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diff --git a/20.02/classarmnn_1_1_cl_lstm_float_workload.xhtml b/20.02/classarmnn_1_1_cl_lstm_float_workload.xhtml new file mode 100644 index 0000000000..d4f318a280 --- /dev/null +++ b/20.02/classarmnn_1_1_cl_lstm_float_workload.xhtml @@ -0,0 +1,244 @@ +<!-- 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: ClLstmFloatWorkload 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_cl_lstm_float_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_cl_lstm_float_workload-members.xhtml">List of all members</a> </div> + <div class="headertitle"> +<div class="title">ClLstmFloatWorkload Class Reference</div> </div> +</div><!--header--> +<div class="contents"> + +<p><code>#include <<a class="el" href="_cl_lstm_float_workload_8hpp_source.xhtml">ClLstmFloatWorkload.hpp</a>></code></p> +<div class="dynheader"> +Inheritance diagram for ClLstmFloatWorkload:</div> +<div class="dyncontent"> + <div class="center"> + <img src="classarmnn_1_1_cl_lstm_float_workload.png" usemap="#ClLstmFloatWorkload_map" alt=""/> + <map id="ClLstmFloatWorkload_map" name="ClLstmFloatWorkload_map"> +<area href="classarmnn_1_1_typed_workload.xhtml" alt="TypedWorkload< QueueDescriptor, DataTypes >" shape="rect" coords="0,112,246,136"/> +<area href="classarmnn_1_1_base_workload.xhtml" alt="BaseWorkload< QueueDescriptor >" shape="rect" coords="0,56,246,80"/> +<area href="classarmnn_1_1_i_workload.xhtml" title="Workload interface to enqueue a layer computation. " alt="IWorkload" shape="rect" coords="0,0,246,24"/> +</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:aba06d1bb61940d3e2eec26ac7dabdc65"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_cl_lstm_float_workload.xhtml#aba06d1bb61940d3e2eec26ac7dabdc65">ClLstmFloatWorkload</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:aba06d1bb61940d3e2eec26ac7dabdc65"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:ae071e8822437c78baea75c3aef3a263a"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_cl_lstm_float_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_typed_workload"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classarmnn_1_1_typed_workload')"><img src="closed.png" alt="-"/> Public Member Functions inherited from <a class="el" href="classarmnn_1_1_typed_workload.xhtml">TypedWorkload< QueueDescriptor, DataTypes ></a></td></tr> +<tr class="memitem:aa617fec9998f9650150a758b68498865 inherit pub_methods_classarmnn_1_1_typed_workload"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_typed_workload.xhtml#aa617fec9998f9650150a758b68498865">TypedWorkload</a> (const <a class="el" href="structarmnn_1_1_queue_descriptor.xhtml">QueueDescriptor</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:aa617fec9998f9650150a758b68498865 inherit pub_methods_classarmnn_1_1_typed_workload"><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< QueueDescriptor ></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_queue_descriptor.xhtml">QueueDescriptor</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: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_queue_descriptor.xhtml">QueueDescriptor</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< QueueDescriptor ></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_queue_descriptor.xhtml">QueueDescriptor</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="_cl_lstm_float_workload_8hpp_source.xhtml#l00018">18</a> of file <a class="el" href="_cl_lstm_float_workload_8hpp_source.xhtml">ClLstmFloatWorkload.hpp</a>.</p> +</div><h2 class="groupheader">Constructor & Destructor Documentation</h2> +<a id="aba06d1bb61940d3e2eec26ac7dabdc65"></a> +<h2 class="memtitle"><span class="permalink"><a href="#aba06d1bb61940d3e2eec26ac7dabdc65">◆ </a></span>ClLstmFloatWorkload()</h2> + +<div class="memitem"> +<div class="memproto"> + <table class="memname"> + <tr> + <td class="memname"><a class="el" href="classarmnn_1_1_cl_lstm_float_workload.xhtml">ClLstmFloatWorkload</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> +</div><div class="memdoc"> + +<p class="definition">Definition at line <a class="el" href="_cl_lstm_float_workload_8cpp_source.xhtml#l00020">20</a> of file <a class="el" href="_cl_lstm_float_workload_8cpp_source.xhtml">ClLstmFloatWorkload.cpp</a>.</p> +<div class="fragment"><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  : FloatWorkload<LstmQueueDescriptor>(descriptor, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>)</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> {</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  arm_compute::LSTMParams<arm_compute::ICLTensor> lstm_param;</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> </div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="comment">// Basic parameters</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  m_InputToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  BuildArmComputeTensor(*m_InputToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputToForgetWeights->GetTensorInfo());</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> </div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  m_InputToCellWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  BuildArmComputeTensor(*m_InputToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputToCellWeights->GetTensorInfo());</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> </div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  m_InputToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  BuildArmComputeTensor(*m_InputToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputToOutputWeights->GetTensorInfo());</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  m_RecurrentToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_RecurrentToForgetWeights->GetTensorInfo());</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  m_RecurrentToCellWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_RecurrentToCellWeights->GetTensorInfo());</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  m_RecurrentToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_RecurrentToOutputWeights->GetTensorInfo());</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> </div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  m_ForgetGateBiasTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  BuildArmComputeTensor(*m_ForgetGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ForgetGateBias->GetTensorInfo());</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  m_CellBiasTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  BuildArmComputeTensor(*m_CellBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellBias->GetTensorInfo());</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  m_OutputGateBiasTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  BuildArmComputeTensor(*m_OutputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_OutputGateBias->GetTensorInfo());</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> </div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="comment">// for future reference: check the AndroidNN API for the logic here</span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_CifgEnabled)</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  {</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  m_InputToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  BuildArmComputeTensor(*m_InputToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputToInputWeights->GetTensorInfo());</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> </div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  m_RecurrentToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_RecurrentToInputWeights->GetTensorInfo());</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  m_CellToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellToInputWeights != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  {</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  BuildArmComputeTensor(*m_CellToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellToInputWeights->GetTensorInfo());</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  }</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> </div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  m_InputGateBiasTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  BuildArmComputeTensor(*m_InputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputGateBias->GetTensorInfo());</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  lstm_param.set_cifg_params(m_InputToInputWeightsTensor.get(),</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  m_RecurrentToInputWeightsTensor.get(),</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellToInputWeights != <span class="keyword">nullptr</span> ? m_CellToInputWeightsTensor.get() : <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  m_InputGateBiasTensor.get());</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  }</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> </div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_ProjectionEnabled)</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  {</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  m_ProjectionWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  BuildArmComputeTensor(*m_ProjectionWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ProjectionWeights->GetTensorInfo());</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> </div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  m_ProjectionBiasTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ProjectionBias != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  {</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  BuildArmComputeTensor(*m_ProjectionBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ProjectionBias->GetTensorInfo());</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  }</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> </div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  lstm_param.set_projection_params(m_ProjectionWeightsTensor.get(),</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ProjectionBias != <span class="keyword">nullptr</span> ? m_ProjectionBiasTensor.get() : <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  }</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> </div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_PeepholeEnabled)</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  {</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  m_CellToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  BuildArmComputeTensor(*m_CellToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellToForgetWeights->GetTensorInfo());</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> </div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  m_CellToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  BuildArmComputeTensor(*m_CellToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellToOutputWeights->GetTensorInfo());</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> </div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  lstm_param.set_peephole_params(m_CellToForgetWeightsTensor.get(), m_CellToOutputWeightsTensor.get());</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  }</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> </div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_LayerNormEnabled)</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>  m_InputLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  m_ForgetLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  m_CellLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  m_OutputLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();</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>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_CifgEnabled)</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  {</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  BuildArmComputeTensor(*m_InputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputLayerNormWeights->GetTensorInfo());</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  }</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  BuildArmComputeTensor(*m_ForgetLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ForgetLayerNormWeights->GetTensorInfo());</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  BuildArmComputeTensor(*m_CellLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellLayerNormWeights->GetTensorInfo());</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  BuildArmComputeTensor(*m_OutputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_OutputLayerNormWeights->GetTensorInfo());</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  lstm_param.set_layer_normalization_params(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_CifgEnabled ? <span class="keyword">nullptr</span> :</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  m_InputLayerNormWeightsTensor.get(),</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  m_ForgetLayerNormWeightsTensor.get(),</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  m_CellLayerNormWeightsTensor.get(),</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  m_OutputLayerNormWeightsTensor.get());</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  }</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> </div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keyword">const</span> arm_compute::ICLTensor& input = <span class="keyword">static_cast<</span>IClTensorHandle*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0])->GetTensor();</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keyword">const</span> arm_compute::ICLTensor& output_state_in = <span class="keyword">static_cast<</span>IClTensorHandle*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[1])->GetTensor();</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keyword">const</span> arm_compute::ICLTensor& cell_state_in = <span class="keyword">static_cast<</span>IClTensorHandle*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[2])->GetTensor();</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> </div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  arm_compute::ICLTensor& output_state_out = <span class="keyword">static_cast<</span>IClTensorHandle*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[1])->GetTensor();</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  arm_compute::ICLTensor& cell_state_out = <span class="keyword">static_cast<</span>IClTensorHandle*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[2])->GetTensor();</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  arm_compute::ICLTensor& output = <span class="keyword">static_cast<</span>IClTensorHandle*<span class="keyword">></span>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[3])->GetTensor();</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> </div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="comment">// Get the batch_size and the num_units from the cellStateIn dimensions</span></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keyword">const</span> TensorInfo& inputTensorInfo = <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>.m_InputTensorInfos[2];</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batch_size = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(inputTensorInfo.GetShape()[0]);</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_units = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(inputTensorInfo.GetShape()[1]);</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> </div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  m_ScratchBuffer = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_CifgEnabled)</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  {</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="comment">// 2D tensor with dimensions [num_units * 3, batch_size] with CIFG</span></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> scratchBuffer1({ batch_size, num_units * 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  BuildArmComputeTensor(*m_ScratchBuffer, scratchBuffer1);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  }</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  {</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="comment">// scratch_buffer [num_units * 4, batch_size] without CIFG</span></div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> scratchBuffer2({ batch_size, num_units * 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  BuildArmComputeTensor(*m_ScratchBuffer, scratchBuffer2);</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> </div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keywordtype">float</span> cell_threshold = <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_ClippingThresCell;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keywordtype">float</span> projection_threshold = <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_ClippingThresProj;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="comment">// for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations</span></div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  arm_compute::ActivationLayerInfo activationLayerInfo;</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_ActivationFunc == 0)</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  {</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="comment">// no activation, do nothing</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_ActivationFunc == 1)</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  {</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  activationLayerInfo = arm_compute::ActivationLayerInfo(</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  arm_compute::ActivationLayerInfo::ActivationFunction::RELU);</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  }</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_ActivationFunc == 3)</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  {</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  activationLayerInfo = arm_compute::ActivationLayerInfo(</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.0);</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  }</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_ActivationFunc == 4)</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  {</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  activationLayerInfo = arm_compute::ActivationLayerInfo(</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  arm_compute::ActivationLayerInfo::ActivationFunction::TANH, 1.0, 1.0);</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  }</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_ActivationFunc == 6)</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  {</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  activationLayerInfo = arm_compute::ActivationLayerInfo(</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  }</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  {</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(<span class="stringliteral">"Wrong Type of Activation Function!"</span>);</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  }</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  m_LstmLayer.configure(&input, m_InputToForgetWeightsTensor.get(), m_InputToCellWeightsTensor.get(),</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  m_InputToOutputWeightsTensor.get(), m_RecurrentToForgetWeightsTensor.get(),</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  m_RecurrentToCellWeightsTensor.get(), m_RecurrentToOutputWeightsTensor.get(),</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  m_ForgetGateBiasTensor.get(), m_CellBiasTensor.get(), m_OutputGateBiasTensor.get(),</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  &output_state_in, &cell_state_in, m_ScratchBuffer.get(), &output_state_out,</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  &cell_state_out, &output, lstm_param, activationLayerInfo,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  cell_threshold, projection_threshold);</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> </div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  armcomputetensorutils::InitialiseArmComputeTensorEmpty(*m_ScratchBuffer);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> </div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_InputToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputToForgetWeights);</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_InputToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputToCellWeights);</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_InputToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputToOutputWeights);</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_RecurrentToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_RecurrentToForgetWeights);</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_RecurrentToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_RecurrentToCellWeights);</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_RecurrentToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_RecurrentToOutputWeights);</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_ForgetGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ForgetGateBias);</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_CellBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellBias);</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_OutputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_OutputGateBias);</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> </div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_CifgEnabled)</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  {</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_InputToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputToInputWeights);</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_RecurrentToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_RecurrentToInputWeights);</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellToInputWeights != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  {</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_CellToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellToInputWeights);</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>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_InputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputGateBias);</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  }</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span> </div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_ProjectionEnabled)</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  {</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_ProjectionWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ProjectionWeights);</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ProjectionBias != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  {</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_ProjectionBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ProjectionBias);</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>  }</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span> </div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_PeepholeEnabled)</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  {</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_CellToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellToForgetWeights);</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_CellToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellToOutputWeights);</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  }</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> </div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_LayerNormEnabled)</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  {</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_Parameters.m_CifgEnabled)</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  {</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_InputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_InputLayerNormWeights);</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  }</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span> </div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_ForgetLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_ForgetLayerNormWeights);</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_CellLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_CellLayerNormWeights);</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <a class="code" href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">InitializeArmComputeClTensorData</a>(*m_OutputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.m_OutputLayerNormWeights);</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  }</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span> </div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="comment">// Force Compute Library to perform the necessary copying and reshaping, after which</span></div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="comment">// delete all the input tensors that will no longer be needed</span></div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  m_LstmLayer.prepare();</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  FreeUnusedTensors();</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> }</div><div class="ttc" id="namespacearmnn_xhtml_a46747c3d0b99968be0b37d74bc9687dd"><div class="ttname"><a href="namespacearmnn.xhtml#a46747c3d0b99968be0b37d74bc9687dd">armnn::InitializeArmComputeClTensorData</a></div><div class="ttdeci">void InitializeArmComputeClTensorData(arm_compute::CLTensor &clTensor, const ConstCpuTensorHandle *handle)</div><div class="ttdef"><b>Definition:</b> <a href="_cl_workload_utils_8hpp_source.xhtml#l00090">ClWorkloadUtils.hpp:90</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div> +<div class="ttc" id="classarmnn_1_1_base_workload_xhtml_a0a487c549c63319505095b855ea3c195"><div class="ttname"><a href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">armnn::BaseWorkload::m_Data</a></div><div class="ttdeci">const QueueDescriptor m_Data</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8hpp_source.xhtml#l00046">Workload.hpp:46</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a37fa39012e90d568df7f774cd6d1e956"><div class="ttname"><a href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">armnn::numeric_cast</a></div><div class="ttdeci">std::enable_if_t< std::is_unsigned< Source >::value &&std::is_unsigned< Dest >::value, Dest > numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00033">NumericCast.hpp:33</a></div></div> +<div class="ttc" id="structarmnn_1_1_queue_descriptor_xhtml_a6abd491bb99ffe88bd472c1ae5a1ed1a"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">armnn::QueueDescriptor::m_Outputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Outputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00031">WorkloadData.hpp:31</a></div></div> +<div class="ttc" id="classarmnn_1_1_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_exception.xhtml">armnn::Exception</a></div><div class="ttdoc">Base class for all ArmNN exceptions so that users can filter to just those. </div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00046">Exceptions.hpp:46</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</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><!-- 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="_cl_lstm_float_workload_8cpp_source.xhtml#l00250">250</a> of file <a class="el" href="_cl_lstm_float_workload_8cpp_source.xhtml">ClLstmFloatWorkload.cpp</a>.</p> + +<p class="reference">References <a class="el" href="_cl_workload_utils_8hpp_source.xhtml#l00020">ARMNN_SCOPED_PROFILING_EVENT_CL</a>, <a class="el" href="_exceptions_8hpp_source.xhtml#l00192">CHECK_LOCATION</a>, and <a class="el" href="_cl_workload_utils_8hpp_source.xhtml#l00131">armnn::RunClFunction()</a>.</p> +<div class="fragment"><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> {</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <a class="code" href="_cl_workload_utils_8hpp.xhtml#a9166fc90a3ea47a2c9499a810b204daf">ARMNN_SCOPED_PROFILING_EVENT_CL</a>(<span class="stringliteral">"ClLstmFloatWorkload_Execute"</span>);</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <a class="code" href="namespacearmnn.xhtml#aff5bee79757341daf750c7dd7c123a15">RunClFunction</a>(m_LstmLayer, <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>());</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span> }</div><div class="ttc" id="_cl_workload_utils_8hpp_xhtml_a9166fc90a3ea47a2c9499a810b204daf"><div class="ttname"><a href="_cl_workload_utils_8hpp.xhtml#a9166fc90a3ea47a2c9499a810b204daf">ARMNN_SCOPED_PROFILING_EVENT_CL</a></div><div class="ttdeci">#define ARMNN_SCOPED_PROFILING_EVENT_CL(name)</div><div class="ttdef"><b>Definition:</b> <a href="_cl_workload_utils_8hpp_source.xhtml#l00020">ClWorkloadUtils.hpp:20</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_aff5bee79757341daf750c7dd7c123a15"><div class="ttname"><a href="namespacearmnn.xhtml#aff5bee79757341daf750c7dd7c123a15">armnn::RunClFunction</a></div><div class="ttdeci">void RunClFunction(arm_compute::IFunction &function, const CheckLocation &location)</div><div class="ttdef"><b>Definition:</b> <a href="_cl_workload_utils_8hpp_source.xhtml#l00131">ClWorkloadUtils.hpp:131</a></div></div> +<div class="ttc" id="_exceptions_8hpp_xhtml_aa3be76aec4ce713822a5ea1ecbb7bc61"><div class="ttname"><a href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a></div><div class="ttdeci">#define CHECK_LOCATION()</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00192">Exceptions.hpp:192</a></div></div> +</div><!-- fragment --> +</div> +</div> +<hr/>The documentation for this class was generated from the following files:<ul> +<li>src/backends/cl/workloads/<a class="el" href="_cl_lstm_float_workload_8hpp_source.xhtml">ClLstmFloatWorkload.hpp</a></li> +<li>src/backends/cl/workloads/<a class="el" href="_cl_lstm_float_workload_8cpp_source.xhtml">ClLstmFloatWorkload.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_cl_lstm_float_workload.xhtml">ClLstmFloatWorkload</a></li> + <li class="footer">Generated on Fri Mar 13 2020 16:09:17 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> |