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| 2 | mjames | 1 | /* |
| 2 | * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. |
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| 3 | * |
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| 4 | * SPDX-License-Identifier: Apache-2.0 |
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| 5 | * |
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| 6 | * Licensed under the Apache License, Version 2.0 (the License); you may |
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| 7 | * not use this file except in compliance with the License. |
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| 8 | * You may obtain a copy of the License at |
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| 9 | * |
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| 10 | * www.apache.org/licenses/LICENSE-2.0 |
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| 11 | * |
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| 12 | * Unless required by applicable law or agreed to in writing, software |
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| 13 | * distributed under the License is distributed on an AS IS BASIS, WITHOUT |
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| 14 | * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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| 15 | * See the License for the specific language governing permissions and |
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| 16 | * limitations under the License. |
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| 17 | */ |
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| 18 | |||
| 19 | /* ---------------------------------------------------------------------- |
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| 20 | * Project: CMSIS NN Library |
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| 21 | * Title: arm_convolve_HWC_q7_fast.c |
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| 22 | * Description: Fast Q7 version of convolution |
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| 23 | * |
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| 24 | * $Date: 17. January 2018 |
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| 25 | * $Revision: V.1.0.0 |
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| 26 | * |
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| 27 | * Target Processor: Cortex-M cores |
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| 28 | * |
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| 29 | * -------------------------------------------------------------------- */ |
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| 30 | |||
| 31 | #include "arm_math.h" |
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| 32 | #include "arm_nnfunctions.h" |
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| 33 | |||
| 34 | /** |
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| 35 | * @ingroup groupNN |
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| 36 | */ |
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| 37 | |||
| 38 | /** |
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| 39 | * @addtogroup NNConv |
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| 40 | * @{ |
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| 41 | */ |
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| 42 | |||
| 43 | /** |
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| 44 | * @brief Fast Q7 convolution function |
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| 45 | * @param[in] Im_in pointer to input tensor |
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| 46 | * @param[in] dim_im_in input tensor dimention |
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| 47 | * @param[in] ch_im_in number of input tensor channels |
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| 48 | * @param[in] wt pointer to kernel weights |
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| 49 | * @param[in] ch_im_out number of filters, i.e., output tensor channels |
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| 50 | * @param[in] dim_kernel filter kernel size |
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| 51 | * @param[in] padding padding sizes |
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| 52 | * @param[in] stride convolution stride |
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| 53 | * @param[in] bias pointer to bias |
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| 54 | * @param[in] bias_shift amount of left-shift for bias |
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| 55 | * @param[in] out_shift amount of right-shift for output |
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| 56 | * @param[in,out] Im_out pointer to output tensor |
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| 57 | * @param[in] dim_im_out output tensor dimension |
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| 58 | * @param[in,out] bufferA pointer to buffer space for input |
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| 59 | * @param[in,out] bufferB pointer to buffer space for output |
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| 60 | * @return The function returns either |
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| 61 | * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking. |
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| 62 | * |
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| 63 | * @details |
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| 64 | * |
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| 65 | * <b>Buffer size:</b> |
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| 66 | * |
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| 67 | * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel |
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| 68 | * |
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| 69 | * bufferB size: 0 |
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| 70 | * |
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| 71 | * <b>Input dimension constraints:</b> |
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| 72 | * |
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| 73 | * ch_im_in is multiple of 4 ( because of the SIMD32 read and swap ) |
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| 74 | * |
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| 75 | * ch_im_out is multipe of 2 ( bacause 2x2 mat_mult kernel ) |
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| 76 | * |
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| 77 | * The im2col converts the Q7 tensor input into Q15 column, which is stored in |
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| 78 | * bufferA. There is reordering happenning during this im2col process with |
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| 79 | * arm_q7_to_q15_reordered_no_shift. For every four elements, the second and |
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| 80 | * third elements are swapped. |
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| 81 | * |
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| 82 | * The computation kernel arm_nn_mat_mult_kernel_q7_q15_reordered does the |
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| 83 | * GEMM computation with the reordered columns. |
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| 84 | * |
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| 85 | * To speed-up the determination of the padding condition, we split the |
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| 86 | * computation into 3x3 parts, i.e., {top, mid, bottom} X {left, mid, right}. |
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| 87 | * This reduces the total number of boundary condition checks and improves |
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| 88 | * the data copying performance. |
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| 89 | */ |
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| 90 | |||
| 91 | arm_status |
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| 92 | arm_convolve_HWC_q7_fast(const q7_t * Im_in, |
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| 93 | const uint16_t dim_im_in, |
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| 94 | const uint16_t ch_im_in, |
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| 95 | const q7_t * wt, |
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| 96 | const uint16_t ch_im_out, |
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| 97 | const uint16_t dim_kernel, |
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| 98 | const uint16_t padding, |
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| 99 | const uint16_t stride, |
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| 100 | const q7_t * bias, |
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| 101 | const uint16_t bias_shift, |
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| 102 | const uint16_t out_shift, |
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| 103 | q7_t * Im_out, |
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| 104 | const uint16_t dim_im_out, |
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| 105 | q15_t * bufferA, |
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| 106 | q7_t * bufferB) |
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| 107 | { |
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| 108 | |||
| 109 | #if defined (ARM_MATH_DSP) |
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| 110 | /* Run the following code for Cortex-M4 and Cortex-M7 */ |
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| 111 | |||
| 112 | int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; |
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| 113 | |||
| 114 | /* |
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| 115 | * Here we use bufferA as q15_t internally as computation are done with q15_t level |
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| 116 | * im2col are done to output in q15_t format from q7_t input |
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| 117 | */ |
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| 118 | |||
| 119 | q15_t *pBuffer = bufferA; |
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| 120 | q7_t *pOut = Im_out; |
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| 121 | |||
| 122 | if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0) |
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| 123 | { |
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| 124 | /* check if the input dimension meets the constraints */ |
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| 125 | return ARM_MATH_SIZE_MISMATCH; |
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| 126 | } |
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| 127 | |||
| 128 | /* |
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| 129 | * Here we split the entire matrix into three regions depending on the padding situation |
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| 130 | * Top: i_out_y from 0 to padding - 1 |
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| 131 | * Middle: i_out_y from padding to dim_im_out-padding-1 |
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| 132 | * Bottom: i_out_y from dim_im_out-padding to dim_im_out-1 |
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| 133 | */ |
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| 134 | |||
| 135 | /* top part */ |
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| 136 | for (i_out_y = 0; i_out_y < padding; i_out_y++) |
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| 137 | { |
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| 138 | for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) |
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| 139 | { |
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| 140 | /* This part implements the im2col function */ |
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| 141 | for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) |
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| 142 | { |
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| 143 | for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) |
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| 144 | { |
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| 145 | if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) |
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| 146 | { |
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| 147 | /* arm_fill_q15(0, pBuffer, ch_im_in); */ |
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| 148 | memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); |
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| 149 | } else |
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| 150 | { |
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| 151 | arm_q7_to_q15_reordered_no_shift |
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| 152 | ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); |
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| 153 | } |
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| 154 | pBuffer += ch_im_in; |
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| 155 | } |
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| 156 | } |
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| 157 | |||
| 158 | if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) |
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| 159 | { |
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| 160 | pOut = |
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| 161 | arm_nn_mat_mult_kernel_q7_q15_reordered(wt, |
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| 162 | bufferA, |
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| 163 | ch_im_out, |
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| 164 | ch_im_in |
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| 165 | * |
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| 166 | dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); |
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| 167 | /* counter reset */ |
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| 168 | pBuffer = bufferA; |
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| 169 | } |
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| 170 | } |
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| 171 | } |
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| 172 | |||
| 173 | /* middle part, here we also divide the x into left, mid and right */ |
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| 174 | for (; i_out_y < dim_im_out - padding; i_out_y++) |
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| 175 | { |
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| 176 | |||
| 177 | /* left part */ |
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| 178 | for (i_out_x = 0; i_out_x < padding; i_out_x++) |
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| 179 | { |
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| 180 | /* This part implements the im2col function */ |
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| 181 | for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) |
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| 182 | { |
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| 183 | for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) |
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| 184 | { |
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| 185 | if (i_ker_x < 0 || i_ker_x >= dim_im_in) |
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| 186 | { |
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| 187 | /* arm_fill_q15(0, pBuffer, ch_im_in); */ |
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| 188 | memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); |
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| 189 | } else |
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| 190 | { |
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| 191 | arm_q7_to_q15_reordered_no_shift |
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| 192 | ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); |
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| 193 | } |
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| 194 | pBuffer += ch_im_in; |
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| 195 | } |
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| 196 | } |
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| 197 | |||
| 198 | if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) |
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| 199 | { |
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| 200 | pOut = |
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| 201 | arm_nn_mat_mult_kernel_q7_q15_reordered(wt, |
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| 202 | bufferA, |
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| 203 | ch_im_out, |
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| 204 | ch_im_in |
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| 205 | * |
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| 206 | dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); |
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| 207 | /* counter reset */ |
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| 208 | pBuffer = bufferA; |
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| 209 | } |
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| 210 | } |
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| 211 | |||
| 212 | /* mid part */ |
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| 213 | for (; i_out_x < dim_im_out - padding; i_out_x++) |
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| 214 | { |
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| 215 | /* This part implements the im2col function */ |
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| 216 | for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) |
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| 217 | { |
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| 218 | arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in |
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| 219 | + |
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| 220 | (i_ker_y * |
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| 221 | dim_im_in + |
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| 222 | i_out_x * |
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| 223 | stride - padding) * ch_im_in, pBuffer, ch_im_in * dim_kernel); |
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| 224 | pBuffer += ch_im_in * dim_kernel; |
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| 225 | } |
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| 226 | |||
| 227 | if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) |
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| 228 | { |
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| 229 | pOut = |
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| 230 | arm_nn_mat_mult_kernel_q7_q15_reordered(wt, |
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| 231 | bufferA, |
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| 232 | ch_im_out, |
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| 233 | ch_im_in |
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| 234 | * |
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| 235 | dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); |
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| 236 | /* counter reset */ |
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| 237 | pBuffer = bufferA; |
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| 238 | } |
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| 239 | } |
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| 240 | |||
| 241 | /* right part */ |
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| 242 | for (; i_out_x < dim_im_out; i_out_x++) |
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| 243 | { |
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| 244 | /* This part implements the im2col function */ |
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| 245 | for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) |
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| 246 | { |
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| 247 | for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) |
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| 248 | { |
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| 249 | if (i_ker_x < 0 || i_ker_x >= dim_im_in) |
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| 250 | { |
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| 251 | /* arm_fill_q15(0, pBuffer, ch_im_in); */ |
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| 252 | memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); |
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| 253 | } else |
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| 254 | { |
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| 255 | arm_q7_to_q15_reordered_no_shift |
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| 256 | ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); |
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| 257 | } |
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| 258 | pBuffer += ch_im_in; |
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| 259 | } |
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| 260 | } |
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| 261 | |||
| 262 | if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) |
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| 263 | { |
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| 264 | pOut = |
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| 265 | arm_nn_mat_mult_kernel_q7_q15_reordered(wt, |
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| 266 | bufferA, |
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| 267 | ch_im_out, |
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| 268 | ch_im_in |
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| 269 | * |
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| 270 | dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); |
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| 271 | /* counter reset */ |
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| 272 | pBuffer = bufferA; |
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| 273 | } |
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| 274 | } |
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| 275 | } |
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| 276 | |||
| 277 | for (; i_out_y < dim_im_out; i_out_y++) |
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| 278 | { |
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| 279 | for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) |
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| 280 | { |
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| 281 | /* This part implements the im2col function */ |
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| 282 | for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) |
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| 283 | { |
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| 284 | for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) |
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| 285 | { |
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| 286 | if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) |
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| 287 | { |
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| 288 | /* arm_fill_q15(0, pBuffer, ch_im_in); */ |
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| 289 | memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); |
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| 290 | } else |
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| 291 | { |
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| 292 | arm_q7_to_q15_reordered_no_shift |
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| 293 | ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); |
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| 294 | } |
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| 295 | pBuffer += ch_im_in; |
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| 296 | } |
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| 297 | } |
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| 298 | |||
| 299 | if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) |
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| 300 | { |
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| 301 | pOut = |
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| 302 | arm_nn_mat_mult_kernel_q7_q15_reordered(wt, |
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| 303 | bufferA, |
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| 304 | ch_im_out, |
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| 305 | ch_im_in |
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| 306 | * |
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| 307 | dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); |
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| 308 | /* counter reset */ |
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| 309 | pBuffer = bufferA; |
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| 310 | } |
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| 311 | } |
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| 312 | } |
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| 313 | |||
| 314 | /* check if there is left-over for compute */ |
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| 315 | if (pBuffer != bufferA) |
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| 316 | { |
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| 317 | const q7_t *pA = wt; |
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| 318 | int i; |
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| 319 | |||
| 320 | for (i = 0; i < ch_im_out; i++) |
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| 321 | { |
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| 322 | q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); |
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| 323 | q15_t *pB = bufferA; |
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| 324 | /* each time it process 4 entries */ |
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| 325 | uint16_t colCnt = ch_im_in * dim_kernel * dim_kernel >> 2; |
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| 326 | |||
| 327 | while (colCnt) |
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| 328 | { |
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| 329 | |||
| 330 | q31_t inA1, inA2; |
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| 331 | q31_t inB1, inB2; |
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| 332 | |||
| 333 | pA = (q7_t *) read_and_pad_reordered((void *)pA, &inA1, &inA2); |
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| 334 | |||
| 335 | inB1 = *__SIMD32(pB)++; |
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| 336 | sum = __SMLAD(inA1, inB1, sum); |
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| 337 | inB2 = *__SIMD32(pB)++; |
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| 338 | sum = __SMLAD(inA2, inB2, sum); |
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| 339 | |||
| 340 | colCnt--; |
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| 341 | } |
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| 342 | colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3; |
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| 343 | while (colCnt) |
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| 344 | { |
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| 345 | q7_t inA1 = *pA++; |
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| 346 | q15_t inB1 = *pB++; |
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| 347 | sum += inA1 * inB1; |
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| 348 | colCnt--; |
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| 349 | } |
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| 350 | *pOut = (q7_t) __SSAT((sum >> out_shift), 8); |
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| 351 | pOut++; |
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| 352 | |||
| 353 | } |
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| 354 | |||
| 355 | } |
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| 356 | #else |
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| 357 | /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ |
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| 358 | |||
| 359 | uint16_t i, j, k, l, m, n; |
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| 360 | int conv_out; |
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| 361 | signed char in_row, in_col; |
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| 362 | |||
| 363 | if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0) |
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| 364 | { |
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| 365 | /* check if the input dimension meets the constraints */ |
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| 366 | return ARM_MATH_SIZE_MISMATCH; |
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| 367 | } |
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| 368 | |||
| 369 | for (i = 0; i < ch_im_out; i++) |
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| 370 | { |
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| 371 | for (j = 0; j < dim_im_out; j++) |
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| 372 | { |
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| 373 | for (k = 0; k < dim_im_out; k++) |
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| 374 | { |
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| 375 | conv_out = (bias[i] << bias_shift) + NN_ROUND(out_shift); |
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| 376 | for (m = 0; m < dim_kernel; m++) |
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| 377 | { |
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| 378 | for (n = 0; n < dim_kernel; n++) |
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| 379 | { |
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| 380 | // if-for implementation |
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| 381 | in_row = stride * j + m - padding; |
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| 382 | in_col = stride * k + n - padding; |
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| 383 | if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in) |
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| 384 | { |
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| 385 | for (l = 0; l < ch_im_in; l++) |
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| 386 | { |
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| 387 | conv_out += |
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| 388 | Im_in[(in_row * dim_im_in + in_col) * ch_im_in + |
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| 389 | l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel + |
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| 390 | n) * ch_im_in + l]; |
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| 391 | } |
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| 392 | } |
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| 393 | } |
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| 394 | } |
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| 395 | Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8); |
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| 396 | } |
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| 397 | } |
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| 398 | } |
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| 399 | |||
| 400 | #endif /* ARM_MATH_DSP */ |
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| 401 | |||
| 402 | /* Return to application */ |
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| 403 | return ARM_MATH_SUCCESS; |
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| 404 | } |
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| 405 | |||
| 406 | /** |
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| 407 | * @} end of NNConv group |
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| 408 | */ |