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| <a href="_c_l_l_s_t_m_layer_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 (c) 2018-2019 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include "<a class="code" href="_c_l_l_s_t_m_layer_8h.xhtml">arm_compute/runtime/CL/functions/CLLSTMLayer.h</a>"</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#include "<a class="code" href="_pixel_value_8h.xhtml">arm_compute/core/PixelValue.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_utils_8h.xhtml">arm_compute/core/Utils.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="_validate_8h.xhtml">arm_compute/core/Validate.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="_shape_calculator_8h.xhtml">arm_compute/core/utils/misc/ShapeCalculator.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="_asymm_helpers_8h.xhtml">arm_compute/core/utils/quantization/AsymmHelpers.h</a>"</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include "<a class="code" href="_c_l_scheduler_8h.xhtml">arm_compute/runtime/CL/CLScheduler.h</a>"</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> </div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include <cmath></span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include <memory></span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#include <tuple></span></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> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a>;</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml">arm_compute::misc::shape_calculator</a>;</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div><div class="line"><a name="l00040"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_l_s_t_m_layer.xhtml#af2e2a062e461a6369a4f2fd330b4e422"> 40</a></span> <a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer.xhtml#af2e2a062e461a6369a4f2fd330b4e422">CLLSTMLayer::CLLSTMLayer</a>(std::shared_ptr<IMemoryManager> memory_manager)</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  : _memory_group(std::move(memory_manager)), _fully_connected_input_gate(), _accum_input_gate1(), _subtract_input_gate(), _pixelwise_mul_input_gate(), _activation_input_gate(),</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  _fully_connected_forget_gate(), _accum_forget_gate1(), _pixelwise_mul_forget_gate(), _activation_forget_gate(), _fully_connected_cell_state(), _gemm_cell_state1(), _transpose_cell_state(),</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  _accum_cell_state1(), _accum_cell_state2(), _pixelwise_mul_cell_state1(), _activation_cell_state(), _cell_clip(), _pixelwise_mul_cell_state2(), _fully_connected_output(),</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  _pixelwise_mul_output_state1(), _accum_output1(), _activation_output(), _activation_output_state(), _pixelwise_mul_output_state2(), _fully_connected_output_state(), _projection_clip(),</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  _copy_cell_state(), _copy_output(), _concat_scratch_buffer(), _concat_inputs_forget_gate(), _concat_weights_forget_gate(), _concat_weights_input_gate(), _concat_weights_output(),</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  _ones_memset_kernel(), _mean_std_norm_input_gate(), _pixelwise_mul_input_gate_coeff(), _accum_input_gate_bias(), _mean_std_norm_forget_gate(), _pixelwise_mul_forget_gate_coeff(),</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  _accum_forget_gate_bias(), _mean_std_norm_cell_gate(), _pixelwise_mul_cell_gate_coeff(), _accum_cell_gate_bias(), _mean_std_norm_output_gate(), _pixelwise_mul_output_gate_coeff(),</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  _accum_output_gate_bias(), _input_gate_out1(), _input_gate_out2(), _input_gate_out3(), _input_gate_out4(), _forget_gate_out1(), _forget_gate_out2(), _forget_gate_out3(), _forget_gate_out4(),</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  _forget_gate_out5(), _forget_gate_out6(), _cell_state_out1(), _cell_state_out2(), _cell_state_out3(), _cell_state_out4(), _cell_state_out5(), _output1(), _output2(), _output3(), _output4(),</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  _cell_state_activation(), _output_state1(), _ones(), _input_layer_norm_out1(), _input_layer_norm_out2(), _forget_layer_norm_out1(), _forget_layer_norm_out2(), _cell_layer_norm_out1(),</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  _cell_layer_norm_out2(), _output_layer_norm_out1(), _output_layer_norm_out2(), _run_peephole_opt(false), _run_cifg_opt(false), _perform_cell_clipping(false), _has_projection_weights(false),</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  _perform_projection_clipping(false), _is_prepared(false), _is_layer_norm_lstm(false)</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> }</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> </div><div class="line"><a name="l00056"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_l_s_t_m_layer.xhtml#aa0f7635e5dffc50c235e8879637f7462"> 56</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer.xhtml#aa0f7635e5dffc50c235e8879637f7462">CLLSTMLayer::configure</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input,</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input_to_forget_weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input_to_cell_weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input_to_output_weights,</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *recurrent_to_forget_weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *recurrent_to_cell_weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *recurrent_to_output_weights,</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *forget_gate_bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *cell_bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *output_gate_bias,</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *output_state_in, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *cell_state_in,</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *scratch_buffer, <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *output_state_out, <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *cell_state_out, <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *output,</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml">LSTMParams<ICLTensor></a> &lstm_params, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> &activation_info, <span class="keywordtype">float</span> cell_threshold, <span class="keywordtype">float</span> projection_threshold)</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> {</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <a class="code" href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a>(input,</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  input_to_forget_weights, input_to_cell_weights, input_to_output_weights,</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  forget_gate_bias, cell_bias, output_gate_bias,</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  output_state_in, cell_state_in,</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  scratch_buffer, output_state_out, cell_state_out, output);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  _is_layer_norm_lstm = lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a2d615f651270885a1b996046e9902a3c">use_layer_norm</a>();</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="comment">// Set lstm parameters</span></div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml">LSTMParams<ITensorInfo></a> lstm_params_info;</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordflow">if</span>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a23be92a19e0d7c174ed444e709518afd">has_peephole_opt</a>())</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  {</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  lstm_params_info.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a28c80440058d5c9b0bc1e1a4622c734a">set_peephole_params</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a477486a9c5189cff8af1cdd9d7e8d573">cell_to_forget_weights</a>()-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a934af5defc72f38841ce8955e2151473">cell_to_output_weights</a>()-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>());</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  }</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordflow">if</span>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a127009377712009a84cd0c48aa7e1edd">has_projection</a>())</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  {</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  lstm_params_info.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#aea777d30779bab2d14630ea7e8516615">set_projection_params</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#ab1b3d5364f11bca8cacef026c8038dba">projection_weights</a>()-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(),</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#ad676992a90d193409fa6a28a001af6c8">projection_bias</a>() != <span class="keyword">nullptr</span> ? lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#ad676992a90d193409fa6a28a001af6c8">projection_bias</a>()-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>() : <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  }</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keywordflow">if</span>(!lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#aae040c52316d86a4df2c7cdf179049bf">has_cifg_opt</a>())</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="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *cell_to_input_weights_info = (lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a23be92a19e0d7c174ed444e709518afd">has_peephole_opt</a>()) ? lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#aafb05bcc27f0879701152cd664c632ce">cell_to_input_weights</a>()-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>() : <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  lstm_params_info.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#adac8095c0cd29d443206dfcaf67f3607">set_cifg_params</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#afa54d4a35e697cb14a38359616709681">input_to_input_weights</a>()-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a35e4b6311397e1f9532fb37560aa9996">recurrent_to_input_weights</a>()-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(),</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  cell_to_input_weights_info, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a29a7a1636c6a8fd9e423d55c36e991a0">input_gate_bias</a>()-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>());</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  }</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="comment">// Validate</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(<a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer.xhtml#aa05bceba37ded272a464a90becd9cd99">CLLSTMLayer::validate</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), input_to_forget_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(),</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  input_to_cell_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), input_to_output_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(),</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  recurrent_to_forget_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), recurrent_to_cell_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), recurrent_to_output_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(),</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  forget_gate_bias-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), cell_bias-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), output_gate_bias-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(),</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  output_state_in-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), cell_state_in-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(),</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  scratch_buffer-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), output_state_out-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), cell_state_out-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(),</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  lstm_params_info, activation_info, cell_threshold, projection_threshold));</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> </div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> cell_state_shape = cell_state_in-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="comment">// Configure block that calculates the forget gate</span></div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="comment">// forget_gate = Activation(input * input_to_forget_weights + output_state_in * recurrent_to_forget_weights + PixelWiseMul(cell_state, cell_to_forget_weights) + forget_gate_bias)</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="comment">// We optimize this as follows:</span></div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="comment">// forget_gate = Activation( (input,output_state_in) * (input_to_forget_weights,recurrent_to_forget_weights) + PixelWiseMul(cell_state, cell_to_forget_weights) + forget_gate_bias</span></div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  _forget_gate_out1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  _forget_gate_out3.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  _forget_gate_out5.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> </div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  std::vector<const ICLTensor *> inputs_vector;</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  inputs_vector.emplace_back(input);</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  inputs_vector.emplace_back(output_state_in);</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> concat_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6100aeb494088632647c3e0d639c99ab">arm_compute::misc::shape_calculator::calculate_concatenate_shape</a>(inputs_vector, 0);</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  _forget_gate_out2.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(concat_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> </div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_forget_gate_out2);</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  _concat_inputs_forget_gate.<a class="code" href="classarm__compute_1_1_c_l_width_concatenate2_tensors_kernel.xhtml#af53d66a8f8dd368d3c06b43c0c6a12f1">configure</a>(input, output_state_in, &_forget_gate_out2);</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  std::vector<const ICLTensor *> weights_vector;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> </div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  weights_vector.emplace_back(input_to_forget_weights);</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  weights_vector.emplace_back(recurrent_to_forget_weights);</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> weights_concat_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6100aeb494088632647c3e0d639c99ab">arm_compute::misc::shape_calculator::calculate_concatenate_shape</a>(weights_vector, 0);</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  _forget_gate_out6.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(weights_concat_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> </div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  _concat_weights_forget_gate.<a class="code" href="classarm__compute_1_1_c_l_width_concatenate2_tensors_kernel.xhtml#af53d66a8f8dd368d3c06b43c0c6a12f1">configure</a>(input_to_forget_weights, recurrent_to_forget_weights, &_forget_gate_out6);</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>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_forget_gate_out5);</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  _fully_connected_forget_gate.<a class="code" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#ab205e8e07c4eff3197d0c8cc85a4488d">configure</a>(&_forget_gate_out2, &_forget_gate_out6, (_is_layer_norm_lstm) ? <span class="keyword">nullptr</span> : forget_gate_bias, &_forget_gate_out5);</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_forget_gate_out1);</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_forget_gate_out3);</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  _forget_gate_out6.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> </div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a> *forget_gate_out = &_forget_gate_out5;</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordflow">if</span>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a23be92a19e0d7c174ed444e709518afd">has_peephole_opt</a>())</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  {</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  _forget_gate_out4.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> </div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  _run_peephole_opt = <span class="keyword">true</span>;</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_forget_gate_out4);</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  _pixelwise_mul_forget_gate.<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a3e0a2f39d9dc0f7083aef3b37335afff">configure</a>(cell_state_in, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a477486a9c5189cff8af1cdd9d7e8d573">cell_to_forget_weights</a>(), &_forget_gate_out4, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>);</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  _accum_forget_gate1.<a class="code" href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml#a7af75ca77a6e9eb53532e7ab1317bdc3">configure</a>(&_forget_gate_out5, &_forget_gate_out4, &_forget_gate_out3, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  _forget_gate_out4.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  _forget_gate_out5.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  forget_gate_out = &_forget_gate_out3;</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  }</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  {</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  _forget_gate_out3.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  }</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keywordflow">if</span>(_is_layer_norm_lstm)</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  {</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  _forget_layer_norm_out1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  _forget_layer_norm_out2.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_forget_layer_norm_out1);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_forget_layer_norm_out2);</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  _mean_std_norm_forget_gate.<a class="code" href="classarm__compute_1_1_c_l_mean_std_dev_normalization_layer.xhtml#a1029bf3c12d8600f803700fc76c11590">configure</a>(forget_gate_out);</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  _pixelwise_mul_forget_gate_coeff.<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a3e0a2f39d9dc0f7083aef3b37335afff">configure</a>(forget_gate_out, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a213908108c07594027bc2b829fe7ee4a">forget_layer_norm_weights</a>(), &_forget_layer_norm_out1, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="comment">// forget_gate_out is going to be reassigned, so allocate the tensor that it was assigned to before</span></div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  forget_gate_out-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  _accum_forget_gate_bias.<a class="code" href="classarm__compute_1_1_c_l_saturated_arithmetic_operation_kernel.xhtml#ad09d2ed1f19a1df9b87da20af7efaff9">configure</a>(<a class="code" href="namespacearm__compute.xhtml#a23d9f0c01c9e120dfb828ee922b7a8aea9eeb52badb613229884838847294b90d">ArithmeticOperation::ADD</a>, &_forget_layer_norm_out1, forget_gate_bias, &_forget_layer_norm_out2, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>);</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  _forget_layer_norm_out1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  forget_gate_out = &_forget_layer_norm_out2;</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  }</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  _activation_forget_gate.<a class="code" href="classarm__compute_1_1_c_l_activation_layer_kernel.xhtml#a239fea32ba46d038ba350dba58026c45">configure</a>(forget_gate_out, <span class="keyword">nullptr</span>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::ActivationFunction::LOGISTIC</a>));</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> </div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="comment">// Configure block that calculates the input gate</span></div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="comment">// input_gate = Activation(input * input_to_input_weights + output_state * recurrent_to_input_weights + PixelWiseMul(cell_state, cell_to_input_weights) + input_gate_bias), without CIFG</span></div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="comment">// input_gate = 1 - forget_gate, with CIFG</span></div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="comment">// We optimize this as follows:</span></div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="comment">// input_gate = Activation((input,output_state) * (input_to_input_weights,recurrent_to_input_weights) + PixelWiseMul(cell_state, cell_to_input_weights) + input_gate_bias), without CIFG</span></div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  _input_gate_out1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a> *input_gate_out = &_input_gate_out1;</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keywordflow">if</span>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#aae040c52316d86a4df2c7cdf179049bf">has_cifg_opt</a>())</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  {</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_input_gate_out1);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  _ones.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  _ones_memset_kernel.<a class="code" href="classarm__compute_1_1_c_l_memset_kernel.xhtml#a8842f3a8e50c91b74a0b0549ac8fa489">configure</a>(&_ones, <a class="code" href="classarm__compute_1_1_pixel_value.xhtml">PixelValue</a>(1, _ones.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>()));</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  _subtract_input_gate.<a class="code" href="classarm__compute_1_1_c_l_saturated_arithmetic_operation_kernel.xhtml#ad09d2ed1f19a1df9b87da20af7efaff9">configure</a>(<a class="code" href="namespacearm__compute.xhtml#a23d9f0c01c9e120dfb828ee922b7a8aea241dd841abade20fcb27b8a9f494e1eb">ArithmeticOperation::SUB</a>, &_ones, forget_gate_out, &_input_gate_out1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>);</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  _ones.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  _run_cifg_opt = <span class="keyword">true</span>;</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  }</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  {</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  _input_gate_out3.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  _input_gate_out4.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> </div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  std::vector<const ICLTensor *> lstm_weights;</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  lstm_weights.emplace_back(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#afa54d4a35e697cb14a38359616709681">input_to_input_weights</a>());</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  lstm_weights.emplace_back(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a35e4b6311397e1f9532fb37560aa9996">recurrent_to_input_weights</a>());</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> lstm_weights_concat_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6100aeb494088632647c3e0d639c99ab">arm_compute::misc::shape_calculator::calculate_concatenate_shape</a>(lstm_weights, 0);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  _input_gate_out2.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(lstm_weights_concat_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> </div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  _concat_weights_input_gate.<a class="code" href="classarm__compute_1_1_c_l_width_concatenate2_tensors_kernel.xhtml#af53d66a8f8dd368d3c06b43c0c6a12f1">configure</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#afa54d4a35e697cb14a38359616709681">input_to_input_weights</a>(), lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a35e4b6311397e1f9532fb37560aa9996">recurrent_to_input_weights</a>(), &_input_gate_out2);</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> </div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_input_gate_out1);</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>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_input_gate_out3);</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  _fully_connected_input_gate.<a class="code" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#ab205e8e07c4eff3197d0c8cc85a4488d">configure</a>(&_forget_gate_out2, &_input_gate_out2, (_is_layer_norm_lstm) ? <span class="keyword">nullptr</span> : lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a29a7a1636c6a8fd9e423d55c36e991a0">input_gate_bias</a>(), &_input_gate_out3);</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  _input_gate_out2.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span> </div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  input_gate_out = &_input_gate_out3;</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="keywordflow">if</span>(_run_peephole_opt)</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  {</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_input_gate_out4);</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  _pixelwise_mul_input_gate.<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a3e0a2f39d9dc0f7083aef3b37335afff">configure</a>(cell_state_in, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#aafb05bcc27f0879701152cd664c632ce">cell_to_input_weights</a>(), &_input_gate_out4, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>);</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  _accum_input_gate1.<a class="code" href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml#a7af75ca77a6e9eb53532e7ab1317bdc3">configure</a>(&_input_gate_out3, &_input_gate_out4, &_input_gate_out1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>);</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  _input_gate_out3.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  _input_gate_out4.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  input_gate_out = &_input_gate_out1;</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  }</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keywordflow">else</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>  _input_gate_out1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  }</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span> </div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keywordflow">if</span>(_is_layer_norm_lstm)</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>  _input_layer_norm_out1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  _input_layer_norm_out2.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_input_layer_norm_out1);</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_input_layer_norm_out2);</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  _mean_std_norm_input_gate.<a class="code" href="classarm__compute_1_1_c_l_mean_std_dev_normalization_layer.xhtml#a1029bf3c12d8600f803700fc76c11590">configure</a>(input_gate_out);</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  _pixelwise_mul_input_gate_coeff.<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a3e0a2f39d9dc0f7083aef3b37335afff">configure</a>(input_gate_out, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a23361ca1393c0dc196fbf4e627e07119">input_layer_norm_weights</a>(), &_input_layer_norm_out1, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>);</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="comment">// input_gate_out is going to be reassigned, so allocate the tensor that it was assigned to before</span></div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  input_gate_out-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  _accum_input_gate_bias.<a class="code" href="classarm__compute_1_1_c_l_saturated_arithmetic_operation_kernel.xhtml#ad09d2ed1f19a1df9b87da20af7efaff9">configure</a>(<a class="code" href="namespacearm__compute.xhtml#a23d9f0c01c9e120dfb828ee922b7a8aea9eeb52badb613229884838847294b90d">ArithmeticOperation::ADD</a>, &_input_layer_norm_out1, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a29a7a1636c6a8fd9e423d55c36e991a0">input_gate_bias</a>(), &_input_layer_norm_out2, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>);</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  _input_layer_norm_out1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  input_gate_out = &_input_layer_norm_out2;</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>  _activation_input_gate.<a class="code" href="classarm__compute_1_1_c_l_activation_layer_kernel.xhtml#a239fea32ba46d038ba350dba58026c45">configure</a>(input_gate_out, <span class="keyword">nullptr</span>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::ActivationFunction::LOGISTIC</a>));</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> </div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="comment">// Configure block that calculates the cell state</span></div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="comment">// cell_state = Clip((PixelwiseMul(input_gate, Activation(input * input_to_cell_weights + output_state_in * recurrent_to_cell_weights + cell_bias)) + PixelwiseMul(forget_gate, cell_state)), cell_threshold)</span></div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> cell_state1_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf">compute_transposed_shape</a>(*recurrent_to_output_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>());</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  _cell_state_out1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  _cell_state_out2.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state1_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  _cell_state_out3.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  _cell_state_out4.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  _cell_state_out5.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> </div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_cell_state_out1);</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  _fully_connected_cell_state.<a class="code" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#ab205e8e07c4eff3197d0c8cc85a4488d">configure</a>(input, input_to_cell_weights, (_is_layer_norm_lstm) ? <span class="keyword">nullptr</span> : cell_bias, &_cell_state_out1);</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_cell_state_out2);</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  _transpose_cell_state.<a class="code" href="classarm__compute_1_1_c_l_transpose_kernel.xhtml#a074e10cfb217e657b9e81adeca2abc68">configure</a>(recurrent_to_cell_weights, &_cell_state_out2);</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_cell_state_out3);</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  _gemm_cell_state1.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#a34e7b882208ff6720bad2e4f2c7565c5">configure</a>(output_state_in, &_cell_state_out2, <span class="keyword">nullptr</span>, &_cell_state_out3, 1.f, 0.f);</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  _cell_state_out2.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_cell_state_out4);</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  _accum_cell_state1.<a class="code" href="classarm__compute_1_1_c_l_saturated_arithmetic_operation_kernel.xhtml#ad09d2ed1f19a1df9b87da20af7efaff9">configure</a>(<a class="code" href="namespacearm__compute.xhtml#a23d9f0c01c9e120dfb828ee922b7a8aea9eeb52badb613229884838847294b90d">ArithmeticOperation::ADD</a>, &_cell_state_out1, &_cell_state_out3, &_cell_state_out4, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>);</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a> *cell_state_out_ptr = &_cell_state_out4;</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="keywordflow">if</span>(_is_layer_norm_lstm)</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  {</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  _cell_layer_norm_out1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  _cell_layer_norm_out2.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_cell_layer_norm_out1);</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_cell_layer_norm_out2);</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  _mean_std_norm_cell_gate.<a class="code" href="classarm__compute_1_1_c_l_mean_std_dev_normalization_layer.xhtml#a1029bf3c12d8600f803700fc76c11590">configure</a>(cell_state_out_ptr);</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  _pixelwise_mul_cell_gate_coeff.<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a3e0a2f39d9dc0f7083aef3b37335afff">configure</a>(cell_state_out_ptr, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#abd478bedc7c65b72ead0d05cbd16d437">cell_layer_norm_weights</a>(), &_cell_layer_norm_out1, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>);</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="comment">// cell_state_out_ptr is going to be reassigned, so allocate the tensor that it was assigned to before</span></div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  cell_state_out_ptr-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  _accum_cell_gate_bias.<a class="code" href="classarm__compute_1_1_c_l_saturated_arithmetic_operation_kernel.xhtml#ad09d2ed1f19a1df9b87da20af7efaff9">configure</a>(<a class="code" href="namespacearm__compute.xhtml#a23d9f0c01c9e120dfb828ee922b7a8aea9eeb52badb613229884838847294b90d">ArithmeticOperation::ADD</a>, &_cell_layer_norm_out1, cell_bias, &_cell_layer_norm_out2, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>);</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  _cell_layer_norm_out1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  cell_state_out_ptr = &_cell_layer_norm_out2;</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  }</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  _activation_cell_state.<a class="code" href="classarm__compute_1_1_c_l_activation_layer_kernel.xhtml#a239fea32ba46d038ba350dba58026c45">configure</a>(cell_state_out_ptr, <span class="keyword">nullptr</span>, activation_info);</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_cell_state_out5);</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  _pixelwise_mul_cell_state1.<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a3e0a2f39d9dc0f7083aef3b37335afff">configure</a>(cell_state_out_ptr, input_gate_out, &_cell_state_out5, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>);</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  cell_state_out_ptr-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  _pixelwise_mul_cell_state2.<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a3e0a2f39d9dc0f7083aef3b37335afff">configure</a>(forget_gate_out, cell_state_in, &_cell_state_out3, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>);</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  _accum_cell_state2.<a class="code" href="classarm__compute_1_1_c_l_saturated_arithmetic_operation_kernel.xhtml#ad09d2ed1f19a1df9b87da20af7efaff9">configure</a>(<a class="code" href="namespacearm__compute.xhtml#a23d9f0c01c9e120dfb828ee922b7a8aea9eeb52badb613229884838847294b90d">ArithmeticOperation::ADD</a>, &_cell_state_out5, &_cell_state_out3, &_cell_state_out1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>);</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  _cell_state_out3.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  _cell_state_out5.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="comment">// Perform clipping</span></div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keywordflow">if</span>(cell_threshold != 0.f)</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  {</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  _perform_cell_clipping = <span class="keyword">true</span>;</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  _cell_clip.<a class="code" href="classarm__compute_1_1_c_l_activation_layer_kernel.xhtml#a239fea32ba46d038ba350dba58026c45">configure</a>(&_cell_state_out1, <span class="keyword">nullptr</span>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a">ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU</a>, -cell_threshold, cell_threshold));</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  }</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>  <span class="comment">// Configure block that calculates the output</span></div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <span class="comment">// output_state_out = Activation(input * input_to_output_weights + output_state_in * recurrent_to_output_weights + PixelWiseMul(cell_state, cell_to_output_weights) + output_gate_bias)</span></div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="comment">// We optimize this as follows:</span></div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="comment">// output_state_out = Activation( (input,output_state_in) * (input_to_output_weights, recurrent_to_output_weights) + PixelWiseMul(cell_state, cell_to_output_weights) + output_gate_bias)</span></div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  _output1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  _output4.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  std::vector<const ICLTensor *> in_out_weights;</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  in_out_weights.emplace_back(input_to_output_weights);</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  in_out_weights.emplace_back(recurrent_to_output_weights);</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> in_out_weights_concat_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6100aeb494088632647c3e0d639c99ab">arm_compute::misc::shape_calculator::calculate_concatenate_shape</a>(in_out_weights, 0);</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  _output2.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(in_out_weights_concat_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span> </div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  _concat_weights_output.<a class="code" href="classarm__compute_1_1_c_l_width_concatenate2_tensors_kernel.xhtml#af53d66a8f8dd368d3c06b43c0c6a12f1">configure</a>(input_to_output_weights, recurrent_to_output_weights, &_output2);</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span> </div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_output1);</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_output4);</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span> </div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  _fully_connected_output.<a class="code" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#ab205e8e07c4eff3197d0c8cc85a4488d">configure</a>(&_forget_gate_out2, &_output2, (_is_layer_norm_lstm) ? <span class="keyword">nullptr</span> : output_gate_bias, &_output4);</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>  _output2.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  _forget_gate_out2.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span> </div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a> *output_gate_out = &_output4;</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keywordflow">if</span>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a23be92a19e0d7c174ed444e709518afd">has_peephole_opt</a>())</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  {</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  _output3.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(_cell_state_out1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>(), 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span> </div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_output3);</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  _pixelwise_mul_output_state1.<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a3e0a2f39d9dc0f7083aef3b37335afff">configure</a>(&_cell_state_out1, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a934af5defc72f38841ce8955e2151473">cell_to_output_weights</a>(), &_output3, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>);</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  _accum_output1.<a class="code" href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml#a7af75ca77a6e9eb53532e7ab1317bdc3">configure</a>(&_output4, &_output3, &_output1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>);</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  _output4.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  output_gate_out = &_output1;</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span> </div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="comment">// Allocate intermediate buffers</span></div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  _output3.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  }</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="keywordflow">else</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>  _output1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</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>  <span class="keywordflow">if</span>(_is_layer_norm_lstm)</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  {</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  _output_layer_norm_out1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  _output_layer_norm_out2.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_output_layer_norm_out1);</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_output_layer_norm_out2);</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  _mean_std_norm_output_gate.<a class="code" href="classarm__compute_1_1_c_l_mean_std_dev_normalization_layer.xhtml#a1029bf3c12d8600f803700fc76c11590">configure</a>(output_gate_out);</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  _pixelwise_mul_output_gate_coeff.<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a3e0a2f39d9dc0f7083aef3b37335afff">configure</a>(output_gate_out, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a208874b46a667263fa309537c5355318">output_layer_norm_weights</a>(), &_output_layer_norm_out1, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>);</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <span class="comment">// output_gate_out is going to be reassigned, so allocate the tensor that it was assigned to before</span></div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  output_gate_out-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  _accum_output_gate_bias.<a class="code" href="classarm__compute_1_1_c_l_saturated_arithmetic_operation_kernel.xhtml#ad09d2ed1f19a1df9b87da20af7efaff9">configure</a>(<a class="code" href="namespacearm__compute.xhtml#a23d9f0c01c9e120dfb828ee922b7a8aea9eeb52badb613229884838847294b90d">ArithmeticOperation::ADD</a>, &_output_layer_norm_out1, output_gate_bias, &_output_layer_norm_out2, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>);</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  _output_layer_norm_out1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  output_gate_out = &_output_layer_norm_out2;</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  }</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  _activation_output.<a class="code" href="classarm__compute_1_1_c_l_activation_layer_kernel.xhtml#a239fea32ba46d038ba350dba58026c45">configure</a>(output_gate_out, <span class="keyword">nullptr</span>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::ActivationFunction::LOGISTIC</a>));</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>  <span class="comment">// Configure block that calculates the output state</span></div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span> <span class="comment"></span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *output_state_out_tmp = lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a127009377712009a84cd0c48aa7e1edd">has_projection</a>() ? &_output_state1 : output_state_out;</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  _cell_state_activation.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  _output_state1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(cell_state_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span> </div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_cell_state_activation);</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  _activation_output_state.<a class="code" href="classarm__compute_1_1_c_l_activation_layer_kernel.xhtml#a239fea32ba46d038ba350dba58026c45">configure</a>(&_cell_state_out1, &_cell_state_activation, activation_info);</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  _pixelwise_mul_output_state2.<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a3e0a2f39d9dc0f7083aef3b37335afff">configure</a>(&_cell_state_activation, output_gate_out, output_state_out_tmp, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>);</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  _cell_state_activation.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span> </div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="keywordflow">if</span>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a127009377712009a84cd0c48aa7e1edd">has_projection</a>())</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>  _has_projection_weights = <span class="keyword">true</span>;</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  _fully_connected_output_state.<a class="code" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#ab205e8e07c4eff3197d0c8cc85a4488d">configure</a>(output_state_out_tmp, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#ab1b3d5364f11bca8cacef026c8038dba">projection_weights</a>(), lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#ad676992a90d193409fa6a28a001af6c8">projection_bias</a>(), output_state_out);</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  _output_state1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <span class="comment">// Perform clipping</span></div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <span class="keywordflow">if</span>(projection_threshold != 0.f)</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>  _perform_projection_clipping = <span class="keyword">true</span>;</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  _projection_clip.<a class="code" href="classarm__compute_1_1_c_l_activation_layer_kernel.xhtml#a239fea32ba46d038ba350dba58026c45">configure</a>(output_state_out, <span class="keyword">nullptr</span>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a">ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU</a>, -projection_threshold, projection_threshold));</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  }</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  }</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span> </div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="comment">// Copy cell state and output</span></div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  _copy_cell_state.<a class="code" href="classarm__compute_1_1_c_l_copy_kernel.xhtml#a71e93e8e995e940376c12cfd1b0a5538">configure</a>(&_cell_state_out1, cell_state_out);</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  _copy_output.<a class="code" href="classarm__compute_1_1_c_l_copy_kernel.xhtml#a71e93e8e995e940376c12cfd1b0a5538">configure</a>(output_state_out, output);</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span> </div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="comment">// Vector for holding the tensors to store in scratch buffer</span></div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  std::vector<ICLTensor *> scratch_inputs;</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="keywordflow">if</span>(!lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#aae040c52316d86a4df2c7cdf179049bf">has_cifg_opt</a>())</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  {</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  scratch_inputs.emplace_back(input_gate_out);</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  }</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  scratch_inputs.emplace_back(&_cell_state_out1);</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  scratch_inputs.emplace_back(forget_gate_out);</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  scratch_inputs.emplace_back(output_gate_out);</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  _concat_scratch_buffer.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6b12b22f2ad4a69582ec1674a2779ec8">configure</a>(scratch_inputs, scratch_buffer, <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>);</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  input_gate_out-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  _cell_state_out1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  forget_gate_out-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  output_gate_out-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span> }</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span> </div><div class="line"><a name="l00388"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_l_s_t_m_layer.xhtml#aa05bceba37ded272a464a90becd9cd99"> 388</a></span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a> <a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer.xhtml#aa05bceba37ded272a464a90becd9cd99">CLLSTMLayer::validate</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input,</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input_to_forget_weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input_to_cell_weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input_to_output_weights,</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *recurrent_to_forget_weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *recurrent_to_cell_weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *recurrent_to_output_weights,</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *forget_gate_bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *cell_bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output_gate_bias,</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output_state_in, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *cell_state_in,</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *scratch_buffer, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output_state_out, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *cell_state_out, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output,</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml">LSTMParams<ITensorInfo></a> &lstm_params, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> &activation_info, <span class="keywordtype">float</span> cell_threshold, <span class="keywordtype">float</span> projection_threshold)</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span> {</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(input,</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  input_to_forget_weights, input_to_cell_weights, input_to_output_weights,</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  forget_gate_bias, cell_bias, output_gate_bias,</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  output_state_in, cell_state_in,</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  scratch_buffer, output_state_out, cell_state_out, output);</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span> </div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="comment">// Check data types</span></div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <a class="code" href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(input, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input,</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  input_to_forget_weights, input_to_cell_weights, input_to_output_weights,</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  forget_gate_bias, cell_bias, output_gate_bias,</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  output_state_in, cell_state_in,</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  scratch_buffer, output_state_out, cell_state_out, output);</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>  <span class="comment">// Check dimensions</span></div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 2);</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(input_to_forget_weights-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 2);</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(input_to_cell_weights-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 2);</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(input_to_output_weights-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 2);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(recurrent_to_forget_weights-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 2);</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(recurrent_to_cell_weights-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 2);</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(recurrent_to_output_weights-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 2);</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(forget_gate_bias-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 1);</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(cell_bias-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 1);</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(output_gate_bias-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 1);</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(output_state_in-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 2);</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(cell_state_in-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 2);</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(scratch_buffer-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 2);</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(output_state_out-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 2);</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(cell_state_out-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 2);</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(output-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 2);</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(cell_bias-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) * 4 != scratch_buffer-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0)</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  && cell_bias-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) * 3 != scratch_buffer-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0));</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span> </div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_batches = input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1);</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_cells = input_to_output_weights-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1);</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span> </div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <span class="keywordflow">if</span>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a2d615f651270885a1b996046e9902a3c">use_layer_norm</a>())</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  {</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <span class="comment">// If CIFG is used, input layer normalization weights tensor is omitted</span></div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  <span class="keywordflow">if</span>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#aae040c52316d86a4df2c7cdf179049bf">has_cifg_opt</a>())</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="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a23361ca1393c0dc196fbf4e627e07119">input_layer_norm_weights</a>() != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  }</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <span class="keywordflow">else</span></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>  <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a23361ca1393c0dc196fbf4e627e07119">input_layer_norm_weights</a>());</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a23361ca1393c0dc196fbf4e627e07119">input_layer_norm_weights</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 1);</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a23361ca1393c0dc196fbf4e627e07119">input_layer_norm_weights</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) != num_batches);</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a23361ca1393c0dc196fbf4e627e07119">input_layer_norm_weights</a>());</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>  <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a213908108c07594027bc2b829fe7ee4a">forget_layer_norm_weights</a>(), lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#abd478bedc7c65b72ead0d05cbd16d437">cell_layer_norm_weights</a>(), lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a208874b46a667263fa309537c5355318">output_layer_norm_weights</a>());</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a213908108c07594027bc2b829fe7ee4a">forget_layer_norm_weights</a>(), lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#abd478bedc7c65b72ead0d05cbd16d437">cell_layer_norm_weights</a>(), lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a208874b46a667263fa309537c5355318">output_layer_norm_weights</a>());</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a213908108c07594027bc2b829fe7ee4a">forget_layer_norm_weights</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 1);</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#abd478bedc7c65b72ead0d05cbd16d437">cell_layer_norm_weights</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 1);</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a208874b46a667263fa309537c5355318">output_layer_norm_weights</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 1);</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a213908108c07594027bc2b829fe7ee4a">forget_layer_norm_weights</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) != num_batches);</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#abd478bedc7c65b72ead0d05cbd16d437">cell_layer_norm_weights</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) != num_batches);</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a208874b46a667263fa309537c5355318">output_layer_norm_weights</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) != num_batches);</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> </div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <span class="comment">// Check peephole optimization</span></div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <span class="keywordflow">if</span>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a23be92a19e0d7c174ed444e709518afd">has_peephole_opt</a>())</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>  <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a934af5defc72f38841ce8955e2151473">cell_to_output_weights</a>(), lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a477486a9c5189cff8af1cdd9d7e8d573">cell_to_forget_weights</a>());</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a477486a9c5189cff8af1cdd9d7e8d573">cell_to_forget_weights</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 1);</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a934af5defc72f38841ce8955e2151473">cell_to_output_weights</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 1);</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  }</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span> </div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> units_out_transposed_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf">compute_transposed_shape</a>(*recurrent_to_output_weights);</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> num_units_transposed_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf">compute_transposed_shape</a>(*forget_gate_bias);</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> units_out_transposed_info = <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(units_out_transposed_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> num_units_transposed_info = <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(num_units_transposed_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span> </div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> input_gate = <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(num_cells, num_batches), 1, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> forget_gate = <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(num_cells, num_batches), 1, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> output_gate_tmp = <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(num_cells, num_batches), 1, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> cell_state_tmp = <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(num_cells, num_batches), 1, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span> </div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <span class="comment">// Validate forget gate</span></div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#a8da875051f2d75a497fb2de9cdd2e6cb">CLFullyConnectedLayer::validate</a>(input, input_to_forget_weights, (lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a2d615f651270885a1b996046e9902a3c">use_layer_norm</a>()) ? <span class="keyword">nullptr</span> : forget_gate_bias, &forget_gate));</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span> </div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  std::vector<const ITensorInfo *> inputs_vector;</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  inputs_vector.emplace_back(input);</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  inputs_vector.emplace_back(output_state_in);</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> concat_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6100aeb494088632647c3e0d639c99ab">arm_compute::misc::shape_calculator::calculate_concatenate_shape</a>(inputs_vector, 0);</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> forget_gate_concat = <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(concat_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span> </div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_width_concatenate2_tensors_kernel.xhtml#afe71126bef1735fe1613a6da30d2c0c4">CLWidthConcatenate2TensorsKernel::validate</a>(input, output_state_in, &forget_gate_concat));</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span> </div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <span class="keywordflow">if</span>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a23be92a19e0d7c174ed444e709518afd">has_peephole_opt</a>())</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  {</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a705182fc799dce8ee017368eea0ca539">CLPixelWiseMultiplicationKernel::validate</a>(cell_state_in, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a477486a9c5189cff8af1cdd9d7e8d573">cell_to_forget_weights</a>(), &forget_gate, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>));</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml#a5e951bf3e414ddcd908245bcf284b08f">CLArithmeticAddition::validate</a>(&forget_gate, &forget_gate, &forget_gate, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>));</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  }</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  <span class="keywordflow">if</span>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a2d615f651270885a1b996046e9902a3c">use_layer_norm</a>())</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  {</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_mean_std_dev_normalization_layer.xhtml#a0a84b209b1d887a523005907e7028e2e">CLMeanStdDevNormalizationLayer::validate</a>(&forget_gate));</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a705182fc799dce8ee017368eea0ca539">CLPixelWiseMultiplicationKernel::validate</a>(&forget_gate, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a213908108c07594027bc2b829fe7ee4a">forget_layer_norm_weights</a>(), &forget_gate, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>,</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>));</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml#a5e951bf3e414ddcd908245bcf284b08f">CLArithmeticAddition::validate</a>(&forget_gate, forget_gate_bias, &forget_gate, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>));</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  }</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_activation_layer_kernel.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">CLActivationLayerKernel::validate</a>(&forget_gate, &forget_gate, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::ActivationFunction::LOGISTIC</a>)));</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">// Validate input gate</span></div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  <span class="keywordflow">if</span>(!lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#aae040c52316d86a4df2c7cdf179049bf">has_cifg_opt</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>  <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#afa54d4a35e697cb14a38359616709681">input_to_input_weights</a>(),</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a35e4b6311397e1f9532fb37560aa9996">recurrent_to_input_weights</a>(),</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a29a7a1636c6a8fd9e423d55c36e991a0">input_gate_bias</a>());</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#afa54d4a35e697cb14a38359616709681">input_to_input_weights</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 2);</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a35e4b6311397e1f9532fb37560aa9996">recurrent_to_input_weights</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 2);</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a29a7a1636c6a8fd9e423d55c36e991a0">input_gate_bias</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 1);</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span> </div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  std::vector<const ITensorInfo *> lstm_weights;</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  lstm_weights.emplace_back(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#afa54d4a35e697cb14a38359616709681">input_to_input_weights</a>());</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  lstm_weights.emplace_back(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a35e4b6311397e1f9532fb37560aa9996">recurrent_to_input_weights</a>());</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> lstm_weights_concat_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6100aeb494088632647c3e0d639c99ab">arm_compute::misc::shape_calculator::calculate_concatenate_shape</a>(lstm_weights, 0);</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> lstm_gate_concat = <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(lstm_weights_concat_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_width_concatenate2_tensors_kernel.xhtml#afe71126bef1735fe1613a6da30d2c0c4">CLWidthConcatenate2TensorsKernel::validate</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#afa54d4a35e697cb14a38359616709681">input_to_input_weights</a>(), lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a35e4b6311397e1f9532fb37560aa9996">recurrent_to_input_weights</a>(), &lstm_gate_concat));</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>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#a8da875051f2d75a497fb2de9cdd2e6cb">CLFullyConnectedLayer::validate</a>(input, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#afa54d4a35e697cb14a38359616709681">input_to_input_weights</a>(), (lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a2d615f651270885a1b996046e9902a3c">use_layer_norm</a>()) ? <span class="keyword">nullptr</span> : lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a29a7a1636c6a8fd9e423d55c36e991a0">input_gate_bias</a>(), &input_gate));</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span> </div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  <span class="keywordflow">if</span>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a23be92a19e0d7c174ed444e709518afd">has_peephole_opt</a>())</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  {</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#aafb05bcc27f0879701152cd664c632ce">cell_to_input_weights</a>());</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#aafb05bcc27f0879701152cd664c632ce">cell_to_input_weights</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 1);</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a705182fc799dce8ee017368eea0ca539">CLPixelWiseMultiplicationKernel::validate</a>(cell_state_in, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#aafb05bcc27f0879701152cd664c632ce">cell_to_input_weights</a>(), &input_gate, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>));</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml#a5e951bf3e414ddcd908245bcf284b08f">CLArithmeticAddition::validate</a>(&input_gate, &input_gate, &input_gate, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>));</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  }</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>  <span class="keywordflow">if</span>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a2d615f651270885a1b996046e9902a3c">use_layer_norm</a>())</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  {</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_mean_std_dev_normalization_layer.xhtml#a0a84b209b1d887a523005907e7028e2e">CLMeanStdDevNormalizationLayer::validate</a>(&input_gate));</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a705182fc799dce8ee017368eea0ca539">CLPixelWiseMultiplicationKernel::validate</a>(&input_gate, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a23361ca1393c0dc196fbf4e627e07119">input_layer_norm_weights</a>(), &input_gate, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>));</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml#a5e951bf3e414ddcd908245bcf284b08f">CLArithmeticAddition::validate</a>(&input_gate, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a29a7a1636c6a8fd9e423d55c36e991a0">input_gate_bias</a>(), &input_gate, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>));</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  }</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_activation_layer_kernel.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">CLActivationLayerKernel::validate</a>(&input_gate, <span class="keyword">nullptr</span>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::ActivationFunction::LOGISTIC</a>)));</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  }</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  {</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_saturated_arithmetic_operation_kernel.xhtml#ad8de41bdd36af838b251be792a8d871f">CLSaturatedArithmeticOperationKernel::validate</a>(<a class="code" href="namespacearm__compute.xhtml#a23d9f0c01c9e120dfb828ee922b7a8aea241dd841abade20fcb27b8a9f494e1eb">ArithmeticOperation::SUB</a>, &forget_gate, &forget_gate, &forget_gate, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>));</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  }</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span> </div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="comment">// Validate cell state</span></div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#a8da875051f2d75a497fb2de9cdd2e6cb">CLFullyConnectedLayer::validate</a>(input, input_to_cell_weights, (lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a2d615f651270885a1b996046e9902a3c">use_layer_norm</a>()) ? <span class="keyword">nullptr</span> : cell_bias, &cell_state_tmp));</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#a3493ba7d1f2057740ff5931fa00a44ac">CLGEMM::validate</a>(output_state_in, &units_out_transposed_info, <span class="keyword">nullptr</span>, &cell_state_tmp, 1.f, 0.f, <a class="code" href="classarm__compute_1_1_g_e_m_m_info.xhtml">GEMMInfo</a>()));</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml#a5e951bf3e414ddcd908245bcf284b08f">CLArithmeticAddition::validate</a>(&cell_state_tmp, &cell_state_tmp, &cell_state_tmp, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>));</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <span class="keywordflow">if</span>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a2d615f651270885a1b996046e9902a3c">use_layer_norm</a>())</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  {</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_mean_std_dev_normalization_layer.xhtml#a0a84b209b1d887a523005907e7028e2e">CLMeanStdDevNormalizationLayer::validate</a>(&cell_state_tmp));</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a705182fc799dce8ee017368eea0ca539">CLPixelWiseMultiplicationKernel::validate</a>(&cell_state_tmp, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#abd478bedc7c65b72ead0d05cbd16d437">cell_layer_norm_weights</a>(), &cell_state_tmp, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>,</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>));</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml#a5e951bf3e414ddcd908245bcf284b08f">CLArithmeticAddition::validate</a>(&cell_state_tmp, cell_bias, &cell_state_tmp, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>));</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  }</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_activation_layer_kernel.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">CLActivationLayerKernel::validate</a>(&cell_state_tmp, <span class="keyword">nullptr</span>, activation_info));</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a705182fc799dce8ee017368eea0ca539">CLPixelWiseMultiplicationKernel::validate</a>(&cell_state_tmp, &input_gate, &cell_state_tmp, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>));</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a705182fc799dce8ee017368eea0ca539">CLPixelWiseMultiplicationKernel::validate</a>(&cell_state_tmp, &forget_gate, &cell_state_tmp, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>));</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml#a5e951bf3e414ddcd908245bcf284b08f">CLArithmeticAddition::validate</a>(&cell_state_tmp, &cell_state_tmp, &cell_state_tmp, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>));</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <span class="keywordflow">if</span>(cell_threshold != 0.f)</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  {</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_activation_layer_kernel.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">CLActivationLayerKernel::validate</a>(&cell_state_tmp, <span class="keyword">nullptr</span>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a">ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU</a>, -cell_threshold,</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  cell_threshold)));</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  }</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span> </div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  std::vector<const ITensorInfo *> in_out_weights;</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  in_out_weights.emplace_back(input_to_output_weights);</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  in_out_weights.emplace_back(recurrent_to_output_weights);</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> in_out_weights_concat_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6100aeb494088632647c3e0d639c99ab">arm_compute::misc::shape_calculator::calculate_concatenate_shape</a>(in_out_weights, 0);</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> in_out_gate_concat = <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(in_out_weights_concat_shape, 1, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_width_concatenate2_tensors_kernel.xhtml#afe71126bef1735fe1613a6da30d2c0c4">CLWidthConcatenate2TensorsKernel::validate</a>(input_to_output_weights, recurrent_to_output_weights, &in_out_gate_concat));</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  <span class="comment">// Validate output gate tmp</span></div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#a8da875051f2d75a497fb2de9cdd2e6cb">CLFullyConnectedLayer::validate</a>(input, input_to_output_weights, (lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a2d615f651270885a1b996046e9902a3c">use_layer_norm</a>()) ? <span class="keyword">nullptr</span> : output_gate_bias, &output_gate_tmp));</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span> </div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <span class="keywordflow">if</span>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a23be92a19e0d7c174ed444e709518afd">has_peephole_opt</a>())</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  {</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a705182fc799dce8ee017368eea0ca539">CLPixelWiseMultiplicationKernel::validate</a>(&cell_state_tmp, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a934af5defc72f38841ce8955e2151473">cell_to_output_weights</a>(), &output_gate_tmp, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>,</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>));</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml#a5e951bf3e414ddcd908245bcf284b08f">CLArithmeticAddition::validate</a>(&output_gate_tmp, &output_gate_tmp, &output_gate_tmp, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>));</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  }</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  <span class="keywordflow">if</span>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a2d615f651270885a1b996046e9902a3c">use_layer_norm</a>())</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  {</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_mean_std_dev_normalization_layer.xhtml#a0a84b209b1d887a523005907e7028e2e">CLMeanStdDevNormalizationLayer::validate</a>(&output_gate_tmp));</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a705182fc799dce8ee017368eea0ca539">CLPixelWiseMultiplicationKernel::validate</a>(&output_gate_tmp, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a208874b46a667263fa309537c5355318">output_layer_norm_weights</a>(), &output_gate_tmp, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>,</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>));</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml#a5e951bf3e414ddcd908245bcf284b08f">CLArithmeticAddition::validate</a>(&output_gate_tmp, output_gate_bias, &output_gate_tmp, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>));</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  }</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_activation_layer_kernel.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">CLActivationLayerKernel::validate</a>(&output_gate_tmp, <span class="keyword">nullptr</span>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::ActivationFunction::LOGISTIC</a>)));</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span> </div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  <span class="comment">// Validate output state</span></div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_activation_layer_kernel.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">CLActivationLayerKernel::validate</a>(&cell_state_tmp, &cell_state_tmp, activation_info));</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a705182fc799dce8ee017368eea0ca539">CLPixelWiseMultiplicationKernel::validate</a>(&cell_state_tmp, &output_gate_tmp, &output_gate_tmp, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">RoundingPolicy::TO_NEAREST_EVEN</a>));</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  <span class="keywordflow">if</span>(lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#a127009377712009a84cd0c48aa7e1edd">has_projection</a>())</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  {</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#a8da875051f2d75a497fb2de9cdd2e6cb">CLFullyConnectedLayer::validate</a>(&output_gate_tmp, lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#ab1b3d5364f11bca8cacef026c8038dba">projection_weights</a>(), lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#ad676992a90d193409fa6a28a001af6c8">projection_bias</a>(), output_state_out));</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  <span class="keywordflow">if</span>(projection_threshold != 0.f)</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  {</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_activation_layer_kernel.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">CLActivationLayerKernel::validate</a>(output_state_out, output_state_out,</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a">ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU</a>, -projection_threshold, projection_threshold)));</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  }</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  }</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span> </div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  <span class="comment">// Validate copy kernel</span></div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_copy_kernel.xhtml#a27b2f705eda7702c5835196e160b111f">CLCopyKernel::validate</a>(&cell_state_tmp, cell_state_out));</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_copy_kernel.xhtml#a27b2f705eda7702c5835196e160b111f">CLCopyKernel::validate</a>(output_state_out, output));</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span> </div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <span class="comment">// Validate scratch concatenation</span></div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  std::vector<ITensorInfo *> inputs_vector_info_raw;</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <span class="keywordflow">if</span>(!lstm_params.<a class="code" href="classarm__compute_1_1_l_s_t_m_params.xhtml#aae040c52316d86a4df2c7cdf179049bf">has_cifg_opt</a>())</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  {</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  inputs_vector_info_raw.push_back(&input_gate);</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  }</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  inputs_vector_info_raw.push_back(&cell_state_tmp);</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  inputs_vector_info_raw.push_back(&forget_gate);</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  inputs_vector_info_raw.push_back(&output_gate_tmp);</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span> </div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6e77b7a36830679af4f991604feab114">CLConcatenateLayer::validate</a>(inputs_vector_info_raw, scratch_buffer, <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>));</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span> }</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span> </div><div class="line"><a name="l00619"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_l_s_t_m_layer.xhtml#ad1717410afd0be936c6213a63c8005fb"> 619</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">CLLSTMLayer::run</a>()</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span> {</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  <a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span> </div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  <a class="code" href="classarm__compute_1_1_memory_group_resource_scope.xhtml">MemoryGroupResourceScope</a> scope_mg(_memory_group);</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span> </div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_concat_inputs_forget_gate);</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span> </div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  _fully_connected_forget_gate.<a class="code" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span> </div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  <span class="keywordflow">if</span>(_run_peephole_opt)</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  {</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_pixelwise_mul_forget_gate);</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  _accum_forget_gate1.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  }</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  <span class="keywordflow">if</span>(_is_layer_norm_lstm)</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  {</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  _mean_std_norm_forget_gate.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_pixelwise_mul_forget_gate_coeff);</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_accum_forget_gate_bias);</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  }</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_activation_forget_gate);</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span> </div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  <span class="keywordflow">if</span>(_run_cifg_opt)</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  {</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_ones_memset_kernel);</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_subtract_input_gate);</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  }</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  {</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  _fully_connected_input_gate.<a class="code" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span> </div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  <span class="keywordflow">if</span>(_run_peephole_opt)</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  {</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_pixelwise_mul_input_gate);</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  _accum_input_gate1.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  }</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span> </div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <span class="keywordflow">if</span>(_is_layer_norm_lstm)</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  {</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  _mean_std_norm_input_gate.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_pixelwise_mul_input_gate_coeff);</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_accum_input_gate_bias);</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  }</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_activation_input_gate);</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  }</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span> </div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  _fully_connected_cell_state.<a class="code" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_transpose_cell_state);</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  _gemm_cell_state1.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_accum_cell_state1);</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  <span class="keywordflow">if</span>(_is_layer_norm_lstm)</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  {</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  _mean_std_norm_cell_gate.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_pixelwise_mul_cell_gate_coeff);</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_accum_cell_gate_bias);</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  }</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_activation_cell_state);</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_pixelwise_mul_cell_state1);</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_pixelwise_mul_cell_state2);</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_accum_cell_state2);</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span> </div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  <span class="keywordflow">if</span>(_perform_cell_clipping)</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  {</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_cell_clip);</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  }</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span> </div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  _fully_connected_output.<a class="code" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span> </div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  <span class="keywordflow">if</span>(_run_peephole_opt)</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  {</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_pixelwise_mul_output_state1);</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  _accum_output1.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  }</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  <span class="keywordflow">if</span>(_is_layer_norm_lstm)</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  {</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  _mean_std_norm_output_gate.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_pixelwise_mul_output_gate_coeff);</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_accum_output_gate_bias);</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  }</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_activation_output);</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span> </div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_activation_output_state);</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_pixelwise_mul_output_state2);</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span> </div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <span class="keywordflow">if</span>(_has_projection_weights)</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  {</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  _fully_connected_output_state.<a class="code" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  <span class="keywordflow">if</span>(_perform_projection_clipping)</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  {</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_projection_clip);</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  }</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  }</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span> </div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_copy_cell_state);</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_copy_output);</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span> </div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  _concat_scratch_buffer.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span> }</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span> </div><div class="line"><a name="l00719"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_l_s_t_m_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77"> 719</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">CLLSTMLayer::prepare</a>()</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span> {</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  <span class="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  {</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_concat_weights_forget_gate);</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  <span class="keywordflow">if</span>(!_run_cifg_opt)</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  {</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_concat_weights_input_gate);</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  }</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_concat_weights_output);</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  }</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span> }</div><div class="ttc" id="_pixel_value_8h_xhtml"><div class="ttname"><a href="_pixel_value_8h.xhtml">PixelValue.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_l_s_t_m_layer_xhtml_aa0f7635e5dffc50c235e8879637f7462"><div class="ttname"><a href="classarm__compute_1_1_c_l_l_s_t_m_layer.xhtml#aa0f7635e5dffc50c235e8879637f7462">arm_compute::CLLSTMLayer::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights, const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights, const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias, const ICLTensor *output_state_in, const ICLTensor *cell_state_in, ICLTensor *scratch_buffer, ICLTensor *output_state_out, ICLTensor *cell_state_out, ICLTensor *output, const LSTMParams< ICLTensor > &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold=0.f, float projection_threshold=0.f)</div><div class="ttdoc">Initialize function's tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_l_s_t_m_layer_8cpp_source.xhtml#l00056">CLLSTMLayer.cpp:56</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_arithmetic_addition_xhtml_a5e951bf3e414ddcd908245bcf284b08f"><div class="ttname"><a href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml#a5e951bf3e414ddcd908245bcf284b08f">arm_compute::CLArithmeticAddition::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLSaturatedArithmeticOpe...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_elementwise_operations_8cpp_source.xhtml#l00058">CLElementwiseOperations.cpp:58</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a1f4e725b8e1ea36b30e09dc08ae6961d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">arm_compute::ITensorInfo::num_dimensions</a></div><div class="ttdeci">virtual size_t num_dimensions() const =0</div><div class="ttdoc">The number of dimensions of the tensor (rank)</div></div> |
| <div class="ttc" id="classarm__compute_1_1_pixel_value_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pixel_value.xhtml">arm_compute::PixelValue</a></div><div class="ttdoc">Class describing the value of a pixel for any image format.</div><div class="ttdef"><b>Definition:</b> <a href="_pixel_value_8h_source.xhtml#l00034">PixelValue.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml_ab1b3d5364f11bca8cacef026c8038dba"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml#ab1b3d5364f11bca8cacef026c8038dba">arm_compute::LSTMParams::projection_weights</a></div><div class="ttdeci">const T * projection_weights() const</div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00150">LSTMParams.h:150</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml_afa54d4a35e697cb14a38359616709681"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml#afa54d4a35e697cb14a38359616709681">arm_compute::LSTMParams::input_to_input_weights</a></div><div class="ttdeci">const T * input_to_input_weights() const</div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00120">LSTMParams.h:120</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a6100aeb494088632647c3e0d639c99ab"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6100aeb494088632647c3e0d639c99ab">arm_compute::misc::shape_calculator::calculate_concatenate_shape</a></div><div class="ttdeci">TensorShape calculate_concatenate_shape(const std::vector< T * > &input, size_t axis)</div><div class="ttdoc">Calculate the concatenate output shape of the concatenate operation along a single axis.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l01268">ShapeCalculator.h:1268</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml_a2d615f651270885a1b996046e9902a3c"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml#a2d615f651270885a1b996046e9902a3c">arm_compute::LSTMParams::use_layer_norm</a></div><div class="ttdeci">bool use_layer_norm() const</div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00195">LSTMParams.h:195</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_ad45f0c01a0713dfb6bd7232c7f396fc4"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">arm_compute::CLTensor::info</a></div><div class="ttdeci">TensorInfo * info() const override</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00035">CLTensor.cpp:35</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_mean_std_dev_normalization_layer_xhtml_a0a84b209b1d887a523005907e7028e2e"><div class="ttname"><a href="classarm__compute_1_1_c_l_mean_std_dev_normalization_layer.xhtml#a0a84b209b1d887a523005907e7028e2e">arm_compute::CLMeanStdDevNormalizationLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output=nullptr, float epsilon=1e-8f)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLMeanStdDevNormalizatio...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_mean_std_dev_normalization_layer_8cpp_source.xhtml#l00039">CLMeanStdDevNormalizationLayer.cpp:39</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLGEMM::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_8cpp_source.xhtml#l00572">CLGEMM.cpp:572</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a178f0d3d87f959e00a743328d95359d2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">arm_compute::ITensorInfo::dimension</a></div><div class="ttdeci">virtual size_t dimension(size_t index) const =0</div><div class="ttdoc">Return the size of the requested dimension.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml_a23be92a19e0d7c174ed444e709518afd"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml#a23be92a19e0d7c174ed444e709518afd">arm_compute::LSTMParams::has_peephole_opt</a></div><div class="ttdeci">bool has_peephole_opt() const</div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00180">LSTMParams.h:180</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a9b58d0eb9a2af8e6d7908695e1557d6c"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">arm_compute::CLScheduler::get</a></div><div class="ttdeci">static CLScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00041">CLScheduler.cpp:41</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a8f3ff7da485ff7e75dab07baadf5b4bd"><div class="ttname"><a href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00545">Validate.h:545</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_width_concatenate2_tensors_kernel_xhtml_af53d66a8f8dd368d3c06b43c0c6a12f1"><div class="ttname"><a href="classarm__compute_1_1_c_l_width_concatenate2_tensors_kernel.xhtml#af53d66a8f8dd368d3c06b43c0c6a12f1">arm_compute::CLWidthConcatenate2TensorsKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output)</div><div class="ttdoc">Initialise the kernel's input1s and output.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_width_concatenate2_tensors_kernel_8cpp_source.xhtml#l00098">CLWidthConcatenate2TensorsKernel.cpp:98</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml_aafb05bcc27f0879701152cd664c632ce"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml#aafb05bcc27f0879701152cd664c632ce">arm_compute::LSTMParams::cell_to_input_weights</a></div><div class="ttdeci">const T * cell_to_input_weights() const</div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00130">LSTMParams.h:130</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml_a28c80440058d5c9b0bc1e1a4622c734a"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml#a28c80440058d5c9b0bc1e1a4622c734a">arm_compute::LSTMParams::set_peephole_params</a></div><div class="ttdeci">LSTMParams & set_peephole_params(const T *cell_to_forget_weights, const T *cell_to_output_weights)</div><div class="ttdoc">Set peephole tensor parameters.</div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00093">LSTMParams.h:93</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a8a1e1c105f0bdaf37db408c7cfcb77a4"><div class="ttname"><a href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ON_ERROR(status)</div><div class="ttdoc">Checks if a status contains an error and returns it.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00193">Error.h:193</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a23d9f0c01c9e120dfb828ee922b7a8aea9eeb52badb613229884838847294b90d"><div class="ttname"><a href="namespacearm__compute.xhtml#a23d9f0c01c9e120dfb828ee922b7a8aea9eeb52badb613229884838847294b90d">arm_compute::ArithmeticOperation::ADD</a></div><div class="ttdoc">(x + y)</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7cfb31af63202568efef5214acfbf3ba"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">arm_compute::ITensorInfo::data_type</a></div><div class="ttdeci">virtual DataType data_type() const =0</div><div class="ttdoc">Data type used for each element of the tensor.</div></div> |
| <div class="ttc" id="_validate_8h_xhtml_ae7eed178dac535c6e727061b1f5bc6eb"><div class="ttname"><a href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00791">Validate.h:791</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel</div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml_aae040c52316d86a4df2c7cdf179049bf"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml#aae040c52316d86a4df2c7cdf179049bf">arm_compute::LSTMParams::has_cifg_opt</a></div><div class="ttdeci">bool has_cifg_opt() const</div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00190">LSTMParams.h:190</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml_a934af5defc72f38841ce8955e2151473"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml#a934af5defc72f38841ce8955e2151473">arm_compute::LSTMParams::cell_to_output_weights</a></div><div class="ttdeci">const T * cell_to_output_weights() const</div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00145">LSTMParams.h:145</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_copy_kernel_xhtml_a71e93e8e995e940376c12cfd1b0a5538"><div class="ttname"><a href="classarm__compute_1_1_c_l_copy_kernel.xhtml#a71e93e8e995e940376c12cfd1b0a5538">arm_compute::CLCopyKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output, const PaddingList &padding=PaddingList(), Window *output_window=nullptr)</div><div class="ttdoc">Initialize the kernel's input, output.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_copy_kernel_8cpp_source.xhtml#l00157">CLCopyKernel.cpp:157</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_arithmetic_addition_xhtml_a7af75ca77a6e9eb53532e7ab1317bdc3"><div class="ttname"><a href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml#a7af75ca77a6e9eb53532e7ab1317bdc3">arm_compute::CLArithmeticAddition::configure</a></div><div class="ttdeci">void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, ConvertPolicy policy)</div><div class="ttdoc">Initialise the kernel's inputs, output and conversion policy.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_elementwise_operations_8cpp_source.xhtml#l00050">CLElementwiseOperations.cpp:50</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml">arm_compute::ITensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_info_8h_source.xhtml#l00040">ITensorInfo.h:40</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_a4083de30daebd6bdee6b35d9c8262108"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">arm_compute::CLTensor::allocator</a></div><div class="ttdeci">CLTensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00055">CLTensor.cpp:55</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_mean_std_dev_normalization_layer_xhtml_a1029bf3c12d8600f803700fc76c11590"><div class="ttname"><a href="classarm__compute_1_1_c_l_mean_std_dev_normalization_layer.xhtml#a1029bf3c12d8600f803700fc76c11590">arm_compute::CLMeanStdDevNormalizationLayer::configure</a></div><div class="ttdeci">void configure(ICLTensor *input, ICLTensor *output=nullptr, float epsilon=1e-8f)</div><div class="ttdoc">Initialise the function's input and outputs.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_mean_std_dev_normalization_layer_8cpp_source.xhtml#l00032">CLMeanStdDevNormalizationLayer.cpp:32</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a938dcd406ce611ef5345ad2531cdb948"><div class="ttname"><a href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_THROW_ON(status)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00327">Error.h:327</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_l_s_t_m_layer_xhtml_af2e2a062e461a6369a4f2fd330b4e422"><div class="ttname"><a href="classarm__compute_1_1_c_l_l_s_t_m_layer.xhtml#af2e2a062e461a6369a4f2fd330b4e422">arm_compute::CLLSTMLayer::CLLSTMLayer</a></div><div class="ttdeci">CLLSTMLayer(std::shared_ptr< IMemoryManager > memory_manager=nullptr)</div><div class="ttdoc">Default constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_l_s_t_m_layer_8cpp_source.xhtml#l00040">CLLSTMLayer.cpp:40</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_status_xhtml"><div class="ttname"><a href="classarm__compute_1_1_status.xhtml">arm_compute::Status</a></div><div class="ttdoc">Status class.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00052">Error.h:52</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a206d6e247e0957ac3dee45d27756fc25"><div class="ttname"><a href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true, an error is returned.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00244">Error.h:244</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml">arm_compute::ActivationLayerInfo</a></div><div class="ttdoc">Activation Layer Information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01517">Types.h:1517</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_allocator_xhtml_af36143939a43fa124312e395975091ed"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">arm_compute::ITensorAllocator::init</a></div><div class="ttdeci">void init(const TensorInfo &input, size_t alignment=0)</div><div class="ttdoc">Initialize a tensor based on the passed TensorInfo.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_allocator_8cpp_source.xhtml#l00038">ITensorAllocator.cpp:38</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">Copyright (c) 2017-2018 ARM Limited.</div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00024">00_introduction.dox:24</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_activation_layer_kernel_xhtml_a239fea32ba46d038ba350dba58026c45"><div class="ttname"><a href="classarm__compute_1_1_c_l_activation_layer_kernel.xhtml#a239fea32ba46d038ba350dba58026c45">arm_compute::CLActivationLayerKernel::configure</a></div><div class="ttdeci">void configure(ICLTensor *input, ICLTensor *output, ActivationLayerInfo act_info)</div><div class="ttdoc">Set the input and output tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_activation_layer_kernel_8cpp_source.xhtml#l00119">CLActivationLayerKernel.cpp:119</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::Format::F16</a></div><div class="ttdoc">1 channel, 1 F16 per channel</div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_activation_layer_kernel_xhtml_aa37e2d0b4cd4f835bfa2a2df4a0bdd2c"><div class="ttname"><a href="classarm__compute_1_1_c_l_activation_layer_kernel.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">arm_compute::CLActivationLayerKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ActivationLayerInfo &act_info)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLActivationLayerKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_activation_layer_kernel_8cpp_source.xhtml#l00228">CLActivationLayerKernel.cpp:228</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a69cb11b5b37f94a6bea9eaad9d13cccf"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf">arm_compute::misc::shape_calculator::compute_transposed_shape</a></div><div class="ttdeci">TensorShape compute_transposed_shape(const ITensorInfo &input)</div><div class="ttdoc">Calculate the transposed shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00390">ShapeCalculator.h:390</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a9a3e72153aeb3ed212e9c3698774e881"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">arm_compute::TensorInfo::data_type</a></div><div class="ttdeci">DataType data_type() const override</div><div class="ttdoc">Data type used for each element of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00256">TensorInfo.h:256</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml_a35e4b6311397e1f9532fb37560aa9996"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml#a35e4b6311397e1f9532fb37560aa9996">arm_compute::LSTMParams::recurrent_to_input_weights</a></div><div class="ttdeci">const T * recurrent_to_input_weights() const</div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00125">LSTMParams.h:125</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml_ad676992a90d193409fa6a28a001af6c8"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml#ad676992a90d193409fa6a28a001af6c8">arm_compute::LSTMParams::projection_bias</a></div><div class="ttdeci">const T * projection_bias() const</div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00155">LSTMParams.h:155</a></div></div> |
| <div class="ttc" id="arm__compute_2core_2_utils_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_utils_8h.xhtml">Utils.h</a></div></div> |
| <div class="ttc" id="_c_l_scheduler_8h_xhtml"><div class="ttname"><a href="_c_l_scheduler_8h.xhtml">CLScheduler.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_fully_connected_layer_xhtml_ab205e8e07c4eff3197d0c8cc85a4488d"><div class="ttname"><a href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#ab205e8e07c4eff3197d0c8cc85a4488d">arm_compute::CLFullyConnectedLayer::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, FullyConnectedLayerInfo fc_info=FullyConnectedLayerInfo())</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_fully_connected_layer_8cpp_source.xhtml#l00140">CLFullyConnectedLayer.cpp:140</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_aa96e81276ee4f87ab386cd05a5539a7d"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">arm_compute::Window::DimX</a></div><div class="ttdeci">static constexpr size_t DimX</div><div class="ttdoc">Alias for dimension 0 also known as X dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00043">Window.h:43</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_memory_group_base_xhtml_ac1f67376afb7822f262a0174ef4a3104"><div class="ttname"><a href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">arm_compute::MemoryGroupBase::manage</a></div><div class="ttdeci">void manage(TensorType *obj)</div><div class="ttdoc">Sets a object to be managed by the given memory group.</div><div class="ttdef"><b>Definition:</b> <a href="_memory_group_base_8h_source.xhtml#l00102">MemoryGroupBase.h:102</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_c_l_simple_function_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::ICLSimpleFunction::run</a></div><div class="ttdeci">void run() override final</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_c_l_simple_function_8cpp_source.xhtml#l00037">ICLSimpleFunction.cpp:37</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7c66505457d00ece3aa4b34cab80757d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">arm_compute::ITensorInfo::tensor_shape</a></div><div class="ttdeci">virtual const TensorShape & tensor_shape() const =0</div><div class="ttdoc">Size for each dimension of the tensor.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_fully_connected_layer_xhtml_a8da875051f2d75a497fb2de9cdd2e6cb"><div class="ttname"><a href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#a8da875051f2d75a497fb2de9cdd2e6cb">arm_compute::CLFullyConnectedLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, FullyConnectedLayerInfo fc_info=FullyConnectedLayerInfo())</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLFullyConnectedLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_fully_connected_layer_8cpp_source.xhtml#l00249">CLFullyConnectedLayer.cpp:249</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml_a477486a9c5189cff8af1cdd9d7e8d573"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml#a477486a9c5189cff8af1cdd9d7e8d573">arm_compute::LSTMParams::cell_to_forget_weights</a></div><div class="ttdeci">const T * cell_to_forget_weights() const</div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00140">LSTMParams.h:140</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_saturated_arithmetic_operation_kernel_xhtml_ad8de41bdd36af838b251be792a8d871f"><div class="ttname"><a href="classarm__compute_1_1_c_l_saturated_arithmetic_operation_kernel.xhtml#ad8de41bdd36af838b251be792a8d871f">arm_compute::CLSaturatedArithmeticOperationKernel::validate</a></div><div class="ttdeci">static Status validate(ArithmeticOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, const ConvertPolicy &policy)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLSaturatedArithmeticOpe...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_elementwise_operation_kernel_8cpp_source.xhtml#l00325">CLElementwiseOperationKernel.cpp:325</a></div></div> |
| <div class="ttc" id="_shape_calculator_8h_xhtml"><div class="ttname"><a href="_shape_calculator_8h.xhtml">ShapeCalculator.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml_abd478bedc7c65b72ead0d05cbd16d437"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml#abd478bedc7c65b72ead0d05cbd16d437">arm_compute::LSTMParams::cell_layer_norm_weights</a></div><div class="ttdeci">const T * cell_layer_norm_weights() const</div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00170">LSTMParams.h:170</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a0e95dc1e53c361348314873b168ae237"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">arm_compute::ITensor::info</a></div><div class="ttdeci">virtual ITensorInfo * info() const =0</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_concatenate_layer_xhtml_a6e77b7a36830679af4f991604feab114"><div class="ttname"><a href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6e77b7a36830679af4f991604feab114">arm_compute::CLConcatenateLayer::validate</a></div><div class="ttdeci">static Status validate(const std::vector< ITensorInfo * > &inputs_vector, const ITensorInfo *output, size_t axis)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLConcatenateLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_concatenate_layer_8cpp_source.xhtml#l00060">CLConcatenateLayer.cpp:60</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_copy_kernel_xhtml_a27b2f705eda7702c5835196e160b111f"><div class="ttname"><a href="classarm__compute_1_1_c_l_copy_kernel.xhtml#a27b2f705eda7702c5835196e160b111f">arm_compute::CLCopyKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const PaddingList &padding=PaddingList(), Window *output_window=nullptr)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLCopyKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_copy_kernel_8cpp_source.xhtml#l00230">CLCopyKernel.cpp:230</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_l_s_t_m_layer_xhtml_aa05bceba37ded272a464a90becd9cd99"><div class="ttname"><a href="classarm__compute_1_1_c_l_l_s_t_m_layer.xhtml#aa05bceba37ded272a464a90becd9cd99">arm_compute::CLLSTMLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights, const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights, const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias, const ITensorInfo *output_state_in, const ITensorInfo *cell_state_in, const ITensorInfo *scratch_buffer, const ITensorInfo *output_state_out, const ITensorInfo *cell_state_out, const ITensorInfo *output, const LSTMParams< ITensorInfo > &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold=0.f, float projection_threshold=0.f)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLLSTMLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_l_s_t_m_layer_8cpp_source.xhtml#l00388">CLLSTMLayer.cpp:388</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_fully_connected_layer_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLFullyConnectedLayer::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_fully_connected_layer_8cpp_source.xhtml#l00347">CLFullyConnectedLayer.cpp:347</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml_a23361ca1393c0dc196fbf4e627e07119"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml#a23361ca1393c0dc196fbf4e627e07119">arm_compute::LSTMParams::input_layer_norm_weights</a></div><div class="ttdeci">const T * input_layer_norm_weights() const</div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00160">LSTMParams.h:160</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC</a></div><div class="ttdoc">Logistic ( )</div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml_aea777d30779bab2d14630ea7e8516615"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml#aea777d30779bab2d14630ea7e8516615">arm_compute::LSTMParams::set_projection_params</a></div><div class="ttdeci">LSTMParams & set_projection_params(const T *projection_weights, const T *projection_bias)</div><div class="ttdoc">Set projection tensor parameters.</div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00079">LSTMParams.h:79</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_ae1a643e517f50bf0392fb6516dd7cf67"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">arm_compute::CLScheduler::enqueue</a></div><div class="ttdeci">void enqueue(ICLKernel &kernel, bool flush=true)</div><div class="ttdoc">Schedule the execution of the passed kernel if possible.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00095">CLScheduler.cpp:95</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_concatenate_layer_xhtml_a6b12b22f2ad4a69582ec1674a2779ec8"><div class="ttname"><a href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6b12b22f2ad4a69582ec1674a2779ec8">arm_compute::CLConcatenateLayer::configure</a></div><div class="ttdeci">void configure(std::vector< ICLTensor * > &inputs_vector, ICLTensor *output, size_t axis)</div><div class="ttdoc">Initialise the kernel's inputs vector and output.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_concatenate_layer_8cpp_source.xhtml#l00050">CLConcatenateLayer.cpp:50</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml_adac8095c0cd29d443206dfcaf67f3607"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml#adac8095c0cd29d443206dfcaf67f3607">arm_compute::LSTMParams::set_cifg_params</a></div><div class="ttdeci">LSTMParams & set_cifg_params(const T *input_to_input_weights, const T *recurrent_to_input_weights, const T *cell_to_input_weights, const T *input_gate_bias)</div><div class="ttdoc">Set CIFG tensor parameters.</div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00063">LSTMParams.h:63</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_aff911654521523937ff24372a870b89f"><div class="ttname"><a href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00163">Validate.h:163</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a">arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU</a></div><div class="ttdoc">Lower and Upper Bounded Rectifier ( )</div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml_a127009377712009a84cd0c48aa7e1edd"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml#a127009377712009a84cd0c48aa7e1edd">arm_compute::LSTMParams::has_projection</a></div><div class="ttdeci">bool has_projection() const</div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00185">LSTMParams.h:185</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::CLTensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of OpenCL memory.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_allocator_8cpp_source.xhtml#l00119">CLTensorAllocator.cpp:119</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_width_concatenate2_tensors_kernel_xhtml_afe71126bef1735fe1613a6da30d2c0c4"><div class="ttname"><a href="classarm__compute_1_1_c_l_width_concatenate2_tensors_kernel.xhtml#afe71126bef1735fe1613a6da30d2c0c4">arm_compute::CLWidthConcatenate2TensorsKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLWidthConcatenate2Tenso...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_width_concatenate2_tensors_kernel_8cpp_source.xhtml#l00091">CLWidthConcatenate2TensorsKernel.cpp:91</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a921b705e9e3e0fe928928447869e62a5"><div class="ttname"><a href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00161">Validate.h:161</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150"><div class="ttname"><a href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea02ff1fff1812f84c89547fcd6c176150">arm_compute::RoundingPolicy::TO_NEAREST_EVEN</a></div><div class="ttdoc">Rounds to nearest value; half rounds to nearest even.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_memory_group_resource_scope_xhtml"><div class="ttname"><a href="classarm__compute_1_1_memory_group_resource_scope.xhtml">arm_compute::MemoryGroupResourceScope</a></div><div class="ttdoc">Memory group resources scope handling class.</div><div class="ttdef"><b>Definition:</b> <a href="_i_memory_group_8h_source.xhtml#l00046">IMemoryGroup.h:46</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_c_l_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_c_l_tensor.xhtml">arm_compute::ICLTensor</a></div><div class="ttdoc">Interface for OpenCL tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_i_c_l_tensor_8h_source.xhtml#l00042">ICLTensor.h:42</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_xhtml_a34e7b882208ff6720bad2e4f2c7565c5"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#a34e7b882208ff6720bad2e4f2c7565c5">arm_compute::CLGEMM::configure</a></div><div class="ttdeci">void configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info=GEMMInfo())</div><div class="ttdoc">Initialise the kernel's inputs and output.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_8cpp_source.xhtml#l00470">CLGEMM.cpp:470</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_transpose_kernel_xhtml_a074e10cfb217e657b9e81adeca2abc68"><div class="ttname"><a href="classarm__compute_1_1_c_l_transpose_kernel.xhtml#a074e10cfb217e657b9e81adeca2abc68">arm_compute::CLTransposeKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output)</div><div class="ttdoc">Initialise the kernel's input and output.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_transpose_kernel_8cpp_source.xhtml#l00111">CLTransposeKernel.cpp:111</a></div></div> |
| <div class="ttc" id="_c_l_l_s_t_m_layer_8h_xhtml"><div class="ttname"><a href="_c_l_l_s_t_m_layer_8h.xhtml">CLLSTMLayer.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml_a208874b46a667263fa309537c5355318"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml#a208874b46a667263fa309537c5355318">arm_compute::LSTMParams::output_layer_norm_weights</a></div><div class="ttdeci">const T * output_layer_norm_weights() const</div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00175">LSTMParams.h:175</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml_a29a7a1636c6a8fd9e423d55c36e991a0"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml#a29a7a1636c6a8fd9e423d55c36e991a0">arm_compute::LSTMParams::input_gate_bias</a></div><div class="ttdeci">const T * input_gate_bias() const</div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00135">LSTMParams.h:135</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel_xhtml_a705182fc799dce8ee017368eea0ca539"><div class="ttname"><a href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a705182fc799dce8ee017368eea0ca539">arm_compute::CLPixelWiseMultiplicationKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLPixelWiseMultiplicatio...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_pixel_wise_multiplication_kernel_8cpp_source.xhtml#l00232">CLPixelWiseMultiplicationKernel.cpp:232</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_xhtml_a3493ba7d1f2057740ff5931fa00a44ac"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#a3493ba7d1f2057740ff5931fa00a44ac">arm_compute::CLGEMM::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info=GEMMInfo())</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLGEMM.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_8cpp_source.xhtml#l00525">CLGEMM.cpp:525</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_saturated_arithmetic_operation_kernel_xhtml_ad09d2ed1f19a1df9b87da20af7efaff9"><div class="ttname"><a href="classarm__compute_1_1_c_l_saturated_arithmetic_operation_kernel.xhtml#ad09d2ed1f19a1df9b87da20af7efaff9">arm_compute::CLSaturatedArithmeticOperationKernel::configure</a></div><div class="ttdeci">void configure(ArithmeticOperation op, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, const ConvertPolicy &policy)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLSaturatedArithmeticOpe...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_elementwise_operation_kernel_8cpp_source.xhtml#l00318">CLElementwiseOperationKernel.cpp:318</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00045">TensorInfo.h:45</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml">arm_compute::misc::shape_calculator</a></div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00040">ShapeCalculator.h:40</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_e_m_m_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_g_e_m_m_info.xhtml">arm_compute::GEMMInfo</a></div><div class="ttdoc">GEMM information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01880">Types.h:1880</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_memset_kernel_xhtml_a8842f3a8e50c91b74a0b0549ac8fa489"><div class="ttname"><a href="classarm__compute_1_1_c_l_memset_kernel.xhtml#a8842f3a8e50c91b74a0b0549ac8fa489">arm_compute::CLMemsetKernel::configure</a></div><div class="ttdeci">void configure(ICLTensor *tensor, const PixelValue &constant_value, Window *window=nullptr)</div><div class="ttdoc">Initialise the kernel's tensor and filling value.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_memset_kernel_8cpp_source.xhtml#l00042">CLMemsetKernel.cpp:42</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel_xhtml_a3e0a2f39d9dc0f7083aef3b37335afff"><div class="ttname"><a href="classarm__compute_1_1_c_l_pixel_wise_multiplication_kernel.xhtml#a3e0a2f39d9dc0f7083aef3b37335afff">arm_compute::CLPixelWiseMultiplicationKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy)</div><div class="ttdoc">Initialise the kernel's input, output and border mode.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_pixel_wise_multiplication_kernel_8cpp_source.xhtml#l00132">CLPixelWiseMultiplicationKernel.cpp:132</a></div></div> |
| <div class="ttc" id="_asymm_helpers_8h_xhtml"><div class="ttname"><a href="_asymm_helpers_8h.xhtml">AsymmHelpers.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86"><div class="ttname"><a href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">arm_compute::ConvertPolicy::SATURATE</a></div><div class="ttdoc">Saturate.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a269b19ce3f357ac65f41f9951906e38e"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">arm_compute::TensorInfo::tensor_shape</a></div><div class="ttdeci">const TensorShape & tensor_shape() const override</div><div class="ttdoc">Size for each dimension of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00252">TensorInfo.h:252</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_concatenate_layer_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLConcatenateLayer::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_concatenate_layer_8cpp_source.xhtml#l00243">CLConcatenateLayer.cpp:243</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_l_s_t_m_layer_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_l_s_t_m_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLLSTMLayer::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_l_s_t_m_layer_8cpp_source.xhtml#l00619">CLLSTMLayer.cpp:619</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a23d9f0c01c9e120dfb828ee922b7a8aea241dd841abade20fcb27b8a9f494e1eb"><div class="ttname"><a href="namespacearm__compute.xhtml#a23d9f0c01c9e120dfb828ee922b7a8aea241dd841abade20fcb27b8a9f494e1eb">arm_compute::ArithmeticOperation::SUB</a></div><div class="ttdoc">(x - y)</div></div> |
| <div class="ttc" id="_validate_8h_xhtml"><div class="ttname"><a href="_validate_8h.xhtml">Validate.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml">arm_compute::LSTMParams</a></div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00038">LSTMParams.h:38</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_l_s_t_m_layer_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_c_l_l_s_t_m_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::CLLSTMLayer::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Prepare the function for executing.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_l_s_t_m_layer_8cpp_source.xhtml#l00719">CLLSTMLayer.cpp:719</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_l_s_t_m_params_xhtml_a213908108c07594027bc2b829fe7ee4a"><div class="ttname"><a href="classarm__compute_1_1_l_s_t_m_params.xhtml#a213908108c07594027bc2b829fe7ee4a">arm_compute::LSTMParams::forget_layer_norm_weights</a></div><div class="ttdeci">const T * forget_layer_norm_weights() const</div><div class="ttdef"><b>Definition:</b> <a href="_l_s_t_m_params_8h_source.xhtml#l00165">LSTMParams.h:165</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml">arm_compute::CLTensor</a></div><div class="ttdoc">Basic implementation of the OpenCL tensor interface.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8h_source.xhtml#l00040">CLTensor.h:40</a></div></div> |
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