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/******************************************************************************
* Copyright (c) Intel Corporation - All rights reserved. *
* This file is part of the LIBXSMM library. *
* *
* For information on the license, see the LICENSE file. *
* Further information: https://github.com/hfp/libxsmm/ *
* SPDX-License-Identifier: BSD-3-Clause *
******************************************************************************/
/* Alexander Heinecke (Intel Corp.)
******************************************************************************/
#ifndef LIBXSMM_DNN_CONVOLUTION_H
#define LIBXSMM_DNN_CONVOLUTION_H
#include "libxsmm_dnn.h"
#include "libxsmm_dnn_tensor.h"
#include "libxsmm_dnn_fusedbatchnorm.h"
/** Opaque handles which represents convolutions and LIBXSMM datatypes */
LIBXSMM_EXTERN_C typedef struct LIBXSMM_RETARGETABLE libxsmm_dnn_layer libxsmm_dnn_layer;
typedef enum libxsmm_dnn_conv_fuse_op {
/* we fuse nothing into convolution */
LIBXSMM_DNN_CONV_FUSE_NONE = 0
} libxsmm_dnn_conv_fuse_op;
/** Type of algorithm used for convolutions. */
typedef enum libxsmm_dnn_conv_algo {
/** let the library decide */
LIBXSMM_DNN_CONV_ALGO_AUTO,
/** direct convolution. */
LIBXSMM_DNN_CONV_ALGO_DIRECT
} libxsmm_dnn_conv_algo;
/** Structure which describes the input and output of data (DNN). */
LIBXSMM_EXTERN_C typedef struct LIBXSMM_RETARGETABLE libxsmm_dnn_conv_desc {
int N; /* number of images in mini-batch */
int C; /* number of input feature maps */
int H; /* height of input image */
int W; /* width of input image */
int K; /* number of output feature maps */
int R; /* height of filter kernel */
int S; /* width of filter kernel */
int u; /* vertical stride */
int v; /* horizontal stride */
int pad_h; /* height of logical rim padding to input
for adjusting output height */
int pad_w; /* width of logical rim padding to input
for adjusting output width */
int pad_h_in; /* height of zero-padding in input buffer,
must equal to pad_h for direct conv */
int pad_w_in; /* width of zero-padding in input buffer,
must equal to pad_w for direct conv */
int pad_h_out; /* height of zero-padding in output buffer */
int pad_w_out; /* width of zero-padding in output buffer */
int threads; /* number of threads to use when running
convolution */
libxsmm_dnn_datatype datatype_in; /* datatypes used for all input related buffer */
libxsmm_dnn_datatype datatype_out; /* datatypes used for all output related buffer */
libxsmm_dnn_tensor_format buffer_format; /* format which is for buffer buffers */
libxsmm_dnn_tensor_format filter_format; /* format which is for filter buffers */
libxsmm_dnn_conv_algo algo; /* convolution algorithm used */
libxsmm_dnn_conv_option options; /* additional options */
libxsmm_dnn_conv_fuse_op fuse_ops; /* used ops into convolutions */
} libxsmm_dnn_conv_desc;
/** Create a layer handle (non-NULL if successful), and pre-build all JIT-code versions. */
LIBXSMM_API libxsmm_dnn_layer* libxsmm_dnn_create_conv_layer(libxsmm_dnn_conv_desc conv_desc, libxsmm_dnn_err_t* status);
LIBXSMM_API libxsmm_dnn_err_t libxsmm_dnn_destroy_conv_layer(const libxsmm_dnn_layer* handle);
/** get layout description of buffers and filters from handle */
LIBXSMM_API libxsmm_dnn_tensor_datalayout* libxsmm_dnn_create_tensor_datalayout(const libxsmm_dnn_layer* handle, const libxsmm_dnn_tensor_type type, libxsmm_dnn_err_t* status);
/** scratch pad management */
LIBXSMM_API size_t libxsmm_dnn_get_scratch_size(const libxsmm_dnn_layer* handle, const libxsmm_dnn_compute_kind kind, libxsmm_dnn_err_t* status);
LIBXSMM_API libxsmm_dnn_err_t libxsmm_dnn_bind_scratch(libxsmm_dnn_layer* handle, const libxsmm_dnn_compute_kind kind, const void* scratch);
LIBXSMM_API libxsmm_dnn_err_t libxsmm_dnn_release_scratch(libxsmm_dnn_layer* handle, const libxsmm_dnn_compute_kind kind);
/** Bind/Release buffers, filters and bias to layer operation */
LIBXSMM_API libxsmm_dnn_err_t libxsmm_dnn_bind_tensor(libxsmm_dnn_layer* handle, const libxsmm_dnn_tensor* tensor, const libxsmm_dnn_tensor_type type);
LIBXSMM_API libxsmm_dnn_tensor* libxsmm_dnn_get_tensor(libxsmm_dnn_layer* handle, const libxsmm_dnn_tensor_type type, libxsmm_dnn_err_t* status);
LIBXSMM_API libxsmm_dnn_err_t libxsmm_dnn_release_tensor(libxsmm_dnn_layer* handle, const libxsmm_dnn_tensor_type type);
/** Run the layer identified by the handle; may use threads internally. */
LIBXSMM_API void libxsmm_dnn_execute(libxsmm_dnn_layer* handle, libxsmm_dnn_compute_kind kind);
LIBXSMM_API libxsmm_dnn_err_t libxsmm_dnn_execute_st(libxsmm_dnn_layer* handle, libxsmm_dnn_compute_kind kind,
/*unsigned*/int start_thread, /*unsigned*/int tid);
/** some helper functions for framework integration */
LIBXSMM_API libxsmm_dnn_err_t libxsmm_dnn_trans_reg_filter(const libxsmm_dnn_layer* handle);
LIBXSMM_API libxsmm_dnn_err_t libxsmm_dnn_trans_reg_bf16_filter(const libxsmm_dnn_layer* handle);
#endif /*LIBXSMM_DNN_CONVOLUTION_H*/
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