mirror of
https://github.com/Relintai/pandemonium_engine.git
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1772 lines
72 KiB
C++
1772 lines
72 KiB
C++
/*******************************************************************************
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* Copyright 2016-2018 Intel Corporation
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*******************************************************************************/
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#ifndef MKLDNN_H
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#define MKLDNN_H
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#ifndef DOXYGEN_SHOULD_SKIP_THIS
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/* All symbols shall be internal unless marked as MKLDNN_API */
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#if defined _WIN32 || defined __CYGWIN__
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# define MKLDNN_HELPER_DLL_IMPORT __declspec(dllimport)
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# define MKLDNN_HELPER_DLL_EXPORT __declspec(dllexport)
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#else
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# if __GNUC__ >= 4
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# define MKLDNN_HELPER_DLL_IMPORT __attribute__ ((visibility ("default")))
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# define MKLDNN_HELPER_DLL_EXPORT __attribute__ ((visibility ("default")))
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# else
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# define MKLDNN_HELPER_DLL_IMPORT
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# define MKLDNN_HELPER_DLL_EXPORT
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# endif
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#endif
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#ifdef MKLDNN_DLL
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# ifdef MKLDNN_DLL_EXPORTS
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# define MKLDNN_API MKLDNN_HELPER_DLL_EXPORT
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# else
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# define MKLDNN_API MKLDNN_HELPER_DLL_IMPORT
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# endif
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#else
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# define MKLDNN_API
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#endif
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#if defined (__GNUC__)
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# define MKLDNN_DEPRECATED __attribute__((deprecated))
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#elif defined(_MSC_VER)
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# define MKLDNN_DEPRECATED __declspec(deprecated)
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#else
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# define MKLDNN_DEPRECATED
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#endif
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#include "mkldnn_types.h"
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#include "mkldnn_version.h"
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#endif /* DOXYGEN_SHOULD_SKIP_THIS */
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#ifdef __cplusplus
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extern "C" {
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#endif
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/** @addtogroup c_api C API
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* @{ */
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/** @addtogroup c_api_primitive Primitive operations
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* @{ */
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/** @addtogroup c_api_primitive_common Common primitive operations
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* @{ */
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/** Creates a primitive descriptor @p iterator for given @p op_desc, @p attr,
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* @p engine, and optionally a hint primitive descriptor from forward
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* propagation (required for backward propagation). Pass @c NULL for forward
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* propagation.
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*/
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mkldnn_status_t MKLDNN_API mkldnn_primitive_desc_iterator_create(
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mkldnn_primitive_desc_iterator_t *iterator,
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const_mkldnn_op_desc_t op_desc, const_mkldnn_primitive_attr_t attr,
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mkldnn_engine_t engine,
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const_mkldnn_primitive_desc_t hint_forward_primitive_desc);
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/** Iterates over primitive descriptors. Returns #mkldnn_iterator_ends if no
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* more primitive descriptors are available. */
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mkldnn_status_t MKLDNN_API mkldnn_primitive_desc_iterator_next(
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mkldnn_primitive_desc_iterator_t iterator);
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/** Fetches the current primitive descriptor.
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*
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* @note
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* The user should delete the fetched primitive descriptor using
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* mkldnn_primitive_desc_destroy() once it is no longer needed. */
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mkldnn_primitive_desc_t MKLDNN_API mkldnn_primitive_desc_iterator_fetch(
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const_mkldnn_primitive_desc_iterator_t iterator);
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/** Deletes a primitive descriptor @p iterator */
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mkldnn_status_t MKLDNN_API mkldnn_primitive_desc_iterator_destroy(
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mkldnn_primitive_desc_iterator_t iterator);
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/** Creates a @p primitive_desc using @p op_desc, @p attr, @p engine, and
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* optionally a hint primitive descriptor from forward propagation. The call is
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* equivalent to creating a primitive descriptor iterator, immediately fetching
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* a primitive descriptor, and then destroying the iterator. */
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mkldnn_status_t MKLDNN_API mkldnn_primitive_desc_create(
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mkldnn_primitive_desc_t *primitive_desc,
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const_mkldnn_op_desc_t op_desc, const_mkldnn_primitive_attr_t attr,
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mkldnn_engine_t engine,
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const_mkldnn_primitive_desc_t hint_forward_primitive_desc);
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/** Makes a copy of a @p primitive_desc. */
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mkldnn_status_t MKLDNN_API mkldnn_primitive_desc_clone(
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mkldnn_primitive_desc_t *primitive_desc,
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const_mkldnn_primitive_desc_t existing_primitive_desc);
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/** Returns a constant reference to the attribute of a @p primitive_desc.
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*
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* @warning
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* The user should not destroy the obtained @p attr.
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*
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* @warning
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* The lifetime of an @p attr is the same as that of a @p primitive_desc,
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* so it is illegal to use the @p attr once @p primitive_desc has been
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* destroyed. */
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mkldnn_status_t MKLDNN_API mkldnn_primitive_desc_get_attr(
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const_mkldnn_primitive_desc_t primitive_desc,
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const_mkldnn_primitive_attr_t *attr);
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/** Deletes a @p primitive_desc. */
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mkldnn_status_t MKLDNN_API mkldnn_primitive_desc_destroy(
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mkldnn_primitive_desc_t primitive_desc);
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/** Queries primitive descriptor
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*
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* One of the most typical use cases is to query a convolution primitive
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* descriptor created with source, weights, and destination formats equal
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* to #mkldnn_format_tag_any about the corresponding memory descriptors
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* (@p what equals #mkldnn_query_src_md, #mkldnn_query_weights_md, and
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* #mkldnn_query_dst_md respectively) to be able to prepare memory and
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* create reorders if required.
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*
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* Another quite typical use case is to query an operation primitive
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* descriptor for a workspace (@p what equals #mkldnn_query_workspace_md).
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* The returned status #mkldnn_not_required indicates that a workspace is
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* not required.
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*
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* A few other possibilities:
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* - query an operation primitive descriptor for the underlying operation
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* descriptor (#mkldnn_query_convolution_d, #mkldnn_query_eltwise_d,
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* #mkldnn_query_rnn_d, etc.)
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* - query an operation primitive descriptor for the implementation
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* information string (#mkldnn_query_impl_info_str)
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* - query an operation primitive descriptor for the number of inputs and
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* outputs (#mkldnn_query_num_of_inputs_s32 and
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* #mkldnn_query_num_of_outputs_s32 respectively)
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*
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* @sa mkldnn_query_t for more options
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*/
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mkldnn_status_t MKLDNN_API mkldnn_primitive_desc_query(
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const_mkldnn_primitive_desc_t primitive_desc, mkldnn_query_t what,
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int index, void *result);
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/** Queries primitive descriptor for memory descriptor
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*
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* @returns NULL in case of any error.
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*
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* This is just a specialized version of mkldnn_primitive_desc_query
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* used for convenience.
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*/
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const mkldnn_memory_desc_t MKLDNN_API *mkldnn_primitive_desc_query_md(
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const_mkldnn_primitive_desc_t primitive_desc, mkldnn_query_t what,
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int index);
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/** Queries primitive descriptor for signed 32bit int
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*
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* @returns 0 in case of any error (in particular if the queried entity is
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* not of type int32_t). Note that 0 might also be the actual returned
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* value.
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*
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* This is just a specialized version of mkldnn_primitive_desc_query
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* used for convenience.
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*/
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int MKLDNN_API mkldnn_primitive_desc_query_s32(
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const_mkldnn_primitive_desc_t primitive_desc, mkldnn_query_t what,
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int index);
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/** Creates a @p primitive using a @p primitive_desc descriptor. */
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mkldnn_status_t MKLDNN_API mkldnn_primitive_create(
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mkldnn_primitive_t *primitive,
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const_mkldnn_primitive_desc_t primitive_desc);
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/** Executes a @p primitive using a @p stream, and @p nargs arguments
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* @p args. */
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mkldnn_status_t MKLDNN_API mkldnn_primitive_execute(
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const_mkldnn_primitive_t primitive, mkldnn_stream_t stream,
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int nargs, const mkldnn_exec_arg_t *args);
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/** Retrieves a reference to the @p primitive_desc descriptor of given @p
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* primitive.
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*
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* @warning
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* The returned object must not be destroyed by the user. The @c const
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* qualifier of the returned object prevents such attempts. */
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mkldnn_status_t MKLDNN_API mkldnn_primitive_get_primitive_desc(
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const_mkldnn_primitive_t primitive,
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const_mkldnn_primitive_desc_t *primitive_desc);
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/** Deletes a @p primitive. */
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mkldnn_status_t MKLDNN_API mkldnn_primitive_destroy(
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mkldnn_primitive_t primitive);
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/** @} */
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/** @addtogroup c_api_attributes Attributes
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* An extension for controlling primitive behavior.
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* @{ */
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/** Creates an empty (default) @p attr attribute. All the parameters are set to
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* default values.
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*
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* An empty attribute is used in primitive descriptor creation whenever it
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* is not passed explicitly, e.g. in mkldnn_primitive_desc_create.
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*/
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mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_create(
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mkldnn_primitive_attr_t *attr);
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/** Makes a copy of an @p existing_attr. */
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mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_clone(
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mkldnn_primitive_attr_t *attr,
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const_mkldnn_primitive_attr_t existing_attr);
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/** Deletes an @p attr. */
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mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_destroy(
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mkldnn_primitive_attr_t attr);
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/** Returns the scratchpad @p mode set in the attribute @p attr */
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mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_get_scratchpad_mode(
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const_mkldnn_primitive_attr_t attr, mkldnn_scratchpad_mode_t *mode);
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/** Sets scratchpad @p mode.
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*
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* The possible values are: #mkldnn_scratchpad_mode_library (default) and
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* #mkldnn_scratchpad_mode_user. */
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mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_set_scratchpad_mode(
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mkldnn_primitive_attr_t attr, mkldnn_scratchpad_mode_t mode);
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/** Returns @p count, correspondence scale @p mask, and a pointer to a constant
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* floating point array of output @p scales for given @p attr, previously set
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* by mkldnn_primitive_attr_set_output_scales.
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*
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* @warning
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* The @p scales array points to the internal @p attr field, so the user
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* should not modify or destroy @p scales.
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*
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* @warning
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* The lifetime of @p scales is the same as that of the @p attr to which it
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* belongs, so it is illegal to use @p scales after @p attr is destroyed.
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*/
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mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_get_output_scales(
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const_mkldnn_primitive_attr_t attr, mkldnn_dim_t *count, int *mask,
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const float **scales);
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/** Sets output @p scales for primitive operations. The number of elements @p
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* count and correspondence scale @p mask are stored for future use.
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*
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* The @p mask argument defines the correspondence between the output tensor
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* dimensions and the @p scales array. Set the i-th bit of @p mask to 1 to use a
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* dedicated scaling factor for each slice of the output tensor over the i-th
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* dimension. Set @p mask to 0 to use a common scaling factor for the whole
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* output tensor.
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*
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* @note
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* The dimension order is always native and does not depend on the actual
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* layout used. Examples:
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* - 2D dimensional data the order of dimensions is always: (n, c)
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* - 4D dimensional data the order is always: (n, c, h, w)
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* - 5D dimensional weights the order is always: (g, oc, ic, kh, kw)
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*
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* Example usage:
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* @code
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* int mb = 32, oc = 32, oh = 14, ow = 14; // convolution output params
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* float scales[oc] = { ... }; // unique output scales per output channel
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* int oc_dim = 1; // mb_dim = 0, channel_dim = 1, height_dim = 2, ...
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*
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* mkldnn_convolution_desc_t cd; // create & configure convolution op_desc
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*
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* mkldnn_primitive_attr_t attr;
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* mkldnn_primitive_attr_create(&attr); // create default attributes
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* mkldnn_primitive_attr_set_output_scales(attr, oc, 1 << oc_dim, scales);
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*
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* mkldnn_primitive_desc_t cpd;
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* mkldnn_primitive_desc_create(&cpd, &cd, attr, NULL);
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* @endcode
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*
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* @note
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* There is no way to check that @p count corresponds to @p mask until an
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* actual primitive descriptor is created, so it is the user's
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* responsibility to set proper values. The following formula must hold:
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*
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* \f[count = \prod\limits_{d \in mask} output.dims[d]\f]
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*/
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mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_set_output_scales(
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mkldnn_primitive_attr_t attr, mkldnn_dim_t count, int mask,
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const float *scales);
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/** Returns @p post_ops for given @p attr.
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*
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* @warning
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* @p post_ops points to the internal @p attr field, so the user should not
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* modify or destroy @p post_ops. Also, the lifetime of @p post_ops is the
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* same as that of the @p attr it belongs to, so it is illegal to use @p
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* post_ops after @p attr has been destroyed.
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*/
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mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_get_post_ops(
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const_mkldnn_primitive_attr_t attr, const_mkldnn_post_ops_t *post_ops);
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/** Sets configured @p post_ops to an attribute @p attr for future use (when
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* primitive descriptor is being created).
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*
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* @note
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* At this point in time, there is no way to check whether the primitive
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* descriptor does or does not support a given sequence of post operations.
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* Therefore the user should handle an error that might occur at the
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* mkldnn_primitive_desc_create call.
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*/
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mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_set_post_ops(
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mkldnn_primitive_attr_t attr, const_mkldnn_post_ops_t post_ops);
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/** @addtogroup c_api_attributes_post_ops Sequence of post operations
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* An extension for performing extra operations after a base operation.
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* @{ */
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/** Creates an empty sequence of post operations @p post_ops. */
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mkldnn_status_t MKLDNN_API mkldnn_post_ops_create(mkldnn_post_ops_t *post_ops);
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/** Deletes a @p post_ops sequence. */
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mkldnn_status_t MKLDNN_API mkldnn_post_ops_destroy(mkldnn_post_ops_t post_ops);
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/** Returns the @p length of post operations for given @p post_ops. */
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int MKLDNN_API mkldnn_post_ops_len(const_mkldnn_post_ops_t post_ops);
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/** Returns the type of post operation with index @p index in given
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* @p post_ops. In case of error, returns #mkldnn_undefined_primitive. */
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mkldnn_primitive_kind_t MKLDNN_API mkldnn_post_ops_get_kind(
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const_mkldnn_post_ops_t post_ops, int index);
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/** Appends accumulation (sum) post operation to the @p post_ops. Prior to
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* accumulating the result, the previous value would be multiplied by @p scale.
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*
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* The kind of this post operation is #mkldnn_sum.
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*
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* This feature might improve performance for cases like residual learning
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* blocks, where the result of convolution is accumulated to the previously
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* computed activations. The parameter @p scale might be extreme for the
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* integer-based computations when the result and previous activations have
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* different logical scaling factors.
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*
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* In the simplest case when the accumulation is the only post operation, the
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* computations would be:
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* dst[] <- scale * dst[] + op(...) // instead of dst[] <- op(...)
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*
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* @note
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* This post operation (as well as all the others) disregards the original
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* layout of the destination; that is, the layout of the original
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* destination is expected to be the same as the layout of the stored
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* destination.
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*/
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mkldnn_status_t MKLDNN_API mkldnn_post_ops_append_sum(
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mkldnn_post_ops_t post_ops, float scale);
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/** Gets the parameters of the accumulation (sum) post operation with index
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* @p index in the sequence of @p post_ops.
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*
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* @note
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* If index @p index would not correspond to the accumulation post
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* operation, the function returns #mkldnn_invalid_arguments.
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*/
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mkldnn_status_t MKLDNN_API mkldnn_post_ops_get_params_sum(
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const_mkldnn_post_ops_t post_ops, int index, float *scale);
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/** Appends eltwise post operation to the @p post_ops with given parameters
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* @p kind, @p alpha, and @p beta (@sa mkldnn_eltwise_forward_desc_init and
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* mkldnn_eltwise_desc_t).
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*
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* The kind of this post operation is #mkldnn_eltwise.
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*
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* In the simplest case when the eltwise is the only post operation, the
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* computations would be:
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* dst[] <- scale * eltwise_op ( op(...) ) // instead of dst[] <- op(...)
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* where eltwise_op is configured with the given parameters.
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*/
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mkldnn_status_t MKLDNN_API mkldnn_post_ops_append_eltwise(
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mkldnn_post_ops_t post_ops, float scale, mkldnn_alg_kind_t alg,
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float alpha, float beta);
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/** Gets the eltwise parameters of the post operation with index @p index in
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* the sequence of @p post_ops.
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*/
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mkldnn_status_t MKLDNN_API mkldnn_post_ops_get_params_eltwise(
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const_mkldnn_post_ops_t post_ops, int index, float *scale,
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mkldnn_alg_kind_t *alg, float *alpha, float *beta);
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/** @} */
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/** @} */
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/** @addtogroup c_api_memory Memory
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* A primitive to describe and store data.
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*
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* The library supports various data types and formats. Memory hierarchy
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* consists of three levels of abstraction:
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* 1. **Memory descriptor** -- engine agnostic logical description of data
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* (number of dimensions, dimensions themselves, and data type), and
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* optionally the format/layout that describes the physical representation
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* of data in memory. If the format is not known yet, one can pass
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* #mkldnn_format_tag_any. This approach is used to allow compute-intensive
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* primitives to specify the most appropriate format on their own with
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* users required to reorder the data if the incoming format doesn't match
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* the primitive's selection. Memory descriptor can be initialized with
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* mkldnn_memory_desc_init_by_tag() or mkldnn_memory_desc_init_by_strides()
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* functions, or by directly filling the mkldnn_memory_desc_t structure.
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* The latter requires deep knowledge of how the physical data
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* representation is mapped to the structure.
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* The @ref understanding_memory_formats topic should shed some light on
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* that.
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* For the fully defined memory descriptors (i.e. where the format kind is
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* not equal to #mkldnn_format_kind_any) a user can the size, using the
|
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* mkldnn_memory_desc_get_size() function. As described in
|
|
* @ref understanding_memory_formats, the size of data sometimes cannot
|
|
* be computed as the product of dimensions times the size of the data
|
|
* type. So users are encouraged to use this function for better code
|
|
* portability.
|
|
* Two memory descriptors can be compared with mkldnn_memory_desc_equal().
|
|
* The comparison is especially useful when checking whether a primitive
|
|
* requires reorder from the user's data format to the primitive's format.
|
|
* 2. **Memory** -- an engine-specific object that handles the data and its
|
|
* description (a memory descriptor). For CPU enigne, the data handle is
|
|
* simply a pointer to @c void. The data handle can be queried using
|
|
* mkldnn_memory_get_data_handle() and set using
|
|
* mkldnn_memory_set_data_handle(). The latter function always sets the
|
|
* memory in the padding region to zero, which is the invariant maintained
|
|
* by all the primitives in Intel MKL-DNN.
|
|
* See @ref understanding_memory_formats for more details.
|
|
* A memory can be created using mkldnn_memory_create() function.
|
|
* A memory can also be queried for the underlying memory descriptor and
|
|
* engine using mkldnn_memory_get_memory_desc() and
|
|
* mkldnn_memory_get_engine() functions.
|
|
*
|
|
* Along with ordinary memory with all dimensions being positive, Intel
|
|
* MKL-DNN supports *zero-volume* memory with one or more dimensions set to
|
|
* zero. This is to support the NumPy\* convention.
|
|
* If a *zero-volume* memory is passed to a primitive, the primitive does
|
|
* not perform any computations on this memory. For example:
|
|
* - Convolution with `(0 batch, 3 input channels, 13 height, 13 width)`
|
|
* source and `(16 output channels, 3 inputs, channel, 3 height, 3 width)`
|
|
* weights would produce `(0 batch, 16 output channels, 11 height, 11 width)`
|
|
* destination (assuming strides are `1` and paddings are zero) and perform
|
|
* zero multiply-add operations.
|
|
* - Concatenation of three memories of shapes `(3, 4, 13, 13)`,
|
|
* `(3, 0, 13, 13)`, and `(3, 1, 13, 13)` along the second axis would produce
|
|
* the output of the shape `(3, 5, 13, 13)`, effectively ignoring the second
|
|
* input (however, if the user created a concatenation primitive descriptor
|
|
* with three inputs they should also provide all three memories to the
|
|
* concatenation primitive, including the one with zero second dimension).
|
|
* - However, Intel MKL-DNN would return an error when attempting to create a
|
|
* convolution with *zero-volume* memory passed for weights because such a
|
|
* convolution is not well-defined:
|
|
* ~~~
|
|
* dst(1, 16, 11, 11) <-- src(1, 0, 13, 13) (*) wei(16, 0, 3, 3)
|
|
* ~~~
|
|
* Should the values in the destination be zeroes or just not accessed at
|
|
* all? Moreover, backward pass w.r.t. weights in such cases is also not
|
|
* well-defined.
|
|
*
|
|
* Data handle of *zero-volume* memory is never accessed and hence can be
|
|
* unset (NULL in case of CPU engine).
|
|
*
|
|
* @sa @ref understanding_memory_formats
|
|
* @{ */
|
|
|
|
/** Initializes a @p memory_desc memory descriptor using @p ndims, @p dims, @p
|
|
* data_type, and @p strides.
|
|
*
|
|
* The @p strides might be NULL, which means the order of physical dimensions
|
|
* is the same as the order of logical ones.
|
|
*
|
|
* @note The logical order of dimensions is defined by a primitive that
|
|
* consumes the memory.
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_memory_desc_init_by_strides(
|
|
mkldnn_memory_desc_t *memory_desc, int ndims, const mkldnn_dims_t dims,
|
|
mkldnn_data_type_t data_type, const mkldnn_dims_t strides);
|
|
|
|
/** Initializes a @p memory_desc memory descriptor using @p ndims, @p dims, @p
|
|
* data_type, and format @p tag.
|
|
*
|
|
* @p tag can be #mkldnn_format_tag_any, which allows a primitive to define
|
|
* the appropriate memory format. In this case, the @p format_kind would be set
|
|
* to #mkldnn_format_kind_any */
|
|
mkldnn_status_t MKLDNN_API mkldnn_memory_desc_init_by_tag(
|
|
mkldnn_memory_desc_t *memory_desc, int ndims, const mkldnn_dims_t dims,
|
|
mkldnn_data_type_t data_type, mkldnn_format_tag_t tag);
|
|
|
|
/** Initializes a @p memory_desc for a given @p parent_memory_desc, with
|
|
* @p dims sizes and @p offsets. May fail if layout used does not allow
|
|
* obtain desired submemory. In this case consider using `extract` or `insert`
|
|
* primitive */
|
|
mkldnn_status_t MKLDNN_API mkldnn_memory_desc_init_submemory(
|
|
mkldnn_memory_desc_t *memory_desc,
|
|
const mkldnn_memory_desc_t *parent_memory_desc,
|
|
const mkldnn_dims_t dims, const mkldnn_dims_t offsets);
|
|
|
|
/** Compares two memory descriptors.
|
|
* @return 1 if the descriptors are the same.
|
|
* @return 0 if the descriptors are different.
|
|
*
|
|
* Use this function to identify whether a reorder is required between the
|
|
* two memories */
|
|
int MKLDNN_API mkldnn_memory_desc_equal(
|
|
const mkldnn_memory_desc_t *lhs,
|
|
const mkldnn_memory_desc_t *rhs);
|
|
|
|
/** Returns the size (in bytes) that is required for given @p memory_desc */
|
|
size_t MKLDNN_API mkldnn_memory_desc_get_size(
|
|
const mkldnn_memory_desc_t *memory_desc);
|
|
|
|
/** Creates a memory for given @p memory_desc and @p engine. Also sets handle
|
|
* to @p native_handle.
|
|
* The @p native_handle can:
|
|
* - point to the user allocated memory, i.e. valid handle. In this case the
|
|
* library doesn't own allocated memory.
|
|
* - be MKLDNN_NATIVE_HANDLE_ALLOCATE to ask the library to allocate and
|
|
* attach memory. In this case the library owns allocated memory.
|
|
* - be MKLDNN_NATIVE_HANDLE_NONE to create mkldnn_memory w/o attached memory.
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_memory_create(mkldnn_memory_t *memory,
|
|
const mkldnn_memory_desc_t *memory_desc, mkldnn_engine_t engine,
|
|
void *native_handle);
|
|
|
|
/** Returns a @p memory_desc associated with @p memory. */
|
|
mkldnn_status_t MKLDNN_API mkldnn_memory_get_memory_desc(
|
|
const_mkldnn_memory_t memory,
|
|
const mkldnn_memory_desc_t **memory_desc);
|
|
|
|
/** Returns an @p engine associated with @p memory. */
|
|
mkldnn_status_t MKLDNN_API mkldnn_memory_get_engine(
|
|
const_mkldnn_memory_t memory, mkldnn_engine_t *engine);
|
|
|
|
/** For a @p memory, returns the data @p handle.
|
|
*
|
|
* For the CPU engine, the data handle is a pointer to the actual data. */
|
|
mkldnn_status_t MKLDNN_API mkldnn_memory_get_data_handle(
|
|
const_mkldnn_memory_t memory, void **handle);
|
|
|
|
/** For a @p memory, sets the data @p handle. */
|
|
mkldnn_status_t MKLDNN_API mkldnn_memory_set_data_handle(
|
|
mkldnn_memory_t memory, void *handle);
|
|
|
|
/** Deletes a @p memory. */
|
|
mkldnn_status_t MKLDNN_API mkldnn_memory_destroy(mkldnn_memory_t memory);
|
|
|
|
/** @} */
|
|
|
|
/** @addtogroup c_api_reorder Reorder
|
|
* A primitive to copy data between memory formats.
|
|
* @{ */
|
|
|
|
/** Initializes a @p reorder_primitive_desc using the description of the source
|
|
* (@p src_engine and @p src_md) and destination (@p dst_engine and @p dst_md)
|
|
* memory, and an @p attr attribute.
|
|
*
|
|
* Inputs:
|
|
* - input (#mkldnn_query_src_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - output (#mkldnn_query_dst_md, 0)
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_reorder_primitive_desc_create(
|
|
mkldnn_primitive_desc_t *reorder_primitive_desc,
|
|
mkldnn_engine_t src_engine, const mkldnn_memory_desc_t *src_md,
|
|
mkldnn_engine_t dst_engine, const mkldnn_memory_desc_t *dst_md,
|
|
const_mkldnn_primitive_attr_t attr);
|
|
|
|
/** @} */
|
|
|
|
/** @addtogroup c_api_concat Concat
|
|
* A primitive to concatenate data by arbitrary dimension.
|
|
* @{ */
|
|
|
|
/** Creates out-of-place @p concat_primitive_desc for concatenation of @p n
|
|
* inputs by @p concat_dimension with resulting @p output_desc memory
|
|
* descriptor. @p output_desc can be NULL or specified with the
|
|
* #mkldnn_format_kind_any format kind -- in this case, the appropriate memory
|
|
* format would be chosen automatically.
|
|
*
|
|
* Inputs:
|
|
* - input 0 (#mkldnn_query_src_md, 0)
|
|
* - input 1 (#mkldnn_query_src_md, 1)
|
|
* - ...
|
|
* - input @p n - 1 (#mkldnn_query_src_md, @p n - 1)
|
|
*
|
|
* Outputs:
|
|
* - output (#mkldnn_query_dst_md, 0)
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_concat_primitive_desc_create(
|
|
mkldnn_primitive_desc_t *concat_primitive_desc,
|
|
const mkldnn_memory_desc_t *dst_md,
|
|
int n, int concat_dimension,
|
|
const mkldnn_memory_desc_t *src_mds,
|
|
const_mkldnn_primitive_attr_t attr,
|
|
mkldnn_engine_t engine);
|
|
|
|
/** @} */
|
|
|
|
/** @addtogroup c_api_sum Sum
|
|
* A primitive to sum data.
|
|
* @{ */
|
|
|
|
/** Creates out-of-place @p sum_primitive_desc for sum of @p n
|
|
* inputs multiplied by scale with resulting @p output_desc memory
|
|
* descriptor. @p output_desc can be NULL or specified with the
|
|
* #mkldnn_format_kind_any format kind -- in this case, the appropriate memory
|
|
* format would be chosen automatically.
|
|
*
|
|
* Inputs:
|
|
* - src 0 (#mkldnn_query_src_md, 0)
|
|
* - src 1 (#mkldnn_query_src_md, 1)
|
|
* - ...
|
|
* - src @p n - 1 (#mkldnn_query_src_md, @p n - 1)
|
|
*
|
|
* Outputs:
|
|
* - output (#mkldnn_query_dst_md, 0)
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_sum_primitive_desc_create(
|
|
mkldnn_primitive_desc_t *sum_primitive_desc,
|
|
const mkldnn_memory_desc_t *dst_mds,
|
|
int n, const float *scales,
|
|
const mkldnn_memory_desc_t *src_mds,
|
|
const_mkldnn_primitive_attr_t attr,
|
|
mkldnn_engine_t engine);
|
|
|
|
/** @} */
|
|
|
|
/** @addtogroup c_api_convolution Convolution
|
|
* A primitive to compute convolution using different algorithms.
|
|
*
|
|
* \f[dst[n][oc][oh][ow] =
|
|
* \sum_{kw=0}^{KW}\sum_{kh=0}^{KH}\sum_{ic=0}^{IC}
|
|
* src[n][ic][oh \cdot s_h - p_l[0] + kh][ow \cdot s_w - p_r[1] + kw]
|
|
* \cdot weights[g][oc][ic][kh][kw]
|
|
* + bias[g][oc],\f]
|
|
*
|
|
* where size of output spatial domain is given by
|
|
* \f$ OH = \left\lfloor{\frac{IH - KH + p_l[0] + p_r[0]}{s_h}}
|
|
* \right\rfloor + 1\f$,
|
|
* \f$ OW = \left\lfloor{\frac{IW - KW + p_l[1] + p_r[1]}{s_w}}
|
|
* \right\rfloor + 1\f$,
|
|
*
|
|
* and summation is carried over input channels \f$ic\f$ in
|
|
* group \f$g\f$, and \f$s_h, s_w\f$ are @p strides and
|
|
* \f$p_l, p_r\f$ are @p padding_l and @p padding_r.
|
|
* @{ */
|
|
|
|
/** Initializes a convolution descriptor @p conv_desc for forward propagation
|
|
* using @p prop_kind (possible values are #mkldnn_forward_training and
|
|
* #mkldnn_forward_inference), @p alg_kind, memory descriptors, @p strides, @p
|
|
* padding_l, @p padding_r, and @p padding_kind. In order to create a
|
|
* convolution without bias, @p bias_desc should either be @c NULL or point to
|
|
* a descriptor with memory format kind equal to #mkldnn_format_kind_undef.
|
|
*
|
|
* @note If @p padding_r is @c NULL, the padding is supposed to be symmetric.
|
|
*
|
|
* @note Memory descriptors are allowed to be initialized with
|
|
* #mkldnn_format_kind_any value of @p format_kind.
|
|
*
|
|
* Inputs:
|
|
* - src (#mkldnn_query_src_md, 0)
|
|
* - weights (#mkldnn_query_weights_md, 0)
|
|
* - bias (#mkldnn_query_weights_md, 1), if created with bias
|
|
*
|
|
* Outputs:
|
|
* - dst (#mkldnn_query_dst_md, 0)
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_convolution_forward_desc_init(
|
|
mkldnn_convolution_desc_t *conv_desc, mkldnn_prop_kind_t prop_kind,
|
|
mkldnn_alg_kind_t alg_kind, const mkldnn_memory_desc_t *src_desc,
|
|
const mkldnn_memory_desc_t *weights_desc,
|
|
const mkldnn_memory_desc_t *bias_desc,
|
|
const mkldnn_memory_desc_t *dst_desc, const mkldnn_dims_t strides,
|
|
const mkldnn_dims_t padding_l, const mkldnn_dims_t padding_r,
|
|
mkldnn_padding_kind_t padding_kind);
|
|
|
|
/** Initializes a dilated convolution descriptor @p conv_desc for forward
|
|
* propagation using @p prop_kind (possible values are #mkldnn_forward_training
|
|
* and #mkldnn_forward_inference), @p alg_kind, memory descriptors, @p strides,
|
|
* @p dilates, @p padding_l, @p padding_r, and @p padding_kind.
|
|
* In order to create a dilated convolution without bias, @p bias_desc
|
|
* should either be @c NULL or point to a descriptor with memory format kind
|
|
* equals #mkldnn_format_kind_undef.
|
|
*
|
|
* @note If @p padding_r is @c NULL, the padding is supposed to be symmetric.
|
|
*
|
|
* @note Memory descriptors are allowed to be initialized with
|
|
* #mkldnn_format_kind_any value of @p format_kind.
|
|
*
|
|
* Inputs:
|
|
* - src (#mkldnn_query_src_md, 0)
|
|
* - weights (#mkldnn_query_weights_md, 0)
|
|
* - bias (#mkldnn_query_weights_md, 1), if created with bias
|
|
*
|
|
* Outputs:
|
|
* - dst (#mkldnn_query_dst_md, 0)
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_dilated_convolution_forward_desc_init(
|
|
mkldnn_convolution_desc_t *conv_desc, mkldnn_prop_kind_t prop_kind,
|
|
mkldnn_alg_kind_t alg_kind, const mkldnn_memory_desc_t *src_desc,
|
|
const mkldnn_memory_desc_t *weights_desc,
|
|
const mkldnn_memory_desc_t *bias_desc,
|
|
const mkldnn_memory_desc_t *dst_desc, const mkldnn_dims_t strides,
|
|
const mkldnn_dims_t dilates, const mkldnn_dims_t padding_l,
|
|
const mkldnn_dims_t padding_r, mkldnn_padding_kind_t padding_kind);
|
|
|
|
/** Initializes a convolution descriptor @p conv_desc for backward propagation
|
|
* with respect to data using @p alg_kind, memory descriptors, @p strides, @p
|
|
* padding_l, @p padding_r, and @p padding_kind.
|
|
*
|
|
* @note Memory descriptors are allowed to be initialized with
|
|
* #mkldnn_format_kind_any value of @p format_kind.
|
|
*
|
|
* Inputs:
|
|
* - diff_dst (#mkldnn_query_diff_dst_md, 0)
|
|
* - weights (#mkldnn_query_weights_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - diff_src (#mkldnn_query_diff_src_md, 0)
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_convolution_backward_data_desc_init(
|
|
mkldnn_convolution_desc_t *conv_desc, mkldnn_alg_kind_t alg_kind,
|
|
const mkldnn_memory_desc_t *diff_src_desc,
|
|
const mkldnn_memory_desc_t *weights_desc,
|
|
const mkldnn_memory_desc_t *diff_dst_desc, const mkldnn_dims_t strides,
|
|
const mkldnn_dims_t padding_l, const mkldnn_dims_t padding_r,
|
|
mkldnn_padding_kind_t padding_kind);
|
|
|
|
/** Initializes a dilated convolution descriptor @p conv_desc for backward
|
|
* propagation with respect to data using @p alg_kind, memory descriptors, @p
|
|
* strides, @p dilates @p padding_l, @p padding_r, and @p padding_kind.
|
|
*
|
|
* @note Memory descriptors are allowed to be initialized with
|
|
* #mkldnn_format_kind_any value of @p format_kind.
|
|
*
|
|
* Inputs:
|
|
* - diff_dst (#mkldnn_query_diff_dst_md, 0)
|
|
* - weights (#mkldnn_query_weights_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - diff_src (#mkldnn_query_diff_src_md, 0)
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_dilated_convolution_backward_data_desc_init(
|
|
mkldnn_convolution_desc_t *conv_desc, mkldnn_alg_kind_t alg_kind,
|
|
const mkldnn_memory_desc_t *diff_src_desc,
|
|
const mkldnn_memory_desc_t *weights_desc,
|
|
const mkldnn_memory_desc_t *diff_dst_desc, const mkldnn_dims_t strides,
|
|
const mkldnn_dims_t dilates, const mkldnn_dims_t padding_l,
|
|
const mkldnn_dims_t padding_r, mkldnn_padding_kind_t padding_kind);
|
|
|
|
/** Initializes a convolution descriptor @p conv_desc for backward propagation
|
|
* with respect to weights using @p alg_kind, memory descriptors, @p strides,
|
|
* @p padding_l, @p padding_r, and @p padding_kind.
|
|
*
|
|
* @note Memory descriptors are allowed to be initialized with
|
|
* #mkldnn_format_kind_any value of @p format_kind.
|
|
*
|
|
* Inputs:
|
|
* - src (#mkldnn_query_src_md, 0)
|
|
* - diff_dst (#mkldnn_query_diff_dst_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - diff_weights (#mkldnn_query_diff_weights_md, 0)
|
|
* - diff_bias (#mkldnn_query_diff_weights_md, 1), if created with bias
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_convolution_backward_weights_desc_init(
|
|
mkldnn_convolution_desc_t *conv_desc, mkldnn_alg_kind_t alg_kind,
|
|
const mkldnn_memory_desc_t *src_desc,
|
|
const mkldnn_memory_desc_t *diff_weights_desc,
|
|
const mkldnn_memory_desc_t *diff_bias_desc,
|
|
const mkldnn_memory_desc_t *diff_dst_desc, const mkldnn_dims_t strides,
|
|
const mkldnn_dims_t padding_l, const mkldnn_dims_t padding_r,
|
|
mkldnn_padding_kind_t padding_kind);
|
|
|
|
/** Initializes a convolution descriptor @p conv_desc for backward propagation
|
|
* with respect to weights using @p alg_kind, memory descriptors, @p strides,
|
|
* @p dilates @p padding_l, @p padding_r, and @p padding_kind.
|
|
*
|
|
* @note Memory descriptors are allowed to be initialized with
|
|
* #mkldnn_format_kind_any value of @p format_kind.
|
|
*
|
|
* Inputs:
|
|
* - src (#mkldnn_query_src_md, 0)
|
|
* - diff_dst (#mkldnn_query_diff_dst_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - diff_weights (#mkldnn_query_diff_weights_md, 0)
|
|
* - diff_bias (#mkldnn_query_diff_weights_md, 1), if created with bias
|
|
*/
|
|
mkldnn_status_t MKLDNN_API
|
|
mkldnn_dilated_convolution_backward_weights_desc_init(
|
|
mkldnn_convolution_desc_t *conv_desc, mkldnn_alg_kind_t alg_kind,
|
|
const mkldnn_memory_desc_t *src_desc,
|
|
const mkldnn_memory_desc_t *diff_weights_desc,
|
|
const mkldnn_memory_desc_t *diff_bias_desc,
|
|
const mkldnn_memory_desc_t *diff_dst_desc, const mkldnn_dims_t strides,
|
|
const mkldnn_dims_t dilates, const mkldnn_dims_t padding_l,
|
|
const mkldnn_dims_t padding_r, mkldnn_padding_kind_t padding_kind);
|
|
|
|
/** @} */
|
|
|
|
/** @addtogroup c_api_deconvolution Deconvolution
|
|
* A primitive to compute deconvolution using different algorithms.
|
|
*
|
|
* @{ */
|
|
|
|
|
|
/** Initializes a deconvolution descriptor @p deconv_desc for forward
|
|
* propagation using @p prop_kind (possible values are #mkldnn_forward_training
|
|
* and #mkldnn_forward_inference), @p alg_kind, memory descriptors, @p strides,
|
|
* @p padding_l, @p padding_r, and @p padding_kind. In order to create a
|
|
* deconvolution without bias, @p bias_desc should either be @c NULL or point to
|
|
* a descriptor with memory format kind equals #mkldnn_format_kind_undef.
|
|
*
|
|
* @note If @p padding_r is @c NULL, the padding is supposed to be symmetric.
|
|
*
|
|
* @note Memory descriptors are allowed to be initialized with
|
|
* #mkldnn_format_kind_any value of @p format_kind.
|
|
*
|
|
* Inputs:
|
|
* - src (#mkldnn_query_src_md, 0)
|
|
* - weights (#mkldnn_query_weights_md, 0)
|
|
* - bias (#mkldnn_query_weights_md, 1), if created with bias
|
|
*
|
|
* Outputs:
|
|
* - dst (#mkldnn_query_dst_md, 0)
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_deconvolution_forward_desc_init(
|
|
mkldnn_deconvolution_desc_t *conv_desc, mkldnn_prop_kind_t prop_kind,
|
|
mkldnn_alg_kind_t alg_kind, const mkldnn_memory_desc_t *src_desc,
|
|
const mkldnn_memory_desc_t *weights_desc,
|
|
const mkldnn_memory_desc_t *bias_desc,
|
|
const mkldnn_memory_desc_t *dst_desc, const mkldnn_dims_t strides,
|
|
const mkldnn_dims_t padding_l, const mkldnn_dims_t padding_r,
|
|
mkldnn_padding_kind_t padding_kind);
|
|
|
|
/** Initializes a dilated deconvolution descriptor @p deconv_desc for forward
|
|
* propagation using @p prop_kind (possible values are #mkldnn_forward_training
|
|
* and #mkldnn_forward_inference), @p alg_kind, memory descriptors, @p strides,
|
|
* @p dilates, @p padding_l, @p padding_r, and @p padding_kind. In order to
|
|
* create a dilated deconvolution without bias, @p bias_desc should either be
|
|
* @c NULL or point to a descriptor with memory format kind equal
|
|
* #mkldnn_format_kind_undef.
|
|
*
|
|
* @note If @p padding_r is @c NULL, the padding is supposed to be symmetric.
|
|
*
|
|
* @note Memory descriptors are allowed to be initialized with
|
|
* #mkldnn_format_kind_any value of @p format_kind.
|
|
*
|
|
* Inputs:
|
|
* - src (#mkldnn_query_src_md, 0)
|
|
* - weights (#mkldnn_query_weights_md, 0)
|
|
* - bias (#mkldnn_query_weights_md, 1), if created with bias
|
|
*
|
|
* Outputs:
|
|
* - dst (#mkldnn_query_dst_md, 0)
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_dilated_deconvolution_forward_desc_init(
|
|
mkldnn_deconvolution_desc_t *conv_desc, mkldnn_prop_kind_t prop_kind,
|
|
mkldnn_alg_kind_t alg_kind, const mkldnn_memory_desc_t *src_desc,
|
|
const mkldnn_memory_desc_t *weights_desc,
|
|
const mkldnn_memory_desc_t *bias_desc,
|
|
const mkldnn_memory_desc_t *dst_desc, const mkldnn_dims_t strides,
|
|
const mkldnn_dims_t dilates, const mkldnn_dims_t padding_l,
|
|
const mkldnn_dims_t padding_r, mkldnn_padding_kind_t padding_kind);
|
|
|
|
/** Initializes a deconvolution descriptor @p conv_desc for backward propagation
|
|
* with respect to data using @p alg_kind, memory descriptors, @p strides, @p
|
|
* padding_l, @p padding_r, and @p padding_kind.
|
|
*
|
|
* @note Memory descriptors are allowed to be initialized with
|
|
* #mkldnn_format_kind_any value of @p format_kind.
|
|
*
|
|
* Inputs:
|
|
* - diff_dst (#mkldnn_query_diff_dst_md, 0)
|
|
* - weights (#mkldnn_query_weights_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - diff_src (#mkldnn_query_diff_src_md, 0)
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_deconvolution_backward_data_desc_init(
|
|
mkldnn_deconvolution_desc_t *conv_desc, mkldnn_alg_kind_t alg_kind,
|
|
const mkldnn_memory_desc_t *diff_src_desc,
|
|
const mkldnn_memory_desc_t *weights_desc,
|
|
const mkldnn_memory_desc_t *diff_dst_desc, const mkldnn_dims_t strides,
|
|
const mkldnn_dims_t padding_l, const mkldnn_dims_t padding_r,
|
|
mkldnn_padding_kind_t padding_kind);
|
|
|
|
/** Initializes a dilated deconvolution descriptor @p conv_desc for backward
|
|
* propagation with respect to data using @p alg_kind, memory descriptors, @p
|
|
* strides, @p dilates, @p padding_l, @p padding_r, and @p padding_kind.
|
|
*
|
|
* @note Memory descriptors are allowed to be initialized with
|
|
* #mkldnn_format_kind_any value of @p format_kind.
|
|
*
|
|
* Inputs:
|
|
* - diff_dst (#mkldnn_query_diff_dst_md, 0)
|
|
* - weights (#mkldnn_query_weights_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - diff_src (#mkldnn_query_diff_src_md, 0)
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_dilated_deconvolution_backward_data_desc_init(
|
|
mkldnn_deconvolution_desc_t *conv_desc, mkldnn_alg_kind_t alg_kind,
|
|
const mkldnn_memory_desc_t *diff_src_desc,
|
|
const mkldnn_memory_desc_t *weights_desc,
|
|
const mkldnn_memory_desc_t *diff_dst_desc, const mkldnn_dims_t strides,
|
|
const mkldnn_dims_t dilates, const mkldnn_dims_t padding_l,
|
|
const mkldnn_dims_t padding_r, mkldnn_padding_kind_t padding_kind);
|
|
|
|
/** Initializes a deconvolution descriptor @p conv_desc for backward propagation
|
|
* with respect to weights using @p alg_kind, memory descriptors, @p strides,
|
|
* @p padding_l, @p padding_r, and @p padding_kind.
|
|
*
|
|
* @note Memory descriptors are allowed to be initialized with
|
|
* #mkldnn_format_kind_any value of @p format_kind.
|
|
*
|
|
* Inputs:
|
|
* - src (#mkldnn_query_src_md, 0)
|
|
* - diff_dst (#mkldnn_query_diff_dst_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - diff_weights (#mkldnn_query_diff_weights_md, 0)
|
|
* - diff_bias (#mkldnn_query_diff_weights_md, 1), if created with bias
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_deconvolution_backward_weights_desc_init(
|
|
mkldnn_deconvolution_desc_t *conv_desc, mkldnn_alg_kind_t alg_kind,
|
|
const mkldnn_memory_desc_t *src_desc,
|
|
const mkldnn_memory_desc_t *diff_weights_desc,
|
|
const mkldnn_memory_desc_t *diff_bias_desc,
|
|
const mkldnn_memory_desc_t *diff_dst_desc, const mkldnn_dims_t strides,
|
|
const mkldnn_dims_t padding_l, const mkldnn_dims_t padding_r,
|
|
mkldnn_padding_kind_t padding_kind);
|
|
|
|
/** Initializes a dilated deconvolution descriptor @p conv_desc for backward
|
|
* propagation with respect to weights using @p alg_kind, memory descriptors,
|
|
* @p strides, @p dilates, @p padding_l, @p padding_r, and @p padding_kind.
|
|
*
|
|
* @note Memory descriptors are allowed to be initialized with
|
|
* #mkldnn_format_kind_any value of @p format_kind.
|
|
*
|
|
* Inputs:
|
|
* - src (#mkldnn_query_src_md, 0)
|
|
* - diff_dst (#mkldnn_query_diff_dst_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - diff_weights (#mkldnn_query_diff_weights_md, 0)
|
|
* - diff_bias (#mkldnn_query_diff_weights_md, 1), if created with bias
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_dilated_deconvolution_backward_weights_desc_init(
|
|
mkldnn_deconvolution_desc_t *conv_desc, mkldnn_alg_kind_t alg_kind,
|
|
const mkldnn_memory_desc_t *src_desc,
|
|
const mkldnn_memory_desc_t *diff_weights_desc,
|
|
const mkldnn_memory_desc_t *diff_bias_desc,
|
|
const mkldnn_memory_desc_t *diff_dst_desc, const mkldnn_dims_t strides,
|
|
const mkldnn_dims_t dilates, const mkldnn_dims_t padding_l,
|
|
const mkldnn_dims_t padding_r, mkldnn_padding_kind_t padding_kind);
|
|
|
|
/** @} */
|
|
|
|
/** @addtogroup c_api_shuffle Shuffle
|
|
* A primitive to shuffle data along the axis.
|
|
* @{ */
|
|
|
|
/** Initializes a @p shuffle_desc for forward propagation using @p prop_kind,
|
|
* memory descriptor @p data_desc, @p axis, and @p group_size.
|
|
*
|
|
* Inputs:
|
|
* - src (#mkldnn_query_src_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - dst (#mkldnn_query_dst_md, 0)
|
|
*
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_shuffle_forward_desc_init(
|
|
mkldnn_shuffle_desc_t *shuffle_desc, mkldnn_prop_kind_t prop_kind,
|
|
const mkldnn_memory_desc_t *data_desc, int axis,
|
|
mkldnn_dim_t group_size);
|
|
|
|
/** Initializes a @p shuffle_desc for backward propagation using memory
|
|
* descriptor @p diff_data_desc, @p axis, and @p group_size.
|
|
*
|
|
*
|
|
* Inputs:
|
|
* - diff_dst (#mkldnn_query_diff_dst_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - diff_src (#mkldnn_query_diff_src_md, 0)
|
|
*
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_shuffle_backward_desc_init(
|
|
mkldnn_shuffle_desc_t *shuffle_desc,
|
|
const mkldnn_memory_desc_t *diff_data_desc, int axis,
|
|
mkldnn_dim_t group_size);
|
|
|
|
/** @} */
|
|
|
|
/** @addtogroup c_api_eltwise Eltwise
|
|
* A primitive to compute element-wise operations like parametric rectifier
|
|
* linear unit (ReLU).
|
|
*
|
|
* Both forward and backward passes support in-place operation; that is, src
|
|
* and dst point to the same memory for forward pass, and diff_dst and diff_src
|
|
* point to the same memory for backward pass.
|
|
*
|
|
* @warning Because the original src is required for backward pass, in-place
|
|
* forward pass in general cannot be applied during training. However, for some
|
|
* kinds of element-wise operations (namely ReLU with alpha parameter equals 0),
|
|
* dst and src can be interchangeable for the backward pass, which enables
|
|
* performing in-place forward even for training.
|
|
*
|
|
* @{ */
|
|
|
|
/** Initializes an @p eltwise_desc for forward propagation using @p prop_kind
|
|
* (possible values are #mkldnn_forward_training and #mkldnn_forward_inference),
|
|
* @p alg_kind algorithm, memory descriptor @p data_desc, @p alpha, and
|
|
* @p beta parameters.
|
|
*
|
|
* @sa mkldnn_eltwise_desc_t for details.
|
|
*
|
|
* Inputs:
|
|
* - src (#mkldnn_query_src_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - dst (#mkldnn_query_dst_md, 0)
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_eltwise_forward_desc_init(
|
|
mkldnn_eltwise_desc_t *eltwise_desc, mkldnn_prop_kind_t prop_kind,
|
|
mkldnn_alg_kind_t alg_kind, const mkldnn_memory_desc_t *data_desc,
|
|
float alpha, float beta);
|
|
|
|
/** Initializes an @p eltwise_desc for backward propagation using @p alg_kind
|
|
* algorithm memory descriptors @p diff_data_desc and @p data_desc, and the
|
|
* @p alpha and @p beta parameters.
|
|
*
|
|
* @sa mkldnn_eltwise_desc_t for details.
|
|
*
|
|
* Inputs:
|
|
* - src (#mkldnn_query_src_md, 0)
|
|
* - diff_dst (#mkldnn_query_diff_dst_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - diff_src (#mkldnn_query_diff_src_md, 0)
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_eltwise_backward_desc_init(
|
|
mkldnn_eltwise_desc_t *eltwise_desc, mkldnn_alg_kind_t alg_kind,
|
|
const mkldnn_memory_desc_t *diff_data_desc,
|
|
const mkldnn_memory_desc_t *data_desc, float alpha, float beta);
|
|
|
|
/** @} */
|
|
|
|
/** @addtogroup c_api_softmax Softmax
|
|
* A primitive to perform softmax.
|
|
*
|
|
* \f[dst[u][c][in] =
|
|
* \frac{\exp(src[ou][c][in]) - \max\limits_{c}(src[ou][c][in])}
|
|
* {\sum\limits_{c}\{\exp(src[ou][c][in])
|
|
* - \max\limits_{c}(src[ou][c][in])\}},\f]
|
|
*
|
|
* where \f$ou, iu\f$ are outer and inner sizes repectively, defined
|
|
* by @p data_desc.dims and @p softmax_axis.
|
|
* @{ */
|
|
|
|
/** Initializes a @p softmax_desc for forward propagation using @p prop_kind
|
|
* (possible values are #mkldnn_forward_training and #mkldnn_forward_inference)
|
|
* and memory descriptor @p data_desc.
|
|
*
|
|
* Inputs:
|
|
* - src (#mkldnn_query_src_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - dst (#mkldnn_query_dst_md, 0)
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_softmax_forward_desc_init(
|
|
mkldnn_softmax_desc_t *softmax_desc, mkldnn_prop_kind_t prop_kind,
|
|
const mkldnn_memory_desc_t *data_desc, int softmax_axis);
|
|
|
|
/** Initializes a @p softmax_desc for backward propagation using memory
|
|
* descriptors @p diff_desc and @p data_desc.
|
|
*
|
|
* Inputs:
|
|
* - dst (#mkldnn_query_dst_md, 0)
|
|
* - diff_dst (#mkldnn_query_diff_dst_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - diff_src (#mkldnn_query_diff_src_md, 0)
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_softmax_backward_desc_init(
|
|
mkldnn_softmax_desc_t *softmax_desc,
|
|
const mkldnn_memory_desc_t *diff_desc,
|
|
const mkldnn_memory_desc_t *data_desc, int softmax_axis);
|
|
|
|
/** @} */
|
|
|
|
/** @addtogroup c_api_pooling Pooling
|
|
* A primitive to perform max or average pooling.
|
|
*
|
|
* Max pooling:
|
|
* \f[dst[n][oc][oh][ow] =
|
|
* \max\limits_{kw,kh}
|
|
* (src[n][ic][oh \cdot s_h - p_l[0] + kh][ow \cdot s_w - p_r[1] + kw]),\f]
|
|
*
|
|
* Average pooling:
|
|
* \f[dst[n][oc][oh][ow] =
|
|
* \frac{1}{KW \cdot KH}\sum\limits_{kw,kh}
|
|
* src[n][ic][oh \cdot s_h - p_l[0] + kh][ow \cdot s_w - p_r[1] + kw],\f]
|
|
*
|
|
* where \f$p_l, p_r\f$ are @p padding_l and @p padding_r respectively, and
|
|
* output spatial dimensions are calculated similarly to how they are done in
|
|
* convolution.
|
|
*
|
|
* During training, max pooling requires a workspace on forward
|
|
* (#mkldnn_forward_training) and backward (#mkldnn_backward) passes to
|
|
* save indices where maximum was found. The workspace layout is opaque, and
|
|
* the indices cannot be restored from it. However, one can use backward
|
|
* pooling to perform up-sampling (used in some detection topologies).
|
|
*
|
|
* @{ */
|
|
|
|
/** Initializes a pooling descriptor @p pool_desc for forward propagation using
|
|
* @p prop_kind (possible values are #mkldnn_forward_training and
|
|
* #mkldnn_forward_inference), @p alg_kind, memory descriptors, and pooling
|
|
* parameters in the spatial domain: @p strides, @p kernel sizes, @p padding_l,
|
|
* @p padding_r, and @p padding_kind.
|
|
*
|
|
* @note If @p padding_r is @c NULL, the padding is supposed to be symmetric.
|
|
*
|
|
* Inputs:
|
|
* - src (#mkldnn_query_src_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - dst (#mkldnn_query_dst_md, 0)
|
|
* - workspace (#mkldnn_query_workspace_md, 0),
|
|
* if @p alg_kind = #mkldnn_pooling_max and
|
|
* @p prop_kind = #mkldnn_forward_training
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_pooling_forward_desc_init(
|
|
mkldnn_pooling_desc_t *pool_desc, mkldnn_prop_kind_t prop_kind,
|
|
mkldnn_alg_kind_t alg_kind, const mkldnn_memory_desc_t *src_desc,
|
|
const mkldnn_memory_desc_t *dst_desc, const mkldnn_dims_t strides,
|
|
const mkldnn_dims_t kernel, const mkldnn_dims_t padding_l,
|
|
const mkldnn_dims_t padding_r, mkldnn_padding_kind_t padding_kind);
|
|
|
|
/** Initializes a pooling descriptor @p pool_desc for backward propagation
|
|
* using @p alg_kind, memory descriptors, and pooling parameters in the spatial
|
|
* domain: @p strides, @p kernel sizes, @p padding_l, @p padding_r, and @p
|
|
* padding_kind.
|
|
*
|
|
* @note If @p padding_r is @c NULL, the padding is supposed to be symmetric.
|
|
*
|
|
* Inputs:
|
|
* - diff_dst (#mkldnn_query_diff_dst_md, 0)
|
|
* - workspace (#mkldnn_query_workspace_md, 0),
|
|
* if @p alg_kind = #mkldnn_pooling_max
|
|
*
|
|
* Outputs:
|
|
* - diff_src (#mkldnn_query_diff_src_md, 0)
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_pooling_backward_desc_init(
|
|
mkldnn_pooling_desc_t *pool_desc, mkldnn_alg_kind_t alg_kind,
|
|
const mkldnn_memory_desc_t *diff_src_desc,
|
|
const mkldnn_memory_desc_t *diff_dst_desc, const mkldnn_dims_t strides,
|
|
const mkldnn_dims_t kernel, const mkldnn_dims_t padding_l,
|
|
const mkldnn_dims_t padding_r, mkldnn_padding_kind_t padding_kind);
|
|
|
|
/** @} */
|
|
|
|
/** @addtogroup c_api_lrn LRN
|
|
* A primitive to perform local response normalization (LRN) across or within
|
|
* channels.
|
|
*
|
|
* LRN accross channels:
|
|
* \f[dst[n][c][h][w] = \left\{k + \frac{\alpha}{n_{l}}
|
|
* \sum\limits_{i=-(n_{l}-1)/2}^{(n_{l}+1)/2}
|
|
* (src[n][c+i][h][w])^2\right\}^{-\beta}
|
|
* src[n][c][h][w],\f]
|
|
*
|
|
* LRN within channels:
|
|
* \f[dst[n][c][h][w] = \left\{k + \frac{\alpha}{n_{l}}
|
|
* \sum\limits_{i=-(n_{l}-1)/2}^{(n_{l}+1)/2}
|
|
* (src[n][c][h+i][w+i])^2\right\}^{-\beta}
|
|
* src[n][c][h][w],\f]
|
|
*
|
|
* where \f$n_{l}\f$ is the @p local_size.
|
|
*
|
|
* During training, LRN might or might not require a workspace on forward
|
|
* (#mkldnn_forward_training) and backward (#mkldnn_backward) passes. The
|
|
* behavior is implementation specific. Optimized implementations typically
|
|
* require a workspace and use it to save some intermediate results from the
|
|
* forward pass that accelerate computations on the backward pass.
|
|
*
|
|
* To check whether a workspace is required, query the LRN primitive descriptor
|
|
* for the workspace (#mkldnn_query_workspace_md). Success indicates that the
|
|
* workspace is required and its description will be returned.
|
|
* @sa mkldnn_primitive_desc_query and mkldnn_primitive_desc_query_pd
|
|
*
|
|
* @{ */
|
|
|
|
/** Initializes an @p lrn_desc for forward propagation using @p prop_kind
|
|
* (possible values are #mkldnn_forward_training and #mkldnn_forward_inference),
|
|
* @p alg_kind, memory descriptor @p data_desc, and regularization
|
|
* parameters @p local_size, @p alpha, @p beta, and @p k.
|
|
*
|
|
* Inputs:
|
|
* - src (#mkldnn_query_src_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - dst (#mkldnn_query_dst_md, 0)
|
|
* - workspace (#mkldnn_query_workspace_md, 0),
|
|
* if the underlying implementation requires
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_lrn_forward_desc_init(
|
|
mkldnn_lrn_desc_t *lrn_desc, mkldnn_prop_kind_t prop_kind,
|
|
mkldnn_alg_kind_t alg_kind, const mkldnn_memory_desc_t *data_desc,
|
|
mkldnn_dim_t local_size, float alpha, float beta, float k);
|
|
|
|
/** Initializes an @p lrn_desc for backward propagation using @p alg_kind,
|
|
* memory descriptors @p data_desc and @p diff_data_desc, and regularization
|
|
* parameters @p local_size, @p alpha, @p beta, and @p k.
|
|
*
|
|
* Inputs:
|
|
* - src (#mkldnn_query_src_md, 0)
|
|
* - diff_dst (#mkldnn_query_diff_dst_md, 0)
|
|
* - workspace (#mkldnn_query_workspace_md, 0),
|
|
* if the underlying implementation requires
|
|
*
|
|
* Outputs:
|
|
* - diff_src (#mkldnn_query_diff_src_md, 0)
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_lrn_backward_desc_init(
|
|
mkldnn_lrn_desc_t *lrn_desc, mkldnn_alg_kind_t alg_kind,
|
|
const mkldnn_memory_desc_t *diff_data_desc,
|
|
const mkldnn_memory_desc_t *data_desc, mkldnn_dim_t local_size,
|
|
float alpha, float beta, float k);
|
|
|
|
/** @} */
|
|
|
|
/** @addtogroup c_api_batch_normalization Batch Normalization
|
|
* A primitive to perform batch normalization.
|
|
*
|
|
* \f[dst[n][c][h][w] = \gamma[c] \frac{src[n][c][h][w] - \mu[c]}
|
|
* {\sqrt{\sigma[c] + eps}} + \beta[c],\f]
|
|
*
|
|
* where \f$\gamma[c], \beta[c]\f$ are weights and bias for a channel and,
|
|
*
|
|
* \f$\mu[c] = \frac{1}{NHW} \sum\limits_{whn} src[n][c][h][w]\f$,
|
|
* \f$\sigma[c] = \frac{1}{NHW} \sum\limits_{whn}
|
|
* (src[n][c][h][w] - \mu[c])^2\f$,
|
|
*
|
|
* and @c eps is a constant to improve numerical stability.
|
|
*
|
|
* Both forward and backward passes support in-place operation; that is, src
|
|
* and dst point to the same memory for forward pass, and diff_dst and diff_src
|
|
* point to the same memory for backward pass.
|
|
*
|
|
* Batch normalization supports different flavors controlled by
|
|
* mkldnn_batch_normalization_desc_t. For example, batch normalization can
|
|
* compute the mean and variance on its own or take them as inputs. It can
|
|
* either perform scaling and shifting using gamma and beta parameters or not.
|
|
* Optionally it can also perform a fused ReLU, which in case of training would
|
|
* also require a workspace.
|
|
*
|
|
* @sa mkldnn_batch_normalization_desc_t
|
|
* @{ */
|
|
|
|
/** Initializes a batch normalization descriptor @p bnrm_desc for forward
|
|
* propagation using @p prop_kind (possible values are
|
|
* #mkldnn_forward_training and #mkldnn_forward_inference), memory descriptor
|
|
* @p data_desc, normalization parameter @p epsilon, and @p flags set using bit
|
|
* flags of type mkldnn_batch_normalization_desc_t.
|
|
*
|
|
* Inputs:
|
|
* - src (#mkldnn_query_src_md, 0)
|
|
* - mean (#mkldnn_query_src_md, 1),
|
|
* if #mkldnn_use_global_stats bit-flags is set in @p flags
|
|
* - variance (#mkldnn_query_src_md, 2),
|
|
* if #mkldnn_use_global_stats bit-flags is set in @p flags
|
|
* - scale_and_shift (#mkldnn_query_weights_md, 0),
|
|
* if #mkldnn_use_scaleshift bit-flags is set in @p flags
|
|
*
|
|
* Outputs:
|
|
* - dst (#mkldnn_query_dst_md, 0)
|
|
* - mean (#mkldnn_query_dst_md, 1),
|
|
* if #mkldnn_use_global_stats bit-flags is not set in @p flags
|
|
* @p prop_kind = #mkldnn_forward_training
|
|
* - variance (#mkldnn_query_dst_md, 2),
|
|
* if #mkldnn_use_global_stats bit-flags is not set in @p flags
|
|
* and @p prop_kind = #mkldnn_forward_training
|
|
* - workspace (#mkldnn_query_workspace_md, 0),
|
|
* if #mkldnn_fuse_bn_relu bit-flags is set in @p flags
|
|
* and @p prop_kind = #mkldnn_forward_training
|
|
*
|
|
* @note In-place operation is supported; that is, dst points to the same memory
|
|
* as src.
|
|
*
|
|
* @sa mkldnn_batch_normalization_desc_t
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_batch_normalization_forward_desc_init(
|
|
mkldnn_batch_normalization_desc_t *bnrm_desc,
|
|
mkldnn_prop_kind_t prop_kind, const mkldnn_memory_desc_t *data_desc,
|
|
float epsilon, unsigned flags);
|
|
|
|
/** Initializes a batch normalization descriptor @p bnrm_desc for backward
|
|
* propagation with respect to data and scale-shift parameters using memory
|
|
* descriptors @p data_desc and @p diff_data_desc, normalization parameter
|
|
* @p epsilon, and @p flags set using bit flags of type
|
|
* mkldnn_batch_normalization_desc_t.
|
|
*
|
|
* Inputs:
|
|
* - src (#mkldnn_query_src_md, 0)
|
|
* - mean (#mkldnn_query_src_md, 1)
|
|
* - variance (#mkldnn_query_src_md, 2)
|
|
* - diff_dst (#mkldnn_query_diff_dst_md, 0)
|
|
* - scale_and_shift (#mkldnn_query_weights_md, 0),
|
|
* if #mkldnn_use_scaleshift bit-flags is set in @p flags
|
|
* - workspace (#mkldnn_query_workspace_md, 0),
|
|
* if #mkldnn_fuse_bn_relu bit-flags is set in @p flags
|
|
*
|
|
* Outputs:
|
|
* - diff_src (#mkldnn_query_diff_src_md, 0)
|
|
* - diff_scale_and_shift (#mkldnn_query_diff_weights_md, 0),
|
|
* if #mkldnn_use_scaleshift bit-flags is set in @p flags
|
|
* and @p prop_kind = #mkldnn_backward
|
|
*
|
|
* @note in-place operation is supported,
|
|
* i.e. diff_src points to the same memory as diff_dst.
|
|
*
|
|
* @sa mkldnn_batch_normalization_desc_t
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_batch_normalization_backward_desc_init(
|
|
mkldnn_batch_normalization_desc_t *bnrm_desc,
|
|
mkldnn_prop_kind_t prop_kind,
|
|
const mkldnn_memory_desc_t *diff_data_desc,
|
|
const mkldnn_memory_desc_t *data_desc,
|
|
float epsilon, unsigned flags);
|
|
|
|
/** @} */
|
|
|
|
/** @addtogroup c_api_inner_product Inner product
|
|
* A primitive to compute an inner product.
|
|
*
|
|
* Inner product layer is also known as fully connected layer.
|
|
* With spatial dimension:
|
|
*
|
|
* \f[dst[n][oc] = \sum\limits_{ic, kh, kw}
|
|
* src[n][ic][kh][kw] \cdot weights[oc][ic][kh][kw]
|
|
* + bias[oc]\f]
|
|
* @{ */
|
|
|
|
/** Initializes an inner product descriptor @p ip_desc for forward propagation
|
|
* using @p prop_kind (possible values are #mkldnn_forward_training and
|
|
* #mkldnn_forward_inference) and memory descriptors. In order to create an
|
|
* inner product without bias, @p bias_desc should be either @c NULL or a
|
|
* pointer to a descriptor with memory format kind equals
|
|
* #mkldnn_format_kind_undef.
|
|
*
|
|
* @note Memory descriptors are allowed to be initialized with
|
|
* #mkldnn_format_kind_any value of @p format_kind.
|
|
*
|
|
* Inputs:
|
|
* - src (#mkldnn_query_src_md, 0)
|
|
* - weights (#mkldnn_query_weights_md, 0)
|
|
* - bias (#mkldnn_query_weights_md, 1), if created with bias
|
|
*
|
|
* Outputs:
|
|
* - dst (#mkldnn_query_dst_md, 0)
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_inner_product_forward_desc_init(
|
|
mkldnn_inner_product_desc_t *ip_desc, mkldnn_prop_kind_t prop_kind,
|
|
const mkldnn_memory_desc_t *src_desc,
|
|
const mkldnn_memory_desc_t *weights_desc,
|
|
const mkldnn_memory_desc_t *bias_desc,
|
|
const mkldnn_memory_desc_t *dst_desc);
|
|
|
|
/** Initializes an inner product descriptor @p ip_desc for backward propagation
|
|
* with respect to data using memory descriptors.
|
|
*
|
|
* @note Memory descriptors are allowed to be initialized with
|
|
* #mkldnn_format_kind_any value of @p format_kind.
|
|
*
|
|
* Inputs:
|
|
* - diff_dst (#mkldnn_query_diff_dst_md, 0)
|
|
* - weights (#mkldnn_query_weights_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - diff_src (#mkldnn_query_diff_src_md, 0)
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_inner_product_backward_data_desc_init(
|
|
mkldnn_inner_product_desc_t *ip_desc,
|
|
const mkldnn_memory_desc_t *diff_src_desc,
|
|
const mkldnn_memory_desc_t *weights_desc,
|
|
const mkldnn_memory_desc_t *diff_dst_desc);
|
|
|
|
/** Initializes an inner product descriptor @p ip_desc for backward propagation
|
|
* with respect to weights using memory descriptors.
|
|
*
|
|
* @note Memory descriptors are allowed to be initialized with
|
|
* #mkldnn_format_kind_any value of @p format_kind.
|
|
*
|
|
* Inputs:
|
|
* - src (#mkldnn_query_src_md, 0)
|
|
* - diff_dst (#mkldnn_query_diff_dst_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - diff_weights (#mkldnn_query_diff_weights_md, 0)
|
|
* - diff_bias (#mkldnn_query_diff_weights_md, 1), if created with bias
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_inner_product_backward_weights_desc_init(
|
|
mkldnn_inner_product_desc_t *ip_desc,
|
|
const mkldnn_memory_desc_t *src_desc,
|
|
const mkldnn_memory_desc_t *diff_weights_desc,
|
|
const mkldnn_memory_desc_t *diff_bias_desc,
|
|
const mkldnn_memory_desc_t *diff_dst_desc);
|
|
|
|
/** @} */
|
|
|
|
/** @addtogroup c_api_rnn RNN
|
|
* A primitive to compute the common recurrent layer.
|
|
* @todo add additional description for the group
|
|
* @{ */
|
|
|
|
/**
|
|
* Initializes a recurrent cell descriptor @p rnn_cell_desc
|
|
* using @p rnn_cell_desc, @p kind (possible values are
|
|
* #mkldnn_vanilla_rnn, #mkldnn_vanilla_lstm, #mkldnn_vanilla_gru, and
|
|
* #mkldnn_gru_linear_before_reset),
|
|
* @p f (possible values are #mkldnn_eltwise_relu and
|
|
* #mkldnn_eltwise_tanh), @p flags, @p alpha, and @p clipping.
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_rnn_cell_desc_init(
|
|
mkldnn_rnn_cell_desc_t *rnn_cell_desc,
|
|
mkldnn_alg_kind_t kind, mkldnn_alg_kind_t f,
|
|
unsigned int flags, float alpha, float clipping);
|
|
|
|
/** Returns the number of gates of a particular @p rnn_cell_desc. */
|
|
int MKLDNN_API mkldnn_rnn_cell_get_gates_count(
|
|
const mkldnn_rnn_cell_desc_t *rnn_cell_desc);
|
|
|
|
/** Returns the number of states of a particular @p rnn_cell_desc. */
|
|
int MKLDNN_API mkldnn_rnn_cell_get_states_count(
|
|
const mkldnn_rnn_cell_desc_t *rnn_cell_desc);
|
|
|
|
/** Sets quantization @p scale and @p shift for RNN data tensors.
|
|
* For performance reasons, low precision configuration of RNN primitive
|
|
* expects input activations to have unsigned int8 data type. Scale and shift
|
|
* used to quantize floating point data to unsigned integer must be passed to
|
|
* RNN primitive using attributes.
|
|
* Example usage:
|
|
* @code
|
|
* // rnn parameters
|
|
* int l = 2, t = 2, mb = 32, sic = 32, slc = 32, dic = 32, dlc = 32;
|
|
* // activations quantization parameters
|
|
* float scale = ..., shift = ..;
|
|
*
|
|
* mkldnn_primitive_attr_t rnn_attr;
|
|
* // create default attributes
|
|
* mkldnn_primitive_attr_create(&rnn_attr);
|
|
*
|
|
* // set scale and shift for int8 quantization of activation
|
|
* mkldnn_primitive_attr_set_rnn_data_qparams(rnn_attr, scale, shift);
|
|
*
|
|
* // create & configure rnn op_desc
|
|
* mkldnn_rnn_desc_t rnn_d;
|
|
* mkldnn_primitive_desc_t rnn_pd;
|
|
* mkldnn_primitive_desc_create(&rnn_pd, &rnn_d, attr, engine, NULL);
|
|
* @endcode
|
|
* @note
|
|
* Quantization scale and shift are common for src_layer, src_iter,
|
|
* dst_iter and dst_layer.
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_set_rnn_data_qparams(
|
|
mkldnn_primitive_attr_t attr, const float scale, const float shift);
|
|
|
|
/** Sets quantization scales @p weights_scales for RNN weights tensors.
|
|
* Low precision configuration of RNN primitive expects input weights to have
|
|
* signed int8 data type. Scales used to quantize floating point data
|
|
* to signed integer must be passed to RNN primitive using attributes.
|
|
* The @p mask argument defines correspondence between output tensor dimensions
|
|
* and the @p weights_scales array. Set i-th bit of @p mask to 1 to use
|
|
* dedicated scaling factor for each slice of the output tensor over i-th
|
|
* dimension. Set @p mask to 0 to use common scaling factor for the whole output
|
|
* tensor. Example usage:
|
|
* @code
|
|
* // rnn parameters
|
|
* int l = 2, t = 2, mb = 32, sic = 32, slc = 32, dic = 32, dlc = 32;
|
|
* // unique output scales per output channel
|
|
* float weights_scales[dic * n_gates] = { ... };
|
|
* // mask that specifies last two dimensions of ldigo format
|
|
* int mask = 0x3;
|
|
*
|
|
* mkldnn_primitive_attr_t attr;
|
|
* // create default attributes
|
|
* mkldnn_primitive_attr_create(&attr);
|
|
*
|
|
* // set output channel-wise weights scales
|
|
* mkldnn_primitive_attr_set_rnn_weights_qparams(attr, dic * n_gates, mask,
|
|
* weights_scales);
|
|
*
|
|
* // create & configure rnn op_desc
|
|
* mkldnn_rnn_desc_t rnn_d;
|
|
* mkldnn_primitive_desc_t rnn_pd;
|
|
* mkldnn_primitive_desc_create(&rnn_pd, &rnn_d, attr, engine, NULL);
|
|
* @endcode
|
|
* @note
|
|
* The dimension order is always native and does not depend on the actual
|
|
* layout used. For example, 5 dimensional weights always have
|
|
* (l, d, i, g, o) logical dimension ordering.
|
|
* @note
|
|
* Quantization sales are common for weights_layer and weights_iteration
|
|
* @note
|
|
* There is no way to check that @p count corresponds to @p mask until an
|
|
* actual primitive descriptor is created, so it is user's responsibility
|
|
* to set proper values. The following formula must be held:
|
|
*
|
|
* \f[count = \prod\limits_{d \in mask} output.dims[d]\f]
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_set_rnn_weights_qparams (
|
|
mkldnn_primitive_attr_t attr, mkldnn_dim_t count, int mask,
|
|
const float *weights_scales);
|
|
|
|
/** Initializes a rnn descriptor @p rnn_desc for forward propagation
|
|
* using @p prop_kind, @p rnn_cell_desc, @p direction, and memory descriptors.
|
|
* @note If @p prop_kind equals #mkldnn_forward_training, you must query a
|
|
* workspace memory descriptor before creating the primitive.
|
|
*
|
|
* @p src_iter_desc, @p bias_desc, and @p dst_iter_desc are allowed to either be
|
|
* @c NULL or point to a zero memory descriptor, which would indicate that the
|
|
* RNN primitive should not use them.
|
|
*
|
|
* @note All memory descriptors except @p src_iter_desc are allowed to be
|
|
* initialized with #mkldnn_format_kind_any value of @p format_kind.
|
|
*
|
|
* Inputs:
|
|
* - src_layer (#mkldnn_query_src_md, 0)
|
|
* - src_iter (#mkldnn_query_src_md, 1), if used
|
|
* - weights_layer (#mkldnn_query_weights_md, 0)
|
|
* - weights_iter (#mkldnn_query_weights_md, 1)
|
|
* - bias (#mkldnn_query_weights_md, 2), if used
|
|
*
|
|
* Outputs:
|
|
* - dst_layer (#mkldnn_query_dst_md, 0)
|
|
* - dst_iter (#mkldnn_query_dst_md, 1), if used
|
|
* - workspace (#mkldnn_query_workspace_md, 0),
|
|
* if @p prop_kind equals #mkldnn_forward_training
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_rnn_forward_desc_init(
|
|
mkldnn_rnn_desc_t *rnn_desc, mkldnn_prop_kind_t prop_kind,
|
|
const mkldnn_rnn_cell_desc_t *rnn_cell_desc,
|
|
const mkldnn_rnn_direction_t direction,
|
|
const mkldnn_memory_desc_t *src_layer_desc,
|
|
const mkldnn_memory_desc_t *src_iter_desc,
|
|
const mkldnn_memory_desc_t *weights_layer_desc,
|
|
const mkldnn_memory_desc_t *weights_iter_desc,
|
|
const mkldnn_memory_desc_t *bias_desc,
|
|
const mkldnn_memory_desc_t *dst_layer_desc,
|
|
const mkldnn_memory_desc_t *dst_iter_desc);
|
|
|
|
/** Initializes a rnn descriptor @p rnn_desc for backward propagation
|
|
* using @p prop_kind, @p rnn_cell_desc, @p direction, and memory descriptors.
|
|
*
|
|
* @note All memory descriptors are allowed to be initialized with
|
|
* #mkldnn_format_kind_any value of @p format_kind.
|
|
*
|
|
* @p src_iter_desc (simultaneously with @p diff_src_iter_desc),
|
|
* @p bias_desc (simultaneously with @p diff_bias_desc), and
|
|
* @p dst_iter_desc (simultaneously with @p diff_src_iter_desc) are allowed to
|
|
* either be @c NULL or point to a zero memory descriptor, which would indicate
|
|
* that the RNN primitive should not use them.
|
|
*
|
|
* Inputs:
|
|
* - src_layer (#mkldnn_query_src_md, 0)
|
|
* - src_iter (#mkldnn_query_src_md, 1), if used
|
|
* - weights_layer (#mkldnn_query_weights_md, 0)
|
|
* - weights_iter (#mkldnn_query_weights_md, 1)
|
|
* - bias (#mkldnn_query_weights_md, 2), if used
|
|
* - dst_layer (#mkldnn_query_dst_md, 0)
|
|
* - dst_iter (#mkldnn_query_dst_md, 1), if used
|
|
* - diff_dst_layer (#mkldnn_query_diff_dst_md, 0)
|
|
* - diff_dst_iter (#mkldnn_query_diff_dst_md, 1), if used
|
|
* - workspace (#mkldnn_query_workspace_md, 0)
|
|
*
|
|
* Outputs:
|
|
* - diff_src_layer (#mkldnn_query_diff_src_md, 0)
|
|
* - diff_src_iter (#mkldnn_query_diff_src_md, 1), if used
|
|
* - diff_weights_layer (#mkldnn_query_diff_weights_md, 0)
|
|
* - diff_weights_iter (#mkldnn_query_diff_weights_md, 1)
|
|
* - diff_bias (#mkldnn_query_diff_weights_md, 2), if used
|
|
*/
|
|
mkldnn_status_t MKLDNN_API mkldnn_rnn_backward_desc_init(
|
|
mkldnn_rnn_desc_t *rnn_desc, mkldnn_prop_kind_t prop_kind,
|
|
const mkldnn_rnn_cell_desc_t *rnn_cell_desc,
|
|
const mkldnn_rnn_direction_t direction,
|
|
const mkldnn_memory_desc_t *src_layer_desc,
|
|
const mkldnn_memory_desc_t *src_iter_desc,
|
|
const mkldnn_memory_desc_t *weights_layer_desc,
|
|
const mkldnn_memory_desc_t *weights_iter_desc,
|
|
const mkldnn_memory_desc_t *bias_desc,
|
|
const mkldnn_memory_desc_t *dst_layer_desc,
|
|
const mkldnn_memory_desc_t *dst_iter_desc,
|
|
const mkldnn_memory_desc_t *diff_src_layer_desc,
|
|
const mkldnn_memory_desc_t *diff_src_iter_desc,
|
|
const mkldnn_memory_desc_t *diff_weights_layer_desc,
|
|
const mkldnn_memory_desc_t *diff_weights_iter_desc,
|
|
const mkldnn_memory_desc_t *diff_bias_desc,
|
|
const mkldnn_memory_desc_t *diff_dst_layer,
|
|
const mkldnn_memory_desc_t *diff_dst_iter_desc);
|
|
|
|
/** @} */
|
|
|
|
/** @} */
|
|
|
|
/** @addtogroup c_api_engine Engine operations
|
|
* @{ */
|
|
|
|
/** Returns the number of engines of a particular @p kind. */
|
|
size_t MKLDNN_API mkldnn_engine_get_count(mkldnn_engine_kind_t kind);
|
|
|
|
/** Creates an @p engine of particular @p kind and @p index. */
|
|
mkldnn_status_t MKLDNN_API mkldnn_engine_create(mkldnn_engine_t *engine,
|
|
mkldnn_engine_kind_t kind, size_t index);
|
|
|
|
/** Returns the kind of an @p engine. */
|
|
mkldnn_status_t MKLDNN_API mkldnn_engine_get_kind(mkldnn_engine_t engine,
|
|
mkldnn_engine_kind_t *kind);
|
|
|
|
/** Destroys an @p engine. */
|
|
mkldnn_status_t MKLDNN_API mkldnn_engine_destroy(mkldnn_engine_t engine);
|
|
|
|
/** @} */
|
|
|
|
/** @addtogroup c_api_stream Execution stream operations
|
|
* @{ */
|
|
|
|
/** Creates an execution @p stream for @p engine and with @p flags. */
|
|
mkldnn_status_t MKLDNN_API mkldnn_stream_create(mkldnn_stream_t *stream,
|
|
mkldnn_engine_t engine, unsigned flags);
|
|
|
|
/** Destroys an execution @p stream. */
|
|
mkldnn_status_t MKLDNN_API mkldnn_stream_destroy(mkldnn_stream_t stream);
|
|
|
|
/** @} */
|
|
|
|
/** @addtogroup c_api_service Service functions
|
|
* @{ */
|
|
|
|
/** Sets verbosity level (print information to stdout).
|
|
* Possible levels are:
|
|
* - 0 -- no verbose output (default)
|
|
* - 1 -- primitive information at execution
|
|
* - 2 -- primitive information at creation and execution
|
|
*
|
|
* @note
|
|
* Dumping information might affect performance.
|
|
* This setting overrides the MKLDNN_VERBOSE environment variable. */
|
|
mkldnn_status_t MKLDNN_API mkldnn_set_verbose(int level);
|
|
|
|
/** Enables or disables dumping of JIT-generated code.
|
|
* The enable parameter can be:
|
|
* - 0 -- disable
|
|
* - any other value -- enable
|
|
*
|
|
* @note
|
|
* This setting overrides the MKLDNN_JIT_DUMP environment variable. */
|
|
mkldnn_status_t MKLDNN_API mkldnn_set_jit_dump(int enable);
|
|
|
|
/** Gets library version information.
|
|
* Version information includes:
|
|
* - major -- major version number
|
|
* - minor -- minor version number
|
|
* - patch -- patch release number
|
|
* - hash -- git commit hash */
|
|
const mkldnn_version_t MKLDNN_API *mkldnn_version();
|
|
|
|
/** @} */
|
|
|
|
/** @addtogroup c_api_blas BLAS functions
|
|
* A subset of Basic Linear ALgebra (BLAS) functions to perform
|
|
* matrix-matrix multiplication.
|
|
* @{ */
|
|
|
|
/** SGEMM performs a matrix-matrix multiplication operation defined as
|
|
*
|
|
* C := alpha*op( A )*op( B ) + beta*C
|
|
*
|
|
* where
|
|
* - op( X ) is one of op( X ) = X or op( X ) = X**T,
|
|
* - alpha and beta are scalars,
|
|
* - A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix
|
|
* and C an m by n matrix.
|
|
*
|
|
* The matrices are assumed to be stored in column-major order (the elements
|
|
* in a matrix columns are contiguous in memory).
|
|
*
|
|
* @note
|
|
* The API is different from the standard BLAS routine
|
|
* because it returns mkldnn_status_t for error handling.
|
|
* XERBLA is not supported: no error message will be printed
|
|
* in case of incorrect parameters. */
|
|
mkldnn_status_t MKLDNN_API mkldnn_sgemm(
|
|
const char *transa, const char *transb,
|
|
const mkldnn_dim_t *M, const mkldnn_dim_t *N, const mkldnn_dim_t *K,
|
|
const float *alpha, const float *A, const mkldnn_dim_t *lda,
|
|
const float *B, const mkldnn_dim_t *ldb,
|
|
const float *beta, float *C, const mkldnn_dim_t *ldc);
|
|
|
|
/** gemm_s8u8s32 and gemm_s8s8s32 perform a matrix-matrix multiplication
|
|
* operation and add the result to a scalar-matrix product. For the final
|
|
* result, a vector is added to each row or column of the output matrix.
|
|
* The operation is defined as:
|
|
*
|
|
* C := alpha*(op(A) + A_offset) * (op(B) + B_offset) + beta*C + C_offset
|
|
*
|
|
* where
|
|
* - op( X ) = X or op( X ) = X**T,
|
|
* - A_offset is an m-by-k matrix with every element equal to the value oa,
|
|
* - B_offset is an k-by-n matrix with every element equal to the value ob,
|
|
* - C_offset is an m-by-n matrix defined by the oc array, size len:
|
|
* - if offsetc = F: len must be at least 1
|
|
* - if offsetc = C: len must be at least max(1, m)
|
|
* - if offsetc = R: len must be at least max(1, n)
|
|
* - alpha and beta are scalars, and A, B and C are matrices, with op( A )
|
|
* an m-by-k matrix, op( B ) a k-by-n matrix and C an m-by-n matrix.
|
|
*
|
|
* The matrices are assumed to be stored in column-major order (the elements
|
|
* in a matrix columns are contiguous in memory).
|
|
*
|
|
* @note
|
|
* The API is different compared with the standard BLAS routine
|
|
* because it returns mkldnn_status_t for error handling.
|
|
* XERBLA is not supported: no error message will be printed
|
|
* in case of incorrect parameters. */
|
|
mkldnn_status_t MKLDNN_API mkldnn_gemm_s8u8s32(
|
|
const char *transa, const char *transb, const char *offsetc,
|
|
const mkldnn_dim_t *M, const mkldnn_dim_t *N, const mkldnn_dim_t *K,
|
|
const float *alpha,
|
|
const int8_t *A, const mkldnn_dim_t *lda, const int8_t *ao,
|
|
const uint8_t *B, const mkldnn_dim_t *ldb, const int8_t *bo,
|
|
const float *beta,
|
|
int32_t *c, const mkldnn_dim_t *ldc, const int32_t *co);
|
|
|
|
mkldnn_status_t MKLDNN_API mkldnn_gemm_s8s8s32(
|
|
const char *transa, const char *transb, const char *offsetc,
|
|
const mkldnn_dim_t *M, const mkldnn_dim_t *N, const mkldnn_dim_t *K,
|
|
const float *alpha,
|
|
const int8_t *A, const mkldnn_dim_t *lda, const int8_t *ao,
|
|
const int8_t *B, const mkldnn_dim_t *ldb, const int8_t *bo,
|
|
const float *beta,
|
|
int32_t *c, const mkldnn_dim_t *ldc, const int32_t *co);
|
|
/** @} */
|
|
|
|
/** @} */
|
|
|
|
#ifdef __cplusplus
|
|
}
|
|
#endif
|
|
|
|
#endif
|