Mercurial > hgrepos > Python2 > PyMuPDF

annotate mupdf-source/thirdparty/tesseract/src/arch/intsimdmatrix.cpp @ 2:b50eed0cc0ef upstream

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ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4. The directory name has changed: no version number in the expanded directory now.
author Franz Glasner <fzglas.hg@dom66.de>
date Mon, 15 Sep 2025 11:43:07 +0200
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1 ///////////////////////////////////////////////////////////////////////
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2 // File: intsimdmatrix.cpp
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3 // Description: Base class for 8-bit int SIMD matrix multipliers.
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4 // Author: Ray Smith
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5 //
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6 // (C) Copyright 2017, Google Inc.
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7 // Licensed under the Apache License, Version 2.0 (the "License");
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8 // you may not use this file except in compliance with the License.
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9 // You may obtain a copy of the License at
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10 // http://www.apache.org/licenses/LICENSE-2.0
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11 // Unless required by applicable law or agreed to in writing, software
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12 // distributed under the License is distributed on an "AS IS" BASIS,
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13 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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14 // See the License for the specific language governing permissions and
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15 // limitations under the License.
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16 ///////////////////////////////////////////////////////////////////////
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18 #include "intsimdmatrix.h"
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19 #include "matrix.h" // for GENERIC_2D_ARRAY
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20 #include "simddetect.h" // for SIMDDetect
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22 namespace tesseract {
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24 const IntSimdMatrix *IntSimdMatrix::intSimdMatrix = nullptr;
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26 // Computes a reshaped copy of the weight matrix w.
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27 void IntSimdMatrix::Init(const GENERIC_2D_ARRAY<int8_t> &w, std::vector<int8_t> &shaped_w,
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28 int32_t &rounded_num_out) const {
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29 const int num_out = w.dim1();
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30 const int num_in = w.dim2() - 1;
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31 // The rounded-up sizes of the reshaped weight matrix, excluding biases.
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32 int rounded_num_in = Roundup(num_in, num_inputs_per_group_);
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33 rounded_num_out = RoundOutputs(num_out);
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34 // Add the bias and compute the required size.
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35 shaped_w.resize((rounded_num_in + 1) * rounded_num_out, 0);
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36 int shaped_index = 0;
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37 int output = 0;
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38 // Each number of registers needs a different format! Iterates over the
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39 // different numbers of registers (each a power of 2).
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40 for (int num_registers = max_output_registers_; num_registers >= 1; num_registers /= 2) {
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41 // The number of outputs that we will generate with this many registers.
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42 int num_outputs_per_register_set = num_registers * num_outputs_per_register_;
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43 // Use the max number of registers until we have to go fewer.
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44 while (output + num_outputs_per_register_set <= rounded_num_out) {
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45 // Accumulating outputs in registers saves iterating over the inputs, so
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46 // we only have to do it once per output register set.
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47 for (int input = 0; input < num_in; input += num_inputs_per_group_) {
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48 // Iterate over the number of outputs in a register set.
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49 for (int j = 0; j < num_outputs_per_register_set; ++j) {
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50 // Inner-most loop corresponds to the number of inputs in an input
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51 // group.
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52 for (int i = 0; i < num_inputs_per_group_; ++i) {
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53 int8_t weight = 0;
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54 if (output + j < num_out && input + i < num_in) {
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55 weight = w(output + j, input + i);
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56 }
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57 shaped_w[shaped_index++] = weight;
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58 }
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59 }
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60 }
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61 // Append the bias weights for the register set.
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62 for (int j = 0; j < num_outputs_per_register_set; ++j) {
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63 int8_t weight = 0;
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64 if (output + j < num_out) {
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65 weight = w(output + j, num_in);
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66 }
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67 shaped_w[shaped_index++] = weight;
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68 }
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69 output += num_outputs_per_register_set;
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70 }
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71 }
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72 }
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73
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74 // Computes matrix.vector v = Wu.
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75 // u is of size W.dim2() - 1 and the output v is of size W.dim1().
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76 // u is imagined to have an extra element at the end with value 1, to
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77 // implement the bias, but it doesn't actually have it.
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78 void IntSimdMatrix::MatrixDotVector(const GENERIC_2D_ARRAY<int8_t> &w,
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79 const std::vector<TFloat> &scales, const int8_t *u, TFloat *v) {
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80 int num_out = w.dim1();
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81 int num_in = w.dim2() - 1;
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82 // Base implementation.
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83 int i;
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84 // Break up into chunks of four to facilitate vectorization
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85 for (i = 0; i < (num_out / 4) * 4; i += 4) {
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86 const int8_t *wi0 = w[i + 0];
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87 const int8_t *wi1 = w[i + 1];
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88 const int8_t *wi2 = w[i + 2];
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89 const int8_t *wi3 = w[i + 3];
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90 int total0 = 0;
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91 int total1 = 0;
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92 int total2 = 0;
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93 int total3 = 0;
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94 for (int j = 0; j < num_in; ++j) {
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95 total0 += wi0[j] * u[j];
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96 total1 += wi1[j] * u[j];
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97 total2 += wi2[j] * u[j];
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98 total3 += wi3[j] * u[j];
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99 }
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100 // Add in the bias and correct for integer values.
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101 v[i + 0] = (total0 + wi0[num_in] * INT8_MAX) * scales[i + 0];
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102 v[i + 1] = (total1 + wi1[num_in] * INT8_MAX) * scales[i + 1];
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103 v[i + 2] = (total2 + wi2[num_in] * INT8_MAX) * scales[i + 2];
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104 v[i + 3] = (total3 + wi3[num_in] * INT8_MAX) * scales[i + 3];
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parents:
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105 }
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106
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107 // Capture the remainder mod four
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108 for (; i < num_out; ++i) {
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109 const int8_t *wi = w[i];
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110 int total = 0;
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parents:
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111 for (int j = 0; j < num_in; ++j) {
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parents:
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112 total += wi[j] * u[j];
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parents:
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113 }
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parents:
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114 // Add in the bias and correct for integer values.
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parents:
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115 v[i] = (total + wi[num_in] * INT8_MAX) * scales[i];
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116 }
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117 }
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118
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119 } // namespace tesseract