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95 <div class="summary">
96<a href="#pub-methods">Public Member Functions</a> &#124;
97<a href="classarmnn_1_1_neon_unidirectional_sequence_lstm_float_workload-members.html">List of all members</a> </div>
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99<div class="title">NeonUnidirectionalSequenceLstmFloatWorkload Class Reference</div> </div>
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101<div class="contents">
102
103<p><code>#include &lt;<a class="el" href="_neon_unidirectional_sequence_lstm_float_workload_8hpp_source.html">NeonUnidirectionalSequenceLstmFloatWorkload.hpp</a>&gt;</code></p>
104<div class="dynheader">
105Inheritance diagram for NeonUnidirectionalSequenceLstmFloatWorkload:</div>
106<div class="dyncontent">
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108</div>
109<center><span class="legend">[<a target="top" href="graph_legend.html">legend</a>]</span></center></div>
110<div class="dynheader">
111Collaboration diagram for NeonUnidirectionalSequenceLstmFloatWorkload:</div>
112<div class="dyncontent">
113<div class="center"><iframe scrolling="no" frameborder="0" src="classarmnn_1_1_neon_unidirectional_sequence_lstm_float_workload__coll__graph.svg" width="271" height="308"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
114</div>
115<center><span class="legend">[<a target="top" href="graph_legend.html">legend</a>]</span></center></div>
116<table class="memberdecls">
117<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
118Public Member Functions</h2></td></tr>
119<tr class="memitem:acceeccc54cc2871ec72da81e48e7ef1c"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_neon_unidirectional_sequence_lstm_float_workload.html#acceeccc54cc2871ec72da81e48e7ef1c">NeonUnidirectionalSequenceLstmFloatWorkload</a> (const <a class="el" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html">UnidirectionalSequenceLstmQueueDescriptor</a> &amp;descriptor, const <a class="el" href="structarmnn_1_1_workload_info.html">WorkloadInfo</a> &amp;info)</td></tr>
120<tr class="separator:acceeccc54cc2871ec72da81e48e7ef1c"><td class="memSeparator" colspan="2">&#160;</td></tr>
121<tr class="memitem:ae071e8822437c78baea75c3aef3a263a"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_neon_unidirectional_sequence_lstm_float_workload.html#ae071e8822437c78baea75c3aef3a263a">Execute</a> () const override</td></tr>
122<tr class="separator:ae071e8822437c78baea75c3aef3a263a"><td class="memSeparator" colspan="2">&#160;</td></tr>
123<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="-"/>&#160;Public Member Functions inherited from <a class="el" href="classarmnn_1_1_typed_workload.html">TypedWorkload&lt; QueueDescriptor, DataTypes &gt;</a></td></tr>
124<tr class="memitem:aa617fec9998f9650150a758b68498865 inherit pub_methods_classarmnn_1_1_typed_workload"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_typed_workload.html#aa617fec9998f9650150a758b68498865">TypedWorkload</a> (const <a class="el" href="structarmnn_1_1_queue_descriptor.html">QueueDescriptor</a> &amp;descriptor, const <a class="el" href="structarmnn_1_1_workload_info.html">WorkloadInfo</a> &amp;info)</td></tr>
125<tr class="separator:aa617fec9998f9650150a758b68498865 inherit pub_methods_classarmnn_1_1_typed_workload"><td class="memSeparator" colspan="2">&#160;</td></tr>
126<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="-"/>&#160;Public Member Functions inherited from <a class="el" href="classarmnn_1_1_base_workload.html">BaseWorkload&lt; QueueDescriptor &gt;</a></td></tr>
127<tr class="memitem:af2ef420610280dc5a661cd3d4836d5a2 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#af2ef420610280dc5a661cd3d4836d5a2">BaseWorkload</a> (const <a class="el" href="structarmnn_1_1_queue_descriptor.html">QueueDescriptor</a> &amp;descriptor, const <a class="el" href="structarmnn_1_1_workload_info.html">WorkloadInfo</a> &amp;info)</td></tr>
128<tr class="separator:af2ef420610280dc5a661cd3d4836d5a2 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2">&#160;</td></tr>
129<tr class="memitem:a163c04b26f9804eafc598a047128f887 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">virtual const std::string &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#a163c04b26f9804eafc598a047128f887">GetName</a> () const override</td></tr>
130<tr class="separator:a163c04b26f9804eafc598a047128f887 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2">&#160;</td></tr>
131<tr class="memitem:ae1c43d025fc90382d7aff7a500937e2c inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#ae1c43d025fc90382d7aff7a500937e2c">ExecuteAsync</a> (<a class="el" href="structarmnn_1_1experimental_1_1_execution_data.html">ExecutionData</a> &amp;executionData) override</td></tr>
132<tr class="separator:ae1c43d025fc90382d7aff7a500937e2c inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2">&#160;</td></tr>
133<tr class="memitem:a81627f96ba06d76e147f7d392a8117ed inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#a81627f96ba06d76e147f7d392a8117ed">PostAllocationConfigure</a> () override</td></tr>
134<tr class="separator:a81627f96ba06d76e147f7d392a8117ed inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2">&#160;</td></tr>
135<tr class="memitem:a965cf380c7adf547d0f14b3f6d1da249 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="structarmnn_1_1_queue_descriptor.html">QueueDescriptor</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#a965cf380c7adf547d0f14b3f6d1da249">GetData</a> () const</td></tr>
136<tr class="separator:a965cf380c7adf547d0f14b3f6d1da249 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2">&#160;</td></tr>
137<tr class="memitem:aaff95a48875d8fb4a616352906660ca9 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">arm::pipe::ProfilingGuid&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#aaff95a48875d8fb4a616352906660ca9">GetGuid</a> () const final</td></tr>
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139<tr class="memitem:a0c326c344355d8423217e9431781f2ee inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">virtual bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#a0c326c344355d8423217e9431781f2ee">SupportsTensorHandleReplacement</a> () const override</td></tr>
140<tr class="separator:a0c326c344355d8423217e9431781f2ee inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2">&#160;</td></tr>
141<tr class="memitem:ab0a67f8179ddb997dda0070a6661f837 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#ab0a67f8179ddb997dda0070a6661f837">ReplaceInputTensorHandle</a> (<a class="el" href="classarmnn_1_1_i_tensor_handle.html">ITensorHandle</a> *tensorHandle, unsigned int slot) override</td></tr>
142<tr class="separator:ab0a67f8179ddb997dda0070a6661f837 inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2">&#160;</td></tr>
143<tr class="memitem:acc08590544f05c641d21c724aedf26dd inherit pub_methods_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#acc08590544f05c641d21c724aedf26dd">ReplaceOutputTensorHandle</a> (<a class="el" href="classarmnn_1_1_i_tensor_handle.html">ITensorHandle</a> *tensorHandle, unsigned int slot) override</td></tr>
144<tr class="separator:acc08590544f05c641d21c724aedf26dd inherit pub_methods_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2">&#160;</td></tr>
145<tr class="inherit_header pub_methods_classarmnn_1_1_i_workload"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classarmnn_1_1_i_workload')"><img src="closed.png" alt="-"/>&#160;Public Member Functions inherited from <a class="el" href="classarmnn_1_1_i_workload.html">IWorkload</a></td></tr>
146<tr class="memitem:a69c83c02ae8de866bc7a46c49e69c1ba inherit pub_methods_classarmnn_1_1_i_workload"><td class="memItemLeft" align="right" valign="top">virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_i_workload.html#a69c83c02ae8de866bc7a46c49e69c1ba">~IWorkload</a> ()</td></tr>
147<tr class="separator:a69c83c02ae8de866bc7a46c49e69c1ba inherit pub_methods_classarmnn_1_1_i_workload"><td class="memSeparator" colspan="2">&#160;</td></tr>
148<tr class="memitem:ab81312bd5e64cbae2803de9f243bdb32 inherit pub_methods_classarmnn_1_1_i_workload"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_i_workload.html#ab81312bd5e64cbae2803de9f243bdb32">RegisterDebugCallback</a> (const <a class="el" href="namespacearmnn.html#a15f3ad9b5e4e3d46b0a6dda246a7bc28">DebugCallbackFunction</a> &amp;)</td></tr>
149<tr class="separator:ab81312bd5e64cbae2803de9f243bdb32 inherit pub_methods_classarmnn_1_1_i_workload"><td class="memSeparator" colspan="2">&#160;</td></tr>
150<tr class="memitem:a2d2834d1029217934f504e3e59579081 inherit pub_methods_classarmnn_1_1_i_workload"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classarmnn_1_1_optional.html">armnn::Optional</a>&lt; <a class="el" href="structarmnn_1_1_memory_requirements.html">armnn::MemoryRequirements</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_i_workload.html#a2d2834d1029217934f504e3e59579081">GetMemoryRequirements</a> ()</td></tr>
151<tr class="separator:a2d2834d1029217934f504e3e59579081 inherit pub_methods_classarmnn_1_1_i_workload"><td class="memSeparator" colspan="2">&#160;</td></tr>
152</table><table class="memberdecls">
153<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="inherited"></a>
154Additional Inherited Members</h2></td></tr>
155<tr class="inherit_header pro_attribs_classarmnn_1_1_base_workload"><td colspan="2" onclick="javascript:toggleInherit('pro_attribs_classarmnn_1_1_base_workload')"><img src="closed.png" alt="-"/>&#160;Protected Attributes inherited from <a class="el" href="classarmnn_1_1_base_workload.html">BaseWorkload&lt; QueueDescriptor &gt;</a></td></tr>
156<tr class="memitem:afb8d2c8817c75de9d01a4c0e0d5c160b inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structarmnn_1_1_queue_descriptor.html">QueueDescriptor</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a></td></tr>
157<tr class="separator:afb8d2c8817c75de9d01a4c0e0d5c160b inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2">&#160;</td></tr>
158<tr class="memitem:a4c2b3ca86eec6c199364671af267cd2c inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">const arm::pipe::ProfilingGuid&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#a4c2b3ca86eec6c199364671af267cd2c">m_Guid</a></td></tr>
159<tr class="separator:a4c2b3ca86eec6c199364671af267cd2c inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2">&#160;</td></tr>
160<tr class="memitem:a77806f89d6edb879d3f6c6b6b18168a7 inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memItemLeft" align="right" valign="top">const std::string&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_base_workload.html#a77806f89d6edb879d3f6c6b6b18168a7">m_Name</a></td></tr>
161<tr class="separator:a77806f89d6edb879d3f6c6b6b18168a7 inherit pro_attribs_classarmnn_1_1_base_workload"><td class="memSeparator" colspan="2">&#160;</td></tr>
162</table>
163<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
164<div class="textblock">
165<p class="definition">Definition at line <a class="el" href="_neon_unidirectional_sequence_lstm_float_workload_8hpp_source.html#l00021">21</a> of file <a class="el" href="_neon_unidirectional_sequence_lstm_float_workload_8hpp_source.html">NeonUnidirectionalSequenceLstmFloatWorkload.hpp</a>.</p>
166</div><h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
167<a id="acceeccc54cc2871ec72da81e48e7ef1c"></a>
168<h2 class="memtitle"><span class="permalink"><a href="#acceeccc54cc2871ec72da81e48e7ef1c">&#9670;&nbsp;</a></span>NeonUnidirectionalSequenceLstmFloatWorkload()</h2>
169
170<div class="memitem">
171<div class="memproto">
172 <table class="memname">
173 <tr>
174 <td class="memname"><a class="el" href="classarmnn_1_1_neon_unidirectional_sequence_lstm_float_workload.html">NeonUnidirectionalSequenceLstmFloatWorkload</a> </td>
175 <td>(</td>
176 <td class="paramtype">const <a class="el" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.html">UnidirectionalSequenceLstmQueueDescriptor</a> &amp;&#160;</td>
177 <td class="paramname"><em>descriptor</em>, </td>
178 </tr>
179 <tr>
180 <td class="paramkey"></td>
181 <td></td>
182 <td class="paramtype">const <a class="el" href="structarmnn_1_1_workload_info.html">WorkloadInfo</a> &amp;&#160;</td>
183 <td class="paramname"><em>info</em>&#160;</td>
184 </tr>
185 <tr>
186 <td></td>
187 <td>)</td>
188 <td></td><td></td>
189 </tr>
190 </table>
191</div><div class="memdoc">
192
193<p class="definition">Definition at line <a class="el" href="_neon_unidirectional_sequence_lstm_float_workload_8cpp_source.html#l00032">32</a> of file <a class="el" href="_neon_unidirectional_sequence_lstm_float_workload_8cpp_source.html">NeonUnidirectionalSequenceLstmFloatWorkload.cpp</a>.</p>
194<div class="fragment"><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160; : FloatWorkload&lt;UnidirectionalSequenceLstmQueueDescriptor&gt;(descriptor, info)</div>
195<div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160;{</div>
196<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160; <span class="comment">// Report Profiling Details</span></div>
197<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160; <a class="code" href="_profiling_8hpp.html#a786492a3881a4c760ab1eec2149f4aba">ARMNN_REPORT_PROFILING_WORKLOAD_DESC</a>(<span class="stringliteral">&quot;NeonUnidirectionalSequenceLstmFloatWorkload_Construct&quot;</span>,</div>
198<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160; descriptor.m_Parameters,</div>
199<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160; info,</div>
200<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160; <a class="code" href="classarmnn_1_1_base_workload.html#aaff95a48875d8fb4a616352906660ca9">GetGuid</a>());</div>
201<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160; </div>
202<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; <span class="keyword">const</span> arm_compute::ITensor&amp; input = <span class="keyword">static_cast&lt;</span>IAclTensorHandle*<span class="keyword">&gt;</span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0])-&gt;GetTensor();</div>
203<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160; arm_compute::ITensor&amp; output = <span class="keyword">static_cast&lt;</span>IAclTensorHandle*<span class="keyword">&gt;</span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[2])-&gt;GetTensor();</div>
204<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; </div>
205<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160; TensorInfo inputInfo = <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>.m_InputTensorInfos[0];</div>
206<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; TensorInfo outputInfo = <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>.m_OutputTensorInfos[0];</div>
207<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; </div>
208<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160; <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a> armComputeDataType = <span class="keyword">static_cast&lt;</span>IAclTensorHandle*<span class="keyword">&gt;</span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0])-&gt;GetDataType();</div>
209<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> armnnDataType = GetArmNNDataType(armComputeDataType);</div>
210<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; </div>
211<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; TensorShape inputLayerShape = <span class="keyword">static_cast&lt;</span>IAclTensorHandle*<span class="keyword">&gt;</span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0])-&gt;GetShape();</div>
212<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; TensorShape cellStateLayerShape = <span class="keyword">static_cast&lt;</span>IAclTensorHandle*<span class="keyword">&gt;</span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[2])-&gt;GetShape();</div>
213<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; TensorShape outputLayerShape = <span class="keyword">static_cast&lt;</span>IAclTensorHandle*<span class="keyword">&gt;</span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[2])-&gt;GetShape();</div>
214<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; </div>
215<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> maxTime = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_TimeMajor ? inputLayerShape[0] : inputLayerShape[1];</div>
216<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_TimeMajor ? inputLayerShape[1] : inputLayerShape[0];</div>
217<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = inputLayerShape[2];</div>
218<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = outputLayerShape[2];</div>
219<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numUnits = cellStateLayerShape[1];</div>
220<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; </div>
221<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; <span class="keyword">const</span> TensorShape timeMajorShapeInput({maxTime, batchSize, inputSize});</div>
222<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; <span class="keyword">const</span> TensorShape timeMajorShapeOutput({maxTime, batchSize, outputSize});</div>
223<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; </div>
224<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; <span class="comment">//</span></div>
225<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; <span class="comment">// Permute: performed if Unidirectional Sequence Layer inputs/outputs are in batch major format.</span></div>
226<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; <span class="comment">//</span></div>
227<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_TimeMajor)</div>
228<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; {</div>
229<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; std::unique_ptr&lt;arm_compute::NEPermute&gt; layer(<span class="keyword">new</span> arm_compute::NEPermute());</div>
230<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; </div>
231<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160; TensorInfo permuteOutInfo = inputInfo;</div>
232<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; permuteOutInfo.SetShape(timeMajorShapeInput);</div>
233<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; BuildArmComputeTensor(m_PermuteFirstOut, permuteOutInfo);</div>
234<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_PermuteFirstOut);</div>
235<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; </div>
236<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; <span class="comment">// Permute to time major format.</span></div>
237<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; layer-&gt;configure(&amp;input, &amp;m_PermuteFirstOut, arm_compute::PermutationVector(0U,2U,1U));</div>
238<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; m_Permute1.reset(layer.release());</div>
239<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; }</div>
240<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; </div>
241<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; <span class="comment">//</span></div>
242<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; <span class="comment">// Split and Concat Tensors</span></div>
243<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; <span class="comment">//</span></div>
244<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; maxTime; ++i)</div>
245<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; {</div>
246<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; arm_compute::Tensor splitter_out;</div>
247<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; arm_compute::Tensor concat_in;</div>
248<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; </div>
249<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160; <span class="keyword">auto</span> splitterTensorInfo = inputInfo;</div>
250<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; <span class="keyword">auto</span> concatTensorInfo = outputInfo;</div>
251<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; splitterTensorInfo.SetShape({batchSize, inputSize});</div>
252<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; concatTensorInfo.SetShape({batchSize, outputSize});</div>
253<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; BuildArmComputeTensor(splitter_out, splitterTensorInfo);</div>
254<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; BuildArmComputeTensor(concat_in, concatTensorInfo);</div>
255<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; </div>
256<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; armcomputetensorutils::InitialiseArmComputeTensorEmpty(splitter_out);</div>
257<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_in);</div>
258<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; </div>
259<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; <span class="comment">// append to std::vector&lt;arm_compute::Tensor&gt;</span></div>
260<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; m_SplitterOutputsTensors.push_back(std::move(splitter_out));</div>
261<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; m_ConcatInputsTensors.push_back(std::move(concat_in));</div>
262<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; }</div>
263<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; </div>
264<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; maxTime; ++i)</div>
265<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; {</div>
266<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; <span class="comment">// append to std::vector&lt;arm_compute::ITensor*&gt;</span></div>
267<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; m_SplitterOutputs.push_back(&amp;m_SplitterOutputsTensors[i]);</div>
268<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; m_ConcatInputs.push_back(&amp;m_ConcatInputsTensors[i]);</div>
269<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; }</div>
270<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; </div>
271<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; <span class="comment">//</span></div>
272<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; <span class="comment">// Split</span></div>
273<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; <span class="comment">//</span></div>
274<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberDimensions = 3;</div>
275<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimension = 0; <span class="comment">// splitting on 0-dimension (i.e. maxTime dimension)</span></div>
276<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; </div>
277<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; <span class="keywordflow">if</span> (maxTime != 1) <span class="comment">// ACL split does not work with only one element to split.</span></div>
278<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; {</div>
279<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; ViewsDescriptor splitterDesc(maxTime, numberDimensions);</div>
280<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitterDimSizes[3] = {1, batchSize, inputSize};</div>
281<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputIdx = 0u; outputIdx &lt; maxTime; ++outputIdx)</div>
282<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; {</div>
283<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; splitterDesc.SetViewOriginCoord(outputIdx, dimension, splitterDimSizes[dimension] * outputIdx);</div>
284<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx = 0u; dimIdx &lt; numberDimensions; ++dimIdx)</div>
285<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; {</div>
286<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; splitterDesc.SetViewSize(outputIdx, dimIdx, splitterDimSizes[dimIdx]);</div>
287<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; }</div>
288<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; }</div>
289<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; </div>
290<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; std::set&lt;unsigned int&gt; splitAxis = <a class="code" href="namespacearmnn.html#a8cbabc875597b3bed0ccdc0adb289fde">ComputeSplitAxis</a>(splitterDesc, timeMajorShapeInput);</div>
291<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; </div>
292<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; std::unique_ptr&lt;arm_compute::NESplit&gt; split_layer(<span class="keyword">new</span> arm_compute::NESplit());</div>
293<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> aclAxisSplit = CalcAclAxis(splitterDesc.GetNumDimensions(),</div>
294<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; *splitAxis.begin());</div>
295<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160; <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_TimeMajor)</div>
296<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; {</div>
297<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; split_layer-&gt;configure(&amp;m_PermuteFirstOut, m_SplitterOutputs, aclAxisSplit);</div>
298<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; } <span class="keywordflow">else</span></div>
299<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; {</div>
300<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; split_layer-&gt;configure(&amp;input, m_SplitterOutputs, aclAxisSplit);</div>
301<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; }</div>
302<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; </div>
303<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; split_layer-&gt;prepare();</div>
304<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160; m_Splitter.reset(split_layer.release());</div>
305<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160; }</div>
306<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160; </div>
307<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; <span class="comment">//</span></div>
308<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; <span class="comment">// Lstm</span></div>
309<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; <span class="comment">//</span></div>
310<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; arm_compute::LSTMParams&lt;arm_compute::ITensor&gt; lstm_param;</div>
311<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; </div>
312<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; m_InputToForgetWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
313<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; BuildArmComputeTensor(*m_InputToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToForgetWeights-&gt;GetTensorInfo());</div>
314<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; </div>
315<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160; m_InputToCellWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
316<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; BuildArmComputeTensor(*m_InputToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToCellWeights-&gt;GetTensorInfo());</div>
317<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; </div>
318<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; m_InputToOutputWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
319<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; BuildArmComputeTensor(*m_InputToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToOutputWeights-&gt;GetTensorInfo());</div>
320<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; </div>
321<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160; m_RecurrentToForgetWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
322<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToForgetWeights-&gt;GetTensorInfo());</div>
323<div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; </div>
324<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; m_RecurrentToCellWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
325<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160; BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToCellWeights-&gt;GetTensorInfo());</div>
326<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; </div>
327<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; m_RecurrentToOutputWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
328<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160; BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToOutputWeights-&gt;GetTensorInfo());</div>
329<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; </div>
330<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160; m_ForgetGateBiasTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
331<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160; BuildArmComputeTensor(*m_ForgetGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ForgetGateBias-&gt;GetTensorInfo());</div>
332<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160; </div>
333<div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; m_CellBiasTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
334<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; BuildArmComputeTensor(*m_CellBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellBias-&gt;GetTensorInfo());</div>
335<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; </div>
336<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160; m_OutputGateBiasTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
337<div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; BuildArmComputeTensor(*m_OutputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_OutputGateBias-&gt;GetTensorInfo());</div>
338<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; </div>
339<div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160; <span class="comment">// for future reference: check the AndroidNN API for the logic here</span></div>
340<div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160; <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</div>
341<div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160; {</div>
342<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; m_InputToInputWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
343<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; BuildArmComputeTensor(*m_InputToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToInputWeights-&gt;GetTensorInfo());</div>
344<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; </div>
345<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; m_RecurrentToInputWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
346<div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToInputWeights-&gt;GetTensorInfo());</div>
347<div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160; </div>
348<div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160; m_CellToInputWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
349<div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160; <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights != <span class="keyword">nullptr</span>)</div>
350<div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160; {</div>
351<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160; BuildArmComputeTensor(*m_CellToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights-&gt;GetTensorInfo());</div>
352<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160; }</div>
353<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160; </div>
354<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160; m_InputGateBiasTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
355<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; BuildArmComputeTensor(*m_InputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputGateBias-&gt;GetTensorInfo());</div>
356<div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160; </div>
357<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; lstm_param.set_cifg_params(m_InputToInputWeightsTensor.get(),</div>
358<div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160; m_RecurrentToInputWeightsTensor.get(),</div>
359<div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights ? m_CellToInputWeightsTensor.get() : <span class="keyword">nullptr</span>,</div>
360<div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160; m_InputGateBiasTensor.get());</div>
361<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; }</div>
362<div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160; </div>
363<div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160; <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ProjectionEnabled)</div>
364<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>&#160; {</div>
365<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160; m_ProjectionWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
366<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160; BuildArmComputeTensor(*m_ProjectionWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionWeights-&gt;GetTensorInfo());</div>
367<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>&#160; </div>
368<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>&#160; m_ProjectionBiasTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
369<div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160; <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias != <span class="keyword">nullptr</span>)</div>
370<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160; {</div>
371<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>&#160; BuildArmComputeTensor(*m_ProjectionBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias-&gt;GetTensorInfo());</div>
372<div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160; }</div>
373<div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160; </div>
374<div class="line"><a name="l00213"></a><span class="lineno"> 213</span>&#160; lstm_param.set_projection_params(m_ProjectionWeightsTensor.get(),</div>
375<div class="line"><a name="l00214"></a><span class="lineno"> 214</span>&#160; <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias ? m_ProjectionBiasTensor.get() : <span class="keyword">nullptr</span>);</div>
376<div class="line"><a name="l00215"></a><span class="lineno"> 215</span>&#160; }</div>
377<div class="line"><a name="l00216"></a><span class="lineno"> 216</span>&#160; </div>
378<div class="line"><a name="l00217"></a><span class="lineno"> 217</span>&#160; <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_PeepholeEnabled)</div>
379<div class="line"><a name="l00218"></a><span class="lineno"> 218</span>&#160; {</div>
380<div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160; m_CellToForgetWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
381<div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160; BuildArmComputeTensor(*m_CellToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToForgetWeights-&gt;GetTensorInfo());</div>
382<div class="line"><a name="l00221"></a><span class="lineno"> 221</span>&#160; </div>
383<div class="line"><a name="l00222"></a><span class="lineno"> 222</span>&#160; m_CellToOutputWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
384<div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160; BuildArmComputeTensor(*m_CellToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToOutputWeights-&gt;GetTensorInfo());</div>
385<div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160; </div>
386<div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160; lstm_param.set_peephole_params(m_CellToForgetWeightsTensor.get(), m_CellToOutputWeightsTensor.get());</div>
387<div class="line"><a name="l00226"></a><span class="lineno"> 226</span>&#160; }</div>
388<div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160; </div>
389<div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160; <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_LayerNormEnabled)</div>
390<div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160; {</div>
391<div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160; m_InputLayerNormWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
392<div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160; <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</div>
393<div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160; {</div>
394<div class="line"><a name="l00233"></a><span class="lineno"> 233</span>&#160; BuildArmComputeTensor(*m_InputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputLayerNormWeights-&gt;GetTensorInfo());</div>
395<div class="line"><a name="l00234"></a><span class="lineno"> 234</span>&#160; }</div>
396<div class="line"><a name="l00235"></a><span class="lineno"> 235</span>&#160; </div>
397<div class="line"><a name="l00236"></a><span class="lineno"> 236</span>&#160; m_ForgetLayerNormWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
398<div class="line"><a name="l00237"></a><span class="lineno"> 237</span>&#160; BuildArmComputeTensor(*m_ForgetLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ForgetLayerNormWeights-&gt;GetTensorInfo());</div>
399<div class="line"><a name="l00238"></a><span class="lineno"> 238</span>&#160; </div>
400<div class="line"><a name="l00239"></a><span class="lineno"> 239</span>&#160; m_CellLayerNormWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
401<div class="line"><a name="l00240"></a><span class="lineno"> 240</span>&#160; BuildArmComputeTensor(*m_CellLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellLayerNormWeights-&gt;GetTensorInfo());</div>
402<div class="line"><a name="l00241"></a><span class="lineno"> 241</span>&#160; </div>
403<div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160; m_OutputLayerNormWeightsTensor = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
404<div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160; BuildArmComputeTensor(*m_OutputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_OutputLayerNormWeights-&gt;GetTensorInfo());</div>
405<div class="line"><a name="l00244"></a><span class="lineno"> 244</span>&#160; </div>
406<div class="line"><a name="l00245"></a><span class="lineno"> 245</span>&#160; <span class="keyword">auto</span> inputNormWeightTensor = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled ? nullptr : m_InputLayerNormWeightsTensor.get();</div>
407<div class="line"><a name="l00246"></a><span class="lineno"> 246</span>&#160; lstm_param.set_layer_normalization_params(inputNormWeightTensor,</div>
408<div class="line"><a name="l00247"></a><span class="lineno"> 247</span>&#160; m_ForgetLayerNormWeightsTensor.get(),</div>
409<div class="line"><a name="l00248"></a><span class="lineno"> 248</span>&#160; m_CellLayerNormWeightsTensor.get(),</div>
410<div class="line"><a name="l00249"></a><span class="lineno"> 249</span>&#160; m_OutputLayerNormWeightsTensor.get());</div>
411<div class="line"><a name="l00250"></a><span class="lineno"> 250</span>&#160; }</div>
412<div class="line"><a name="l00251"></a><span class="lineno"> 251</span>&#160; </div>
413<div class="line"><a name="l00252"></a><span class="lineno"> 252</span>&#160; arm_compute::ITensor&amp; output_state_in = <span class="keyword">static_cast&lt;</span>IAclTensorHandle*<span class="keyword">&gt;</span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[1])-&gt;GetTensor();</div>
414<div class="line"><a name="l00253"></a><span class="lineno"> 253</span>&#160; arm_compute::ITensor&amp; cell_state_in = <span class="keyword">static_cast&lt;</span>IAclTensorHandle*<span class="keyword">&gt;</span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[2])-&gt;GetTensor();</div>
415<div class="line"><a name="l00254"></a><span class="lineno"> 254</span>&#160; </div>
416<div class="line"><a name="l00255"></a><span class="lineno"> 255</span>&#160; arm_compute::ITensor&amp; output_state_out = <span class="keyword">static_cast&lt;</span>IAclTensorHandle*<span class="keyword">&gt;</span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[1])-&gt;GetTensor();</div>
417<div class="line"><a name="l00256"></a><span class="lineno"> 256</span>&#160; arm_compute::ITensor&amp; cell_state_out = <span class="keyword">static_cast&lt;</span>IAclTensorHandle*<span class="keyword">&gt;</span>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[2])-&gt;GetTensor();</div>
418<div class="line"><a name="l00257"></a><span class="lineno"> 257</span>&#160; </div>
419<div class="line"><a name="l00258"></a><span class="lineno"> 258</span>&#160; m_ScratchBuffer = std::make_unique&lt;arm_compute::Tensor&gt;();</div>
420<div class="line"><a name="l00259"></a><span class="lineno"> 259</span>&#160; <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</div>
421<div class="line"><a name="l00260"></a><span class="lineno"> 260</span>&#160; {</div>
422<div class="line"><a name="l00261"></a><span class="lineno"> 261</span>&#160; <span class="comment">// scratch_buffer [num_units * 3, batch_size] with CIFG</span></div>
423<div class="line"><a name="l00262"></a><span class="lineno"> 262</span>&#160; BuildArmComputeTensor(*m_ScratchBuffer, TensorInfo({batchSize, numUnits * 3}, armnnDataType));</div>
424<div class="line"><a name="l00263"></a><span class="lineno"> 263</span>&#160; }</div>
425<div class="line"><a name="l00264"></a><span class="lineno"> 264</span>&#160; <span class="keywordflow">else</span></div>
426<div class="line"><a name="l00265"></a><span class="lineno"> 265</span>&#160; {</div>
427<div class="line"><a name="l00266"></a><span class="lineno"> 266</span>&#160; <span class="comment">// scratch_buffer [num_units * 4, batch_size] without CIFG</span></div>
428<div class="line"><a name="l00267"></a><span class="lineno"> 267</span>&#160; BuildArmComputeTensor(*m_ScratchBuffer, TensorInfo({batchSize, numUnits * 4}, armnnDataType));</div>
429<div class="line"><a name="l00268"></a><span class="lineno"> 268</span>&#160; }</div>
430<div class="line"><a name="l00269"></a><span class="lineno"> 269</span>&#160; </div>
431<div class="line"><a name="l00270"></a><span class="lineno"> 270</span>&#160; <span class="comment">// Need to be set at negative threshold to be compatible for ACL</span></div>
432<div class="line"><a name="l00271"></a><span class="lineno"> 271</span>&#160; <span class="keywordtype">float</span> cell_threshold = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ClippingThresCell;</div>
433<div class="line"><a name="l00272"></a><span class="lineno"> 272</span>&#160; <span class="keywordtype">float</span> projection_threshold = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ClippingThresProj;</div>
434<div class="line"><a name="l00273"></a><span class="lineno"> 273</span>&#160; </div>
435<div class="line"><a name="l00274"></a><span class="lineno"> 274</span>&#160; <span class="comment">// For preparing the object for the class ActivationLayerInfo, consider 5 situations</span></div>
436<div class="line"><a name="l00275"></a><span class="lineno"> 275</span>&#160; arm_compute::ActivationLayerInfo activationLayerInfo =</div>
437<div class="line"><a name="l00276"></a><span class="lineno"> 276</span>&#160; <a class="code" href="namespacearmnn.html#aa1e93ef5f9ee3dbb5e7faa9578f180ae">ConvertLstmActivationFuncToAclLayerInfo</a>(<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ActivationFunc);</div>
438<div class="line"><a name="l00277"></a><span class="lineno"> 277</span>&#160; </div>
439<div class="line"><a name="l00278"></a><span class="lineno"> 278</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i != maxTime; ++i)</div>
440<div class="line"><a name="l00279"></a><span class="lineno"> 279</span>&#160; {</div>
441<div class="line"><a name="l00280"></a><span class="lineno"> 280</span>&#160; <span class="comment">// Set LSTM input and output ITensors depending on:</span></div>
442<div class="line"><a name="l00281"></a><span class="lineno"> 281</span>&#160; <span class="comment">// input format (timeMajor) &amp; number of LSTM batches (maxTime).</span></div>
443<div class="line"><a name="l00282"></a><span class="lineno"> 282</span>&#160; arm_compute::ITensor* outputLSTM;</div>
444<div class="line"><a name="l00283"></a><span class="lineno"> 283</span>&#160; arm_compute::ITensor* inputLSTM;</div>
445<div class="line"><a name="l00284"></a><span class="lineno"> 284</span>&#160; </div>
446<div class="line"><a name="l00285"></a><span class="lineno"> 285</span>&#160; <span class="comment">// If there is only one LSTM time major batch, we will not concat OR permute.</span></div>
447<div class="line"><a name="l00286"></a><span class="lineno"> 286</span>&#160; <span class="comment">// Set input of LSTM to be first input ITensor.</span></div>
448<div class="line"><a name="l00287"></a><span class="lineno"> 287</span>&#160; <span class="comment">// Set output of LSTM to be final output ITensor.</span></div>
449<div class="line"><a name="l00288"></a><span class="lineno"> 288</span>&#160; <span class="comment">// LSTM input/output cannot be &gt; 2 dimensions so need to resize its TensorInfo.</span></div>
450<div class="line"><a name="l00289"></a><span class="lineno"> 289</span>&#160; <span class="keywordflow">if</span> (maxTime == 1 &amp;&amp; <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_TimeMajor)</div>
451<div class="line"><a name="l00290"></a><span class="lineno"> 290</span>&#160; {</div>
452<div class="line"><a name="l00291"></a><span class="lineno"> 291</span>&#160; TensorShape inputShape = <a class="code" href="namespacearmnn_utils.html#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(input.info()-&gt;tensor_shape(), 1U);</div>
453<div class="line"><a name="l00292"></a><span class="lineno"> 292</span>&#160; TensorShape outputShape = <a class="code" href="namespacearmnn_utils.html#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>((&amp;output)-&gt;<a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>()-&gt;tensor_shape(), 1U);</div>
454<div class="line"><a name="l00293"></a><span class="lineno"> 293</span>&#160; </div>
455<div class="line"><a name="l00294"></a><span class="lineno"> 294</span>&#160; TensorShape inputShapeShrink({inputShape[1], inputShape[2]});</div>
456<div class="line"><a name="l00295"></a><span class="lineno"> 295</span>&#160; TensorShape outputShapeShrink({outputShape[1], outputShape[2]});</div>
457<div class="line"><a name="l00296"></a><span class="lineno"> 296</span>&#160; </div>
458<div class="line"><a name="l00297"></a><span class="lineno"> 297</span>&#160; <span class="keyword">auto</span> acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);</div>
459<div class="line"><a name="l00298"></a><span class="lineno"> 298</span>&#160; <span class="keyword">auto</span> acl_output_shape_shrink = BuildArmComputeTensorShape(outputShapeShrink);</div>
460<div class="line"><a name="l00299"></a><span class="lineno"> 299</span>&#160; </div>
461<div class="line"><a name="l00300"></a><span class="lineno"> 300</span>&#160; input.info()-&gt;set_tensor_shape(acl_input_shape_shrink);</div>
462<div class="line"><a name="l00301"></a><span class="lineno"> 301</span>&#160; inputLSTM = <span class="keyword">const_cast&lt;</span>arm_compute::ITensor*<span class="keyword">&gt;</span>(&amp;input);</div>
463<div class="line"><a name="l00302"></a><span class="lineno"> 302</span>&#160; </div>
464<div class="line"><a name="l00303"></a><span class="lineno"> 303</span>&#160; output.info()-&gt;set_tensor_shape(acl_output_shape_shrink);</div>
465<div class="line"><a name="l00304"></a><span class="lineno"> 304</span>&#160; outputLSTM = &amp;output;</div>
466<div class="line"><a name="l00305"></a><span class="lineno"> 305</span>&#160; }</div>
467<div class="line"><a name="l00306"></a><span class="lineno"> 306</span>&#160; <span class="comment">// If there is only one LSTM batch major batch, we will not concat, only permute.</span></div>
468<div class="line"><a name="l00307"></a><span class="lineno"> 307</span>&#160; <span class="comment">// Set input of LSTM to be output of initial permute.</span></div>
469<div class="line"><a name="l00308"></a><span class="lineno"> 308</span>&#160; <span class="comment">// Set output of LSTM to be first element of m_ConcatInputs &amp; use that value later in permute.</span></div>
470<div class="line"><a name="l00309"></a><span class="lineno"> 309</span>&#160; <span class="comment">// LSTM output cannot be &gt; 2 dimensions so need to resize its TensorInfo.</span></div>
471<div class="line"><a name="l00310"></a><span class="lineno"> 310</span>&#160; <span class="keywordflow">else</span> <span class="keywordflow">if</span> (maxTime == 1 &amp;&amp; !<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_TimeMajor)</div>
472<div class="line"><a name="l00311"></a><span class="lineno"> 311</span>&#160; {</div>
473<div class="line"><a name="l00312"></a><span class="lineno"> 312</span>&#160; TensorShape inputShape = <a class="code" href="namespacearmnn_utils.html#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(m_PermuteFirstOut.info()-&gt;tensor_shape(), 1U);</div>
474<div class="line"><a name="l00313"></a><span class="lineno"> 313</span>&#160; TensorShape inputShapeShrink({inputShape[1], inputShape[2]});</div>
475<div class="line"><a name="l00314"></a><span class="lineno"> 314</span>&#160; <span class="keyword">auto</span> acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);</div>
476<div class="line"><a name="l00315"></a><span class="lineno"> 315</span>&#160; m_PermuteFirstOut.info()-&gt;set_tensor_shape(acl_input_shape_shrink);</div>
477<div class="line"><a name="l00316"></a><span class="lineno"> 316</span>&#160; inputLSTM = &amp;m_PermuteFirstOut;</div>
478<div class="line"><a name="l00317"></a><span class="lineno"> 317</span>&#160; </div>
479<div class="line"><a name="l00318"></a><span class="lineno"> 318</span>&#160; outputLSTM = <span class="keyword">const_cast&lt;</span>arm_compute::ITensor*<span class="keyword">&gt;</span>(m_ConcatInputs[i]);</div>
480<div class="line"><a name="l00319"></a><span class="lineno"> 319</span>&#160; }</div>
481<div class="line"><a name="l00320"></a><span class="lineno"> 320</span>&#160; <span class="comment">// Batch major AND/OR 2+ LSTM batches so will use concat AND/OR permute later on.</span></div>
482<div class="line"><a name="l00321"></a><span class="lineno"> 321</span>&#160; <span class="keywordflow">else</span></div>
483<div class="line"><a name="l00322"></a><span class="lineno"> 322</span>&#160; {</div>
484<div class="line"><a name="l00323"></a><span class="lineno"> 323</span>&#160; inputLSTM = m_SplitterOutputs[i];</div>
485<div class="line"><a name="l00324"></a><span class="lineno"> 324</span>&#160; outputLSTM = <span class="keyword">const_cast&lt;</span>arm_compute::ITensor*<span class="keyword">&gt;</span>(m_ConcatInputs[i]);</div>
486<div class="line"><a name="l00325"></a><span class="lineno"> 325</span>&#160; }</div>
487<div class="line"><a name="l00326"></a><span class="lineno"> 326</span>&#160; </div>
488<div class="line"><a name="l00327"></a><span class="lineno"> 327</span>&#160; std::unique_ptr&lt;arm_compute::NELSTMLayer&gt; lstm_layer(<span class="keyword">new</span> arm_compute::NELSTMLayer());</div>
489<div class="line"><a name="l00328"></a><span class="lineno"> 328</span>&#160; lstm_layer-&gt;configure(inputLSTM,</div>
490<div class="line"><a name="l00329"></a><span class="lineno"> 329</span>&#160; m_InputToForgetWeightsTensor.get(),</div>
491<div class="line"><a name="l00330"></a><span class="lineno"> 330</span>&#160; m_InputToCellWeightsTensor.get(),</div>
492<div class="line"><a name="l00331"></a><span class="lineno"> 331</span>&#160; m_InputToOutputWeightsTensor.get(),</div>
493<div class="line"><a name="l00332"></a><span class="lineno"> 332</span>&#160; m_RecurrentToForgetWeightsTensor.get(),</div>
494<div class="line"><a name="l00333"></a><span class="lineno"> 333</span>&#160; m_RecurrentToCellWeightsTensor.get(),</div>
495<div class="line"><a name="l00334"></a><span class="lineno"> 334</span>&#160; m_RecurrentToOutputWeightsTensor.get(),</div>
496<div class="line"><a name="l00335"></a><span class="lineno"> 335</span>&#160; m_ForgetGateBiasTensor.get(),</div>
497<div class="line"><a name="l00336"></a><span class="lineno"> 336</span>&#160; m_CellBiasTensor.get(),</div>
498<div class="line"><a name="l00337"></a><span class="lineno"> 337</span>&#160; m_OutputGateBiasTensor.get(),</div>
499<div class="line"><a name="l00338"></a><span class="lineno"> 338</span>&#160; &amp;output_state_in,</div>
500<div class="line"><a name="l00339"></a><span class="lineno"> 339</span>&#160; &amp;cell_state_in,</div>
501<div class="line"><a name="l00340"></a><span class="lineno"> 340</span>&#160; m_ScratchBuffer.get(),</div>
502<div class="line"><a name="l00341"></a><span class="lineno"> 341</span>&#160; &amp;output_state_out,</div>
503<div class="line"><a name="l00342"></a><span class="lineno"> 342</span>&#160; &amp;cell_state_out,</div>
504<div class="line"><a name="l00343"></a><span class="lineno"> 343</span>&#160; outputLSTM,</div>
505<div class="line"><a name="l00344"></a><span class="lineno"> 344</span>&#160; lstm_param,</div>
506<div class="line"><a name="l00345"></a><span class="lineno"> 345</span>&#160; activationLayerInfo,</div>
507<div class="line"><a name="l00346"></a><span class="lineno"> 346</span>&#160; cell_threshold,</div>
508<div class="line"><a name="l00347"></a><span class="lineno"> 347</span>&#160; projection_threshold);</div>
509<div class="line"><a name="l00348"></a><span class="lineno"> 348</span>&#160; </div>
510<div class="line"><a name="l00349"></a><span class="lineno"> 349</span>&#160; m_Layers.emplace_back(std::move(lstm_layer));</div>
511<div class="line"><a name="l00350"></a><span class="lineno"> 350</span>&#160; }</div>
512<div class="line"><a name="l00351"></a><span class="lineno"> 351</span>&#160; </div>
513<div class="line"><a name="l00352"></a><span class="lineno"> 352</span>&#160; armcomputetensorutils::InitialiseArmComputeTensorEmpty(*m_ScratchBuffer);</div>
514<div class="line"><a name="l00353"></a><span class="lineno"> 353</span>&#160; </div>
515<div class="line"><a name="l00354"></a><span class="lineno"> 354</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_InputToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToForgetWeights);</div>
516<div class="line"><a name="l00355"></a><span class="lineno"> 355</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_InputToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToCellWeights);</div>
517<div class="line"><a name="l00356"></a><span class="lineno"> 356</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_InputToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToOutputWeights);</div>
518<div class="line"><a name="l00357"></a><span class="lineno"> 357</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_RecurrentToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToForgetWeights);</div>
519<div class="line"><a name="l00358"></a><span class="lineno"> 358</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_RecurrentToCellWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToCellWeights);</div>
520<div class="line"><a name="l00359"></a><span class="lineno"> 359</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_RecurrentToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToOutputWeights);</div>
521<div class="line"><a name="l00360"></a><span class="lineno"> 360</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_ForgetGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ForgetGateBias);</div>
522<div class="line"><a name="l00361"></a><span class="lineno"> 361</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_CellBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellBias);</div>
523<div class="line"><a name="l00362"></a><span class="lineno"> 362</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_OutputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_OutputGateBias);</div>
524<div class="line"><a name="l00363"></a><span class="lineno"> 363</span>&#160; </div>
525<div class="line"><a name="l00364"></a><span class="lineno"> 364</span>&#160; <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</div>
526<div class="line"><a name="l00365"></a><span class="lineno"> 365</span>&#160; {</div>
527<div class="line"><a name="l00366"></a><span class="lineno"> 366</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_InputToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputToInputWeights);</div>
528<div class="line"><a name="l00367"></a><span class="lineno"> 367</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_RecurrentToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_RecurrentToInputWeights);</div>
529<div class="line"><a name="l00368"></a><span class="lineno"> 368</span>&#160; <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights != <span class="keyword">nullptr</span>)</div>
530<div class="line"><a name="l00369"></a><span class="lineno"> 369</span>&#160; {</div>
531<div class="line"><a name="l00370"></a><span class="lineno"> 370</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_CellToInputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToInputWeights);</div>
532<div class="line"><a name="l00371"></a><span class="lineno"> 371</span>&#160; }</div>
533<div class="line"><a name="l00372"></a><span class="lineno"> 372</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_InputGateBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputGateBias);</div>
534<div class="line"><a name="l00373"></a><span class="lineno"> 373</span>&#160; }</div>
535<div class="line"><a name="l00374"></a><span class="lineno"> 374</span>&#160; </div>
536<div class="line"><a name="l00375"></a><span class="lineno"> 375</span>&#160; <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_ProjectionEnabled)</div>
537<div class="line"><a name="l00376"></a><span class="lineno"> 376</span>&#160; {</div>
538<div class="line"><a name="l00377"></a><span class="lineno"> 377</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_ProjectionWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionWeights);</div>
539<div class="line"><a name="l00378"></a><span class="lineno"> 378</span>&#160; <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias != <span class="keyword">nullptr</span>)</div>
540<div class="line"><a name="l00379"></a><span class="lineno"> 379</span>&#160; {</div>
541<div class="line"><a name="l00380"></a><span class="lineno"> 380</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_ProjectionBiasTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ProjectionBias);</div>
542<div class="line"><a name="l00381"></a><span class="lineno"> 381</span>&#160; }</div>
543<div class="line"><a name="l00382"></a><span class="lineno"> 382</span>&#160; }</div>
544<div class="line"><a name="l00383"></a><span class="lineno"> 383</span>&#160; </div>
545<div class="line"><a name="l00384"></a><span class="lineno"> 384</span>&#160; <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_PeepholeEnabled)</div>
546<div class="line"><a name="l00385"></a><span class="lineno"> 385</span>&#160; {</div>
547<div class="line"><a name="l00386"></a><span class="lineno"> 386</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_CellToForgetWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToForgetWeights);</div>
548<div class="line"><a name="l00387"></a><span class="lineno"> 387</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_CellToOutputWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellToOutputWeights);</div>
549<div class="line"><a name="l00388"></a><span class="lineno"> 388</span>&#160; }</div>
550<div class="line"><a name="l00389"></a><span class="lineno"> 389</span>&#160; </div>
551<div class="line"><a name="l00390"></a><span class="lineno"> 390</span>&#160; <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_LayerNormEnabled)</div>
552<div class="line"><a name="l00391"></a><span class="lineno"> 391</span>&#160; {</div>
553<div class="line"><a name="l00392"></a><span class="lineno"> 392</span>&#160; <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_CifgEnabled)</div>
554<div class="line"><a name="l00393"></a><span class="lineno"> 393</span>&#160; {</div>
555<div class="line"><a name="l00394"></a><span class="lineno"> 394</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_InputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_InputLayerNormWeights);</div>
556<div class="line"><a name="l00395"></a><span class="lineno"> 395</span>&#160; }</div>
557<div class="line"><a name="l00396"></a><span class="lineno"> 396</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_ForgetLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_ForgetLayerNormWeights);</div>
558<div class="line"><a name="l00397"></a><span class="lineno"> 397</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_CellLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_CellLayerNormWeights);</div>
559<div class="line"><a name="l00398"></a><span class="lineno"> 398</span>&#160; <a class="code" href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">InitializeArmComputeTensorData</a>(*m_OutputLayerNormWeightsTensor, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_OutputLayerNormWeights);</div>
560<div class="line"><a name="l00399"></a><span class="lineno"> 399</span>&#160; }</div>
561<div class="line"><a name="l00400"></a><span class="lineno"> 400</span>&#160; </div>
562<div class="line"><a name="l00401"></a><span class="lineno"> 401</span>&#160; <span class="comment">// Force Compute Library to perform the necessary copying and reshaping.</span></div>
563<div class="line"><a name="l00402"></a><span class="lineno"> 402</span>&#160; <span class="comment">// After which delete all the input tensors that will no longer be needed.</span></div>
564<div class="line"><a name="l00403"></a><span class="lineno"> 403</span>&#160; <span class="keywordflow">for</span> (uint32_t i = 0; i &lt; m_Layers.size(); ++i)</div>
565<div class="line"><a name="l00404"></a><span class="lineno"> 404</span>&#160; {</div>
566<div class="line"><a name="l00405"></a><span class="lineno"> 405</span>&#160; m_Layers[i]-&gt;prepare();</div>
567<div class="line"><a name="l00406"></a><span class="lineno"> 406</span>&#160; }</div>
568<div class="line"><a name="l00407"></a><span class="lineno"> 407</span>&#160; </div>
569<div class="line"><a name="l00408"></a><span class="lineno"> 408</span>&#160; <span class="comment">//</span></div>
570<div class="line"><a name="l00409"></a><span class="lineno"> 409</span>&#160; <span class="comment">// Concat</span></div>
571<div class="line"><a name="l00410"></a><span class="lineno"> 410</span>&#160; <span class="comment">//</span></div>
572<div class="line"><a name="l00411"></a><span class="lineno"> 411</span>&#160; </div>
573<div class="line"><a name="l00412"></a><span class="lineno"> 412</span>&#160; <span class="comment">// Expand dimensions of LSTM outputs adding one empty dimension to fit concatenate inputs.</span></div>
574<div class="line"><a name="l00413"></a><span class="lineno"> 413</span>&#160; TensorShape shape = <a class="code" href="namespacearmnn_utils.html#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(m_ConcatInputs[0]-&gt;<a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>()-&gt;tensor_shape(), 1U);</div>
575<div class="line"><a name="l00414"></a><span class="lineno"> 414</span>&#160; TensorShape shapeExpandTimeMajor({1, shape[0], shape[1]});</div>
576<div class="line"><a name="l00415"></a><span class="lineno"> 415</span>&#160; TensorShape shapeExpandBatchMajor({shape[0], 1, shape[1]});</div>
577<div class="line"><a name="l00416"></a><span class="lineno"> 416</span>&#160; </div>
578<div class="line"><a name="l00417"></a><span class="lineno"> 417</span>&#160; <span class="keywordflow">if</span> (maxTime != 1) <span class="comment">// ACL concat does not work with only one element to concatenate.</span></div>
579<div class="line"><a name="l00418"></a><span class="lineno"> 418</span>&#160; {</div>
580<div class="line"><a name="l00419"></a><span class="lineno"> 419</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; maxTime; ++i)</div>
581<div class="line"><a name="l00420"></a><span class="lineno"> 420</span>&#160; {</div>
582<div class="line"><a name="l00421"></a><span class="lineno"> 421</span>&#160; m_ConcatInputs[i]-&gt;info()-&gt;set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor));</div>
583<div class="line"><a name="l00422"></a><span class="lineno"> 422</span>&#160; }</div>
584<div class="line"><a name="l00423"></a><span class="lineno"> 423</span>&#160; </div>
585<div class="line"><a name="l00424"></a><span class="lineno"> 424</span>&#160; <a class="code" href="namespacearmnn.html#a7863c179ff92feec660c48ab7b95ae55">ConcatDescriptor</a> concatDescriptor(maxTime, numberDimensions); <span class="comment">// maxTime = num inputs (aka. number of views).</span></div>
586<div class="line"><a name="l00425"></a><span class="lineno"> 425</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputIdx = 0u; inputIdx &lt; maxTime; ++inputIdx)</div>
587<div class="line"><a name="l00426"></a><span class="lineno"> 426</span>&#160; {</div>
588<div class="line"><a name="l00427"></a><span class="lineno"> 427</span>&#160; concatDescriptor.SetViewOriginCoord(inputIdx, dimension, inputIdx);</div>
589<div class="line"><a name="l00428"></a><span class="lineno"> 428</span>&#160; concatDescriptor.SetConcatAxis(dimension);</div>
590<div class="line"><a name="l00429"></a><span class="lineno"> 429</span>&#160; }</div>
591<div class="line"><a name="l00430"></a><span class="lineno"> 430</span>&#160; </div>
592<div class="line"><a name="l00431"></a><span class="lineno"> 431</span>&#160; m_Concat.reset(<span class="keyword">new</span> arm_compute::NEConcatenateLayer());</div>
593<div class="line"><a name="l00432"></a><span class="lineno"> 432</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> aclAxisConcat = CalcAclAxis(concatDescriptor.GetNumDimensions(), concatDescriptor.GetConcatAxis());</div>
594<div class="line"><a name="l00433"></a><span class="lineno"> 433</span>&#160; <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_TimeMajor)</div>
595<div class="line"><a name="l00434"></a><span class="lineno"> 434</span>&#160; {</div>
596<div class="line"><a name="l00435"></a><span class="lineno"> 435</span>&#160; TensorInfo concatOutputTensorInfo = outputInfo;</div>
597<div class="line"><a name="l00436"></a><span class="lineno"> 436</span>&#160; concatOutputTensorInfo.SetShape(timeMajorShapeOutput);</div>
598<div class="line"><a name="l00437"></a><span class="lineno"> 437</span>&#160; BuildArmComputeTensor(concat_out, concatOutputTensorInfo);</div>
599<div class="line"><a name="l00438"></a><span class="lineno"> 438</span>&#160; armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_out);</div>
600<div class="line"><a name="l00439"></a><span class="lineno"> 439</span>&#160; </div>
601<div class="line"><a name="l00440"></a><span class="lineno"> 440</span>&#160; m_Concat-&gt;configure(m_ConcatInputs, &amp;concat_out, aclAxisConcat);</div>
602<div class="line"><a name="l00441"></a><span class="lineno"> 441</span>&#160; }</div>
603<div class="line"><a name="l00442"></a><span class="lineno"> 442</span>&#160; <span class="keywordflow">else</span></div>
604<div class="line"><a name="l00443"></a><span class="lineno"> 443</span>&#160; {</div>
605<div class="line"><a name="l00444"></a><span class="lineno"> 444</span>&#160; m_Concat-&gt;configure(m_ConcatInputs, &amp;output, aclAxisConcat);</div>
606<div class="line"><a name="l00445"></a><span class="lineno"> 445</span>&#160; }</div>
607<div class="line"><a name="l00446"></a><span class="lineno"> 446</span>&#160; </div>
608<div class="line"><a name="l00447"></a><span class="lineno"> 447</span>&#160; m_Concat-&gt;prepare();</div>
609<div class="line"><a name="l00448"></a><span class="lineno"> 448</span>&#160; }</div>
610<div class="line"><a name="l00449"></a><span class="lineno"> 449</span>&#160; <span class="comment">// If only one LSTM batch, we do not concat and/or permute.</span></div>
611<div class="line"><a name="l00450"></a><span class="lineno"> 450</span>&#160; <span class="comment">// Must ensure final output info is expanded to correct batch major dimensions.</span></div>
612<div class="line"><a name="l00451"></a><span class="lineno"> 451</span>&#160; <span class="keywordflow">else</span></div>
613<div class="line"><a name="l00452"></a><span class="lineno"> 452</span>&#160; {</div>
614<div class="line"><a name="l00453"></a><span class="lineno"> 453</span>&#160; <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_TimeMajor)</div>
615<div class="line"><a name="l00454"></a><span class="lineno"> 454</span>&#160; {</div>
616<div class="line"><a name="l00455"></a><span class="lineno"> 455</span>&#160; output.info()-&gt;set_tensor_shape(BuildArmComputeTensorShape(shapeExpandBatchMajor));</div>
617<div class="line"><a name="l00456"></a><span class="lineno"> 456</span>&#160; }</div>
618<div class="line"><a name="l00457"></a><span class="lineno"> 457</span>&#160; <span class="keywordflow">else</span></div>
619<div class="line"><a name="l00458"></a><span class="lineno"> 458</span>&#160; {</div>
620<div class="line"><a name="l00459"></a><span class="lineno"> 459</span>&#160; output.info()-&gt;set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor));</div>
621<div class="line"><a name="l00460"></a><span class="lineno"> 460</span>&#160; }</div>
622<div class="line"><a name="l00461"></a><span class="lineno"> 461</span>&#160; }</div>
623<div class="line"><a name="l00462"></a><span class="lineno"> 462</span>&#160; </div>
624<div class="line"><a name="l00463"></a><span class="lineno"> 463</span>&#160; <span class="comment">//</span></div>
625<div class="line"><a name="l00464"></a><span class="lineno"> 464</span>&#160; <span class="comment">// Permute: only done if input/output are in batch major format.</span></div>
626<div class="line"><a name="l00465"></a><span class="lineno"> 465</span>&#160; <span class="comment">//</span></div>
627<div class="line"><a name="l00466"></a><span class="lineno"> 466</span>&#160; <span class="keywordflow">if</span> (!<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.m_Parameters.m_TimeMajor)</div>
628<div class="line"><a name="l00467"></a><span class="lineno"> 467</span>&#160; {</div>
629<div class="line"><a name="l00468"></a><span class="lineno"> 468</span>&#160; <span class="comment">// Output now time major. Permute output back to batch major.</span></div>
630<div class="line"><a name="l00469"></a><span class="lineno"> 469</span>&#160; std::unique_ptr&lt;arm_compute::NEPermute&gt; layer(<span class="keyword">new</span> arm_compute::NEPermute());</div>
631<div class="line"><a name="l00470"></a><span class="lineno"> 470</span>&#160; <span class="keywordflow">if</span> (maxTime != 1)</div>
632<div class="line"><a name="l00471"></a><span class="lineno"> 471</span>&#160; {</div>
633<div class="line"><a name="l00472"></a><span class="lineno"> 472</span>&#160; layer-&gt;configure(&amp;concat_out, &amp;output, arm_compute::PermutationVector(0U, 2U, 1U));</div>
634<div class="line"><a name="l00473"></a><span class="lineno"> 473</span>&#160; }</div>
635<div class="line"><a name="l00474"></a><span class="lineno"> 474</span>&#160; <span class="keywordflow">else</span></div>
636<div class="line"><a name="l00475"></a><span class="lineno"> 475</span>&#160; {</div>
637<div class="line"><a name="l00476"></a><span class="lineno"> 476</span>&#160; layer-&gt;configure(m_ConcatInputs[0], &amp;output, arm_compute::PermutationVector(0U, 2U, 1U));</div>
638<div class="line"><a name="l00477"></a><span class="lineno"> 477</span>&#160; }</div>
639<div class="line"><a name="l00478"></a><span class="lineno"> 478</span>&#160; m_Permute2.reset(layer.release());</div>
640<div class="line"><a name="l00479"></a><span class="lineno"> 479</span>&#160; }</div>
641<div class="line"><a name="l00480"></a><span class="lineno"> 480</span>&#160; </div>
642<div class="line"><a name="l00481"></a><span class="lineno"> 481</span>&#160; FreeUnusedTensors();</div>
643<div class="line"><a name="l00482"></a><span class="lineno"> 482</span>&#160;}</div>
644</div><!-- fragment -->
645<p class="reference">References <a class="el" href="_profiling_8hpp_source.html#l00227">ARMNN_REPORT_PROFILING_WORKLOAD_DESC</a>, <a class="el" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, and <a class="el" href="_workload_data_8hpp_source.html#l00066">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>.</p>
646
647</div>
648</div>
649<h2 class="groupheader">Member Function Documentation</h2>
650<a id="ae071e8822437c78baea75c3aef3a263a"></a>
651<h2 class="memtitle"><span class="permalink"><a href="#ae071e8822437c78baea75c3aef3a263a">&#9670;&nbsp;</a></span>Execute()</h2>
652
653<div class="memitem">
654<div class="memproto">
655<table class="mlabels">
656 <tr>
657 <td class="mlabels-left">
658 <table class="memname">
659 <tr>
660 <td class="memname">void Execute </td>
661 <td>(</td>
662 <td class="paramname"></td><td>)</td>
663 <td> const</td>
664 </tr>
665 </table>
666 </td>
667 <td class="mlabels-right">
668<span class="mlabels"><span class="mlabel">override</span><span class="mlabel">virtual</span></span> </td>
669 </tr>
670</table>
671</div><div class="memdoc">
672
673<p>Implements <a class="el" href="classarmnn_1_1_i_workload.html#a72ae00e6604850c8798c5e0d825ee7e4">IWorkload</a>.</p>
674
675<p class="definition">Definition at line <a class="el" href="_neon_unidirectional_sequence_lstm_float_workload_8cpp_source.html#l00484">484</a> of file <a class="el" href="_neon_unidirectional_sequence_lstm_float_workload_8cpp_source.html">NeonUnidirectionalSequenceLstmFloatWorkload.cpp</a>.</p>
676<div class="fragment"><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>&#160;{</div>
677<div class="line"><a name="l00486"></a><span class="lineno"> 486</span>&#160; <a class="code" href="_neon_workload_utils_8hpp.html#a7f97eedf3c9436b110df92c947bbb55d">ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID</a>(<span class="stringliteral">&quot;NeonUnidirectionalSequenceLstmFloatWorkload_Execute&quot;</span>);</div>
678<div class="line"><a name="l00487"></a><span class="lineno"> 487</span>&#160; <span class="keywordflow">if</span> (m_Permute1)</div>
679<div class="line"><a name="l00488"></a><span class="lineno"> 488</span>&#160; {</div>
680<div class="line"><a name="l00489"></a><span class="lineno"> 489</span>&#160; m_Permute1-&gt;run();</div>
681<div class="line"><a name="l00490"></a><span class="lineno"> 490</span>&#160; }</div>
682<div class="line"><a name="l00491"></a><span class="lineno"> 491</span>&#160; <span class="keywordflow">if</span> (m_Splitter)</div>
683<div class="line"><a name="l00492"></a><span class="lineno"> 492</span>&#160; {</div>
684<div class="line"><a name="l00493"></a><span class="lineno"> 493</span>&#160; m_Splitter-&gt;run();</div>
685<div class="line"><a name="l00494"></a><span class="lineno"> 494</span>&#160; }</div>
686<div class="line"><a name="l00495"></a><span class="lineno"> 495</span>&#160; <span class="keywordflow">for</span> (uint32_t i = 0; i &lt; m_Layers.size(); ++i)</div>
687<div class="line"><a name="l00496"></a><span class="lineno"> 496</span>&#160; {</div>
688<div class="line"><a name="l00497"></a><span class="lineno"> 497</span>&#160; m_Layers[i]-&gt;run();</div>
689<div class="line"><a name="l00498"></a><span class="lineno"> 498</span>&#160; }</div>
690<div class="line"><a name="l00499"></a><span class="lineno"> 499</span>&#160; <span class="keywordflow">if</span> (m_Concat)</div>
691<div class="line"><a name="l00500"></a><span class="lineno"> 500</span>&#160; {</div>
692<div class="line"><a name="l00501"></a><span class="lineno"> 501</span>&#160; m_Concat-&gt;run();</div>
693<div class="line"><a name="l00502"></a><span class="lineno"> 502</span>&#160; }</div>
694<div class="line"><a name="l00503"></a><span class="lineno"> 503</span>&#160; <span class="keywordflow">if</span> (m_Permute2)</div>
695<div class="line"><a name="l00504"></a><span class="lineno"> 504</span>&#160; {</div>
696<div class="line"><a name="l00505"></a><span class="lineno"> 505</span>&#160; m_Permute2-&gt;run();</div>
697<div class="line"><a name="l00506"></a><span class="lineno"> 506</span>&#160; }</div>
698<div class="line"><a name="l00507"></a><span class="lineno"> 507</span>&#160;}</div>
699</div><!-- fragment -->
700<p class="reference">References <a class="el" href="_neon_workload_utils_8hpp_source.html#l00032">ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID</a>.</p>
701
702</div>
703</div>
704<hr/>The documentation for this class was generated from the following files:<ul>
705<li>src/backends/neon/workloads/<a class="el" href="_neon_unidirectional_sequence_lstm_float_workload_8hpp_source.html">NeonUnidirectionalSequenceLstmFloatWorkload.hpp</a></li>
706<li>src/backends/neon/workloads/<a class="el" href="_neon_unidirectional_sequence_lstm_float_workload_8cpp_source.html">NeonUnidirectionalSequenceLstmFloatWorkload.cpp</a></li>
707</ul>
708</div><!-- contents -->
709</div><!-- doc-content -->
710<div class="ttc" id="anamespacearmnn_html_a7863c179ff92feec660c48ab7b95ae55"><div class="ttname"><a href="namespacearmnn.html#a7863c179ff92feec660c48ab7b95ae55">armnn::ConcatDescriptor</a></div><div class="ttdeci">OriginsDescriptor ConcatDescriptor</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_fwd_8hpp_source.html#l00059">DescriptorsFwd.hpp:59</a></div></div>
711<div class="ttc" id="anamespacearmnn_html_a8cbabc875597b3bed0ccdc0adb289fde"><div class="ttname"><a href="namespacearmnn.html#a8cbabc875597b3bed0ccdc0adb289fde">armnn::ComputeSplitAxis</a></div><div class="ttdeci">std::set&lt; unsigned int &gt; ComputeSplitAxis(const armnn::SplitterDescriptor &amp;desc, const TensorShape &amp;input)</div><div class="ttdef"><b>Definition:</b> <a href="_arm_compute_utils_8hpp_source.html#l00246">ArmComputeUtils.hpp:246</a></div></div>
712<div class="ttc" id="anamespacearmnn_html_a611208865d55ea576cc89ac86d7c19b7"><div class="ttname"><a href="namespacearmnn.html#a611208865d55ea576cc89ac86d7c19b7">armnn::InitializeArmComputeTensorData</a></div><div class="ttdeci">void InitializeArmComputeTensorData(arm_compute::Tensor &amp;tensor, TensorInfo tensorInfo, const ITensorHandle *handle)</div><div class="ttdef"><b>Definition:</b> <a href="_neon_workload_utils_8hpp_source.html#l00068">NeonWorkloadUtils.hpp:68</a></div></div>
713<div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.html#l00048">Types.hpp:48</a></div></div>
714<div class="ttc" id="anamespacearmnn_html_aa1e93ef5f9ee3dbb5e7faa9578f180ae"><div class="ttname"><a href="namespacearmnn.html#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.html#l00118">ArmComputeUtils.hpp:118</a></div></div>
715<div class="ttc" id="anamespacearmnn_html_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div><div class="ttdeci">@ info</div></div>
716<div class="ttc" id="astructarmnn_1_1_queue_descriptor_html_a6abd491bb99ffe88bd472c1ae5a1ed1a"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">armnn::QueueDescriptor::m_Outputs</a></div><div class="ttdeci">std::vector&lt; ITensorHandle * &gt; m_Outputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00027">WorkloadData.hpp:27</a></div></div>
717<div class="ttc" id="a_profiling_8hpp_html_a786492a3881a4c760ab1eec2149f4aba"><div class="ttname"><a href="_profiling_8hpp.html#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.html#l00227">Profiling.hpp:227</a></div></div>
718<div class="ttc" id="aclassarmnn_1_1_base_workload_html_aaff95a48875d8fb4a616352906660ca9"><div class="ttname"><a href="classarmnn_1_1_base_workload.html#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.html#l00067">Workload.hpp:67</a></div></div>
719<div class="ttc" id="aclassarmnn_1_1_base_workload_html_afb8d2c8817c75de9d01a4c0e0d5c160b"><div class="ttname"><a href="classarmnn_1_1_base_workload.html#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.html#l00089">Workload.hpp:89</a></div></div>
720<div class="ttc" id="a_neon_workload_utils_8hpp_html_a7f97eedf3c9436b110df92c947bbb55d"><div class="ttname"><a href="_neon_workload_utils_8hpp.html#a7f97eedf3c9436b110df92c947bbb55d">ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID</a></div><div class="ttdeci">#define ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID(label)</div><div class="ttdoc">Creates a profiling event that uses GetGuid() and GetName() from the calling class.</div><div class="ttdef"><b>Definition:</b> <a href="_neon_workload_utils_8hpp_source.html#l00032">NeonWorkloadUtils.hpp:32</a></div></div>
721<div class="ttc" id="anamespacearmnn_utils_html_ab53d94ea22b51c6bcdf9584644bd67bb"><div class="ttname"><a href="namespacearmnn_utils.html#ab53d94ea22b51c6bcdf9584644bd67bb">armnnUtils::GetTensorShape</a></div><div class="ttdeci">armnn::TensorShape GetTensorShape(unsigned int numberOfBatches, unsigned int numberOfChannels, unsigned int height, unsigned int width, const armnn::DataLayout dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.html#l00021">TensorUtils.cpp:21</a></div></div>
722<div class="ttc" id="astructarmnn_1_1_queue_descriptor_html_a4b50e46a6810018f3edecfb68b2a76b3"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">armnn::QueueDescriptor::m_Inputs</a></div><div class="ttdeci">std::vector&lt; ITensorHandle * &gt; m_Inputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00026">WorkloadData.hpp:26</a></div></div>
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