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diff --git a/20.02/_neon_lstm_float_workload_8cpp_source.xhtml b/20.02/_neon_lstm_float_workload_8cpp_source.xhtml new file mode 100644 index 0000000000..11ddef61da --- /dev/null +++ b/20.02/_neon_lstm_float_workload_8cpp_source.xhtml @@ -0,0 +1,168 @@ +<!-- Copyright (c) 2020 ARM Limited. --> +<!-- --> +<!-- SPDX-License-Identifier: MIT --> +<!-- --> +<!-- HTML header for doxygen 1.8.13--> +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> +<html xmlns="http://www.w3.org/1999/xhtml"> +<head> +<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/> +<meta http-equiv="X-UA-Compatible" content="IE=9"/> +<meta name="generator" content="Doxygen 1.8.13"/> +<meta name="robots" content="NOINDEX, NOFOLLOW" /> +<meta name="viewport" content="width=device-width, initial-scale=1"/> +<title>ArmNN: src/backends/neon/workloads/NeonLstmFloatWorkload.cpp Source File</title> +<link href="tabs.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="jquery.js"></script> +<script type="text/javascript" src="dynsections.js"></script> +<link href="navtree.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="resize.js"></script> +<script type="text/javascript" src="navtreedata.js"></script> +<script type="text/javascript" src="navtree.js"></script> +<script type="text/javascript"> + $(document).ready(initResizable); +</script> +<link href="search/search.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="search/searchdata.js"></script> +<script type="text/javascript" src="search/search.js"></script> +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + extensions: ["tex2jax.js"], + jax: ["input/TeX","output/HTML-CSS"], +}); +</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script> +<link href="doxygen.css" rel="stylesheet" type="text/css" /> +<link href="stylesheet.css" rel="stylesheet" type="text/css"/> +</head> +<body> +<div id="top"><!-- do not remove this div, it is closed by doxygen! --> +<div id="titlearea"> +<table cellspacing="0" cellpadding="0"> + <tbody> + <tr style="height: 56px;"> + <img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/> + <td style="padding-left: 0.5em;"> + <div id="projectname"> +  <span id="projectnumber">20.02</span> + </div> + </td> + </tr> + </tbody> +</table> +</div> +<!-- end header part --> +<!-- Generated by Doxygen 1.8.13 --> +<script type="text/javascript"> +var searchBox = new SearchBox("searchBox", "search",false,'Search'); +</script> +<script type="text/javascript" src="menudata.js"></script> +<script type="text/javascript" src="menu.js"></script> +<script type="text/javascript"> +$(function() { + initMenu('',true,false,'search.php','Search'); + $(document).ready(function() { init_search(); }); +}); +</script> +<div id="main-nav"></div> +</div><!-- top --> +<div id="side-nav" class="ui-resizable side-nav-resizable"> + <div id="nav-tree"> + <div id="nav-tree-contents"> + <div id="nav-sync" class="sync"></div> + </div> + </div> + <div id="splitbar" style="-moz-user-select:none;" + class="ui-resizable-handle"> + </div> +</div> +<script type="text/javascript"> +$(document).ready(function(){initNavTree('_neon_lstm_float_workload_8cpp_source.xhtml','');}); +</script> +<div id="doc-content"> +<!-- window showing the filter options --> +<div id="MSearchSelectWindow" + onmouseover="return searchBox.OnSearchSelectShow()" + onmouseout="return searchBox.OnSearchSelectHide()" + onkeydown="return searchBox.OnSearchSelectKey(event)"> +</div> + +<!-- iframe showing the search results (closed by default) --> +<div id="MSearchResultsWindow"> +<iframe src="javascript:void(0)" frameborder="0" + name="MSearchResults" id="MSearchResults"> +</iframe> +</div> + +<div class="header"> + <div class="headertitle"> +<div class="title">NeonLstmFloatWorkload.cpp</div> </div> +</div><!--header--> +<div class="contents"> +<a href="_neon_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 © 2017 Arm Ltd. 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="_neon_lstm_float_workload_8hpp.xhtml">NeonLstmFloatWorkload.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include "<a class="code" href="_neon_workload_utils_8hpp.xhtml">NeonWorkloadUtils.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_tensor_utils_8hpp.xhtml">aclCommon/ArmComputeTensorUtils.hpp</a>"</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include "<a class="code" href="_neon_tensor_handle_8hpp.xhtml">neon/NeonTensorHandle.hpp</a>"</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> {</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="keyword">using namespace </span>armcomputetensorutils;</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"><a class="line" href="classarmnn_1_1_neon_lstm_float_workload.xhtml#a471d876fac9cbcc3347eec34e558fe46"> 17</a></span> <a class="code" href="classarmnn_1_1_neon_lstm_float_workload.xhtml#a471d876fac9cbcc3347eec34e558fe46">NeonLstmFloatWorkload::NeonLstmFloatWorkload</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml">LstmQueueDescriptor</a> &descriptor, <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="l00018"></a><span class="lineno"> 18</span>  : <a class="code" href="classarmnn_1_1_typed_workload.xhtml">FloatWorkload</a><<a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml">LstmQueueDescriptor</a>>(descriptor, info)</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> {</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  arm_compute::LSTMParams<arm_compute::ITensor> lstm_param;</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  <span class="comment">// Basic parameters</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  m_InputToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  BuildArmComputeTensor(*m_InputToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputToForgetWeights->GetTensorInfo());</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  m_InputToCellWeightsTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  BuildArmComputeTensor(*m_InputToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputToCellWeights->GetTensorInfo());</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>  m_InputToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  BuildArmComputeTensor(*m_InputToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputToOutputWeights->GetTensorInfo());</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> </div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  m_RecurrentToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_RecurrentToForgetWeights->GetTensorInfo());</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  m_RecurrentToCellWeightsTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_RecurrentToCellWeights->GetTensorInfo());</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  m_RecurrentToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_RecurrentToOutputWeights->GetTensorInfo());</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  m_ForgetGateBiasTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  BuildArmComputeTensor(*m_ForgetGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ForgetGateBias->GetTensorInfo());</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> </div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  m_CellBiasTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  BuildArmComputeTensor(*m_CellBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellBias->GetTensorInfo());</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  m_OutputGateBiasTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  BuildArmComputeTensor(*m_OutputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_OutputGateBias->GetTensorInfo());</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="comment">// for future reference: check the AndroidNN API for the logic here</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_CifgEnabled)</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  {</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  m_InputToInputWeightsTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  BuildArmComputeTensor(*m_InputToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputToInputWeights->GetTensorInfo());</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>  m_RecurrentToInputWeightsTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_RecurrentToInputWeights->GetTensorInfo());</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> </div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  m_CellToInputWeightsTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellToInputWeights != <span class="keyword">nullptr</span>)</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>  BuildArmComputeTensor(*m_CellToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellToInputWeights->GetTensorInfo());</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  }</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> </div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  m_InputGateBiasTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  BuildArmComputeTensor(*m_InputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputGateBias->GetTensorInfo());</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> </div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  lstm_param.set_cifg_params(m_InputToInputWeightsTensor.get(),</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  m_RecurrentToInputWeightsTensor.get(),</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellToInputWeights != <span class="keyword">nullptr</span> ? m_CellToInputWeightsTensor.get() : <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  m_InputGateBiasTensor.get());</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  }</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_ProjectionEnabled)</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  {</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  m_ProjectionWeightsTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  BuildArmComputeTensor(*m_ProjectionWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ProjectionWeights->GetTensorInfo());</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  m_ProjectionBiasTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ProjectionBias != <span class="keyword">nullptr</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>  BuildArmComputeTensor(*m_ProjectionBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ProjectionBias->GetTensorInfo());</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  }</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> </div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  lstm_param.set_projection_params(m_ProjectionWeightsTensor.get(),</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ProjectionBias != <span class="keyword">nullptr</span> ? m_ProjectionBiasTensor.get() : <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  }</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> </div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_PeepholeEnabled)</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  {</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  m_CellToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  BuildArmComputeTensor(*m_CellToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellToForgetWeights->GetTensorInfo());</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> </div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  m_CellToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  BuildArmComputeTensor(*m_CellToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellToOutputWeights->GetTensorInfo());</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>  lstm_param.set_peephole_params(m_CellToForgetWeightsTensor.get(), m_CellToOutputWeightsTensor.get());</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  }</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> </div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_LayerNormEnabled)</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  {</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  m_InputLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_CifgEnabled)</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>  BuildArmComputeTensor(*m_InputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputLayerNormWeights->GetTensorInfo());</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> </div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  m_ForgetLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  BuildArmComputeTensor(*m_ForgetLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ForgetLayerNormWeights->GetTensorInfo());</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>  m_CellLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  BuildArmComputeTensor(*m_CellLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellLayerNormWeights->GetTensorInfo());</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> </div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  m_OutputLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  BuildArmComputeTensor(*m_OutputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_OutputLayerNormWeights->GetTensorInfo());</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> </div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  lstm_param.set_layer_normalization_params(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_CifgEnabled ?</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keyword">nullptr</span> : m_InputLayerNormWeightsTensor.get(),</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  m_ForgetLayerNormWeightsTensor.get(),</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  m_CellLayerNormWeightsTensor.get(),</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  m_OutputLayerNormWeightsTensor.get());</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  }</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> </div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keyword">const</span> arm_compute::ITensor& input = <span class="keyword">static_cast<</span><a class="code" href="classarmnn_1_1_i_acl_tensor_handle.xhtml">IAclTensorHandle</a>*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0])->GetTensor();</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keyword">const</span> arm_compute::ITensor& output_state_in = <span class="keyword">static_cast<</span><a class="code" href="classarmnn_1_1_i_acl_tensor_handle.xhtml">IAclTensorHandle</a>*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[1])->GetTensor();</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keyword">const</span> arm_compute::ITensor& cell_state_in = <span class="keyword">static_cast<</span><a class="code" href="classarmnn_1_1_i_acl_tensor_handle.xhtml">IAclTensorHandle</a>*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[2])->GetTensor();</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  arm_compute::ITensor& output_state_out = <span class="keyword">static_cast<</span><a class="code" href="classarmnn_1_1_i_acl_tensor_handle.xhtml">IAclTensorHandle</a>*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[1])->GetTensor();</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  arm_compute::ITensor& cell_state_out = <span class="keyword">static_cast<</span><a class="code" href="classarmnn_1_1_i_acl_tensor_handle.xhtml">IAclTensorHandle</a>*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[2])->GetTensor();</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  arm_compute::ITensor& output = <span class="keyword">static_cast<</span><a class="code" href="classarmnn_1_1_i_acl_tensor_handle.xhtml">IAclTensorHandle</a>*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[3])->GetTensor();</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> </div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="comment">// Get the batch_size and the num_units from the cellStateIn dimensions</span></div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = info.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[2];</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batch_size = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0]);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_units = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1]);</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>  m_ScratchBuffer = std::make_unique<arm_compute::Tensor>();</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_CifgEnabled)</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  {</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="comment">// 2D tensor with dimensions [num_units * 3, batch_size] with CIFG</span></div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> scratchBuffer1({ batch_size, num_units * 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  BuildArmComputeTensor(*m_ScratchBuffer, scratchBuffer1);</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  {</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="comment">// scratch_buffer [num_units * 4, batch_size] without CIFG</span></div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> scratchBuffer2({ batch_size, num_units * 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  BuildArmComputeTensor(*m_ScratchBuffer, scratchBuffer2);</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  }</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> </div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordtype">float</span> cell_threshold = <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_ClippingThresCell;</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keywordtype">float</span> projection_threshold = <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_ClippingThresProj;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> </div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="comment">// for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations</span></div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  arm_compute::ActivationLayerInfo activationLayerInfo;</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_ActivationFunc == 0)</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  {</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="comment">// no activation, do nothing</span></div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  }</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_ActivationFunc == 1)</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>  activationLayerInfo = arm_compute::ActivationLayerInfo(</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  arm_compute::ActivationLayerInfo::ActivationFunction::RELU);</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  }</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_ActivationFunc == 3)</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  {</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  activationLayerInfo = arm_compute::ActivationLayerInfo(</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.0);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  }</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_ActivationFunc == 4)</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  {</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  activationLayerInfo = arm_compute::ActivationLayerInfo(</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  arm_compute::ActivationLayerInfo::ActivationFunction::TANH, 1.0, 1.0);</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  }</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_ActivationFunc == 6)</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>  activationLayerInfo = arm_compute::ActivationLayerInfo(</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC);</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  }</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  {</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(<span class="stringliteral">"Wrong Type of Activation Function!"</span>);</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  }</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span> </div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  m_LstmLayer.configure(&input, m_InputToForgetWeightsTensor.get(), m_InputToCellWeightsTensor.get(),</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  m_InputToOutputWeightsTensor.get(), m_RecurrentToForgetWeightsTensor.get(),</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  m_RecurrentToCellWeightsTensor.get(), m_RecurrentToOutputWeightsTensor.get(),</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  m_ForgetGateBiasTensor.get(), m_CellBiasTensor.get(), m_OutputGateBiasTensor.get(),</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  &output_state_in, &cell_state_in, m_ScratchBuffer.get(), &output_state_out,</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  &cell_state_out, &output, lstm_param, activationLayerInfo,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  cell_threshold, projection_threshold);</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>  armcomputetensorutils::InitialiseArmComputeTensorEmpty(*m_ScratchBuffer);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> </div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_InputToForgetWeightsTensor,</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputToForgetWeights);</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_InputToCellWeightsTensor,</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputToCellWeights);</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_InputToOutputWeightsTensor,</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputToOutputWeights);</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_RecurrentToForgetWeightsTensor,</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_RecurrentToForgetWeights);</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_RecurrentToCellWeightsTensor,</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_RecurrentToCellWeights);</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_RecurrentToOutputWeightsTensor,</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_RecurrentToOutputWeights);</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_ForgetGateBiasTensor,</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ForgetGateBias);</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_CellBiasTensor,</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellBias);</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_OutputGateBiasTensor,</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_OutputGateBias);</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span> </div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_CifgEnabled)</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  {</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_InputToInputWeightsTensor,</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputToInputWeights);</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_RecurrentToInputWeightsTensor,</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_RecurrentToInputWeights);</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellToInputWeights != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  {</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_CellToInputWeightsTensor,</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellToInputWeights);</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  }</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_InputGateBiasTensor,</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputGateBias);</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> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_ProjectionEnabled)</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>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_ProjectionWeightsTensor,</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ProjectionWeights);</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ProjectionBias != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  {</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_ProjectionBiasTensor,</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ProjectionBias);</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  }</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  }</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> </div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_PeepholeEnabled)</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  {</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_CellToForgetWeightsTensor,</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellToForgetWeights);</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_CellToOutputWeightsTensor,</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellToOutputWeights);</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  }</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> </div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_LayerNormEnabled)</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  {</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_CifgEnabled)</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>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_InputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputLayerNormWeights);</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  }</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_ForgetLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ForgetLayerNormWeights);</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_CellLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellLayerNormWeights);</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <a class="code" href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">InitializeArmComputeTensorData</a>(*m_OutputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_OutputLayerNormWeights);</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  }</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> </div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="comment">// Force Compute Library to perform the necessary copying and reshaping, after which</span></div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="comment">// delete all the input tensors that will no longer be needed</span></div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  m_LstmLayer.prepare();</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  FreeUnusedTensors();</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span> }</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span> </div><div class="line"><a name="l00266"></a><span class="lineno"><a class="line" href="classarmnn_1_1_neon_lstm_float_workload.xhtml#ae071e8822437c78baea75c3aef3a263a"> 266</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_neon_lstm_float_workload.xhtml#ae071e8822437c78baea75c3aef3a263a">NeonLstmFloatWorkload::Execute</a>()<span class="keyword"> const</span></div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> <span class="keyword"></span>{</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  m_LstmLayer.run();</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> }</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> </div><div class="line"><a name="l00271"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a9e06cc2a2ac8b88fc72972695a17910f"> 271</a></span> <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a> <a class="code" href="namespacearmnn.xhtml#a9e06cc2a2ac8b88fc72972695a17910f">NeonLstmFloatWorkloadValidate</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="l00272"></a><span class="lineno"> 272</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="l00273"></a><span class="lineno"> 273</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="l00274"></a><span class="lineno"> 274</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& scratchBuffer,</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputStateOut,</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& cellStateOut,</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</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="l00278"></a><span class="lineno"> 278</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">LstmDescriptor</a>& descriptor,</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</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="l00280"></a><span class="lineno"> 280</span> {</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  arm_compute::LSTMParams<arm_compute::ITensorInfo> lstm_params_info;</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> </div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="comment">// The inputs and outputs</span></div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input);</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclOutputStateInInfo = BuildArmComputeTensorInfo(outputStateIn);</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclCellStateInInfo = BuildArmComputeTensorInfo(cellStateIn);</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclScratchBufferInfo = BuildArmComputeTensorInfo(scratchBuffer);</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclOutputStateOutInfo = BuildArmComputeTensorInfo(outputStateOut);</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclCellStateOutInfo = BuildArmComputeTensorInfo(cellStateOut);</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output);</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span> </div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="comment">// Basic parameters</span></div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclInputToForgetWeightsInfo</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a7dac08f19a1b235d5256d39136848a09">GetInputToForgetWeights</a>());</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclInputToCellWeightsInfo</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a3b3c26330a05bf4ea40f8a6b402be354">GetInputToCellWeights</a>());</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclInputToOutputWeightsInfo</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a800adf0f61e84d706060f63037c1a336">GetInputToOutputWeights</a>());</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclRecurrentToForgetWeightsInfo</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a534af7e4f3a6d50a6dab05abc245133d">GetRecurrentToForgetWeights</a>());</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclRecurrentToCellWeightsInfo</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ae5bfdd423b16f990c1713ef9f91f947b">GetRecurrentToCellWeights</a>());</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclRecurrentToOutputWeightsInfo</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#afe4d25acd31b98dee6f6b28d4d756071">GetRecurrentToOutputWeights</a>());</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclForgetGateBiasInfo</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ac81393ef433b0c7c337f9f0d55f41ae4">GetForgetGateBias</a>());</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclCellBiasInfo</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ad5f4be37766b41f342dd196cb1c6e141">GetCellBias</a>());</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclOutputGateBiasInfo</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  = BuildArmComputeTensorInfo(paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#ae0da94ba17ce67b95b5b9d6e5adc4271">GetOutputGateBias</a>());</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span> </div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  arm_compute::TensorInfo aclInputToInputWeightsInfo;</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  arm_compute::TensorInfo aclRecurrentToInputWeightsInfo;</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  arm_compute::TensorInfo aclCellToInputWeightsInfo;</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  arm_compute::TensorInfo aclInputGateBiasInfo;</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  arm_compute::TensorInfo aclProjectionWeightsInfo;</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  arm_compute::TensorInfo aclProjectionBiasInfo;</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  arm_compute::TensorInfo aclCellToForgetWeightsInfo;</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  arm_compute::TensorInfo aclCellToOutputWeightsInfo;</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span> </div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  arm_compute::TensorInfo aclInputLayerNormWeightsInfo;</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  arm_compute::TensorInfo aclForgetLayerNormWeightsInfo;</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  arm_compute::TensorInfo aclCellLayerNormWeightsInfo;</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  arm_compute::TensorInfo aclOutputLayerNormWeightsInfo;</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span> </div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> </div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <span class="keywordflow">if</span> (!descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a>)</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  {</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</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="l00330"></a><span class="lineno"> 330</span>  {</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</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="l00332"></a><span class="lineno"> 332</span>  }</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</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="l00334"></a><span class="lineno"> 334</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="l00335"></a><span class="lineno"> 335</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="l00336"></a><span class="lineno"> 336</span> </div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  lstm_params_info.set_cifg_params(&aclInputToInputWeightsInfo, &aclRecurrentToInputWeightsInfo,</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</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="l00339"></a><span class="lineno"> 339</span>  &aclInputGateBiasInfo);</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  }</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span> </div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keywordflow">if</span> (descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a>)</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  {</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <span class="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="l00345"></a><span class="lineno"> 345</span>  {</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</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="l00347"></a><span class="lineno"> 347</span>  }</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</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="l00349"></a><span class="lineno"> 349</span> </div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  lstm_params_info.set_projection_params(&aclProjectionWeightsInfo,</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</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="l00352"></a><span class="lineno"> 352</span>  &aclProjectionBiasInfo : <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  }</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span> </div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <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="l00356"></a><span class="lineno"> 356</span>  {</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</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="l00358"></a><span class="lineno"> 358</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="l00359"></a><span class="lineno"> 359</span> </div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  lstm_params_info.set_peephole_params(&aclCellToForgetWeightsInfo, &aclCellToOutputWeightsInfo);</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  }</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>  <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="l00364"></a><span class="lineno"> 364</span>  {</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</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="l00366"></a><span class="lineno"> 366</span>  {</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</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="l00368"></a><span class="lineno"> 368</span>  }</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</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="l00370"></a><span class="lineno"> 370</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="l00371"></a><span class="lineno"> 371</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="l00372"></a><span class="lineno"> 372</span> </div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  lstm_params_info.set_layer_normalization_params(descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> ?</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="keyword">nullptr</span> : &aclInputLayerNormWeightsInfo,</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  &aclForgetLayerNormWeightsInfo,</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  &aclCellLayerNormWeightsInfo,</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  &aclOutputLayerNormWeightsInfo);</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>  <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="l00381"></a><span class="lineno"> 381</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="l00382"></a><span class="lineno"> 382</span> </div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="comment">// for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations</span></div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  arm_compute::ActivationLayerInfo activationLayerInfo;</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <span class="keywordflow">switch</span> (descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a>)</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">case</span> 0:</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <span class="comment">// no activation, do nothing</span></div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="keywordflow">case</span> 1:</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  activationLayerInfo = arm_compute::ActivationLayerInfo(</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  arm_compute::ActivationLayerInfo::ActivationFunction::RELU);</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="keywordflow">case</span> 3:</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  activationLayerInfo = arm_compute::ActivationLayerInfo(</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.0);</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="keywordflow">case</span> 4:</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  activationLayerInfo = arm_compute::ActivationLayerInfo(</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  arm_compute::ActivationLayerInfo::ActivationFunction::TANH, 1.0, 1.0);</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="keywordflow">case</span> 6:</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  activationLayerInfo = arm_compute::ActivationLayerInfo(</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC);</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(<span class="stringliteral">"Wrong Type of Activation Function!"</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> </div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <span class="keywordflow">return</span> arm_compute::NELSTMLayer::validate(&aclInputInfo,</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  &aclInputToForgetWeightsInfo,</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  &aclInputToCellWeightsInfo,</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  &aclInputToOutputWeightsInfo,</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  &aclRecurrentToForgetWeightsInfo,</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  &aclRecurrentToCellWeightsInfo,</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  &aclRecurrentToOutputWeightsInfo,</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  &aclForgetGateBiasInfo,</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  &aclCellBiasInfo,</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  &aclOutputGateBiasInfo,</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  &aclOutputStateInInfo,</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  &aclCellStateInInfo,</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  &aclScratchBufferInfo,</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  &aclOutputStateOutInfo,</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  &aclCellStateOutInfo,</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  &aclOutputInfo,</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  lstm_params_info,</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  activationLayerInfo,</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  cell_threshold,</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  projection_threshold);</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span> }</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span> </div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span> <span class="keywordtype">void</span> NeonLstmFloatWorkload::FreeUnusedTensors()</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span> {</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  FreeTensorIfUnused(m_InputToInputWeightsTensor);</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  FreeTensorIfUnused(m_InputToForgetWeightsTensor);</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  FreeTensorIfUnused(m_InputToCellWeightsTensor);</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  FreeTensorIfUnused(m_InputToOutputWeightsTensor);</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  FreeTensorIfUnused(m_RecurrentToInputWeightsTensor);</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  FreeTensorIfUnused(m_RecurrentToForgetWeightsTensor);</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  FreeTensorIfUnused(m_RecurrentToCellWeightsTensor);</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  FreeTensorIfUnused(m_RecurrentToOutputWeightsTensor);</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  FreeTensorIfUnused(m_CellToInputWeightsTensor);</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  FreeTensorIfUnused(m_CellToForgetWeightsTensor);</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  FreeTensorIfUnused(m_CellToOutputWeightsTensor);</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  FreeTensorIfUnused(m_InputGateBiasTensor);</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  FreeTensorIfUnused(m_ForgetGateBiasTensor);</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  FreeTensorIfUnused(m_CellBiasTensor);</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  FreeTensorIfUnused(m_OutputGateBiasTensor);</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  FreeTensorIfUnused(m_ProjectionWeightsTensor);</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  FreeTensorIfUnused(m_ProjectionBiasTensor);</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  FreeTensorIfUnused(m_ScratchBuffer);</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  FreeTensorIfUnused(m_InputLayerNormWeightsTensor);</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  FreeTensorIfUnused(m_ForgetLayerNormWeightsTensor);</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  FreeTensorIfUnused(m_CellLayerNormWeightsTensor);</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  FreeTensorIfUnused(m_OutputLayerNormWeightsTensor);</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> </div><div class="line"><a name="l00458"></a><span class="lineno"> 458</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#l00871">Descriptors.hpp:871</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="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#l00088">Tensor.hpp:88</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#l00865">Descriptors.hpp:865</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#l00053">Tensor.hpp:53</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="classarmnn_1_1_base_workload_xhtml_a0a487c549c63319505095b855ea3c195"><div class="ttname"><a href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">armnn::BaseWorkload::m_Data</a></div><div class="ttdeci">const QueueDescriptor m_Data</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8hpp_source.xhtml#l00046">Workload.hpp:46</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="classarmnn_1_1_neon_lstm_float_workload_xhtml_ae071e8822437c78baea75c3aef3a263a"><div class="ttname"><a href="classarmnn_1_1_neon_lstm_float_workload.xhtml#ae071e8822437c78baea75c3aef3a263a">armnn::NeonLstmFloatWorkload::Execute</a></div><div class="ttdeci">virtual void Execute() const override</div><div class="ttdef"><b>Definition:</b> <a href="_neon_lstm_float_workload_8cpp_source.xhtml#l00266">NeonLstmFloatWorkload.cpp:266</a></div></div> +<div class="ttc" id="_neon_tensor_handle_8hpp_xhtml"><div class="ttname"><a href="_neon_tensor_handle_8hpp.xhtml">NeonTensorHandle.hpp</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a9e06cc2a2ac8b88fc72972695a17910f"><div class="ttname"><a href="namespacearmnn.xhtml#a9e06cc2a2ac8b88fc72972695a17910f">armnn::NeonLstmFloatWorkloadValidate</a></div><div class="ttdeci">arm_compute::Status NeonLstmFloatWorkloadValidate(const TensorInfo &input, const TensorInfo &outputStateIn, const TensorInfo &cellStateIn, const TensorInfo &scratchBuffer, const TensorInfo &outputStateOut, const TensorInfo &cellStateOut, const TensorInfo &output, const LstmDescriptor &descriptor, const LstmInputParamsInfo &paramsInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_neon_lstm_float_workload_8cpp_source.xhtml#l00271">NeonLstmFloatWorkload.cpp:271</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="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2020 ARM Limited. </div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00025">00_introduction.dox:25</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="classarmnn_1_1_i_acl_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_acl_tensor_handle.xhtml">armnn::IAclTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_arm_compute_tensor_handle_8hpp_source.xhtml#l00016">ArmComputeTensorHandle.hpp:16</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="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="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="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#l00837">Descriptors.hpp:837</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml">armnn::LstmQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00358">WorkloadData.hpp:358</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="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#l00869">Descriptors.hpp:869</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#l00026">Types.hpp:26</a></div></div> +<div class="ttc" id="_neon_workload_utils_8hpp_xhtml"><div class="ttname"><a href="_neon_workload_utils_8hpp.xhtml">NeonWorkloadUtils.hpp</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="_workload_8hpp_source.xhtml#l00052">Workload.hpp:52</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a37fa39012e90d568df7f774cd6d1e956"><div class="ttname"><a href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">armnn::numeric_cast</a></div><div class="ttdeci">std::enable_if_t< std::is_unsigned< Source >::value &&std::is_unsigned< Dest >::value, Dest > numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00033">NumericCast.hpp:33</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#l00861">Descriptors.hpp:861</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#l00863">Descriptors.hpp:863</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad9aa8d49d42ada3f757290033af39857"><div class="ttname"><a href="namespacearmnn.xhtml#ad9aa8d49d42ada3f757290033af39857">armnn::InitializeArmComputeTensorData</a></div><div class="ttdeci">void InitializeArmComputeTensorData(arm_compute::Tensor &tensor, const ConstCpuTensorHandle *handle)</div><div class="ttdef"><b>Definition:</b> <a href="_neon_workload_utils_8hpp_source.xhtml#l00035">NeonWorkloadUtils.hpp:35</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#l00867">Descriptors.hpp:867</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="structarmnn_1_1_queue_descriptor_xhtml_a6abd491bb99ffe88bd472c1ae5a1ed1a"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">armnn::QueueDescriptor::m_Outputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Outputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00031">WorkloadData.hpp:31</a></div></div> +<div class="ttc" id="classarmnn_1_1_neon_lstm_float_workload_xhtml_a471d876fac9cbcc3347eec34e558fe46"><div class="ttname"><a href="classarmnn_1_1_neon_lstm_float_workload.xhtml#a471d876fac9cbcc3347eec34e558fe46">armnn::NeonLstmFloatWorkload::NeonLstmFloatWorkload</a></div><div class="ttdeci">NeonLstmFloatWorkload(const LstmQueueDescriptor &descriptor, const WorkloadInfo &info)</div><div class="ttdef"><b>Definition:</b> <a href="_neon_lstm_float_workload_8cpp_source.xhtml#l00017">NeonLstmFloatWorkload.cpp:17</a></div></div> +<div class="ttc" id="classarmnn_1_1_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_exception.xhtml">armnn::Exception</a></div><div class="ttdoc">Base class for all ArmNN exceptions so that users can filter to just those. </div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00046">Exceptions.hpp:46</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="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#l00873">Descriptors.hpp:873</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="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</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 inputs and outputs to 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_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_queue_descriptor_xhtml_a4b50e46a6810018f3edecfb68b2a76b3"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">armnn::QueueDescriptor::m_Inputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Inputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00030">WorkloadData.hpp:30</a></div></div> +<div class="ttc" id="_neon_lstm_float_workload_8hpp_xhtml"><div class="ttname"><a href="_neon_lstm_float_workload_8hpp.xhtml">NeonLstmFloatWorkload.hpp</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_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><!-- fragment --></div><!-- contents --> +</div><!-- doc-content --> +<!-- start footer part --> +<div id="nav-path" class="navpath"><!-- id is needed for treeview function! --> + <ul> + <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.xhtml">src</a></li><li class="navelem"><a class="el" href="dir_0f3cdec46afbc61a1ded8e1687c9c9a0.xhtml">backends</a></li><li class="navelem"><a class="el" href="dir_d86eb514662c7c08e168285f21d00ea1.xhtml">neon</a></li><li class="navelem"><a class="el" href="dir_369c3c20501d0d10bd0354bf11c2f559.xhtml">workloads</a></li><li class="navelem"><a class="el" href="_neon_lstm_float_workload_8cpp.xhtml">NeonLstmFloatWorkload.cpp</a></li> + <li class="footer">Generated on Fri Mar 13 2020 16:09:12 for ArmNN by + <a href="http://www.doxygen.org/index.html"> + <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.13 </li> + </ul> +</div> +</body> +</html> |