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<div class="title">ClUnidirectionalSequenceLstmFloatWorkload.cpp</div> </div>
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<a href="_cl_unidirectional_sequence_lstm_float_workload_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="_cl_unidirectional_sequence_lstm_float_workload_8hpp.xhtml">ClUnidirectionalSequenceLstmFloatWorkload.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include "<a class="code" href="_cl_workload_utils_8hpp.xhtml">ClWorkloadUtils.hpp</a>"</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_arm_compute_utils_8hpp.xhtml">aclCommon/ArmComputeUtils.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_arm_compute_tensor_utils_8hpp.xhtml">aclCommon/ArmComputeTensorUtils.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="preprocessor">#include <<a class="code" href="_numeric_cast_8hpp.xhtml">armnn/utility/NumericCast.hpp</a>></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <<a class="code" href="_permute_8hpp.xhtml">armnnUtils/Permute.hpp</a>></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <<a class="code" href="_cl_workload_factory_helper_8hpp.xhtml">cl/test/ClWorkloadFactoryHelper.hpp</a>></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <<a class="code" href="_workload_utils_8hpp.xhtml">backendsCommon/WorkloadUtils.hpp</a>></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include "<a class="code" href="_cl_tensor_handle_8hpp.xhtml">cl/ClTensorHandle.hpp</a>"</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> </div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> {</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> CalcAclAxis(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDimensions, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> axis)</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="keywordflow">return</span> (numDimensions - axis) - 1;</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="comment">//namespace</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> {</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="keyword">using namespace </span>armcomputetensorutils;</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> <a class="code" href="classarmnn_1_1_cl_unidirectional_sequence_lstm_float_workload.xhtml#a9d2fcde9a15c84c5cca2d5a26aa5bbec">ClUnidirectionalSequenceLstmFloatWorkload::ClUnidirectionalSequenceLstmFloatWorkload</a></div><div class="line"><a name="l00032"></a><span class="lineno"><a class="line" href="classarmnn_1_1_cl_unidirectional_sequence_lstm_float_workload.xhtml#a9d2fcde9a15c84c5cca2d5a26aa5bbec"> 32</a></span>  (<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml">UnidirectionalSequenceLstmQueueDescriptor</a>& descriptor,</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>,</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keyword">const</span> arm_compute::CLCompileContext& clCompileContext)</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  : <a class="code" href="classarmnn_1_1_typed_workload.xhtml">FloatWorkload<UnidirectionalSequenceLstmQueueDescriptor></a>(descriptor, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>)</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="comment">// Report Profiling Details</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <a class="code" href="_profiling_8hpp.xhtml#a786492a3881a4c760ab1eec2149f4aba">ARMNN_REPORT_PROFILING_WORKLOAD_DESC</a>(<span class="stringliteral">"ClUnidirectionalSequenceLstmFloatWorkload_Construct"</span>,</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  descriptor.m_Parameters,</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  info,</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  GetGuid());</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> </div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keyword">const</span> arm_compute::ICLTensor& input = <span class="keyword">static_cast<</span><a class="code" href="classarmnn_1_1_i_cl_tensor_handle.xhtml">IClTensorHandle</a>*<span class="keyword">></span>(m_Data.m_Inputs[0])->GetTensor();</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  arm_compute::ICLTensor& output = <span class="keyword">static_cast<</span><a class="code" href="classarmnn_1_1_i_cl_tensor_handle.xhtml">IClTensorHandle</a>*<span class="keyword">></span>(m_Data.m_Outputs[2])->GetTensor();</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo = info.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo = info.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[2];</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>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a> armComputeDataType = <span class="keyword">static_cast<</span><a class="code" href="classarmnn_1_1_i_cl_tensor_handle.xhtml">IClTensorHandle</a>*<span class="keyword">></span>(m_Data.m_Inputs[0])->GetDataType();</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> armnnDataType = GetArmNNDataType(armComputeDataType);</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>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputLayerShape = <span class="keyword">static_cast<</span><a class="code" href="classarmnn_1_1_i_cl_tensor_handle.xhtml">IClTensorHandle</a>*<span class="keyword">></span>(m_Data.m_Inputs[0])->GetShape();</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> cellStateLayerShape = <span class="keyword">static_cast<</span><a class="code" href="classarmnn_1_1_i_cl_tensor_handle.xhtml">IClTensorHandle</a>*<span class="keyword">></span>(m_Data.m_Inputs[2])->GetShape();</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputLayerShape = <span class="keyword">static_cast<</span><a class="code" href="classarmnn_1_1_i_cl_tensor_handle.xhtml">IClTensorHandle</a>*<span class="keyword">></span>(m_Data.m_Outputs[2])->GetShape();</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> maxTime = m_Data.m_Parameters.m_TimeMajor ? inputLayerShape[0] : inputLayerShape[1];</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = m_Data.m_Parameters.m_TimeMajor ? inputLayerShape[1] : inputLayerShape[0];</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = inputLayerShape[2];</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = outputLayerShape[2];</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numUnits = cellStateLayerShape[1];</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> timeMajorShapeInput({maxTime, batchSize, inputSize});</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> timeMajorShapeOutput({maxTime, batchSize, outputSize});</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>  <span class="comment">//</span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="comment">// Permute: performed if Unidirectional Sequence Layer inputs/outputs are in batch major format.</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="comment">//</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keywordflow">if</span> (!m_Data.m_Parameters.m_TimeMajor)</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>  std::unique_ptr<arm_compute::CLPermute> layer(<span class="keyword">new</span> arm_compute::CLPermute());</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> permuteOutInfo = inputInfo;</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  permuteOutInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(timeMajorShapeInput);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  BuildArmComputeTensor(m_PermuteFirstOut, permuteOutInfo);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_PermuteFirstOut);</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="comment">// Permute to time major format.</span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  layer->configure(clCompileContext, &input, &m_PermuteFirstOut, arm_compute::PermutationVector(0U,2U,1U));</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  m_Permute1.reset(layer.release());</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> </div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="comment">//</span></div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="comment">// Split and Concat Tensors</span></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="comment">//</span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < maxTime; ++i)</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>  arm_compute::CLTensor splitter_out;</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  arm_compute::CLTensor concat_in;</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="keyword">auto</span> splitterTensorInfo = inputInfo;</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keyword">auto</span> concatTensorInfo = outputInfo;</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  splitterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({batchSize, inputSize});</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  concatTensorInfo.SetShape({batchSize, outputSize});</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  BuildArmComputeTensor(splitter_out, splitterTensorInfo);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  BuildArmComputeTensor(concat_in, concatTensorInfo);</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>  armcomputetensorutils::InitialiseArmComputeTensorEmpty(splitter_out);</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_in);</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> </div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="comment">// append to std::vector<arm_compute::CLTensor></span></div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  m_SplitterOutputsTensors.push_back(std::move(splitter_out));</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  m_ConcatInputsTensors.push_back(std::move(concat_in));</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> </div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < maxTime; ++i)</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="comment">// append to std::vector<arm_compute::ICLTensor*></span></div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  m_SplitterOutputs.push_back(&m_SplitterOutputsTensors[i]);</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  m_ConcatInputs.push_back(&m_ConcatInputsTensors[i]);</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  }</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> </div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="comment">//</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="comment">// Split</span></div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="comment">//</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberDimensions = 3;</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimension = 0; <span class="comment">// splitting on 0-dimension (i.e. maxTime dimension)</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">if</span> (maxTime != 1) <span class="comment">// ACL split does not work with only one element to split.</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>  <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">ViewsDescriptor</a> splitterDesc(maxTime, numberDimensions);</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitterDimSizes[3] = {1, batchSize, inputSize};</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputIdx = 0u; outputIdx < maxTime; ++outputIdx)</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  {</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  splitterDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(outputIdx, dimension, splitterDimSizes[dimension] * outputIdx);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx = 0u; dimIdx < numberDimensions; ++dimIdx)</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  {</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  splitterDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">SetViewSize</a>(outputIdx, dimIdx, splitterDimSizes[dimIdx]);</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  }</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> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  std::set<unsigned int> splitAxis = <a class="code" href="namespacearmnn.xhtml#a8cbabc875597b3bed0ccdc0adb289fde">ComputeSplitAxis</a>(splitterDesc, timeMajorShapeInput);</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> </div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  std::unique_ptr<arm_compute::CLSplit> split_layer(<span class="keyword">new</span> arm_compute::CLSplit());</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> aclAxisSplit = CalcAclAxis(splitterDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#a78e8266be865fdd92cadd04d6e25ae1f">GetNumDimensions</a>(), *splitAxis.begin());</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keywordflow">if</span> (!m_Data.m_Parameters.m_TimeMajor)</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>  split_layer->configure(&m_PermuteFirstOut, m_SplitterOutputs, aclAxisSplit);</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  }</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  {</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  split_layer->configure(&input, m_SplitterOutputs, aclAxisSplit);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</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>  split_layer->prepare();</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  m_Splitter.reset(split_layer.release());</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  }</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>  <span class="comment">//</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="comment">// Lstm</span></div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="comment">//</span></div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  arm_compute::LSTMParams<arm_compute::ICLTensor> lstm_param;</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>  m_InputToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  BuildArmComputeTensor(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights->GetTensorInfo());</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> </div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  m_InputToCellWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  BuildArmComputeTensor(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights->GetTensorInfo());</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> </div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  m_InputToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  BuildArmComputeTensor(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights->GetTensorInfo());</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>  m_RecurrentToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights->GetTensorInfo());</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>  m_RecurrentToCellWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights->GetTensorInfo());</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>  m_RecurrentToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights->GetTensorInfo());</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>  m_ForgetGateBiasTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  BuildArmComputeTensor(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias->GetTensorInfo());</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> </div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  m_CellBiasTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  BuildArmComputeTensor(*m_CellBiasTensor, m_Data.m_CellBias->GetTensorInfo());</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>  m_OutputGateBiasTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  BuildArmComputeTensor(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias->GetTensorInfo());</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="comment">// for future reference: check the AndroidNN API for the logic here</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="keywordflow">if</span> (!m_Data.m_Parameters.m_CifgEnabled)</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>  m_InputToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  BuildArmComputeTensor(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights->GetTensorInfo());</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  m_RecurrentToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights->GetTensorInfo());</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> </div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  m_CellToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keywordflow">if</span> (m_Data.m_CellToInputWeights != <span class="keyword">nullptr</span>)</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>  BuildArmComputeTensor(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights->GetTensorInfo());</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> </div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  m_InputGateBiasTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  BuildArmComputeTensor(*m_InputGateBiasTensor, m_Data.m_InputGateBias->GetTensorInfo());</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>  lstm_param.set_cifg_params(m_InputToInputWeightsTensor.get(),</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  m_RecurrentToInputWeightsTensor.get(),</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  m_Data.m_CellToInputWeights ? m_CellToInputWeightsTensor.get() : <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  m_InputGateBiasTensor.get());</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> </div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keywordflow">if</span> (m_Data.m_Parameters.m_ProjectionEnabled)</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  {</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  m_ProjectionWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  BuildArmComputeTensor(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights->GetTensorInfo());</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span> </div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  m_ProjectionBiasTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keywordflow">if</span> (m_Data.m_ProjectionBias != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  {</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  BuildArmComputeTensor(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias->GetTensorInfo());</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>  lstm_param.set_projection_params(m_ProjectionWeightsTensor.get(),</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  m_Data.m_ProjectionBias ? m_ProjectionBiasTensor.get() : <span class="keyword">nullptr</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> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keywordflow">if</span> (m_Data.m_Parameters.m_PeepholeEnabled)</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>  m_CellToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  BuildArmComputeTensor(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights->GetTensorInfo());</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>  m_CellToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  BuildArmComputeTensor(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights->GetTensorInfo());</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>  lstm_param.set_peephole_params(m_CellToForgetWeightsTensor.get(), m_CellToOutputWeightsTensor.get());</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  }</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>  <span class="keywordflow">if</span> (m_Data.m_Parameters.m_LayerNormEnabled)</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  {</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  m_InputLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="keywordflow">if</span> (!m_Data.m_Parameters.m_CifgEnabled)</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>  BuildArmComputeTensor(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights->GetTensorInfo());</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  }</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>  m_ForgetLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  BuildArmComputeTensor(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights->GetTensorInfo());</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>  m_CellLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  BuildArmComputeTensor(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights->GetTensorInfo());</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span> </div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  m_OutputLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  BuildArmComputeTensor(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights->GetTensorInfo());</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span> </div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="keyword">auto</span> inputNormWeightTensor = m_Data.m_Parameters.m_CifgEnabled ? nullptr : m_InputLayerNormWeightsTensor.get();</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  lstm_param.set_layer_normalization_params(inputNormWeightTensor,</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  m_ForgetLayerNormWeightsTensor.get(),</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  m_CellLayerNormWeightsTensor.get(),</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  m_OutputLayerNormWeightsTensor.get());</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  }</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>  arm_compute::ICLTensor& output_state_in = <span class="keyword">static_cast<</span><a class="code" href="classarmnn_1_1_i_cl_tensor_handle.xhtml">IClTensorHandle</a>*<span class="keyword">></span>(m_Data.m_Inputs[1])->GetTensor();</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  arm_compute::ICLTensor& cell_state_in = <span class="keyword">static_cast<</span><a class="code" href="classarmnn_1_1_i_cl_tensor_handle.xhtml">IClTensorHandle</a>*<span class="keyword">></span>(m_Data.m_Inputs[2])->GetTensor();</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span> </div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  arm_compute::ICLTensor& output_state_out = <span class="keyword">static_cast<</span><a class="code" href="classarmnn_1_1_i_cl_tensor_handle.xhtml">IClTensorHandle</a>*<span class="keyword">></span>(m_Data.m_Inputs[1])->GetTensor();</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  arm_compute::ICLTensor& cell_state_out = <span class="keyword">static_cast<</span><a class="code" href="classarmnn_1_1_i_cl_tensor_handle.xhtml">IClTensorHandle</a>*<span class="keyword">></span>(m_Data.m_Inputs[2])->GetTensor();</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> </div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  m_ScratchBuffer = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="keywordflow">if</span> (m_Data.m_Parameters.m_CifgEnabled)</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  {</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="comment">// scratch_buffer [num_units * 3, batch_size] with CIFG</span></div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  BuildArmComputeTensor(*m_ScratchBuffer, <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({batchSize, numUnits * 3}, armnnDataType));</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  }</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  {</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="comment">// scratch_buffer [num_units * 4, batch_size] without CIFG</span></div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  BuildArmComputeTensor(*m_ScratchBuffer, <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({batchSize, numUnits * 4}, armnnDataType));</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> </div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="comment">// Need to be set at negative threshold to be compatible for ACL</span></div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keywordtype">float</span> cell_threshold = m_Data.m_Parameters.m_ClippingThresCell;</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="keywordtype">float</span> projection_threshold = m_Data.m_Parameters.m_ClippingThresProj;</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">// For preparing the object for the class ActivationLayerInfo, consider 5 situations</span></div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  arm_compute::ActivationLayerInfo activationLayerInfo =</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <a class="code" href="namespacearmnn.xhtml#aa1e93ef5f9ee3dbb5e7faa9578f180ae">ConvertLstmActivationFuncToAclLayerInfo</a>(m_Data.m_Parameters.m_ActivationFunc);</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span> </div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i != maxTime; ++i)</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  {</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <span class="comment">// Set LSTM input and output ITensors depending on:</span></div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="comment">// input format (timeMajor) & number of LSTM batches (maxTime).</span></div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  arm_compute::ICLTensor* outputLSTM;</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  arm_compute::ICLTensor* inputLSTM;</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="comment">// If there is only one LSTM time major batch, we will not concat OR permute.</span></div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="comment">// Set input of LSTM to be first input ITensor.</span></div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="comment">// Set output of LSTM to be final output ITensor.</span></div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="comment">// LSTM input/output cannot be > 2 dimensions so need to resize its TensorInfo.</span></div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="keywordflow">if</span> (maxTime == 1 && m_Data.m_Parameters.m_TimeMajor)</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>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape = <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>((&input)-><a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>()->tensor_shape(), 1U);</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape = <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>((&output)-><a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>()->tensor_shape(), 1U);</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShapeShrink({inputShape[1], inputShape[2]});</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShapeShrink({outputShape[1], outputShape[2]});</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <span class="keyword">auto</span> acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="keyword">auto</span> acl_output_shape_shrink = BuildArmComputeTensorShape(outputShapeShrink);</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  (&input)-><a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>()->set_tensor_shape(acl_input_shape_shrink);</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  inputLSTM = <span class="keyword">const_cast<</span>arm_compute::ICLTensor*<span class="keyword">></span>(&input);</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  (&output)-><a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>()->set_tensor_shape(acl_output_shape_shrink);</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  outputLSTM = &output;</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  }</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="comment">// If there is only one LSTM batch major batch, we will not concat, only permute.</span></div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="comment">// Set input of LSTM to be output of initial permute.</span></div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="comment">// Set output of LSTM to be first element of m_ConcatInputs & use that value later in permute.</span></div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="comment">// LSTM output cannot be > 2 dimensions so need to resize its TensorInfo.</span></div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (maxTime == 1 && !m_Data.m_Parameters.m_TimeMajor)</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  {</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape = <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(m_PermuteFirstOut.info()->tensor_shape(), 1U);</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShapeShrink({inputShape[1], inputShape[2]});</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="keyword">auto</span> acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  m_PermuteFirstOut.info()->set_tensor_shape(acl_input_shape_shrink);</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  inputLSTM = &m_PermuteFirstOut;</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  outputLSTM = <span class="keyword">const_cast<</span>arm_compute::ICLTensor*<span class="keyword">></span>(m_ConcatInputs[i]);</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>  <span class="comment">// Batch major AND/OR 2+ LSTM batches so will use concat AND/OR permute later on.</span></div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="keywordflow">else</span></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>  inputLSTM = m_SplitterOutputs[i];</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  outputLSTM = <span class="keyword">const_cast<</span>arm_compute::ICLTensor*<span class="keyword">></span>(m_ConcatInputs[i]);</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  }</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span> </div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  std::unique_ptr<arm_compute::CLLSTMLayer> lstm_layer(<span class="keyword">new</span> arm_compute::CLLSTMLayer());</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  lstm_layer->configure(clCompileContext,</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  inputLSTM,</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  m_InputToForgetWeightsTensor.get(),</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  m_InputToCellWeightsTensor.get(),</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  m_InputToOutputWeightsTensor.get(),</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  m_RecurrentToForgetWeightsTensor.get(),</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  m_RecurrentToCellWeightsTensor.get(),</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  m_RecurrentToOutputWeightsTensor.get(),</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  m_ForgetGateBiasTensor.get(),</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  m_CellBiasTensor.get(),</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  m_OutputGateBiasTensor.get(),</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  &output_state_in,</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  &cell_state_in,</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  m_ScratchBuffer.get(),</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  &output_state_out,</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  &cell_state_out,</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  outputLSTM,</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  lstm_param,</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  activationLayerInfo,</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  cell_threshold,</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  projection_threshold);</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> </div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  m_Layers.emplace_back(std::move(lstm_layer));</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>  armcomputetensorutils::InitialiseArmComputeTensorEmpty(*m_ScratchBuffer);</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span> </div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights);</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights);</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights);</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights);</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights);</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights);</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias);</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_CellBiasTensor, m_Data.m_CellBias);</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias);</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span> </div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <span class="keywordflow">if</span> (!m_Data.m_Parameters.m_CifgEnabled)</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  {</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights);</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights);</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  <span class="keywordflow">if</span> (m_Data.m_CellToInputWeights != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  {</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights);</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  }</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_InputGateBiasTensor, m_Data.m_InputGateBias);</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  }</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span> </div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="keywordflow">if</span> (m_Data.m_Parameters.m_ProjectionEnabled)</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>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights);</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <span class="keywordflow">if</span> (m_Data.m_ProjectionBias != <span class="keyword">nullptr</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>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias);</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>  }</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span> </div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="keywordflow">if</span> (m_Data.m_Parameters.m_PeepholeEnabled)</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>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights);</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights);</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  }</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span> </div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="keywordflow">if</span> (m_Data.m_Parameters.m_LayerNormEnabled)</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  {</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keywordflow">if</span> (!m_Data.m_Parameters.m_CifgEnabled)</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  {</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights);</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  }</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights);</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights);</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights);</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="comment">// Force Compute Library to perform the necessary copying and reshaping.</span></div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="comment">// After which delete all the input tensors that will no longer be needed.</span></div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keywordflow">for</span> (uint32_t i = 0; i < m_Layers.size(); ++i)</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>  m_Layers[i]->prepare();</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> </div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="comment">//</span></div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="comment">// Concat</span></div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="comment">//</span></div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span> </div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="comment">// Expand dimensions of LSTM outputs adding one empty dimension to fit concatenate inputs.</span></div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape = <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(m_ConcatInputs[0]-><a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>()->tensor_shape(), 1U);</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shapeExpandTimeMajor({1, shape[0], shape[1]});</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shapeExpandBatchMajor({shape[0], 1, shape[1]});</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span> </div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <span class="keywordflow">if</span> (maxTime != 1) <span class="comment">// ACL concat does not work with only one element to concatenate.</span></div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  {</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < maxTime; ++i)</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  {</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  m_ConcatInputs[i]->info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor));</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  }</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span> </div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">ConcatDescriptor</a> concatDescriptor(maxTime, numberDimensions); <span class="comment">// maxTime = num inputs (aka. number of views).</span></div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputIdx = 0u; inputIdx < maxTime; ++inputIdx)</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>  concatDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(inputIdx, dimension, inputIdx);</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  concatDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a5b192c5fcd96a0f75542524cf646b355">SetConcatAxis</a>(dimension);</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  }</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>  m_Concat.reset(<span class="keyword">new</span> arm_compute::CLConcatenateLayer());</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> aclAxisConcat = CalcAclAxis(concatDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a78e8266be865fdd92cadd04d6e25ae1f">GetNumDimensions</a>(),</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  concatDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a379929e3b277f1ef94f3ce645870589d">GetConcatAxis</a>());</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <span class="keywordflow">if</span> (!m_Data.m_Parameters.m_TimeMajor)</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  {</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> concatOuputTensorInfo = outputInfo;</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  concatOuputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(timeMajorShapeOutput);</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  BuildArmComputeTensor(concat_out, concatOuputTensorInfo);</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_out);</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span> </div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  m_Concat->configure(m_ConcatInputs, &concat_out, aclAxisConcat);</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  }</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  {</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  m_Concat->configure(m_ConcatInputs, &output, aclAxisConcat);</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  }</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span> </div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  m_Concat->prepare();</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  }</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <span class="comment">// If only one LSTM batch, we do not concat and/or permute.</span></div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <span class="comment">// Must ensure final output info is expanded to correct batch major dimensions.</span></div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  {</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="keywordflow">if</span> (!m_Data.m_Parameters.m_TimeMajor)</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  {</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  (&output)-><a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandBatchMajor));</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  }</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  {</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  (&output)-><a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor));</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  }</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  }</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span> </div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <span class="comment">//</span></div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="comment">// Permute: only done if input/output are in batch major format.</span></div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="comment">//</span></div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <span class="keywordflow">if</span> (!m_Data.m_Parameters.m_TimeMajor)</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>  <span class="comment">// Output now time major. Permute output back to batch major.</span></div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  std::unique_ptr<arm_compute::CLPermute> layer(<span class="keyword">new</span> arm_compute::CLPermute());</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <span class="keywordflow">if</span> (maxTime != 1)</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  {</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  layer->configure(clCompileContext, &concat_out, &output, arm_compute::PermutationVector(0U, 2U, 1U));</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  }</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  {</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  layer->configure(clCompileContext, m_ConcatInputs[0], &output, arm_compute::PermutationVector(0U, 2U, 1U));</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  }</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  m_Permute2.reset(layer.release());</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  }</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span> </div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  FreeUnusedTensors();</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span> }</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span> </div><div class="line"><a name="l00482"></a><span class="lineno"><a class="line" href="classarmnn_1_1_cl_unidirectional_sequence_lstm_float_workload.xhtml#ae071e8822437c78baea75c3aef3a263a"> 482</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_cl_unidirectional_sequence_lstm_float_workload.xhtml#ae071e8822437c78baea75c3aef3a263a">ClUnidirectionalSequenceLstmFloatWorkload::Execute</a>()<span class="keyword"> const</span></div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span> <span class="keyword"></span>{</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <a class="code" href="_cl_workload_utils_8hpp.xhtml#ae96fe8349d05e83e891129d63d8e2263">ARMNN_SCOPED_PROFILING_EVENT_CL_GUID</a>(<span class="stringliteral">"ClUnidirectionalSequenceLstmFloatWorkload_Execute"</span>, GetGuid());</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <span class="keywordflow">if</span> (m_Permute1)</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  {</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  m_Permute1->run();</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  }</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <span class="keywordflow">if</span> (m_Splitter)</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  {</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  m_Splitter->run();</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  }</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  <span class="keywordflow">for</span> (uint32_t i = 0; i < m_Layers.size(); ++i)</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  {</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  m_Layers[i]->run();</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  }</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  <span class="keywordflow">if</span> (m_Concat)</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  {</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  m_Concat->run();</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  }</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <span class="keywordflow">if</span> (m_Permute2)</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  {</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  m_Permute2->run();</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  }</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span> }</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span> </div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span> <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a></div><div class="line"><a name="l00508"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a924063ef859ec5f9a1466a42e7409c85"> 508</a></span> <a class="code" href="namespacearmnn.xhtml#a924063ef859ec5f9a1466a42e7409c85">ClUnidirectionalSequenceLstmFloatWorkloadValidate</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& input,</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputStateIn,</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& cellStateIn,</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& output,</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<TensorInfo></a>& hiddenStateOutput,</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<TensorInfo></a>& cellStateOutput,</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">UnidirectionalSequenceLstmDescriptor</a>& descriptor,</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml">LstmInputParamsInfo</a>& paramsInfo)</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span> {</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(hiddenStateOutput, cellStateOutput);</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span> </div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputLayerShape = input.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputLayerShape = outputStateIn.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span> </div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> maxTime = descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a>?inputLayerShape[0]:inputLayerShape[1];</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a>?inputLayerShape[1]:inputLayerShape[0];</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = inputLayerShape[2];</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = outputLayerShape[2];</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span> </div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> timeMajorShapeInput({maxTime, batchSize, inputSize});</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> timeMajorShapeOutput({maxTime, batchSize, outputSize});</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span> </div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a> statusPermute1 = <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a>(arm_compute::ErrorCode::OK,</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <span class="stringliteral">"Permute1 status"</span>);</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a> statusSplit = <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a>(arm_compute::ErrorCode::OK,</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="stringliteral">"Split status"</span>);</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a> statusLSTM = <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a>(arm_compute::ErrorCode::OK,</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <span class="stringliteral">"LSTM status"</span>);</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a> statusConcat = <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a>(arm_compute::ErrorCode::OK,</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <span class="stringliteral">"Concat status"</span>);</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a> statusPermute2 = <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a>(arm_compute::ErrorCode::OK,</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  <span class="stringliteral">"Permute2 status"</span>);</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span> </div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclInputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(input);</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output);</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span> </div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <span class="comment">//</span></div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <span class="comment">// Permute validate</span></div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  <span class="comment">//</span></div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> permuteOutInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(input);</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  arm_compute::TensorInfo aclPermuteOutInfo = armcomputetensorutils::BuildArmComputeTensorInfo(permuteOutInfo);</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <span class="keywordflow">if</span> (!descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a>)</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  {</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  statusPermute1 = arm_compute::CLPermute::validate(&aclInputInfo,</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  &aclPermuteOutInfo,</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  arm_compute::PermutationVector(0U, 2U, 1U));</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  }</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span> </div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <span class="comment">//</span></div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <span class="comment">// Split and Concat Tensors validate</span></div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <span class="comment">//</span></div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  std::vector<arm_compute::TensorInfo> splitterOutputsTensorInfos;</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  std::vector<arm_compute::TensorInfo> concatInputsTensorInfos;</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  std::vector<arm_compute::ITensorInfo*> splitterOutputsTensorInfosPtr;</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  std::vector<const arm_compute::ITensorInfo*> concatInputsTensorInfosPtr;</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  splitterOutputsTensorInfos.reserve(maxTime);</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  concatInputsTensorInfos.reserve(maxTime);</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < maxTime; ++i)</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  {</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  arm_compute::TensorInfo splitter_out;</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  arm_compute::TensorInfo concat_in;</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span> </div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  <span class="keyword">auto</span> splitterTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(input);</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <span class="keyword">auto</span> concatTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(output);</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  splitterTensorInfo.SetShape({batchSize, inputSize});</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  concatTensorInfo.SetShape({batchSize, outputSize});</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span> </div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  arm_compute::TensorInfo aclSplitterTensorInfo</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  = armcomputetensorutils::BuildArmComputeTensorInfo(splitterTensorInfo);</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  arm_compute::TensorInfo aclConcatTensorInfo</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  = armcomputetensorutils::BuildArmComputeTensorInfo(concatTensorInfo);</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span> </div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  splitterOutputsTensorInfos.emplace_back(aclSplitterTensorInfo);</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  concatInputsTensorInfos.emplace_back(aclConcatTensorInfo);</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  splitterOutputsTensorInfosPtr.emplace_back(&splitterOutputsTensorInfos[i]);</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  concatInputsTensorInfosPtr.emplace_back(&concatInputsTensorInfos[i]);</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  }</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span> </div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  <span class="comment">//</span></div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <span class="comment">// Split validate</span></div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  <span class="comment">//</span></div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberDimensions = 3;</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimension = 0; <span class="comment">// splitting on 0-dimension (i.e. maxTime dimension)</span></div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> aclAxisSplit = CalcAclAxis(numberDimensions, dimension);</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span> </div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <span class="keywordflow">if</span> (maxTime != 1) <span class="comment">// ACL split does not work with only one element to split.</span></div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  {</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  <span class="keywordflow">if</span> (!descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a>)</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  {</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  statusSplit = arm_compute::CLSplit::validate(&aclPermuteOutInfo,</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  splitterOutputsTensorInfosPtr,</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  aclAxisSplit);</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  }</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  {</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  statusSplit = arm_compute::CLSplit::validate(&aclInputInfo, splitterOutputsTensorInfosPtr, aclAxisSplit);</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  }</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  }</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span> </div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <span class="comment">//</span></div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  <span class="comment">// LSTM validate</span></div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  <span class="comment">//</span></div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span> </div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  arm_compute::LSTMParams<arm_compute::ITensorInfo> lstm_params_info;</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span> </div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& scratchBuffer = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(cellStateIn.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), input.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>());</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputStateOut = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(outputStateIn.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), input.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>());</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& cellStateOut = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(cellStateIn.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), input.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>());</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span> </div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <span class="comment">// The inputs and outputs</span></div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclOutputStateInInfo = BuildArmComputeTensorInfo(outputStateIn);</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclCellStateInInfo = BuildArmComputeTensorInfo(cellStateIn);</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclScratchBufferInfo = BuildArmComputeTensorInfo(scratchBuffer);</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclOutputStateOutInfo = BuildArmComputeTensorInfo(outputStateOut);</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclCellStateOutInfo = BuildArmComputeTensorInfo(cellStateOut);</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span> </div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  <span class="comment">// Basic parameters</span></div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclInputToForgetWeightsInfo</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a7dac08f19a1b235d5256d39136848a09">GetInputToForgetWeights</a>());</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclInputToCellWeightsInfo</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a3b3c26330a05bf4ea40f8a6b402be354">GetInputToCellWeights</a>());</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclInputToOutputWeightsInfo</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a800adf0f61e84d706060f63037c1a336">GetInputToOutputWeights</a>());</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclRecurrentToForgetWeightsInfo</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a534af7e4f3a6d50a6dab05abc245133d">GetRecurrentToForgetWeights</a>());</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclRecurrentToCellWeightsInfo</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ae5bfdd423b16f990c1713ef9f91f947b">GetRecurrentToCellWeights</a>());</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclRecurrentToOutputWeightsInfo</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#afe4d25acd31b98dee6f6b28d4d756071">GetRecurrentToOutputWeights</a>());</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclForgetGateBiasInfo</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ac81393ef433b0c7c337f9f0d55f41ae4">GetForgetGateBias</a>());</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclCellBiasInfo</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ad5f4be37766b41f342dd196cb1c6e141">GetCellBias</a>());</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclOutputGateBiasInfo</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ae0da94ba17ce67b95b5b9d6e5adc4271">GetOutputGateBias</a>());</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span> </div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  arm_compute::TensorInfo aclInputToInputWeightsInfo;</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  arm_compute::TensorInfo aclRecurrentToInputWeightsInfo;</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  arm_compute::TensorInfo aclCellToInputWeightsInfo;</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  arm_compute::TensorInfo aclInputGateBiasInfo;</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  arm_compute::TensorInfo aclProjectionWeightsInfo;</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  arm_compute::TensorInfo aclProjectionBiasInfo;</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  arm_compute::TensorInfo aclCellToForgetWeightsInfo;</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  arm_compute::TensorInfo aclCellToOutputWeightsInfo;</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span> </div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  arm_compute::TensorInfo aclInputLayerNormWeightsInfo;</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  arm_compute::TensorInfo aclForgetLayerNormWeightsInfo;</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  arm_compute::TensorInfo aclCellLayerNormWeightsInfo;</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  arm_compute::TensorInfo aclOutputLayerNormWeightsInfo;</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span> </div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span> </div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <span class="keywordflow">if</span> (!descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a>)</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  {</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  <span class="keywordflow">if</span> (descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a>)</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  {</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  aclCellToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a36fa9439fda2e72234411956a1c7e64f">GetCellToInputWeights</a>());</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  }</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  aclInputToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#afa2b04197a764428a8c3a648de8058fc">GetInputToInputWeights</a>());</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  aclRecurrentToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ad159f9edbddeeb6cf6ff0ba042481ba8">GetRecurrentToInputWeights</a>());</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  aclInputGateBiasInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ae1d5a487fcd13852927c8a2b9f9dfeb6">GetInputGateBias</a>());</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span> </div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  lstm_params_info.set_cifg_params(&aclInputToInputWeightsInfo,</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  &aclRecurrentToInputWeightsInfo,</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a> ? &aclCellToInputWeightsInfo : <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  &aclInputGateBiasInfo);</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  }</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span> </div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <span class="keywordflow">if</span> (descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a>)</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  {</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  <span class="keywordflow">if</span> (paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ae22fc962c59e7c24986718f5af0020db">m_ProjectionBias</a> != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  {</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  aclProjectionBiasInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a9f2cce936b4df49c487eaca513bf55ca">GetProjectionBias</a>());</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  }</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  aclProjectionWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a18038725f71bb5c5bd03c02cc164f879">GetProjectionWeights</a>());</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span> </div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  lstm_params_info.set_projection_params(&aclProjectionWeightsInfo,</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ae22fc962c59e7c24986718f5af0020db">m_ProjectionBias</a> ? &aclProjectionBiasInfo : <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  }</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span> </div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  <span class="keywordflow">if</span> (descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a>)</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  {</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  aclCellToForgetWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a0e31db1891d11bbe0d8556c01e9812ef">GetCellToForgetWeights</a>());</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  aclCellToOutputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a35825b1ec5bc2b14c8eac60887dbcf19">GetCellToOutputWeights</a>());</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span> </div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  lstm_params_info.set_peephole_params(&aclCellToForgetWeightsInfo, &aclCellToOutputWeightsInfo);</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  }</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span> </div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  <span class="keywordflow">if</span> (descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">m_LayerNormEnabled</a>)</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  {</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <span class="keywordflow">if</span> (!descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a>)</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  {</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  aclInputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a3d2f638ba83ae5dad0094c006220c232">GetInputLayerNormWeights</a>());</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  }</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  aclForgetLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ab50b4ccb0b84f6427996f76083a4107a">GetForgetLayerNormWeights</a>());</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  aclCellLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#aaf1af3bc828c5daa4a5c0bac28f63cc3">GetCellLayerNormWeights</a>());</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  aclOutputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a045674b768295e617d7060f96f162366">GetOutputLayerNormWeights</a>());</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span> </div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  lstm_params_info.set_layer_normalization_params(descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> ? <span class="keyword">nullptr</span> :</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  &aclInputLayerNormWeightsInfo,</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  &aclForgetLayerNormWeightsInfo,</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  &aclCellLayerNormWeightsInfo,</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  &aclOutputLayerNormWeightsInfo);</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  }</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span> </div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  <span class="comment">// Need to be set at negative threshold to be compatible for ACL</span></div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  <span class="keywordtype">float</span> cell_threshold = descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a>;</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  <span class="keywordtype">float</span> projection_threshold = descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a>;</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span> </div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  arm_compute::ActivationLayerInfo activationLayerInfo =</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  <a class="code" href="namespacearmnn.xhtml#aa1e93ef5f9ee3dbb5e7faa9578f180ae">ConvertLstmActivationFuncToAclLayerInfo</a>(descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a>);</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span> </div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i != maxTime; ++i)</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  {</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span> </div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  <span class="comment">// Set LSTM input and output ITensors depending on:</span></div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  <span class="comment">// input format (timeMajor) & number of LSTM batches (maxTime).</span></div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  arm_compute::ITensorInfo* outputLSTM;</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  arm_compute::ITensorInfo* inputLSTM;</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  <span class="comment">// If there is only one LSTM time major batch, we will not concat OR permute.</span></div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  <span class="comment">// Set input of LSTM to be first input ITensor.</span></div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  <span class="comment">// Set output of LSTM to be final output ITensor.</span></div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  <span class="comment">// LSTM input/output cannot be > 2 dimensions so need to resize its TensorInfo.</span></div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  <span class="keywordflow">if</span> (maxTime == 1 && !descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a>)</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  {</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape = <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(aclInputInfo.tensor_shape(), 1U);</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape = <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(aclOutputInfo.tensor_shape(), 1U);</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShapeShrink({inputShape[1], inputShape[2]});</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShapeShrink({outputShape[1], outputShape[2]});</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  <span class="keyword">auto</span> acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  <span class="keyword">auto</span> acl_output_shape_shrink = BuildArmComputeTensorShape(outputShapeShrink);</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  <span class="keyword">const_cast<</span>arm_compute::TensorInfo*<span class="keyword">></span>(&aclInputInfo)->set_tensor_shape(acl_input_shape_shrink);</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  inputLSTM = <span class="keyword">const_cast<</span>arm_compute::TensorInfo*<span class="keyword">></span>(&aclInputInfo);</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  <span class="keyword">const_cast<</span>arm_compute::TensorInfo*<span class="keyword">></span>(&aclOutputInfo)->set_tensor_shape(acl_output_shape_shrink);</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  outputLSTM = <span class="keyword">const_cast<</span>arm_compute::TensorInfo*<span class="keyword">></span>(&aclOutputInfo);</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  }</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  <span class="comment">// If there is only one LSTM batch major batch, we will not concat, only permute.</span></div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  <span class="comment">// Set input of LSTM to be output of initial permute.</span></div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  <span class="comment">// Set output of LSTM to be first element of m_ConcatInputs & use that value later in permute.</span></div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  <span class="comment">// LSTM output cannot be > 2 dimensions so need to resize its TensorInfo.</span></div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (maxTime == 1 && !descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a>)</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  {</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape = <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(aclPermuteOutInfo.tensor_shape(), 1U);</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShapeShrink({inputShape[1], inputShape[2]});</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  <span class="keyword">auto</span> acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  aclPermuteOutInfo.set_tensor_shape(acl_input_shape_shrink);</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  inputLSTM = &aclPermuteOutInfo;</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  outputLSTM = <span class="keyword">const_cast<</span>arm_compute::ITensorInfo*<span class="keyword">></span>(concatInputsTensorInfosPtr[i]);</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  }</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  <span class="comment">// Batch major AND/OR 2+ LSTM batches so will use concat AND/OR permute later on.</span></div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  {</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  inputLSTM = splitterOutputsTensorInfosPtr[i];</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  outputLSTM = <span class="keyword">const_cast<</span>arm_compute::ITensorInfo*<span class="keyword">></span>(concatInputsTensorInfosPtr[i]);</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  }</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span> </div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  statusLSTM = arm_compute::CLLSTMLayer::validate(inputLSTM,</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  &aclInputToForgetWeightsInfo,</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  &aclInputToCellWeightsInfo,</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  &aclInputToOutputWeightsInfo,</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  &aclRecurrentToForgetWeightsInfo,</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  &aclRecurrentToCellWeightsInfo,</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  &aclRecurrentToOutputWeightsInfo,</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  &aclForgetGateBiasInfo,</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  &aclCellBiasInfo,</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  &aclOutputGateBiasInfo,</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  &aclOutputStateInInfo,</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  &aclCellStateInInfo,</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  &aclScratchBufferInfo,</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  &aclOutputStateOutInfo,</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  &aclCellStateOutInfo,</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  outputLSTM,</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  lstm_params_info,</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  activationLayerInfo,</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  cell_threshold,</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  projection_threshold);</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span> </div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  <span class="keywordflow">if</span> (statusLSTM.error_code() != arm_compute::ErrorCode::OK)</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  {</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  }</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  }</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span> </div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  <span class="comment">//</span></div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  <span class="comment">// Concat validate</span></div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  <span class="comment">//</span></div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span> </div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  <span class="comment">// Expand dimensions of LSTM outputs adding one empty dimension to fit concatenate inputs.</span></div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape = <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(concatInputsTensorInfosPtr[0]->tensor_shape(), 1U);</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shapeExpandTimeMajor({1, shape[0], shape[1]});</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shapeExpandBatchMajor({shape[0], 1, shape[1]});</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span> </div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> concatOuputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(output);</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  concatOuputTensorInfo.SetShape(timeMajorShapeOutput);</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  arm_compute::TensorInfo aclConcatOuputTensorInfo= BuildArmComputeTensorInfo(concatOuputTensorInfo);</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span> </div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  <span class="keywordflow">if</span> (maxTime != 1) <span class="comment">// ACL concat does not work with only one element to concatenate.</span></div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  {</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < maxTime; ++i)</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  {</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  <span class="keyword">auto</span> acl_shape_expand = BuildArmComputeTensorShape(shapeExpandTimeMajor);</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  concatInputsTensorInfos[i].set_tensor_shape(acl_shape_expand);</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  }</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span> </div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> aclAxisConcat = CalcAclAxis(numberDimensions, dimension);</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  <span class="keywordflow">if</span> (!descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a>)</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  {</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  statusConcat = arm_compute::CLConcatenateLayer::validate(concatInputsTensorInfosPtr,</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  &aclConcatOuputTensorInfo,</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  aclAxisConcat);</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  }</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  {</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  statusConcat = arm_compute::CLConcatenateLayer::validate(concatInputsTensorInfosPtr,</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  &aclOutputInfo,</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  aclAxisConcat);</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  }</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  }</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  <span class="comment">// If only one LSTM batch, we do not concat and/or permute.</span></div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  <span class="comment">// Must ensure final output info is expanded to correct batch major dimensions.</span></div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  {</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  <span class="keywordflow">if</span> (!descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a>)</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  {</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  <span class="keyword">const_cast<</span>arm_compute::TensorInfo*<span class="keyword">></span>(&aclInputInfo)->set_tensor_shape(</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  BuildArmComputeTensorShape(shapeExpandBatchMajor));</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  }</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  {</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  <span class="keyword">const_cast<</span>arm_compute::TensorInfo*<span class="keyword">></span>(&aclInputInfo)->set_tensor_shape(</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  BuildArmComputeTensorShape(shapeExpandTimeMajor));</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  }</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  }</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  <span class="comment">//</span></div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  <span class="comment">// Permute validate</span></div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  <span class="comment">//</span></div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  <span class="keywordflow">if</span> (!descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a3dcd10ca3ea2e132558b1e2814668c15">m_TimeMajor</a>)</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  {</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  <span class="comment">// Output now time major. Permute output back to batch major.</span></div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  <span class="keywordflow">if</span> (maxTime != 1)</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  {</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>  statusPermute2 = arm_compute::CLPermute::validate(&aclConcatOuputTensorInfo,</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  &aclOutputInfo,</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  arm_compute::PermutationVector(0U, 2U, 1U));</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  }</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  {</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  statusPermute2 = arm_compute::CLPermute::validate(concatInputsTensorInfosPtr[0],</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  &aclOutputInfo,</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  arm_compute::PermutationVector(0U, 2U, 1U));</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  }</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  }</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span> </div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  <span class="keyword">auto</span> okCode = arm_compute::ErrorCode::OK;</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  <span class="keywordflow">if</span> (statusPermute1.error_code() == okCode &&</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  statusSplit.error_code() == okCode &&</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  statusLSTM .error_code() == okCode &&</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  statusConcat.error_code() == okCode &&</div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  statusPermute2.error_code() == okCode)</div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  {</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a>(arm_compute::ErrorCode::OK,</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  <span class="stringliteral">"All Unidirectional Sequence LSTM layer validate status OK."</span>);</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  }</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  {</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a>(arm_compute::ErrorCode::RUNTIME_ERROR,</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  <span class="stringliteral">"Unidirectional Sequence LSTM layer validate status failed."</span>);</div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  }</div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span> }</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span> </div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span> <span class="keywordtype">void</span> ClUnidirectionalSequenceLstmFloatWorkload::FreeUnusedTensors()</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span> {</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  FreeTensorIfUnused(m_InputToInputWeightsTensor);</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  FreeTensorIfUnused(m_InputToForgetWeightsTensor);</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  FreeTensorIfUnused(m_InputToCellWeightsTensor);</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  FreeTensorIfUnused(m_InputToOutputWeightsTensor);</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  FreeTensorIfUnused(m_RecurrentToInputWeightsTensor);</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  FreeTensorIfUnused(m_RecurrentToForgetWeightsTensor);</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span>  FreeTensorIfUnused(m_RecurrentToCellWeightsTensor);</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span>  FreeTensorIfUnused(m_RecurrentToOutputWeightsTensor);</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  FreeTensorIfUnused(m_CellToInputWeightsTensor);</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span>  FreeTensorIfUnused(m_CellToForgetWeightsTensor);</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  FreeTensorIfUnused(m_CellToOutputWeightsTensor);</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span>  FreeTensorIfUnused(m_InputGateBiasTensor);</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span>  FreeTensorIfUnused(m_ForgetGateBiasTensor);</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  FreeTensorIfUnused(m_CellBiasTensor);</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  FreeTensorIfUnused(m_OutputGateBiasTensor);</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span>  FreeTensorIfUnused(m_ProjectionWeightsTensor);</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  FreeTensorIfUnused(m_ProjectionBiasTensor);</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  FreeTensorIfUnused(m_InputLayerNormWeightsTensor);</div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  FreeTensorIfUnused(m_ForgetLayerNormWeightsTensor);</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>  FreeTensorIfUnused(m_CellLayerNormWeightsTensor);</div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  FreeTensorIfUnused(m_OutputLayerNormWeightsTensor);</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  FreeTensorIfUnused(m_ScratchBuffer);</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span> }</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span> </div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span> } <span class="comment">//namespace armnn</span></div><div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a6c9de81fc65b3c4924cab11907075a17"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">armnn::LstmDescriptor::m_ProjectionEnabled</a></div><div class="ttdeci">bool m_ProjectionEnabled</div><div class="ttdoc">Enable/disable the projection layer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01131">Descriptors.hpp:1131</a></div></div>
<div class="ttc" id="_cl_unidirectional_sequence_lstm_float_workload_8hpp_xhtml"><div class="ttname"><a href="_cl_unidirectional_sequence_lstm_float_workload_8hpp.xhtml">ClUnidirectionalSequenceLstmFloatWorkload.hpp</a></div></div>
<div class="ttc" id="_cl_workload_utils_8hpp_xhtml_ae96fe8349d05e83e891129d63d8e2263"><div class="ttname"><a href="_cl_workload_utils_8hpp.xhtml#ae96fe8349d05e83e891129d63d8e2263">ARMNN_SCOPED_PROFILING_EVENT_CL_GUID</a></div><div class="ttdeci">#define ARMNN_SCOPED_PROFILING_EVENT_CL_GUID(name, guid)</div><div class="ttdef"><b>Definition:</b> <a href="_cl_workload_utils_8hpp_source.xhtml#l00027">ClWorkloadUtils.hpp:27</a></div></div>
<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml">armnn::ViewsDescriptor</a></div><div class="ttdoc">A ViewsDescriptor for the SplitterLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00217">Descriptors.hpp:217</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_ae5bfdd423b16f990c1713ef9f91f947b"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#ae5bfdd423b16f990c1713ef9f91f947b">armnn::LstmInputParamsInfo::GetRecurrentToCellWeights</a></div><div class="ttdeci">const TensorInfo & GetRecurrentToCellWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00145">LstmParams.hpp:145</a></div></div>
<div class="ttc" id="_cl_workload_factory_helper_8hpp_xhtml"><div class="ttname"><a href="_cl_workload_factory_helper_8hpp.xhtml">ClWorkloadFactoryHelper.hpp</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="structarmnn_1_1_lstm_input_params_info_xhtml_ad5f4be37766b41f342dd196cb1c6e141"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#ad5f4be37766b41f342dd196cb1c6e141">armnn::LstmInputParamsInfo::GetCellBias</a></div><div class="ttdeci">const TensorInfo & GetCellBias() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00173">LstmParams.hpp:173</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a86e88bef0df4df96df752b4b8955a3af"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">armnn::LstmDescriptor::m_ClippingThresProj</a></div><div class="ttdeci">float m_ClippingThresProj</div><div class="ttdoc">Clipping threshold value for the projection. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01125">Descriptors.hpp:1125</a></div></div>
<div class="ttc" id="classarmnn_1_1_optional_xhtml"><div class="ttname"><a href="classarmnn_1_1_optional.xhtml">armnn::Optional</a></div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00270">Optional.hpp:270</a></div></div>
<div class="ttc" id="_arm_compute_utils_8hpp_xhtml"><div class="ttname"><a href="_arm_compute_utils_8hpp.xhtml">ArmComputeUtils.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_ad159f9edbddeeb6cf6ff0ba042481ba8"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#ad159f9edbddeeb6cf6ff0ba042481ba8">armnn::LstmInputParamsInfo::GetRecurrentToInputWeights</a></div><div class="ttdeci">const TensorInfo & GetRecurrentToInputWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00137">LstmParams.hpp:137</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_aaf1af3bc828c5daa4a5c0bac28f63cc3"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#aaf1af3bc828c5daa4a5c0bac28f63cc3">armnn::LstmInputParamsInfo::GetCellLayerNormWeights</a></div><div class="ttdeci">const TensorInfo & GetCellLayerNormWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00197">LstmParams.hpp:197</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_views_descriptor_xhtml_a78e8266be865fdd92cadd04d6e25ae1f"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml#a78e8266be865fdd92cadd04d6e25ae1f">armnn::ViewsDescriptor::GetNumDimensions</a></div><div class="ttdeci">uint32_t GetNumDimensions() const</div><div class="ttdoc">Get the number of dimensions. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00300">Descriptors.cpp:300</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_afe4d25acd31b98dee6f6b28d4d756071"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#afe4d25acd31b98dee6f6b28d4d756071">armnn::LstmInputParamsInfo::GetRecurrentToOutputWeights</a></div><div class="ttdeci">const TensorInfo & GetRecurrentToOutputWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00149">LstmParams.hpp:149</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_a36fa9439fda2e72234411956a1c7e64f"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a36fa9439fda2e72234411956a1c7e64f">armnn::LstmInputParamsInfo::GetCellToInputWeights</a></div><div class="ttdeci">const TensorInfo & GetCellToInputWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00153">LstmParams.hpp:153</a></div></div>
<div class="ttc" id="_arm_compute_tensor_utils_8hpp_xhtml"><div class="ttname"><a href="_arm_compute_tensor_utils_8hpp.xhtml">ArmComputeTensorUtils.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a3dcd10ca3ea2e132558b1e2814668c15"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a3dcd10ca3ea2e132558b1e2814668c15">armnn::LstmDescriptor::m_TimeMajor</a></div><div class="ttdeci">bool m_TimeMajor</div><div class="ttdoc">Enable/disable time major. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01135">Descriptors.hpp:1135</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="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &&...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_aa1e93ef5f9ee3dbb5e7faa9578f180ae"><div class="ttname"><a href="namespacearmnn.xhtml#aa1e93ef5f9ee3dbb5e7faa9578f180ae">armnn::ConvertLstmActivationFuncToAclLayerInfo</a></div><div class="ttdeci">arm_compute::ActivationLayerInfo ConvertLstmActivationFuncToAclLayerInfo(uint32_t activationFunction)</div><div class="ttdef"><b>Definition:</b> <a href="_arm_compute_utils_8hpp_source.xhtml#l00116">ArmComputeUtils.hpp:116</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_a0e31db1891d11bbe0d8556c01e9812ef"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a0e31db1891d11bbe0d8556c01e9812ef">armnn::LstmInputParamsInfo::GetCellToForgetWeights</a></div><div class="ttdeci">const TensorInfo & GetCellToForgetWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00157">LstmParams.hpp:157</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a8cbabc875597b3bed0ccdc0adb289fde"><div class="ttname"><a href="namespacearmnn.xhtml#a8cbabc875597b3bed0ccdc0adb289fde">armnn::ComputeSplitAxis</a></div><div class="ttdeci">std::set< unsigned int > ComputeSplitAxis(const armnn::SplitterDescriptor &desc, const TensorShape &input)</div><div class="ttdef"><b>Definition:</b> <a href="_arm_compute_utils_8hpp_source.xhtml#l00244">ArmComputeUtils.hpp:244</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_ab50b4ccb0b84f6427996f76083a4107a"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#ab50b4ccb0b84f6427996f76083a4107a">armnn::LstmInputParamsInfo::GetForgetLayerNormWeights</a></div><div class="ttdeci">const TensorInfo & GetForgetLayerNormWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00193">LstmParams.hpp:193</a></div></div>
<div class="ttc" id="_numeric_cast_8hpp_xhtml"><div class="ttname"><a href="_numeric_cast_8hpp.xhtml">NumericCast.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_a35825b1ec5bc2b14c8eac60887dbcf19"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a35825b1ec5bc2b14c8eac60887dbcf19">armnn::LstmInputParamsInfo::GetCellToOutputWeights</a></div><div class="ttdeci">const TensorInfo & GetCellToOutputWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00161">LstmParams.hpp:161</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_abe8889e8150beef5fd204b2d87b49298"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">armnn::TensorInfo::SetShape</a></div><div class="ttdeci">void SetShape(const TensorShape &newShape)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00193">Tensor.hpp:193</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_a3b3c26330a05bf4ea40f8a6b402be354"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a3b3c26330a05bf4ea40f8a6b402be354">armnn::LstmInputParamsInfo::GetInputToCellWeights</a></div><div class="ttdeci">const TensorInfo & GetInputToCellWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00129">LstmParams.hpp:129</a></div></div>
<div class="ttc" id="structarmnn_1_1_workload_info_xhtml_ac97905bfa0daab357b91df1347600309"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">armnn::WorkloadInfo::m_InputTensorInfos</a></div><div class="ttdeci">std::vector< TensorInfo > m_InputTensorInfos</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00018">WorkloadInfo.hpp:18</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml">armnn::LstmInputParamsInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00063">LstmParams.hpp:63</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_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00048">Types.hpp:48</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a></div><div class="ttdoc">An LstmDescriptor for the LstmLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01083">Descriptors.hpp:1083</a></div></div>
<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml_aae0893695f5803a3517985c7cb1ccb2e"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">armnn::ViewsDescriptor::SetViewSize</a></div><div class="ttdeci">Status SetViewSize(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">Set the size of the views. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00315">Descriptors.cpp:315</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_a800adf0f61e84d706060f63037c1a336"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a800adf0f61e84d706060f63037c1a336">armnn::LstmInputParamsInfo::GetInputToOutputWeights</a></div><div class="ttdeci">const TensorInfo & GetInputToOutputWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00133">LstmParams.hpp:133</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">armnn::TensorInfo::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00198">Tensor.hpp:198</a></div></div>
<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a></div><div class="ttdoc">An OriginsDescriptor for the ConcatLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00174">Descriptors.hpp:174</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_ae22fc962c59e7c24986718f5af0020db"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#ae22fc962c59e7c24986718f5af0020db">armnn::LstmInputParamsInfo::m_ProjectionBias</a></div><div class="ttdeci">const TensorInfo * m_ProjectionBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00105">LstmParams.hpp:105</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a2837b4396f20c956952d1a7286cab5f8"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">armnn::LstmDescriptor::m_PeepholeEnabled</a></div><div class="ttdeci">bool m_PeepholeEnabled</div><div class="ttdoc">Enable/disable peephole. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01129">Descriptors.hpp:1129</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a67a0db04d321a74b7e7fcfd3f1a3f70b"><div class="ttname"><a href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">armnn::Status</a></div><div class="ttdeci">Status</div><div class="ttdoc">enumeration </div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00042">Types.hpp:42</a></div></div>
<div class="ttc" id="classarmnn_1_1_typed_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_typed_workload.xhtml">armnn::TypedWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_8hpp_source.xhtml#l00092">Workload.hpp:92</a></div></div>
<div class="ttc" id="namespacearmnn_utils_xhtml_ab53d94ea22b51c6bcdf9584644bd67bb"><div class="ttname"><a href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">armnnUtils::GetTensorShape</a></div><div class="ttdeci">armnn::TensorShape GetTensorShape(unsigned int numberOfBatches, unsigned int numberOfChannels, unsigned int height, unsigned int width, const armnn::DataLayout dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.xhtml#l00019">TensorUtils.cpp:19</a></div></div>
<div class="ttc" id="structarmnn_1_1_workload_info_xhtml_a67b178f8a836bc1e52b8de109760adfd"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">armnn::WorkloadInfo::m_OutputTensorInfos</a></div><div class="ttdeci">std::vector< TensorInfo > m_OutputTensorInfos</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00019">WorkloadInfo.hpp:19</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_a534af7e4f3a6d50a6dab05abc245133d"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a534af7e4f3a6d50a6dab05abc245133d">armnn::LstmInputParamsInfo::GetRecurrentToForgetWeights</a></div><div class="ttdeci">const TensorInfo & GetRecurrentToForgetWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00141">LstmParams.hpp:141</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_ae1b07ed928036004bd257169e5aeeef4"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">armnn::LstmDescriptor::m_ActivationFunc</a></div><div class="ttdeci">uint32_t m_ActivationFunc</div><div class="ttdoc">The activation function to use. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01121">Descriptors.hpp:1121</a></div></div>
<div class="ttc" id="classarmnn_1_1_cl_unidirectional_sequence_lstm_float_workload_xhtml_ae071e8822437c78baea75c3aef3a263a"><div class="ttname"><a href="classarmnn_1_1_cl_unidirectional_sequence_lstm_float_workload.xhtml#ae071e8822437c78baea75c3aef3a263a">armnn::ClUnidirectionalSequenceLstmFloatWorkload::Execute</a></div><div class="ttdeci">virtual void Execute() const override</div><div class="ttdef"><b>Definition:</b> <a href="_cl_unidirectional_sequence_lstm_float_workload_8cpp_source.xhtml#l00482">ClUnidirectionalSequenceLstmFloatWorkload.cpp:482</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a531a3907ec13d3772370da88030191a5"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">armnn::LstmDescriptor::m_ClippingThresCell</a></div><div class="ttdeci">float m_ClippingThresCell</div><div class="ttdoc">Clipping threshold value for the cell state. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01123">Descriptors.hpp:1123</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_afa2b04197a764428a8c3a648de8058fc"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#afa2b04197a764428a8c3a648de8058fc">armnn::LstmInputParamsInfo::GetInputToInputWeights</a></div><div class="ttdeci">const TensorInfo & GetInputToInputWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00121">LstmParams.hpp:121</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_a045674b768295e617d7060f96f162366"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a045674b768295e617d7060f96f162366">armnn::LstmInputParamsInfo::GetOutputLayerNormWeights</a></div><div class="ttdeci">const TensorInfo & GetOutputLayerNormWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00201">LstmParams.hpp:201</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_ad474e5c51a0b194ef32e812b86c0cbdb"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">armnn::LstmDescriptor::m_CifgEnabled</a></div><div class="ttdeci">bool m_CifgEnabled</div><div class="ttdoc">Enable/disable cifg (coupled input & forget gate). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01127">Descriptors.hpp:1127</a></div></div>
<div class="ttc" id="_cl_tensor_handle_8hpp_xhtml"><div class="ttname"><a href="_cl_tensor_handle_8hpp.xhtml">ClTensorHandle.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_ac81393ef433b0c7c337f9f0d55f41ae4"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#ac81393ef433b0c7c337f9f0d55f41ae4">armnn::LstmInputParamsInfo::GetForgetGateBias</a></div><div class="ttdeci">const TensorInfo & GetForgetGateBias() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00169">LstmParams.hpp:169</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_cl_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_cl_tensor_handle.xhtml">armnn::IClTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_cl_tensor_handle_8hpp_source.xhtml#l00013">IClTensorHandle.hpp:13</a></div></div>
<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml_a5b192c5fcd96a0f75542524cf646b355"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#a5b192c5fcd96a0f75542524cf646b355">armnn::OriginsDescriptor::SetConcatAxis</a></div><div class="ttdeci">void SetConcatAxis(unsigned int concatAxis)</div><div class="ttdoc">Set the concatenation axis value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00158">Descriptors.cpp:158</a></div></div>
<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml_a78e8266be865fdd92cadd04d6e25ae1f"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#a78e8266be865fdd92cadd04d6e25ae1f">armnn::OriginsDescriptor::GetNumDimensions</a></div><div class="ttdeci">uint32_t GetNumDimensions() const</div><div class="ttdoc">Get the number of dimensions. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00192">Descriptors.cpp:192</a></div></div>
<div class="ttc" id="_profiling_8hpp_xhtml_a786492a3881a4c760ab1eec2149f4aba"><div class="ttname"><a href="_profiling_8hpp.xhtml#a786492a3881a4c760ab1eec2149f4aba">ARMNN_REPORT_PROFILING_WORKLOAD_DESC</a></div><div class="ttdeci">#define ARMNN_REPORT_PROFILING_WORKLOAD_DESC(name, desc, infos, guid)</div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8hpp_source.xhtml#l00227">Profiling.hpp:227</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a924063ef859ec5f9a1466a42e7409c85"><div class="ttname"><a href="namespacearmnn.xhtml#a924063ef859ec5f9a1466a42e7409c85">armnn::ClUnidirectionalSequenceLstmFloatWorkloadValidate</a></div><div class="ttdeci">arm_compute::Status ClUnidirectionalSequenceLstmFloatWorkloadValidate(const TensorInfo &input, const TensorInfo &outputStateIn, const TensorInfo &cellStateIn, const TensorInfo &output, const Optional< TensorInfo > &hiddenStateOutput, const Optional< TensorInfo > &cellStateOutput, const UnidirectionalSequenceLstmDescriptor &descriptor, const LstmInputParamsInfo &paramsInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_cl_unidirectional_sequence_lstm_float_workload_8cpp_source.xhtml#l00508">ClUnidirectionalSequenceLstmFloatWorkload.cpp:508</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a4a8ec49f130084445d44297549254780"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">armnn::LstmDescriptor::m_LayerNormEnabled</a></div><div class="ttdeci">bool m_LayerNormEnabled</div><div class="ttdoc">Enable/disable layer normalization. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01133">Descriptors.hpp:1133</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_ae1d5a487fcd13852927c8a2b9f9dfeb6"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#ae1d5a487fcd13852927c8a2b9f9dfeb6">armnn::LstmInputParamsInfo::GetInputGateBias</a></div><div class="ttdeci">const TensorInfo & GetInputGateBias() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00165">LstmParams.hpp:165</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_a18038725f71bb5c5bd03c02cc164f879"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a18038725f71bb5c5bd03c02cc164f879">armnn::LstmInputParamsInfo::GetProjectionWeights</a></div><div class="ttdeci">const TensorInfo & GetProjectionWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00181">LstmParams.hpp:181</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_a7dac08f19a1b235d5256d39136848a09"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a7dac08f19a1b235d5256d39136848a09">armnn::LstmInputParamsInfo::GetInputToForgetWeights</a></div><div class="ttdeci">const TensorInfo & GetInputToForgetWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00125">LstmParams.hpp:125</a></div></div>
<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about TensorInfos of a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml">armnn::UnidirectionalSequenceLstmQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00731">WorkloadData.hpp:731</a></div></div>
<div class="ttc" id="classarmnn_1_1_cl_unidirectional_sequence_lstm_float_workload_xhtml_a9d2fcde9a15c84c5cca2d5a26aa5bbec"><div class="ttname"><a href="classarmnn_1_1_cl_unidirectional_sequence_lstm_float_workload.xhtml#a9d2fcde9a15c84c5cca2d5a26aa5bbec">armnn::ClUnidirectionalSequenceLstmFloatWorkload::ClUnidirectionalSequenceLstmFloatWorkload</a></div><div class="ttdeci">ClUnidirectionalSequenceLstmFloatWorkload(const UnidirectionalSequenceLstmQueueDescriptor &descriptor, const WorkloadInfo &info, const arm_compute::CLCompileContext &clCompileContext)</div><div class="ttdef"><b>Definition:</b> <a href="_cl_unidirectional_sequence_lstm_float_workload_8cpp_source.xhtml#l00032">ClUnidirectionalSequenceLstmFloatWorkload.cpp:32</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a0eec4a463a166fad55307d9f26ba3a68"><div class="ttname"><a href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">armnn::InitializeArmComputeClTensorData</a></div><div class="ttdeci">void InitializeArmComputeClTensorData(arm_compute::CLTensor &clTensor, const ConstTensorHandle *handle)</div><div class="ttdef"><b>Definition:</b> <a href="_cl_workload_utils_8hpp_source.xhtml#l00115">ClWorkloadUtils.hpp:115</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_a3d2f638ba83ae5dad0094c006220c232"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a3d2f638ba83ae5dad0094c006220c232">armnn::LstmInputParamsInfo::GetInputLayerNormWeights</a></div><div class="ttdeci">const TensorInfo & GetInputLayerNormWeights() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00189">LstmParams.hpp:189</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_ae0da94ba17ce67b95b5b9d6e5adc4271"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#ae0da94ba17ce67b95b5b9d6e5adc4271">armnn::LstmInputParamsInfo::GetOutputGateBias</a></div><div class="ttdeci">const TensorInfo & GetOutputGateBias() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00177">LstmParams.hpp:177</a></div></div>
<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml_a379929e3b277f1ef94f3ce645870589d"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#a379929e3b277f1ef94f3ce645870589d">armnn::OriginsDescriptor::GetConcatAxis</a></div><div class="ttdeci">unsigned int GetConcatAxis() const</div><div class="ttdoc">Get the concatenation axis value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00162">Descriptors.cpp:162</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_a9f2cce936b4df49c487eaca513bf55ca"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a9f2cce936b4df49c487eaca513bf55ca">armnn::LstmInputParamsInfo::GetProjectionBias</a></div><div class="ttdeci">const TensorInfo & GetProjectionBias() const</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00185">LstmParams.hpp:185</a></div></div>
<div class="ttc" id="_cl_workload_utils_8hpp_xhtml"><div class="ttname"><a href="_cl_workload_utils_8hpp.xhtml">ClWorkloadUtils.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml_a2b125117aa61f9baf3a9cb8658aa61a2"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">armnn::ViewsDescriptor::SetViewOriginCoord</a></div><div class="ttdeci">Status SetViewOriginCoord(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">Set the view origin coordinates. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00310">Descriptors.cpp:310</a></div></div>
<div class="ttc" id="_workload_utils_8hpp_xhtml"><div class="ttname"><a href="_workload_utils_8hpp.xhtml">WorkloadUtils.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml_a2b125117aa61f9baf3a9cb8658aa61a2"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">armnn::OriginsDescriptor::SetViewOriginCoord</a></div><div class="ttdeci">Status SetViewOriginCoord(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">Set the view origin coordinates. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00167">Descriptors.cpp:167</a></div></div>
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