Attributes

Overview

A container for parameters that extend primitives behavior. More…

// typedefs

typedef struct dnnl_primitive_attr* dnnl_primitive_attr_t;
typedef const struct dnnl_primitive_attr* const_dnnl_primitive_attr_t;
typedef struct dnnl_post_ops* dnnl_post_ops_t;
typedef const struct dnnl_post_ops* const_dnnl_post_ops_t;

// enums

enum dnnl::algorithm;
enum dnnl_scratchpad_mode_t;
enum dnnl::prop_kind;
enum dnnl::scratchpad_mode;

// structs

struct dnnl_post_ops;
struct dnnl_primitive_attr;
struct dnnl::post_ops;
struct dnnl::primitive_attr;

// global functions

dnnl_scratchpad_mode_t dnnl::convert_to_c(scratchpad_mode mode);
dnnl_prop_kind_t dnnl::convert_to_c(prop_kind akind);
dnnl_alg_kind_t dnnl::convert_to_c(algorithm aalgorithm);
dnnl_status_t DNNL_API dnnl_primitive_attr_create(dnnl_primitive_attr_t* attr);

dnnl_status_t DNNL_API dnnl_primitive_attr_clone(
    dnnl_primitive_attr_t* attr,
    const_dnnl_primitive_attr_t existing_attr
    );

dnnl_status_t DNNL_API dnnl_primitive_attr_destroy(dnnl_primitive_attr_t attr);

dnnl_status_t DNNL_API dnnl_primitive_attr_get_scratchpad_mode(
    const_dnnl_primitive_attr_t attr,
    dnnl_scratchpad_mode_t* mode
    );

dnnl_status_t DNNL_API dnnl_primitive_attr_set_scratchpad_mode(
    dnnl_primitive_attr_t attr,
    dnnl_scratchpad_mode_t mode
    );

dnnl_status_t DNNL_API dnnl_primitive_attr_get_output_scales(
    const_dnnl_primitive_attr_t attr,
    dnnl_dim_t* count,
    int* mask,
    const float** scales
    );

dnnl_status_t DNNL_API dnnl_primitive_attr_set_output_scales(
    dnnl_primitive_attr_t attr,
    dnnl_dim_t count,
    int mask,
    const float* scales
    );

dnnl_status_t DNNL_API dnnl_primitive_attr_get_scales(
    dnnl_primitive_attr_t attr,
    int arg,
    dnnl_dim_t* count,
    int* mask,
    const float** scales
    );

dnnl_status_t DNNL_API dnnl_primitive_attr_set_scales(
    dnnl_primitive_attr_t attr,
    int arg,
    dnnl_dim_t count,
    int mask,
    const float* scales
    );

dnnl_status_t DNNL_API dnnl_primitive_attr_get_zero_points(
    const_dnnl_primitive_attr_t attr,
    int arg,
    dnnl_dim_t* count,
    int* mask,
    const int32_t** zero_points
    );

dnnl_status_t DNNL_API dnnl_primitive_attr_set_zero_points(
    dnnl_primitive_attr_t attr,
    int arg,
    dnnl_dim_t count,
    int mask,
    const int32_t* zero_points
    );

dnnl_status_t DNNL_API dnnl_primitive_attr_get_post_ops(
    const_dnnl_primitive_attr_t attr,
    const_dnnl_post_ops_t* post_ops
    );

dnnl_status_t DNNL_API dnnl_primitive_attr_set_post_ops(
    dnnl_primitive_attr_t attr,
    const_dnnl_post_ops_t post_ops
    );

dnnl_status_t DNNL_API dnnl_post_ops_create(dnnl_post_ops_t* post_ops);
dnnl_status_t DNNL_API dnnl_post_ops_destroy(dnnl_post_ops_t post_ops);
int DNNL_API dnnl_post_ops_len(const_dnnl_post_ops_t post_ops);

dnnl_primitive_kind_t DNNL_API dnnl_post_ops_get_kind(
    const_dnnl_post_ops_t post_ops,
    int index
    );

dnnl_status_t DNNL_API dnnl_post_ops_append_sum(
    dnnl_post_ops_t post_ops,
    float scale
    );

dnnl_status_t DNNL_API dnnl_post_ops_append_sum_v2(
    dnnl_post_ops_t post_ops,
    float scale,
    dnnl_data_type_t data_type
    );

dnnl_status_t DNNL_API dnnl_post_ops_get_params_sum(
    const_dnnl_post_ops_t post_ops,
    int index,
    float* scale
    );

dnnl_status_t DNNL_API dnnl_post_ops_get_params_sum_v2(
    const_dnnl_post_ops_t post_ops,
    int index,
    float* scale,
    dnnl_data_type_t* data_type
    );

dnnl_status_t DNNL_API dnnl_post_ops_append_eltwise(
    dnnl_post_ops_t post_ops,
    float scale,
    dnnl_alg_kind_t alg_kind,
    float alpha,
    float beta
    );

dnnl_status_t DNNL_API dnnl_post_ops_get_params_eltwise(
    const_dnnl_post_ops_t post_ops,
    int index,
    float* scale,
    dnnl_alg_kind_t* alg_kind,
    float* alpha,
    float* beta
    );

dnnl_status_t DNNL_API dnnl_post_ops_append_dw_k3s1p1(
    dnnl_post_ops_t post_ops,
    dnnl_data_type_t weights_data_type,
    dnnl_data_type_t bias_data_type,
    dnnl_data_type_t dst_data_type,
    dnnl_dim_t count,
    int mask,
    const float* scales
    );

dnnl_status_t DNNL_API dnnl_post_ops_get_params_dw_k3s1p1(
    const_dnnl_post_ops_t post_ops,
    int index,
    dnnl_data_type_t* weights_data_type,
    dnnl_data_type_t* bias_data_type,
    dnnl_data_type_t* dst_data_type,
    dnnl_dim_t* count,
    int* mask,
    const float** scales
    );

dnnl_status_t DNNL_API dnnl_post_ops_append_dw_k3s2p1(
    dnnl_post_ops_t post_ops,
    dnnl_data_type_t weights_data_type,
    dnnl_data_type_t bias_data_type,
    dnnl_data_type_t dst_data_type,
    dnnl_dim_t count,
    int mask,
    const float* scales
    );

dnnl_status_t DNNL_API dnnl_post_ops_get_params_dw_k3s2p1(
    const_dnnl_post_ops_t post_ops,
    int index,
    dnnl_data_type_t* weights_data_type,
    dnnl_data_type_t* bias_data_type,
    dnnl_data_type_t* dst_data_type,
    dnnl_dim_t* count,
    int* mask,
    const float** scales
    );

dnnl_status_t DNNL_API dnnl_post_ops_append_binary(
    dnnl_post_ops_t post_ops,
    dnnl_alg_kind_t alg_kind,
    const dnnl_memory_desc_t* src1_desc
    );

dnnl_status_t DNNL_API dnnl_post_ops_get_params_binary(
    const_dnnl_post_ops_t post_ops,
    int index,
    dnnl_alg_kind_t* alg_kind,
    const dnnl_memory_desc_t** src1_desc
    );

dnnl_status_t DNNL_API dnnl_primitive_attr_set_rnn_data_qparams(
    dnnl_primitive_attr_t attr,
    const float scale,
    const float shift
    );

dnnl_status_t DNNL_API dnnl_primitive_attr_get_rnn_data_qparams(
    const_dnnl_primitive_attr_t attr,
    float* scale,
    float* shift
    );

dnnl_status_t DNNL_API dnnl_primitive_attr_set_rnn_weights_qparams(
    dnnl_primitive_attr_t attr,
    dnnl_dim_t count,
    int mask,
    const float* scales
    );

dnnl_status_t DNNL_API dnnl_primitive_attr_get_rnn_weights_qparams(
    const_dnnl_primitive_attr_t attr,
    dnnl_dim_t* count,
    int* mask,
    const float** scales
    );

dnnl_status_t DNNL_API dnnl_primitive_attr_set_rnn_weights_projection_qparams(
    dnnl_primitive_attr_t attr,
    dnnl_dim_t count,
    int mask,
    const float* scales
    );

dnnl_status_t DNNL_API dnnl_primitive_attr_get_rnn_weights_projection_qparams(
    const_dnnl_primitive_attr_t attr,
    dnnl_dim_t* count,
    int* mask,
    const float** scales
    );

Detailed Documentation

A container for parameters that extend primitives behavior.

Attributes can also contain Post-ops, which are computations executed after the primitive.

See also:

Primitive Attributes

Primitive Attributes: Post-ops

Typedefs

typedef struct dnnl_primitive_attr* dnnl_primitive_attr_t

A primitive descriptor attributes handle that controls primitive behavior.

typedef const struct dnnl_primitive_attr* const_dnnl_primitive_attr_t

A constant primitive descriptor attributes handle.

typedef struct dnnl_post_ops* dnnl_post_ops_t

A post operation chain handle.

typedef const struct dnnl_post_ops* const_dnnl_post_ops_t

A constant post operation chain handle.

Global Functions

dnnl_scratchpad_mode_t dnnl::convert_to_c(scratchpad_mode mode)

Converts a scratchpad mode enum value from C++ API to C API type.

Parameters:

mode

C++ API scratchpad mode enum value.

Returns:

Corresponding C API scratchpad mode enum value.

dnnl_prop_kind_t dnnl::convert_to_c(prop_kind akind)

Converts propagation kind enum value from C++ API to C API type.

Parameters:

akind

C++ API propagation kind enum value.

Returns:

Corresponding C API propagation kind enum value.

dnnl_alg_kind_t dnnl::convert_to_c(algorithm aalgorithm)

Converts algorithm kind enum value from C++ API to C API type.

Parameters:

aalgorithm

C++ API algorithm kind enum value.

Returns:

Corresponding C API algorithm kind enum value.

dnnl_status_t DNNL_API dnnl_primitive_attr_create(dnnl_primitive_attr_t* attr)

Creates an empty (default) primitive attributes with all the parameters set to their default values.

Empty attributes are implied whenever the respective argument is NULL.

Parameters:

attr

Output primitive attributes.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_primitive_attr_clone(
    dnnl_primitive_attr_t* attr,
    const_dnnl_primitive_attr_t existing_attr
    )

Clones primitive attributes.

Parameters:

attr

Output primitive attributes.

existing_attr

Primitive attributes to clone.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_primitive_attr_destroy(dnnl_primitive_attr_t attr)

Destroys primitive attributes.

Parameters:

attr

Primitive attributes to destroy.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_primitive_attr_get_scratchpad_mode(
    const_dnnl_primitive_attr_t attr,
    dnnl_scratchpad_mode_t* mode
    )

Returns the primitive attributes scratchpad mode.

Parameters:

attr

Primitive attributes.

mode

Output scratchpad mode.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_primitive_attr_set_scratchpad_mode(
    dnnl_primitive_attr_t attr,
    dnnl_scratchpad_mode_t mode
    )

Sets primitive attributes scratchpad mode.

Parameters:

attr

Primitive attributes.

mode

Scratchpad mode. The possible values are: dnnl_scratchpad_mode_library (default) and dnnl_scratchpad_mode_user.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_primitive_attr_get_output_scales(
    const_dnnl_primitive_attr_t attr,
    dnnl_dim_t* count,
    int* mask,
    const float** scales
    )

Returns primitive attributes output scaling factors correspondence mask and values.

Warning

The scales array is an internal part of the primitive attributes attr, so it is an error to modify or destroy the scales array.

Warning

The lifetime of scales array is the same as that of the primitive attributes attr to which it belongs, so it is an error to use scales after attr is destroyed.

Parameters:

attr

Primitive attributes.

count

Output length of the array of scaling factors scales.

mask

Output scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the scales vector. The set i-th bit indicates that a dedicated output scaling factor is used for each index along that dimension. The mask value of 0 implies a common output scaling factor for the whole output tensor.

scales

Output pointer to a constant array of scaling factors.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_primitive_attr_set_output_scales(
    dnnl_primitive_attr_t attr,
    dnnl_dim_t count,
    int mask,
    const float* scales
    )

Sets output scaling factors correspondence mask and values.

Note

The order of dimensions does not depend on how elements are laid out in memory. For example:

  • for a 2D CNN activations tensor the order is always (n, c)

  • for a 4D CNN activations tensor the order is always (n, c, h, w)

  • for a 5D CNN weights tensor the order is always (g, oc, ic, kh, kw)

Example usage:

int mb = 32, oc = 32, oh = 14, ow = 14; // convolution output params
float scales[oc] = { ... }; // unique output scales per output channel
int oc_dim = 1; // mb_dim = 0, channel_dim = 1, height_dim = 2, ...

dnnl_convolution_desc_t conv_d; // create a convolution descriptor

dnnl_primitive_attr_t attr;
dnnl_primitive_attr_create(&attr); // create primitive attributes
dnnl_primitive_attr_set_output_scales(attr, oc, 1 << oc_dim, scales);

dnnl_primitive_desc_t conv_pd;
dnnl_primitive_desc_create(&conv_pd, &conv_d, attr, engine, NULL);

Parameters:

attr

Primitive attributes.

count

Length of the array of scaling factors scales.

mask

Scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the scales array. The set i-th bit indicates that a dedicated output scaling factor is used for each index along that dimension. The mask value of 0 implies a common output scaling factor for the whole output tensor.

scales

Array of output scaling factors. If the output scaling factors are known at the time of this call, this array must contain count values and the following equality must hold:

\[count = \prod\limits_{d \in mask} output.dims[d].\]

Violations can only be detected when the attributes are used to create a primitive descriptor. If the output scaling factors are not known at the time of the call, this array must contain a single DNNL_RUNTIME_F32_VAL value and the output scaling factors must be passed at execution time as an argument with index DNNL_ARG_ATTR_OUTPUT_SCALES.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_primitive_attr_get_scales(
    dnnl_primitive_attr_t attr,
    int arg,
    dnnl_dim_t* count,
    int* mask,
    const float** scales
    )

Returns primitive attributes scaling factors correspondence mask and values for a given memory argument.

Warning

The output scales array is an internal part of the primitive attributes attr, so it is an error to modify or destroy the scales array.

Warning

The lifetime of the scales array is the same as that of the primitive attributes attr to which it belongs, so it is an error to use scales after attr is destroyed.

Parameters:

attr

Primitive attributes.

arg

Parameter argument index as passed to the dnnl_primitive_execute() call.

count

Output length of the array of scaling factors scales.

mask

Output scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the scales array. The set i-th bit indicates that a dedicated output scaling factor is used for each index along that dimension. The mask value of 0 implies a common scaling factor for the whole output tensor.

scales

Output pointer to a constant array of float scaling factors.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_primitive_attr_set_scales(
    dnnl_primitive_attr_t attr,
    int arg,
    dnnl_dim_t count,
    int mask,
    const float* scales
    )

Sets primitive attributes scaling factors for primitive operations for a given memory argument.

Parameters:

attr

Primitive attributes.

arg

Parameter argument index as passed to the dnnl_primitive_execute() call.

count

Length of the array of scaling factors scales.

mask

Scaling factors correspondence mask that defines the correspondence between the tensor dimensions and the scales array. The set i-th bit indicates that a dedicated scaling factor is used for each index along that dimension. Set the mask to 0 to use a common scaling factor for the whole output tensor.

scales

Constant array of float scaling factors. This array must contain count scales and the following equality must hold:

\[count = \prod\limits_{d \in mask} output.dims[d].\]

Returns:

dnnl_success on success and a status describing the error otherwise.

See also:

dnnl_primitive_attr_set_output_scales

dnnl_status_t DNNL_API dnnl_primitive_attr_get_zero_points(
    const_dnnl_primitive_attr_t attr,
    int arg,
    dnnl_dim_t* count,
    int* mask,
    const int32_t** zero_points
    )

Returns count, correspondence zero point mask, and a pointer to a constant int32_t array of zero_points for given attr and memory argument (index), previously set by dnnl_primitive_attr_set_zero_points.

Warning

The output zero_points array is an internal part of the primitive attributes attr, so it is an error to modify or destroy the zero_points array.

Warning

The lifetime of zero_points array is the same as that of the primitive attributes attr to which it belongs, so it is an error to use zero_points after attr is destroyed.

Parameters:

attr

Primitive attributes.

arg

Parameter argument index as passed to the dnnl_primitive_execute() call.

count

Output length of the array of zero points zero_points.

mask

Output zero points correspondence mask that defines the correspondence between the output tensor dimensions and the zero_points array. The set i-th bit indicates that a dedicated output zero point is used for each index along that dimension. The mask value of 0 implies a common zero point for the whole output tensor.

zero_points

Output pointer to a constant array of int32_t zero points.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_primitive_attr_set_zero_points(
    dnnl_primitive_attr_t attr,
    int arg,
    dnnl_dim_t count,
    int mask,
    const int32_t* zero_points
    )

Sets primitive attributes zero points for primitive operations for a given memory argument.

Parameters:

attr

Primitive attributes.

arg

Parameter argument index as passed to the dnnl_primitive_execute() call.

count

Length of the array of zero points zero_points.

mask

Zero point correspondence mask that defines the correspondence between the tensor dimensions and the zero_points array. The set i-th bit indicates that a dedicated zero point is used for each index along that dimension. Set the mask to 0 to use a common zero point for the whole output tensor.

zero_points

Constant array of int32_t zero points. If the zero points are known at the time of this call, this array must contain count zero points and the following equality must hold:

\[count = \prod\limits_{d \in mask} output.dims[d].\]

If the zero points are not known at the time of the call, this array must contain a single DNNL_RUNTIME_S32_VAL and the zero points must be passed at execution time as an argument with index DNNL_ARG_ATTR_ZERO_POINTS.

Returns:

dnnl_success on success and a status describing the error otherwise.

See also:

dnnl_primitive_attr_set_output_scales

dnnl_status_t DNNL_API dnnl_primitive_attr_get_post_ops(
    const_dnnl_primitive_attr_t attr,
    const_dnnl_post_ops_t* post_ops
    )

Returns primitive attributes post-ops.

Warning

The output post_ops points to the internal attr field, so it is an error to modify or destroy them. The lifetime of post_ops is the same as that of the attr it belongs to, so it is an error to use post_ops after attr has been destroyed.

Parameters:

attr

Primitive attributes.

post_ops

Output post-ops.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_primitive_attr_set_post_ops(
    dnnl_primitive_attr_t attr,
    const_dnnl_post_ops_t post_ops
    )

Sets primitive attributes post-ops.

Note

There is no way to check whether the post-ops would be supported by the target primitive. Any error will be reported by the dnnl_primitive_desc_create() function call.

Parameters:

attr

Primitive attributes.

post_ops

Post-ops to set.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_post_ops_create(dnnl_post_ops_t* post_ops)

Creates empty post-ops sequence.

Parameters:

post_ops

Output post-ops.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_post_ops_destroy(dnnl_post_ops_t post_ops)

Destroys post-ops.

Parameters:

post_ops

Post-ops to destroy.

Returns:

dnnl_success on success and a status describing the error otherwise.

int DNNL_API dnnl_post_ops_len(const_dnnl_post_ops_t post_ops)

Returns the length of post-ops.

Parameters:

post_ops

Post-ops.

Returns:

The number of post-ops entries.

dnnl_primitive_kind_t DNNL_API dnnl_post_ops_get_kind(
    const_dnnl_post_ops_t post_ops,
    int index
    )

Returns the kind of a post-op entry.

Parameters:

post_ops

Post-ops.

index

Post-op entry index.

Returns:

The kind of the post-op with the specified index.

dnnl_undefined_primitive if there is no post-op at the specified index.

dnnl_status_t DNNL_API dnnl_post_ops_append_sum(
    dnnl_post_ops_t post_ops,
    float scale
    )

Appends an accumulation (sum) to post-ops.

Prior to accumulating the result, the previous value is multiplied by a scale.

The kind of this post-op is dnnl_sum.

This feature may improve performance for cases like residual learning blocks, where the result of convolution is accumulated to the previously computed activations. The parameter scale may be used for the integer-based computations when the result and previous activations have different logical scaling factors.

In the simplest case when the accumulation is the only post-op, the computations would be:

dst[:] <- scale * dst[:] + op(...) // instead of dst[:] <- op(...)

Note

This post-op executes in-place and does not change the destination layout.

Parameters:

post_ops

Post-ops.

scale

Accumulation scaling factor.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_post_ops_append_sum_v2(
    dnnl_post_ops_t post_ops,
    float scale,
    dnnl_data_type_t data_type
    )

Appends an accumulation v2 (sum) to post-ops.

Prior to accumulating the result, the previous value is multiplied by a scale.

The kind of this post-op is dnnl_sum.

This feature may improve performance for cases like residual learning blocks, where the result of convolution is accumulated to the previously computed activations. The parameter scale may be used for the integer-based computations when the result and previous activations have different logical scaling factors.

In the simplest case when the accumulation is the only post-op, the computations would be:

dst[:] <- scale * dst[:] + op(...) // instead of dst[:] <- op(...)

If data_type is specified, original dst tensor will be reinterpreted as a tensor with provided data type. Since it is reinterpretation, data_type and dst data type should have same size. As a result, computations would be:

dst[:] <- scale * as_data_type(dst[:]) + op(...)
                                   // instead of dst[:] <- op(...)

Note

This post-op executes in-place and does not change the destination layout.

Parameters:

post_ops

Post-ops.

scale

Accumulation scaling factor.

data_type

Accumulation data_type.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_post_ops_get_params_sum(
    const_dnnl_post_ops_t post_ops,
    int index,
    float* scale
    )

Returns the parameters of an accumulation (sum) post-op.

Parameters:

post_ops

Post-ops.

index

Index of the sum post-op.

scale

Output accumulation scaling factor.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_invalid_arguments if index does not refer to a sum post-op.

dnnl_status_t DNNL_API dnnl_post_ops_get_params_sum_v2(
    const_dnnl_post_ops_t post_ops,
    int index,
    float* scale,
    dnnl_data_type_t* data_type
    )

Returns the parameters of an accumulation (sum) post-op with a data type parameter.

Parameters:

post_ops

Post-ops.

index

Index of the sum post-op.

scale

Output accumulation scaling factor.

data_type

Data type for accumulation.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_post_ops_append_eltwise(
    dnnl_post_ops_t post_ops,
    float scale,
    dnnl_alg_kind_t alg_kind,
    float alpha,
    float beta
    )

Appends an elementwise post-op.

The kind of this post operation is dnnl_eltwise.

In the simplest case when the elementwise is the only post operation, the computations would be:

dst[:] <- scale * eltwise_op (op(...)) // instead of dst[:] <- op(...)

where eltwise_op is configured with the given parameters.

Parameters:

post_ops

Post-ops.

scale

Scaling factor.

alg_kind

Elementwise algorithm for the post-op.

alpha

Alpha parameter for the elementwise algorithm.

beta

Beta parameter for the elementwise algorithm.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_post_ops_get_params_eltwise(
    const_dnnl_post_ops_t post_ops,
    int index,
    float* scale,
    dnnl_alg_kind_t* alg_kind,
    float* alpha,
    float* beta
    )

Returns the parameters of an elementwise post-op.

Parameters:

post_ops

Post-ops.

index

Index of the elementwise post-op.

scale

Output scaling factor.

alg_kind

Output elementwise algorithm kind.

alpha

Output alpha parameter for the elementwise algorithm.

beta

Output beta parameter for the elementwise algorithm.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_invalid_arguments if index does not refer to an elementwise post-op.

dnnl_status_t DNNL_API dnnl_post_ops_append_dw_k3s1p1(
    dnnl_post_ops_t post_ops,
    dnnl_data_type_t weights_data_type,
    dnnl_data_type_t bias_data_type,
    dnnl_data_type_t dst_data_type,
    dnnl_dim_t count,
    int mask,
    const float* scales
    )

Appends a depthwise post-op convolution with stride 1.

This post-op can only be fused with a 2D 1x1 convolution (convolution with weights spatial dimension equal to 1 i.e., kh=kw=1).

The kind of this post-op is dnnl_convolution.

The number of outputs for primitive remain same as before. The output size remain same as the original primitive due to stride=1.

The Post-op can be defined as:

dst[:] <- scales * (conv_dw(conv_1x1))

See dev_guide_attributes_post_ops_depthwise and dev_guide_attributes_post_ops_depthwise_fusion for more info.

Parameters:

post_ops

Post-ops.

weights_data_type

Weights data type of depthwise post-op

bias_data_type

Bias data type of depthwise post-op

dst_data_type

Output data type of depthwise post-op

count

Output length of the array of scaling factors scales.

mask

Output scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the scales array. The set i-th bit indicates that a dedicated output scaling factor is used for each index along that dimension. The mask value of 0 implies a common scaling factor for the whole output tensor.

scales

Output pointer to a constant array of float scaling factors.

Returns:

dnnl_success on success and a status describing the error otherwise

dnnl_status_t DNNL_API dnnl_post_ops_get_params_dw_k3s1p1(
    const_dnnl_post_ops_t post_ops,
    int index,
    dnnl_data_type_t* weights_data_type,
    dnnl_data_type_t* bias_data_type,
    dnnl_data_type_t* dst_data_type,
    dnnl_dim_t* count,
    int* mask,
    const float** scales
    )

Returns the parameters of an depthwise post-op with stride 1.

Parameters:

post_ops

Post-ops.

index

Index of the elementwise post-op.

weights_data_type

Weights data type of depthwise post-op

bias_data_type

Bias data type of depthwise post-op

dst_data_type

Output data type of depthwise post-op

count

Output length of the array of scaling factors scales.

mask

Output scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the scales array. The set i-th bit indicates that a dedicated output scaling factor is used for each index along that dimension. The mask value of 0 implies a common scaling factor for the whole output tensor.

scales

Output pointer to a constant array of float scaling factors.

Returns:

dnnl_success on success and a status describing the error otherwise

dnnl_status_t DNNL_API dnnl_post_ops_append_dw_k3s2p1(
    dnnl_post_ops_t post_ops,
    dnnl_data_type_t weights_data_type,
    dnnl_data_type_t bias_data_type,
    dnnl_data_type_t dst_data_type,
    dnnl_dim_t count,
    int mask,
    const float* scales
    )

Appends a depthwise post-op convolution with stride 2.

This post-op can only be fused with a 2D 1x1 convolution (convolution with weights spatial dimension equal to 1 i.e., kh=kw=1).

The kind of this post-op is dnnl_convolution.

The number of outputs for primitive remain same as before. The output spatial size can be derived as below:

output_height = ceil(output_height_1x1_convolution, stride) output_width = ceil(output_width_1x1_convolution, stride)

The Post-op can be defined as:

dst[:] <- scales * (conv_dw(conv_1x1))

See dev_guide_attributes_post_ops_depthwise and dev_guide_attributes_post_ops_depthwise_fusion for more info.

Parameters:

post_ops

Post-ops.

weights_data_type

Weights data type of depthwise post-op

bias_data_type

Bias data type of depthwise post-op

dst_data_type

Output data type of depthwise post-op

count

Output length of the array of scaling factors scales.

mask

Output scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the scales array. The set i-th bit indicates that a dedicated output scaling factor is used for each index along that dimension. The mask value of 0 implies a common scaling factor for the whole output tensor.

scales

Output pointer to a constant array of float scaling factors.

Returns:

dnnl_success on success and a status describing the error otherwise

dnnl_status_t DNNL_API dnnl_post_ops_get_params_dw_k3s2p1(
    const_dnnl_post_ops_t post_ops,
    int index,
    dnnl_data_type_t* weights_data_type,
    dnnl_data_type_t* bias_data_type,
    dnnl_data_type_t* dst_data_type,
    dnnl_dim_t* count,
    int* mask,
    const float** scales
    )

Returns the parameters of an depthwise post-op with stride 2.

Parameters:

post_ops

Post-ops.

index

Index of the elementwise post-op.

weights_data_type

Weights data type of depthwise post-op

bias_data_type

Bias data type of depthwise post-op

dst_data_type

Output data type of depthwise post-op

count

Output length of the array of scaling factors scales.

mask

Output scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the scales array. The set i-th bit indicates that a dedicated output scaling factor is used for each index along that dimension. The mask value of 0 implies a common scaling factor for the whole output tensor.

scales

Output pointer to a constant array of float scaling factors.

Returns:

dnnl_success on success and a status describing the error otherwise

dnnl_status_t DNNL_API dnnl_post_ops_append_binary(
    dnnl_post_ops_t post_ops,
    dnnl_alg_kind_t alg_kind,
    const dnnl_memory_desc_t* src1_desc
    )

Appends a binary post-op.

The kind of this post operation is dnnl_binary.

In the simplest case when the binary is the only post operation, the computations would be:

dst[:] <- binary_op (dst[:], another_input[:])

where binary_op is configured with the given parameters. binary_op supports broadcast semantics for a second operand.

Parameters:

post_ops

Post-ops.

alg_kind

Binary algorithm for the post-op.

src1_desc

Memory descriptor of a second operand.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_post_ops_get_params_binary(
    const_dnnl_post_ops_t post_ops,
    int index,
    dnnl_alg_kind_t* alg_kind,
    const dnnl_memory_desc_t** src1_desc
    )

Returns the parameters of a binary post-op.

Parameters:

post_ops

Post-ops.

index

Index of the binary post-op.

alg_kind

Output binary algorithm kind.

src1_desc

Output memory descriptor of a second operand.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_invalid_arguments if index does not refer to a binary post-op.

dnnl_status_t DNNL_API dnnl_primitive_attr_set_rnn_data_qparams(
    dnnl_primitive_attr_t attr,
    const float scale,
    const float shift
    )

Set quantization scale and shift parameters for RNN data tensors.

For performance reasons, the low-precision configuration of the RNN primitives expects input activations to have the unsigned 8-bit integer data type. The scale and shift parameters are used to quantize floating-point data to unsigned integer and must be passed to the RNN primitive using attributes.

The quantization formula is scale * data + shift.

Note

Quantization scale and shift are common for src_layer, src_iter, dst_iter, and dst_layer.

Example usage:

// RNN parameters
int l = 2, t = 2, mb = 32, sic = 32, slc = 32, dic = 32, dlc = 32;
// Activations quantization parameters
float scale = 63.f, shift = 64.f;

dnnl_primitive_attr_t rnn_attr;
// Create default attributes
dnnl_primitive_attr_create(&rnn_attr);

// Set scale and shift for int8 quantization of activation
dnnl_primitive_attr_set_rnn_data_qparams(rnn_attr, scale, shift);

// Create and configure rnn op_desc
dnnl_rnn_desc_t rnn_d;
dnnl_primitive_desc_t rnn_pd;
dnnl_primitive_desc_create(&rnn_pd, &rnn_d, attr, engine, NULL);

Parameters:

attr

Primitive attributes.

scale

The value to scale the data by.

shift

The value to shift the data by.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_primitive_attr_get_rnn_data_qparams(
    const_dnnl_primitive_attr_t attr,
    float* scale,
    float* shift
    )

Returns the quantization scale and shift parameters for RNN data tensors.

Note

Quantization scale and shift are common for src_layer, src_iter, dst_iter, and dst_layer.

Parameters:

attr

Primitive attributes.

scale

The value to scale the data by.

shift

The value to shift the data by.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_primitive_attr_set_rnn_weights_qparams(
    dnnl_primitive_attr_t attr,
    dnnl_dim_t count,
    int mask,
    const float* scales
    )

Sets quantization scaling factors for RNN weights tensors.

The low-precision configuration of the RNN primitives expects input weights to use the signed 8-bit integer data type. The scaling factors are used to quantize floating-point data to signed integer and must be passed to RNN primitives using attributes.

Note

The dimension order is always native and does not depend on the actual layout used. For example, five-dimensional weights always have (l, d, i, g, o) logical dimension ordering.

Note

Quantization scales are common for weights_layer and weights_iteration

Parameters:

attr

Primitive attributes.

count

Number of elements in the scales array.

mask

Scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the scales vector. The set i-th bit indicates that a dedicated scaling factor should be used for each index along that dimension. Set the mask to 0 to use a common scaling factor for the whole output tensor.

scales

Array of output scaling factors that must contain count values and the following equality must hold:

\[count = \prod\limits_{d \in mask} weights.dims[d].\]

Violations can only be detected when the attributes are used to create a primitive descriptor.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_primitive_attr_get_rnn_weights_qparams(
    const_dnnl_primitive_attr_t attr,
    dnnl_dim_t* count,
    int* mask,
    const float** scales
    )

Returns the quantization scaling factors for RNN weights tensors.

Parameters:

attr

Primitive attributes.

count

Number of elements in the scales array.

mask

Scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the scales vector. The set i-th bit indicates that a dedicated scaling factor should be used for each index along that dimension. Set the mask to 0 to use a common scaling factor for the whole output tensor.

scales

Array of output scaling factors that contain count values and the following equality must hold:

\[count = \prod\limits_{d \in mask} weights.dims[d].\]

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_primitive_attr_set_rnn_weights_projection_qparams(
    dnnl_primitive_attr_t attr,
    dnnl_dim_t count,
    int mask,
    const float* scales
    )

Sets quantization scaling factors for RNN projection weights tensors.

The low-precision configuration of the RNN primitives expects input weights to use the signed 8-bit integer data type. The scaling factors are used to quantize floating-point data to signed integer and must be passed to RNN primitives using attributes.

Note

The dimension order is always native and does not depend on the actual layout used. For example, five-dimensional weights always have (l, d, i, g, o) logical dimension ordering.

Parameters:

attr

Primitive attributes.

count

Number of elements in the scales array.

mask

Scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the scales vector. The set i-th bit indicates that a dedicated scaling factor should be used for each index along that dimension. Set the mask to 0 to use a common scaling factor for the whole output tensor.

scales

Array of output scaling factors that must contain count values and the following equality must hold:

\[count = \prod\limits_{d \in mask} weights.dims[d].\]

Violations can only be detected when the attributes are used to create a primitive descriptor.

Returns:

dnnl_success on success and a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_primitive_attr_get_rnn_weights_projection_qparams(
    const_dnnl_primitive_attr_t attr,
    dnnl_dim_t* count,
    int* mask,
    const float** scales
    )

Returns the quantization scaling factors for RNN projection weights tensors.

Parameters:

attr

Primitive attributes.

count

Number of elements in the scales array.

mask

Scaling factors correspondence mask that defines the correspondence between the output tensor dimensions and the scales vector. The set i-th bit indicates that a dedicated scaling factor should be used for each index along that dimension. Set the mask to 0 to use a common scaling factor for the whole output tensor.

scales

Array of output scaling factors that contain count values and the following equality must hold:

\[count = \prod\limits_{d \in mask} weights.dims[d].\]

Returns:

dnnl_success on success and a status describing the error otherwise.