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Contents

Convolution

Overview

A primitive to perform 1D, 2D or 3D convolution. More…

// structs

struct dnnl::convolution_backward_data;
struct dnnl::convolution_backward_weights;
struct dnnl::convolution_forward;
struct dnnl_convolution_desc_t;

// global functions

dnnl_status_t DNNL_API dnnl_convolution_forward_desc_init(
    dnnl_convolution_desc_t* conv_desc,
    dnnl_prop_kind_t prop_kind,
    dnnl_alg_kind_t alg_kind,
    const dnnl_memory_desc_t* src_desc,
    const dnnl_memory_desc_t* weights_desc,
    const dnnl_memory_desc_t* bias_desc,
    const dnnl_memory_desc_t* dst_desc,
    const dnnl_dims_t strides,
    const dnnl_dims_t padding_l,
    const dnnl_dims_t padding_r
    );

dnnl_status_t DNNL_API dnnl_dilated_convolution_forward_desc_init(
    dnnl_convolution_desc_t* conv_desc,
    dnnl_prop_kind_t prop_kind,
    dnnl_alg_kind_t alg_kind,
    const dnnl_memory_desc_t* src_desc,
    const dnnl_memory_desc_t* weights_desc,
    const dnnl_memory_desc_t* bias_desc,
    const dnnl_memory_desc_t* dst_desc,
    const dnnl_dims_t strides,
    const dnnl_dims_t dilates,
    const dnnl_dims_t padding_l,
    const dnnl_dims_t padding_r
    );

dnnl_status_t DNNL_API dnnl_convolution_backward_data_desc_init(
    dnnl_convolution_desc_t* conv_desc,
    dnnl_alg_kind_t alg_kind,
    const dnnl_memory_desc_t* diff_src_desc,
    const dnnl_memory_desc_t* weights_desc,
    const dnnl_memory_desc_t* diff_dst_desc,
    const dnnl_dims_t strides,
    const dnnl_dims_t padding_l,
    const dnnl_dims_t padding_r
    );

dnnl_status_t DNNL_API dnnl_dilated_convolution_backward_data_desc_init(
    dnnl_convolution_desc_t* conv_desc,
    dnnl_alg_kind_t alg_kind,
    const dnnl_memory_desc_t* diff_src_desc,
    const dnnl_memory_desc_t* weights_desc,
    const dnnl_memory_desc_t* diff_dst_desc,
    const dnnl_dims_t strides,
    const dnnl_dims_t dilates,
    const dnnl_dims_t padding_l,
    const dnnl_dims_t padding_r
    );

dnnl_status_t DNNL_API dnnl_convolution_backward_weights_desc_init(
    dnnl_convolution_desc_t* conv_desc,
    dnnl_alg_kind_t alg_kind,
    const dnnl_memory_desc_t* src_desc,
    const dnnl_memory_desc_t* diff_weights_desc,
    const dnnl_memory_desc_t* diff_bias_desc,
    const dnnl_memory_desc_t* diff_dst_desc,
    const dnnl_dims_t strides,
    const dnnl_dims_t padding_l,
    const dnnl_dims_t padding_r
    );

dnnl_status_t DNNL_API dnnl_dilated_convolution_backward_weights_desc_init(
    dnnl_convolution_desc_t* conv_desc,
    dnnl_alg_kind_t alg_kind,
    const dnnl_memory_desc_t* src_desc,
    const dnnl_memory_desc_t* diff_weights_desc,
    const dnnl_memory_desc_t* diff_bias_desc,
    const dnnl_memory_desc_t* diff_dst_desc,
    const dnnl_dims_t strides,
    const dnnl_dims_t dilates,
    const dnnl_dims_t padding_l,
    const dnnl_dims_t padding_r
    );

Detailed Documentation

A primitive to perform 1D, 2D or 3D convolution.

Supported variants are forward propagation, backward propagation, and weights gradient with or without bias.

See also:

Convolution in developer guide

Global Functions

dnnl_status_t DNNL_API dnnl_convolution_forward_desc_init(
    dnnl_convolution_desc_t* conv_desc,
    dnnl_prop_kind_t prop_kind,
    dnnl_alg_kind_t alg_kind,
    const dnnl_memory_desc_t* src_desc,
    const dnnl_memory_desc_t* weights_desc,
    const dnnl_memory_desc_t* bias_desc,
    const dnnl_memory_desc_t* dst_desc,
    const dnnl_dims_t strides,
    const dnnl_dims_t padding_l,
    const dnnl_dims_t padding_r
    )

Initializes a descriptor for a convolution forward propagation primitive.

Note

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

Arrays strides, padding_l, and padding_r contain values for spatial dimensions only and hence must have the same number of elements as there are spatial dimensions. The order of values is the same as in the tensor: depth (for 3D tensors), height (for 3D and 2D tensors), and width.

Parameters:

conv_desc	Output descriptor for a convolution primitive.
prop_kind	Propagation kind. Possible values are dnnl_forward_training and dnnl_forward_inference.
alg_kind	Convolution algorithm. Possible values are dnnl_convolution_direct, dnnl_convolution_winograd, dnnl_convolution_auto.
src_desc	Source memory descriptor.
weights_desc	Weights memory descriptor.
bias_desc	Bias memory descriptor. Passing NULL, a zero memory descriptor, or a memory descriptor with format_kind set to dnnl_format_kind_undef disables the bias term.
dst_desc	Destination memory descriptor.
strides	Array of strides for spatial dimension.
padding_l	Array of padding values for low indices for each spatial dimension ([[front,] top,] left).
padding_r	Array of padding values for high indices for each spatial dimension ([[back,] bottom,] right). Can be NULL in which case padding is assumed to be symmetrical.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_dilated_convolution_forward_desc_init(
    dnnl_convolution_desc_t* conv_desc,
    dnnl_prop_kind_t prop_kind,
    dnnl_alg_kind_t alg_kind,
    const dnnl_memory_desc_t* src_desc,
    const dnnl_memory_desc_t* weights_desc,
    const dnnl_memory_desc_t* bias_desc,
    const dnnl_memory_desc_t* dst_desc,
    const dnnl_dims_t strides,
    const dnnl_dims_t dilates,
    const dnnl_dims_t padding_l,
    const dnnl_dims_t padding_r
    )

Initializes a descriptor for a dilated convolution forward propagation primitive.

Note

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

Arrays strides, dilates, padding_l, and padding_r contain values for spatial dimensions only and hence must have the same number of elements as there are spatial dimensions. The order of values is the same as in the tensor: depth (for 3D tensors), height (for 3D and 2D tensors), and width.

Parameters:

conv_desc	Output descriptor for a convolution primitive.
prop_kind	Propagation kind. Possible values are dnnl_forward_training and dnnl_forward_inference.
alg_kind	Convolution algorithm. Possible values are dnnl_convolution_direct, dnnl_convolution_winograd, dnnl_convolution_auto.
src_desc	Source memory descriptor.
weights_desc	Weights memory descriptor.
bias_desc	Bias memory descriptor. Passing NULL, a zero memory descriptor, or a memory descriptor with format_kind set to dnnl_format_kind_undef disables the bias term.
dst_desc	Destination memory descriptor.
strides	Array of strides for spatial dimension.
dilates	Array of dilations for spatial dimension. A zero value means no dilation in the corresponding dimension.
padding_l	Array of padding values for low indices for each spatial dimension ([[front,] top,] left).
padding_r	Array of padding values for high indices for each spatial dimension ([[back,] bottom,] right). Can be NULL in which case padding is considered to be symmetrical.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_convolution_backward_data_desc_init(
    dnnl_convolution_desc_t* conv_desc,
    dnnl_alg_kind_t alg_kind,
    const dnnl_memory_desc_t* diff_src_desc,
    const dnnl_memory_desc_t* weights_desc,
    const dnnl_memory_desc_t* diff_dst_desc,
    const dnnl_dims_t strides,
    const dnnl_dims_t padding_l,
    const dnnl_dims_t padding_r
    )

Initializes a descriptor for a convolution backward propagation primitive.

Note

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

Arrays strides, padding_l, and padding_r contain values for spatial dimensions only and hence must have the same number of elements as there are spatial dimensions. The order of values is the same as in the tensor: depth (for 3D tensors), height (for 3D and 2D tensors), and width.

Parameters:

conv_desc	Output descriptor for a convolution primitive.
alg_kind	Convolution algorithm. Possible values are dnnl_convolution_direct, dnnl_convolution_winograd, dnnl_convolution_auto.
diff_src_desc	Diff source memory descriptor.
weights_desc	Weights memory descriptor.
diff_dst_desc	Diff destination memory descriptor.
strides	Array of strides for spatial dimension.
padding_l	Array of padding values for low indices for each spatial dimension ([[front,] top,] left).
padding_r	Array of padding values for high indices for each spatial dimension ([[back,] bottom,] right). Can be NULL in which case padding is assumed to be symmetrical.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_dilated_convolution_backward_data_desc_init(
    dnnl_convolution_desc_t* conv_desc,
    dnnl_alg_kind_t alg_kind,
    const dnnl_memory_desc_t* diff_src_desc,
    const dnnl_memory_desc_t* weights_desc,
    const dnnl_memory_desc_t* diff_dst_desc,
    const dnnl_dims_t strides,
    const dnnl_dims_t dilates,
    const dnnl_dims_t padding_l,
    const dnnl_dims_t padding_r
    )

Initializes a descriptor for a dilated convolution backward propagation primitive.

Note

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

Arrays strides, dilates, padding_l, and padding_r contain values for spatial dimensions only and hence must have the same number of elements as there are spatial dimensions. The order of values is the same as in the tensor: depth (for 3D tensors), height (for 3D and 2D tensors), and width.

Parameters:

conv_desc	Output descriptor for a convolution primitive.
alg_kind	Convolution algorithm. Possible values are dnnl_convolution_direct, dnnl_convolution_winograd, dnnl_convolution_auto.
diff_src_desc	Diff source memory descriptor.
weights_desc	Weights memory descriptor.
diff_dst_desc	Diff destination memory descriptor.
strides	Array of strides for spatial dimension.
dilates	Array of dilations for spatial dimension. A zero value means no dilation in the corresponding dimension.
padding_l	Array of padding values for low indices for each spatial dimension ([[front,] top,] left).
padding_r	Array of padding values for high indices for each spatial dimension ([[back,] bottom,] right). Can be NULL in which case padding is considered to be symmetrical.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_convolution_backward_weights_desc_init(
    dnnl_convolution_desc_t* conv_desc,
    dnnl_alg_kind_t alg_kind,
    const dnnl_memory_desc_t* src_desc,
    const dnnl_memory_desc_t* diff_weights_desc,
    const dnnl_memory_desc_t* diff_bias_desc,
    const dnnl_memory_desc_t* diff_dst_desc,
    const dnnl_dims_t strides,
    const dnnl_dims_t padding_l,
    const dnnl_dims_t padding_r
    )

Initializes a descriptor for a convolution weights gradient primitive.

Note

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

Arrays strides, padding_l, and padding_r contain values for spatial dimensions only and hence must have the same number of elements as there are spatial dimensions. The order of values is the same as in the tensor: depth (for 3D tensors), height (for 3D and 2D tensors), and width.

Parameters:

conv_desc	Output descriptor for a convolution primitive.
alg_kind	Convolution algorithm. Possible values are dnnl_convolution_direct, dnnl_convolution_winograd, dnnl_convolution_auto.
src_desc	Source memory descriptor.
diff_weights_desc	Diff weights memory descriptor.
diff_bias_desc	Diff bias memory descriptor. Passing NULL, a zero memory descriptor, or a memory descriptor with format_kind set to dnnl_format_kind_undef disables the bias term.
diff_dst_desc	Diff destination memory descriptor.
strides	Array of strides for spatial dimension.
padding_l	Array of padding values for low indices for each spatial dimension ([[front,] top,] left).
padding_r	Array of padding values for high indices for each spatial dimension ([[back,] bottom,] right). Can be NULL in which case padding is considered to be symmetrical.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_dilated_convolution_backward_weights_desc_init(
    dnnl_convolution_desc_t* conv_desc,
    dnnl_alg_kind_t alg_kind,
    const dnnl_memory_desc_t* src_desc,
    const dnnl_memory_desc_t* diff_weights_desc,
    const dnnl_memory_desc_t* diff_bias_desc,
    const dnnl_memory_desc_t* diff_dst_desc,
    const dnnl_dims_t strides,
    const dnnl_dims_t dilates,
    const dnnl_dims_t padding_l,
    const dnnl_dims_t padding_r
    )

Initializes a descriptor for a dilated convolution weights gradient primitive.

Note

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

Arrays strides, dilates, padding_l, and padding_r contain values for spatial dimensions only and hence must have the same number of elements as there are spatial dimensions. The order of values is the same as in the tensor: depth (for 3D tensors), height (for 3D and 2D tensors), and width.

Parameters:

conv_desc	Output descriptor for a convolution primitive.
alg_kind	Convolution algorithm. Possible values are dnnl_convolution_direct, dnnl_convolution_winograd, dnnl_convolution_auto.
src_desc	Source memory descriptor.
diff_weights_desc	Diff weights memory descriptor.
diff_bias_desc	Diff bias memory descriptor. Passing NULL, a zero memory descriptor, or a memory descriptor with format_kind set to dnnl_format_kind_undef disables the bias term.
diff_dst_desc	Diff destination memory descriptor.
strides	Array of strides for spatial dimension.
dilates	Array of dilations for spatial dimension. A zero value means no dilation in the corresponding dimension.
padding_l	Array of padding values for low indices for each spatial dimension ([[front,] top,] left).
padding_r	Array of padding values for high indices for each spatial dimension ([[back,] bottom,] right). Can be NULL in which case padding is considered to be symmetrical.

Returns:

dnnl_success on success and a status describing the error otherwise.

struct dnnl_binary_desc_t struct dnnl::convolution_backward_data