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95 <div class="headertitle">
96<div class="title">RefQLstmWorkload.cpp</div> </div>
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98<div class="contents">
99<a href="_ref_q_lstm_workload_8cpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span>&#160;<span class="comment">//</span></div>
100<div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment">// Copyright © 2020-2023 Arm Ltd and Contributors. All rights reserved.</span></div>
101<div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment">// SPDX-License-Identifier: MIT</span></div>
102<div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment">//</span></div>
103<div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160; </div>
104<div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_ref_q_lstm_workload_8hpp.html">RefQLstmWorkload.hpp</a>&quot;</span></div>
105<div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_activation_8hpp.html">Activation.hpp</a>&quot;</span></div>
106<div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_encoders_8hpp.html">Encoders.hpp</a>&quot;</span></div>
107<div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_decoders_8hpp.html">Decoders.hpp</a>&quot;</span></div>
108<div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_lstm_utils_8hpp.html">LstmUtils.hpp</a>&quot;</span></div>
109<div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_ref_workload_utils_8hpp.html">RefWorkloadUtils.hpp</a>&quot;</span></div>
110<div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160; </div>
111<div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearmnn.html">armnn</a></div>
112<div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;{</div>
113<div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160; </div>
114<div class="line"><a name="l00016"></a><span class="lineno"><a class="line" href="classarmnn_1_1_ref_q_lstm_workload.html#a143c3dfe9ce674a65bb5b93f91ec54e3"> 16</a></span>&#160;<a class="code" href="classarmnn_1_1_ref_q_lstm_workload.html#a143c3dfe9ce674a65bb5b93f91ec54e3">RefQLstmWorkload::RefQLstmWorkload</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_q_lstm_queue_descriptor.html">QLstmQueueDescriptor</a> &amp;descriptor, <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.html">WorkloadInfo</a> &amp;info)</div>
115<div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160; : <a class="code" href="classarmnn_1_1_ref_base_workload.html">RefBaseWorkload</a>&lt;<a class="code" href="structarmnn_1_1_q_lstm_queue_descriptor.html">QLstmQueueDescriptor</a>&gt;(descriptor, <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>)</div>
116<div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160; , m_InputToInputWeightsTensor (<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_InputToInputWeights))</div>
117<div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160; , m_InputToForgetWeightsTensor (<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_InputToForgetWeights))</div>
118<div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160; , m_InputToCellWeightsTensor (<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_InputToCellWeights))</div>
119<div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160; , m_InputToOutputWeightsTensor (<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_InputToOutputWeights))</div>
120<div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160; </div>
121<div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160; , m_RecurrentToInputWeightsTensor (<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_RecurrentToInputWeights))</div>
122<div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160; , m_RecurrentToForgetWeightsTensor(<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_RecurrentToForgetWeights))</div>
123<div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160; , m_RecurrentToCellWeightsTensor (<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_RecurrentToCellWeights))</div>
124<div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160; , m_RecurrentToOutputWeightsTensor(<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_RecurrentToOutputWeights))</div>
125<div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160; </div>
126<div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160; , m_CellToInputWeightsTensor (<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_CellToInputWeights))</div>
127<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160; , m_CellToForgetWeightsTensor (<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_CellToForgetWeights))</div>
128<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160; , m_CellToOutputWeightsTensor (<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_CellToOutputWeights))</div>
129<div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160; </div>
130<div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160; , m_InputGateBiasTensor (<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_InputGateBias))</div>
131<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160; , m_ForgetGateBiasTensor (<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_ForgetGateBias))</div>
132<div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160; , m_CellBiasTensor (<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_CellBias))</div>
133<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160; , m_OutputGateBiasTensor (<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_OutputGateBias))</div>
134<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160; </div>
135<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160; , m_ProjectionWeightsTensor (<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_ProjectionWeights))</div>
136<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160; , m_ProjectionBiasTensor (<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_ProjectionBias))</div>
137<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160; </div>
138<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160; , m_InputLayerNormWeightsTensor (<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_InputLayerNormWeights))</div>
139<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; , m_ForgetLayerNormWeightsTensor (<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_ForgetLayerNormWeights))</div>
140<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160; , m_CellLayerNormWeightsTensor (<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_CellLayerNormWeights))</div>
141<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; , m_OutputLayerNormWeightsTensor (<a class="code" href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a>(descriptor.m_OutputLayerNormWeights))</div>
142<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160;{}</div>
143<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; </div>
144<div class="line"><a name="l00046"></a><span class="lineno"><a class="line" href="classarmnn_1_1_ref_q_lstm_workload.html#ae071e8822437c78baea75c3aef3a263a"> 46</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_ref_q_lstm_workload.html#ae071e8822437c78baea75c3aef3a263a">RefQLstmWorkload::Execute</a>()<span class="keyword"> const</span></div>
145<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160;<span class="keyword"></span>{</div>
146<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; <a class="code" href="classarmnn_1_1_ref_q_lstm_workload.html#ae071e8822437c78baea75c3aef3a263a">Execute</a>(<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>, <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>);</div>
147<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160;}</div>
148<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; </div>
149<div class="line"><a name="l00051"></a><span class="lineno"><a class="line" href="classarmnn_1_1_ref_q_lstm_workload.html#ae1c43d025fc90382d7aff7a500937e2c"> 51</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_ref_q_lstm_workload.html#ae1c43d025fc90382d7aff7a500937e2c">RefQLstmWorkload::ExecuteAsync</a>(<a class="code" href="structarmnn_1_1experimental_1_1_execution_data.html">ExecutionData</a>&amp; executionData)</div>
150<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160;{</div>
151<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; <a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.html">WorkingMemDescriptor</a>* workingMemDescriptor = <span class="keyword">static_cast&lt;</span><a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.html">WorkingMemDescriptor</a>*<span class="keyword">&gt;</span>(executionData.<a class="code" href="structarmnn_1_1experimental_1_1_execution_data.html#ad2b382076f26f48cd44783cfca2e3642">m_Data</a>);</div>
152<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; <a class="code" href="classarmnn_1_1_ref_q_lstm_workload.html#ae071e8822437c78baea75c3aef3a263a">Execute</a>(workingMemDescriptor-&gt;<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>, workingMemDescriptor-&gt;<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>);</div>
153<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160;}</div>
154<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; </div>
155<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_ref_q_lstm_workload.html#ae071e8822437c78baea75c3aef3a263a">RefQLstmWorkload::Execute</a>(std::vector&lt;ITensorHandle*&gt; inputs, std::vector&lt;ITensorHandle*&gt; outputs)<span class="keyword"> const</span></div>
156<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160;<span class="keyword"></span>{</div>
157<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; <a class="code" href="_ref_workload_utils_8hpp.html#a06acca4fd832e0fb179604112c0505af">ARMNN_SCOPED_PROFILING_EVENT_REF_NAME_GUID</a>(<span class="stringliteral">&quot;RefQLstmWorkload_Execute&quot;</span>);</div>
158<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; </div>
159<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; <span class="comment">// This is a porting of the QLSTM::Execute(std::vector&lt;ITensorHandle*&gt; inputs, std::vector&lt;ITensorHandle*&gt; outputs)</span></div>
160<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; <span class="comment">// method in the Android code base</span></div>
161<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; <span class="comment">// Note: this implementation wraps the arithmetic functions of the LSTM cell in Quantize/Dequantize ops, so all</span></div>
162<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; <span class="comment">// computation is done in the floating point domain. Arithmetic functions are found in LstmUtils.cpp.</span></div>
163<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; <span class="comment">// Refer to: android/frameworks/ml/nn/common/operations/QLSTM.cpp</span></div>
164<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>&amp; internalType = <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>;</div>
165<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; </div>
166<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>&amp; inputInfo = <a class="code" href="namespacearmnn.html#aa815fde54f6d8e8aa5b4f0301cf4178b">GetTensorInfo</a>(inputs[0]);</div>
167<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>&amp; outputStateInInfo = <a class="code" href="namespacearmnn.html#aa815fde54f6d8e8aa5b4f0301cf4178b">GetTensorInfo</a>(inputs[1]);</div>
168<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>&amp; cellStateInInfo = <a class="code" href="namespacearmnn.html#aa815fde54f6d8e8aa5b4f0301cf4178b">GetTensorInfo</a>(inputs[2]);</div>
169<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; </div>
170<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>&amp; outputStateOutInfo = <a class="code" href="namespacearmnn.html#aa815fde54f6d8e8aa5b4f0301cf4178b">GetTensorInfo</a>(outputs[0]);</div>
171<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>&amp; cellStateOutInfo = <a class="code" href="namespacearmnn.html#aa815fde54f6d8e8aa5b4f0301cf4178b">GetTensorInfo</a>(outputs[1]);</div>
172<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>&amp; outputInfo = <a class="code" href="namespacearmnn.html#aa815fde54f6d8e8aa5b4f0301cf4178b">GetTensorInfo</a>(outputs[2]);</div>
173<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; </div>
174<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>&amp; inputShape = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div>
175<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>&amp; outputStateInShape = outputStateInInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div>
176<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>&amp; cellStateInShape = cellStateInInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div>
177<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; </div>
178<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; <span class="comment">// Infer numBatches, inputSize, outputSize and numUnits</span></div>
179<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; <span class="keyword">const</span> uint32_t numBatches = inputShape[0];</div>
180<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; <span class="keyword">const</span> uint32_t inputSize = inputShape[1];</div>
181<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; <span class="keyword">const</span> uint32_t outputSize = outputStateInShape[1];</div>
182<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; <span class="keyword">const</span> uint32_t numUnits = cellStateInShape[1];</div>
183<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; </div>
184<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; <span class="comment">// Optional param settings</span></div>
185<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; <span class="keyword">const</span> <span class="keywordtype">bool</span> cifgEnabled = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.html#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a>;</div>
186<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160; <span class="keyword">const</span> <span class="keywordtype">bool</span> peepholeEnabled = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.html#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a>;</div>
187<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; <span class="keyword">const</span> <span class="keywordtype">bool</span> projectionEnabled = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.html#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a>;</div>
188<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; <span class="keyword">const</span> <span class="keywordtype">bool</span> layerNormEnabled = <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.html#a4a8ec49f130084445d44297549254780">m_LayerNormEnabled</a>;</div>
189<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; </div>
190<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; <span class="comment">// Input decoders</span></div>
191<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputDecoder =</div>
192<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; MakeDecoder&lt;float&gt;(inputInfo, inputs[0]-&gt;<a class="code" href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4a46f3ea056caa3126b91f3f70beea068c">Map</a>());</div>
193<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputStateInDecoder =</div>
194<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; MakeDecoder&lt;float&gt;(outputStateInInfo, inputs[1]-&gt;<a class="code" href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4a46f3ea056caa3126b91f3f70beea068c">Map</a>());</div>
195<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellStateInDecoder =</div>
196<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; MakeDecoder&lt;float&gt;(cellStateInInfo, inputs[2]-&gt;<a class="code" href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4a46f3ea056caa3126b91f3f70beea068c">Map</a>());</div>
197<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; </div>
198<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; <span class="comment">// Output decoders</span></div>
199<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputStateOutDecoder =</div>
200<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; MakeDecoder&lt;float&gt;(outputStateOutInfo, outputs[0]-&gt;<a class="code" href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4a46f3ea056caa3126b91f3f70beea068c">Map</a>());</div>
201<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellStateOutDecoder =</div>
202<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; MakeDecoder&lt;float&gt;(cellStateOutInfo, outputs[1]-&gt;<a class="code" href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4a46f3ea056caa3126b91f3f70beea068c">Map</a>());</div>
203<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputDecoder =</div>
204<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; MakeDecoder&lt;float&gt;(outputInfo, outputs[2]-&gt;<a class="code" href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4a46f3ea056caa3126b91f3f70beea068c">Map</a>());</div>
205<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; </div>
206<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; <span class="comment">// Output encoders</span></div>
207<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; std::unique_ptr&lt;Encoder&lt;float&gt;&gt; outputStateOutEncoder =</div>
208<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; MakeEncoder&lt;float&gt;(outputStateOutInfo, outputs[0]-&gt;<a class="code" href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4a46f3ea056caa3126b91f3f70beea068c">Map</a>());</div>
209<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; std::unique_ptr&lt;Encoder&lt;float&gt;&gt; cellStateOutEncoder =</div>
210<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; MakeEncoder&lt;float&gt;(cellStateOutInfo, outputs[1]-&gt;<a class="code" href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4a46f3ea056caa3126b91f3f70beea068c">Map</a>());</div>
211<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; std::unique_ptr&lt;Encoder&lt;float&gt;&gt; outputEncoder =</div>
212<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; MakeEncoder&lt;float&gt;(outputInfo, outputs[2]-&gt;<a class="code" href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4a46f3ea056caa3126b91f3f70beea068c">Map</a>());</div>
213<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; </div>
214<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; <span class="comment">// Weights decoders</span></div>
215<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputToForgetWeightsDecoder = MakeDecoder&lt;float&gt;(</div>
216<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; m_InputToForgetWeightsTensor-&gt;GetTensorInfo(), m_InputToForgetWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
217<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputToCellWeightsDecoder = MakeDecoder&lt;float&gt;(</div>
218<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; m_InputToCellWeightsTensor-&gt;GetTensorInfo(), m_InputToCellWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
219<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputToOutputWeightsDecoder = MakeDecoder&lt;float&gt;(</div>
220<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; m_InputToOutputWeightsTensor-&gt;GetTensorInfo(), m_InputToOutputWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
221<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; </div>
222<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; recurrentToForgetWeightsDecoder = MakeDecoder&lt;float&gt;(</div>
223<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; m_RecurrentToForgetWeightsTensor-&gt;GetTensorInfo(),</div>
224<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; m_RecurrentToForgetWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
225<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; recurrentToCellWeightsDecoder = MakeDecoder&lt;float&gt;(</div>
226<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; m_RecurrentToCellWeightsTensor-&gt;GetTensorInfo(), m_RecurrentToCellWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
227<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; recurrentToOutputWeightsDecoder = MakeDecoder&lt;float&gt;(</div>
228<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; m_RecurrentToOutputWeightsTensor-&gt;GetTensorInfo(),</div>
229<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; m_RecurrentToOutputWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
230<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; </div>
231<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; <span class="comment">// Optional CIFG params</span></div>
232<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputToInputWeightsDecoder;</div>
233<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; recurrentToInputWeightsDecoder;</div>
234<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputGateBiasDecoder;</div>
235<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; </div>
236<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; <span class="comment">// Optional Peephole params</span></div>
237<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellToInputWeightsDecoder;</div>
238<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellToForgetWeightsDecoder;</div>
239<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellToOutputWeightsDecoder;</div>
240<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; </div>
241<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160; <span class="comment">// Optional Projection params</span></div>
242<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; projectionWeightsDecoder;</div>
243<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; projectionBiasDecoder;</div>
244<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; </div>
245<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; <span class="comment">// Optional Layer Norm params</span></div>
246<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputLayerNormWeightsDecoder;</div>
247<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; forgetLayerNormWeightsDecoder;</div>
248<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellLayerNormWeightsDecoder;</div>
249<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputLayerNormWeightsDecoder;</div>
250<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; </div>
251<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; <span class="comment">// Biases are only used when Layer Norm is enabled. Scale is defined as (XLayerNormWeights Scale / 1024)</span></div>
252<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; forgetGateBiasDecoder;</div>
253<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellGateBiasDecoder;</div>
254<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputGateBiasDecoder;</div>
255<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; </div>
256<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; <span class="comment">// Int16 vectors for internal state data (to be decoded/encoded)</span></div>
257<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; <span class="keyword">const</span> uint32_t stateTensorSize = numBatches * numUnits;</div>
258<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160; std::vector&lt;int16_t&gt; inputGateData(stateTensorSize);</div>
259<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; std::vector&lt;int16_t&gt; cellGateData(stateTensorSize);</div>
260<div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; std::vector&lt;int16_t&gt; forgetGateData(stateTensorSize);</div>
261<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; std::vector&lt;int16_t&gt; outputGateData(stateTensorSize);</div>
262<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160; std::vector&lt;int32_t&gt; hiddenStateData(stateTensorSize);</div>
263<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; std::vector&lt;int16_t&gt; outputInt16Data(numBatches * outputSize);</div>
264<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; </div>
265<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputGateInfo(</div>
266<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; {numBatches , numUnits}, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.html#a09e1f097944f61cc901240f9300364cf">m_InputIntermediateScale</a>, 0);</div>
267<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> cellGateInfo(</div>
268<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160; {numBatches , numUnits}, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.html#a0477ee1b44ace6090119178eea78cb0b">m_CellIntermediateScale</a>, 0);</div>
269<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> forgetGateInfo(</div>
270<div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; {numBatches , numUnits}, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.html#afec7f36158448f723b426a9527acb189">m_ForgetIntermediateScale</a>, 0);</div>
271<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputGateInfo(</div>
272<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; {numBatches , numUnits}, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.html#aa43409f9b457352c95c89f20ce5d844d">m_OutputIntermediateScale</a>, 0);</div>
273<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> hiddenStateInfo({numBatches, numUnits},</div>
274<div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>,</div>
275<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.html#af8f724af7210b52529216feefa993c98">m_HiddenStateScale</a>,</div>
276<div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160; <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.html#a4556cbd764d4848d8ad0637a9eed580d">m_HiddenStateZeroPoint</a>);</div>
277<div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputInt16Info({numBatches , outputSize},</div>
278<div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160; <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div>
279<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div>
280<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>());</div>
281<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; </div>
282<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; <span class="comment">// Decoders/Encoders for internal states</span></div>
283<div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; inputGateDecoder =</div>
284<div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160; MakeDecoder&lt;float&gt;(inputGateInfo, inputGateData.data());</div>
285<div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; cellGateDecoder =</div>
286<div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160; MakeDecoder&lt;float&gt;(cellGateInfo, cellGateData.data());</div>
287<div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; forgetGateDecoder =</div>
288<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160; MakeDecoder&lt;float&gt;(forgetGateInfo, forgetGateData.data());</div>
289<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputGateDecoder =</div>
290<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160; MakeDecoder&lt;float&gt;(outputGateInfo, outputGateData.data());</div>
291<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; hiddenStateDecoder =</div>
292<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; MakeDecoder&lt;float&gt;(hiddenStateInfo, hiddenStateData.data());</div>
293<div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160; </div>
294<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; std::unique_ptr&lt;Encoder&lt;float&gt;&gt; inputGateEncoder =</div>
295<div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160; MakeEncoder&lt;float&gt;(inputGateInfo, inputGateData.data());</div>
296<div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; std::unique_ptr&lt;Encoder&lt;float&gt;&gt; cellGateEncoder =</div>
297<div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160; MakeEncoder&lt;float&gt;(cellGateInfo, cellGateData.data());</div>
298<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; std::unique_ptr&lt;Encoder&lt;float&gt;&gt; forgetGateEncoder =</div>
299<div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160; MakeEncoder&lt;float&gt;(forgetGateInfo, forgetGateData.data());</div>
300<div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160; std::unique_ptr&lt;Encoder&lt;float&gt;&gt; outputGateEncoder =</div>
301<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>&#160; MakeEncoder&lt;float&gt;(outputGateInfo, outputGateData.data());</div>
302<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160; std::unique_ptr&lt;Encoder&lt;float&gt;&gt; hiddenStateEncoder =</div>
303<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160; MakeEncoder&lt;float&gt;(hiddenStateInfo, hiddenStateData.data());</div>
304<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>&#160; </div>
305<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>&#160; <span class="comment">// Int16 used to accumulate output to prevent overflowing (after Projection MatMul)</span></div>
306<div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160; std::unique_ptr&lt;Decoder&lt;float&gt;&gt; outputInt16Decoder =</div>
307<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160; MakeDecoder&lt;float&gt;(outputInt16Info, outputInt16Data.data());</div>
308<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>&#160; std::unique_ptr&lt;Encoder&lt;float&gt;&gt; outputInt16Encoder =</div>
309<div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160; MakeEncoder&lt;float&gt;(outputInt16Info, outputInt16Data.data());</div>
310<div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160; </div>
311<div class="line"><a name="l00213"></a><span class="lineno"> 213</span>&#160; <span class="comment">// Create decoders for optional params if they are enabled</span></div>
312<div class="line"><a name="l00214"></a><span class="lineno"> 214</span>&#160; <span class="keywordflow">if</span> (!cifgEnabled)</div>
313<div class="line"><a name="l00215"></a><span class="lineno"> 215</span>&#160; {</div>
314<div class="line"><a name="l00216"></a><span class="lineno"> 216</span>&#160; inputToInputWeightsDecoder = MakeDecoder&lt;float&gt;(</div>
315<div class="line"><a name="l00217"></a><span class="lineno"> 217</span>&#160; m_InputToInputWeightsTensor-&gt;GetTensorInfo(), m_InputToInputWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
316<div class="line"><a name="l00218"></a><span class="lineno"> 218</span>&#160; recurrentToInputWeightsDecoder = MakeDecoder&lt;float&gt;(m_RecurrentToInputWeightsTensor-&gt;GetTensorInfo(),</div>
317<div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160; m_RecurrentToInputWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
318<div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160; }</div>
319<div class="line"><a name="l00221"></a><span class="lineno"> 221</span>&#160; </div>
320<div class="line"><a name="l00222"></a><span class="lineno"> 222</span>&#160; <span class="keywordflow">if</span> (peepholeEnabled)</div>
321<div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160; {</div>
322<div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160; <span class="keywordflow">if</span> (!cifgEnabled)</div>
323<div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160; {</div>
324<div class="line"><a name="l00226"></a><span class="lineno"> 226</span>&#160; cellToInputWeightsDecoder = MakeDecoder&lt;float&gt;(</div>
325<div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160; m_CellToInputWeightsTensor-&gt;GetTensorInfo(), m_CellToInputWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
326<div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160; }</div>
327<div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160; cellToForgetWeightsDecoder = MakeDecoder&lt;float&gt;(</div>
328<div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160; m_CellToForgetWeightsTensor-&gt;GetTensorInfo(), m_CellToForgetWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
329<div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160; cellToOutputWeightsDecoder = MakeDecoder&lt;float&gt;(</div>
330<div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160; m_CellToOutputWeightsTensor-&gt;GetTensorInfo(), m_CellToOutputWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
331<div class="line"><a name="l00233"></a><span class="lineno"> 233</span>&#160; }</div>
332<div class="line"><a name="l00234"></a><span class="lineno"> 234</span>&#160; </div>
333<div class="line"><a name="l00235"></a><span class="lineno"> 235</span>&#160; <span class="keywordflow">if</span> (projectionEnabled)</div>
334<div class="line"><a name="l00236"></a><span class="lineno"> 236</span>&#160; {</div>
335<div class="line"><a name="l00237"></a><span class="lineno"> 237</span>&#160; projectionWeightsDecoder = MakeDecoder&lt;float&gt;(</div>
336<div class="line"><a name="l00238"></a><span class="lineno"> 238</span>&#160; m_ProjectionWeightsTensor-&gt;GetTensorInfo(), m_ProjectionWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
337<div class="line"><a name="l00239"></a><span class="lineno"> 239</span>&#160; <span class="keywordflow">if</span> (m_ProjectionBiasTensor)</div>
338<div class="line"><a name="l00240"></a><span class="lineno"> 240</span>&#160; {</div>
339<div class="line"><a name="l00241"></a><span class="lineno"> 241</span>&#160; projectionBiasDecoder = MakeDecoder&lt;float&gt;(</div>
340<div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160; m_ProjectionBiasTensor-&gt;GetTensorInfo(), m_ProjectionBiasTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
341<div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160; }</div>
342<div class="line"><a name="l00244"></a><span class="lineno"> 244</span>&#160; }</div>
343<div class="line"><a name="l00245"></a><span class="lineno"> 245</span>&#160; </div>
344<div class="line"><a name="l00246"></a><span class="lineno"> 246</span>&#160; <span class="keywordflow">if</span> (layerNormEnabled)</div>
345<div class="line"><a name="l00247"></a><span class="lineno"> 247</span>&#160; {</div>
346<div class="line"><a name="l00248"></a><span class="lineno"> 248</span>&#160; <span class="keywordflow">if</span> (!cifgEnabled)</div>
347<div class="line"><a name="l00249"></a><span class="lineno"> 249</span>&#160; {</div>
348<div class="line"><a name="l00250"></a><span class="lineno"> 250</span>&#160; inputLayerNormWeightsDecoder = MakeDecoder&lt;float&gt;(m_InputLayerNormWeightsTensor-&gt;GetTensorInfo(),</div>
349<div class="line"><a name="l00251"></a><span class="lineno"> 251</span>&#160; m_InputLayerNormWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
350<div class="line"><a name="l00252"></a><span class="lineno"> 252</span>&#160; </div>
351<div class="line"><a name="l00253"></a><span class="lineno"> 253</span>&#160; <span class="comment">// Bias only used if layer norm enabled</span></div>
352<div class="line"><a name="l00254"></a><span class="lineno"> 254</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputGateBiasTensorInfo({outputSize}, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div>
353<div class="line"><a name="l00255"></a><span class="lineno"> 255</span>&#160; m_InputLayerNormWeightsTensor-&gt;GetTensorInfo().GetQuantizationScale() / 1024, 0);</div>
354<div class="line"><a name="l00256"></a><span class="lineno"> 256</span>&#160; inputGateBiasDecoder = MakeDecoder&lt;float&gt;(</div>
355<div class="line"><a name="l00257"></a><span class="lineno"> 257</span>&#160; inputGateBiasTensorInfo, m_InputGateBiasTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
356<div class="line"><a name="l00258"></a><span class="lineno"> 258</span>&#160; }</div>
357<div class="line"><a name="l00259"></a><span class="lineno"> 259</span>&#160; </div>
358<div class="line"><a name="l00260"></a><span class="lineno"> 260</span>&#160; forgetLayerNormWeightsDecoder = MakeDecoder&lt;float&gt;(</div>
359<div class="line"><a name="l00261"></a><span class="lineno"> 261</span>&#160; m_ForgetLayerNormWeightsTensor-&gt;GetTensorInfo(),</div>
360<div class="line"><a name="l00262"></a><span class="lineno"> 262</span>&#160; m_ForgetLayerNormWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
361<div class="line"><a name="l00263"></a><span class="lineno"> 263</span>&#160; cellLayerNormWeightsDecoder = MakeDecoder&lt;float&gt;(</div>
362<div class="line"><a name="l00264"></a><span class="lineno"> 264</span>&#160; m_CellLayerNormWeightsTensor-&gt;GetTensorInfo(), m_CellLayerNormWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
363<div class="line"><a name="l00265"></a><span class="lineno"> 265</span>&#160; outputLayerNormWeightsDecoder = MakeDecoder&lt;float&gt;(</div>
364<div class="line"><a name="l00266"></a><span class="lineno"> 266</span>&#160; m_OutputLayerNormWeightsTensor-&gt;GetTensorInfo(),</div>
365<div class="line"><a name="l00267"></a><span class="lineno"> 267</span>&#160; m_OutputLayerNormWeightsTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
366<div class="line"><a name="l00268"></a><span class="lineno"> 268</span>&#160; </div>
367<div class="line"><a name="l00269"></a><span class="lineno"> 269</span>&#160; <span class="comment">// Bias only used if layer norm enabled</span></div>
368<div class="line"><a name="l00270"></a><span class="lineno"> 270</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> forgetGateBiasTensorInfo({outputSize}, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div>
369<div class="line"><a name="l00271"></a><span class="lineno"> 271</span>&#160; m_ForgetLayerNormWeightsTensor-&gt;GetTensorInfo().GetQuantizationScale() / 1024, 0);</div>
370<div class="line"><a name="l00272"></a><span class="lineno"> 272</span>&#160; forgetGateBiasDecoder = MakeDecoder&lt;float&gt;(</div>
371<div class="line"><a name="l00273"></a><span class="lineno"> 273</span>&#160; forgetGateBiasTensorInfo, m_ForgetGateBiasTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
372<div class="line"><a name="l00274"></a><span class="lineno"> 274</span>&#160; </div>
373<div class="line"><a name="l00275"></a><span class="lineno"> 275</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> cellGateBiasTensorInfo({outputSize}, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div>
374<div class="line"><a name="l00276"></a><span class="lineno"> 276</span>&#160; m_CellLayerNormWeightsTensor-&gt;GetTensorInfo().GetQuantizationScale() / 1024, 0);</div>
375<div class="line"><a name="l00277"></a><span class="lineno"> 277</span>&#160; cellGateBiasDecoder = MakeDecoder&lt;float&gt;(</div>
376<div class="line"><a name="l00278"></a><span class="lineno"> 278</span>&#160; cellGateBiasTensorInfo, m_CellBiasTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
377<div class="line"><a name="l00279"></a><span class="lineno"> 279</span>&#160; </div>
378<div class="line"><a name="l00280"></a><span class="lineno"> 280</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputGateBiasTensorInfo({outputSize}, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div>
379<div class="line"><a name="l00281"></a><span class="lineno"> 281</span>&#160; m_OutputLayerNormWeightsTensor-&gt;GetTensorInfo().GetQuantizationScale() / 1024, 0);</div>
380<div class="line"><a name="l00282"></a><span class="lineno"> 282</span>&#160; outputGateBiasDecoder = MakeDecoder&lt;float&gt;(</div>
381<div class="line"><a name="l00283"></a><span class="lineno"> 283</span>&#160; outputGateBiasTensorInfo, m_OutputGateBiasTensor-&gt;GetConstTensor&lt;<span class="keywordtype">void</span>&gt;());</div>
382<div class="line"><a name="l00284"></a><span class="lineno"> 284</span>&#160; }</div>
383<div class="line"><a name="l00285"></a><span class="lineno"> 285</span>&#160; </div>
384<div class="line"><a name="l00286"></a><span class="lineno"> 286</span>&#160; <span class="comment">// Initialize internal state tensors with zeroes.</span></div>
385<div class="line"><a name="l00287"></a><span class="lineno"> 287</span>&#160; <span class="keywordflow">if</span> (!cifgEnabled)</div>
386<div class="line"><a name="l00288"></a><span class="lineno"> 288</span>&#160; {</div>
387<div class="line"><a name="l00289"></a><span class="lineno"> 289</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*inputGateEncoder, stateTensorSize);</div>
388<div class="line"><a name="l00290"></a><span class="lineno"> 290</span>&#160; }</div>
389<div class="line"><a name="l00291"></a><span class="lineno"> 291</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*forgetGateEncoder, stateTensorSize);</div>
390<div class="line"><a name="l00292"></a><span class="lineno"> 292</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*cellGateEncoder, stateTensorSize);</div>
391<div class="line"><a name="l00293"></a><span class="lineno"> 293</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*outputGateEncoder, stateTensorSize);</div>
392<div class="line"><a name="l00294"></a><span class="lineno"> 294</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*hiddenStateEncoder, stateTensorSize);</div>
393<div class="line"><a name="l00295"></a><span class="lineno"> 295</span>&#160; </div>
394<div class="line"><a name="l00296"></a><span class="lineno"> 296</span>&#160; <span class="comment">// Input weights * Input</span></div>
395<div class="line"><a name="l00297"></a><span class="lineno"> 297</span>&#160; <span class="keywordflow">if</span> (!cifgEnabled)</div>
396<div class="line"><a name="l00298"></a><span class="lineno"> 298</span>&#160; {</div>
397<div class="line"><a name="l00299"></a><span class="lineno"> 299</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*inputToInputWeightsDecoder,</div>
398<div class="line"><a name="l00300"></a><span class="lineno"> 300</span>&#160; numUnits, inputSize, *inputDecoder, numBatches, *inputGateEncoder);</div>
399<div class="line"><a name="l00301"></a><span class="lineno"> 301</span>&#160; }</div>
400<div class="line"><a name="l00302"></a><span class="lineno"> 302</span>&#160; </div>
401<div class="line"><a name="l00303"></a><span class="lineno"> 303</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*inputToForgetWeightsDecoder,</div>
402<div class="line"><a name="l00304"></a><span class="lineno"> 304</span>&#160; numUnits, inputSize, *inputDecoder, numBatches, *forgetGateEncoder);</div>
403<div class="line"><a name="l00305"></a><span class="lineno"> 305</span>&#160; </div>
404<div class="line"><a name="l00306"></a><span class="lineno"> 306</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*inputToCellWeightsDecoder,</div>
405<div class="line"><a name="l00307"></a><span class="lineno"> 307</span>&#160; numUnits, inputSize, *inputDecoder, numBatches, *cellGateEncoder);</div>
406<div class="line"><a name="l00308"></a><span class="lineno"> 308</span>&#160; </div>
407<div class="line"><a name="l00309"></a><span class="lineno"> 309</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*inputToOutputWeightsDecoder,</div>
408<div class="line"><a name="l00310"></a><span class="lineno"> 310</span>&#160; numUnits, inputSize, *inputDecoder, numBatches, *outputGateEncoder);</div>
409<div class="line"><a name="l00311"></a><span class="lineno"> 311</span>&#160; </div>
410<div class="line"><a name="l00312"></a><span class="lineno"> 312</span>&#160; <span class="comment">// Recurrent weights * OutputStateIn</span></div>
411<div class="line"><a name="l00313"></a><span class="lineno"> 313</span>&#160; <span class="keywordflow">if</span> (!cifgEnabled)</div>
412<div class="line"><a name="l00314"></a><span class="lineno"> 314</span>&#160; {</div>
413<div class="line"><a name="l00315"></a><span class="lineno"> 315</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*recurrentToInputWeightsDecoder,</div>
414<div class="line"><a name="l00316"></a><span class="lineno"> 316</span>&#160; numUnits, outputSize, *outputStateInDecoder, numBatches, *inputGateEncoder);</div>
415<div class="line"><a name="l00317"></a><span class="lineno"> 317</span>&#160; }</div>
416<div class="line"><a name="l00318"></a><span class="lineno"> 318</span>&#160; </div>
417<div class="line"><a name="l00319"></a><span class="lineno"> 319</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*recurrentToForgetWeightsDecoder,</div>
418<div class="line"><a name="l00320"></a><span class="lineno"> 320</span>&#160; numUnits, outputSize, *outputStateInDecoder, numBatches, *forgetGateEncoder);</div>
419<div class="line"><a name="l00321"></a><span class="lineno"> 321</span>&#160; </div>
420<div class="line"><a name="l00322"></a><span class="lineno"> 322</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*recurrentToCellWeightsDecoder,</div>
421<div class="line"><a name="l00323"></a><span class="lineno"> 323</span>&#160; numUnits, outputSize, *outputStateInDecoder, numBatches, *cellGateEncoder);</div>
422<div class="line"><a name="l00324"></a><span class="lineno"> 324</span>&#160; </div>
423<div class="line"><a name="l00325"></a><span class="lineno"> 325</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*recurrentToOutputWeightsDecoder,</div>
424<div class="line"><a name="l00326"></a><span class="lineno"> 326</span>&#160; numUnits, outputSize, *outputStateInDecoder, numBatches, *outputGateEncoder);</div>
425<div class="line"><a name="l00327"></a><span class="lineno"> 327</span>&#160; </div>
426<div class="line"><a name="l00328"></a><span class="lineno"> 328</span>&#160; <span class="comment">// Input gate.</span></div>
427<div class="line"><a name="l00329"></a><span class="lineno"> 329</span>&#160; <span class="keywordflow">if</span> (!cifgEnabled)</div>
428<div class="line"><a name="l00330"></a><span class="lineno"> 330</span>&#160; {</div>
429<div class="line"><a name="l00331"></a><span class="lineno"> 331</span>&#160; <span class="keywordflow">if</span> (peepholeEnabled)</div>
430<div class="line"><a name="l00332"></a><span class="lineno"> 332</span>&#160; {</div>
431<div class="line"><a name="l00333"></a><span class="lineno"> 333</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a796323e16216b880043dc5ebbaa2372b">VectorBatchVectorCwiseProductAccumulate</a>(*cellToInputWeightsDecoder,</div>
432<div class="line"><a name="l00334"></a><span class="lineno"> 334</span>&#160; numUnits, *cellStateInDecoder, numBatches, *inputGateEncoder);</div>
433<div class="line"><a name="l00335"></a><span class="lineno"> 335</span>&#160; }</div>
434<div class="line"><a name="l00336"></a><span class="lineno"> 336</span>&#160; </div>
435<div class="line"><a name="l00337"></a><span class="lineno"> 337</span>&#160; <span class="keywordflow">if</span> (layerNormEnabled)</div>
436<div class="line"><a name="l00338"></a><span class="lineno"> 338</span>&#160; {</div>
437<div class="line"><a name="l00339"></a><span class="lineno"> 339</span>&#160; inputGateInfo.SetQuantizationScale(inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>() *</div>
438<div class="line"><a name="l00340"></a><span class="lineno"> 340</span>&#160; m_InputLayerNormWeightsTensor-&gt;GetTensorInfo().GetQuantizationScale() *</div>
439<div class="line"><a name="l00341"></a><span class="lineno"> 341</span>&#160; 1024);</div>
440<div class="line"><a name="l00342"></a><span class="lineno"> 342</span>&#160; inputGateEncoder = MakeEncoder&lt;float&gt;(inputGateInfo, inputGateData.data());</div>
441<div class="line"><a name="l00343"></a><span class="lineno"> 343</span>&#160; </div>
442<div class="line"><a name="l00344"></a><span class="lineno"> 344</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a>(*inputGateDecoder,</div>
443<div class="line"><a name="l00345"></a><span class="lineno"> 345</span>&#160; *inputGateEncoder, numUnits, numBatches, m_LayerNormEpsilon);</div>
444<div class="line"><a name="l00346"></a><span class="lineno"> 346</span>&#160; </div>
445<div class="line"><a name="l00347"></a><span class="lineno"> 347</span>&#160; inputGateDecoder = MakeDecoder&lt;float&gt;(inputGateInfo, inputGateData.data());</div>
446<div class="line"><a name="l00348"></a><span class="lineno"> 348</span>&#160; </div>
447<div class="line"><a name="l00349"></a><span class="lineno"> 349</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a>(*inputLayerNormWeightsDecoder,</div>
448<div class="line"><a name="l00350"></a><span class="lineno"> 350</span>&#160; numUnits, *inputGateDecoder, numBatches, *inputGateEncoder);</div>
449<div class="line"><a name="l00351"></a><span class="lineno"> 351</span>&#160; </div>
450<div class="line"><a name="l00352"></a><span class="lineno"> 352</span>&#160; inputGateInfo.SetQuantizationScale(1.f / 4096);</div>
451<div class="line"><a name="l00353"></a><span class="lineno"> 353</span>&#160; inputGateEncoder = MakeEncoder&lt;float&gt;(inputGateInfo, inputGateData.data());</div>
452<div class="line"><a name="l00354"></a><span class="lineno"> 354</span>&#160; </div>
453<div class="line"><a name="l00355"></a><span class="lineno"> 355</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a>(*inputGateBiasDecoder,</div>
454<div class="line"><a name="l00356"></a><span class="lineno"> 356</span>&#160; numUnits, *inputGateDecoder, numBatches, *inputGateEncoder);</div>
455<div class="line"><a name="l00357"></a><span class="lineno"> 357</span>&#160; </div>
456<div class="line"><a name="l00358"></a><span class="lineno"> 358</span>&#160; inputGateDecoder = MakeDecoder&lt;float&gt;(inputGateInfo, inputGateData.data());</div>
457<div class="line"><a name="l00359"></a><span class="lineno"> 359</span>&#160; }</div>
458<div class="line"><a name="l00360"></a><span class="lineno"> 360</span>&#160; </div>
459<div class="line"><a name="l00361"></a><span class="lineno"> 361</span>&#160; inputGateInfo.SetQuantizationScale(cellStateOutInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>());</div>
460<div class="line"><a name="l00362"></a><span class="lineno"> 362</span>&#160; inputGateEncoder = MakeEncoder&lt;float&gt;(inputGateInfo, inputGateData.data());</div>
461<div class="line"><a name="l00363"></a><span class="lineno"> 363</span>&#160; </div>
462<div class="line"><a name="l00364"></a><span class="lineno"> 364</span>&#160; <span class="comment">// Input gate sigmoid</span></div>
463<div class="line"><a name="l00365"></a><span class="lineno"> 365</span>&#160; <a class="code" href="namespacearmnn.html#a7636fbbc4f8ea2d0cf9f3ac2d12a4c62">Activation</a>(*inputGateDecoder, *inputGateEncoder,</div>
464<div class="line"><a name="l00366"></a><span class="lineno"> 366</span>&#160; TensorInfo({numUnits, numBatches}, internalType),</div>
465<div class="line"><a name="l00367"></a><span class="lineno"> 367</span>&#160; <a class="code" href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">ActivationFunction::Sigmoid</a>, 0, 0);</div>
466<div class="line"><a name="l00368"></a><span class="lineno"> 368</span>&#160; </div>
467<div class="line"><a name="l00369"></a><span class="lineno"> 369</span>&#160; inputGateDecoder = MakeDecoder&lt;float&gt;(inputGateInfo, inputGateData.data());</div>
468<div class="line"><a name="l00370"></a><span class="lineno"> 370</span>&#160; }</div>
469<div class="line"><a name="l00371"></a><span class="lineno"> 371</span>&#160; </div>
470<div class="line"><a name="l00372"></a><span class="lineno"> 372</span>&#160; <span class="comment">// Forget gate</span></div>
471<div class="line"><a name="l00373"></a><span class="lineno"> 373</span>&#160; <span class="keywordflow">if</span> (peepholeEnabled)</div>
472<div class="line"><a name="l00374"></a><span class="lineno"> 374</span>&#160; {</div>
473<div class="line"><a name="l00375"></a><span class="lineno"> 375</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a796323e16216b880043dc5ebbaa2372b">VectorBatchVectorCwiseProductAccumulate</a>(*cellToForgetWeightsDecoder, numUnits,</div>
474<div class="line"><a name="l00376"></a><span class="lineno"> 376</span>&#160; *cellStateInDecoder, numBatches, *forgetGateEncoder);</div>
475<div class="line"><a name="l00377"></a><span class="lineno"> 377</span>&#160; }</div>
476<div class="line"><a name="l00378"></a><span class="lineno"> 378</span>&#160; </div>
477<div class="line"><a name="l00379"></a><span class="lineno"> 379</span>&#160; <span class="keywordflow">if</span> (layerNormEnabled)</div>
478<div class="line"><a name="l00380"></a><span class="lineno"> 380</span>&#160; {</div>
479<div class="line"><a name="l00381"></a><span class="lineno"> 381</span>&#160; <span class="comment">// Quantize layer norm output to Input Scale * m_ForgetLayerNormWeightsTensor * 1024</span></div>
480<div class="line"><a name="l00382"></a><span class="lineno"> 382</span>&#160; forgetGateInfo.SetQuantizationScale(inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>() *</div>
481<div class="line"><a name="l00383"></a><span class="lineno"> 383</span>&#160; m_ForgetLayerNormWeightsTensor-&gt;GetTensorInfo().GetQuantizationScale() *</div>
482<div class="line"><a name="l00384"></a><span class="lineno"> 384</span>&#160; 1024);</div>
483<div class="line"><a name="l00385"></a><span class="lineno"> 385</span>&#160; forgetGateEncoder = MakeEncoder&lt;float&gt;(forgetGateInfo, forgetGateData.data());</div>
484<div class="line"><a name="l00386"></a><span class="lineno"> 386</span>&#160; </div>
485<div class="line"><a name="l00387"></a><span class="lineno"> 387</span>&#160; </div>
486<div class="line"><a name="l00388"></a><span class="lineno"> 388</span>&#160; </div>
487<div class="line"><a name="l00389"></a><span class="lineno"> 389</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a>(*forgetGateDecoder,</div>
488<div class="line"><a name="l00390"></a><span class="lineno"> 390</span>&#160; *forgetGateEncoder, numUnits, numBatches, m_LayerNormEpsilon);</div>
489<div class="line"><a name="l00391"></a><span class="lineno"> 391</span>&#160; </div>
490<div class="line"><a name="l00392"></a><span class="lineno"> 392</span>&#160; </div>
491<div class="line"><a name="l00393"></a><span class="lineno"> 393</span>&#160; forgetGateDecoder = MakeDecoder&lt;float&gt;(forgetGateInfo, forgetGateData.data());</div>
492<div class="line"><a name="l00394"></a><span class="lineno"> 394</span>&#160; </div>
493<div class="line"><a name="l00395"></a><span class="lineno"> 395</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a>(*forgetLayerNormWeightsDecoder,</div>
494<div class="line"><a name="l00396"></a><span class="lineno"> 396</span>&#160; numUnits, *forgetGateDecoder, numBatches, *forgetGateEncoder);</div>
495<div class="line"><a name="l00397"></a><span class="lineno"> 397</span>&#160; </div>
496<div class="line"><a name="l00398"></a><span class="lineno"> 398</span>&#160; </div>
497<div class="line"><a name="l00399"></a><span class="lineno"> 399</span>&#160; <span class="comment">// Dequantize layer norm output to (1 / 4096)</span></div>
498<div class="line"><a name="l00400"></a><span class="lineno"> 400</span>&#160; forgetGateInfo.SetQuantizationScale(1.f / 4096);</div>
499<div class="line"><a name="l00401"></a><span class="lineno"> 401</span>&#160; forgetGateEncoder = MakeEncoder&lt;float&gt;(forgetGateInfo, forgetGateData.data());</div>
500<div class="line"><a name="l00402"></a><span class="lineno"> 402</span>&#160; </div>
501<div class="line"><a name="l00403"></a><span class="lineno"> 403</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a>(*forgetGateBiasDecoder,</div>
502<div class="line"><a name="l00404"></a><span class="lineno"> 404</span>&#160; numUnits, *forgetGateDecoder, numBatches, *forgetGateEncoder);</div>
503<div class="line"><a name="l00405"></a><span class="lineno"> 405</span>&#160; </div>
504<div class="line"><a name="l00406"></a><span class="lineno"> 406</span>&#160; </div>
505<div class="line"><a name="l00407"></a><span class="lineno"> 407</span>&#160; forgetGateDecoder = MakeDecoder&lt;float&gt;(forgetGateInfo, forgetGateData.data());</div>
506<div class="line"><a name="l00408"></a><span class="lineno"> 408</span>&#160; }</div>
507<div class="line"><a name="l00409"></a><span class="lineno"> 409</span>&#160; </div>
508<div class="line"><a name="l00410"></a><span class="lineno"> 410</span>&#160; forgetGateInfo.SetQuantizationScale(cellStateOutInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>());</div>
509<div class="line"><a name="l00411"></a><span class="lineno"> 411</span>&#160; forgetGateEncoder = MakeEncoder&lt;float&gt;(forgetGateInfo, forgetGateData.data());</div>
510<div class="line"><a name="l00412"></a><span class="lineno"> 412</span>&#160; </div>
511<div class="line"><a name="l00413"></a><span class="lineno"> 413</span>&#160; <span class="comment">// Forget gate sigmoid</span></div>
512<div class="line"><a name="l00414"></a><span class="lineno"> 414</span>&#160; <a class="code" href="namespacearmnn.html#a7636fbbc4f8ea2d0cf9f3ac2d12a4c62">Activation</a>(*forgetGateDecoder, *forgetGateEncoder,</div>
513<div class="line"><a name="l00415"></a><span class="lineno"> 415</span>&#160; TensorInfo({numUnits, numBatches}, internalType),</div>
514<div class="line"><a name="l00416"></a><span class="lineno"> 416</span>&#160; <a class="code" href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">ActivationFunction::Sigmoid</a>, 0, 0);</div>
515<div class="line"><a name="l00417"></a><span class="lineno"> 417</span>&#160; </div>
516<div class="line"><a name="l00418"></a><span class="lineno"> 418</span>&#160; forgetGateDecoder = MakeDecoder&lt;float&gt;(forgetGateInfo, forgetGateData.data());</div>
517<div class="line"><a name="l00419"></a><span class="lineno"> 419</span>&#160; </div>
518<div class="line"><a name="l00420"></a><span class="lineno"> 420</span>&#160; <span class="comment">// Cell (Modulation) gate</span></div>
519<div class="line"><a name="l00421"></a><span class="lineno"> 421</span>&#160; <span class="keywordflow">if</span> (layerNormEnabled)</div>
520<div class="line"><a name="l00422"></a><span class="lineno"> 422</span>&#160; {</div>
521<div class="line"><a name="l00423"></a><span class="lineno"> 423</span>&#160; cellGateInfo.SetQuantizationScale(inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>() *</div>
522<div class="line"><a name="l00424"></a><span class="lineno"> 424</span>&#160; m_CellLayerNormWeightsTensor-&gt;GetTensorInfo().GetQuantizationScale() *</div>
523<div class="line"><a name="l00425"></a><span class="lineno"> 425</span>&#160; 1024);</div>
524<div class="line"><a name="l00426"></a><span class="lineno"> 426</span>&#160; cellGateEncoder = MakeEncoder&lt;float&gt;(cellGateInfo, cellGateData.data());</div>
525<div class="line"><a name="l00427"></a><span class="lineno"> 427</span>&#160; </div>
526<div class="line"><a name="l00428"></a><span class="lineno"> 428</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a>(*cellGateDecoder, *cellGateEncoder, numUnits, numBatches, m_LayerNormEpsilon);</div>
527<div class="line"><a name="l00429"></a><span class="lineno"> 429</span>&#160; </div>
528<div class="line"><a name="l00430"></a><span class="lineno"> 430</span>&#160; cellGateDecoder = MakeDecoder&lt;float&gt;(cellGateInfo, cellGateData.data());</div>
529<div class="line"><a name="l00431"></a><span class="lineno"> 431</span>&#160; </div>
530<div class="line"><a name="l00432"></a><span class="lineno"> 432</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a>(*cellLayerNormWeightsDecoder,</div>
531<div class="line"><a name="l00433"></a><span class="lineno"> 433</span>&#160; numUnits, *cellGateDecoder, numBatches, *cellGateEncoder);</div>
532<div class="line"><a name="l00434"></a><span class="lineno"> 434</span>&#160; </div>
533<div class="line"><a name="l00435"></a><span class="lineno"> 435</span>&#160; cellGateInfo.SetQuantizationScale(1.f / 4096);</div>
534<div class="line"><a name="l00436"></a><span class="lineno"> 436</span>&#160; cellGateEncoder = MakeEncoder&lt;float&gt;(cellGateInfo, cellGateData.data());</div>
535<div class="line"><a name="l00437"></a><span class="lineno"> 437</span>&#160; </div>
536<div class="line"><a name="l00438"></a><span class="lineno"> 438</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a>(*cellGateBiasDecoder,</div>
537<div class="line"><a name="l00439"></a><span class="lineno"> 439</span>&#160; numUnits, *cellGateDecoder, numBatches, *cellGateEncoder);</div>
538<div class="line"><a name="l00440"></a><span class="lineno"> 440</span>&#160; </div>
539<div class="line"><a name="l00441"></a><span class="lineno"> 441</span>&#160; cellGateDecoder = MakeDecoder&lt;float&gt;(cellGateInfo, cellGateData.data());</div>
540<div class="line"><a name="l00442"></a><span class="lineno"> 442</span>&#160; }</div>
541<div class="line"><a name="l00443"></a><span class="lineno"> 443</span>&#160; </div>
542<div class="line"><a name="l00444"></a><span class="lineno"> 444</span>&#160; cellGateInfo.SetQuantizationScale(cellStateOutInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>());</div>
543<div class="line"><a name="l00445"></a><span class="lineno"> 445</span>&#160; cellGateEncoder = MakeEncoder&lt;float&gt;(cellGateInfo, cellGateData.data());</div>
544<div class="line"><a name="l00446"></a><span class="lineno"> 446</span>&#160; </div>
545<div class="line"><a name="l00447"></a><span class="lineno"> 447</span>&#160; <span class="comment">// Cell (Modulation) gate tanH</span></div>
546<div class="line"><a name="l00448"></a><span class="lineno"> 448</span>&#160; <a class="code" href="namespacearmnn.html#a7636fbbc4f8ea2d0cf9f3ac2d12a4c62">Activation</a>(*cellGateDecoder, *cellGateEncoder,</div>
547<div class="line"><a name="l00449"></a><span class="lineno"> 449</span>&#160; TensorInfo({numUnits, numBatches}, internalType),</div>
548<div class="line"><a name="l00450"></a><span class="lineno"> 450</span>&#160; <a class="code" href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">ActivationFunction::TanH</a>, 1.0f, 1.0f);</div>
549<div class="line"><a name="l00451"></a><span class="lineno"> 451</span>&#160; </div>
550<div class="line"><a name="l00452"></a><span class="lineno"> 452</span>&#160; cellGateDecoder = MakeDecoder&lt;float&gt;(cellGateInfo, cellGateData.data());</div>
551<div class="line"><a name="l00453"></a><span class="lineno"> 453</span>&#160; </div>
552<div class="line"><a name="l00454"></a><span class="lineno"> 454</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a5b81dc0a1a9a2bccab8bb79dfa3e77b7">VectorVectorCwiseProduct</a>(*forgetGateDecoder, *cellStateInDecoder, stateTensorSize, *cellStateOutEncoder);</div>
553<div class="line"><a name="l00455"></a><span class="lineno"> 455</span>&#160; </div>
554<div class="line"><a name="l00456"></a><span class="lineno"> 456</span>&#160; <span class="keywordflow">if</span> (cifgEnabled)</div>
555<div class="line"><a name="l00457"></a><span class="lineno"> 457</span>&#160; {</div>
556<div class="line"><a name="l00458"></a><span class="lineno"> 458</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#aca7bd1dff180b6a5de894537f8220793">Sub1Vector</a>(*forgetGateDecoder, stateTensorSize, *forgetGateEncoder);</div>
557<div class="line"><a name="l00459"></a><span class="lineno"> 459</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a2e653f948d93f4177f267a7b1b4ed47d">VectorVectorCwiseProductAccumulate</a>(</div>
558<div class="line"><a name="l00460"></a><span class="lineno"> 460</span>&#160; *cellGateDecoder, *forgetGateDecoder, stateTensorSize, *cellStateOutEncoder);</div>
559<div class="line"><a name="l00461"></a><span class="lineno"> 461</span>&#160; }</div>
560<div class="line"><a name="l00462"></a><span class="lineno"> 462</span>&#160; <span class="keywordflow">else</span></div>
561<div class="line"><a name="l00463"></a><span class="lineno"> 463</span>&#160; {</div>
562<div class="line"><a name="l00464"></a><span class="lineno"> 464</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a2e653f948d93f4177f267a7b1b4ed47d">VectorVectorCwiseProductAccumulate</a>(</div>
563<div class="line"><a name="l00465"></a><span class="lineno"> 465</span>&#160; *cellGateDecoder, *inputGateDecoder, stateTensorSize, *cellStateOutEncoder);</div>
564<div class="line"><a name="l00466"></a><span class="lineno"> 466</span>&#160; }</div>
565<div class="line"><a name="l00467"></a><span class="lineno"> 467</span>&#160; </div>
566<div class="line"><a name="l00468"></a><span class="lineno"> 468</span>&#160; <span class="comment">// Final cell state out calculated here</span></div>
567<div class="line"><a name="l00469"></a><span class="lineno"> 469</span>&#160; <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.html#ac81fb0e66dc623dc37c77f219f53a6d3">m_CellClip</a> &gt; 0.0)</div>
568<div class="line"><a name="l00470"></a><span class="lineno"> 470</span>&#160; {</div>
569<div class="line"><a name="l00471"></a><span class="lineno"> 471</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a117781e8e9b7321722bbdd8ff74b484a">ClipVector</a>(*cellStateOutDecoder, stateTensorSize, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.html#ac81fb0e66dc623dc37c77f219f53a6d3">m_CellClip</a>, *cellStateOutEncoder);</div>
570<div class="line"><a name="l00472"></a><span class="lineno"> 472</span>&#160; }</div>
571<div class="line"><a name="l00473"></a><span class="lineno"> 473</span>&#160; </div>
572<div class="line"><a name="l00474"></a><span class="lineno"> 474</span>&#160; <span class="comment">// Output gate.</span></div>
573<div class="line"><a name="l00475"></a><span class="lineno"> 475</span>&#160; <span class="keywordflow">if</span> (peepholeEnabled)</div>
574<div class="line"><a name="l00476"></a><span class="lineno"> 476</span>&#160; {</div>
575<div class="line"><a name="l00477"></a><span class="lineno"> 477</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a796323e16216b880043dc5ebbaa2372b">VectorBatchVectorCwiseProductAccumulate</a>(*cellToOutputWeightsDecoder,</div>
576<div class="line"><a name="l00478"></a><span class="lineno"> 478</span>&#160; numUnits, *cellStateOutDecoder, numBatches, *outputGateEncoder);</div>
577<div class="line"><a name="l00479"></a><span class="lineno"> 479</span>&#160; }</div>
578<div class="line"><a name="l00480"></a><span class="lineno"> 480</span>&#160; </div>
579<div class="line"><a name="l00481"></a><span class="lineno"> 481</span>&#160; <span class="keywordflow">if</span> (layerNormEnabled)</div>
580<div class="line"><a name="l00482"></a><span class="lineno"> 482</span>&#160; {</div>
581<div class="line"><a name="l00483"></a><span class="lineno"> 483</span>&#160; outputGateInfo.SetQuantizationScale(inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>() *</div>
582<div class="line"><a name="l00484"></a><span class="lineno"> 484</span>&#160; m_OutputLayerNormWeightsTensor-&gt;GetTensorInfo().GetQuantizationScale() *</div>
583<div class="line"><a name="l00485"></a><span class="lineno"> 485</span>&#160; 1024);</div>
584<div class="line"><a name="l00486"></a><span class="lineno"> 486</span>&#160; outputGateEncoder = MakeEncoder&lt;float&gt;(outputGateInfo, outputGateData.data());</div>
585<div class="line"><a name="l00487"></a><span class="lineno"> 487</span>&#160; </div>
586<div class="line"><a name="l00488"></a><span class="lineno"> 488</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a>(*outputGateDecoder, *outputGateEncoder, numUnits, numBatches, m_LayerNormEpsilon);</div>
587<div class="line"><a name="l00489"></a><span class="lineno"> 489</span>&#160; </div>
588<div class="line"><a name="l00490"></a><span class="lineno"> 490</span>&#160; outputGateDecoder = MakeDecoder&lt;float&gt;(outputGateInfo, outputGateData.data());</div>
589<div class="line"><a name="l00491"></a><span class="lineno"> 491</span>&#160; </div>
590<div class="line"><a name="l00492"></a><span class="lineno"> 492</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a>(*outputLayerNormWeightsDecoder, numUnits, *outputGateDecoder,</div>
591<div class="line"><a name="l00493"></a><span class="lineno"> 493</span>&#160; numBatches, *outputGateEncoder);</div>
592<div class="line"><a name="l00494"></a><span class="lineno"> 494</span>&#160; </div>
593<div class="line"><a name="l00495"></a><span class="lineno"> 495</span>&#160; outputGateInfo.SetQuantizationScale(1.f / 4096);</div>
594<div class="line"><a name="l00496"></a><span class="lineno"> 496</span>&#160; outputGateEncoder = MakeEncoder&lt;float&gt;(outputGateInfo, outputGateData.data());</div>
595<div class="line"><a name="l00497"></a><span class="lineno"> 497</span>&#160; </div>
596<div class="line"><a name="l00498"></a><span class="lineno"> 498</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a>(*outputGateBiasDecoder, numUnits, *outputGateDecoder, numBatches, *outputGateEncoder);</div>
597<div class="line"><a name="l00499"></a><span class="lineno"> 499</span>&#160; </div>
598<div class="line"><a name="l00500"></a><span class="lineno"> 500</span>&#160; outputGateDecoder = MakeDecoder&lt;float&gt;(outputGateInfo, outputGateData.data());</div>
599<div class="line"><a name="l00501"></a><span class="lineno"> 501</span>&#160; }</div>
600<div class="line"><a name="l00502"></a><span class="lineno"> 502</span>&#160; </div>
601<div class="line"><a name="l00503"></a><span class="lineno"> 503</span>&#160; outputGateInfo.SetQuantizationScale(cellStateOutInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>());</div>
602<div class="line"><a name="l00504"></a><span class="lineno"> 504</span>&#160; outputGateEncoder = MakeEncoder&lt;float&gt;(outputGateInfo, outputGateData.data());</div>
603<div class="line"><a name="l00505"></a><span class="lineno"> 505</span>&#160; </div>
604<div class="line"><a name="l00506"></a><span class="lineno"> 506</span>&#160; <span class="comment">// Output gate sigmoid</span></div>
605<div class="line"><a name="l00507"></a><span class="lineno"> 507</span>&#160; <a class="code" href="namespacearmnn.html#a7636fbbc4f8ea2d0cf9f3ac2d12a4c62">Activation</a>(*outputGateDecoder, *outputGateEncoder,</div>
606<div class="line"><a name="l00508"></a><span class="lineno"> 508</span>&#160; TensorInfo({numUnits, numBatches}, internalType),</div>
607<div class="line"><a name="l00509"></a><span class="lineno"> 509</span>&#160; <a class="code" href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">ActivationFunction::Sigmoid</a>, 0, 0);</div>
608<div class="line"><a name="l00510"></a><span class="lineno"> 510</span>&#160; </div>
609<div class="line"><a name="l00511"></a><span class="lineno"> 511</span>&#160; outputGateDecoder = MakeDecoder&lt;float&gt;(outputGateInfo, outputGateData.data());</div>
610<div class="line"><a name="l00512"></a><span class="lineno"> 512</span>&#160; </div>
611<div class="line"><a name="l00513"></a><span class="lineno"> 513</span>&#160; <span class="comment">// Hidden state tanH</span></div>
612<div class="line"><a name="l00514"></a><span class="lineno"> 514</span>&#160; <a class="code" href="namespacearmnn.html#a7636fbbc4f8ea2d0cf9f3ac2d12a4c62">Activation</a>(*cellStateOutDecoder, *cellGateEncoder,</div>
613<div class="line"><a name="l00515"></a><span class="lineno"> 515</span>&#160; TensorInfo({numUnits, numBatches}, internalType),</div>
614<div class="line"><a name="l00516"></a><span class="lineno"> 516</span>&#160; <a class="code" href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">ActivationFunction::TanH</a>, 1.0f, 1.0f);</div>
615<div class="line"><a name="l00517"></a><span class="lineno"> 517</span>&#160; </div>
616<div class="line"><a name="l00518"></a><span class="lineno"> 518</span>&#160; <span class="comment">// Final hidden state output</span></div>
617<div class="line"><a name="l00519"></a><span class="lineno"> 519</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a5b81dc0a1a9a2bccab8bb79dfa3e77b7">VectorVectorCwiseProduct</a>(*outputGateDecoder, *cellGateDecoder, stateTensorSize, *hiddenStateEncoder);</div>
618<div class="line"><a name="l00520"></a><span class="lineno"> 520</span>&#160; </div>
619<div class="line"><a name="l00521"></a><span class="lineno"> 521</span>&#160; <span class="comment">// Projection</span></div>
620<div class="line"><a name="l00522"></a><span class="lineno"> 522</span>&#160; <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.html#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a>)</div>
621<div class="line"><a name="l00523"></a><span class="lineno"> 523</span>&#160; {</div>
622<div class="line"><a name="l00524"></a><span class="lineno"> 524</span>&#160; <span class="keywordflow">if</span> (m_ProjectionBiasTensor)</div>
623<div class="line"><a name="l00525"></a><span class="lineno"> 525</span>&#160; {</div>
624<div class="line"><a name="l00526"></a><span class="lineno"> 526</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a8c4a96233c9b62c76d611316da11124b">VectorBatchVectorAssign</a>(*projectionBiasDecoder, outputSize, numBatches, *outputInt16Encoder);</div>
625<div class="line"><a name="l00527"></a><span class="lineno"> 527</span>&#160; }</div>
626<div class="line"><a name="l00528"></a><span class="lineno"> 528</span>&#160; </div>
627<div class="line"><a name="l00529"></a><span class="lineno"> 529</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*projectionWeightsDecoder, outputSize, numUnits, *hiddenStateDecoder,</div>
628<div class="line"><a name="l00530"></a><span class="lineno"> 530</span>&#160; numBatches, *outputInt16Encoder);</div>
629<div class="line"><a name="l00531"></a><span class="lineno"> 531</span>&#160; </div>
630<div class="line"><a name="l00532"></a><span class="lineno"> 532</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a00d8a623c04f1120f6fee3fe38d1cee9">CopyVector</a>(*outputInt16Decoder, numBatches * outputSize, *outputEncoder);</div>
631<div class="line"><a name="l00533"></a><span class="lineno"> 533</span>&#160; </div>
632<div class="line"><a name="l00534"></a><span class="lineno"> 534</span>&#160; <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.html#aa6a518b65088f34803b3214334bdff61">m_ProjectionClip</a> &gt; 0.0)</div>
633<div class="line"><a name="l00535"></a><span class="lineno"> 535</span>&#160; {</div>
634<div class="line"><a name="l00536"></a><span class="lineno"> 536</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a117781e8e9b7321722bbdd8ff74b484a">ClipVector</a>(*outputDecoder, numBatches * outputSize, <a class="code" href="classarmnn_1_1_base_workload.html#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.html#aa6a518b65088f34803b3214334bdff61">m_ProjectionClip</a>, *outputEncoder);</div>
635<div class="line"><a name="l00537"></a><span class="lineno"> 537</span>&#160; }</div>
636<div class="line"><a name="l00538"></a><span class="lineno"> 538</span>&#160; }</div>
637<div class="line"><a name="l00539"></a><span class="lineno"> 539</span>&#160; <span class="keywordflow">else</span></div>
638<div class="line"><a name="l00540"></a><span class="lineno"> 540</span>&#160; {</div>
639<div class="line"><a name="l00541"></a><span class="lineno"> 541</span>&#160; <span class="comment">// Output has same quantization scale as hidden state if projection is disabled</span></div>
640<div class="line"><a name="l00542"></a><span class="lineno"> 542</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a00d8a623c04f1120f6fee3fe38d1cee9">CopyVector</a>(*hiddenStateDecoder, numBatches * outputSize, *outputEncoder);</div>
641<div class="line"><a name="l00543"></a><span class="lineno"> 543</span>&#160; }</div>
642<div class="line"><a name="l00544"></a><span class="lineno"> 544</span>&#160; </div>
643<div class="line"><a name="l00545"></a><span class="lineno"> 545</span>&#160; <span class="comment">// output == outputStateOut</span></div>
644<div class="line"><a name="l00546"></a><span class="lineno"> 546</span>&#160; <a class="code" href="_lstm_utils_8cpp.html#a00d8a623c04f1120f6fee3fe38d1cee9">CopyVector</a>(*outputDecoder, numBatches * outputSize, *outputStateOutEncoder);</div>
645<div class="line"><a name="l00547"></a><span class="lineno"> 547</span>&#160;}</div>
646<div class="line"><a name="l00548"></a><span class="lineno"> 548</span>&#160; </div>
647<div class="line"><a name="l00549"></a><span class="lineno"> 549</span>&#160;} <span class="comment">//namespace armnn</span></div>
648</div><!-- fragment --></div><!-- contents -->
649</div><!-- doc-content -->
650<div class="ttc" id="a_lstm_utils_8cpp_html_a5b81dc0a1a9a2bccab8bb79dfa3e77b7"><div class="ttname"><a href="_lstm_utils_8cpp.html#a5b81dc0a1a9a2bccab8bb79dfa3e77b7">VectorVectorCwiseProduct</a></div><div class="ttdeci">void VectorVectorCwiseProduct(armnn::Decoder&lt; float &gt; &amp;vector1, armnn::Decoder&lt; float &gt; &amp;vector2, uint32_t vSize, armnn::Encoder&lt; float &gt; &amp;outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.html#l00187">LstmUtils.cpp:187</a></div></div>
651<div class="ttc" id="astructarmnn_1_1_q_lstm_descriptor_html_afec7f36158448f723b426a9527acb189"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.html#afec7f36158448f723b426a9527acb189">armnn::QLstmDescriptor::m_ForgetIntermediateScale</a></div><div class="ttdeci">float m_ForgetIntermediateScale</div><div class="ttdoc">Forget intermediate quantization scale.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01428">Descriptors.hpp:1428</a></div></div>
652<div class="ttc" id="astructarmnn_1_1_q_lstm_descriptor_html_a6c9de81fc65b3c4924cab11907075a17"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.html#a6c9de81fc65b3c4924cab11907075a17">armnn::QLstmDescriptor::m_ProjectionEnabled</a></div><div class="ttdeci">bool m_ProjectionEnabled</div><div class="ttdoc">Enable/disable the projection layer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01422">Descriptors.hpp:1422</a></div></div>
653<div class="ttc" id="a_lstm_utils_8cpp_html_a0ed27dd6d6125a06bf654080f4184360"><div class="ttname"><a href="_lstm_utils_8cpp.html#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a></div><div class="ttdeci">void MeanStddevNormalization(armnn::Decoder&lt; float &gt; &amp;input_vector, armnn::Encoder&lt; float &gt; &amp;output_vector, uint32_t v_size, uint32_t n_batch, float normalization_epsilon)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.html#l00040">LstmUtils.cpp:40</a></div></div>
654<div class="ttc" id="a_lstm_utils_8cpp_html_a796323e16216b880043dc5ebbaa2372b"><div class="ttname"><a href="_lstm_utils_8cpp.html#a796323e16216b880043dc5ebbaa2372b">VectorBatchVectorCwiseProductAccumulate</a></div><div class="ttdeci">void VectorBatchVectorCwiseProductAccumulate(armnn::Decoder&lt; float &gt; &amp;vector, uint32_t vSize, armnn::Decoder&lt; float &gt; &amp;batchVector, uint32_t nBatch, armnn::Encoder&lt; float &gt; &amp;outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.html#l00131">LstmUtils.cpp:131</a></div></div>
655<div class="ttc" id="a_lstm_utils_8cpp_html_a389c4bbafd0fff7060cbb183f20a2134"><div class="ttname"><a href="_lstm_utils_8cpp.html#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a></div><div class="ttdeci">void VectorBatchVectorAdd(armnn::Decoder&lt; float &gt; &amp;vector, uint32_t vSize, armnn::Decoder&lt; float &gt; &amp;batchVector, uint32_t nBatch, armnn::Encoder&lt; float &gt; &amp;outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.html#l00016">LstmUtils.cpp:16</a></div></div>
656<div class="ttc" id="astructarmnn_1_1experimental_1_1_execution_data_html_ad2b382076f26f48cd44783cfca2e3642"><div class="ttname"><a href="structarmnn_1_1experimental_1_1_execution_data.html#ad2b382076f26f48cd44783cfca2e3642">armnn::experimental::ExecutionData::m_Data</a></div><div class="ttdeci">void * m_Data</div><div class="ttdef"><b>Definition:</b> <a href="_execution_data_8hpp_source.html#l00016">ExecutionData.hpp:16</a></div></div>
657<div class="ttc" id="aclassarmnn_1_1_tensor_info_html_a047ca888c43bd7fb5702853bf72410d0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a047ca888c43bd7fb5702853bf72410d0">armnn::TensorInfo::GetQuantizationScale</a></div><div class="ttdeci">float GetQuantizationScale() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00461">Tensor.cpp:461</a></div></div>
658<div class="ttc" id="aclassarmnn_1_1_tensor_info_html"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00152">Tensor.hpp:152</a></div></div>
659<div class="ttc" id="aclassarmnn_1_1_ref_q_lstm_workload_html_a143c3dfe9ce674a65bb5b93f91ec54e3"><div class="ttname"><a href="classarmnn_1_1_ref_q_lstm_workload.html#a143c3dfe9ce674a65bb5b93f91ec54e3">armnn::RefQLstmWorkload::RefQLstmWorkload</a></div><div class="ttdeci">RefQLstmWorkload(const QLstmQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info)</div><div class="ttdef"><b>Definition:</b> <a href="_ref_q_lstm_workload_8cpp_source.html#l00016">RefQLstmWorkload.cpp:16</a></div></div>
660<div class="ttc" id="anamespacearmnn_html_a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e"><div class="ttname"><a href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">armnn::ActivationFunction::TanH</a></div><div class="ttdeci">@ TanH</div></div>
661<div class="ttc" id="a_lstm_utils_8cpp_html_a1d7ad9698b02282a57fdb17b3af745f9"><div class="ttname"><a href="_lstm_utils_8cpp.html#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a></div><div class="ttdeci">void VectorBatchVectorCwiseProduct(armnn::Decoder&lt; float &gt; &amp;vector, uint32_t vSize, armnn::Decoder&lt; float &gt; &amp;batchVector, uint32_t nBatch, armnn::Encoder&lt; float &gt; &amp;outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.html#l00152">LstmUtils.cpp:152</a></div></div>
662<div class="ttc" id="a_lstm_utils_8cpp_html_a117781e8e9b7321722bbdd8ff74b484a"><div class="ttname"><a href="_lstm_utils_8cpp.html#a117781e8e9b7321722bbdd8ff74b484a">ClipVector</a></div><div class="ttdeci">void ClipVector(armnn::Decoder&lt; float &gt; &amp;vector, uint32_t vSize, float absLimit, armnn::Encoder&lt; float &gt; &amp;outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.html#l00229">LstmUtils.cpp:229</a></div></div>
663<div class="ttc" id="a_lstm_utils_8cpp_html_a8618ae0c77638e01069fdb0063cabb3f"><div class="ttname"><a href="_lstm_utils_8cpp.html#a8618ae0c77638e01069fdb0063cabb3f">AssignScopedTensorHandle</a></div><div class="ttdeci">std::unique_ptr&lt; armnn::ScopedTensorHandle &gt; AssignScopedTensorHandle(const armnn::ConstTensorHandle *ptr)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.html#l00299">LstmUtils.cpp:299</a></div></div>
664<div class="ttc" id="a_lstm_utils_8cpp_html_ab93a2c78551c3d3aba8ddcafb792a36d"><div class="ttname"><a href="_lstm_utils_8cpp.html#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a></div><div class="ttdeci">void MatrixBatchVectorMultiplyAccumulate(armnn::Decoder&lt; float &gt; &amp;matrix, uint32_t mRows, uint32_t mCols, armnn::Decoder&lt; float &gt; &amp;vector, uint32_t nBatch, armnn::Encoder&lt; float &gt; &amp;outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.html#l00087">LstmUtils.cpp:87</a></div></div>
665<div class="ttc" id="a_ref_workload_utils_8hpp_html_a06acca4fd832e0fb179604112c0505af"><div class="ttname"><a href="_ref_workload_utils_8hpp.html#a06acca4fd832e0fb179604112c0505af">ARMNN_SCOPED_PROFILING_EVENT_REF_NAME_GUID</a></div><div class="ttdeci">#define ARMNN_SCOPED_PROFILING_EVENT_REF_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="_ref_workload_utils_8hpp_source.html#l00022">RefWorkloadUtils.hpp:22</a></div></div>
666<div class="ttc" id="astructarmnn_1_1_q_lstm_descriptor_html_a09e1f097944f61cc901240f9300364cf"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.html#a09e1f097944f61cc901240f9300364cf">armnn::QLstmDescriptor::m_InputIntermediateScale</a></div><div class="ttdeci">float m_InputIntermediateScale</div><div class="ttdoc">Input intermediate quantization scale.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01426">Descriptors.hpp:1426</a></div></div>
667<div class="ttc" id="a_lstm_utils_8cpp_html_a00d8a623c04f1120f6fee3fe38d1cee9"><div class="ttname"><a href="_lstm_utils_8cpp.html#a00d8a623c04f1120f6fee3fe38d1cee9">CopyVector</a></div><div class="ttdeci">void CopyVector(armnn::Decoder&lt; float &gt; &amp;vector, uint32_t vSize, armnn::Encoder&lt; float &gt; &amp;outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.html#l00244">LstmUtils.cpp:244</a></div></div>
668<div class="ttc" id="a_lstm_utils_8cpp_html_aca7bd1dff180b6a5de894537f8220793"><div class="ttname"><a href="_lstm_utils_8cpp.html#aca7bd1dff180b6a5de894537f8220793">Sub1Vector</a></div><div class="ttdeci">void Sub1Vector(armnn::Decoder&lt; float &gt; &amp;vector, uint32_t vSize, armnn::Encoder&lt; float &gt; &amp;result)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.html#l00173">LstmUtils.cpp:173</a></div></div>
669<div class="ttc" id="astructarmnn_1_1_q_lstm_descriptor_html_a0477ee1b44ace6090119178eea78cb0b"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.html#a0477ee1b44ace6090119178eea78cb0b">armnn::QLstmDescriptor::m_CellIntermediateScale</a></div><div class="ttdeci">float m_CellIntermediateScale</div><div class="ttdoc">Cell intermediate quantization scale.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01430">Descriptors.hpp:1430</a></div></div>
670<div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div><div class="ttdeci">@ QSymmS16</div></div>
671<div class="ttc" id="a_ref_q_lstm_workload_8hpp_html"><div class="ttname"><a href="_ref_q_lstm_workload_8hpp.html">RefQLstmWorkload.hpp</a></div></div>
672<div class="ttc" id="aclassarmnn_1_1_tensor_shape_html"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00020">Tensor.hpp:20</a></div></div>
673<div class="ttc" id="astructarmnn_1_1_queue_descriptor_with_parameters_html_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00066">WorkloadData.hpp:66</a></div></div>
674<div class="ttc" id="astructarmnn_1_1_workload_info_html"><div class="ttname"><a href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about TensorInfos of a layer.</div><div class="ttdef"><b>Definition:</b> <a href="_workload_info_8hpp_source.html#l00016">WorkloadInfo.hpp:16</a></div></div>
675<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>
676<div class="ttc" id="anamespacearmnn_html_aa815fde54f6d8e8aa5b4f0301cf4178b"><div class="ttname"><a href="namespacearmnn.html#aa815fde54f6d8e8aa5b4f0301cf4178b">armnn::GetTensorInfo</a></div><div class="ttdeci">const TensorInfo &amp; GetTensorInfo(const ITensorHandle *tensorHandle)</div><div class="ttdoc">float32 helpers</div><div class="ttdef"><b>Definition:</b> <a href="_ref_workload_utils_8hpp_source.html#l00033">RefWorkloadUtils.hpp:33</a></div></div>
677<div class="ttc" id="a_activation_8hpp_html"><div class="ttname"><a href="_activation_8hpp.html">Activation.hpp</a></div></div>
678<div class="ttc" id="astructarmnn_1_1_q_lstm_descriptor_html_aa6a518b65088f34803b3214334bdff61"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.html#aa6a518b65088f34803b3214334bdff61">armnn::QLstmDescriptor::m_ProjectionClip</a></div><div class="ttdeci">float m_ProjectionClip</div><div class="ttdoc">Clipping threshold value for the projection.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01416">Descriptors.hpp:1416</a></div></div>
679<div class="ttc" id="astructarmnn_1_1_q_lstm_queue_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_q_lstm_queue_descriptor.html">armnn::QLstmQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00562">WorkloadData.hpp:562</a></div></div>
680<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>
681<div class="ttc" id="astructarmnn_1_1_q_lstm_descriptor_html_aa43409f9b457352c95c89f20ce5d844d"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.html#aa43409f9b457352c95c89f20ce5d844d">armnn::QLstmDescriptor::m_OutputIntermediateScale</a></div><div class="ttdeci">float m_OutputIntermediateScale</div><div class="ttdoc">Output intermediate quantization scale.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01432">Descriptors.hpp:1432</a></div></div>
682<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>
683<div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div><div class="ttdeci">@ Signed32</div></div>
684<div class="ttc" id="a_lstm_utils_8cpp_html_a4c20bc573b70e89327b334f924da97b5"><div class="ttname"><a href="_lstm_utils_8cpp.html#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a></div><div class="ttdeci">void ZeroVector(armnn::Encoder&lt; float &gt; &amp;vector, uint32_t vSize)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.html#l00076">LstmUtils.cpp:76</a></div></div>
685<div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a></div><div class="ttdeci">@ QAsymmS8</div></div>
686<div class="ttc" id="a_ref_workload_utils_8hpp_html"><div class="ttname"><a href="_ref_workload_utils_8hpp.html">RefWorkloadUtils.hpp</a></div></div>
687<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&lt; QLstmQueueDescriptor &gt;::m_Data</a></div><div class="ttdeci">QLstmQueueDescriptor m_Data</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8hpp_source.html#l00089">Workload.hpp:89</a></div></div>
688<div class="ttc" id="aclassarmnn_1_1_tensor_info_html_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape &amp; GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00193">Tensor.hpp:193</a></div></div>
689<div class="ttc" id="astructarmnn_1_1_q_lstm_descriptor_html_a4556cbd764d4848d8ad0637a9eed580d"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.html#a4556cbd764d4848d8ad0637a9eed580d">armnn::QLstmDescriptor::m_HiddenStateZeroPoint</a></div><div class="ttdeci">int32_t m_HiddenStateZeroPoint</div><div class="ttdoc">Hidden State zero point.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01434">Descriptors.hpp:1434</a></div></div>
690<div class="ttc" id="aclassarmnn_1_1_ref_q_lstm_workload_html_ae071e8822437c78baea75c3aef3a263a"><div class="ttname"><a href="classarmnn_1_1_ref_q_lstm_workload.html#ae071e8822437c78baea75c3aef3a263a">armnn::RefQLstmWorkload::Execute</a></div><div class="ttdeci">void Execute() const override</div><div class="ttdef"><b>Definition:</b> <a href="_ref_q_lstm_workload_8cpp_source.html#l00046">RefQLstmWorkload.cpp:46</a></div></div>
691<div class="ttc" id="astructarmnn_1_1_q_lstm_descriptor_html_ad474e5c51a0b194ef32e812b86c0cbdb"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.html#ad474e5c51a0b194ef32e812b86c0cbdb">armnn::QLstmDescriptor::m_CifgEnabled</a></div><div class="ttdeci">bool m_CifgEnabled</div><div class="ttdoc">Enable/disable CIFG (coupled input &amp; forget gate).</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01418">Descriptors.hpp:1418</a></div></div>
692<div class="ttc" id="a_decoders_8hpp_html"><div class="ttname"><a href="_decoders_8hpp.html">Decoders.hpp</a></div></div>
693<div class="ttc" id="anamespacearmnn_html_a56943a0946e5f15e5e58054b8e7a04a4a46f3ea056caa3126b91f3f70beea068c"><div class="ttname"><a href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4a46f3ea056caa3126b91f3f70beea068c">armnn::LayerType::Map</a></div><div class="ttdeci">@ Map</div></div>
694<div class="ttc" id="a_lstm_utils_8cpp_html_a2e653f948d93f4177f267a7b1b4ed47d"><div class="ttname"><a href="_lstm_utils_8cpp.html#a2e653f948d93f4177f267a7b1b4ed47d">VectorVectorCwiseProductAccumulate</a></div><div class="ttdeci">void VectorVectorCwiseProductAccumulate(armnn::Decoder&lt; float &gt; &amp;vector1, armnn::Decoder&lt; float &gt; &amp;vector2, uint32_t vSize, armnn::Encoder&lt; float &gt; &amp;outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.html#l00204">LstmUtils.cpp:204</a></div></div>
695<div class="ttc" id="astructarmnn_1_1experimental_1_1_working_mem_descriptor_html_a4b50e46a6810018f3edecfb68b2a76b3"><div class="ttname"><a href="structarmnn_1_1experimental_1_1_working_mem_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">armnn::experimental::WorkingMemDescriptor::m_Inputs</a></div><div class="ttdeci">std::vector&lt; ITensorHandle * &gt; m_Inputs</div><div class="ttdef"><b>Definition:</b> <a href="_working_mem_descriptor_8hpp_source.html#l00020">WorkingMemDescriptor.hpp:20</a></div></div>
696<div class="ttc" id="anamespacearmnn_html"><div class="ttname"><a href="namespacearmnn.html">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors.</div><div class="ttdef"><b>Definition:</b> <a href="01__00__quick__start_8dox_source.html#l00006">01_00_quick_start.dox:6</a></div></div>
697<div class="ttc" id="astructarmnn_1_1experimental_1_1_working_mem_descriptor_html"><div class="ttname"><a href="structarmnn_1_1experimental_1_1_working_mem_descriptor.html">armnn::experimental::WorkingMemDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_working_mem_descriptor_8hpp_source.html#l00018">WorkingMemDescriptor.hpp:18</a></div></div>
698<div class="ttc" id="astructarmnn_1_1_q_lstm_descriptor_html_af8f724af7210b52529216feefa993c98"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.html#af8f724af7210b52529216feefa993c98">armnn::QLstmDescriptor::m_HiddenStateScale</a></div><div class="ttdeci">float m_HiddenStateScale</div><div class="ttdoc">Hidden State quantization scale.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01436">Descriptors.hpp:1436</a></div></div>
699<div class="ttc" id="astructarmnn_1_1_q_lstm_descriptor_html_ac81fb0e66dc623dc37c77f219f53a6d3"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.html#ac81fb0e66dc623dc37c77f219f53a6d3">armnn::QLstmDescriptor::m_CellClip</a></div><div class="ttdeci">float m_CellClip</div><div class="ttdoc">Clipping threshold value for the cell state.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01414">Descriptors.hpp:1414</a></div></div>
700<div class="ttc" id="anamespacearmnn_html_a7636fbbc4f8ea2d0cf9f3ac2d12a4c62"><div class="ttname"><a href="namespacearmnn.html#a7636fbbc4f8ea2d0cf9f3ac2d12a4c62">armnn::Activation</a></div><div class="ttdeci">float Activation(float in, ActivationFunction function, float a, float b)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_8cpp_source.html#l00013">Activation.cpp:13</a></div></div>
701<div class="ttc" id="astructarmnn_1_1_q_lstm_descriptor_html_a4a8ec49f130084445d44297549254780"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.html#a4a8ec49f130084445d44297549254780">armnn::QLstmDescriptor::m_LayerNormEnabled</a></div><div class="ttdeci">bool m_LayerNormEnabled</div><div class="ttdoc">Enable/disable layer normalization.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01424">Descriptors.hpp:1424</a></div></div>
702<div class="ttc" id="a_lstm_utils_8hpp_html"><div class="ttname"><a href="_lstm_utils_8hpp.html">LstmUtils.hpp</a></div></div>
703<div class="ttc" id="aclassarmnn_1_1_tensor_info_html_a770b51078da02f44a819e9f95d8058b5"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a770b51078da02f44a819e9f95d8058b5">armnn::TensorInfo::GetQuantizationOffset</a></div><div class="ttdeci">int32_t GetQuantizationOffset() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00482">Tensor.cpp:482</a></div></div>
704<div class="ttc" id="a_encoders_8hpp_html"><div class="ttname"><a href="_encoders_8hpp.html">Encoders.hpp</a></div></div>
705<div class="ttc" id="aclassarmnn_1_1_ref_base_workload_html"><div class="ttname"><a href="classarmnn_1_1_ref_base_workload.html">armnn::RefBaseWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_base_workload_8hpp_source.html#l00013">RefBaseWorkload.hpp:13</a></div></div>
706<div class="ttc" id="astructarmnn_1_1_q_lstm_descriptor_html_a2837b4396f20c956952d1a7286cab5f8"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.html#a2837b4396f20c956952d1a7286cab5f8">armnn::QLstmDescriptor::m_PeepholeEnabled</a></div><div class="ttdeci">bool m_PeepholeEnabled</div><div class="ttdoc">Enable/disable peephole.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01420">Descriptors.hpp:1420</a></div></div>
707<div class="ttc" id="aclassarmnn_1_1_ref_q_lstm_workload_html_ae1c43d025fc90382d7aff7a500937e2c"><div class="ttname"><a href="classarmnn_1_1_ref_q_lstm_workload.html#ae1c43d025fc90382d7aff7a500937e2c">armnn::RefQLstmWorkload::ExecuteAsync</a></div><div class="ttdeci">void ExecuteAsync(ExecutionData &amp;executionData) override</div><div class="ttdef"><b>Definition:</b> <a href="_ref_q_lstm_workload_8cpp_source.html#l00051">RefQLstmWorkload.cpp:51</a></div></div>
708<div class="ttc" id="astructarmnn_1_1experimental_1_1_working_mem_descriptor_html_a6abd491bb99ffe88bd472c1ae5a1ed1a"><div class="ttname"><a href="structarmnn_1_1experimental_1_1_working_mem_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">armnn::experimental::WorkingMemDescriptor::m_Outputs</a></div><div class="ttdeci">std::vector&lt; ITensorHandle * &gt; m_Outputs</div><div class="ttdef"><b>Definition:</b> <a href="_working_mem_descriptor_8hpp_source.html#l00021">WorkingMemDescriptor.hpp:21</a></div></div>
709<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>
710<div class="ttc" id="anamespacearmnn_html_a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4"><div class="ttname"><a href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">armnn::ActivationFunction::Sigmoid</a></div><div class="ttdeci">@ Sigmoid</div></div>
711<div class="ttc" id="a_lstm_utils_8cpp_html_a8c4a96233c9b62c76d611316da11124b"><div class="ttname"><a href="_lstm_utils_8cpp.html#a8c4a96233c9b62c76d611316da11124b">VectorBatchVectorAssign</a></div><div class="ttdeci">void VectorBatchVectorAssign(armnn::Decoder&lt; float &gt; &amp;vector, uint32_t vSize, uint32_t nBatch, armnn::Encoder&lt; float &gt; &amp;outBatchVector)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.html#l00113">LstmUtils.cpp:113</a></div></div>
712<div class="ttc" id="astructarmnn_1_1experimental_1_1_execution_data_html"><div class="ttname"><a href="structarmnn_1_1experimental_1_1_execution_data.html">armnn::experimental::ExecutionData</a></div><div class="ttdef"><b>Definition:</b> <a href="_execution_data_8hpp_source.html#l00014">ExecutionData.hpp:14</a></div></div>
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