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| author | David Monahan <david.monahan@arm.com> | 2023-03-22 16:48:58 +0000 |
|---|---|---|
| committer | David Monahan <david.monahan@arm.com> | 2023-03-22 16:48:58 +0000 |
| commit | ae050524109f1ce827962665436ef7430f2ac479 (patch) | |
| tree | a087fe0c77570971dd7979f2757426c24e91afc7 /23.02/classarmnn_1_1_cl_lstm_float_workload.xhtml | |
| parent | 8d2ca734165a068478df7cffa46185680b05cd20 (diff) | |
| download | armnn-ae050524109f1ce827962665436ef7430f2ac479.tar.gz | |
IVGCVSW-7255 Update Doxygen Documentation and publish on GitHub.
* Updating Doxygen documentation for 23.02 release.
Signed-off-by: David Monahan <david.monahan@arm.com>
Change-Id: I545574ff7664b4595d2fe6a91a3c35d2ad55df82
Diffstat (limited to '23.02/classarmnn_1_1_cl_lstm_float_workload.xhtml')
| -rw-r--r-- | 23.02/classarmnn_1_1_cl_lstm_float_workload.xhtml | 333 |
1 files changed, 286 insertions, 47 deletions
diff --git a/23.02/classarmnn_1_1_cl_lstm_float_workload.xhtml b/23.02/classarmnn_1_1_cl_lstm_float_workload.xhtml index 9db7a906ce..aa16f16f95 100644 --- a/23.02/classarmnn_1_1_cl_lstm_float_workload.xhtml +++ b/23.02/classarmnn_1_1_cl_lstm_float_workload.xhtml @@ -8,7 +8,7 @@ <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="generator" content="Doxygen 1.8.17"/> <meta name="robots" content="NOINDEX, NOFOLLOW" /> <meta name="viewport" content="width=device-width, initial-scale=1"/> <title>ArmNN: ClLstmFloatWorkload Class Reference</title> @@ -19,9 +19,6 @@ <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> @@ -30,7 +27,8 @@ extensions: ["tex2jax.js"], jax: ["input/TeX","output/HTML-CSS"], }); -</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script> +</script> +<script type="text/javascript" async="async" 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> @@ -51,18 +49,21 @@ </table> </div> <!-- end header part --> -<!-- Generated by Doxygen 1.8.13 --> +<!-- Generated by Doxygen 1.8.17 --> <script type="text/javascript"> +/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt GPL-v2 */ var searchBox = new SearchBox("searchBox", "search",false,'Search'); +/* @license-end */ </script> <script type="text/javascript" src="menudata.js"></script> <script type="text/javascript" src="menu.js"></script> <script type="text/javascript"> +/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt GPL-v2 */ $(function() { initMenu('',true,false,'search.php','Search'); $(document).ready(function() { init_search(); }); }); -</script> +/* @license-end */</script> <div id="main-nav"></div> </div><!-- top --> <div id="side-nav" class="ui-resizable side-nav-resizable"> @@ -76,7 +77,9 @@ $(function() { </div> </div> <script type="text/javascript"> -$(document).ready(function(){initNavTree('classarmnn_1_1_cl_lstm_float_workload.xhtml','');}); +/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt GPL-v2 */ +$(document).ready(function(){initNavTree('classarmnn_1_1_cl_lstm_float_workload.xhtml',''); initResizable(); }); +/* @license-end */ </script> <div id="doc-content"> <!-- window showing the filter options --> @@ -111,13 +114,13 @@ Inheritance diagram for ClLstmFloatWorkload:</div> <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> +<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:a76748f8bb92f17003b47fbc2887986cc"><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#a76748f8bb92f17003b47fbc2887986cc">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>, const arm_compute::CLCompileContext &clCompileContext)</td></tr> +<tr class="memitem:a76748f8bb92f17003b47fbc2887986cc"><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#a76748f8bb92f17003b47fbc2887986cc">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> &info, const arm_compute::CLCompileContext &clCompileContext)</td></tr> <tr class="separator:a76748f8bb92f17003b47fbc2887986cc"><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> @@ -126,10 +129,10 @@ Public Member Functions</h2></td></tr> <tr class="memitem:acc08590544f05c641d21c724aedf26dd"><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#acc08590544f05c641d21c724aedf26dd">ReplaceOutputTensorHandle</a> (<a class="el" href="classarmnn_1_1_i_tensor_handle.xhtml">ITensorHandle</a> *tensorHandle, unsigned int slot) override</td></tr> <tr class="separator:acc08590544f05c641d21c724aedf26dd"><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="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> &info)</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="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> &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:ae1c43d025fc90382d7aff7a500937e2c 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#ae1c43d025fc90382d7aff7a500937e2c">ExecuteAsync</a> (<a class="el" href="structarmnn_1_1experimental_1_1_execution_data.xhtml">ExecutionData</a> &executionData) override</td></tr> <tr class="separator:ae1c43d025fc90382d7aff7a500937e2c inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2"> </td></tr> @@ -194,22 +197,220 @@ Additional Inherited Members</h2></td></tr> </div><div class="memdoc"> <p class="definition">Definition at line <a class="el" href="_cl_lstm_float_workload_8cpp_source.xhtml#l00023">23</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="_profiling_8hpp_source.xhtml#l00227">ARMNN_REPORT_PROFILING_WORKLOAD_DESC</a>, <a class="el" href="_workload_8hpp_source.xhtml#l00061">BaseWorkload< QueueDescriptor >::GetGuid()</a>, and <a class="el" href="_workload_data_8hpp_source.xhtml#l00066">QueueDescriptorWithParameters< LayerDescriptor >::m_Parameters</a>.</p> -<div class="fragment"><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  : FloatWorkload<LstmQueueDescriptor>(descriptor, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>)</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> {</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="comment">// Report Profiling Details</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <a class="code" href="_profiling_8hpp.xhtml#a786492a3881a4c760ab1eec2149f4aba">ARMNN_REPORT_PROFILING_WORKLOAD_DESC</a>(<span class="stringliteral">"ClLstmFloatWorkload_Construct"</span>,</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  descriptor.m_Parameters,</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>,</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#aaff95a48875d8fb4a616352906660ca9">GetGuid</a>());</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  arm_compute::LSTMParams<arm_compute::ICLTensor> lstm_param;</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> </div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="comment">// Basic parameters</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  m_InputToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  BuildArmComputeTensor(*m_InputToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToForgetWeights->GetTensorInfo());</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  m_InputToCellWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  BuildArmComputeTensor(*m_InputToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToCellWeights->GetTensorInfo());</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> </div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  m_InputToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  BuildArmComputeTensor(*m_InputToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToOutputWeights->GetTensorInfo());</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>  m_RecurrentToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToForgetWeights->GetTensorInfo());</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>  m_RecurrentToCellWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToCellWeights->GetTensorInfo());</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>  m_RecurrentToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToOutputWeights->GetTensorInfo());</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> </div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  m_ForgetGateBiasTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  BuildArmComputeTensor(*m_ForgetGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ForgetGateBias->GetTensorInfo());</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  m_CellBiasTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  BuildArmComputeTensor(*m_CellBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellBias->GetTensorInfo());</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  m_OutputGateBiasTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  BuildArmComputeTensor(*m_OutputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_OutputGateBias->GetTensorInfo());</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="comment">// for future reference: check the AndroidNN API for the logic here</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</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>  m_InputToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  BuildArmComputeTensor(*m_InputToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToInputWeights->GetTensorInfo());</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> </div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  m_RecurrentToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToInputWeights->GetTensorInfo());</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>  m_CellToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights != <span class="keyword">nullptr</span>)</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>  BuildArmComputeTensor(*m_CellToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights->GetTensorInfo());</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  }</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_InputGateBiasTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  BuildArmComputeTensor(*m_InputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputGateBias->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>  lstm_param.set_cifg_params(m_InputToInputWeightsTensor.get(),</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  m_RecurrentToInputWeightsTensor.get(),</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights != <span class="keyword">nullptr</span> ? m_CellToInputWeightsTensor.get() : <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  m_InputGateBiasTensor.get());</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>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ProjectionEnabled)</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>  m_ProjectionWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  BuildArmComputeTensor(*m_ProjectionWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionWeights->GetTensorInfo());</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> </div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  m_ProjectionBiasTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  {</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  BuildArmComputeTensor(*m_ProjectionBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias->GetTensorInfo());</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> </div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  lstm_param.set_projection_params(m_ProjectionWeightsTensor.get(),</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias != <span class="keyword">nullptr</span> ? m_ProjectionBiasTensor.get() : <span class="keyword">nullptr</span>);</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#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_PeepholeEnabled)</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_CellToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  BuildArmComputeTensor(*m_CellToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToForgetWeights->GetTensorInfo());</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> </div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  m_CellToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  BuildArmComputeTensor(*m_CellToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToOutputWeights->GetTensorInfo());</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> </div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  lstm_param.set_peephole_params(m_CellToForgetWeightsTensor.get(), m_CellToOutputWeightsTensor.get());</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  }</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>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_LayerNormEnabled)</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  {</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  m_InputLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  m_ForgetLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  m_CellLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  m_OutputLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> </div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  {</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  BuildArmComputeTensor(*m_InputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputLayerNormWeights->GetTensorInfo());</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>  BuildArmComputeTensor(*m_ForgetLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ForgetLayerNormWeights->GetTensorInfo());</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  BuildArmComputeTensor(*m_CellLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellLayerNormWeights->GetTensorInfo());</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  BuildArmComputeTensor(*m_OutputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_OutputLayerNormWeights->GetTensorInfo());</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>  lstm_param.set_layer_normalization_params(<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled ? <span class="keyword">nullptr</span> :</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  m_InputLayerNormWeightsTensor.get(),</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  m_ForgetLayerNormWeightsTensor.get(),</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  m_CellLayerNormWeightsTensor.get(),</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  m_OutputLayerNormWeightsTensor.get());</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> </div><div class="line"><a name="l00136"></a><span class="lineno"> 136</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#afb8d2c8817c75de9d01a4c0e0d5c160b">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="l00137"></a><span class="lineno"> 137</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#afb8d2c8817c75de9d01a4c0e0d5c160b">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="l00138"></a><span class="lineno"> 138</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#afb8d2c8817c75de9d01a4c0e0d5c160b">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="l00139"></a><span class="lineno"> 139</span> </div><div class="line"><a name="l00140"></a><span class="lineno"> 140</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#afb8d2c8817c75de9d01a4c0e0d5c160b">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="l00141"></a><span class="lineno"> 141</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#afb8d2c8817c75de9d01a4c0e0d5c160b">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="l00142"></a><span class="lineno"> 142</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#afb8d2c8817c75de9d01a4c0e0d5c160b">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="l00143"></a><span class="lineno"> 143</span> </div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="comment">// Get the batch_size and the num_units from the cellStateIn dimensions</span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</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="l00146"></a><span class="lineno"> 146</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batch_size = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(inputTensorInfo.GetShape()[0]);</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_units = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(inputTensorInfo.GetShape()[1]);</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>  m_ScratchBuffer = std::make_unique<arm_compute::CLTensor>();</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</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="comment">// 2D tensor with dimensions [num_units * 3, batch_size] with CIFG</span></div><div class="line"><a name="l00153"></a><span class="lineno"> 153</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="l00154"></a><span class="lineno"> 154</span>  BuildArmComputeTensor(*m_ScratchBuffer, scratchBuffer1);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  }</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  {</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="comment">// scratch_buffer [num_units * 4, batch_size] without CIFG</span></div><div class="line"><a name="l00159"></a><span class="lineno"> 159</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="l00160"></a><span class="lineno"> 160</span>  BuildArmComputeTensor(*m_ScratchBuffer, scratchBuffer2);</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> </div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keywordtype">float</span> cell_threshold = <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ClippingThresCell;</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="keywordtype">float</span> projection_threshold = <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ClippingThresProj;</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="comment">// for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations</span></div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  arm_compute::ActivationLayerInfo activationLayerInfo =</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="namespacearmnn.xhtml#aa1e93ef5f9ee3dbb5e7faa9578f180ae">ConvertLstmActivationFuncToAclLayerInfo</a>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ActivationFunc);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> </div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  {</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <a class="code" href="_profiling_8hpp.xhtml#a5ccc65e2c464ac05ce311fdae7ede03a">ARMNN_SCOPED_PROFILING_EVENT</a>(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">Compute::Undefined</a>, <span class="stringliteral">"ClLstmFloatWorkload_configure"</span>);</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  m_LstmLayer.configure(clCompileContext, &input, m_InputToForgetWeightsTensor.get(),</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  m_InputToCellWeightsTensor.get(), m_InputToOutputWeightsTensor.get(),</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  m_RecurrentToForgetWeightsTensor.get(), m_RecurrentToCellWeightsTensor.get(),</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  m_RecurrentToOutputWeightsTensor.get(), m_ForgetGateBiasTensor.get(),</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  m_CellBiasTensor.get(), m_OutputGateBiasTensor.get(), &output_state_in,</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  &cell_state_in, m_ScratchBuffer.get(), &output_state_out,</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  &cell_state_out, &output, lstm_param, activationLayerInfo,</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  cell_threshold, projection_threshold);</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> </div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  armcomputetensorutils::InitialiseArmComputeTensorEmpty(*m_ScratchBuffer);</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> </div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_InputToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToForgetWeights);</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_InputToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToCellWeights);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_InputToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToOutputWeights);</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_RecurrentToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToForgetWeights);</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_RecurrentToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToCellWeights);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_RecurrentToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToOutputWeights);</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_ForgetGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ForgetGateBias);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_CellBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellBias);</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_OutputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_OutputGateBias);</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> </div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</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#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_InputToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToInputWeights);</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_RecurrentToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToInputWeights);</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  {</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_CellToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights);</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="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_InputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputGateBias);</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  }</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span> </div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ProjectionEnabled)</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  {</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_ProjectionWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionWeights);</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  {</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_ProjectionBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias);</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>  }</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="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_PeepholeEnabled)</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>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_CellToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToForgetWeights);</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_CellToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToOutputWeights);</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> </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#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_LayerNormEnabled)</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>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</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>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_InputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputLayerNormWeights);</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> </div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_ForgetLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ForgetLayerNormWeights);</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_CellLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellLayerNormWeights);</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_OutputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_OutputLayerNormWeights);</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="comment">// Force Compute Library to perform the necessary copying and reshaping, after which</span></div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="comment">// delete all the input tensors that will no longer be needed</span></div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  m_LstmLayer.prepare();</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  FreeUnusedTensors();</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> }</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div> -<div class="ttc" id="classarmnn_1_1_base_workload_xhtml_aaff95a48875d8fb4a616352906660ca9"><div class="ttname"><a href="classarmnn_1_1_base_workload.xhtml#aaff95a48875d8fb4a616352906660ca9">armnn::BaseWorkload::GetGuid</a></div><div class="ttdeci">arm::pipe::ProfilingGuid GetGuid() const final</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8hpp_source.xhtml#l00061">Workload.hpp:61</a></div></div> -<div class="ttc" id="namespacearmnn_xhtml_aa1e93ef5f9ee3dbb5e7faa9578f180ae"><div class="ttname"><a href="namespacearmnn.xhtml#aa1e93ef5f9ee3dbb5e7faa9578f180ae">armnn::ConvertLstmActivationFuncToAclLayerInfo</a></div><div class="ttdeci">arm_compute::ActivationLayerInfo ConvertLstmActivationFuncToAclLayerInfo(uint32_t activationFunction)</div><div class="ttdef"><b>Definition:</b> <a href="_arm_compute_utils_8hpp_source.xhtml#l00116">ArmComputeUtils.hpp:116</a></div></div> -<div class="ttc" id="_profiling_8hpp_xhtml_a5ccc65e2c464ac05ce311fdae7ede03a"><div class="ttname"><a href="_profiling_8hpp.xhtml#a5ccc65e2c464ac05ce311fdae7ede03a">ARMNN_SCOPED_PROFILING_EVENT</a></div><div class="ttdeci">#define ARMNN_SCOPED_PROFILING_EVENT(backendId, name)</div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8hpp_source.xhtml#l00220">Profiling.hpp:220</a></div></div> -<div class="ttc" id="classarmnn_1_1_base_workload_xhtml_afb8d2c8817c75de9d01a4c0e0d5c160b"><div class="ttname"><a href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">armnn::BaseWorkload::m_Data</a></div><div class="ttdeci">QueueDescriptor m_Data</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8hpp_source.xhtml#l00083">Workload.hpp:83</a></div></div> -<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">armnn::Compute::Undefined</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#l00027">WorkloadData.hpp:27</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="_profiling_8hpp_xhtml_a786492a3881a4c760ab1eec2149f4aba"><div class="ttname"><a href="_profiling_8hpp.xhtml#a786492a3881a4c760ab1eec2149f4aba">ARMNN_REPORT_PROFILING_WORKLOAD_DESC</a></div><div class="ttdeci">#define ARMNN_REPORT_PROFILING_WORKLOAD_DESC(name, desc, infos, guid)</div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8hpp_source.xhtml#l00227">Profiling.hpp:227</a></div></div> -<div class="ttc" id="namespacearmnn_xhtml_a375ca3cff9f1b005d1412dc5f3cf5b6e"><div class="ttname"><a href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">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#l00035">NumericCast.hpp:35</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="namespacearmnn_xhtml_a0eec4a463a166fad55307d9f26ba3a68"><div class="ttname"><a href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">armnn::InitializeArmComputeClTensorData</a></div><div class="ttdeci">void InitializeArmComputeClTensorData(arm_compute::CLTensor &clTensor, const ConstTensorHandle *handle)</div><div class="ttdef"><b>Definition:</b> <a href="_cl_workload_utils_8hpp_source.xhtml#l00116">ClWorkloadUtils.hpp:116</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#l00026">WorkloadData.hpp:26</a></div></div> +<div class="fragment"><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  : FloatWorkload<LstmQueueDescriptor>(descriptor, info)</div> +<div class="line"><a name="l00027"></a><span class="lineno"> 27</span> {</div> +<div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="comment">// Report Profiling Details</span></div> +<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <a class="code" href="_profiling_8hpp.xhtml#a786492a3881a4c760ab1eec2149f4aba">ARMNN_REPORT_PROFILING_WORKLOAD_DESC</a>(<span class="stringliteral">"ClLstmFloatWorkload_Construct"</span>,</div> +<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  descriptor.m_Parameters,</div> +<div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  info,</div> +<div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#aaff95a48875d8fb4a616352906660ca9">GetGuid</a>());</div> +<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  </div> +<div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  arm_compute::LSTMParams<arm_compute::ICLTensor> lstm_param;</div> +<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  </div> +<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="comment">// Basic parameters</span></div> +<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  m_InputToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  BuildArmComputeTensor(*m_InputToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToForgetWeights->GetTensorInfo());</div> +<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  </div> +<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  m_InputToCellWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  BuildArmComputeTensor(*m_InputToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToCellWeights->GetTensorInfo());</div> +<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  </div> +<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  m_InputToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  BuildArmComputeTensor(*m_InputToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToOutputWeights->GetTensorInfo());</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>  m_RecurrentToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToForgetWeights->GetTensorInfo());</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>  m_RecurrentToCellWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToCellWeights->GetTensorInfo());</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>  m_RecurrentToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToOutputWeights->GetTensorInfo());</div> +<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  </div> +<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  m_ForgetGateBiasTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  BuildArmComputeTensor(*m_ForgetGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ForgetGateBias->GetTensorInfo());</div> +<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  </div> +<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  m_CellBiasTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  BuildArmComputeTensor(*m_CellBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellBias->GetTensorInfo());</div> +<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  </div> +<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  m_OutputGateBiasTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  BuildArmComputeTensor(*m_OutputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_OutputGateBias->GetTensorInfo());</div> +<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  </div> +<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="comment">// for future reference: check the AndroidNN API for the logic here</span></div> +<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</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>  m_InputToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  BuildArmComputeTensor(*m_InputToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToInputWeights->GetTensorInfo());</div> +<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  </div> +<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  m_RecurrentToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToInputWeights->GetTensorInfo());</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>  m_CellToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights != <span class="keyword">nullptr</span>)</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>  BuildArmComputeTensor(*m_CellToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights->GetTensorInfo());</div> +<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  }</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_InputGateBiasTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  BuildArmComputeTensor(*m_InputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputGateBias->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>  lstm_param.set_cifg_params(m_InputToInputWeightsTensor.get(),</div> +<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  m_RecurrentToInputWeightsTensor.get(),</div> +<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights != <span class="keyword">nullptr</span> ? m_CellToInputWeightsTensor.get() : <span class="keyword">nullptr</span>,</div> +<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  m_InputGateBiasTensor.get());</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>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ProjectionEnabled)</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>  m_ProjectionWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  BuildArmComputeTensor(*m_ProjectionWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionWeights->GetTensorInfo());</div> +<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  </div> +<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  m_ProjectionBiasTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias != <span class="keyword">nullptr</span>)</div> +<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  {</div> +<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  BuildArmComputeTensor(*m_ProjectionBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias->GetTensorInfo());</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>  </div> +<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  lstm_param.set_projection_params(m_ProjectionWeightsTensor.get(),</div> +<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias != <span class="keyword">nullptr</span> ? m_ProjectionBiasTensor.get() : <span class="keyword">nullptr</span>);</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#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_PeepholeEnabled)</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_CellToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  BuildArmComputeTensor(*m_CellToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToForgetWeights->GetTensorInfo());</div> +<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  </div> +<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  m_CellToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  BuildArmComputeTensor(*m_CellToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToOutputWeights->GetTensorInfo());</div> +<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  </div> +<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  lstm_param.set_peephole_params(m_CellToForgetWeightsTensor.get(), m_CellToOutputWeightsTensor.get());</div> +<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  }</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>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_LayerNormEnabled)</div> +<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  {</div> +<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  m_InputLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  m_ForgetLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  m_CellLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  m_OutputLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  </div> +<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</div> +<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  {</div> +<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  BuildArmComputeTensor(*m_InputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputLayerNormWeights->GetTensorInfo());</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>  BuildArmComputeTensor(*m_ForgetLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ForgetLayerNormWeights->GetTensorInfo());</div> +<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  BuildArmComputeTensor(*m_CellLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellLayerNormWeights->GetTensorInfo());</div> +<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  BuildArmComputeTensor(*m_OutputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_OutputLayerNormWeights->GetTensorInfo());</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>  lstm_param.set_layer_normalization_params(<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled ? <span class="keyword">nullptr</span> :</div> +<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  m_InputLayerNormWeightsTensor.get(),</div> +<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  m_ForgetLayerNormWeightsTensor.get(),</div> +<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  m_CellLayerNormWeightsTensor.get(),</div> +<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  m_OutputLayerNormWeightsTensor.get());</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>  </div> +<div class="line"><a name="l00136"></a><span class="lineno"> 136</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#afb8d2c8817c75de9d01a4c0e0d5c160b">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="l00137"></a><span class="lineno"> 137</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#afb8d2c8817c75de9d01a4c0e0d5c160b">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="l00138"></a><span class="lineno"> 138</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#afb8d2c8817c75de9d01a4c0e0d5c160b">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="l00139"></a><span class="lineno"> 139</span>  </div> +<div class="line"><a name="l00140"></a><span class="lineno"> 140</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#afb8d2c8817c75de9d01a4c0e0d5c160b">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="l00141"></a><span class="lineno"> 141</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#afb8d2c8817c75de9d01a4c0e0d5c160b">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="l00142"></a><span class="lineno"> 142</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#afb8d2c8817c75de9d01a4c0e0d5c160b">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="l00143"></a><span class="lineno"> 143</span>  </div> +<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="comment">// Get the batch_size and the num_units from the cellStateIn dimensions</span></div> +<div class="line"><a name="l00145"></a><span class="lineno"> 145</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="l00146"></a><span class="lineno"> 146</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batch_size = armnn::numeric_cast<unsigned int>(inputTensorInfo.GetShape()[0]);</div> +<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_units = armnn::numeric_cast<unsigned int>(inputTensorInfo.GetShape()[1]);</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>  m_ScratchBuffer = std::make_unique<arm_compute::CLTensor>();</div> +<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</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="comment">// 2D tensor with dimensions [num_units * 3, batch_size] with CIFG</span></div> +<div class="line"><a name="l00153"></a><span class="lineno"> 153</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="l00154"></a><span class="lineno"> 154</span>  BuildArmComputeTensor(*m_ScratchBuffer, scratchBuffer1);</div> +<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  }</div> +<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keywordflow">else</span></div> +<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  {</div> +<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="comment">// scratch_buffer [num_units * 4, batch_size] without CIFG</span></div> +<div class="line"><a name="l00159"></a><span class="lineno"> 159</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="l00160"></a><span class="lineno"> 160</span>  BuildArmComputeTensor(*m_ScratchBuffer, scratchBuffer2);</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>  </div> +<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keywordtype">float</span> cell_threshold = <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ClippingThresCell;</div> +<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="keywordtype">float</span> projection_threshold = <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ClippingThresProj;</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="comment">// for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations</span></div> +<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  arm_compute::ActivationLayerInfo activationLayerInfo =</div> +<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="namespacearmnn.xhtml#aa1e93ef5f9ee3dbb5e7faa9578f180ae">ConvertLstmActivationFuncToAclLayerInfo</a>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ActivationFunc);</div> +<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  </div> +<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  {</div> +<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <a class="code" href="_profiling_8hpp.xhtml#a5ccc65e2c464ac05ce311fdae7ede03a">ARMNN_SCOPED_PROFILING_EVENT</a>(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">Compute::Undefined</a>, <span class="stringliteral">"ClLstmFloatWorkload_configure"</span>);</div> +<div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  m_LstmLayer.configure(clCompileContext, &input, m_InputToForgetWeightsTensor.get(),</div> +<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  m_InputToCellWeightsTensor.get(), m_InputToOutputWeightsTensor.get(),</div> +<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  m_RecurrentToForgetWeightsTensor.get(), m_RecurrentToCellWeightsTensor.get(),</div> +<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  m_RecurrentToOutputWeightsTensor.get(), m_ForgetGateBiasTensor.get(),</div> +<div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  m_CellBiasTensor.get(), m_OutputGateBiasTensor.get(), &output_state_in,</div> +<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  &cell_state_in, m_ScratchBuffer.get(), &output_state_out,</div> +<div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  &cell_state_out, &output, lstm_param, activationLayerInfo,</div> +<div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  cell_threshold, projection_threshold);</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>  </div> +<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  armcomputetensorutils::InitialiseArmComputeTensorEmpty(*m_ScratchBuffer);</div> +<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  </div> +<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_InputToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToForgetWeights);</div> +<div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_InputToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToCellWeights);</div> +<div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_InputToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToOutputWeights);</div> +<div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_RecurrentToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToForgetWeights);</div> +<div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_RecurrentToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToCellWeights);</div> +<div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_RecurrentToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToOutputWeights);</div> +<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_ForgetGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ForgetGateBias);</div> +<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_CellBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellBias);</div> +<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_OutputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_OutputGateBias);</div> +<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  </div> +<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</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#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_InputToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToInputWeights);</div> +<div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_RecurrentToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToInputWeights);</div> +<div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights != <span class="keyword">nullptr</span>)</div> +<div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  {</div> +<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_CellToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights);</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="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_InputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputGateBias);</div> +<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  }</div> +<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  </div> +<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ProjectionEnabled)</div> +<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  {</div> +<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_ProjectionWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionWeights);</div> +<div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias != <span class="keyword">nullptr</span>)</div> +<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  {</div> +<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_ProjectionBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias);</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>  }</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="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_PeepholeEnabled)</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>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_CellToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToForgetWeights);</div> +<div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_CellToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToOutputWeights);</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>  </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#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_LayerNormEnabled)</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>  <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</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>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_InputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputLayerNormWeights);</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>  </div> +<div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_ForgetLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ForgetLayerNormWeights);</div> +<div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_CellLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellLayerNormWeights);</div> +<div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <a class="code" href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">InitializeArmComputeClTensorData</a>(*m_OutputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_OutputLayerNormWeights);</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="comment">// Force Compute Library to perform the necessary copying and reshaping, after which</span></div> +<div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="comment">// delete all the input tensors that will no longer be needed</span></div> +<div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  m_LstmLayer.prepare();</div> +<div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  FreeUnusedTensors();</div> +<div class="line"><a name="l00236"></a><span class="lineno"> 236</span> }</div> </div><!-- fragment --> +<p class="reference">References <a class="el" href="_profiling_8hpp_source.xhtml#l00227">ARMNN_REPORT_PROFILING_WORKLOAD_DESC</a>, <a class="el" href="_workload_8hpp_source.xhtml#l00061">BaseWorkload< QueueDescriptor >::GetGuid()</a>, <a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, and <a class="el" href="_workload_data_8hpp_source.xhtml#l00066">QueueDescriptorWithParameters< LayerDescriptor >::m_Parameters</a>.</p> + </div> </div> <h2 class="groupheader">Member Function Documentation</h2> @@ -239,13 +440,13 @@ Additional Inherited Members</h2></td></tr> <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#l00238">238</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#l00028">ARMNN_SCOPED_PROFILING_EVENT_CL_GUID</a>, <a class="el" href="_exceptions_8hpp_source.xhtml#l00203">CHECK_LOCATION</a>, <a class="el" href="_workload_8hpp_source.xhtml#l00061">BaseWorkload< QueueDescriptor >::GetGuid()</a>, and <a class="el" href="_cl_workload_utils_8hpp_source.xhtml#l00160">armnn::RunClFunction()</a>.</p> -<div class="fragment"><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="_cl_workload_utils_8hpp.xhtml#ae96fe8349d05e83e891129d63d8e2263">ARMNN_SCOPED_PROFILING_EVENT_CL_GUID</a>(<span class="stringliteral">"ClLstmFloatWorkload_Execute"</span>, <a class="code" href="classarmnn_1_1_base_workload.xhtml#aaff95a48875d8fb4a616352906660ca9">GetGuid</a>());</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</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="l00242"></a><span class="lineno"> 242</span> }</div><div class="ttc" id="_cl_workload_utils_8hpp_xhtml_ae96fe8349d05e83e891129d63d8e2263"><div class="ttname"><a href="_cl_workload_utils_8hpp.xhtml#ae96fe8349d05e83e891129d63d8e2263">ARMNN_SCOPED_PROFILING_EVENT_CL_GUID</a></div><div class="ttdeci">#define ARMNN_SCOPED_PROFILING_EVENT_CL_GUID(name, guid)</div><div class="ttdef"><b>Definition:</b> <a href="_cl_workload_utils_8hpp_source.xhtml#l00028">ClWorkloadUtils.hpp:28</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#l00160">ClWorkloadUtils.hpp:160</a></div></div> -<div class="ttc" id="classarmnn_1_1_base_workload_xhtml_aaff95a48875d8fb4a616352906660ca9"><div class="ttname"><a href="classarmnn_1_1_base_workload.xhtml#aaff95a48875d8fb4a616352906660ca9">armnn::BaseWorkload::GetGuid</a></div><div class="ttdeci">arm::pipe::ProfilingGuid GetGuid() const final</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8hpp_source.xhtml#l00061">Workload.hpp:61</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#l00203">Exceptions.hpp:203</a></div></div> +<div class="fragment"><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="_cl_workload_utils_8hpp.xhtml#ae96fe8349d05e83e891129d63d8e2263">ARMNN_SCOPED_PROFILING_EVENT_CL_GUID</a>(<span class="stringliteral">"ClLstmFloatWorkload_Execute"</span>, <a class="code" href="classarmnn_1_1_base_workload.xhtml#aaff95a48875d8fb4a616352906660ca9">GetGuid</a>());</div> +<div class="line"><a name="l00241"></a><span class="lineno"> 241</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="l00242"></a><span class="lineno"> 242</span> }</div> </div><!-- fragment --> +<p class="reference">References <a class="el" href="_cl_workload_utils_8hpp_source.xhtml#l00028">ARMNN_SCOPED_PROFILING_EVENT_CL_GUID</a>, <a class="el" href="_exceptions_8hpp_source.xhtml#l00203">CHECK_LOCATION</a>, <a class="el" href="_workload_8hpp_source.xhtml#l00061">BaseWorkload< QueueDescriptor >::GetGuid()</a>, and <a class="el" href="_cl_workload_utils_8hpp_source.xhtml#l00160">armnn::RunClFunction()</a>.</p> + </div> </div> <a id="ab0a67f8179ddb997dda0070a6661f837"></a> @@ -285,12 +486,23 @@ Additional Inherited Members</h2></td></tr> <p>Reimplemented from <a class="el" href="classarmnn_1_1_base_workload.xhtml#ab0a67f8179ddb997dda0070a6661f837">BaseWorkload< QueueDescriptor ></a>.</p> <p class="definition">Definition at line <a class="el" href="_cl_lstm_float_workload_8cpp_source.xhtml#l00396">396</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="_workload_8hpp_source.xhtml#l00083">BaseWorkload< QueueDescriptor >::m_Data</a>, and <a class="el" href="_workload_data_8hpp_source.xhtml#l00026">QueueDescriptor::m_Inputs</a>.</p> -<div class="fragment"><div class="line"><a name="l00397"></a><span class="lineno"> 397</span> {</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  ITensorHandle* backupHandle = this-><a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[slot];</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  this-><a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[slot] = tensorHandle;</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  {</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  Reconfigure();</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  }</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="keywordflow">catch</span>(<a class="code" href="classarmnn_1_1_unimplemented_exception.xhtml">armnn::UnimplementedException</a>& e)</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  {</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="comment">// Cannot reconfigure, revert the slot back and throw the exception.</span></div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  this-><a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[slot] = backupHandle;</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="keywordflow">throw</span> e;</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  }</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span> }</div><div class="ttc" id="classarmnn_1_1_unimplemented_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_unimplemented_exception.xhtml">armnn::UnimplementedException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00098">Exceptions.hpp:98</a></div></div> -<div class="ttc" id="classarmnn_1_1_base_workload_xhtml_afb8d2c8817c75de9d01a4c0e0d5c160b"><div class="ttname"><a href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">armnn::BaseWorkload::m_Data</a></div><div class="ttdeci">QueueDescriptor m_Data</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8hpp_source.xhtml#l00083">Workload.hpp:83</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#l00026">WorkloadData.hpp:26</a></div></div> +<div class="fragment"><div class="line"><a name="l00397"></a><span class="lineno"> 397</span> {</div> +<div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  ITensorHandle* backupHandle = this-><a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[slot];</div> +<div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  this-><a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[slot] = tensorHandle;</div> +<div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keywordflow">try</span></div> +<div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  {</div> +<div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  Reconfigure();</div> +<div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  }</div> +<div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="keywordflow">catch</span>(<a class="code" href="classarmnn_1_1_unimplemented_exception.xhtml">armnn::UnimplementedException</a>& e)</div> +<div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  {</div> +<div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="comment">// Cannot reconfigure, revert the slot back and throw the exception.</span></div> +<div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  this-><a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[slot] = backupHandle;</div> +<div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="keywordflow">throw</span> e;</div> +<div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  }</div> +<div class="line"><a name="l00410"></a><span class="lineno"> 410</span> }</div> </div><!-- fragment --> +<p class="reference">References <a class="el" href="_workload_8hpp_source.xhtml#l00083">BaseWorkload< QueueDescriptor >::m_Data</a>, and <a class="el" href="_workload_data_8hpp_source.xhtml#l00026">QueueDescriptor::m_Inputs</a>.</p> + </div> </div> <a id="acc08590544f05c641d21c724aedf26dd"></a> @@ -330,12 +542,23 @@ Additional Inherited Members</h2></td></tr> <p>Reimplemented from <a class="el" href="classarmnn_1_1_base_workload.xhtml#acc08590544f05c641d21c724aedf26dd">BaseWorkload< QueueDescriptor ></a>.</p> <p class="definition">Definition at line <a class="el" href="_cl_lstm_float_workload_8cpp_source.xhtml#l00413">413</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="_workload_8hpp_source.xhtml#l00083">BaseWorkload< QueueDescriptor >::m_Data</a>, and <a class="el" href="_workload_data_8hpp_source.xhtml#l00026">QueueDescriptor::m_Inputs</a>.</p> -<div class="fragment"><div class="line"><a name="l00414"></a><span class="lineno"> 414</span> {</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  ITensorHandle* backupHandle = this-><a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[slot];</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  this-><a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[slot] = tensorHandle;</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  {</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  Reconfigure();</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  }</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keywordflow">catch</span>(<a class="code" href="classarmnn_1_1_unimplemented_exception.xhtml">armnn::UnimplementedException</a>& e)</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  {</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <span class="comment">// Cannot reconfigure, revert the slot back and throw the exception.</span></div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  this-><a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[slot] = backupHandle;</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="keywordflow">throw</span> e;</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  }</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span> }</div><div class="ttc" id="classarmnn_1_1_unimplemented_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_unimplemented_exception.xhtml">armnn::UnimplementedException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00098">Exceptions.hpp:98</a></div></div> -<div class="ttc" id="classarmnn_1_1_base_workload_xhtml_afb8d2c8817c75de9d01a4c0e0d5c160b"><div class="ttname"><a href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">armnn::BaseWorkload::m_Data</a></div><div class="ttdeci">QueueDescriptor m_Data</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8hpp_source.xhtml#l00083">Workload.hpp:83</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#l00026">WorkloadData.hpp:26</a></div></div> +<div class="fragment"><div class="line"><a name="l00414"></a><span class="lineno"> 414</span> {</div> +<div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  ITensorHandle* backupHandle = this-><a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[slot];</div> +<div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  this-><a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[slot] = tensorHandle;</div> +<div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <span class="keywordflow">try</span></div> +<div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  {</div> +<div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  Reconfigure();</div> +<div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  }</div> +<div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keywordflow">catch</span>(<a class="code" href="classarmnn_1_1_unimplemented_exception.xhtml">armnn::UnimplementedException</a>& e)</div> +<div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  {</div> +<div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <span class="comment">// Cannot reconfigure, revert the slot back and throw the exception.</span></div> +<div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  this-><a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[slot] = backupHandle;</div> +<div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="keywordflow">throw</span> e;</div> +<div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  }</div> +<div class="line"><a name="l00427"></a><span class="lineno"> 427</span> }</div> </div><!-- fragment --> +<p class="reference">References <a class="el" href="_workload_8hpp_source.xhtml#l00083">BaseWorkload< QueueDescriptor >::m_Data</a>, and <a class="el" href="_workload_data_8hpp_source.xhtml#l00026">QueueDescriptor::m_Inputs</a>.</p> + </div> </div> <hr/>The documentation for this class was generated from the following files:<ul> @@ -344,13 +567,29 @@ Additional Inherited Members</h2></td></tr> </ul> </div><!-- contents --> </div><!-- doc-content --> +<div class="ttc" id="anamespacearmnn_xhtml_a0eec4a463a166fad55307d9f26ba3a68"><div class="ttname"><a href="namespacearmnn.xhtml#a0eec4a463a166fad55307d9f26ba3a68">armnn::InitializeArmComputeClTensorData</a></div><div class="ttdeci">void InitializeArmComputeClTensorData(arm_compute::CLTensor &clTensor, const ConstTensorHandle *handle)</div><div class="ttdef"><b>Definition:</b> <a href="_cl_workload_utils_8hpp_source.xhtml#l00116">ClWorkloadUtils.hpp:116</a></div></div> +<div class="ttc" id="aclassarmnn_1_1_base_workload_xhtml_aaff95a48875d8fb4a616352906660ca9"><div class="ttname"><a href="classarmnn_1_1_base_workload.xhtml#aaff95a48875d8fb4a616352906660ca9">armnn::BaseWorkload::GetGuid</a></div><div class="ttdeci">arm::pipe::ProfilingGuid GetGuid() const final</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8hpp_source.xhtml#l00061">Workload.hpp:61</a></div></div> +<div class="ttc" id="a_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#l00203">Exceptions.hpp:203</a></div></div> +<div class="ttc" id="anamespacearmnn_xhtml_aa1e93ef5f9ee3dbb5e7faa9578f180ae"><div class="ttname"><a href="namespacearmnn.xhtml#aa1e93ef5f9ee3dbb5e7faa9578f180ae">armnn::ConvertLstmActivationFuncToAclLayerInfo</a></div><div class="ttdeci">arm_compute::ActivationLayerInfo ConvertLstmActivationFuncToAclLayerInfo(uint32_t activationFunction)</div><div class="ttdef"><b>Definition:</b> <a href="_arm_compute_utils_8hpp_source.xhtml#l00116">ArmComputeUtils.hpp:116</a></div></div> +<div class="ttc" id="aclassarmnn_1_1_base_workload_xhtml_afb8d2c8817c75de9d01a4c0e0d5c160b"><div class="ttname"><a href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">armnn::BaseWorkload::m_Data</a></div><div class="ttdeci">QueueDescriptor m_Data</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8hpp_source.xhtml#l00083">Workload.hpp:83</a></div></div> +<div class="ttc" id="aclassarmnn_1_1_unimplemented_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_unimplemented_exception.xhtml">armnn::UnimplementedException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00098">Exceptions.hpp:98</a></div></div> +<div class="ttc" id="a_profiling_8hpp_xhtml_a5ccc65e2c464ac05ce311fdae7ede03a"><div class="ttname"><a href="_profiling_8hpp.xhtml#a5ccc65e2c464ac05ce311fdae7ede03a">ARMNN_SCOPED_PROFILING_EVENT</a></div><div class="ttdeci">#define ARMNN_SCOPED_PROFILING_EVENT(backendId, name)</div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8hpp_source.xhtml#l00220">Profiling.hpp:220</a></div></div> +<div class="ttc" id="anamespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div><div class="ttdeci">@ Float32</div></div> +<div class="ttc" id="aclassarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div> +<div class="ttc" id="anamespacearmnn_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#l00160">ClWorkloadUtils.hpp:160</a></div></div> +<div class="ttc" id="anamespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">armnn::Compute::Undefined</a></div><div class="ttdeci">@ Undefined</div></div> +<div class="ttc" id="a_profiling_8hpp_xhtml_a786492a3881a4c760ab1eec2149f4aba"><div class="ttname"><a href="_profiling_8hpp.xhtml#a786492a3881a4c760ab1eec2149f4aba">ARMNN_REPORT_PROFILING_WORKLOAD_DESC</a></div><div class="ttdeci">#define ARMNN_REPORT_PROFILING_WORKLOAD_DESC(name, desc, infos, guid)</div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8hpp_source.xhtml#l00227">Profiling.hpp:227</a></div></div> +<div class="ttc" id="astructarmnn_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#l00027">WorkloadData.hpp:27</a></div></div> +<div class="ttc" id="a_cl_workload_utils_8hpp_xhtml_ae96fe8349d05e83e891129d63d8e2263"><div class="ttname"><a href="_cl_workload_utils_8hpp.xhtml#ae96fe8349d05e83e891129d63d8e2263">ARMNN_SCOPED_PROFILING_EVENT_CL_GUID</a></div><div class="ttdeci">#define ARMNN_SCOPED_PROFILING_EVENT_CL_GUID(name, guid)</div><div class="ttdef"><b>Definition:</b> <a href="_cl_workload_utils_8hpp_source.xhtml#l00028">ClWorkloadUtils.hpp:28</a></div></div> +<div class="ttc" id="astructarmnn_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#l00026">WorkloadData.hpp:26</a></div></div> +<div class="ttc" id="anamespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div><div class="ttdeci">@ info</div></div> <!-- 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 Feb 24 2023 10:24:29 for ArmNN by + <li class="footer">Generated on Wed Mar 22 2023 15:53:05 for ArmNN by <a href="http://www.doxygen.org/index.html"> - <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.13 </li> + <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.17 </li> </ul> </div> </body> |