.. index:: pair: group; Attributes .. _doxid-group__dnnl__api__attributes: Attributes ========== .. toctree:: :hidden: enum_dnnl_algorithm.rst enum_dnnl_fpmath_mode_t.rst enum_dnnl_scratchpad_mode_t.rst enum_dnnl_fpmath_mode.rst enum_dnnl_prop_kind.rst enum_dnnl_scratchpad_mode.rst struct_dnnl_post_ops.rst struct_dnnl_primitive_attr.rst struct_dnnl_post_ops-2.rst Overview ~~~~~~~~ A container for parameters that extend primitives behavior. :ref:`More...` .. ref-code-block:: cpp :class: doxyrest-overview-code-block // typedefs typedef struct :ref:`dnnl_primitive_attr`* :ref:`dnnl_primitive_attr_t`; typedef const struct :ref:`dnnl_primitive_attr`* :ref:`const_dnnl_primitive_attr_t`; typedef struct :ref:`dnnl_post_ops`* :ref:`dnnl_post_ops_t`; typedef const struct :ref:`dnnl_post_ops`* :ref:`const_dnnl_post_ops_t`; // enums enum :ref:`dnnl::algorithm`; enum :ref:`dnnl_fpmath_mode_t`; enum :ref:`dnnl_scratchpad_mode_t`; enum :ref:`dnnl::fpmath_mode`; enum :ref:`dnnl::prop_kind`; enum :ref:`dnnl::scratchpad_mode`; // structs struct :ref:`dnnl_post_ops`; struct :ref:`dnnl_primitive_attr`; struct :ref:`dnnl::post_ops`; // global functions :ref:`dnnl_fpmath_mode_t` :ref:`dnnl::convert_to_c`(:ref:`fpmath_mode` mode); :ref:`dnnl_scratchpad_mode_t` :ref:`dnnl::convert_to_c`(:ref:`scratchpad_mode` mode); :ref:`dnnl_prop_kind_t` :ref:`dnnl::convert_to_c`(:ref:`prop_kind` akind); :ref:`dnnl_alg_kind_t` :ref:`dnnl::convert_to_c`(:ref:`algorithm` aalgorithm); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_create`(:ref:`dnnl_primitive_attr_t`* attr); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_clone`( :ref:`dnnl_primitive_attr_t`* attr, :ref:`const_dnnl_primitive_attr_t` existing_attr ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_destroy`(:ref:`dnnl_primitive_attr_t` attr); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_get_fpmath_mode`( :ref:`const_dnnl_primitive_attr_t` attr, :ref:`dnnl_fpmath_mode_t`* mode ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_set_fpmath_mode`( :ref:`dnnl_primitive_attr_t` attr, :ref:`dnnl_fpmath_mode_t` mode ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_get_scratchpad_mode`( :ref:`const_dnnl_primitive_attr_t` attr, :ref:`dnnl_scratchpad_mode_t`* mode ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_set_scratchpad_mode`( :ref:`dnnl_primitive_attr_t` attr, :ref:`dnnl_scratchpad_mode_t` mode ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_get_output_scales`( :ref:`const_dnnl_primitive_attr_t` attr, :ref:`dnnl_dim_t`* count, int* mask, const float** scales ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_set_output_scales`( :ref:`dnnl_primitive_attr_t` attr, :ref:`dnnl_dim_t` count, int mask, const float* scales ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_get_scales`( :ref:`dnnl_primitive_attr_t` attr, int arg, :ref:`dnnl_dim_t`* count, int* mask, const float** scales ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_set_scales`( :ref:`dnnl_primitive_attr_t` attr, int arg, :ref:`dnnl_dim_t` count, int mask, const float* scales ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_get_zero_points`( :ref:`const_dnnl_primitive_attr_t` attr, int arg, :ref:`dnnl_dim_t`* count, int* mask, const int32_t** zero_points ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_set_zero_points`( :ref:`dnnl_primitive_attr_t` attr, int arg, :ref:`dnnl_dim_t` count, int mask, const int32_t* zero_points ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_get_post_ops`( :ref:`const_dnnl_primitive_attr_t` attr, :ref:`const_dnnl_post_ops_t`* post_ops ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_set_post_ops`( :ref:`dnnl_primitive_attr_t` attr, :ref:`const_dnnl_post_ops_t` post_ops ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_post_ops_create`(:ref:`dnnl_post_ops_t`* post_ops); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_post_ops_destroy`(:ref:`dnnl_post_ops_t` post_ops); int DNNL_API :ref:`dnnl_post_ops_len`(:ref:`const_dnnl_post_ops_t` post_ops); :ref:`dnnl_primitive_kind_t` DNNL_API :ref:`dnnl_post_ops_get_kind`( :ref:`const_dnnl_post_ops_t` post_ops, int index ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_post_ops_append_sum`( :ref:`dnnl_post_ops_t` post_ops, float scale ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_post_ops_append_sum_v2`( :ref:`dnnl_post_ops_t` post_ops, float scale, :ref:`dnnl_data_type_t` data_type ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_post_ops_append_sum_v3`( :ref:`dnnl_post_ops_t` post_ops, float scale, int32_t zero_point, :ref:`dnnl_data_type_t` data_type ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_post_ops_get_params_sum`( :ref:`const_dnnl_post_ops_t` post_ops, int index, float* scale ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_post_ops_get_params_sum_v2`( :ref:`const_dnnl_post_ops_t` post_ops, int index, float* scale, :ref:`dnnl_data_type_t`* data_type ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_post_ops_get_params_sum_v3`( :ref:`const_dnnl_post_ops_t` post_ops, int index, float* scale, int32_t* zero_point, :ref:`dnnl_data_type_t`* data_type ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_post_ops_append_eltwise`( :ref:`dnnl_post_ops_t` post_ops, float scale, :ref:`dnnl_alg_kind_t` alg_kind, float alpha, float beta ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_post_ops_get_params_eltwise`( :ref:`const_dnnl_post_ops_t` post_ops, int index, float* scale, :ref:`dnnl_alg_kind_t`* alg_kind, float* alpha, float* beta ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_post_ops_append_dw_k3s1p1`( :ref:`dnnl_post_ops_t` post_ops, :ref:`dnnl_data_type_t` weights_data_type, :ref:`dnnl_data_type_t` bias_data_type, :ref:`dnnl_data_type_t` dst_data_type, :ref:`dnnl_dim_t` count, int mask, const float* scales ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_post_ops_get_params_dw_k3s1p1`( :ref:`const_dnnl_post_ops_t` post_ops, int index, :ref:`dnnl_data_type_t`* weights_data_type, :ref:`dnnl_data_type_t`* bias_data_type, :ref:`dnnl_data_type_t`* dst_data_type, :ref:`dnnl_dim_t`* count, int* mask, const float** scales ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_post_ops_append_dw_k3s2p1`( :ref:`dnnl_post_ops_t` post_ops, :ref:`dnnl_data_type_t` weights_data_type, :ref:`dnnl_data_type_t` bias_data_type, :ref:`dnnl_data_type_t` dst_data_type, :ref:`dnnl_dim_t` count, int mask, const float* scales ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_post_ops_get_params_dw_k3s2p1`( :ref:`const_dnnl_post_ops_t` post_ops, int index, :ref:`dnnl_data_type_t`* weights_data_type, :ref:`dnnl_data_type_t`* bias_data_type, :ref:`dnnl_data_type_t`* dst_data_type, :ref:`dnnl_dim_t`* count, int* mask, const float** scales ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_post_ops_append_binary`( :ref:`dnnl_post_ops_t` post_ops, :ref:`dnnl_alg_kind_t` alg_kind, const :ref:`dnnl_memory_desc_t`* src1_desc ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_post_ops_get_params_binary`( :ref:`const_dnnl_post_ops_t` post_ops, int index, :ref:`dnnl_alg_kind_t`* alg_kind, const :ref:`dnnl_memory_desc_t`** src1_desc ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_post_ops_append_prelu`( :ref:`dnnl_post_ops_t` post_ops, int mask ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_post_ops_get_params_prelu`( :ref:`const_dnnl_post_ops_t` post_ops, int index, int* mask ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_set_rnn_data_qparams`( :ref:`dnnl_primitive_attr_t` attr, const float scale, const float shift ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_get_rnn_data_qparams`( :ref:`const_dnnl_primitive_attr_t` attr, float* scale, float* shift ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_set_rnn_weights_qparams`( :ref:`dnnl_primitive_attr_t` attr, :ref:`dnnl_dim_t` count, int mask, const float* scales ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_get_rnn_weights_qparams`( :ref:`const_dnnl_primitive_attr_t` attr, :ref:`dnnl_dim_t`* count, int* mask, const float** scales ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_set_rnn_weights_projection_qparams`( :ref:`dnnl_primitive_attr_t` attr, :ref:`dnnl_dim_t` count, int mask, const float* scales ); :ref:`dnnl_status_t` DNNL_API :ref:`dnnl_primitive_attr_get_rnn_weights_projection_qparams`( :ref:`const_dnnl_primitive_attr_t` attr, :ref:`dnnl_dim_t`* count, int* mask, const float** scales ); .. _details-group__dnnl__api__attributes: Detailed Documentation ~~~~~~~~~~~~~~~~~~~~~~ A container for parameters that extend primitives behavior. Attributes can also contain Post-ops, which are computations executed after the primitive. .. rubric:: See also: :ref:`Primitive Attributes ` :ref:`Primitive Attributes: Post-ops ` Typedefs -------- .. index:: pair: typedef; dnnl_primitive_attr_t .. _doxid-group__dnnl__api__attributes_1ga06d701a25b82d4c8a93aaabb93e03dc3: .. ref-code-block:: cpp :class: doxyrest-title-code-block typedef struct :ref:`dnnl_primitive_attr`* dnnl_primitive_attr_t A primitive descriptor attributes handle that controls primitive behavior. .. index:: pair: typedef; const_dnnl_primitive_attr_t .. _doxid-group__dnnl__api__attributes_1ga871d7ee732a90fec43f1c878581bb59a: .. ref-code-block:: cpp :class: doxyrest-title-code-block typedef const struct :ref:`dnnl_primitive_attr`* const_dnnl_primitive_attr_t A constant primitive descriptor attributes handle. .. index:: pair: typedef; dnnl_post_ops_t .. _doxid-group__dnnl__api__attributes_1ga7d715ce1a81606584df9dcf045976401: .. ref-code-block:: cpp :class: doxyrest-title-code-block typedef struct :ref:`dnnl_post_ops`* dnnl_post_ops_t A post operation chain handle. .. index:: pair: typedef; const_dnnl_post_ops_t .. _doxid-group__dnnl__api__attributes_1ga997bc4a3d9d2ce50238b1c035963fc39: .. ref-code-block:: cpp :class: doxyrest-title-code-block typedef const struct :ref:`dnnl_post_ops`* const_dnnl_post_ops_t A constant post operation chain handle. Global Functions ---------------- .. index:: pair: function; convert_to_c .. _doxid-group__dnnl__api__attributes_1gad095d0686c7020ce49be483cb44e8535: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_fpmath_mode_t` dnnl::convert_to_c(:ref:`fpmath_mode` mode) Converts an fpmath mode enum value from C++ API to C API type. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - mode - C++ API fpmath mode enum value. .. rubric:: Returns: Corresponding C API fpmath mode enum value. .. index:: pair: function; convert_to_c .. _doxid-group__dnnl__api__attributes_1gaa30f540e1ed09b2865f153fd599c967b: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_scratchpad_mode_t` dnnl::convert_to_c(:ref:`scratchpad_mode` mode) Converts a scratchpad mode enum value from C++ API to C API type. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - mode - C++ API scratchpad mode enum value. .. rubric:: Returns: Corresponding C API scratchpad mode enum value. .. index:: pair: function; convert_to_c .. _doxid-group__dnnl__api__attributes_1gae13881206fecd43ce0e0daead7f0009e: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_prop_kind_t` dnnl::convert_to_c(:ref:`prop_kind` akind) Converts propagation kind enum value from C++ API to C API type. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - akind - C++ API propagation kind enum value. .. rubric:: Returns: Corresponding C API propagation kind enum value. .. index:: pair: function; convert_to_c .. _doxid-group__dnnl__api__attributes_1gad4c07d30e46391ce7ce0900d18cbfa30: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_alg_kind_t` dnnl::convert_to_c(:ref:`algorithm` aalgorithm) Converts algorithm kind enum value from C++ API to C API type. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - aalgorithm - C++ API algorithm kind enum value. .. rubric:: Returns: Corresponding C API algorithm kind enum value. .. index:: pair: function; dnnl_primitive_attr_create .. _doxid-group__dnnl__api__attributes_1gaf630fdc0d8d0fd8522ec93852a559081: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_create(:ref:`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. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - attr - Output primitive attributes. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_primitive_attr_clone .. _doxid-group__dnnl__api__attributes_1gab6ac5a4b13fa1ab3251c51f3c750bd63: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_clone( :ref:`dnnl_primitive_attr_t`* attr, :ref:`const_dnnl_primitive_attr_t` existing_attr ) Clones primitive attributes. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - attr - Output primitive attributes. * - existing_attr - Primitive attributes to clone. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_primitive_attr_destroy .. _doxid-group__dnnl__api__attributes_1ga96a7539382945195627f2932bff8fadb: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_destroy(:ref:`dnnl_primitive_attr_t` attr) Destroys primitive attributes. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - attr - Primitive attributes to destroy. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_primitive_attr_get_fpmath_mode .. _doxid-group__dnnl__api__attributes_1gac63b70ab1d2fe88c31f03c961b2e924a: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_get_fpmath_mode( :ref:`const_dnnl_primitive_attr_t` attr, :ref:`dnnl_fpmath_mode_t`* mode ) Returns the floating-point math mode primitive attribute. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - attr - Primitive attributes. * - mode - Output FP math mode. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_primitive_attr_set_fpmath_mode .. _doxid-group__dnnl__api__attributes_1gafe55fa618bc10b65b6c0b6eca7e43840: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_set_fpmath_mode( :ref:`dnnl_primitive_attr_t` attr, :ref:`dnnl_fpmath_mode_t` mode ) Sets the floating-point math mode primitive attributes. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - attr - Primitive attributes. * - mode - FP math mode. The possible values are: :ref:`dnnl_fpmath_mode_strict ` (default), :ref:`dnnl_fpmath_mode_bf16 `, :ref:`dnnl_fpmath_mode_f16 `, :ref:`dnnl_fpmath_mode_any `. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_primitive_attr_get_scratchpad_mode .. _doxid-group__dnnl__api__attributes_1gab14d8e830a52510a75a917f75764a6b8: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_get_scratchpad_mode( :ref:`const_dnnl_primitive_attr_t` attr, :ref:`dnnl_scratchpad_mode_t`* mode ) Returns the primitive attributes scratchpad mode. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - attr - Primitive attributes. * - mode - Output scratchpad mode. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_primitive_attr_set_scratchpad_mode .. _doxid-group__dnnl__api__attributes_1ga4adeb17e538392ec3a16d2f6ef3f7cca: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_set_scratchpad_mode( :ref:`dnnl_primitive_attr_t` attr, :ref:`dnnl_scratchpad_mode_t` mode ) Sets primitive attributes scratchpad mode. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - attr - Primitive attributes. * - mode - Scratchpad mode. The possible values are: :ref:`dnnl_scratchpad_mode_library ` (default) and :ref:`dnnl_scratchpad_mode_user `. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_primitive_attr_get_output_scales .. _doxid-group__dnnl__api__attributes_1gaf85a41b0666a93ba20c756b49fdcb0c2: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_get_output_scales( :ref:`const_dnnl_primitive_attr_t` attr, :ref:`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. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - 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. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_primitive_attr_set_output_scales .. _doxid-group__dnnl__api__attributes_1gae3ff1fe9449f2d9b1cd02ad864b57057: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_set_output_scales( :ref:`dnnl_primitive_attr_t` attr, :ref:`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: .. ref-code-block:: cpp 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); .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - 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: .. math:: 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 :ref:`DNNL_RUNTIME_F32_VAL ` value and the output scaling factors must be passed at execution time as an argument with index :ref:`DNNL_ARG_ATTR_OUTPUT_SCALES `. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_primitive_attr_get_scales .. _doxid-group__dnnl__api__attributes_1gaf405f0476714bdcb5d0dad1c9e720030: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_get_scales( :ref:`dnnl_primitive_attr_t` attr, int arg, :ref:`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. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - attr - Primitive attributes. * - arg - Parameter argument index as passed to the :ref:`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. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_primitive_attr_set_scales .. _doxid-group__dnnl__api__attributes_1gaaa6b2f9a167cfd6ddee2ec93be8ea41e: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_set_scales( :ref:`dnnl_primitive_attr_t` attr, int arg, :ref:`dnnl_dim_t` count, int mask, const float* scales ) Sets primitive attributes scaling factors for primitive operations for a given memory argument. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - attr - Primitive attributes. * - arg - Parameter argument index as passed to the :ref:`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: .. math:: count = \prod\limits_{d \in mask} output.dims[d]. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. rubric:: See also: :ref:`dnnl_primitive_attr_set_output_scales ` .. index:: pair: function; dnnl_primitive_attr_get_zero_points .. _doxid-group__dnnl__api__attributes_1gadd27e1e35b2854f593360f90acc8965f: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_get_zero_points( :ref:`const_dnnl_primitive_attr_t` attr, int arg, :ref:`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. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - attr - Primitive attributes. * - arg - Parameter argument index as passed to the :ref:`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. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_primitive_attr_set_zero_points .. _doxid-group__dnnl__api__attributes_1ga6f4b625c7f238883fe32cbf6829b7f30: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_set_zero_points( :ref:`dnnl_primitive_attr_t` attr, int arg, :ref:`dnnl_dim_t` count, int mask, const int32_t* zero_points ) Sets primitive attributes zero points for primitive operations for a given memory argument. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - attr - Primitive attributes. * - arg - Parameter argument index as passed to the :ref:`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: .. math:: 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 :ref:`DNNL_RUNTIME_S32_VAL ` and the zero points must be passed at execution time as an argument with index :ref:`DNNL_ARG_ATTR_ZERO_POINTS `. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. rubric:: See also: :ref:`dnnl_primitive_attr_set_output_scales ` .. index:: pair: function; dnnl_primitive_attr_get_post_ops .. _doxid-group__dnnl__api__attributes_1ga50c92661cc69e1eeb17b61f006320a05: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_get_post_ops( :ref:`const_dnnl_primitive_attr_t` attr, :ref:`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. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - attr - Primitive attributes. * - post_ops - Output post-ops. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_primitive_attr_set_post_ops .. _doxid-group__dnnl__api__attributes_1ga7045d42606599f156bfca69820c21ea2: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_set_post_ops( :ref:`dnnl_primitive_attr_t` attr, :ref:`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 :ref:`dnnl_primitive_desc_create() ` function call. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - attr - Primitive attributes. * - post_ops - Post-ops to set. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_post_ops_create .. _doxid-group__dnnl__api__attributes_1gaa8d8c32ad4472de464e47336ad702a48: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_post_ops_create(:ref:`dnnl_post_ops_t`* post_ops) Creates empty post-ops sequence. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - post_ops - Output post-ops. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_post_ops_destroy .. _doxid-group__dnnl__api__attributes_1ga67487a65afa2e2066f4b4eb12d47535b: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_post_ops_destroy(:ref:`dnnl_post_ops_t` post_ops) Destroys post-ops. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - post_ops - Post-ops to destroy. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_post_ops_len .. _doxid-group__dnnl__api__attributes_1ga98550f7eddff153ea819a6c4a68e7eec: .. ref-code-block:: cpp :class: doxyrest-title-code-block int DNNL_API dnnl_post_ops_len(:ref:`const_dnnl_post_ops_t` post_ops) Returns the length of post-ops. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - post_ops - Post-ops. .. rubric:: Returns: The number of post-ops entries. .. index:: pair: function; dnnl_post_ops_get_kind .. _doxid-group__dnnl__api__attributes_1gabb9d82e4e8f1c83f169468d4b92f4109: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_primitive_kind_t` DNNL_API dnnl_post_ops_get_kind( :ref:`const_dnnl_post_ops_t` post_ops, int index ) Returns the kind of a post-op entry. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - post_ops - Post-ops. * - index - Post-op entry index. .. rubric:: Returns: The kind of the post-op with the specified index. :ref:`dnnl_undefined_primitive ` if there is no post-op at the specified index. .. index:: pair: function; dnnl_post_ops_append_sum .. _doxid-group__dnnl__api__attributes_1ga7175edf83658bd14dd5d7cd9e9193e92: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_post_ops_append_sum( :ref:`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 :ref:`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 where the accumulation is the only post-op, the computations will be: .. code-block:: cpp dst[:] <- scale * dst[:] + op(...) // instead of dst[:] <- op(...) .. note:: This post-op executes in-place and does not change the destination layout. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - post_ops - Post-ops. * - scale - Accumulation scaling factor. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_post_ops_append_sum_v2 .. _doxid-group__dnnl__api__attributes_1ga97ce3bde45e1a8f1970c899e4e519497: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_post_ops_append_sum_v2( :ref:`dnnl_post_ops_t` post_ops, float scale, :ref:`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 :ref:`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 where the accumulation is the only post-op, the computations will be: .. code-block:: cpp 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 the same size. As a result, computations will be: .. code-block:: cpp 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. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - post_ops - Post-ops. * - scale - Accumulation scaling factor. * - data_type - Accumulation data_type. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_post_ops_append_sum_v3 .. _doxid-group__dnnl__api__attributes_1ga74d7cdc5f9954e42c662d81fa1a83f3a: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_post_ops_append_sum_v3( :ref:`dnnl_post_ops_t` post_ops, float scale, int32_t zero_point, :ref:`dnnl_data_type_t` data_type ) Appends an accumulation v3 (sum) to post-ops. Prior to accumulating the result, a zero point is subtracted from the previous value and is multiplied by the scale. The kind of this post-op is :ref:`dnnl_sum `. This feature may improve performance for cases like dequantize the asymmetrically quantized sum's src1 tensor to f32 domain before performing the sum operation by subtracting the ``zero_point`` before the scaling. In the simplest case where accumulation is the only post-op, the computations will be: .. code-block:: cpp dst[:] <- scale * (dst[:] - zero_point) + 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 the same size. As a result, computations will be: .. code-block:: cpp dst[:] <- scale * (as_data_type(dst[:]) - zero_point) + op(...) // instead of dst[:] <- op(...) .. note:: This post-op executes in-place and does not change the destination layout. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - post_ops - Post-ops. * - scale - Accumulation scaling factor. * - zero_point - Single scalar int32_t value of zero point. * - data_type - Accumulation data_type. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_post_ops_get_params_sum .. _doxid-group__dnnl__api__attributes_1gade0dbf6e22ef699e31166c341f9d043a: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_post_ops_get_params_sum( :ref:`const_dnnl_post_ops_t` post_ops, int index, float* scale ) Returns the parameters of an accumulation (sum) post-op. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - post_ops - Post-ops. * - index - Index of the sum post-op. * - scale - Output accumulation scaling factor. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. :ref:`dnnl_invalid_arguments ` if ``index`` does not refer to a sum post-op. .. index:: pair: function; dnnl_post_ops_get_params_sum_v2 .. _doxid-group__dnnl__api__attributes_1ga0546f6cb1352f898ef9444a4e0a79c06: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_post_ops_get_params_sum_v2( :ref:`const_dnnl_post_ops_t` post_ops, int index, float* scale, :ref:`dnnl_data_type_t`* data_type ) Returns the parameters of an accumulation (sum) post-op with a data type parameter. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - post_ops - Post-ops. * - index - Index of the sum post-op. * - scale - Output accumulation scaling factor. * - data_type - Data type for accumulation. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_post_ops_get_params_sum_v3 .. _doxid-group__dnnl__api__attributes_1ga9b09d3bfddb7e539fe900d6aa7690a09: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_post_ops_get_params_sum_v3( :ref:`const_dnnl_post_ops_t` post_ops, int index, float* scale, int32_t* zero_point, :ref:`dnnl_data_type_t`* data_type ) Returns the parameters of an accumulation (sum) post-op with zero point and data type parameter. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - post_ops - Post-ops. * - index - Index of the sum post-op. * - scale - Output accumulation scaling factor. * - zero_point - Zero point. * - data_type - Data type for accumulation. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_post_ops_append_eltwise .. _doxid-group__dnnl__api__attributes_1gad3a93e3c396b07066d42eadbe119b7a4: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_post_ops_append_eltwise( :ref:`dnnl_post_ops_t` post_ops, float scale, :ref:`dnnl_alg_kind_t` alg_kind, float alpha, float beta ) Appends an elementwise post-op. The kind of this post operation is :ref:`dnnl_eltwise `. In the simplest case when the elementwise is the only post operation, the computations would be: .. code-block:: cpp dst[:] <- scale * eltwise_op (op(...)) // instead of dst[:] <- op(...) where eltwise_op is configured with the given parameters. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - 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. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_post_ops_get_params_eltwise .. _doxid-group__dnnl__api__attributes_1gaa57e61baf091b255b4cc0c3648bac3a5: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_post_ops_get_params_eltwise( :ref:`const_dnnl_post_ops_t` post_ops, int index, float* scale, :ref:`dnnl_alg_kind_t`* alg_kind, float* alpha, float* beta ) Returns the parameters of an elementwise post-op. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - 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. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. :ref:`dnnl_invalid_arguments ` if ``index`` does not refer to an elementwise post-op. .. index:: pair: function; dnnl_post_ops_append_dw_k3s1p1 .. _doxid-group__dnnl__api__attributes_1ga60126f67fa1dd9df8cca16e2dd471184: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_post_ops_append_dw_k3s1p1( :ref:`dnnl_post_ops_t` post_ops, :ref:`dnnl_data_type_t` weights_data_type, :ref:`dnnl_data_type_t` bias_data_type, :ref:`dnnl_data_type_t` dst_data_type, :ref:`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 :ref:`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: .. code-block:: cpp dst[:] <- scales * (conv_dw(conv_1x1)) See :ref:`dev_guide_attributes_post_ops_depthwise ` and :ref:`dev_guide_attributes_post_ops_depthwise_fusion ` for more info. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - 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. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise .. index:: pair: function; dnnl_post_ops_get_params_dw_k3s1p1 .. _doxid-group__dnnl__api__attributes_1gab7b3337bc132438f4dada22f6fe72a0f: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_post_ops_get_params_dw_k3s1p1( :ref:`const_dnnl_post_ops_t` post_ops, int index, :ref:`dnnl_data_type_t`* weights_data_type, :ref:`dnnl_data_type_t`* bias_data_type, :ref:`dnnl_data_type_t`* dst_data_type, :ref:`dnnl_dim_t`* count, int* mask, const float** scales ) Returns the parameters of an depthwise post-op with stride 1. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - 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. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise .. index:: pair: function; dnnl_post_ops_append_dw_k3s2p1 .. _doxid-group__dnnl__api__attributes_1gaa31670d933930b7c7940379bff0de326: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_post_ops_append_dw_k3s2p1( :ref:`dnnl_post_ops_t` post_ops, :ref:`dnnl_data_type_t` weights_data_type, :ref:`dnnl_data_type_t` bias_data_type, :ref:`dnnl_data_type_t` dst_data_type, :ref:`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 :ref:`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: .. code-block:: cpp dst[:] <- scales * (conv_dw(conv_1x1)) See :ref:`dev_guide_attributes_post_ops_depthwise ` and :ref:`dev_guide_attributes_post_ops_depthwise_fusion ` for more info. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - 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. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise .. index:: pair: function; dnnl_post_ops_get_params_dw_k3s2p1 .. _doxid-group__dnnl__api__attributes_1ga0b82c6ac2d4bd5c8ddee4a807fd2e527: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_post_ops_get_params_dw_k3s2p1( :ref:`const_dnnl_post_ops_t` post_ops, int index, :ref:`dnnl_data_type_t`* weights_data_type, :ref:`dnnl_data_type_t`* bias_data_type, :ref:`dnnl_data_type_t`* dst_data_type, :ref:`dnnl_dim_t`* count, int* mask, const float** scales ) Returns the parameters of an depthwise post-op with stride 2. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - 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. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise .. index:: pair: function; dnnl_post_ops_append_binary .. _doxid-group__dnnl__api__attributes_1ga9d6524a5808500e78138dd2125ac2cbe: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_post_ops_append_binary( :ref:`dnnl_post_ops_t` post_ops, :ref:`dnnl_alg_kind_t` alg_kind, const :ref:`dnnl_memory_desc_t`* src1_desc ) Appends a binary post-op. The kind of this post operation is :ref:`dnnl_binary `. In the simplest case when the binary is the only post operation, the computations would be: .. code-block:: cpp dst[:] <- binary_op (dst[:], another_input[:]) where binary_op is configured with the given parameters. binary_op supports broadcast semantics for a second operand. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - post_ops - Post-ops. * - alg_kind - Binary algorithm for the post-op. * - src1_desc - Memory descriptor of a second operand. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_post_ops_get_params_binary .. _doxid-group__dnnl__api__attributes_1gade0e47fd1f367bf5d8119b21751038ba: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_post_ops_get_params_binary( :ref:`const_dnnl_post_ops_t` post_ops, int index, :ref:`dnnl_alg_kind_t`* alg_kind, const :ref:`dnnl_memory_desc_t`** src1_desc ) Returns the parameters of a binary post-op. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - 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. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. :ref:`dnnl_invalid_arguments ` if ``index`` does not refer to a binary post-op. .. index:: pair: function; dnnl_post_ops_append_prelu .. _doxid-group__dnnl__api__attributes_1ga833465b0aac349988b29245e1112656f: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_post_ops_append_prelu( :ref:`dnnl_post_ops_t` post_ops, int mask ) Appends a prelu forward post-op. The kind of this post-op is :ref:`dnnl::primitive::kind::prelu `. The post-op can be defined as: .. code-block:: cpp dst[:] <- prelu(dst[:], weights[:]) prelu: dst[:] <- dst[:] if dst[:] > 0 dst[:] <- dst[:] * weights[:] if dst[:] <= 0 .. 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) Prelu weights tensor is passed in runtime execution phase. Prelu weights tensor data type is implicitly assumed as f32 using plain layout (a, ab, acb, acdb, acdeb) .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - mask - Defines the correspondence between the output tensor dimensions and the prelu weights tensor. The set i-th bit indicates that a dedicated weights value is used for each index along that dimension. Set the mask to 0 to use a common weights value for the whole output tensor. .. index:: pair: function; dnnl_post_ops_get_params_prelu .. _doxid-group__dnnl__api__attributes_1ga5207e88213978239909da6e9f346cda7: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_post_ops_get_params_prelu( :ref:`const_dnnl_post_ops_t` post_ops, int index, int* mask ) Returns the parameters of a prelu post-op. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - post_ops - Post-ops. * - index - Index of the preu post-op. * - mask - Mask of the prelu post-op. .. index:: pair: function; dnnl_primitive_attr_set_rnn_data_qparams .. _doxid-group__dnnl__api__attributes_1ga0067a4b6e5dd2fe7578cd4a25dddfe39: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_set_rnn_data_qparams( :ref:`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: .. ref-code-block:: cpp // 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); .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - attr - Primitive attributes. * - scale - The value to scale the data by. * - shift - The value to shift the data by. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_primitive_attr_get_rnn_data_qparams .. _doxid-group__dnnl__api__attributes_1gae04744b95cdabcbcda1087229759be04: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_get_rnn_data_qparams( :ref:`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. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - attr - Primitive attributes. * - scale - The value to scale the data by. * - shift - The value to shift the data by. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_primitive_attr_set_rnn_weights_qparams .. _doxid-group__dnnl__api__attributes_1ga815dbfe548cfcb70076fe091888e5466: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_set_rnn_weights_qparams( :ref:`dnnl_primitive_attr_t` attr, :ref:`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 .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - 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: .. math:: count = \prod\limits_{d \in mask} weights.dims[d]. Violations can only be detected when the attributes are used to create a primitive descriptor. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_primitive_attr_get_rnn_weights_qparams .. _doxid-group__dnnl__api__attributes_1ga5bb88cfe42454f01884ddcdb906f6f7c: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_get_rnn_weights_qparams( :ref:`const_dnnl_primitive_attr_t` attr, :ref:`dnnl_dim_t`* count, int* mask, const float** scales ) Returns the quantization scaling factors for RNN weights tensors. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - 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: .. math:: count = \prod\limits_{d \in mask} weights.dims[d]. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_primitive_attr_set_rnn_weights_projection_qparams .. _doxid-group__dnnl__api__attributes_1gac7973cc7b4c62eb6766e9ac96c51d49d: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_set_rnn_weights_projection_qparams( :ref:`dnnl_primitive_attr_t` attr, :ref:`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. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - 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: .. math:: count = \prod\limits_{d \in mask} weights.dims[d]. Violations can only be detected when the attributes are used to create a primitive descriptor. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise. .. index:: pair: function; dnnl_primitive_attr_get_rnn_weights_projection_qparams .. _doxid-group__dnnl__api__attributes_1gaa33206be6e7a0b7de2341041da75cc90: .. ref-code-block:: cpp :class: doxyrest-title-code-block :ref:`dnnl_status_t` DNNL_API dnnl_primitive_attr_get_rnn_weights_projection_qparams( :ref:`const_dnnl_primitive_attr_t` attr, :ref:`dnnl_dim_t`* count, int* mask, const float** scales ) Returns the quantization scaling factors for RNN projection weights tensors. .. rubric:: Parameters: .. list-table:: :widths: 20 80 * - 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: .. math:: count = \prod\limits_{d \in mask} weights.dims[d]. .. rubric:: Returns: :ref:`dnnl_success ` on success and a status describing the error otherwise.