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<div class="title">intsimdmatrix.cpp</div> </div>
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<a href="a00041.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">// File: intsimdmatrix.cpp</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// Description: Base class for 8-bit int SIMD matrix multipliers.</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">// Author: Ray Smith</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment">// Created: Tue Aug 15 08:01:32 PST 2017</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment">//</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment">// (C) Copyright 2017, Google Inc.</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment">// Licensed under the Apache License, Version 2.0 (the "License");</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment">// you may not use this file except in compliance with the License.</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment">// You may obtain a copy of the License at</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment">// http://www.apache.org/licenses/LICENSE-2.0</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment">// Unless required by applicable law or agreed to in writing, software</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment">// distributed under the License is distributed on an "AS IS" BASIS,</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment">// See the License for the specific language governing permissions and</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment">// limitations under the License.</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"></span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include "<a class="code" href="a00044.html">intsimdmatrix.h</a>"</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#include "<a class="code" href="a00518.html">genericvector.h</a>"</span> <span class="comment">// for GenericVector</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor">#include "<a class="code" href="a00050.html">intsimdmatrixavx2.h</a>"</span> <span class="comment">// for IntSimdMatrixAVX2</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="preprocessor">#include "<a class="code" href="a00056.html">intsimdmatrixsse.h</a>"</span> <span class="comment">// for IntSimdMatrixSSE</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="preprocessor">#include "<a class="code" href="a00302.html">matrix.h</a>"</span> <span class="comment">// for GENERIC_2D_ARRAY</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include "<a class="code" href="a00062.html">simddetect.h</a>"</span> <span class="comment">// for SIMDDetect</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="keyword">namespace </span><a class="code" href="a01629.html">tesseract</a> {</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> </div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="comment">// Factory makes and returns an IntSimdMatrix (sub)class of the best</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="comment">// available type for the current architecture.</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="comment">/* static */</span></div><div class="line"><a name="l00031"></a><span class="lineno"><a class="line" href="a02226.html#a8b7dc48f425a7d108df0f5f489c92cb7"> 31</a></span> <a class="code" href="a02226.html">IntSimdMatrix</a>* <a class="code" href="a02226.html#a8b7dc48f425a7d108df0f5f489c92cb7">IntSimdMatrix::GetFastestMultiplier</a>() {</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <a class="code" href="a02226.html">IntSimdMatrix</a>* multiplier = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordflow">if</span> (<a class="code" href="a02238.html#acf8a5862e2ea087d915d41d4ef5a2c85">SIMDDetect::IsAVX2Available</a>()) {</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  multiplier = <span class="keyword">new</span> <a class="code" href="a02230.html">IntSimdMatrixAVX2</a>();</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (<a class="code" href="a02238.html#ad82ce72ec36f91946ff1796e25e7ff7f">SIMDDetect::IsSSEAvailable</a>()) {</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  multiplier = <span class="keyword">new</span> <a class="code" href="a02234.html">IntSimdMatrixSSE</a>();</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="comment">// Default c++ implementation.</span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  multiplier = <span class="keyword">new</span> <a class="code" href="a02226.html#a12c832ad24fd4c1581259b7a2743625d">IntSimdMatrix</a>();</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  }</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keywordflow">return</span> multiplier;</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> }</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> </div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="comment">// Computes a reshaped copy of the weight matrix w. If there are no</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="comment">// partial_funcs_, it does nothing.</span></div><div class="line"><a name="l00046"></a><span class="lineno"><a class="line" href="a02226.html#a48e759a483e6ce9574f0e05d7a7eb4b6"> 46</a></span> <span class="keywordtype">void</span> <a class="code" href="a02226.html#a48e759a483e6ce9574f0e05d7a7eb4b6">IntSimdMatrix::Init</a>(<span class="keyword">const</span> <a class="code" href="a02222.html">GENERIC_2D_ARRAY<int8_t></a>& w) {</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keywordflow">if</span> (<a class="code" href="a02226.html#a1fcea4ce2c16453174e3386ebd81fdf1">partial_funcs_</a>.empty()) <span class="keywordflow">return</span>;</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keywordtype">int</span> num_out = w.<a class="code" href="a02222.html#adfa4379cd2d6ba96123fd1b20be9e74d">dim1</a>();</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keywordtype">int</span> num_in = w.<a class="code" href="a02222.html#a112504ebf7329913119401c851a6b24b">dim2</a>() - 1;</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="comment">// The rounded-up sizes of the reshaped weight matrix, excluding biases.</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keywordtype">int</span> rounded_num_in = <a class="code" href="a02226.html#acf9f1a5093e7fa9de9c107b543404d0f">Roundup</a>(num_in, <a class="code" href="a02226.html#a23256c8836310012b8a80e61f9dd5a35">num_inputs_per_group_</a>);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordtype">int</span> rounded_num_out = <a class="code" href="a02226.html#a74a99972e32fe1659f839791bb7dde49">RoundOutputs</a>(num_out);</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="comment">// Add the bias and compute the required size.</span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <a class="code" href="a02226.html#a6de2b2f60bec1231ebebee433ed900f7">shaped_w_</a>.resize((rounded_num_in + 1) * rounded_num_out, 0);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordtype">int</span> shaped_index = 0;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordtype">int</span> output = 0;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="comment">// Each number of registers needs a different format! Iterates over the</span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="comment">// different numbers of registers (each a power of 2).</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> num_registers = <a class="code" href="a02226.html#a6c071f8e8104b7f4ff744622fa21edc1">max_output_registers_</a>; num_registers >= 1;</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  num_registers /= 2) {</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="comment">// The number of outputs that we will generate with this many registers.</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordtype">int</span> num_outputs_per_register_set =</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  num_registers * <a class="code" href="a02226.html#a8760aa1d64a18e46cc77698f2381177c">num_outputs_per_register_</a>;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="comment">// Use the max number of registers until we have to go fewer.</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordflow">while</span> (output + num_outputs_per_register_set <= rounded_num_out) {</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="comment">// Accumulating outputs in registers saves iterating over the inputs, so</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="comment">// we only have to do it once per output register set.</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> input = 0; input < num_in; input += <a class="code" href="a02226.html#a23256c8836310012b8a80e61f9dd5a35">num_inputs_per_group_</a>) {</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="comment">// Iterate over the number of outputs in a register set.</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < num_outputs_per_register_set; ++j) {</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="comment">// Inner-most loop corresponds to the number of inputs in an input</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="comment">// group.</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < <a class="code" href="a02226.html#a23256c8836310012b8a80e61f9dd5a35">num_inputs_per_group_</a>; ++i) {</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  int8_t weight = 0;</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordflow">if</span> (output + j < num_out && input + i < num_in)</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  weight = w(output + j, input + i);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <a class="code" href="a02226.html#a6de2b2f60bec1231ebebee433ed900f7">shaped_w_</a>[shaped_index++] = weight;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  }</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  }</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  }</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="comment">// Append the bias weights for the register set.</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < num_outputs_per_register_set; ++j) {</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  int8_t weight = 0;</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keywordflow">if</span> (output + j < num_out) weight = w(output + j, num_in);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <a class="code" href="a02226.html#a6de2b2f60bec1231ebebee433ed900f7">shaped_w_</a>[shaped_index++] = weight;</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  }</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  output += num_outputs_per_register_set;</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  }</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  }</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> }</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> </div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> <span class="comment">// Computes matrix.vector v = Wu.</span></div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> <span class="comment">// u is of size W.dim2() - 1 and the output v is of size W.dim1().</span></div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> <span class="comment">// u is imagined to have an extra element at the end with value 1, to</span></div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> <span class="comment">// implement the bias, but it doesn't actually have it.</span></div><div class="line"><a name="l00096"></a><span class="lineno"><a class="line" href="a02226.html#a442dc784affc6b5b9c62d03997fee9e2"> 96</a></span> <span class="keywordtype">void</span> <a class="code" href="a02226.html#a442dc784affc6b5b9c62d03997fee9e2">IntSimdMatrix::MatrixDotVector</a>(<span class="keyword">const</span> <a class="code" href="a02222.html">GENERIC_2D_ARRAY<int8_t></a>& w,</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keyword">const</span> <a class="code" href="a02182.html">GenericVector<double></a>& scales,</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keyword">const</span> int8_t* u, <span class="keywordtype">double</span>* v)<span class="keyword"> const </span>{</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keywordtype">int</span> num_out = w.<a class="code" href="a02222.html#adfa4379cd2d6ba96123fd1b20be9e74d">dim1</a>();</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keywordtype">int</span> num_in = w.<a class="code" href="a02222.html#a112504ebf7329913119401c851a6b24b">dim2</a>() - 1;</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keywordflow">if</span> (<a class="code" href="a02226.html#a1fcea4ce2c16453174e3386ebd81fdf1">partial_funcs_</a>.empty()) {</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="comment">// Base implementation.</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < num_out; ++i) {</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keyword">const</span> int8_t* wi = w[i];</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keywordtype">int</span> total = 0;</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < num_in; ++j) total += wi[j] * u[j];</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="comment">// Add in the bias and correct for integer values.</span></div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  v[i] = (<span class="keyword">static_cast<</span><span class="keywordtype">double</span><span class="keyword">></span>(total) / INT8_MAX + wi[num_in]) * scales[i];</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  }</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keyword">const</span> int8_t* w_data = <a class="code" href="a02226.html#a6de2b2f60bec1231ebebee433ed900f7">shaped_w_</a>.data();</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keyword">const</span> <span class="keywordtype">double</span>* scales_data = &scales[0];</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="comment">// Each call to a partial_func_ produces group_size outputs, except the</span></div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="comment">// last one, which can produce less.</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="keywordtype">int</span> group_size = <a class="code" href="a02226.html#a8760aa1d64a18e46cc77698f2381177c">num_outputs_per_register_</a> * <a class="code" href="a02226.html#a6c071f8e8104b7f4ff744622fa21edc1">max_output_registers_</a>;</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keywordtype">int</span> rounded_num_in = <a class="code" href="a02226.html#acf9f1a5093e7fa9de9c107b543404d0f">Roundup</a>(num_in, <a class="code" href="a02226.html#a23256c8836310012b8a80e61f9dd5a35">num_inputs_per_group_</a>);</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keywordtype">int</span> rounded_num_out = <a class="code" href="a02226.html#a74a99972e32fe1659f839791bb7dde49">RoundOutputs</a>(num_out);</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keywordtype">int</span> output = 0;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> fn : <a class="code" href="a02226.html#a1fcea4ce2c16453174e3386ebd81fdf1">partial_funcs_</a>) {</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="comment">// The amount of w_data consumed by each call to fn.</span></div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keywordtype">int</span> w_step = (rounded_num_in + 1) * group_size;</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="comment">// Run with this group size, until it would produce too much output, then</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="comment">// switch to a smaller size.</span></div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keywordflow">for</span> (; output + group_size <= rounded_num_out; output += group_size) {</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  (*fn)(w_data, scales_data, u, rounded_num_in, num_out - output, v);</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  w_data += w_step;</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  scales_data += group_size;</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  v += group_size;</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  }</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  group_size /= 2;</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  }</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  }</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> }</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> </div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> } <span class="comment">// namespace tesseract</span></div><div class="ttc" id="a02226_html_a6c071f8e8104b7f4ff744622fa21edc1"><div class="ttname"><a href="a02226.html#a6c071f8e8104b7f4ff744622fa21edc1">tesseract::IntSimdMatrix::max_output_registers_</a></div><div class="ttdeci">int max_output_registers_</div><div class="ttdef"><b>Definition:</b> <a href="a00044_source.html#l00121">intsimdmatrix.h:121</a></div></div>
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