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<div class="title">BatchMatMulImpl.cpp</div> </div>
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<a href="_batch_mat_mul_impl_8cpp.xhtml">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">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "<a class="code" href="_batch_mat_mul_impl_8hpp.xhtml">BatchMatMulImpl.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <<a class="code" href="_workload_data_8hpp.xhtml">armnn/backends/WorkloadData.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_logging_8hpp.xhtml">armnn/Logging.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_permute_8hpp.xhtml">armnnUtils/Permute.hpp</a>></span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> </div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> {</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"><a class="line" href="classarmnn_1_1_batch_mat_mul.xhtml#a7c4e7bac563e596b1a775dd7e19b9e7f"> 15</a></span> <a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#a7c4e7bac563e596b1a775dd7e19b9e7f">BatchMatMul::BatchMatMul</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml">BatchMatMulDescriptor</a>& params,</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputXInfo,</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputYInfo,</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputInfo,</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  <a class="code" href="classarmnn_1_1_decoder.xhtml">Decoder<float></a>& inputXDecoder,</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  <a class="code" href="classarmnn_1_1_decoder.xhtml">Decoder<float></a>& inputYDecoder,</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  <a class="code" href="classarmnn_1_1_encoder.xhtml">Encoder<float></a>& outputEncoder)</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  : params(params),</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  inputXInfo(inputXInfo),</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  inputYInfo(inputYInfo),</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  outputInfo(outputInfo),</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  inputXDecoder(inputXDecoder),</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  inputYDecoder(inputYDecoder),</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  outputEncoder(outputEncoder)</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> {</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  inputXData = this->inputXDecoder.<a class="code" href="classarmnn_1_1_decoder.xhtml#aafe0168dd5ece89e7c62e8d83a4e57cd">DecodeTensor</a>(inputXInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  inputYData = this->inputYDecoder.<a class="code" href="classarmnn_1_1_decoder.xhtml#aafe0168dd5ece89e7c62e8d83a4e57cd">DecodeTensor</a>(inputYInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="comment">// At this point, we don't touch the input decoders - just the resultant vectors</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  ApplyParams();</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> </div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  ApplyBatchMatMul();</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> }</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> </div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="keywordtype">void</span> BatchMatMul::ApplyBatchMatMul()</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="keyword">auto</span> axesXToMul = <a class="code" href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#adea0557f6519a2d7f1f1424e3de0fc4a">BatchMatMulDescriptor::GetAxesToMul</a>(params.<a class="code" href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#aedca000a005e091c23191e82d7e81b1d">m_DataLayoutX</a>,</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  inputXInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keyword">auto</span> axesYToMul = <a class="code" href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#adea0557f6519a2d7f1f1424e3de0fc4a">BatchMatMulDescriptor::GetAxesToMul</a>(params.<a class="code" href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#aaf7828880989b4b9378d3e86aa6dc843">m_DataLayoutY</a>,</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  inputYInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  AdjustAxesToMulForUnequalRanks(axesXToMul, axesYToMul);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputXColDim = axesXToMul.second;</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputYRowDim = axesYToMul.first;</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputYRowSize = inputYInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[inputYRowDim];</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keyword">auto</span> batchMatMulOperation = [&](<span class="keyword">const</span> std::vector<unsigned int>& curIdx)</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  {</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordtype">float</span> sum = 0.0f;</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> </div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="comment">// InputYRowSize is synonymous with inputXColSize</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputYRowIdx = 0; inputYRowIdx < inputYRowSize; inputYRowIdx++) {</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keyword">auto</span> xIdx = curIdx;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  xIdx[inputXColDim] = inputYRowIdx;</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keyword">auto</span> yIdx = curIdx;</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  yIdx[inputYRowDim] = inputYRowIdx;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  sum += (GetValueAt(DataSlot::InputX, xIdx) * GetValueAt(DataSlot::InputY, yIdx));</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  }</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  SetValueAt(sum, <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">DataSlot::Output</a>, curIdx);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  };</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> </div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keyword">auto</span> startIdx = std::vector<unsigned int>(outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(), 0);</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  RecurseTensor(outputInfo,</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  batchMatMulOperation,</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  startIdx,</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  0);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> }</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> </div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> <span class="keywordtype">void</span> BatchMatMul::ApplyParams()</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>  <span class="keywordflow">if</span>(params.<a class="code" href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#acb441bb8db19bcce78d15cdd8ceb5ea0">m_TransposeX</a>)</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>  Transpose(DataSlot::InputX);</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  }</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(params.<a class="code" href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#a0cf8306be7d301de0f095fff9901a525">m_AdjointX</a>)</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  {</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  Adjoint(DataSlot::InputX);</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>  <span class="keywordflow">if</span>(params.<a class="code" href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#a112b466e5d2ab9d1887178adbe3afa1c">m_TransposeY</a>)</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>  Transpose(DataSlot::InputY);</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(params.<a class="code" href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#ad945fc98770356dd886a68e98a52e26b">m_AdjointY</a>)</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  {</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  Adjoint(DataSlot::InputY);</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  }</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> }</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> </div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> <span class="keywordtype">void</span> BatchMatMul::Transpose(DataSlot type)</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> {</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="comment">// AKA the permute of the tensor</span></div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="comment">// This modifies the tensor's info.</span></div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> </div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keywordflow">switch</span>(type)</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  {</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keywordflow">case</span> DataSlot::InputX:</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  {</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="keyword">auto</span> permuteVec = <a class="code" href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#a85e74c2aeaf6fc124e9582329a82d72b">BatchMatMulDescriptor::GetPermuteVec</a>(params.<a class="code" href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#aedca000a005e091c23191e82d7e81b1d">m_DataLayoutX</a>,</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  inputXInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  inputXInfo = <a class="code" href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a>(inputXInfo, permuteVec);</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  std::vector<float> temp(inputXData.size());</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7">armnnUtils::Permute</a>(inputXInfo.GetShape(),</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  permuteVec,</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  inputXData.data(),</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  temp.data(),</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="keyword">sizeof</span>(float));</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  inputXData = temp;</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  }</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keywordflow">case</span> DataSlot::InputY:</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  {</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keyword">auto</span> permuteVec = <a class="code" href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#a85e74c2aeaf6fc124e9582329a82d72b">BatchMatMulDescriptor::GetPermuteVec</a>(params.<a class="code" href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#aaf7828880989b4b9378d3e86aa6dc843">m_DataLayoutY</a>,</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  inputYInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  inputYInfo = <a class="code" href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a>(inputYInfo, permuteVec);</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  std::vector<float> temp(inputYData.size());</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7">armnnUtils::Permute</a>(inputYInfo.GetShape(),</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  permuteVec,</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  inputYData.data(),</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  temp.data(),</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keyword">sizeof</span>(float));</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  inputYData = temp;</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keywordflow">break</span>;</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">DataSlot::Output</a>: <span class="comment">// We needn't transpose the output tensor</span></div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  }</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> }</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> </div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> <span class="keywordtype">void</span> BatchMatMul::Adjoint(DataSlot type)</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> {</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="comment">// Finding the adjoint of a square matrix:</span></div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="comment">// Calculate the cofactor of each element (using Gauss elimination here)</span></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="comment">// Apply a transpose to it (this also modifies the tensor's info)</span></div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> </div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo = (type == DataSlot::InputX) ? inputXInfo : inputYInfo;</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& dataLayout = (type == DataSlot::InputX) ? params.<a class="code" href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#aedca000a005e091c23191e82d7e81b1d">m_DataLayoutX</a> : params.<a class="code" href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#aaf7828880989b4b9378d3e86aa6dc843">m_DataLayoutY</a>;</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keyword">const</span> <span class="keyword">auto</span> axesToAdjoint = <a class="code" href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#adea0557f6519a2d7f1f1424e3de0fc4a">BatchMatMulDescriptor::GetAxesToMul</a>(dataLayout,inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> </div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[axesToAdjoint.first] == inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[axesToAdjoint.second]);</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="comment">// We grab a copy of the tensor data to prevent overwriting</span></div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  std::vector<float> inputDataClone = (type == DataSlot::InputX) ? inputXData : inputYData;</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> </div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="comment">// The sub-matrix is the resultant matrix when the row and column of the current index is removed</span></div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> subMatAxisSize = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[axesToAdjoint.first] - 1;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  std::vector<std::vector<float>> subMat(subMatAxisSize,</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  std::vector<float>(subMatAxisSize));</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> </div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="comment">// Lambdas for each sub-step of the cofactor operation</span></div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keyword">auto</span> almostEquals = [&](<span class="keyword">const</span> <span class="keywordtype">float</span>& a, <span class="keyword">const</span> <span class="keywordtype">float</span>& b, <span class="keywordtype">float</span> unitsInLastPlace = 2.0f)</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  {</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keywordtype">float</span> diff = std::fabs(a-b);</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keywordtype">float</span> bound = diff * std::numeric_limits<float>::epsilon() * unitsInLastPlace;</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keywordflow">return</span> (diff <= bound) || (diff < std::numeric_limits<float>::min());</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  };</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> </div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="keywordtype">float</span> swapMultiplier = std::numeric_limits<float>::max();</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="keyword">auto</span> swapRows = [&](<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> rowIdxA, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> rowIdxB)</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  {</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="comment">// Every row swap flips this around by the negative (set to 1 at the beginning of each cofactor op run)</span></div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> colIdx = 0; colIdx < subMatAxisSize; colIdx++)</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  {</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keywordtype">float</span> tmp = subMat[rowIdxA][colIdx];</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  subMat[rowIdxA][colIdx] = subMat[rowIdxB][colIdx];</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  subMat[rowIdxB][colIdx] = tmp;</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  }</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  swapMultiplier *= -1.0f;</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  };</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> </div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keyword">auto</span> findNextValidPivotRowIdx = [&](<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> colIdx)</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  {</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> result = std::numeric_limits<unsigned int>::max();</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> </div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="comment">// The original diagonal has been checked and is invalid</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> rowIdx = colIdx+1; rowIdx < subMatAxisSize; rowIdx++)</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  {</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keywordflow">if</span>(!almostEquals(subMat[rowIdx][colIdx], 0.0f))</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  {</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  result = rowIdx;</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  }</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  }</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  };</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> </div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="keyword">auto</span> eliminate = [&](<span class="keyword">const</span> <span class="keywordtype">float</span>& pivot, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pivotPos)</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  {</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> rowIdx = pivotPos+1; rowIdx < subMatAxisSize; rowIdx++)</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  {</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keywordtype">float</span> multiplierNumerator = subMat[rowIdx][pivotPos];</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="keywordflow">if</span>(almostEquals(multiplierNumerator, 0.0f))</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  {</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="keywordflow">continue</span>;</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  }</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keywordtype">float</span> multiplier = multiplierNumerator / pivot; <span class="comment">// Susceptible to floating point inaccuracies</span></div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="comment">// Hence the almostEquals usage to counteract this</span></div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> colIdx = pivotPos; colIdx < subMatAxisSize; colIdx++)</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  {</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="comment">// We start at col=pivotPos as we have assumed that all elements</span></div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="comment">// to our left have been eliminated to zero already</span></div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> </div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="comment">// We subtract based on the element directly above us in our pivot row</span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  subMat[rowIdx][colIdx] -= multiplier * subMat[pivotPos][colIdx];</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  }</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  }</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  };</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span> </div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keyword">auto</span> cofactorOperation = [&](<span class="keyword">const</span> std::vector<unsigned int>& curIdx)</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  {</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keyword">auto</span> row = curIdx[axesToAdjoint.first];</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keyword">auto</span> col = curIdx[axesToAdjoint.second];</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> </div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="keywordtype">float</span> minorMultiplier = <span class="keyword">static_cast<</span><span class="keywordtype">float</span><span class="keyword">></span>(std::pow(-1, (row + 1 + col + 1)));</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span> </div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> subRow = 0; subRow < subMatAxisSize; subRow++)</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  {</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> subCol = 0; subCol < subMatAxisSize; subCol++)</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  {</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outerRow = (subRow >= row)?subRow + 1:subRow;</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outerCol = (subCol >= col)?subCol + 1:subCol;</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keyword">auto</span> cloneIdx = curIdx;</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  cloneIdx[axesToAdjoint.first] = outerRow;</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  cloneIdx[axesToAdjoint.second] = outerCol;</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  subMat[subRow][subCol] = GetValueAt(type,cloneIdx,inputDataClone);</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  }</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  }</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> </div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="keywordtype">float</span> determinant = 1.0f;</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> </div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="comment">// Cover the edge cases and simple base cases before resorting to Gauss elimination for larger matrices</span></div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keywordflow">switch</span>(subMatAxisSize)</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  {</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keywordflow">case</span> 0:</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  {</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  determinant = GetValueAt(type, curIdx, inputDataClone);</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  }</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keywordflow">case</span> 1:</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  {</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="comment">// If the resultant sub-matrix is just one element - that's the determinant</span></div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  determinant = subMat[0][0];</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  }</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="keywordflow">case</span> 2:</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  {</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="comment">// For a 2x2 sub-matrix, the determinant is just a*d-b*c</span></div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  determinant = subMat[0][0] * subMat[1][1] -</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  subMat[0][1] * subMat[1][0];</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  }</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  {</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="comment">// Gaussian elimination to find the determinant of this sub-matrix</span></div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  swapMultiplier = 1.0f;</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="comment">// March diagonally down the pivots and if it's invalid (a zero), swap the row with the</span></div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="comment">// nearest non-zero down within the column</span></div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pivotRow = 0, pivotCol = 0;</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  pivotRow < subMatAxisSize;</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  pivotRow++, pivotCol++)</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  {</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  <span class="keywordtype">float</span>& pivot = subMat[pivotRow][pivotCol];</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> </div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="keywordflow">if</span>(almostEquals(pivot, 0.0f))</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  {</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> nextValidPivotRowIdx = findNextValidPivotRowIdx(pivotCol);</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="keywordflow">if</span>(nextValidPivotRowIdx == std::numeric_limits<unsigned int>::max())</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  {</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="comment">// No valid pivot down this column, which means that this pivot remains a zero.</span></div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="comment">// This results in the determinant for this entire sub-matrix to just be zero.</span></div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  determinant = 0.0f;</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  }</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  swapRows(pivotRow, nextValidPivotRowIdx);</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  }</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  determinant *= pivot;</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="comment">// The actual elimination bit (which will update/propagate to the pivots down the line)</span></div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  eliminate(pivot, pivotRow); <span class="comment">// Synonymous with pivotCol</span></div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  }</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span> </div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  determinant *= swapMultiplier;</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  }</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  }</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="keywordtype">float</span> cofactor = minorMultiplier * determinant;</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  SetValueAt(cofactor, type, curIdx);</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  };</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span> </div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <span class="keyword">auto</span> startIdx = std::vector<unsigned int>(inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(), 0);</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  RecurseTensor(inputInfo,</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  cofactorOperation,</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  startIdx,</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  0);</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span> </div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  Transpose(type);</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> }</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span> </div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span> <span class="keywordtype">void</span> BatchMatMul::RecurseTensor(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensorInfo,</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keyword">const</span> std::function<<span class="keywordtype">void</span>(<span class="keyword">const</span> std::vector<unsigned int>&)>& operation,</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  std::vector<unsigned int>& curIdx,</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> curDim)</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span> {</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="keywordflow">if</span>(!(curDim < tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()))</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  {</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="comment">// We're at the leaf level of this call tree, so we operate here (each leaf is a data point)</span></div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  operation(curIdx);</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  }</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span> </div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[curDim]; i++)</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  {</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  curIdx[curDim] = i;</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  RecurseTensor(tensorInfo,</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  operation,</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  curIdx,</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  curDim + 1);</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  }</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span> }</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> </div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span> <span class="keywordtype">void</span> BatchMatMul::AdjustAxesToMulForUnequalRanks(std::pair<unsigned int, unsigned int>& axesXToMul,</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  std::pair<unsigned int, unsigned int>& axesYToMul)</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span> {</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  <span class="keywordtype">int</span> rankDiff = <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(inputXInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()) -</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  static_cast<int>(inputYInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>());</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="keywordflow">if</span>(rankDiff == 0)</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  {</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  }</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(rankDiff < 0)</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  {</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="comment">// Y is the larger one</span></div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  axesXToMul.first += <span class="keyword">static_cast<</span>std::make_unsigned<unsigned int>::type<span class="keyword">></span>(std::abs(rankDiff));</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  axesXToMul.second += <span class="keyword">static_cast<</span>std::make_unsigned<unsigned int>::type<span class="keyword">></span>(std::abs(rankDiff));</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  }</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(rankDiff > 0)</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  {</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <span class="comment">// X is the larger one</span></div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  axesYToMul.first += <span class="keyword">static_cast<</span>std::make_unsigned<unsigned int>::type<span class="keyword">></span>(std::abs(rankDiff));</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  axesYToMul.second += <span class="keyword">static_cast<</span>std::make_unsigned<unsigned int>::type<span class="keyword">></span>(std::abs(rankDiff));</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  }</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span> }</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span> </div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span> <span class="keywordtype">float</span> BatchMatMul::GetValueAt(DataSlot type, std::vector<unsigned int> idx, <span class="keyword">const</span> std::vector<float>& customData)</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span> {</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="comment">// This gets the data from the input vector that we have, Not the decoder</span></div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="comment">// But for the output, it is operating on the encoder itself</span></div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span> </div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  AdjustToSafeIdx(type, idx);</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> flatIdx = CalcFlatIdx(type, idx);</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <span class="keywordtype">float</span> value = 0.0f;</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="keywordflow">switch</span>(type)</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  {</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <span class="keywordflow">case</span> DataSlot::InputX:</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  value = customData.empty() ? inputXData[flatIdx] : customData[flatIdx];</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="keywordflow">case</span> DataSlot::InputY:</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  value = customData.empty() ? inputYData[flatIdx] : customData[flatIdx];</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">DataSlot::Output</a>:</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  outputEncoder[flatIdx];</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  value = outputEncoder.<a class="code" href="classarmnn_1_1_encoder.xhtml#ac729108381e2340bea12877971713ecb">Get</a>();</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  }</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span> </div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="keywordflow">return</span> value;</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span> }</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span> </div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span> <span class="keywordtype">void</span> BatchMatMul::SetValueAt(<span class="keywordtype">float</span> value, DataSlot type, std::vector<unsigned int> idx)</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span> {</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  AdjustToSafeIdx(type, idx);</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> flatIdx = CalcFlatIdx(type, idx);</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="keywordflow">switch</span>(type)</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  {</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="keywordflow">case</span> DataSlot::InputX:</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  inputXData[flatIdx] = value;</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <span class="keywordflow">case</span> DataSlot::InputY:</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  inputYData[flatIdx] = value;</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">DataSlot::Output</a>:</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  outputEncoder[flatIdx];</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  outputEncoder.<a class="code" href="classarmnn_1_1_encoder.xhtml#ae3b62b846a9c239f332830b9e36030eb">Set</a>(value);</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  }</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span> }</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span> </div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span> <span class="keywordtype">void</span> BatchMatMul::AdjustToSafeIdx(DataSlot type, std::vector<unsigned int>& idx)</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span> {</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dim = 0; dim < idx.size(); dim++)</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  {</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="keywordflow">switch</span>(type)</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  {</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="keywordflow">case</span> DataSlot::InputX:</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  {</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="keyword">auto</span> xRank = inputXInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keyword">auto</span> xDiff = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - xRank;</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="keywordflow">if</span> (dim < xDiff ||</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  idx[dim] > inputXInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[dim-xDiff]-1)</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  {</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  idx[dim] = 0; <span class="comment">// Broadcasting</span></div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  }</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  }</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <span class="keywordflow">case</span> DataSlot::InputY:</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  {</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <span class="keyword">auto</span> yRank = inputYInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="keyword">auto</span> yDiff = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - yRank;</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="keywordflow">if</span> (dim < yDiff ||</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  idx[dim] > inputYInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[dim-yDiff]-1)</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  {</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  idx[dim] = 0;</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  }</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  }</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">DataSlot::Output</a>:</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  {</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <span class="comment">// Our indices are based off the output</span></div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  }</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  }</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  }</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span> }</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span> </div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> BatchMatMul::CalcFlatIdx(DataSlot type, <span class="keyword">const</span> std::vector<unsigned int>& idx)</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span> {</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> result = idx[idx.size()-1];</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimMultiplier = 1;</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> offset;</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span> </div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <span class="comment">// -2 because final dim is already accounted for in the multiplier (last dim is just a multiplier of 1x)</span></div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = static_cast<unsigned int>(idx.size()-2); <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(i) >= 0; i--)</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  {</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <span class="keywordflow">switch</span>(type)</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  {</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <span class="keywordflow">case</span> DataSlot::InputX:</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  offset = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - inputXInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  dimMultiplier *= inputXInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i + 1 - offset];</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <span class="keywordflow">case</span> DataSlot::InputY:</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  offset = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - inputYInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  dimMultiplier *= inputYInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i + 1 - offset];</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">DataSlot::Output</a>:</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  dimMultiplier *= outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i+1];</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  }</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  result += (idx[i] * dimMultiplier);</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  }</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span> }</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span> </div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span> } <span class="comment">// namespace armnn</span></div><div class="ttc" id="structarmnn_1_1_batch_mat_mul_descriptor_xhtml_ad945fc98770356dd886a68e98a52e26b"><div class="ttname"><a href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#ad945fc98770356dd886a68e98a52e26b">armnn::BatchMatMulDescriptor::m_AdjointY</a></div><div class="ttdeci">bool m_AdjointY</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01551">Descriptors.hpp:1551</a></div></div>
<div class="ttc" id="_workload_data_8hpp_xhtml"><div class="ttname"><a href="_workload_data_8hpp.xhtml">WorkloadData.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_mat_mul_descriptor_xhtml_aaf7828880989b4b9378d3e86aa6dc843"><div class="ttname"><a href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#aaf7828880989b4b9378d3e86aa6dc843">armnn::BatchMatMulDescriptor::m_DataLayoutY</a></div><div class="ttdeci">DataLayout m_DataLayoutY</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01555">Descriptors.hpp:1555</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00191">Tensor.hpp:191</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_mat_mul_descriptor_xhtml_acb441bb8db19bcce78d15cdd8ceb5ea0"><div class="ttname"><a href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#acb441bb8db19bcce78d15cdd8ceb5ea0">armnn::BatchMatMulDescriptor::m_TransposeX</a></div><div class="ttdeci">bool m_TransposeX</div><div class="ttdoc">Transpose the slices of each input tensor Transpose and Adjoint can not both be set to true for the s...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01545">Descriptors.hpp:1545</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a></div></div>
<div class="ttc" id="classarmnn_1_1_decoder_xhtml_aafe0168dd5ece89e7c62e8d83a4e57cd"><div class="ttname"><a href="classarmnn_1_1_decoder.xhtml#aafe0168dd5ece89e7c62e8d83a4e57cd">armnn::Decoder::DecodeTensor</a></div><div class="ttdeci">virtual std::vector< float > DecodeTensor(const TensorShape &tensorShape, bool isDepthwise=false)=0</div></div>
<div class="ttc" id="classarmnn_1_1_encoder_xhtml_ae3b62b846a9c239f332830b9e36030eb"><div class="ttname"><a href="classarmnn_1_1_encoder.xhtml#ae3b62b846a9c239f332830b9e36030eb">armnn::Encoder::Set</a></div><div class="ttdeci">virtual void Set(IType right)=0</div></div>
<div class="ttc" id="structarmnn_1_1_batch_mat_mul_descriptor_xhtml_a0cf8306be7d301de0f095fff9901a525"><div class="ttname"><a href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#a0cf8306be7d301de0f095fff9901a525">armnn::BatchMatMulDescriptor::m_AdjointX</a></div><div class="ttdeci">bool m_AdjointX</div><div class="ttdoc">Adjoint the slices of each input tensor Transpose and Adjoint can not both be set to true for the sam...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01550">Descriptors.hpp:1550</a></div></div>
<div class="ttc" id="classarmnn_1_1_encoder_xhtml"><div class="ttname"><a href="classarmnn_1_1_encoder.xhtml">armnn::Encoder< float ></a></div></div>
<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__quick__start_8dox_source.xhtml#l00006">01_00_quick_start.dox:6</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_mat_mul_descriptor_xhtml_a85e74c2aeaf6fc124e9582329a82d72b"><div class="ttname"><a href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#a85e74c2aeaf6fc124e9582329a82d72b">armnn::BatchMatMulDescriptor::GetPermuteVec</a></div><div class="ttdeci">static PermutationVector GetPermuteVec(DataLayout dataLayout, const TensorShape &tensorShape)</div><div class="ttdoc">Static helper to get the axes which will be transposed. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00514">Descriptors.cpp:514</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_mat_mul_descriptor_xhtml_aedca000a005e091c23191e82d7e81b1d"><div class="ttname"><a href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#aedca000a005e091c23191e82d7e81b1d">armnn::BatchMatMulDescriptor::m_DataLayoutX</a></div><div class="ttdeci">DataLayout m_DataLayoutX</div><div class="ttdoc">Data layout of each input tensor, such as NHWC/NDHWC (leave as default for arbitrary layout) ...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01554">Descriptors.hpp:1554</a></div></div>
<div class="ttc" id="_permute_8hpp_xhtml"><div class="ttname"><a href="_permute_8hpp.xhtml">Permute.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7">armnn::LayerType::Permute</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_mat_mul_descriptor_xhtml_adea0557f6519a2d7f1f1424e3de0fc4a"><div class="ttname"><a href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#adea0557f6519a2d7f1f1424e3de0fc4a">armnn::BatchMatMulDescriptor::GetAxesToMul</a></div><div class="ttdeci">static std::pair< std::pair< unsigned int, unsigned int >, std::pair< unsigned int, unsigned int > > GetAxesToMul(const BatchMatMulDescriptor &desc, const TensorShape &tensorXShape, const TensorShape &tensorYShape)</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00459">Descriptors.cpp:459</a></div></div>
<div class="ttc" id="_assert_8hpp_xhtml_a5698be69cbd5dfe6c28fcd9867e8cbed"><div class="ttname"><a href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a></div><div class="ttdeci">#define ARMNN_ASSERT(COND)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.xhtml#l00014">Assert.hpp:14</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_mat_mul_descriptor_xhtml_a112b466e5d2ab9d1887178adbe3afa1c"><div class="ttname"><a href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#a112b466e5d2ab9d1887178adbe3afa1c">armnn::BatchMatMulDescriptor::m_TransposeY</a></div><div class="ttdeci">bool m_TransposeY</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01546">Descriptors.hpp:1546</a></div></div>
<div class="ttc" id="classarmnn_1_1_batch_mat_mul_xhtml_a7c4e7bac563e596b1a775dd7e19b9e7f"><div class="ttname"><a href="classarmnn_1_1_batch_mat_mul.xhtml#a7c4e7bac563e596b1a775dd7e19b9e7f">armnn::BatchMatMul::BatchMatMul</a></div><div class="ttdeci">BatchMatMul(const BatchMatMulDescriptor &params, const TensorInfo &inputXInfo, const TensorInfo &inputYInfo, const TensorInfo &outputInfo, Decoder< float > &inputXDecoder, Decoder< float > &inputYDecoder, Encoder< float > &outputEncoder)</div><div class="ttdef"><b>Definition:</b> <a href="_batch_mat_mul_impl_8cpp_source.xhtml#l00015">BatchMatMulImpl.cpp:15</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_mat_mul_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml">armnn::BatchMatMulDescriptor</a></div><div class="ttdoc">A BatchMatMulDescriptor for the BatchMatMul operator. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01517">Descriptors.hpp:1517</a></div></div>
<div class="ttc" id="_logging_8hpp_xhtml"><div class="ttname"><a href="_logging_8hpp.xhtml">Logging.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_encoder_xhtml_ac729108381e2340bea12877971713ecb"><div class="ttname"><a href="classarmnn_1_1_encoder.xhtml#ac729108381e2340bea12877971713ecb">armnn::Encoder::Get</a></div><div class="ttdeci">virtual IType Get() const =0</div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorInfo::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00195">Tensor.hpp:195</a></div></div>
<div class="ttc" id="namespacearmnn_utils_xhtml_abeaf4f6785039866fd075f4569ba8e84"><div class="ttname"><a href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a></div><div class="ttdeci">armnn::TensorShape Permuted(const armnn::TensorShape &srcShape, const armnn::PermutationVector &mappings)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.xhtml#l00098">Permute.cpp:98</a></div></div>
<div class="ttc" id="_batch_mat_mul_impl_8hpp_xhtml"><div class="ttname"><a href="_batch_mat_mul_impl_8hpp.xhtml">BatchMatMulImpl.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_decoder_xhtml"><div class="ttname"><a href="classarmnn_1_1_decoder.xhtml">armnn::Decoder< float ></a></div></div>
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