dnnl::convolution_forward::desc Struct Reference

Descriptor for a convolution forward propagation primitive. More...

#include <dnnl.hpp>

Collaboration diagram for dnnl::convolution_forward::desc:

Public Member Functions
	desc (prop_kind aprop_kind, algorithm aalgorithm, const memory::desc &src_desc, const memory::desc &weights_desc, const memory::desc &bias_desc, const memory::desc &dst_desc, const memory::dims &strides, const memory::dims &padding_l, const memory::dims &padding_r)
	Constructs a descriptor for a convolution forward propagation primitive with bias. More...
	desc (prop_kind aprop_kind, algorithm aalgorithm, const memory::desc &src_desc, const memory::desc &weights_desc, const memory::desc &dst_desc, const memory::dims &strides, const memory::dims &padding_l, const memory::dims &padding_r)
	Constructs a descriptor for a convolution forward propagation primitive without bias. More...
	desc (prop_kind aprop_kind, algorithm aalgorithm, const memory::desc &src_desc, const memory::desc &weights_desc, const memory::desc &bias_desc, const memory::desc &dst_desc, const memory::dims &strides, const memory::dims &dilates, const memory::dims &padding_l, const memory::dims &padding_r)
	Constructs a descriptor for a dilated convolution forward propagation primitive with bias. More...
	desc (prop_kind aprop_kind, algorithm aalgorithm, const memory::desc &src_desc, const memory::desc &weights_desc, const memory::desc &dst_desc, const memory::dims &strides, const memory::dims &dilates, const memory::dims &padding_l, const memory::dims &padding_r)
	Constructs a descriptor for a dilated convolution forward propagation primitive without bias. More...

Detailed Description

Descriptor for a convolution forward propagation primitive.

Examples:

cnn_inference_f32.cpp, cnn_inference_int8.cpp, cnn_training_bf16.cpp, cnn_training_f32.cpp, convolution.cpp, and performance_profiling.cpp.

Constructor & Destructor Documentation

desc() [1/4]

dnnl::convolution_forward::desc::desc ( prop_kind  aprop_kind, algorithm  aalgorithm, const memory::desc &  src_desc, const memory::desc &  weights_desc, const memory::desc &  bias_desc, const memory::desc &  dst_desc, const memory::dims &  strides, const memory::dims &  padding_l, const memory::dims &  padding_r  )

inline

Constructs a descriptor for a convolution forward propagation primitive with bias.

Note

All the memory descriptors may be initialized with the dnnl::memory::format_tag::any value of format_tag.

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

aprop_kind	Propagation kind. Possible values are dnnl::prop_kind::forward_training, and dnnl::prop_kind::forward_inference.
aalgorithm	Convolution algorithm. Possible values are dnnl::algorithm::convolution_direct, dnnl::algorithm::convolution_winograd, and dnnl::algorithm::convolution_auto.
src_desc	Source memory descriptor.
weights_desc	Weights memory descriptor.
bias_desc	Bias memory descriptor. Passing zero memory descriptor disables the bias term.
dst_desc	Destination memory descriptor.
strides	Strides for each spatial dimension.
padding_l	Vector of padding values for low indices for each spatial dimension ([[front,] top,] left).
padding_r	Vector of padding values for high indices for each spatial dimension ([[back,] bottom,] right).

desc() [2/4]

dnnl::convolution_forward::desc::desc ( prop_kind  aprop_kind, algorithm  aalgorithm, const memory::desc &  src_desc, const memory::desc &  weights_desc, const memory::desc &  dst_desc, const memory::dims &  strides, const memory::dims &  padding_l, const memory::dims &  padding_r  )

inline

Constructs a descriptor for a convolution forward propagation primitive without bias.

Note

All the memory descriptors may be initialized with the dnnl::memory::format_tag::any value of format_tag.

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

aprop_kind	Propagation kind. Possible values are dnnl::prop_kind::forward_training, and dnnl::prop_kind::forward_inference.
aalgorithm	Convolution algorithm. Possible values are dnnl::algorithm::convolution_direct, dnnl::algorithm::convolution_winograd, and dnnl::algorithm::convolution_auto.
src_desc	Source memory descriptor.
weights_desc	Weights memory descriptor.
dst_desc	Destination memory descriptor.
strides	Strides for each spatial dimension.
padding_l	Vector of padding values for low indices for each spatial dimension ([[front,] top,] left).
padding_r	Vector of padding values for high indices for each spatial dimension ([[back,] bottom,] right).

desc() [3/4]

dnnl::convolution_forward::desc::desc ( prop_kind  aprop_kind, algorithm  aalgorithm, const memory::desc &  src_desc, const memory::desc &  weights_desc, const memory::desc &  bias_desc, const memory::desc &  dst_desc, const memory::dims &  strides, const memory::dims &  dilates, const memory::dims &  padding_l, const memory::dims &  padding_r  )

inline

Constructs a descriptor for a dilated convolution forward propagation primitive with bias.

Note

All the memory descriptors may be initialized with the dnnl::memory::format_tag::any value of format_tag.

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

aprop_kind	Propagation kind. Possible values are dnnl::prop_kind::forward_training, and dnnl::prop_kind::forward_inference.
aalgorithm	Convolution algorithm. Possible values are dnnl::algorithm::convolution_direct, dnnl::algorithm::convolution_winograd, and dnnl::algorithm::convolution_auto.
src_desc	Source memory descriptor.
weights_desc	Weights memory descriptor.
bias_desc	Bias memory descriptor. Passing zero memory descriptor disables the bias term.
dst_desc	Destination memory descriptor.
strides	Strides for each spatial dimension.
dilates	Dilations for each spatial dimension. A zero value means no dilation in the corresponding dimension.
padding_l	Vector of padding values for low indices for each spatial dimension ([[front,] top,] left).
padding_r	Vector of padding values for high indices for each spatial dimension ([[back,] bottom,] right).

desc() [4/4]

dnnl::convolution_forward::desc::desc ( prop_kind  aprop_kind, algorithm  aalgorithm, const memory::desc &  src_desc, const memory::desc &  weights_desc, const memory::desc &  dst_desc, const memory::dims &  strides, const memory::dims &  dilates, const memory::dims &  padding_l, const memory::dims &  padding_r  )

inline

Constructs a descriptor for a dilated convolution forward propagation primitive without bias.

Note

All the memory descriptors may be initialized with the dnnl::memory::format_tag::any value of format_tag.

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

aprop_kind	Propagation kind. Possible values are dnnl::prop_kind::forward_training, and dnnl::prop_kind::forward_inference.
aalgorithm	Convolution algorithm. Possible values are dnnl::algorithm::convolution_direct, dnnl::algorithm::convolution_winograd, and dnnl::algorithm::convolution_auto.
src_desc	Source memory descriptor.
weights_desc	Weights memory descriptor.
dst_desc	Destination memory descriptor.
strides	Strides for each spatial dimension.
dilates	Dilations for each spatial dimension. A zero value means no dilation in the corresponding dimension.
padding_l	Vector of padding values for low indices for each spatial dimension ([[front,] top,] left).
padding_r	Vector of padding values for high indices for each spatial dimension ([[back,] bottom,] right).

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The documentation for this struct was generated from the following file:

• include/oneapi/dnnl/dnnl.hpp