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<div class="title">RefQLstmWorkload.cpp</div> </div>
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<a href="_ref_q_lstm_workload_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2020 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "<a class="code" href="_ref_q_lstm_workload_8hpp.xhtml">RefQLstmWorkload.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include "<a class="code" href="_activation_8hpp.xhtml">Activation.hpp</a>"</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include "<a class="code" href="_encoders_8hpp.xhtml">Encoders.hpp</a>"</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include "<a class="code" href="_decoders_8hpp.xhtml">Decoders.hpp</a>"</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include "<a class="code" href="_lstm_utils_8hpp.xhtml">LstmUtils.hpp</a>"</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include "<a class="code" href="_ref_workload_utils_8hpp.xhtml">RefWorkloadUtils.hpp</a>"</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> {</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> </div><div class="line"><a name="l00016"></a><span class="lineno"><a class="line" href="classarmnn_1_1_ref_q_lstm_workload.xhtml#a143c3dfe9ce674a65bb5b93f91ec54e3"> 16</a></span> <a class="code" href="classarmnn_1_1_ref_q_lstm_workload.xhtml#a143c3dfe9ce674a65bb5b93f91ec54e3">RefQLstmWorkload::RefQLstmWorkload</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_q_lstm_queue_descriptor.xhtml">QLstmQueueDescriptor</a> &descriptor, <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> &info)</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  : <a class="code" href="classarmnn_1_1_base_workload.xhtml">BaseWorkload</a><<a class="code" href="structarmnn_1_1_q_lstm_queue_descriptor.xhtml">QLstmQueueDescriptor</a>>(descriptor, info)</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  , m_InputToInputWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_InputToInputWeights))</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  , m_InputToForgetWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_InputToForgetWeights))</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  , m_InputToCellWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_InputToCellWeights))</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  , m_InputToOutputWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_InputToOutputWeights))</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> </div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  , m_RecurrentToInputWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_RecurrentToInputWeights))</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  , m_RecurrentToForgetWeightsTensor(<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_RecurrentToForgetWeights))</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  , m_RecurrentToCellWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_RecurrentToCellWeights))</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  , m_RecurrentToOutputWeightsTensor(<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_RecurrentToOutputWeights))</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> </div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  , m_CellToInputWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_CellToInputWeights))</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  , m_CellToForgetWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_CellToForgetWeights))</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  , m_CellToOutputWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_CellToOutputWeights))</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> </div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  , m_InputGateBiasTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_InputGateBias))</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  , m_ForgetGateBiasTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_ForgetGateBias))</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  , m_CellBiasTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_CellBias))</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  , m_OutputGateBiasTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_OutputGateBias))</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> </div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  , m_ProjectionWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_ProjectionWeights))</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  , m_ProjectionBiasTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_ProjectionBias))</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  , m_InputLayerNormWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_InputLayerNormWeights))</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  , m_ForgetLayerNormWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_ForgetLayerNormWeights))</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  , m_CellLayerNormWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_CellLayerNormWeights))</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  , m_OutputLayerNormWeightsTensor (<a class="code" href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a>(descriptor.m_OutputLayerNormWeights))</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> {}</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div><div class="line"><a name="l00046"></a><span class="lineno"><a class="line" href="classarmnn_1_1_ref_q_lstm_workload.xhtml#ae071e8822437c78baea75c3aef3a263a"> 46</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_ref_q_lstm_workload.xhtml#ae071e8822437c78baea75c3aef3a263a">RefQLstmWorkload::Execute</a>()<span class="keyword"> const</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="keyword"></span>{</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="comment">// This is a porting of the QLSTM::Execute() method in the Android code base</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="comment">// Note: this implementation wraps the arithmetic functions of the LSTM cell in Quantize/Dequantize ops, so all</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="comment">// computation is done in the floating point domain. Arithmetic functions are found in LstmUtils.cpp.</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="comment">// Refer to: android/frameworks/ml/nn/common/operations/QLSTM.cpp</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>& internalType = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>;</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> </div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo = <a class="code" href="namespacearmnn.xhtml#af7ec4c0fa4375a45a70e4e31f3d8af47">GetTensorInfo</a>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0]);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputStateInInfo = <a class="code" href="namespacearmnn.xhtml#af7ec4c0fa4375a45a70e4e31f3d8af47">GetTensorInfo</a>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[1]);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& cellStateInInfo = <a class="code" href="namespacearmnn.xhtml#af7ec4c0fa4375a45a70e4e31f3d8af47">GetTensorInfo</a>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[2]);</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputStateOutInfo = <a class="code" href="namespacearmnn.xhtml#af7ec4c0fa4375a45a70e4e31f3d8af47">GetTensorInfo</a>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[0]);</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& cellStateOutInfo = <a class="code" href="namespacearmnn.xhtml#af7ec4c0fa4375a45a70e4e31f3d8af47">GetTensorInfo</a>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[1]);</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputInfo = <a class="code" href="namespacearmnn.xhtml#af7ec4c0fa4375a45a70e4e31f3d8af47">GetTensorInfo</a>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[2]);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputStateInShape = outputStateInInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& cellStateInShape = cellStateInInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="comment">// Infer numBatches, inputSize, outputSize and numUnits</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keyword">const</span> uint32_t numBatches = inputShape[0];</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keyword">const</span> uint32_t inputSize = inputShape[1];</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keyword">const</span> uint32_t outputSize = outputStateInShape[1];</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keyword">const</span> uint32_t numUnits = cellStateInShape[1];</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> </div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="comment">// Optional param settings</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> cifgEnabled = <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a>;</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> peepholeEnabled = <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a>;</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> projectionEnabled = <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a>;</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> layerNormEnabled = <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">m_LayerNormEnabled</a>;</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> </div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="comment">// Input decoders</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  std::unique_ptr<Decoder<float>> inputDecoder =</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  MakeDecoder<float>(inputInfo, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0]->Map());</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  std::unique_ptr<Decoder<float>> outputStateInDecoder =</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  MakeDecoder<float>(outputStateInInfo, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[1]->Map());</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  std::unique_ptr<Decoder<float>> cellStateInDecoder =</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  MakeDecoder<float>(cellStateInInfo, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[2]->Map());</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="comment">// Output decoders</span></div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  std::unique_ptr<Decoder<float>> outputStateOutDecoder =</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  MakeDecoder<float>(outputStateOutInfo, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[0]->Map());</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  std::unique_ptr<Decoder<float>> cellStateOutDecoder =</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  MakeDecoder<float>(cellStateOutInfo, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[1]->Map());</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  std::unique_ptr<Decoder<float>> outputDecoder =</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  MakeDecoder<float>(outputInfo, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[2]->Map());</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> </div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="comment">// Output encoders</span></div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  std::unique_ptr<Encoder<float>> outputStateOutEncoder =</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  MakeEncoder<float>(outputStateOutInfo, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[0]->Map());</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  std::unique_ptr<Encoder<float>> cellStateOutEncoder =</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  MakeEncoder<float>(cellStateOutInfo, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[1]->Map());</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  std::unique_ptr<Encoder<float>> outputEncoder =</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  MakeEncoder<float>(outputInfo, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[2]->Map());</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> </div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="comment">// Weights decoders</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  std::unique_ptr<Decoder<float>> inputToForgetWeightsDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  m_InputToForgetWeightsTensor->GetTensorInfo(), m_InputToForgetWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  std::unique_ptr<Decoder<float>> inputToCellWeightsDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  m_InputToCellWeightsTensor->GetTensorInfo(), m_InputToCellWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  std::unique_ptr<Decoder<float>> inputToOutputWeightsDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  m_InputToOutputWeightsTensor->GetTensorInfo(), m_InputToOutputWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> </div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  std::unique_ptr<Decoder<float>> recurrentToForgetWeightsDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  m_RecurrentToForgetWeightsTensor->GetTensorInfo(), m_RecurrentToForgetWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  std::unique_ptr<Decoder<float>> recurrentToCellWeightsDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  m_RecurrentToCellWeightsTensor->GetTensorInfo(), m_RecurrentToCellWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  std::unique_ptr<Decoder<float>> recurrentToOutputWeightsDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  m_RecurrentToOutputWeightsTensor->GetTensorInfo(), m_RecurrentToOutputWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> </div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="comment">// Optional CIFG params</span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  std::unique_ptr<Decoder<float>> inputToInputWeightsDecoder;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  std::unique_ptr<Decoder<float>> recurrentToInputWeightsDecoder;</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  std::unique_ptr<Decoder<float>> inputGateBiasDecoder;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> </div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="comment">// Optional Peephole params</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  std::unique_ptr<Decoder<float>> cellToInputWeightsDecoder;</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  std::unique_ptr<Decoder<float>> cellToForgetWeightsDecoder;</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  std::unique_ptr<Decoder<float>> cellToOutputWeightsDecoder;</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> </div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="comment">// Optional Projection params</span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  std::unique_ptr<Decoder<float>> projectionWeightsDecoder;</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  std::unique_ptr<Decoder<float>> projectionBiasDecoder;</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="comment">// Optional Layer Norm params</span></div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  std::unique_ptr<Decoder<float>> inputLayerNormWeightsDecoder;</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  std::unique_ptr<Decoder<float>> forgetLayerNormWeightsDecoder;</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  std::unique_ptr<Decoder<float>> cellLayerNormWeightsDecoder;</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  std::unique_ptr<Decoder<float>> outputLayerNormWeightsDecoder;</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> </div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="comment">// Biases are only used when Layer Norm is enabled. Scale is defined as (XLayerNormWeights Scale / 1024)</span></div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  std::unique_ptr<Decoder<float>> forgetGateBiasDecoder;</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  std::unique_ptr<Decoder<float>> cellGateBiasDecoder;</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  std::unique_ptr<Decoder<float>> outputGateBiasDecoder;</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> </div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="comment">// Int16 vectors for internal state data (to be decoded/encoded)</span></div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keyword">const</span> uint32_t stateTensorSize = numBatches * numUnits;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  std::vector<int16_t> inputGateData(stateTensorSize);</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  std::vector<int16_t> cellGateData(stateTensorSize);</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  std::vector<int16_t> forgetGateData(stateTensorSize);</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  std::vector<int16_t> outputGateData(stateTensorSize);</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  std::vector<int32_t> hiddenStateData(stateTensorSize);</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  std::vector<int16_t> outputInt16Data(numBatches * outputSize);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> </div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputGateInfo(</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  {numBatches , numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a09e1f097944f61cc901240f9300364cf">m_InputIntermediateScale</a>, 0);</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellGateInfo(</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  {numBatches , numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a0477ee1b44ace6090119178eea78cb0b">m_CellIntermediateScale</a>, 0);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> forgetGateInfo(</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  {numBatches , numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#afec7f36158448f723b426a9527acb189">m_ForgetIntermediateScale</a>, 0);</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputGateInfo(</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  {numBatches , numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa43409f9b457352c95c89f20ce5d844d">m_OutputIntermediateScale</a>, 0);</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> hiddenStateInfo({numBatches, numUnits},</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>,</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#af8f724af7210b52529216feefa993c98">m_HiddenStateScale</a>,</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4556cbd764d4848d8ad0637a9eed580d">m_HiddenStateZeroPoint</a>);</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInt16Info({numBatches , outputSize},</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  outputInfo.GetQuantizationScale(),</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  outputInfo.GetQuantizationOffset());</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> </div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="comment">// Decoders/Encoders for internal states</span></div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  std::unique_ptr<Decoder<float>> inputGateDecoder =</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  MakeDecoder<float>(inputGateInfo, inputGateData.data());</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  std::unique_ptr<Decoder<float>> cellGateDecoder =</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  MakeDecoder<float>(cellGateInfo, cellGateData.data());</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  std::unique_ptr<Decoder<float>> forgetGateDecoder =</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  MakeDecoder<float>(forgetGateInfo, forgetGateData.data());</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  std::unique_ptr<Decoder<float>> outputGateDecoder =</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  MakeDecoder<float>(outputGateInfo, outputGateData.data());</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  std::unique_ptr<Decoder<float>> hiddenStateDecoder =</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  MakeDecoder<float>(hiddenStateInfo, hiddenStateData.data());</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> </div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  std::unique_ptr<Encoder<float>> inputGateEncoder =</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  MakeEncoder<float>(inputGateInfo, inputGateData.data());</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  std::unique_ptr<Encoder<float>> cellGateEncoder =</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  MakeEncoder<float>(cellGateInfo, cellGateData.data());</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  std::unique_ptr<Encoder<float>> forgetGateEncoder =</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  MakeEncoder<float>(forgetGateInfo, forgetGateData.data());</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  std::unique_ptr<Encoder<float>> outputGateEncoder =</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  MakeEncoder<float>(outputGateInfo, outputGateData.data());</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  std::unique_ptr<Encoder<float>> hiddenStateEncoder =</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  MakeEncoder<float>(hiddenStateInfo, hiddenStateData.data());</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span> </div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="comment">// Int16 used to accumulate output to prevent overflowing (after Projection MatMul)</span></div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  std::unique_ptr<Decoder<float>> outputInt16Decoder =</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  MakeDecoder<float>(outputInt16Info, outputInt16Data.data());</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  std::unique_ptr<Encoder<float>> outputInt16Encoder =</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  MakeEncoder<float>(outputInt16Info, outputInt16Data.data());</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="comment">// Create decoders for optional params if they are enabled</span></div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keywordflow">if</span> (!cifgEnabled)</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  {</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  inputToInputWeightsDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  m_InputToInputWeightsTensor->GetTensorInfo(), m_InputToInputWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  recurrentToInputWeightsDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  m_RecurrentToInputWeightsTensor->GetTensorInfo(), m_RecurrentToInputWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  }</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> </div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keywordflow">if</span> (peepholeEnabled)</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  {</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordflow">if</span> (!cifgEnabled)</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  {</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  cellToInputWeightsDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  m_CellToInputWeightsTensor->GetTensorInfo(), m_CellToInputWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  }</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  cellToForgetWeightsDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  m_CellToForgetWeightsTensor->GetTensorInfo(), m_CellToForgetWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  cellToOutputWeightsDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  m_CellToOutputWeightsTensor->GetTensorInfo(), m_CellToOutputWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  }</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keywordflow">if</span> (projectionEnabled)</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  {</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  projectionWeightsDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  m_ProjectionWeightsTensor->GetTensorInfo(), m_ProjectionWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keywordflow">if</span> (m_ProjectionBiasTensor)</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  {</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  projectionBiasDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  m_ProjectionBiasTensor->GetTensorInfo(), m_ProjectionBiasTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  }</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  }</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span> </div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keywordflow">if</span> (layerNormEnabled)</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  {</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keywordflow">if</span> (!cifgEnabled)</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  {</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  inputLayerNormWeightsDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  m_InputLayerNormWeightsTensor->GetTensorInfo(), m_InputLayerNormWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> </div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="comment">// Bias only used if layer norm enabled</span></div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputGateBiasTensorInfo({outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  m_InputLayerNormWeightsTensor->GetTensorInfo().GetQuantizationScale() / 1024, 0);</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  inputGateBiasDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  inputGateBiasTensorInfo, m_InputGateBiasTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  }</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span> </div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  forgetLayerNormWeightsDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  m_ForgetLayerNormWeightsTensor->GetTensorInfo(), m_ForgetLayerNormWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  cellLayerNormWeightsDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  m_CellLayerNormWeightsTensor->GetTensorInfo(), m_CellLayerNormWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  outputLayerNormWeightsDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  m_OutputLayerNormWeightsTensor->GetTensorInfo(), m_OutputLayerNormWeightsTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> </div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="comment">// Bias only used if layer norm enabled</span></div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> forgetGateBiasTensorInfo({outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  m_ForgetLayerNormWeightsTensor->GetTensorInfo().GetQuantizationScale() / 1024, 0);</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  forgetGateBiasDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  forgetGateBiasTensorInfo, m_ForgetGateBiasTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span> </div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellGateBiasTensorInfo({outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  m_CellLayerNormWeightsTensor->GetTensorInfo().GetQuantizationScale() / 1024, 0);</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  cellGateBiasDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  cellGateBiasTensorInfo, m_CellBiasTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> </div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputGateBiasTensorInfo({outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  m_OutputLayerNormWeightsTensor->GetTensorInfo().GetQuantizationScale() / 1024, 0);</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  outputGateBiasDecoder = MakeDecoder<float>(</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  outputGateBiasTensorInfo, m_OutputGateBiasTensor->GetTensor<<span class="keywordtype">void</span>>());</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  }</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> </div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="comment">// Initialize internal state tensors with zeroes.</span></div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  <span class="keywordflow">if</span> (!cifgEnabled)</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  {</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*inputGateEncoder, stateTensorSize);</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  }</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*forgetGateEncoder, stateTensorSize);</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*cellGateEncoder, stateTensorSize);</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*outputGateEncoder, stateTensorSize);</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a>(*hiddenStateEncoder, stateTensorSize);</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span> </div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="comment">// Input weights * Input</span></div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keywordflow">if</span> (!cifgEnabled)</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  {</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*inputToInputWeightsDecoder,</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  numUnits, inputSize, *inputDecoder, numBatches, *inputGateEncoder);</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  }</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span> </div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*inputToForgetWeightsDecoder,</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  numUnits, inputSize, *inputDecoder, numBatches, *forgetGateEncoder);</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span> </div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*inputToCellWeightsDecoder,</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  numUnits, inputSize, *inputDecoder, numBatches, *cellGateEncoder);</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span> </div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*inputToOutputWeightsDecoder,</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  numUnits, inputSize, *inputDecoder, numBatches, *outputGateEncoder);</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span> </div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <span class="comment">// Recurrent weights * OutputStateIn</span></div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <span class="keywordflow">if</span> (!cifgEnabled)</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  {</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*recurrentToInputWeightsDecoder,</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  numUnits, outputSize, *outputStateInDecoder, numBatches, *inputGateEncoder);</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  }</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span> </div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*recurrentToForgetWeightsDecoder,</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  numUnits, outputSize, *outputStateInDecoder, numBatches, *forgetGateEncoder);</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> </div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*recurrentToCellWeightsDecoder,</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  numUnits, outputSize, *outputStateInDecoder, numBatches, *cellGateEncoder);</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span> </div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*recurrentToOutputWeightsDecoder,</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  numUnits, outputSize, *outputStateInDecoder, numBatches, *outputGateEncoder);</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span> </div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="comment">// Input gate.</span></div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="keywordflow">if</span> (!cifgEnabled)</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  {</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keywordflow">if</span> (peepholeEnabled)</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  {</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a796323e16216b880043dc5ebbaa2372b">VectorBatchVectorCwiseProductAccumulate</a>(*cellToInputWeightsDecoder,</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  numUnits, *cellStateInDecoder, numBatches, *inputGateEncoder);</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  }</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span> </div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <span class="keywordflow">if</span> (layerNormEnabled)</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  {</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  inputGateInfo.SetQuantizationScale(inputInfo.GetQuantizationScale() *</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  m_InputLayerNormWeightsTensor->GetTensorInfo().GetQuantizationScale() *</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  1024);</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  inputGateEncoder = MakeEncoder<float>(inputGateInfo, inputGateData.data());</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span> </div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a>(*inputGateDecoder,</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  *inputGateEncoder, numUnits, numBatches, m_LayerNormEpsilon);</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span> </div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  inputGateDecoder = MakeDecoder<float>(inputGateInfo, inputGateData.data());</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span> </div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a>(*inputLayerNormWeightsDecoder,</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  numUnits, *inputGateDecoder, numBatches, *inputGateEncoder);</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span> </div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  inputGateInfo.SetQuantizationScale(1.f / 4096);</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  inputGateEncoder = MakeEncoder<float>(inputGateInfo, inputGateData.data());</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span> </div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a>(*inputGateBiasDecoder,</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  numUnits, *inputGateDecoder, numBatches, *inputGateEncoder);</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span> </div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  inputGateDecoder = MakeDecoder<float>(inputGateInfo, inputGateData.data());</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  }</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span> </div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  inputGateInfo.SetQuantizationScale(cellStateOutInfo.GetQuantizationScale());</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  inputGateEncoder = MakeEncoder<float>(inputGateInfo, inputGateData.data());</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> </div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <span class="comment">// Input gate sigmoid</span></div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">Activation</a>(*inputGateDecoder, *inputGateEncoder,</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({numUnits, numBatches}, internalType),</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">ActivationFunction::Sigmoid</a>, 0, 0);</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span> </div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  inputGateDecoder = MakeDecoder<float>(inputGateInfo, inputGateData.data());</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  }</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span> </div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <span class="comment">// Forget gate</span></div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="keywordflow">if</span> (peepholeEnabled)</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  {</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a796323e16216b880043dc5ebbaa2372b">VectorBatchVectorCwiseProductAccumulate</a>(*cellToForgetWeightsDecoder, numUnits,</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  *cellStateInDecoder, numBatches, *forgetGateEncoder);</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  }</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span> </div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <span class="keywordflow">if</span> (layerNormEnabled)</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  {</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="comment">// Quantize layer norm output to Input Scale * m_ForgetLayerNormWeightsTensor * 1024</span></div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  forgetGateInfo.SetQuantizationScale(inputInfo.GetQuantizationScale() *</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  m_ForgetLayerNormWeightsTensor->GetTensorInfo().GetQuantizationScale() *</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  1024);</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  forgetGateEncoder = MakeEncoder<float>(forgetGateInfo, forgetGateData.data());</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span> </div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span> </div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span> </div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a>(*forgetGateDecoder,</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  *forgetGateEncoder, numUnits, numBatches, m_LayerNormEpsilon);</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span> </div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span> </div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  forgetGateDecoder = MakeDecoder<float>(forgetGateInfo, forgetGateData.data());</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span> </div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a>(*forgetLayerNormWeightsDecoder,</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  numUnits, *forgetGateDecoder, numBatches, *forgetGateEncoder);</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span> </div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span> </div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="comment">// Dequantize layer norm output to (1 / 4096)</span></div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  forgetGateInfo.SetQuantizationScale(1.f / 4096);</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  forgetGateEncoder = MakeEncoder<float>(forgetGateInfo, forgetGateData.data());</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span> </div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a>(*forgetGateBiasDecoder,</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  numUnits, *forgetGateDecoder, numBatches, *forgetGateEncoder);</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span> </div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span> </div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  forgetGateDecoder = MakeDecoder<float>(forgetGateInfo, forgetGateData.data());</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  }</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span> </div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  forgetGateInfo.SetQuantizationScale(cellStateOutInfo.GetQuantizationScale());</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  forgetGateEncoder = MakeEncoder<float>(forgetGateInfo, forgetGateData.data());</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span> </div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <span class="comment">// Forget gate sigmoid</span></div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">Activation</a>(*forgetGateDecoder, *forgetGateEncoder,</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({numUnits, numBatches}, internalType),</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">ActivationFunction::Sigmoid</a>, 0, 0);</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span> </div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  forgetGateDecoder = MakeDecoder<float>(forgetGateInfo, forgetGateData.data());</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span> </div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="comment">// Cell (Modulation) gate</span></div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="keywordflow">if</span> (layerNormEnabled)</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  {</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  cellGateInfo.SetQuantizationScale(inputInfo.GetQuantizationScale() *</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  m_CellLayerNormWeightsTensor->GetTensorInfo().GetQuantizationScale() *</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  1024);</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  cellGateEncoder = MakeEncoder<float>(cellGateInfo, cellGateData.data());</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span> </div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a>(*cellGateDecoder, *cellGateEncoder, numUnits, numBatches, m_LayerNormEpsilon);</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span> </div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  cellGateDecoder = MakeDecoder<float>(cellGateInfo, cellGateData.data());</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span> </div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a>(*cellLayerNormWeightsDecoder,</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  numUnits, *cellGateDecoder, numBatches, *cellGateEncoder);</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span> </div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  cellGateInfo.SetQuantizationScale(1.f / 4096);</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  cellGateEncoder = MakeEncoder<float>(cellGateInfo, cellGateData.data());</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> </div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a>(*cellGateBiasDecoder,</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  numUnits, *cellGateDecoder, numBatches, *cellGateEncoder);</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span> </div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  cellGateDecoder = MakeDecoder<float>(cellGateInfo, cellGateData.data());</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  }</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span> </div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  cellGateInfo.SetQuantizationScale(cellStateOutInfo.GetQuantizationScale());</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  cellGateEncoder = MakeEncoder<float>(cellGateInfo, cellGateData.data());</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span> </div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="comment">// Cell (Modulation) gate tanH</span></div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">Activation</a>(*cellGateDecoder, *cellGateEncoder,</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({numUnits, numBatches}, internalType),</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">ActivationFunction::TanH</a>, 1.0f, 1.0f);</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span> </div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  cellGateDecoder = MakeDecoder<float>(cellGateInfo, cellGateData.data());</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span> </div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a5b81dc0a1a9a2bccab8bb79dfa3e77b7">VectorVectorCwiseProduct</a>(*forgetGateDecoder, *cellStateInDecoder, stateTensorSize, *cellStateOutEncoder);</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span> </div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  <span class="keywordflow">if</span> (cifgEnabled)</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  {</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#aca7bd1dff180b6a5de894537f8220793">Sub1Vector</a>(*forgetGateDecoder, stateTensorSize, *forgetGateEncoder);</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a2e653f948d93f4177f267a7b1b4ed47d">VectorVectorCwiseProductAccumulate</a>(</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  *cellGateDecoder, *forgetGateDecoder, stateTensorSize, *cellStateOutEncoder);</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  }</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  {</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a2e653f948d93f4177f267a7b1b4ed47d">VectorVectorCwiseProductAccumulate</a>(</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  *cellGateDecoder, *inputGateDecoder, stateTensorSize, *cellStateOutEncoder);</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  }</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span> </div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  <span class="comment">// Final cell state out calculated here</span></div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#ac81fb0e66dc623dc37c77f219f53a6d3">m_CellClip</a> > 0.0)</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  {</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a117781e8e9b7321722bbdd8ff74b484a">ClipVector</a>(*cellStateOutDecoder, stateTensorSize, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#ac81fb0e66dc623dc37c77f219f53a6d3">m_CellClip</a>, *cellStateOutEncoder);</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  }</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span> </div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <span class="comment">// Output gate.</span></div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <span class="keywordflow">if</span> (peepholeEnabled)</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  {</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a796323e16216b880043dc5ebbaa2372b">VectorBatchVectorCwiseProductAccumulate</a>(*cellToOutputWeightsDecoder,</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  numUnits, *cellStateOutDecoder, numBatches, *outputGateEncoder);</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  }</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span> </div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="keywordflow">if</span> (layerNormEnabled)</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  {</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  outputGateInfo.SetQuantizationScale(inputInfo.GetQuantizationScale() *</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  m_OutputLayerNormWeightsTensor->GetTensorInfo().GetQuantizationScale() *</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  1024);</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  outputGateEncoder = MakeEncoder<float>(outputGateInfo, outputGateData.data());</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span> </div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a>(*outputGateDecoder, *outputGateEncoder, numUnits, numBatches, m_LayerNormEpsilon);</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span> </div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  outputGateDecoder = MakeDecoder<float>(outputGateInfo, outputGateData.data());</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span> </div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a>(*outputLayerNormWeightsDecoder, numUnits, *outputGateDecoder,</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  numBatches, *outputGateEncoder);</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span> </div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  outputGateInfo.SetQuantizationScale(1.f / 4096);</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  outputGateEncoder = MakeEncoder<float>(outputGateInfo, outputGateData.data());</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span> </div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a>(*outputGateBiasDecoder, numUnits, *outputGateDecoder, numBatches, *outputGateEncoder);</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span> </div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  outputGateDecoder = MakeDecoder<float>(outputGateInfo, outputGateData.data());</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  }</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span> </div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  outputGateInfo.SetQuantizationScale(cellStateOutInfo.GetQuantizationScale());</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  outputGateEncoder = MakeEncoder<float>(outputGateInfo, outputGateData.data());</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span> </div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <span class="comment">// Output gate sigmoid</span></div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">Activation</a>(*outputGateDecoder, *outputGateEncoder,</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({numUnits, numBatches}, internalType),</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">ActivationFunction::Sigmoid</a>, 0, 0);</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span> </div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  outputGateDecoder = MakeDecoder<float>(outputGateInfo, outputGateData.data());</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span> </div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <span class="comment">// Hidden state tanH</span></div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">Activation</a>(*cellStateOutDecoder, *cellGateEncoder,</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({numUnits, numBatches}, internalType),</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">ActivationFunction::TanH</a>, 1.0f, 1.0f);</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span> </div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <span class="comment">// Final hidden state output</span></div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a5b81dc0a1a9a2bccab8bb79dfa3e77b7">VectorVectorCwiseProduct</a>(*outputGateDecoder, *cellGateDecoder, stateTensorSize, *hiddenStateEncoder);</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span> </div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  <span class="comment">// Projection</span></div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a>)</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  {</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  <span class="keywordflow">if</span> (m_ProjectionBiasTensor)</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  {</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a8c4a96233c9b62c76d611316da11124b">VectorBatchVectorAssign</a>(*projectionBiasDecoder, outputSize, numBatches, *outputInt16Encoder);</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  }</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span> </div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a>(*projectionWeightsDecoder, outputSize, numUnits, *hiddenStateDecoder,</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  numBatches, *outputInt16Encoder);</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span> </div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a00d8a623c04f1120f6fee3fe38d1cee9">CopyVector</a>(*outputInt16Decoder, numBatches * outputSize, *outputEncoder);</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span> </div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  <span class="keywordflow">if</span> (<a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa6a518b65088f34803b3214334bdff61">m_ProjectionClip</a> > 0.0)</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  {</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a117781e8e9b7321722bbdd8ff74b484a">ClipVector</a>(*outputDecoder, numBatches * outputSize, <a class="code" href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa6a518b65088f34803b3214334bdff61">m_ProjectionClip</a>, *outputEncoder);</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  }</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  }</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  {</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <span class="comment">// Output has same quantization scale as hidden state if projection is disabled</span></div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a00d8a623c04f1120f6fee3fe38d1cee9">CopyVector</a>(*hiddenStateDecoder, numBatches * outputSize, *outputEncoder);</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  }</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span> </div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  <span class="comment">// output == outputStateOut</span></div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  <a class="code" href="_lstm_utils_8cpp.xhtml#a00d8a623c04f1120f6fee3fe38d1cee9">CopyVector</a>(*outputDecoder, numBatches * outputSize, *outputStateOutEncoder);</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span> }</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span> </div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span> } <span class="comment">//namespace armnn</span></div><div class="ttc" id="_lstm_utils_8cpp_xhtml_a0ed27dd6d6125a06bf654080f4184360"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a0ed27dd6d6125a06bf654080f4184360">MeanStddevNormalization</a></div><div class="ttdeci">void MeanStddevNormalization(armnn::Decoder< float > &input_vector, armnn::Encoder< float > &output_vector, uint32_t v_size, uint32_t n_batch, float normalization_epsilon)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00040">LstmUtils.cpp:40</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a389c4bbafd0fff7060cbb183f20a2134"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a389c4bbafd0fff7060cbb183f20a2134">VectorBatchVectorAdd</a></div><div class="ttdeci">void VectorBatchVectorAdd(armnn::Decoder< float > &vector, uint32_t vSize, armnn::Decoder< float > &batchVector, uint32_t nBatch, armnn::Encoder< float > &outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00016">LstmUtils.cpp:16</a></div></div>
<div class="ttc" id="_activation_8hpp_xhtml"><div class="ttname"><a href="_activation_8hpp.xhtml">Activation.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00187">Tensor.hpp:187</a></div></div>
<div class="ttc" id="_ref_workload_utils_8hpp_xhtml"><div class="ttname"><a href="_ref_workload_utils_8hpp.xhtml">RefWorkloadUtils.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_ref_q_lstm_workload_xhtml_ae071e8822437c78baea75c3aef3a263a"><div class="ttname"><a href="classarmnn_1_1_ref_q_lstm_workload.xhtml#ae071e8822437c78baea75c3aef3a263a">armnn::RefQLstmWorkload::Execute</a></div><div class="ttdeci">virtual void Execute() const override</div><div class="ttdef"><b>Definition:</b> <a href="_ref_q_lstm_workload_8cpp_source.xhtml#l00046">RefQLstmWorkload.cpp:46</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a117781e8e9b7321722bbdd8ff74b484a"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a117781e8e9b7321722bbdd8ff74b484a">ClipVector</a></div><div class="ttdeci">void ClipVector(armnn::Decoder< float > &vector, uint32_t vSize, float absLimit, armnn::Encoder< float > &outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00229">LstmUtils.cpp:229</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a2837b4396f20c956952d1a7286cab5f8"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#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.xhtml#l01193">Descriptors.hpp:1193</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_aca7bd1dff180b6a5de894537f8220793"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#aca7bd1dff180b6a5de894537f8220793">Sub1Vector</a></div><div class="ttdeci">void Sub1Vector(armnn::Decoder< float > &vector, uint32_t vSize, armnn::Encoder< float > &result)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00173">LstmUtils.cpp:173</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_af8f724af7210b52529216feefa993c98"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#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.xhtml#l01209">Descriptors.hpp:1209</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">armnn::LayerType::Activation</a></div></div>
<div class="ttc" id="classarmnn_1_1_base_workload_xhtml_a0a487c549c63319505095b855ea3c195"><div class="ttname"><a href="classarmnn_1_1_base_workload.xhtml#a0a487c549c63319505095b855ea3c195">armnn::BaseWorkload< QLstmQueueDescriptor >::m_Data</a></div><div class="ttdeci">const QLstmQueueDescriptor m_Data</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8hpp_source.xhtml#l00046">Workload.hpp:46</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_aa43409f9b457352c95c89f20ce5d844d"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#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.xhtml#l01205">Descriptors.hpp:1205</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a00d8a623c04f1120f6fee3fe38d1cee9"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a00d8a623c04f1120f6fee3fe38d1cee9">CopyVector</a></div><div class="ttdeci">void CopyVector(armnn::Decoder< float > &vector, uint32_t vSize, armnn::Encoder< float > &outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00244">LstmUtils.cpp:244</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">armnn::ActivationFunction::Sigmoid</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a796323e16216b880043dc5ebbaa2372b"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a796323e16216b880043dc5ebbaa2372b">VectorBatchVectorCwiseProductAccumulate</a></div><div class="ttdeci">void VectorBatchVectorCwiseProductAccumulate(armnn::Decoder< float > &vector, uint32_t vSize, armnn::Decoder< float > &batchVector, uint32_t nBatch, armnn::Encoder< float > &outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00131">LstmUtils.cpp:131</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a4c20bc573b70e89327b334f924da97b5"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a4c20bc573b70e89327b334f924da97b5">ZeroVector</a></div><div class="ttdeci">void ZeroVector(armnn::Encoder< float > &vector, uint32_t vSize)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00076">LstmUtils.cpp:76</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__software__tools_8dox_source.xhtml#l00006">01_00_software_tools.dox:6</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a5b81dc0a1a9a2bccab8bb79dfa3e77b7"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a5b81dc0a1a9a2bccab8bb79dfa3e77b7">VectorVectorCwiseProduct</a></div><div class="ttdeci">void VectorVectorCwiseProduct(armnn::Decoder< float > &vector1, armnn::Decoder< float > &vector2, uint32_t vSize, armnn::Encoder< float > &outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00187">LstmUtils.cpp:187</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div>
<div class="ttc" id="structarmnn_1_1_queue_descriptor_with_parameters_xhtml_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00057">WorkloadData.hpp:57</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a1d7ad9698b02282a57fdb17b3af745f9"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a1d7ad9698b02282a57fdb17b3af745f9">VectorBatchVectorCwiseProduct</a></div><div class="ttdeci">void VectorBatchVectorCwiseProduct(armnn::Decoder< float > &vector, uint32_t vSize, armnn::Decoder< float > &batchVector, uint32_t nBatch, armnn::Encoder< float > &outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00152">LstmUtils.cpp:152</a></div></div>
<div class="ttc" id="_encoders_8hpp_xhtml"><div class="ttname"><a href="_encoders_8hpp.xhtml">Encoders.hpp</a></div></div>
<div class="ttc" id="_ref_q_lstm_workload_8hpp_xhtml"><div class="ttname"><a href="_ref_q_lstm_workload_8hpp.xhtml">RefQLstmWorkload.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_base_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_base_workload.xhtml">armnn::BaseWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_8hpp_source.xhtml#l00028">Workload.hpp:28</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_ab93a2c78551c3d3aba8ddcafb792a36d"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#ab93a2c78551c3d3aba8ddcafb792a36d">MatrixBatchVectorMultiplyAccumulate</a></div><div class="ttdeci">void MatrixBatchVectorMultiplyAccumulate(armnn::Decoder< float > &matrix, uint32_t mRows, uint32_t mCols, armnn::Decoder< float > &vector, uint32_t nBatch, armnn::Encoder< float > &outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00087">LstmUtils.cpp:87</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a4a8ec49f130084445d44297549254780"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#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.xhtml#l01197">Descriptors.hpp:1197</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00032">Types.hpp:32</a></div></div>
<div class="ttc" id="classarmnn_1_1_ref_q_lstm_workload_xhtml_a143c3dfe9ce674a65bb5b93f91ec54e3"><div class="ttname"><a href="classarmnn_1_1_ref_q_lstm_workload.xhtml#a143c3dfe9ce674a65bb5b93f91ec54e3">armnn::RefQLstmWorkload::RefQLstmWorkload</a></div><div class="ttdeci">RefQLstmWorkload(const QLstmQueueDescriptor &descriptor, const WorkloadInfo &info)</div><div class="ttdef"><b>Definition:</b> <a href="_ref_q_lstm_workload_8cpp_source.xhtml#l00016">RefQLstmWorkload.cpp:16</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_aa6a518b65088f34803b3214334bdff61"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#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.xhtml#l01189">Descriptors.hpp:1189</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a09e1f097944f61cc901240f9300364cf"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#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.xhtml#l01199">Descriptors.hpp:1199</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a2e653f948d93f4177f267a7b1b4ed47d"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a2e653f948d93f4177f267a7b1b4ed47d">VectorVectorCwiseProductAccumulate</a></div><div class="ttdeci">void VectorVectorCwiseProductAccumulate(armnn::Decoder< float > &vector1, armnn::Decoder< float > &vector2, uint32_t vSize, armnn::Encoder< float > &outResult)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00204">LstmUtils.cpp:204</a></div></div>
<div class="ttc" id="_decoders_8hpp_xhtml"><div class="ttname"><a href="_decoders_8hpp.xhtml">Decoders.hpp</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a8c4a96233c9b62c76d611316da11124b"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a8c4a96233c9b62c76d611316da11124b">VectorBatchVectorAssign</a></div><div class="ttdeci">void VectorBatchVectorAssign(armnn::Decoder< float > &vector, uint32_t vSize, uint32_t nBatch, armnn::Encoder< float > &outBatchVector)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00113">LstmUtils.cpp:113</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_afec7f36158448f723b426a9527acb189"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#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.xhtml#l01201">Descriptors.hpp:1201</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_ac81fb0e66dc623dc37c77f219f53a6d3"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#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.xhtml#l01187">Descriptors.hpp:1187</a></div></div>
<div class="ttc" id="structarmnn_1_1_queue_descriptor_xhtml_a6abd491bb99ffe88bd472c1ae5a1ed1a"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">armnn::QueueDescriptor::m_Outputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Outputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00031">WorkloadData.hpp:31</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_q_lstm_queue_descriptor.xhtml">armnn::QLstmQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00552">WorkloadData.hpp:552</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a6c9de81fc65b3c4924cab11907075a17"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#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.xhtml#l01195">Descriptors.hpp:1195</a></div></div>
<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
<div class="ttc" id="structarmnn_1_1_queue_descriptor_xhtml_a4b50e46a6810018f3edecfb68b2a76b3"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">armnn::QueueDescriptor::m_Inputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Inputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00030">WorkloadData.hpp:30</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_af7ec4c0fa4375a45a70e4e31f3d8af47"><div class="ttname"><a href="namespacearmnn.xhtml#af7ec4c0fa4375a45a70e4e31f3d8af47">armnn::GetTensorInfo</a></div><div class="ttdeci">const TensorInfo & GetTensorInfo(const ITensorHandle *tensorHandle)</div><div class="ttdoc">float32 helpers </div><div class="ttdef"><b>Definition:</b> <a href="_ref_workload_utils_8hpp_source.xhtml#l00026">RefWorkloadUtils.hpp:26</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a0477ee1b44ace6090119178eea78cb0b"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#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.xhtml#l01203">Descriptors.hpp:1203</a></div></div>
<div class="ttc" id="_lstm_utils_8hpp_xhtml"><div class="ttname"><a href="_lstm_utils_8hpp.xhtml">LstmUtils.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_ad474e5c51a0b194ef32e812b86c0cbdb"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">armnn::QLstmDescriptor::m_CifgEnabled</a></div><div class="ttdeci">bool m_CifgEnabled</div><div class="ttdoc">Enable/disable CIFG (coupled input & forget gate). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01191">Descriptors.hpp:1191</a></div></div>
<div class="ttc" id="_lstm_utils_8cpp_xhtml_a1aef5d233d1f569f34d6c36bdf5ae9e5"><div class="ttname"><a href="_lstm_utils_8cpp.xhtml#a1aef5d233d1f569f34d6c36bdf5ae9e5">AssignScopedCpuTensorHandle</a></div><div class="ttdeci">std::unique_ptr< armnn::ScopedCpuTensorHandle > AssignScopedCpuTensorHandle(const armnn::ConstCpuTensorHandle *ptr)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_utils_8cpp_source.xhtml#l00299">LstmUtils.cpp:299</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a4556cbd764d4848d8ad0637a9eed580d"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#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.xhtml#l01207">Descriptors.hpp:1207</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">armnn::ActivationFunction::TanH</a></div></div>
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