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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="include_2armnn_2backends_2_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> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> {</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> </div><div class="line"><a name="l00014"></a><span class="lineno"><a class="line" href="classarmnn_1_1_batch_mat_mul.xhtml#a188b8d270e02af10e876e60e54e7587c"> 14</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#a188b8d270e02af10e876e60e54e7587c">BatchMatMul::BatchMatMulImpl</a>()</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> {</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  inputXData = 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="l00017"></a><span class="lineno"> 17</span>  inputYData = 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="l00018"></a><span class="lineno"> 18</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="l00019"></a><span class="lineno"> 19</span> </div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  <span class="comment">// Pre-transpose and pre-adjoint if their vectors aren't empty</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  <span class="comment">// and also DataLayouts which may change with permutations/adjoints</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> </div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <span class="comment">// Todo: Have you updated input validation and inferred output shapes to accommodate for these pre-permutes?</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> </div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keyword">auto</span> idx = 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="l00026"></a><span class="lineno"> 26</span>  <a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#af989fe83accce35c5072407e82a7063e">RecurseBMM</a>(idx, 0);</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> </div><div class="line"><a name="l00029"></a><span class="lineno"><a class="line" href="classarmnn_1_1_batch_mat_mul.xhtml#af989fe83accce35c5072407e82a7063e"> 29</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#af989fe83accce35c5072407e82a7063e">BatchMatMul::RecurseBMM</a>(std::vector<unsigned int>& curIdx, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> curDim)</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> {</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="comment">// We're working off of the indexes of the output tensor (the max possible shape)</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> </div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordflow">if</span>(!(curDim < outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()))</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  {</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</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="l00036"></a><span class="lineno"> 36</span> </div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keyword">auto</span> axesToMul = <a class="code" href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml#adea0557f6519a2d7f1f1424e3de0fc4a">BatchMatMulDescriptor::GetAxesToMul</a>(params,</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  inputXInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(),</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  inputYInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#ae15c1a71e9465ac3698f32a572746804">AdjustAxesToMulForUnequalRanks</a>(axesToMul);</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> </div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputXColDim = axesToMul.first.second;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputYRowDim = axesToMul.second.first;</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> </div><div class="line"><a name="l00045"></a><span class="lineno"> 45</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="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keywordtype">float</span> sum = 0.0f;</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="comment">// You could also use inputXColSize</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</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="l00051"></a><span class="lineno"> 51</span>  <span class="keyword">auto</span> xIdx = curIdx;</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  xIdx[inputXColDim] = inputYRowIdx;</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="keyword">auto</span> yIdx = curIdx;</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  yIdx[inputYRowDim] = inputYRowIdx;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> </div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  sum += (<a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#a47068479ce7cab2a3d7c3e1ae53cf760">GetValueAt</a>(DataSlot::InputX, xIdx)</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  * <a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#a47068479ce7cab2a3d7c3e1ae53cf760">GetValueAt</a>(DataSlot::InputY, yIdx));</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  }</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>  <a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#a23985d27f79a44c973bd1e0c2633c191">SetValueAt</a>(sum, <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">DataSlot::Output</a>, curIdx);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> </div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  }</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>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[curDim]; i++)</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  {</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  curIdx[curDim] = i;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#af989fe83accce35c5072407e82a7063e">RecurseBMM</a>(curIdx, curDim+1);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  }</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> }</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div><div class="line"><a name="l00073"></a><span class="lineno"><a class="line" href="classarmnn_1_1_batch_mat_mul.xhtml#ae15c1a71e9465ac3698f32a572746804"> 73</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#ae15c1a71e9465ac3698f32a572746804">BatchMatMul::AdjustAxesToMulForUnequalRanks</a>(</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  std::pair<std::pair<unsigned int, unsigned int>, std::pair<unsigned int, unsigned int>>& axesToMul)</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>  <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="l00077"></a><span class="lineno"> 77</span>  static_cast<int>(inputYInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>());</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keywordflow">if</span>(rankDiff == 0)</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>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  }</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(rankDiff < 0)</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  {</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="comment">// Y is the larger one</span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  axesToMul.first.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="l00086"></a><span class="lineno"> 86</span>  axesToMul.first.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="l00087"></a><span class="lineno"> 87</span>  }</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(rankDiff > 0)</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>  <span class="comment">// X is the larger one</span></div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  axesToMul.second.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="l00092"></a><span class="lineno"> 92</span>  axesToMul.second.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="l00093"></a><span class="lineno"> 93</span>  }</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"><a class="line" href="classarmnn_1_1_batch_mat_mul.xhtml#a47068479ce7cab2a3d7c3e1ae53cf760"> 96</a></span> <span class="keywordtype">float</span> <a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#a47068479ce7cab2a3d7c3e1ae53cf760">BatchMatMul::GetValueAt</a>(<a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#a34243d3189830ed8c4d5cb077e68ccb3">DataSlot</a> type, std::vector<unsigned int> idx)</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> {</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</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="l00099"></a><span class="lineno"> 99</span>  <span class="comment">// But for the output, it is operating on the encoder itself</span></div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> </div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#a0664b61334b45316933938fa4514a387">AdjustToSafeIdx</a>(type, idx);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> flatIdx = <a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#af390ba429c0633b64e71f4228bdbbadf">CalcFlatIdx</a>(type, idx);</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keywordtype">float</span> value = 0.0f;</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keywordflow">switch</span>(type)</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  {</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="keywordflow">case</span> DataSlot::InputX:</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  value = inputXData[flatIdx];</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keywordflow">case</span> DataSlot::InputY:</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  value = inputYData[flatIdx];</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">DataSlot::Output</a>:</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  outputEncoder[flatIdx];</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  value = outputEncoder.<a class="code" href="classarmnn_1_1_encoder.xhtml#ac729108381e2340bea12877971713ecb">Get</a>();</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>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keywordflow">break</span>;</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> </div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keywordflow">return</span> value;</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> }</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> </div><div class="line"><a name="l00124"></a><span class="lineno"><a class="line" href="classarmnn_1_1_batch_mat_mul.xhtml#a23985d27f79a44c973bd1e0c2633c191"> 124</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#a23985d27f79a44c973bd1e0c2633c191">BatchMatMul::SetValueAt</a>(<span class="keywordtype">float</span> value, <a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#a34243d3189830ed8c4d5cb077e68ccb3">DataSlot</a> type, std::vector<unsigned int> idx)</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> {</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#a0664b61334b45316933938fa4514a387">AdjustToSafeIdx</a>(type, idx);</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> flatIdx = <a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#af390ba429c0633b64e71f4228bdbbadf">CalcFlatIdx</a>(type, idx);</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>  <span class="keywordflow">switch</span>(type)</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> DataSlot::InputX:</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  inputXData[flatIdx] = value;</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>  <span class="keywordflow">case</span> DataSlot::InputY:</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  inputYData[flatIdx] = value;</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">DataSlot::Output</a>:</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  outputEncoder[flatIdx];</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  outputEncoder.<a class="code" href="classarmnn_1_1_encoder.xhtml#ae3b62b846a9c239f332830b9e36030eb">Set</a>(value);</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  }</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> }</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> </div><div class="line"><a name="l00147"></a><span class="lineno"><a class="line" href="classarmnn_1_1_batch_mat_mul.xhtml#a0664b61334b45316933938fa4514a387"> 147</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#a0664b61334b45316933938fa4514a387">BatchMatMul::AdjustToSafeIdx</a>(<a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#a34243d3189830ed8c4d5cb077e68ccb3">DataSlot</a> type, std::vector<unsigned int>& idx)</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> {</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</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="l00150"></a><span class="lineno"> 150</span>  {</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordflow">switch</span>(type)</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  {</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keywordflow">case</span> DataSlot::InputX:</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  {</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</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="l00156"></a><span class="lineno"> 156</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="l00157"></a><span class="lineno"> 157</span>  <span class="keywordflow">if</span> (dim < xDiff ||</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</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="l00159"></a><span class="lineno"> 159</span>  {</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  idx[dim] = 0; <span class="comment">// Broadcasting</span></div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  }</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keywordflow">break</span>;</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>  <span class="keywordflow">case</span> DataSlot::InputY:</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  {</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</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="l00167"></a><span class="lineno"> 167</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="l00168"></a><span class="lineno"> 168</span>  <span class="keywordflow">if</span> (dim < yDiff ||</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</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="l00170"></a><span class="lineno"> 170</span>  {</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  idx[dim] = 0;</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  }</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keywordflow">break</span>;</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">DataSlot::Output</a>:</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>  <span class="comment">// Our indices are based off the output</span></div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordflow">break</span>;</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="keywordflow">default</span>:</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  }</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  }</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> </div><div class="line"><a name="l00186"></a><span class="lineno"><a class="line" href="classarmnn_1_1_batch_mat_mul.xhtml#af390ba429c0633b64e71f4228bdbbadf"> 186</a></span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#af390ba429c0633b64e71f4228bdbbadf">BatchMatMul::CalcFlatIdx</a>(<a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#a34243d3189830ed8c4d5cb077e68ccb3">DataSlot</a> type, <span class="keyword">const</span> std::vector<unsigned int>& idx)</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> {</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> result = idx[idx.size()-1];</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>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimMultiplier = 1;</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> </div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> offset;</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="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="l00195"></a><span class="lineno"> 195</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="l00196"></a><span class="lineno"> 196</span>  {</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keywordflow">switch</span>(type)</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  {</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="keywordflow">case</span> DataSlot::InputX:</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</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="l00201"></a><span class="lineno"> 201</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="l00202"></a><span class="lineno"> 202</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keywordflow">case</span> DataSlot::InputY:</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</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="l00205"></a><span class="lineno"> 205</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="l00206"></a><span class="lineno"> 206</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">DataSlot::Output</a>:</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  dimMultiplier *= outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i+1];</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keywordflow">break</span>;</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>  result += (idx[i] * dimMultiplier);</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>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span> }</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">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00219"></a><span class="lineno"><a class="line" href="classarmnn_1_1_batch_mat_mul.xhtml#a139ced0af01e092a71f47d443ef75d74"> 219</a></span> std::string <a class="code" href="classarmnn_1_1_batch_mat_mul.xhtml#a139ced0af01e092a71f47d443ef75d74">BatchMatMul::StringifyVec</a>(<span class="keyword">const</span> std::vector<T>& vec)</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>  std::string res = <span class="stringliteral">"{ "</span>;</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> x : vec)</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  {</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  res += std::to_string(x);</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  res += <span class="stringliteral">" "</span>;</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>  res += <span class="stringliteral">"}"</span>;</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keywordflow">return</span> res;</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span> }</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span> </div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> } <span class="comment">// namespace armnn</span></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_batch_mat_mul_xhtml_af390ba429c0633b64e71f4228bdbbadf"><div class="ttname"><a href="classarmnn_1_1_batch_mat_mul.xhtml#af390ba429c0633b64e71f4228bdbbadf">armnn::BatchMatMul::CalcFlatIdx</a></div><div class="ttdeci">unsigned int CalcFlatIdx(DataSlot type, const std::vector< unsigned int > &idx)</div><div class="ttdef"><b>Definition:</b> <a href="_batch_mat_mul_impl_8cpp_source.xhtml#l00186">BatchMatMulImpl.cpp:186</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="classarmnn_1_1_batch_mat_mul_xhtml_a0664b61334b45316933938fa4514a387"><div class="ttname"><a href="classarmnn_1_1_batch_mat_mul.xhtml#a0664b61334b45316933938fa4514a387">armnn::BatchMatMul::AdjustToSafeIdx</a></div><div class="ttdeci">void AdjustToSafeIdx(DataSlot type, std::vector< unsigned int > &idx)</div><div class="ttdef"><b>Definition:</b> <a href="_batch_mat_mul_impl_8cpp_source.xhtml#l00147">BatchMatMulImpl.cpp:147</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="classarmnn_1_1_batch_mat_mul_xhtml_a34243d3189830ed8c4d5cb077e68ccb3"><div class="ttname"><a href="classarmnn_1_1_batch_mat_mul.xhtml#a34243d3189830ed8c4d5cb077e68ccb3">armnn::BatchMatMul::DataSlot</a></div><div class="ttdeci">DataSlot</div><div class="ttdef"><b>Definition:</b> <a href="_batch_mat_mul_impl_8hpp_source.xhtml#l00018">BatchMatMulImpl.hpp:18</a></div></div>
<div class="ttc" id="classarmnn_1_1_batch_mat_mul_xhtml_a188b8d270e02af10e876e60e54e7587c"><div class="ttname"><a href="classarmnn_1_1_batch_mat_mul.xhtml#a188b8d270e02af10e876e60e54e7587c">armnn::BatchMatMul::BatchMatMulImpl</a></div><div class="ttdeci">void BatchMatMulImpl()</div><div class="ttdef"><b>Definition:</b> <a href="_batch_mat_mul_impl_8cpp_source.xhtml#l00014">BatchMatMulImpl.cpp:14</a></div></div>
<div class="ttc" id="classarmnn_1_1_batch_mat_mul_xhtml_a139ced0af01e092a71f47d443ef75d74"><div class="ttname"><a href="classarmnn_1_1_batch_mat_mul.xhtml#a139ced0af01e092a71f47d443ef75d74">armnn::BatchMatMul::StringifyVec</a></div><div class="ttdeci">std::string StringifyVec(const std::vector< T > &vec)</div><div class="ttdef"><b>Definition:</b> <a href="_batch_mat_mul_impl_8cpp_source.xhtml#l00219">BatchMatMulImpl.cpp:219</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="ttdoc">Static helper to get the two axes (for each input) for multiplication. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00459">Descriptors.cpp:459</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_batch_mat_mul_xhtml_af989fe83accce35c5072407e82a7063e"><div class="ttname"><a href="classarmnn_1_1_batch_mat_mul.xhtml#af989fe83accce35c5072407e82a7063e">armnn::BatchMatMul::RecurseBMM</a></div><div class="ttdeci">void RecurseBMM(std::vector< unsigned int > &curIdx, unsigned int curDim)</div><div class="ttdef"><b>Definition:</b> <a href="_batch_mat_mul_impl_8cpp_source.xhtml#l00029">BatchMatMulImpl.cpp:29</a></div></div>
<div class="ttc" id="classarmnn_1_1_batch_mat_mul_xhtml_ae15c1a71e9465ac3698f32a572746804"><div class="ttname"><a href="classarmnn_1_1_batch_mat_mul.xhtml#ae15c1a71e9465ac3698f32a572746804">armnn::BatchMatMul::AdjustAxesToMulForUnequalRanks</a></div><div class="ttdeci">void AdjustAxesToMulForUnequalRanks(std::pair< std::pair< unsigned int, unsigned int >, std::pair< unsigned int, unsigned int >> &axesToMul)</div><div class="ttdef"><b>Definition:</b> <a href="_batch_mat_mul_impl_8cpp_source.xhtml#l00073">BatchMatMulImpl.cpp:73</a></div></div>
<div class="ttc" id="classarmnn_1_1_batch_mat_mul_xhtml_a23985d27f79a44c973bd1e0c2633c191"><div class="ttname"><a href="classarmnn_1_1_batch_mat_mul.xhtml#a23985d27f79a44c973bd1e0c2633c191">armnn::BatchMatMul::SetValueAt</a></div><div class="ttdeci">void SetValueAt(float value, DataSlot type, std::vector< unsigned int > idx)</div><div class="ttdef"><b>Definition:</b> <a href="_batch_mat_mul_impl_8cpp_source.xhtml#l00124">BatchMatMulImpl.cpp:124</a></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="include_2armnn_2backends_2_workload_data_8hpp_xhtml"><div class="ttname"><a href="include_2armnn_2backends_2_workload_data_8hpp.xhtml">WorkloadData.hpp</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_batch_mat_mul_xhtml_a47068479ce7cab2a3d7c3e1ae53cf760"><div class="ttname"><a href="classarmnn_1_1_batch_mat_mul.xhtml#a47068479ce7cab2a3d7c3e1ae53cf760">armnn::BatchMatMul::GetValueAt</a></div><div class="ttdeci">float GetValueAt(DataSlot type, std::vector< unsigned int > idx)</div><div class="ttdef"><b>Definition:</b> <a href="_batch_mat_mul_impl_8cpp_source.xhtml#l00096">BatchMatMulImpl.cpp:96</a></div></div>
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