/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifndef HL_MATRIX_H_
#define HL_MATRIX_H_
#include "hl_base.h"
/**
* @brief Matrix addition: C_d[i] = alpha * A_d[i] + beta * B_d[i].
*
* @param[in] A_d input matrix (M x N).
* @param[in] B_d input matrix (M x N).
* @param[out] C_d output matrix (M x N).
* @param[in] dimM matrix height.
* @param[in] dimN matrix width.
* @param[in] alpha scalar used for addition.
* @param[in] beta scalar used for addition.
*
*/
extern void hl_matrix_add(
real* A_d, real* B_d, real* C_d, int dimM, int dimN, real alpha, real beta);
/**
* @brief Matrix Softmax.
*
* @param[in] A_d input maxtrix (M x N).
* @param[out] C_d output matrix (M x N).
* @param[in] dimM matrix height.
* @param[in] dimN matrix width.
*
*/
extern void hl_matrix_softmax(real* A_d, real* C_d, int dimM, int dimN);
/**
* @brief Matrix softmax derivative.
*
* @param[out] grad_d intput matrix (M x N).
* @param[in] output_d output matrix (M x N).
* @param[in] sftmaxSum_d softmax sum (M * 1).
* @param[in] dimM matrix height.
* @param[in] dimN matrix width.
*
*/
extern void hl_matrix_softmax_derivative(
real* grad_d, real* output_d, real* sftmaxSum_d, int dimM, int dimN);
/**
* @brief Sequence softmax.
*
* @param[in] A_d input vector.
* @param[out] C_d output vector.
* @param[in] index start positions of sequence.
* @param[in] numSequence sequence number.
*
*/
extern void hl_sequence_softmax_forward(real* A_d,
real* C_d,
const int* index,
int numSequence);
/**
* @brief Matrix cross entropy.
*
* @param[in] A_d input matrix (M x N).
* @param[out] C_d output matrix (M X 1).
* @param[in] label_d input matrix (M x 1).
* @param[in] dimM matrix height.
* @param[in] dimN matrix width.
*
*/
extern void hl_matrix_cross_entropy(
real* A_d, real* C_d, int* label_d, int dimM, int dimN);
/**
* @brief Matrix cross entropy back propagation.
*
* @param[out] grad_d output matrix (M x N).
* @param[in] output_d input matrix (M x N).
* @param[in] label_d input vector (M x 1).
* @param[in] dimM matrix height.
* @param[in] dimN matrix width.
*
*/
extern void hl_matrix_cross_entropy_bp(
real* grad_d, real* output_d, int* label_d, int dimM, int dimN);
/**
* @brief Matrix multi-binary label cross entropy
*
* @param[in] output input matrix (M x N).
* @param[out] entropy output matrix (M x 1).
* @param[in] mat input sparse matrix.
* @param[in] dimM matrix height.
* @param[in] dimN matrix width.
*/
extern void hl_matrix_multi_binary_cross_entropy(
real* output, real* entropy, hl_sparse_matrix_s mat, int dimM, int dimN);
/**
* @brief Matrix multi-binary label cross entropy backprop
*
* @param[in] output input matrix (M x N).
* @param[out] grad output matrix (M x N).
* @param[in] mat input sparse matrix.
* @param[in] dimM matrix height.
* @param[in] dimN matrix width.
*/
extern void hl_matrix_multi_binary_cross_entropy_bp(
real* output, real* grad, hl_sparse_matrix_s mat, int dimM, int dimN);
/**
* @brief Matrix zero memory.
*
* @param[in,out] data input data.
* @param[in] num length of data.
*
*/
extern void hl_matrix_zero_mem(real* data, int num);
/**
* @brief parameter relu forward
*
* @param[out] output output data
* @param[in] input input data
* @param[in] w parameter data
* @param[in] width matrix width
* @param[in] height matrix height
* @param[in] partial_sum
*/
extern void hl_param_relu_forward(
real* output, real* input, real* w, int width, int height, int partial_sum);
/**
* @brief parameter relu backward w
*
* @param[out] grad_w w grad
* @param[in] grad_o output grad
* @param[in] input input data
* @param[in] width matrix width
* @param[in] height matrix height
* @param[in] partial_sum
*/
extern void hl_param_relu_backward_w(real* grad_w,
real* grad_o,
real* input,
int width,
int height,
int partial_sum);
/**
* @brief parameter relu backward diff
*
* @param[in] grad_o output grad
* @param[in] input input data
* @param[in] w parameter
* @param[out] diff diff
* @param[in] width matrix width
* @param[in] height matrix height
* @param[in] partial_sum
*/
extern void hl_param_relu_backward_diff(real* grad_o,
real* input,
real* w,
real* diff,
int width,
int height,
int partial_sum);
/**
* @brief Matrix addition: A_d[i][j] += scale * B_d[j/channel].
*
* @param[in] A_d input matrix (M x N).
* @param[in] B_d input matrix (1 x channel).
* @param[in] channel width of B.
* @param[in] dimM height of A.
* @param[in] dimN width of A.
* @param[in] scale scalar used for addition.
*
*/
extern void hl_matrix_add_shared_bias(real* A_d,
real* B_d,
const int channel,
const int dimM,
const int dimN,
real scale);
/**
* @brief Matrix addition: A_d[i][j] += scale * B_d[j/channel].
*
* @param[in] B_d input matrix (1 x channel).
* @param[in] A_d input matrix (M x N).
* @param[in] channel width of B.
* @param[in] dimM height of A.
* @param[in] dimN width of A.
* @param[in] scale scalar used for addition.
*
*/
extern void hl_matrix_collect_shared_bias(real* B_d,
real* A_d,
const int channel,
const int dimM,
const int dimN,
real scale);
/**
* @brief Matrix rotation in 90 degrees
*
* @param[in] mat input matrix (M x N).
* @param[out] matRot output matrix (N x M).
* @param[in] dimM input matrix height.
* @param[in] dimN input matrix width.
* @param[in] clockWise rotation direction
*/
extern void hl_matrix_rotate(
real* mat, real* matRot, int dimM, int dimN, bool clockWise);
/**
* @brief Matrix vol2Col: Convert 3D volume into col matrix
*
* @param[in] matSrc input matrix.
* @param[in] channel channel of matSrc.
* @param[in] depth depth of matSrc.
* @param[in] height height of matSrc.
* @param[in] width width of matSrc.
* @param[in] filterD depth of filter.
* @param[in] filterH height of filter.
* @param[in] filterW width of filter.
* @param[in] strideD stride in the depth.
* @param[in] strideH stride in the height.
* @param[in] strideW stride in the width.
* @param[in] paddingD padding in the depth.
* @param[in] paddingH padding in the height.
* @param[in] paddingW padding in the width.
* @param[out] dataDst output matrix.
*
*/
extern void hl_matrix_vol2Col(const real* dataSrc,
int channels,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW,
real* dataDst);
/**
* @brief Matrix col2Vol: Convert col matrix into 3D volume
*
* @param[out] matDst output matrix.
* @param[in] channel channel of matDst.
* @param[in] depth depth of matDst.
* @param[in] height height of matDst.
* @param[in] width width of matDst.
* @param[in] filterD depth of filter.
* @param[in] filterH height of filter.
* @param[in] filterW width of filter.
* @param[in] strideD stride in the depth.
* @param[in] strideH stride in the height.
* @param[in] strideW stride in the width.
* @param[in] paddingD padding in the depth.
* @param[in] paddingH padding in the height.
* @param[in] paddingW padding in the width.
* @param[in] matSrc input matrix.
* @param[in] beta input
* @param[in] alpha input
*
*/
extern void hl_matrix_col2Vol(real* dataDst,
int channels,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW,
const real* dataSrc,
real alpha,
real beta);
#endif /* HL_MATRIX_H_ */