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diff --git a/20.02/classarmnn_1_1_ref_lstm_workload.xhtml b/20.02/classarmnn_1_1_ref_lstm_workload.xhtml new file mode 100644 index 0000000000..899883bf17 --- /dev/null +++ b/20.02/classarmnn_1_1_ref_lstm_workload.xhtml @@ -0,0 +1,267 @@ +<!-- 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: RefLstmWorkload Class Reference</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('classarmnn_1_1_ref_lstm_workload.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="summary"> +<a href="#pub-methods">Public Member Functions</a> | +<a href="classarmnn_1_1_ref_lstm_workload-members.xhtml">List of all members</a> </div> + <div class="headertitle"> +<div class="title">RefLstmWorkload Class Reference</div> </div> +</div><!--header--> +<div class="contents"> + +<p><code>#include <<a class="el" href="_ref_lstm_workload_8hpp_source.xhtml">RefLstmWorkload.hpp</a>></code></p> +<div class="dynheader"> +Inheritance diagram for RefLstmWorkload:</div> +<div class="dyncontent"> + <div class="center"> + <img src="classarmnn_1_1_ref_lstm_workload.png" usemap="#RefLstmWorkload_map" alt=""/> + <map id="RefLstmWorkload_map" name="RefLstmWorkload_map"> +<area href="classarmnn_1_1_base_workload.xhtml" alt="BaseWorkload< LstmQueueDescriptor >" shape="rect" coords="0,56,239,80"/> +<area href="classarmnn_1_1_i_workload.xhtml" title="Workload interface to enqueue a layer computation. " alt="IWorkload" shape="rect" coords="0,0,239,24"/> +</map> + </div></div> +<table class="memberdecls"> +<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a> +Public Member Functions</h2></td></tr> +<tr class="memitem:a236c28ffd299dcfa88d6c0260e69f000"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_ref_lstm_workload.xhtml#a236c28ffd299dcfa88d6c0260e69f000">RefLstmWorkload</a> (const <a class="el" href="structarmnn_1_1_lstm_queue_descriptor.xhtml">LstmQueueDescriptor</a> &descriptor, const <a class="el" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> &<a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>)</td></tr> +<tr class="separator:a236c28ffd299dcfa88d6c0260e69f000"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:ae071e8822437c78baea75c3aef3a263a"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_ref_lstm_workload.xhtml#ae071e8822437c78baea75c3aef3a263a">Execute</a> () const override</td></tr> +<tr class="separator:ae071e8822437c78baea75c3aef3a263a"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="inherit_header pub_methods_classarmnn_1_1_base_workload"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classarmnn_1_1_base_workload')"><img src="closed.png" alt="-"/> Public Member Functions inherited from <a class="el" href="classarmnn_1_1_base_workload.xhtml">BaseWorkload< LstmQueueDescriptor ></a></td></tr> +<tr class="memitem:af2ef420610280dc5a661cd3d4836d5a2 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.xhtml#af2ef420610280dc5a661cd3d4836d5a2">BaseWorkload</a> (const <a class="el" href="structarmnn_1_1_lstm_queue_descriptor.xhtml">LstmQueueDescriptor</a> &descriptor, const <a class="el" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> &info)</td></tr> +<tr class="separator:af2ef420610280dc5a661cd3d4836d5a2 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:a81627f96ba06d76e147f7d392a8117ed inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.xhtml#a81627f96ba06d76e147f7d392a8117ed">PostAllocationConfigure</a> () override</td></tr> +<tr class="separator:a81627f96ba06d76e147f7d392a8117ed inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:a965cf380c7adf547d0f14b3f6d1da249 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="structarmnn_1_1_lstm_queue_descriptor.xhtml">LstmQueueDescriptor</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.xhtml#a965cf380c7adf547d0f14b3f6d1da249">GetData</a> () const</td></tr> +<tr class="separator:a965cf380c7adf547d0f14b3f6d1da249 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:a29c2c6dd77c6fe10674fc6876751cbce inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarmnn_1_1profiling_1_1_profiling_guid.xhtml">profiling::ProfilingGuid</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.xhtml#a29c2c6dd77c6fe10674fc6876751cbce">GetGuid</a> () const final</td></tr> +<tr class="separator:a29c2c6dd77c6fe10674fc6876751cbce inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="inherit_header pub_methods_classarmnn_1_1_i_workload"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classarmnn_1_1_i_workload')"><img src="closed.png" alt="-"/> Public Member Functions inherited from <a class="el" href="classarmnn_1_1_i_workload.xhtml">IWorkload</a></td></tr> +<tr class="memitem:a69c83c02ae8de866bc7a46c49e69c1ba inherit pub_methods_classarmnn_1_1_i_workload"><td class="memItemLeft" align="right" valign="top">virtual </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_i_workload.xhtml#a69c83c02ae8de866bc7a46c49e69c1ba">~IWorkload</a> ()</td></tr> +<tr class="separator:a69c83c02ae8de866bc7a46c49e69c1ba inherit pub_methods_classarmnn_1_1_i_workload"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:ab81312bd5e64cbae2803de9f243bdb32 inherit pub_methods_classarmnn_1_1_i_workload"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_i_workload.xhtml#ab81312bd5e64cbae2803de9f243bdb32">RegisterDebugCallback</a> (const <a class="el" href="namespacearmnn.xhtml#a15f3ad9b5e4e3d46b0a6dda246a7bc28">DebugCallbackFunction</a> &)</td></tr> +<tr class="separator:ab81312bd5e64cbae2803de9f243bdb32 inherit pub_methods_classarmnn_1_1_i_workload"><td class="memSeparator" colspan="2"> </td></tr> +</table><table class="memberdecls"> +<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="inherited"></a> +Additional Inherited Members</h2></td></tr> +<tr class="inherit_header pro_attribs_classarmnn_1_1_base_workload"><td colspan="2" onclick="javascript:toggleInherit('pro_attribs_classarmnn_1_1_base_workload')"><img src="closed.png" alt="-"/> Protected Attributes inherited from <a class="el" href="classarmnn_1_1_base_workload.xhtml">BaseWorkload< LstmQueueDescriptor ></a></td></tr> +<tr class="memitem:a0a487c549c63319505095b855ea3c195 inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="structarmnn_1_1_lstm_queue_descriptor.xhtml">LstmQueueDescriptor</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a></td></tr> +<tr class="separator:a0a487c549c63319505095b855ea3c195 inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:a902044db290170b8467ed4697b7fed24 inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="classarmnn_1_1profiling_1_1_profiling_guid.xhtml">profiling::ProfilingGuid</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.xhtml#a902044db290170b8467ed4697b7fed24">m_Guid</a></td></tr> +<tr class="separator:a902044db290170b8467ed4697b7fed24 inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2"> </td></tr> +</table> +<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2> +<div class="textblock"> +<p class="definition">Definition at line <a class="el" href="_ref_lstm_workload_8hpp_source.xhtml#l00016">16</a> of file <a class="el" href="_ref_lstm_workload_8hpp_source.xhtml">RefLstmWorkload.hpp</a>.</p> +</div><h2 class="groupheader">Constructor & Destructor Documentation</h2> +<a id="a236c28ffd299dcfa88d6c0260e69f000"></a> +<h2 class="memtitle"><span class="permalink"><a href="#a236c28ffd299dcfa88d6c0260e69f000">◆ </a></span>RefLstmWorkload()</h2> + +<div class="memitem"> +<div class="memproto"> +<table class="mlabels"> + <tr> + <td class="mlabels-left"> + <table class="memname"> + <tr> + <td class="memname"><a class="el" href="classarmnn_1_1_ref_lstm_workload.xhtml">RefLstmWorkload</a> </td> + <td>(</td> + <td class="paramtype">const <a class="el" href="structarmnn_1_1_lstm_queue_descriptor.xhtml">LstmQueueDescriptor</a> & </td> + <td class="paramname"><em>descriptor</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const <a class="el" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> & </td> + <td class="paramname"><em>info</em> </td> + </tr> + <tr> + <td></td> + <td>)</td> + <td></td><td></td> + </tr> + </table> + </td> + <td class="mlabels-right"> +<span class="mlabels"><span class="mlabel">explicit</span></span> </td> + </tr> +</table> +</div><div class="memdoc"> + +<p class="definition">Definition at line <a class="el" href="_ref_lstm_workload_8cpp_source.xhtml#l00016">16</a> of file <a class="el" href="_ref_lstm_workload_8cpp_source.xhtml">RefLstmWorkload.cpp</a>.</p> +<div class="fragment"><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  : BaseWorkload<LstmQueueDescriptor>(descriptor, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>)</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  , m_InputToInputWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_InputToInputWeights))</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  , m_InputToForgetWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_InputToForgetWeights))</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  , m_InputToCellWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_InputToCellWeights))</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  , m_InputToOutputWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_InputToOutputWeights))</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  , m_RecurrentToInputWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_RecurrentToInputWeights))</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  , m_RecurrentToForgetWeightsTensor(<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_RecurrentToForgetWeights))</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  , m_RecurrentToCellWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_RecurrentToCellWeights))</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  , m_RecurrentToOutputWeightsTensor(<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_RecurrentToOutputWeights))</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  , m_CellToInputWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_CellToInputWeights))</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  , m_CellToForgetWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_CellToForgetWeights))</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  , m_CellToOutputWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_CellToOutputWeights))</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  , m_InputGateBiasTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_InputGateBias))</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  , m_ForgetGateBiasTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_ForgetGateBias))</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  , m_CellBiasTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_CellBias))</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  , m_OutputGateBiasTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_OutputGateBias))</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  , m_ProjectionWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_ProjectionWeights))</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  , m_ProjectionBiasTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_ProjectionBias))</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  , m_InputLayerNormWeights (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_InputLayerNormWeights))</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  , m_ForgetLayerNormWeights (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_ForgetLayerNormWeights))</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  , m_CellLayerNormWeights (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_CellLayerNormWeights))</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  , m_OutputLayerNormWeights (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_OutputLayerNormWeights))</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> {}</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="_lstm_utils_8cpp_xhtml_a1aef5d233d1f569f34d6c36bdf5ae9e5"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a></div><div class="ttdeci">std::unique_ptr< armnn::ScopedCpuTensorHandle > AssignScopedCpuTensorHandle(const armnn::ConstCpuTensorHandle *ptr)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00299">LstmUtils.cpp:299</a></div></div> +</div><!-- fragment --> +</div> +</div> +<h2 class="groupheader">Member Function Documentation</h2> +<a id="ae071e8822437c78baea75c3aef3a263a"></a> +<h2 class="memtitle"><span class="permalink"><a href="#ae071e8822437c78baea75c3aef3a263a">◆ </a></span>Execute()</h2> + +<div class="memitem"> +<div class="memproto"> +<table class="mlabels"> + <tr> + <td class="mlabels-left"> + <table class="memname"> + <tr> + <td class="memname">void Execute </td> + <td>(</td> + <td class="paramname"></td><td>)</td> + <td> const</td> + </tr> + </table> + </td> + <td class="mlabels-right"> +<span class="mlabels"><span class="mlabel">override</span><span class="mlabel">virtual</span></span> </td> + </tr> +</table> +</div><div class="memdoc"> + +<p>Implements <a class="el" href="classarmnn_1_1_i_workload.xhtml#a72ae00e6604850c8798c5e0d825ee7e4">IWorkload</a>.</p> + +<p class="definition">Definition at line <a class="el" href="_ref_lstm_workload_8cpp_source.xhtml#l00041">41</a> of file <a class="el" href="_ref_lstm_workload_8cpp_source.xhtml">RefLstmWorkload.cpp</a>.</p> + +<p class="reference">References <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">armnn::Activation</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00229">ClipVector()</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00244">CopyVector()</a>, <a class="el" href="_tensor_8hpp_source.xhtml#l00095">TensorInfo::GetDataType()</a>, <a class="el" href="_tensor_8hpp_source.xhtml#l00088">TensorInfo::GetShape()</a>, <a class="el" href="_ref_workload_utils_8hpp_source.xhtml#l00025">armnn::GetTensorInfo()</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00861">LstmDescriptor::m_ActivationFunc</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00867">LstmDescriptor::m_CifgEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00863">LstmDescriptor::m_ClippingThresCell</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00865">LstmDescriptor::m_ClippingThresProj</a>, <a class="el" href="_workload_8hpp_source.xhtml#l00046">BaseWorkload< LstmQueueDescriptor >::m_Data</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00030">QueueDescriptor::m_Inputs</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00873">LstmDescriptor::m_LayerNormEnabled</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00031">QueueDescriptor::m_Outputs</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00049">QueueDescriptorWithParameters< LayerDescriptor >::m_Parameters</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00869">LstmDescriptor::m_PeepholeEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00871">LstmDescriptor::m_ProjectionEnabled</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00087">MatrixBatchVectorMultiplyAccumulate()</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00040">MeanStddevNormalization()</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00258">SetActivationParameters()</a>, <a class="el" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">armnn::Sigmoid</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00173">Sub1Vector()</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00016">VectorBatchVectorAdd()</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00113">VectorBatchVectorAssign()</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00152">VectorBatchVectorCwiseProduct()</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00131">VectorBatchVectorCwiseProductAccumulate()</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00187">VectorVectorCwiseProduct()</a>, <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00204">VectorVectorCwiseProductAccumulate()</a>, and <a class="el" href="_lstm_utils_8cpp_source.xhtml#l00076">ZeroVector()</a>.</p> +<div class="fragment"><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> {</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="comment">// This is a porting of the LSTM::Eval() method in the Android code base</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="comment">// Refer to: android/frameworks/ml/nn/common/operations/LSTM.cpp</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keyword">const</span> TensorInfo& inputInfo = <a class="code" href="namespacearmnn.xhtml#a93d269806f34407695dc10f510001c30">GetTensorInfo</a>(<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]);</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">const</span> TensorInfo& outputInfo = <a class="code" href="namespacearmnn.xhtml#a93d269806f34407695dc10f510001c30">GetTensorInfo</a>(<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>[0]);</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keyword">const</span> TensorShape& inputShape = inputInfo.GetShape();</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>& outputType = outputInfo.GetDataType();</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  std::unique_ptr<Encoder<float>> outputStateOut = MakeEncoder<float>(outputInfo, <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]->Map());</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  std::unique_ptr<Encoder<float>> cellStateOut = MakeEncoder<float>(outputInfo, <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]->Map());</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  std::unique_ptr<Encoder<float>> output = MakeEncoder<float>(outputInfo, <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]->Map());</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>  std::unique_ptr<Decoder<float>> cellStateOutDecoder = MakeDecoder<float>(outputInfo, <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]->Map());</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  std::unique_ptr<Decoder<float>> outputDecoder = MakeDecoder<float>(outputInfo, <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]->Map());</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>  std::unique_ptr<Decoder<float>> inputData = MakeDecoder<float>(inputInfo, <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]->Map());</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  std::unique_ptr<Decoder<float>> outputStateIn = MakeDecoder<float>(inputInfo, <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]->Map());</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  std::unique_ptr<Decoder<float>> cellStateIn = MakeDecoder<float>(inputInfo, <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]->Map());</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> </div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keyword">const</span> uint32_t nBatch = inputShape[0];</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keyword">const</span> uint32_t nInput = inputShape[1];</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="keyword">const</span> uint32_t nCell = m_InputToOutputWeightsTensor->GetShape()[0];</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keyword">const</span> uint32_t nOutput = m_RecurrentToOutputWeightsTensor->GetShape()[1];</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> </div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> useCifg = <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a>;</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> usePeephole = <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a>;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> useLayerNorm = <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">m_LayerNormEnabled</a>;</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>  <span class="comment">// Index the scratch buffers pointers to the global scratch buffer.</span></div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  std::unique_ptr<Encoder<float>> inputGateScratch = MakeEncoder<float>(outputInfo, <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>[0]->Map());</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  std::unique_ptr<Encoder<float>> cellScratch = MakeEncoder<float>(outputInfo, <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>[0]->Map());</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  std::unique_ptr<Encoder<float>> forgetGateScratch = MakeEncoder<float>(outputInfo, <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>[0]->Map());</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  std::unique_ptr<Encoder<float>> outputGateScratch = MakeEncoder<float>(outputInfo, <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>[0]->Map());</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>  std::unique_ptr<Decoder<float>> inputGateScratchDecoder =</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  MakeDecoder<float>(outputInfo, <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>[0]->Map());</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  std::unique_ptr<Decoder<float>> cellScratchDecoder =</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  MakeDecoder<float>(outputInfo, <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>[0]->Map());</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  std::unique_ptr<Decoder<float>> forgetGateScratchDecoder =</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  MakeDecoder<float>(outputInfo, <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>[0]->Map());</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  std::unique_ptr<Decoder<float>> outputGateScratchDecoder =</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  MakeDecoder<float>(outputInfo, <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>[0]->Map());</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> </div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keywordflow">if</span> (useCifg)</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  {</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  *cellScratch += (0 * nCell * nBatch);</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  *forgetGateScratch += (1 * nCell * nBatch);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  *outputGateScratch += (2 * nCell * nBatch);</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>  *cellScratchDecoder += (0 * nCell * nBatch);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  *forgetGateScratchDecoder += (1 * nCell * nBatch);</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  *outputGateScratchDecoder += (2 * nCell * nBatch);</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  }</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keywordflow">else</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>  *inputGateScratch += (0 * nCell * nBatch);</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  *cellScratch += (1 * nCell * nBatch);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  *forgetGateScratch += (2 * nCell * nBatch);</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  *outputGateScratch += (3 * nCell * nBatch);</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>  *inputGateScratchDecoder += (0 * nCell * nBatch);</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  *cellScratchDecoder += (1 * nCell * nBatch);</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  *forgetGateScratchDecoder += (2 * nCell * nBatch);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  *outputGateScratchDecoder += (3 * nCell * nBatch);</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  }</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> </div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  std::unique_ptr<Decoder<float>> inputToInputWeightsTensor;</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  std::unique_ptr<Decoder<float>> inputToForgetWeightsTensor = MakeDecoder<float>(</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  m_InputToForgetWeightsTensor->GetTensorInfo(), m_InputToForgetWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  std::unique_ptr<Decoder<float>> inputToCellWeightsTensor = MakeDecoder<float>(</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  m_InputToCellWeightsTensor->GetTensorInfo(), m_InputToCellWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  std::unique_ptr<Decoder<float>> inputToOutputWeightsTensor = MakeDecoder<float>(</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  m_InputToOutputWeightsTensor->GetTensorInfo(), m_InputToOutputWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  std::unique_ptr<Decoder<float>> recurrentToInputWeightsTensor;</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  std::unique_ptr<Decoder<float>> recurrentToForgetWeightsTensor = MakeDecoder<float>(</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  m_RecurrentToForgetWeightsTensor->GetTensorInfo(), m_RecurrentToForgetWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  std::unique_ptr<Decoder<float>> recurrentToCellWeightsTensor = MakeDecoder<float>(</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  m_RecurrentToCellWeightsTensor->GetTensorInfo(), m_RecurrentToCellWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  std::unique_ptr<Decoder<float>> recurrentToOutputWeightsTensor = MakeDecoder<float>(</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  m_RecurrentToOutputWeightsTensor->GetTensorInfo(), m_RecurrentToOutputWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> </div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  std::unique_ptr<Decoder<float>> inputGateBiasTensor;</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  std::unique_ptr<Decoder<float>> forgetGateBiasTensor = MakeDecoder<float>(</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  m_ForgetGateBiasTensor->GetTensorInfo(), m_ForgetGateBiasTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  std::unique_ptr<Decoder<float>> cellBiasTensor = MakeDecoder<float>(</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  m_CellBiasTensor->GetTensorInfo(), m_CellBiasTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  std::unique_ptr<Decoder<float>> outputGateBiasTensor = MakeDecoder<float>(</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  m_OutputGateBiasTensor->GetTensorInfo(), m_OutputGateBiasTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> </div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  std::unique_ptr<Decoder<float>> cellToInputWeightsTensor;</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  std::unique_ptr<Decoder<float>> cellToForgetWeightsTensor;</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  std::unique_ptr<Decoder<float>> cellToOutputWeightsTensor;</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> </div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  std::unique_ptr<Decoder<float>> projectionWeightsTensor;</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  std::unique_ptr<Decoder<float>> projectionBiasTensor;</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> </div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  std::unique_ptr<Decoder<float>> inputLayerNormWeights;</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  std::unique_ptr<Decoder<float>> forgetLayerNormWeights;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  std::unique_ptr<Decoder<float>> cellLayerNormWeights;</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  std::unique_ptr<Decoder<float>> outputLayerNormWeights;</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> </div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keywordflow">if</span> (useLayerNorm)</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  {</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="keywordflow">if</span> (!useCifg)</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>  inputLayerNormWeights = MakeDecoder<float>(</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  m_InputLayerNormWeights->GetTensorInfo(), m_InputLayerNormWeights->GetTensor<<span class="keywordtype">void</span>>());</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>  forgetLayerNormWeights = MakeDecoder<float>(</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  m_ForgetLayerNormWeights->GetTensorInfo(), m_ForgetLayerNormWeights->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  cellLayerNormWeights = MakeDecoder<float>(</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  m_CellLayerNormWeights->GetTensorInfo(), m_CellLayerNormWeights->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  outputLayerNormWeights = MakeDecoder<float>(</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  m_OutputLayerNormWeights->GetTensorInfo(), m_OutputLayerNormWeights->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  }</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> </div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keywordflow">if</span> (!useCifg)</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  {</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  inputToInputWeightsTensor = MakeDecoder<float>(</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  m_InputToInputWeightsTensor->GetTensorInfo(), m_InputToInputWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  inputGateBiasTensor = MakeDecoder<float>(</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  m_InputGateBiasTensor->GetTensorInfo(), m_InputGateBiasTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  recurrentToInputWeightsTensor = MakeDecoder<float>(</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  m_RecurrentToInputWeightsTensor->GetTensorInfo(), m_RecurrentToInputWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  }</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span> </div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keywordflow">if</span> (usePeephole)</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  {</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  cellToForgetWeightsTensor = MakeDecoder<float>(</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  m_CellToForgetWeightsTensor->GetTensorInfo(), m_CellToForgetWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  cellToOutputWeightsTensor = MakeDecoder<float>(</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  m_CellToOutputWeightsTensor->GetTensorInfo(), m_CellToOutputWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  }</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> </div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keywordflow">if</span> (!useCifg && usePeephole)</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>  cellToInputWeightsTensor = MakeDecoder<float>(</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  m_CellToInputWeightsTensor->GetTensorInfo(), m_CellToInputWeightsTensor->GetTensor<<span class="keywordtype">void</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>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a>)</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  {</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  projectionWeightsTensor = MakeDecoder<float>(</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  m_ProjectionWeightsTensor->GetTensorInfo(), m_ProjectionWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keywordflow">if</span> (m_ProjectionBiasTensor)</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  {</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  projectionBiasTensor = MakeDecoder<float>(</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  m_ProjectionBiasTensor->GetTensorInfo(), m_ProjectionBiasTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  }</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  }</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keywordflow">if</span> (!useLayerNorm)</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  {</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="comment">// Initialize scratch buffers with bias.</span></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keywordflow">if</span> (!useCifg)</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  {</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a8c4a96233c9b62c76d611316da11124b">VectorBatchVectorAssign</a>(*inputGateBiasTensor,</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  nCell, nBatch, *inputGateScratch);</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  }</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a8c4a96233c9b62c76d611316da11124b">VectorBatchVectorAssign</a>(*forgetGateBiasTensor,</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  nCell, nBatch, *forgetGateScratch);</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a8c4a96233c9b62c76d611316da11124b">VectorBatchVectorAssign</a>(*cellBiasTensor,</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  nCell, nBatch, *cellScratch);</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a8c4a96233c9b62c76d611316da11124b">VectorBatchVectorAssign</a>(*outputGateBiasTensor,</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  nCell, nBatch, *outputGateScratch);</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  }</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  {</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="comment">// Initialize scratch buffers with zeroes.</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keywordflow">if</span> (!useCifg)</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="_lstm_utils_8cpp.xhtml#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*inputGateScratch, nCell * nBatch);</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  }</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*forgetGateScratch, nCell * nBatch);</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*cellScratch , nCell * nBatch);</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*outputGateScratch, nCell * nBatch);</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> </div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="comment">// For each batch and cell: compute input_weight * input.</span></div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keywordflow">if</span> (!useCifg)</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  {</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*inputToInputWeightsTensor,</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  nCell, nInput, *inputData, nBatch, *inputGateScratch);</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>  <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*inputToForgetWeightsTensor,</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  nCell, nInput, *inputData, nBatch, *forgetGateScratch);</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*inputToCellWeightsTensor,</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  nCell, nInput, *inputData, nBatch, *cellScratch);</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*inputToOutputWeightsTensor,</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  nCell, nInput, *inputData, nBatch, *outputGateScratch);</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> </div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="comment">// For each batch and cell: compute recurrent_weight * output_state.</span></div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keywordflow">if</span> (!useCifg)</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>  <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*recurrentToInputWeightsTensor,</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  nCell, nOutput, *outputStateIn, nBatch, *inputGateScratch);</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="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*recurrentToForgetWeightsTensor,</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  nCell, nOutput, *outputStateIn, nBatch, *forgetGateScratch);</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*recurrentToCellWeightsTensor,</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  nCell, nOutput, *outputStateIn, nBatch, *cellScratch);</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*recurrentToOutputWeightsTensor,</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  nCell, nOutput, *outputStateIn, nBatch, *outputGateScratch);</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span> </div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="comment">// For each batch and cell: update input gate.</span></div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keywordflow">if</span> (!useCifg)</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>  <span class="keywordflow">if</span> (usePeephole)</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="_lstm_utils_8cpp.xhtml#a796323e16216b880043dc5ebbaa2372b">VectorBatchVectorCwiseProductAccumulate</a>(*cellToInputWeightsTensor,</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  nCell, *cellStateIn, nBatch, *inputGateScratch);</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  }</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="keywordflow">if</span> (useLayerNorm)</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>  <a class="code" href="_lstm_utils_8cpp.xhtml#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a>(*inputGateScratchDecoder,</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  *inputGateScratch, nCell, nBatch, m_LayerNormEpsilon);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a>(*inputLayerNormWeights,</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  nCell, *inputGateScratchDecoder, nBatch, *inputGateScratch);</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a>(*inputGateBiasTensor,</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  nCell, *inputGateScratchDecoder, nBatch, *inputGateScratch);</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  }</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">Activation</a>(*inputGateScratchDecoder, *inputGateScratch,</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  TensorInfo({nCell, nBatch}, outputType),</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">ActivationFunction::Sigmoid</a>, 0, 0);</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  }</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span> </div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="comment">// For each batch and cell: update forget gate.</span></div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keywordflow">if</span> (usePeephole)</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  {</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a796323e16216b880043dc5ebbaa2372b">VectorBatchVectorCwiseProductAccumulate</a>(*cellToForgetWeightsTensor, nCell,</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  *cellStateIn, nBatch, *forgetGateScratch);</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  }</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keywordflow">if</span> (useLayerNorm)</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  {</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a>(*forgetGateScratchDecoder,</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  *forgetGateScratch, nCell, nBatch, m_LayerNormEpsilon);</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a>(*forgetLayerNormWeights,</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  nCell, *forgetGateScratchDecoder, nBatch, *forgetGateScratch);</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a>(*forgetGateBiasTensor,</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  nCell, *forgetGateScratchDecoder, nBatch, *forgetGateScratch);</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  }</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">Activation</a>(*forgetGateScratchDecoder, *forgetGateScratch,</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  TensorInfo({nCell, nBatch}, outputType),</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">ActivationFunction::Sigmoid</a>, 0, 0);</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span> </div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="comment">// For each batch and cell: update the cell.</span></div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="keywordflow">if</span> (useLayerNorm)</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  {</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a>(*cellScratchDecoder,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  *cellScratch, nCell, nBatch, m_LayerNormEpsilon);</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a>(*cellLayerNormWeights,</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  nCell, *cellScratchDecoder, nBatch, *cellScratch);</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a>(*cellBiasTensor,</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  nCell, *cellScratchDecoder, nBatch, *cellScratch);</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  }</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span> </div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a5b81dc0a1a9a2bccab8bb79dfa3e77b7">VectorVectorCwiseProduct</a>(*forgetGateScratchDecoder, *cellStateIn, nBatch * nCell, *cellStateOut);</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> </div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9ea">ActivationFunction</a> armnnActivationFunc = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">ActivationFunction::Sigmoid</a>;</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keywordtype">float</span> a = 0;</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keywordtype">float</span> b = 0;</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#afc83b9418e9721cee21de245cb8533de">SetActivationParameters</a>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a>, armnnActivationFunc, a, b);</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span> </div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a> > 0)</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  {</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">Activation</a>(*cellScratchDecoder, *cellScratch,</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  TensorInfo({nCell, nBatch}, outputType),</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  armnnActivationFunc, a, b);</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  }</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="keywordflow">if</span> (useCifg)</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  {</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#aca7bd1dff180b6a5de894537f8220793">Sub1Vector</a>(*forgetGateScratchDecoder, nBatch * nCell, *forgetGateScratch);</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a2e653f948d93f4177f267a7b1b4ed47d">VectorVectorCwiseProductAccumulate</a>(</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  *cellScratchDecoder, *forgetGateScratchDecoder, nBatch * nCell, *cellStateOut);</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  {</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a2e653f948d93f4177f267a7b1b4ed47d">VectorVectorCwiseProductAccumulate</a>(</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  *cellScratchDecoder, *inputGateScratchDecoder, nBatch * nCell, *cellStateOut);</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>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a> > 0.0)</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  {</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a117781e8e9b7321722bbdd8ff74b484a">ClipVector</a>(*cellStateOutDecoder, nBatch * nCell, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a>, *cellStateOut);</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  }</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span> </div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <span class="comment">// For each batch and cell: update the output gate.</span></div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="keywordflow">if</span> (usePeephole)</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  {</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a796323e16216b880043dc5ebbaa2372b">VectorBatchVectorCwiseProductAccumulate</a>(*cellToOutputWeightsTensor,</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  nCell, *cellStateOutDecoder, nBatch, *outputGateScratch);</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>  <span class="keywordflow">if</span> (useLayerNorm)</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  {</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a>(*outputGateScratchDecoder,</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  *outputGateScratch, nCell, nBatch, m_LayerNormEpsilon);</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a>(*outputLayerNormWeights,</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  nCell, *outputGateScratchDecoder, nBatch, *outputGateScratch);</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a>(*outputGateBiasTensor,</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  nCell, *outputGateScratchDecoder, nBatch, *outputGateScratch);</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>  <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">Activation</a>(*outputGateScratchDecoder, *outputGateScratch,</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  TensorInfo({nCell, nBatch}, outputType),</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">ActivationFunction::Sigmoid</a>, 0, 0);</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span> </div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a> > 0)</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  {</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">Activation</a>(*cellStateOutDecoder, *cellScratch,</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  TensorInfo({nCell, nBatch}, outputType),</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  armnnActivationFunc, a, b);</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  }</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>  <a class="code" href="_lstm_utils_8cpp.xhtml#a5b81dc0a1a9a2bccab8bb79dfa3e77b7">VectorVectorCwiseProduct</a>(*outputGateScratchDecoder, *cellScratchDecoder, nBatch * nCell, *outputGateScratch);</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span> </div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <span class="comment">// For each batch: update the projection and output_state.</span></div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a>)</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>  <span class="keywordflow">if</span> (m_ProjectionBiasTensor)</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  {</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a8c4a96233c9b62c76d611316da11124b">VectorBatchVectorAssign</a>(*projectionBiasTensor,</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  nOutput, nBatch, *output);</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  }</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*projectionWeightsTensor,</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  nOutput, nCell, *outputGateScratchDecoder, nBatch, *output);</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span> </div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a> > 0.0)</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  {</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a117781e8e9b7321722bbdd8ff74b484a">ClipVector</a>(*outputDecoder, nBatch * nOutput, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a>, *output);</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  }</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  }</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  {</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a00d8a623c04f1120f6fee3fe38d1cee9">CopyVector</a>(*outputGateScratchDecoder, nBatch * nOutput, *output);</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>  <a class="code" href="_lstm_utils_8cpp.xhtml#a00d8a623c04f1120f6fee3fe38d1cee9">CopyVector</a>(*outputDecoder, nBatch * nOutput, *outputStateOut);</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span> }</div><div class="ttc" id="_lstm_utils_8cpp_xhtml_a0ed27dd6d6125a06bf654080f4184360"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a></div><div class="ttdeci">void MeanStddevNormalization(armnn::Decoder< float > &input_vector, armnn::Encoder< float > &output_vector, uint32_t v_size, uint32_t n_batch, float normalization_epsilon)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00040">LstmUtils.cpp:40</a></div></div> +<div class="ttc" id="_lstm_utils_8cpp_xhtml_a389c4bbafd0fff7060cbb183f20a2134"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a></div><div class="ttdeci">void VectorBatchVectorAdd(armnn::Decoder< float > &vector, uint32_t vSize, armnn::Decoder< float > &batchVector, uint32_t nBatch, armnn::Encoder< float > &outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00016">LstmUtils.cpp:16</a></div></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_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="_lstm_utils_8cpp_xhtml_a117781e8e9b7321722bbdd8ff74b484a"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a117781e8e9b7321722bbdd8ff74b484a">ClipVector</a></div><div class="ttdeci">void ClipVector(armnn::Decoder< float > &vector, uint32_t vSize, float absLimit, armnn::Encoder< float > &outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00229">LstmUtils.cpp:229</a></div></div> +<div class="ttc" id="_lstm_utils_8cpp_xhtml_aca7bd1dff180b6a5de894537f8220793"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#aca7bd1dff180b6a5de894537f8220793">Sub1Vector</a></div><div class="ttdeci">void Sub1Vector(armnn::Decoder< float > &vector, uint32_t vSize, armnn::Encoder< float > &result)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00173">LstmUtils.cpp:173</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">armnn::LayerType::Activation</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< LstmQueueDescriptor >::m_Data</a></div><div class="ttdeci">const LstmQueueDescriptor 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="_lstm_utils_8cpp_xhtml_a00d8a623c04f1120f6fee3fe38d1cee9"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a00d8a623c04f1120f6fee3fe38d1cee9">CopyVector</a></div><div class="ttdeci">void CopyVector(armnn::Decoder< float > &vector, uint32_t vSize, armnn::Encoder< float > &outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00244">LstmUtils.cpp:244</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a93d269806f34407695dc10f510001c30"><div class="ttname"><a href="namespacearmnn.xhtml#a93d269806f34407695dc10f510001c30">armnn::GetTensorInfo</a></div><div class="ttdeci">const TensorInfo & GetTensorInfo(const ITensorHandle *tensorHandle)</div><div class="ttdoc">float32 helpers </div><div class="ttdef"><b>Definition:</b> <a href="_ref_workload_utils_8hpp_source.xhtml#l00025">RefWorkloadUtils.hpp:25</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">armnn::ActivationFunction::Sigmoid</a></div></div> +<div class="ttc" id="_lstm_utils_8cpp_xhtml_a796323e16216b880043dc5ebbaa2372b"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a796323e16216b880043dc5ebbaa2372b">VectorBatchVectorCwiseProductAccumulate</a></div><div class="ttdeci">void VectorBatchVectorCwiseProductAccumulate(armnn::Decoder< float > &vector, uint32_t vSize, armnn::Decoder< float > &batchVector, uint32_t nBatch, armnn::Encoder< float > &outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00131">LstmUtils.cpp:131</a></div></div> +<div class="ttc" id="_lstm_utils_8cpp_xhtml_a4c20bc573b70e89327b334f924da97b5"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a></div><div class="ttdeci">void ZeroVector(armnn::Encoder< float > &vector, uint32_t vSize)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00076">LstmUtils.cpp:76</a></div></div> +<div class="ttc" id="_lstm_utils_8cpp_xhtml_a5b81dc0a1a9a2bccab8bb79dfa3e77b7"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a5b81dc0a1a9a2bccab8bb79dfa3e77b7">VectorVectorCwiseProduct</a></div><div class="ttdeci">void VectorVectorCwiseProduct(armnn::Decoder< float > &vector1, armnn::Decoder< float > &vector2, uint32_t vSize, armnn::Encoder< float > &outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00187">LstmUtils.cpp:187</a></div></div> +<div class="ttc" id="structarmnn_1_1_queue_descriptor_with_parameters_xhtml_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00049">WorkloadData.hpp:49</a></div></div> +<div class="ttc" id="_lstm_utils_8cpp_xhtml_a1d7ad9698b02282a57fdb17b3af745f9"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a></div><div class="ttdeci">void VectorBatchVectorCwiseProduct(armnn::Decoder< float > &vector, uint32_t vSize, armnn::Decoder< float > &batchVector, uint32_t nBatch, armnn::Encoder< float > &outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00152">LstmUtils.cpp:152</a></div></div> +<div class="ttc" id="_lstm_utils_8cpp_xhtml_ab93a2c78551c3d3aba8ddcafb792a36d"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a></div><div class="ttdeci">void MatrixBatchVectorMultiplyAccumulate(armnn::Decoder< float > &matrix, uint32_t mRows, uint32_t mCols, armnn::Decoder< float > &vector, uint32_t nBatch, armnn::Encoder< float > &outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00087">LstmUtils.cpp:87</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00032">Types.hpp:32</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="_lstm_utils_8cpp_xhtml_a2e653f948d93f4177f267a7b1b4ed47d"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a2e653f948d93f4177f267a7b1b4ed47d">VectorVectorCwiseProductAccumulate</a></div><div class="ttdeci">void VectorVectorCwiseProductAccumulate(armnn::Decoder< float > &vector1, armnn::Decoder< float > &vector2, uint32_t vSize, armnn::Encoder< float > &outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00204">LstmUtils.cpp:204</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="_lstm_utils_8cpp_xhtml_a8c4a96233c9b62c76d611316da11124b"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a8c4a96233c9b62c76d611316da11124b">VectorBatchVectorAssign</a></div><div class="ttdeci">void VectorBatchVectorAssign(armnn::Decoder< float > &vector, uint32_t vSize, uint32_t nBatch, armnn::Encoder< float > &outBatchVector)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00113">LstmUtils.cpp:113</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="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_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="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_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="_lstm_utils_8cpp_xhtml_afc83b9418e9721cee21de245cb8533de"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#afc83b9418e9721cee21de245cb8533de">SetActivationParameters</a></div><div class="ttdeci">void SetActivationParameters(uint32_t activation, armnn::ActivationFunction &outArmnnActivation, float &outA, float &outB)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00258">LstmUtils.cpp:258</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9ea"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9ea">armnn::ActivationFunction</a></div><div class="ttdeci">ActivationFunction</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00055">Types.hpp:55</a></div></div> +</div><!-- fragment --> +</div> +</div> +<hr/>The documentation for this class was generated from the following files:<ul> +<li>src/backends/reference/workloads/<a class="el" href="_ref_lstm_workload_8hpp_source.xhtml">RefLstmWorkload.hpp</a></li> +<li>src/backends/reference/workloads/<a class="el" href="_ref_lstm_workload_8cpp_source.xhtml">RefLstmWorkload.cpp</a></li> +</ul> +</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="namespacearmnn.xhtml">armnn</a></li><li class="navelem"><a class="el" href="classarmnn_1_1_ref_lstm_workload.xhtml">RefLstmWorkload</a></li> + <li class="footer">Generated on Fri Mar 13 2020 16:09:20 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> |