const;
    memory::desc weights_layer_desc() const;
    memory::desc weights_iter_desc() const;
    memory::desc bias_desc() const;
    memory::desc dst_layer_desc() const;
    memory::desc dst_iter_desc() const;
    memory::desc workspace_desc() const;
    memory::desc diff_src_layer_desc() const;
    memory::desc diff_src_iter_desc() const;
    memory::desc diff_weights_layer_desc() const;
    memory::desc diff_weights_iter_desc() const;
    memory::desc diff_bias_desc() const;
    memory::desc diff_dst_layer_desc() const;
    memory::desc diff_dst_iter_desc() const;
    algorithm get_cell_kind() const;
    prop_kind get_prop_kind() const;
    algorithm get_activation_kind() const;
    rnn_direction get_direction() const;
    float get_alpha() const;
    float get_beta() const;
};

Inherited Members

public:
    // methods

    handle<T, traits>& operator = (const handle<T, traits>&);
    handle<T, traits>& operator = (handle<T, traits>&&);
    void reset(T t, bool weak = false);
    T get(bool allow_empty = false) const;
    operator T () const;
    operator bool () const;
    bool operator == (const handle<T, traits>& other) const;
    bool operator != (const handle& other) const;
    engine get_engine() const;
    const char* impl_info_str() const;
    memory::dim query_s64(query what) const;
    memory::dims get_strides() const;
    memory::dims get_dilations() const;
    memory::dims get_padding_l() const;
    memory::dims get_padding_r() const;
    float get_epsilon() const;

    template <typename T = unsigned>
    T get_flags() const;

    dnnl::algorithm get_algorithm() const;
    float get_alpha() const;
    float get_beta() const;
    int get_axis() const;
    memory::dim get_local_size() const;
    float get_k() const;
    float get_p() const;
    std::vector<float> get_factors() const;
    dnnl::algorithm get_cell_kind() const;
    dnnl::rnn_direction get_direction() const;
    dnnl::algorithm get_activation_kind() const;
    memory::dims get_kernel() const;
    memory::dim get_group_size() const;
    dnnl::prop_kind get_prop_kind() const;
    memory::desc query_md(query what, int idx = 0) const;
    memory::desc src_desc(int idx) const;
    memory::desc dst_desc(int idx) const;
    memory::desc weights_desc(int idx) const;
    memory::desc diff_src_desc(int idx) const;
    memory::desc diff_dst_desc(int idx) const;
    memory::desc diff_weights_desc(int idx) const;
    memory::desc src_desc() const;
    memory::desc dst_desc() const;
    memory::desc weights_desc() const;
    memory::desc diff_src_desc() const;
    memory::desc diff_dst_desc() const;
    memory::desc diff_weights_desc() const;
    memory::desc workspace_desc() const;
    memory::desc scratchpad_desc() const;
    engine scratchpad_engine() const;
    primitive_attr get_primitive_attr() const;
    dnnl::primitive::kind get_kind() const;
    std::vector<uint8_t> get_cache_blob_id() const;
    bool next_impl();
    primitive_desc_base();
    primitive_desc_base(dnnl_primitive_desc_t pd, dnnl::primitive::kind prim_kind);

    primitive_desc_base(
        dnnl_primitive_desc_t pd,
        dnnl::primitive::kind prim_kind,
        dnnl::prop_kind aprop_kind
        );

    primitive_desc_base(
        dnnl_primitive_desc_t pd,
        dnnl::primitive::kind prim_kind,
        dnnl::prop_kind prop_kind1,
        dnnl::prop_kind prop_kind2
        );

    memory::desc src_layer_desc() const;
    memory::desc augru_attention_desc() const;
    memory::desc src_iter_desc() const;
    memory::desc src_iter_c_desc() const;
    memory::desc weights_layer_desc() const;
    memory::desc weights_iter_desc() const;
    memory::desc weights_peephole_desc() const;
    memory::desc weights_projection_desc() const;
    memory::desc bias_desc() const;
    memory::desc dst_layer_desc() const;
    memory::desc dst_iter_desc() const;
    memory::desc dst_iter_c_desc() const;
    memory::desc diff_src_layer_desc() const;
    memory::desc diff_augru_attention_desc() const;
    memory::desc diff_src_iter_desc() const;
    memory::desc diff_src_iter_c_desc() const;
    memory::desc diff_weights_layer_desc() const;
    memory::desc diff_weights_iter_desc() const;
    memory::desc diff_weights_peephole_desc() const;
    memory::desc diff_weights_projection_desc() const;
    memory::desc diff_bias_desc() const;
    memory::desc diff_dst_layer_desc() const;
    memory::desc diff_dst_iter_desc() const;
    memory::desc diff_dst_iter_c_desc() const;
    primitive_desc();

Detailed Documentation

Primitive descriptor for an RNN backward propagation primitive.

Construction

primitive_desc()

Default constructor. Produces an empty object.

primitive_desc(
    const engine& aengine,
    prop_kind aprop_kind,
    algorithm activation,
    rnn_direction direction,
    const memory::desc& src_layer_desc,
    const memory::desc& src_iter_desc,
    const memory::desc& weights_layer_desc,
    const memory::desc& weights_iter_desc,
    const memory::desc& bias_desc,
    const memory::desc& dst_layer_desc,
    const memory::desc& dst_iter_desc,
    const memory::desc& diff_src_layer_desc,
    const memory::desc& diff_src_iter_desc,
    const memory::desc& diff_weights_layer_desc,
    const memory::desc& diff_weights_iter_desc,
    const memory::desc& diff_bias_desc,
    const memory::desc& diff_dst_layer_desc,
    const memory::desc& diff_dst_iter_desc,
    const vanilla_rnn_forward::primitive_desc& hint_fwd_pd,
    const primitive_attr& attr = default_attr(),
    bool allow_empty = false
    )

Constructs a primitive descriptor for a vanilla RNN backward propagation primitive.

The following arguments may 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 the memory descriptors may be initialized with the dnnl::memory::format_tag::any value of format_tag.

Parameters:

aengine	Engine to use.
aprop_kind	Propagation kind. Must be dnnl::prop_kind::backward.
activation	Activation kind. Possible values are dnnl::algorithm::eltwise_relu, dnnl::algorithm::eltwise_tanh, or dnnl::algorithm::eltwise_logistic.
direction	RNN direction. See dnnl::rnn_direction 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.
hint_fwd_pd	Primitive descriptor for a vanilla RNN forward propagation primitive. It is used as a hint for deciding which memory format to use.
attr	Primitive attributes to use. Attributes are optional and default to empty attributes.
allow_empty	A flag signifying whether construction is allowed to fail without throwing an exception. In this case an empty object will be produced. This flag is optional and defaults to false.

primitive_desc(
    const engine& aengine,
    prop_kind aprop_kind,
    algorithm activation,
    rnn_direction direction,
    const memory::desc& src_layer_desc,
    const memory::desc& src_iter_desc,
    const memory::desc& weights_layer_desc,
    const memory::desc& weights_iter_desc,
    const memory::desc& bias_desc,
    const memory::desc& dst_layer_desc,
    const memory::desc& dst_iter_desc,
    const memory::desc& diff_src_layer_desc,
    const memory::desc& diff_src_iter_desc,
    const memory::desc& diff_weights_layer_desc,
    const memory::desc& diff_weights_iter_desc,
    const memory::desc& diff_bias_desc,
    const memory::desc& diff_dst_layer_desc,
    const memory::desc& diff_dst_iter_desc,
    float alpha,
    const vanilla_rnn_forward::primitive_desc& hint_fwd_pd,
    const primitive_attr& attr = default_attr(),
    bool allow_empty = false
    )

Constructs a primitive descriptor for a vanilla RNN backward propagation primitive with an alpha parameter.

The following arguments may 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 the memory descriptors may be initialized with the dnnl::memory::format_tag::any value of format_tag.

Parameters:

aengine	Engine to use.
aprop_kind	Propagation kind. Must be dnnl::prop_kind::backward.
activation	Activation kind. Possible values are dnnl::algorithm::eltwise_relu, dnnl::algorithm::eltwise_tanh, or dnnl::algorithm::eltwise_logistic.
direction	RNN direction. See dnnl::rnn_direction 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.
alpha	Negative slope if activation is dnnl::algorithm::eltwise_relu.
hint_fwd_pd	Primitive descriptor for a vanilla RNN forward propagation primitive. It is used as a hint for deciding which memory format to use.
attr	Primitive attributes to use. Attributes are optional and default to empty attributes.
allow_empty	A flag signifying whether construction is allowed to fail without throwing an exception. In this case an empty object will be produced. This flag is optional and defaults to false.

primitive_desc(dnnl_primitive_desc_t pd)

Constructs a primitive descriptor for a vanilla RNN backward propagation primitive from a C API primitive descriptor that must have a matching kind.

Parameters:

pd	C API primitive descriptor for a vanilla RNN backward propagation primitive.

Methods

memory::desc src_layer_desc() const

Returns source layer memory descriptor.

Returns:

Source layer memory descriptor.

memory::desc src_iter_desc() const

Returns source iteration memory descriptor.

Returns:

Source iteration memory descriptor.

A zero memory descriptor if the primitive does not have a source iteration parameter.

memory::desc weights_layer_desc() const

Returns weights layer memory descriptor.

Returns:

Weights layer memory descriptor.

memory::desc weights_iter_desc() const

Returns weights iteration memory descriptor.

Returns:

Weights iteration memory descriptor.

memory::desc bias_desc() const

Returns bias memory descriptor.

Returns:

Bias memory descriptor.

A zero memory descriptor if the primitive does not have a bias parameter.

memory::desc dst_layer_desc() const

Returns destination layer memory descriptor.

Returns:

Destination layer memory descriptor.

memory::desc dst_iter_desc() const

Returns destination iteration memory descriptor.

Returns:

Destination iteration memory descriptor.

A zero memory descriptor if the primitive does not have a destination iteration parameter.

memory::desc workspace_desc() const

Returns the workspace memory descriptor.

Returns:

Workspace memory descriptor.

A zero memory descriptor if the primitive does not require workspace parameter.

memory::desc diff_src_layer_desc() const

Returns diff source layer memory descriptor.

Returns:

Diff source layer memory descriptor.

memory::desc diff_src_iter_desc() const

Returns diff source iteration memory descriptor.

Returns:

Diff source iteration memory descriptor.

A zero memory descriptor if the primitive does not have a diff source iteration parameter.

memory::desc diff_weights_layer_desc() const

Returns diff weights layer memory descriptor.

Returns:

Diff weights layer memory descriptor.

memory::desc diff_weights_iter_desc() const

Returns diff weights iteration memory descriptor.

Returns:

Diff weights iteration memory descriptor.

memory::desc diff_bias_desc() const

Returns diff bias memory descriptor.

Returns:

Diff bias memory descriptor.

A zero memory descriptor if the primitive does not have a diff bias parameter.

memory::desc diff_dst_layer_desc() const

Returns diff destination layer memory descriptor.

Returns:

Diff destination layer memory descriptor.

memory::desc diff_dst_iter_desc() const

Returns diff destination iteration memory descriptor.

Returns:

Diff destination iteration memory descriptor.

A zero memory descriptor if the primitive does not have a diff destination iteration parameter.

algorithm get_cell_kind() const

Returns an RNN cell kind parameter.

Returns:

An RNN cell kind parameter.

dnnl::algorithm::undef if the primitive does not have an RNN cell kind parameter.

prop_kind get_prop_kind() const

Returns a propagation kind.

Returns:

A propagation kind.

dnnl::prop_kind::undef if the primitive does not have a propagation parameter.

algorithm get_activation_kind() const

Returns an RNN activation kind parameter.

Returns:

An RNN activation kind parameter.

dnnl::algorithm::undef if the primitive does not have an RNN activation kind parameter.

rnn_direction get_direction() const

Returns an RNN direction parameter.

Returns:

An RNN direction parameter.

dnnl::rnn_direction::undef if the primitive does not have an RNN direction parameter.

float get_alpha() const

Returns an alpha.

Returns:

An alpha.

Zero if the primitive does not have an alpha parameter.

float get_beta() const

Returns a beta.

Returns:

A beta.

Zero if the primitive does not have a beta parameter.

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Intel® oneAPI Deep Neural Network Developer Guide and Reference

struct dnnl::vanilla_rnn_backward::primitive_desc

Overview

Detailed Documentation