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| <a href="_c_l_l_s_t_m_layer_quantized_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) 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> </div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#include "<a class="code" href="_c_l_l_s_t_m_layer_quantized_8h.xhtml">arm_compute/runtime/CL/functions/CLLSTMLayerQuantized.h</a>"</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</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="_asymm_helpers_8h.xhtml">arm_compute/core/utils/quantization/AsymmHelpers.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include <cmath></span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include <memory></span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include <tuple></span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></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">namespace</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> {</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="comment">// Quantization info structures used in the LSTMQuantize layer</span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="keyword">const</span> QuantizationInfo qasymm(1.f / 128.f, 128);</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="keyword">const</span> QuantizationInfo qsymm_3(8.f / 32768.f, 0); <span class="comment">// qsymm16 with 3 integer bit</span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="keyword">const</span> QuantizationInfo qsymm_4(16.f / 32768.f, 0); <span class="comment">// qsymm16 with 4 integer bit</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="keyword">const</span> QuantizationInfo qsymm_0(1.f / 32768.f, 0); <span class="comment">// qsymm16 with 0 integer bit</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> } <span class="comment">// namespace</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div><div class="line"><a name="l00046"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#a9e382561b8278103570de8550f83e718"> 46</a></span> <a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#a9e382561b8278103570de8550f83e718">CLLSTMLayerQuantized::CLLSTMLayerQuantized</a>(std::shared_ptr<IMemoryManager> memory_manager)</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  : _memory_group(std::move(memory_manager)), _gemmlowp(), _output_stage(), _transpose_weights(), _concat_input_weights(), _concat_recurrent_weights(), _concat_weights(), _concat_inputs(),</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  _concat_bias(), _sigmoid_forget_gate(), _sigmoid_input_gate(), _sigmoid_output_gate(), _tanh_modulation_gate(), _tanh_output_state(), _add_cell_state_tmps(), _add2(), _mul_forget_gate_cell_state(),</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  _mul_input_gate_input_mod_gate(), _mul_output_state_tmp_output_gate(), _slice_input_tensor(), _slice_forget_tensor(), _slice_cell_tensor(), _slice_output_tensor(), _dequantize(), _quantize(),</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  _input_to_input_weights(nullptr), _input_to_forget_weights(nullptr), _input_to_cell_weights(nullptr), _input_to_output_weights(nullptr), _recurrent_to_input_weights(nullptr),</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  _recurrent_to_forget_weights(nullptr), _recurrent_to_cell_weights(nullptr), _recurrent_to_output_weights(nullptr), _input_gate_bias(nullptr), _forget_gate_bias(nullptr), _cell_bias(nullptr),</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  _output_gate_bias(nullptr), _recurrent_weights(), _input_weights(), _weights(), _input(), _weights_transposed(), _output_highp(), _output_lowp(), _bias(), _forget_gate_input(), _input_gate_input(),</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  _output_gate_input(), _input_modulation_gate_input(), _forget_gate_output(), _input_gate_output(), _output_gate_output(), _input_modulation_gate_output(), _cell_state_tmp1(), _cell_state_tmp2(),</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  _output_state_tmp(), _output_state_out_symm(), _output_state_out_f32(), _is_prepared(false)</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> {</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> }</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ab90e9ae19db4dbc4f316851b03402bfa"> 58</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ab90e9ae19db4dbc4f316851b03402bfa">CLLSTMLayerQuantized::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="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> *input_to_input_weights, <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="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> *recurrent_to_input_weights, <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="l00061"></a><span class="lineno"> 61</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input_gate_bias, <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="l00062"></a><span class="lineno"> 62</span>  <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *cell_state_in, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *output_state_in,</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <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_state_out)</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> {</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <a class="code" href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a>(input, input_to_input_weights, 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_input_weights, 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>  input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in, cell_state_out, output_state_out);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> </div><div class="line"><a name="l00069"></a><span class="lineno"> 69</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_quantized.xhtml#aea08b58ba60ca803b947023ce07c5f79">CLLSTMLayerQuantized::validate</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), input_to_input_weights-><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>(), input_to_cell_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(),</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  input_to_output_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(),</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  recurrent_to_input_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), 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="l00072"></a><span class="lineno"> 72</span>  input_gate_bias-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), 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>(), cell_state_in-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), output_state_in-><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_state_out-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()));</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> input_size = 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#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch_size = 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#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> output_size = input_to_input_weights-><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#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> </div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a> qweights = input_to_input_weights-><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#a3f3e1a3200223e6a304a533b1016e749">quantization_info</a>(); <span class="comment">// Weights quantization</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*cell_state_out-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), <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>(batch_size, output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_4));</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*output_state_out-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), <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>(batch_size, output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, qasymm));</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> </div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  _input_to_input_weights = input_to_input_weights;</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  _input_to_forget_weights = input_to_forget_weights;</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  _input_to_cell_weights = input_to_cell_weights;</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  _input_to_output_weights = input_to_output_weights;</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  _recurrent_to_input_weights = recurrent_to_input_weights;</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  _recurrent_to_forget_weights = recurrent_to_forget_weights;</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  _recurrent_to_cell_weights = recurrent_to_cell_weights;</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  _recurrent_to_output_weights = recurrent_to_output_weights;</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  _input_gate_bias = input_gate_bias;</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  _forget_gate_bias = forget_gate_bias;</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  _cell_bias = cell_bias;</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  _output_gate_bias = output_gate_bias;</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> </div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="comment">// Weights concatenation</span></div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  std::vector<const ICLTensor *> inputs_weights_vector;</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  inputs_weights_vector.emplace_back(input_to_input_weights);</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  inputs_weights_vector.emplace_back(input_to_forget_weights);</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  inputs_weights_vector.emplace_back(input_to_cell_weights);</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  inputs_weights_vector.emplace_back(input_to_output_weights);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> </div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  std::vector<const ICLTensor *> recurrent_weights_vector;</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  recurrent_weights_vector.emplace_back(recurrent_to_input_weights);</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  recurrent_weights_vector.emplace_back(recurrent_to_forget_weights);</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  recurrent_weights_vector.emplace_back(recurrent_to_cell_weights);</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  recurrent_weights_vector.emplace_back(recurrent_to_output_weights);</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>  _input_weights.<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>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(input_size, 4 * output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, qweights));</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  _concat_input_weights.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6b12b22f2ad4a69582ec1674a2779ec8">configure</a>(inputs_weights_vector, &_input_weights, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> </div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  _recurrent_weights.<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>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(output_size, 4 * output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, qweights));</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  _concat_recurrent_weights.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6b12b22f2ad4a69582ec1674a2779ec8">configure</a>(recurrent_weights_vector, &_recurrent_weights, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</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>  std::vector<const ICLTensor *> weights_vector;</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  weights_vector.emplace_back(&_recurrent_weights);</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  weights_vector.emplace_back(&_input_weights);</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  _weights.<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>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(output_size + input_size, 4 * output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, qweights));</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  _concat_weights.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6b12b22f2ad4a69582ec1674a2779ec8">configure</a>(weights_vector, &_weights, <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>);</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  _transpose_weights.<a class="code" href="classarm__compute_1_1_c_l_transpose.xhtml#a074e10cfb217e657b9e81adeca2abc68">configure</a>(&_weights, &_weights_transposed);</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> </div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="comment">// Input concatenation</span></div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  std::vector<const ICLTensor *> input_vector;</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  input_vector.emplace_back(input);</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  input_vector.emplace_back(output_state_in);</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_input);</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  _input.<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>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(output_size + input_size, batch_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, qasymm));</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  _concat_inputs.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6b12b22f2ad4a69582ec1674a2779ec8">configure</a>(input_vector, &_input, <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>);</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> </div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="comment">// Bias concatenation</span></div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  std::vector<const ICLTensor *> bias_vector;</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  bias_vector.emplace_back(input_gate_bias);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  bias_vector.emplace_back(forget_gate_bias);</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  bias_vector.emplace_back(cell_bias);</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  bias_vector.emplace_back(output_gate_bias);</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> </div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  _bias.<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>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4 * output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>));</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  _concat_bias.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#a6b12b22f2ad4a69582ec1674a2779ec8">configure</a>(bias_vector, &_bias, <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>);</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> </div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="comment">// Invert the offset for gemmlowp</span></div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  _input.<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#a78839e7861ba8ffed52ca55da2745761">set_quantization_info</a>(<a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(qasymm.uniform().scale, -qasymm.uniform().offset));</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  _weights_transposed.<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#a78839e7861ba8ffed52ca55da2745761">set_quantization_info</a>(<a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(qweights.<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>().<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a>, -qweights.<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>().<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a97bd6c077f3c7769f575b82988b9b668">offset</a>));</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="comment">// Run gemmlowp</span></div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_output_highp);</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  _output_highp.<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>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4 * output_size, batch_size), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>));</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  _gemmlowp.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml#a0ae54876c8c3394f2e207f6b884f2b58">configure</a>(&_input, &_weights_transposed, <span class="keyword">nullptr</span>, &_output_highp);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  _input.<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="l00151"></a><span class="lineno"> 151</span> </div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="comment">// Set the offset back</span></div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  _input.<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#a78839e7861ba8ffed52ca55da2745761">set_quantization_info</a>(<a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(qasymm.uniform().scale, qasymm.uniform().offset));</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  _weights_transposed.<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#a78839e7861ba8ffed52ca55da2745761">set_quantization_info</a>(<a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(qweights.<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>().<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a>, qweights.<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>().<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a97bd6c077f3c7769f575b82988b9b668">offset</a>));</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> </div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="comment">// multiplier = (input_scale * weights_scale) / output_scale (2 ^ (-12))</span></div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  _output_lowp.<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>(_output_highp.<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, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_3));</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> </div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> multiplier = 4096.f * qasymm.uniform().scale * qweights.<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>().<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a>;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keywordtype">int</span> output_multiplier = 0;</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keywordtype">int</span> output_shift = 0;</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> </div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <a class="code" href="namespacearm__compute_1_1quantization.xhtml#a22032f9cf47deae265eafb65ff55b594">quantization::calculate_quantized_multiplier_less_than_one</a>(multiplier, &output_multiplier, &output_shift);</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> </div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_output_lowp);</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  _output_stage.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_int16_scale_by_fixed_point.xhtml#a5c7abeeecc0f2d6c50cc1dafaadf7c19">configure</a>(&_output_highp, &_bias, &_output_lowp, output_multiplier, output_shift);</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  _output_highp.<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="l00168"></a><span class="lineno"> 168</span>  _bias.<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="l00169"></a><span class="lineno"> 169</span> </div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="comment">// Get the gate tensors</span></div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keywordflow">if</span>(batch_size > 1)</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  {</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_input_gate_input);</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  _slice_input_tensor.<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#ae883a7cb96f6111b0e8bf3a64842c438">configure</a>(&_output_lowp, &_input_gate_input, { 0, 0 }, { output_size, batch_size });</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>(&_forget_gate_input);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  _slice_forget_tensor.<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#ae883a7cb96f6111b0e8bf3a64842c438">configure</a>(&_output_lowp, &_forget_gate_input, { output_size, 0 }, { 2 * output_size, batch_size });</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_input_modulation_gate_input);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  _slice_cell_tensor.<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#ae883a7cb96f6111b0e8bf3a64842c438">configure</a>(&_output_lowp, &_input_modulation_gate_input, { 2 * output_size, 0 }, { 3 * output_size, batch_size });</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_output_gate_input);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  _slice_output_tensor.<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#ae883a7cb96f6111b0e8bf3a64842c438">configure</a>(&_output_lowp, &_output_gate_input, { 3 * output_size, 0 }, { 4 * output_size, batch_size });</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  _output_lowp.<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="l00182"></a><span class="lineno"> 182</span>  }</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  {</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_input_gate_input);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  _slice_input_tensor.<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#ae883a7cb96f6111b0e8bf3a64842c438">configure</a>(&_output_lowp, &_input_gate_input, { 0 }, { output_size });</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_forget_gate_input);</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  _slice_forget_tensor.<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#ae883a7cb96f6111b0e8bf3a64842c438">configure</a>(&_output_lowp, &_forget_gate_input, { output_size }, { 2 * output_size });</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_input_modulation_gate_input);</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  _slice_cell_tensor.<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#ae883a7cb96f6111b0e8bf3a64842c438">configure</a>(&_output_lowp, &_input_modulation_gate_input, { 2 * output_size }, { 3 * output_size });</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_output_gate_input);</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  _slice_output_tensor.<a class="code" href="classarm__compute_1_1_c_l_slice.xhtml#ae883a7cb96f6111b0e8bf3a64842c438">configure</a>(&_output_lowp, &_output_gate_input, { 3 * output_size }, { 4 * output_size });</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  _output_lowp.<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="l00194"></a><span class="lineno"> 194</span>  }</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> </div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="comment">// Forget gate</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>(&_forget_gate_output);</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  _forget_gate_output.<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>(_forget_gate_input.<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, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0));</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  _sigmoid_forget_gate.<a class="code" href="classarm__compute_1_1_c_l_activation_layer.xhtml#a239fea32ba46d038ba350dba58026c45">configure</a>(&_forget_gate_input, &_forget_gate_output, <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="l00200"></a><span class="lineno"> 200</span>  _forget_gate_input.<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="l00201"></a><span class="lineno"> 201</span> </div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="comment">// Input gate</span></div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_input_gate_output);</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  _input_gate_output.<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>(_input_gate_input.<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, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0));</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  _sigmoid_input_gate.<a class="code" href="classarm__compute_1_1_c_l_activation_layer.xhtml#a239fea32ba46d038ba350dba58026c45">configure</a>(&_input_gate_input, &_input_gate_output, <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="l00206"></a><span class="lineno"> 206</span>  _input_gate_input.<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="l00207"></a><span class="lineno"> 207</span> </div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="comment">// Input modulation gate equation</span></div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_input_modulation_gate_output);</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  _input_modulation_gate_output.<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>(_input_modulation_gate_input.<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, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0));</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  _tanh_modulation_gate.<a class="code" href="classarm__compute_1_1_c_l_activation_layer.xhtml#a239fea32ba46d038ba350dba58026c45">configure</a>(&_input_modulation_gate_input, &_input_modulation_gate_output, <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#a56297e0f7b215eea46c818cb7528d9eaa143c8c6f51b9bb893ce71e38702e3cc1">ActivationLayerInfo::ActivationFunction::TANH</a>, 1.0f, 1.0f));</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  _input_modulation_gate_input.<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="l00213"></a><span class="lineno"> 213</span> </div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="comment">// Output gate</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_output_gate_output);</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  _output_gate_output.<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>(_output_gate_input.<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, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0));</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  _sigmoid_output_gate.<a class="code" href="classarm__compute_1_1_c_l_activation_layer.xhtml#a239fea32ba46d038ba350dba58026c45">configure</a>(&_output_gate_input, &_output_gate_output, <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="l00218"></a><span class="lineno"> 218</span>  _output_gate_input.<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="l00219"></a><span class="lineno"> 219</span> </div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="comment">// Long term memory</span></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>(&_cell_state_tmp1);</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  _cell_state_tmp1.<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>(_forget_gate_output.<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, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_4));</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  _mul_forget_gate_cell_state.<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication.xhtml#ab49eaee7b0a9443f7cbd3a73fd0bf2b2">configure</a>(&_forget_gate_output, cell_state_in, &_cell_state_tmp1, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06">RoundingPolicy::TO_ZERO</a>);</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  _forget_gate_output.<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="l00225"></a><span class="lineno"> 225</span> </div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_cell_state_tmp2);</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  _cell_state_tmp2.<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>(_input_gate_output.<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, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_4));</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  _mul_input_gate_input_mod_gate.<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication.xhtml#ab49eaee7b0a9443f7cbd3a73fd0bf2b2">configure</a>(&_input_gate_output, &_input_modulation_gate_output, &_cell_state_tmp2, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06">RoundingPolicy::TO_ZERO</a>);</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  _input_modulation_gate_output.<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="l00230"></a><span class="lineno"> 230</span>  _input_gate_output.<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="l00231"></a><span class="lineno"> 231</span> </div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  _add_cell_state_tmps.<a class="code" href="classarm__compute_1_1_c_l_arithmetic_addition.xhtml#a7af75ca77a6e9eb53532e7ab1317bdc3">configure</a>(&_cell_state_tmp1, &_cell_state_tmp2, cell_state_out, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>);</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  _cell_state_tmp1.<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="l00234"></a><span class="lineno"> 234</span>  _cell_state_tmp2.<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="l00235"></a><span class="lineno"> 235</span> </div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="comment">// Short term memory</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_output_state_tmp);</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  _output_state_tmp.<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_out-><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>(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0));</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  _tanh_output_state.<a class="code" href="classarm__compute_1_1_c_l_activation_layer.xhtml#a239fea32ba46d038ba350dba58026c45">configure</a>(cell_state_out, &_output_state_tmp, <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#a56297e0f7b215eea46c818cb7528d9eaa143c8c6f51b9bb893ce71e38702e3cc1">ActivationLayerInfo::ActivationFunction::TANH</a>, 1.0f, 1.0f));</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> </div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_output_state_out_symm);</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  _output_state_out_symm.<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>(_output_gate_output.<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, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0));</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  _mul_output_state_tmp_output_gate.<a class="code" href="classarm__compute_1_1_c_l_pixel_wise_multiplication.xhtml#ab49eaee7b0a9443f7cbd3a73fd0bf2b2">configure</a>(&_output_state_tmp, &_output_gate_output, &_output_state_out_symm, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06">RoundingPolicy::TO_ZERO</a>);</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  _output_gate_output.<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="l00245"></a><span class="lineno"> 245</span>  _output_state_tmp.<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="l00246"></a><span class="lineno"> 246</span> </div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="comment">// Requantize the output state from QSYMM16 to QASYMM8</span></div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_output_state_out_f32);</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  _output_state_out_f32.<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>(_output_state_out_symm.<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, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  _dequantize.<a class="code" href="classarm__compute_1_1_c_l_dequantization_layer.xhtml#a074e10cfb217e657b9e81adeca2abc68">configure</a>(&_output_state_out_symm, &_output_state_out_f32);</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  _output_state_out_symm.<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="l00252"></a><span class="lineno"> 252</span> </div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  _quantize.<a class="code" href="classarm__compute_1_1_c_l_quantization_layer.xhtml#a074e10cfb217e657b9e81adeca2abc68">configure</a>(&_output_state_out_f32, output_state_out);</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  _output_state_out_f32.<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="l00255"></a><span class="lineno"> 255</span> }</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span> </div><div class="line"><a name="l00257"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#aea08b58ba60ca803b947023ce07c5f79"> 257</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_quantized.xhtml#aea08b58ba60ca803b947023ce07c5f79">CLLSTMLayerQuantized::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="l00258"></a><span class="lineno"> 258</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input_to_input_weights, <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="l00259"></a><span class="lineno"> 259</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *recurrent_to_input_weights, <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="l00260"></a><span class="lineno"> 260</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input_gate_bias, <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="l00261"></a><span class="lineno"> 261</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *cell_state_in, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output_state_in,</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <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_state_out)</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span> {</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_input_weights,</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, cell_state_in,</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  output_state_in, cell_state_out, output_state_out);</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> </div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> input_size = input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0);</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch_size = input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1);</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> output_size = input_to_input_weights-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1);</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span> </div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="comment">// Dimensionality checks</span></div><div class="line"><a name="l00273"></a><span class="lineno"> 273</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="l00274"></a><span class="lineno"> 274</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(input_to_input_weights-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 2);</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(input_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="l00276"></a><span class="lineno"> 276</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="l00277"></a><span class="lineno"> 277</span> </div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> input_weights_info(input_to_input_weights-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_tensor_shape(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(input_size, output_size)).set_data_type(<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>));</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> recurrent_weights_info(input_to_input_weights-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_tensor_shape(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(output_size, output_size)).set_data_type(<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>));</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a43c38c81ff3058e36ceb9904a944d1ea">bias_info</a>(input_gate_bias-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_tensor_shape(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(output_size)).set_data_type(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>));</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> output_state_info(cell_state_in-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_tensor_shape(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(output_size, batch_size)).set_data_type(<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>).set_quantization_info(qasymm));</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> cell_state_info(cell_state_in-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_tensor_shape(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(output_size, batch_size)).set_data_type(<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>).set_quantization_info(qsymm_4));</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> </div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="comment">// Shape checks</span></div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <a class="code" href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>(&input_weights_info, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights);</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <a class="code" href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>(&recurrent_weights_info, recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights);</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <a class="code" href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>(&<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a43c38c81ff3058e36ceb9904a944d1ea">bias_info</a>, input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias);</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <a class="code" href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>(&cell_state_info, cell_state_in);</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <a class="code" href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>(&output_state_info, output_state_in);</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span> </div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="comment">// Data type checks</span></div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(&input_weights_info, input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights);</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(&recurrent_weights_info, recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights);</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(&<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a43c38c81ff3058e36ceb9904a944d1ea">bias_info</a>, input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias);</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(&cell_state_info, cell_state_in);</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(&output_state_info, output_state_in);</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>  <span class="comment">// Quantization checks</span></div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <a class="code" href="_validate_8h.xhtml#aba910b683652be1f65437ef37a9da2a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO</a>(input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights);</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <a class="code" href="_validate_8h.xhtml#aba910b683652be1f65437ef37a9da2a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO</a>(recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights);</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <a class="code" href="_validate_8h.xhtml#aba910b683652be1f65437ef37a9da2a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO</a>(&cell_state_info, cell_state_in);</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <a class="code" href="_validate_8h.xhtml#aba910b683652be1f65437ef37a9da2a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO</a>(&output_state_info, output_state_in);</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> </div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="comment">// Validate internal functions</span></div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="comment">// _concat_input_weights</span></div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  std::vector<const ITensorInfo *> inputs_weights_vector;</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  inputs_weights_vector.emplace_back(input_to_input_weights);</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  inputs_weights_vector.emplace_back(input_to_forget_weights);</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  inputs_weights_vector.emplace_back(input_to_cell_weights);</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  inputs_weights_vector.emplace_back(input_to_output_weights);</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a> qweights = input_to_input_weights-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">quantization_info</a>(); <span class="comment">// Weights quantization</span></div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> input_weights(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(input_size, 4 * output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, qweights);</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</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_weights_vector, &input_weights, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>));</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span> </div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="comment">// _concat_recurrent_weights</span></div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  std::vector<const ITensorInfo *> recurrent_weights_vector;</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  recurrent_weights_vector.emplace_back(recurrent_to_input_weights);</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  recurrent_weights_vector.emplace_back(recurrent_to_forget_weights);</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  recurrent_weights_vector.emplace_back(recurrent_to_cell_weights);</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  recurrent_weights_vector.emplace_back(recurrent_to_output_weights);</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> recurrent_weights(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(output_size, 4 * output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, qweights);</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</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>(recurrent_weights_vector, &recurrent_weights, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>));</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span> </div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="comment">// _concat_weights</span></div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  std::vector<const ITensorInfo *> weights_vector;</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  weights_vector.emplace_back(&recurrent_weights);</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  weights_vector.emplace_back(&input_weights);</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(input_size + output_size, 4 * output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, qweights);</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</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>(weights_vector, &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>));</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  <span class="comment">// _transpose_weights</span></div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> weights_transposed_shape(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.tensor_shape()[1], <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.tensor_shape()[0]);</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> weights_transposed = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.clone()->set_is_resizable(<span class="keyword">true</span>).set_tensor_shape(weights_transposed_shape);</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</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_transpose.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">CLTranspose::validate</a>(&<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &weights_transposed));</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>  <span class="comment">// _concat_inputs</span></div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  std::vector<const ITensorInfo *> input_vector;</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  input_vector.emplace_back(input);</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  input_vector.emplace_back(output_state_in);</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> input_concatenated(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(output_size + input_size, batch_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, qasymm);</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</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>(input_vector, &input_concatenated, <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>));</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span> </div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="comment">// _concat_bias</span></div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  std::vector<const ITensorInfo *> bias_vector;</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  bias_vector.emplace_back(input_gate_bias);</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  bias_vector.emplace_back(forget_gate_bias);</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  bias_vector.emplace_back(cell_bias);</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  bias_vector.emplace_back(output_gate_bias);</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span> </div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> bias_concatenated(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4 * output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</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>(bias_vector, &bias_concatenated, <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>));</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span> </div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="comment">// Invert the offset for gemmlowp</span></div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  input_concatenated.<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a78839e7861ba8ffed52ca55da2745761">set_quantization_info</a>(<a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(qasymm.uniform().scale, -qasymm.uniform().offset));</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  weights_transposed.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a575d37eaf8a905c8ca3c0250757c2b81">set_quantization_info</a>(<a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(qweights.<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>().<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a>, -qweights.<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>().<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a97bd6c077f3c7769f575b82988b9b668">offset</a>));</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span> </div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <span class="comment">// _gemmlowp</span></div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> output_highp(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4 * output_size, batch_size), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</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_lowp_matrix_multiply_core.xhtml#a8c3cf2d65afb288e39909171ada19566">CLGEMMLowpMatrixMultiplyCore::validate</a>(&input_concatenated, &weights_transposed, <span class="keyword">nullptr</span>, &output_highp));</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span> </div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <span class="comment">// Set the offset back</span></div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  input_concatenated.<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a78839e7861ba8ffed52ca55da2745761">set_quantization_info</a>(<a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(qasymm.uniform().scale, qasymm.uniform().offset));</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  weights_transposed.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a575d37eaf8a905c8ca3c0250757c2b81">set_quantization_info</a>(<a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(qweights.<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>().<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a>, qweights.<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>().<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a97bd6c077f3c7769f575b82988b9b668">offset</a>));</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span> </div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <span class="comment">// multiplier = (input_scale * weights_scale) / output_scale (2 ^ (-12))</span></div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> output_lowp(output_highp.<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_3);</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>  <span class="keyword">const</span> <span class="keywordtype">float</span> multiplier = 4096.f * qasymm.uniform().scale * qweights.<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>().<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a>;</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(multiplier);</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(multiplier > 1.0f);</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="comment">// _output_stage</span></div><div class="line"><a name="l00371"></a><span class="lineno"> 371</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_lowp_quantize_down_int32_to_int16_scale_by_fixed_point.xhtml#aee63e7671cf04d15be2da1b83d90e61b">CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::validate</a>(&output_highp, &bias_concatenated, &output_lowp));</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span> </div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> input_gate_input;</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> forget_gate_input;</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> input_modulation_gate_input;</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> output_gate_input;</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>  <span class="keywordflow">if</span>(batch_size > 1)</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  {</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <span class="comment">// _slice_input_tensor</span></div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  input_gate_input = <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>(output_size, batch_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_3);</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</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_slice.xhtml#afb48914092f4bb2fdf249fa1e3fcd17c">CLSlice::validate</a>(&output_lowp, &input_gate_input, { 0, 0 }, { output_size, batch_size }));</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="comment">// _slice_forget_tensor</span></div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  forget_gate_input = <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>(output_size, batch_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_3);</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</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_slice.xhtml#afb48914092f4bb2fdf249fa1e3fcd17c">CLSlice::validate</a>(&output_lowp, &forget_gate_input, { output_size, 0 }, { 2 * output_size, batch_size }));</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <span class="comment">// _slice_cell_tensor</span></div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  input_modulation_gate_input = <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>(output_size, batch_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_3);</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</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_slice.xhtml#afb48914092f4bb2fdf249fa1e3fcd17c">CLSlice::validate</a>(&output_lowp, &input_modulation_gate_input, { 2 * output_size, 0 }, { 3 * output_size, batch_size }));</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="comment">// _slice_output_tensor</span></div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  output_gate_input = <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>(output_size, batch_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_3);</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</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_slice.xhtml#afb48914092f4bb2fdf249fa1e3fcd17c">CLSlice::validate</a>(&output_lowp, &output_gate_input, { 3 * output_size, 0 }, { 4 * output_size, batch_size }));</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  }</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  {</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <span class="comment">// _slice_input_tensor</span></div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  input_gate_input = <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>(output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_3);</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</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_slice.xhtml#afb48914092f4bb2fdf249fa1e3fcd17c">CLSlice::validate</a>(&output_lowp, &input_gate_input, { 0 }, { output_size }));</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="comment">// _slice_forget_tensor</span></div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  forget_gate_input = <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>(output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_3);</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</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_slice.xhtml#afb48914092f4bb2fdf249fa1e3fcd17c">CLSlice::validate</a>(&output_lowp, &forget_gate_input, { output_size }, { 2 * output_size }));</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="comment">// _slice_cell_tensor</span></div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  input_modulation_gate_input = <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>(output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_3);</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</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_slice.xhtml#afb48914092f4bb2fdf249fa1e3fcd17c">CLSlice::validate</a>(&output_lowp, &input_modulation_gate_input, { 2 * output_size }, { 3 * output_size }));</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="comment">// _slice_output_tensor</span></div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  output_gate_input = <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>(output_size), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_3);</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</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_slice.xhtml#afb48914092f4bb2fdf249fa1e3fcd17c">CLSlice::validate</a>(&output_lowp, &output_gate_input, { 3 * output_size }, { 4 * output_size }));</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  }</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span> </div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="comment">// _sigmoid_forget_gate</span></div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> forget_gate_output(forget_gate_input.<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0);</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</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.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">CLActivationLayer::validate</a>(&forget_gate_input, &forget_gate_output, <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="l00412"></a><span class="lineno"> 412</span>  <span class="comment">// _sigmoid_input_gate</span></div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> input_gate_output(input_gate_input.<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0);</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</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.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">CLActivationLayer::validate</a>(&input_gate_input, &input_gate_output, <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="l00415"></a><span class="lineno"> 415</span>  <span class="comment">// _tanh_modulation_gate</span></div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> input_modulation_gate_output(input_modulation_gate_input.<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</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.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">CLActivationLayer::validate</a>(&input_modulation_gate_input, &input_modulation_gate_output, <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#a56297e0f7b215eea46c818cb7528d9eaa143c8c6f51b9bb893ce71e38702e3cc1">ActivationLayerInfo::ActivationFunction::TANH</a>, 1.0f, 1.0f)));</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="comment">// _sigmoid_output_gate</span></div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> output_gate_output(output_gate_input.<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0);</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</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.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">CLActivationLayer::validate</a>(&output_gate_input, &output_gate_output, <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="l00421"></a><span class="lineno"> 421</span> </div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="comment">// _mul_forget_gate_cell_state</span></div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> cell_state_tmp1(forget_gate_output.tensor_shape(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_4);</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</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.xhtml#a705182fc799dce8ee017368eea0ca539">CLPixelWiseMultiplication::validate</a>(&forget_gate_output, cell_state_in, &cell_state_tmp1, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06">RoundingPolicy::TO_ZERO</a>));</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span> </div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="comment">// _mul_input_gate_input_mod_gate</span></div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> cell_state_tmp2(input_gate_output.tensor_shape(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_4);</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</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.xhtml#a705182fc799dce8ee017368eea0ca539">CLPixelWiseMultiplication::validate</a>(&input_gate_output, &input_modulation_gate_output, &cell_state_tmp2, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06">RoundingPolicy::TO_ZERO</a>));</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span> </div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <span class="comment">// _add_cell_state_tmps</span></div><div class="line"><a name="l00431"></a><span class="lineno"> 431</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_tmp1, &cell_state_tmp2, cell_state_out, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>));</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span> </div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="comment">// _tanh_modulation_gate</span></div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> output_state_tmp(cell_state_out-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0);</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</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.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">CLActivationLayer::validate</a>(cell_state_out, &output_state_tmp, <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#a56297e0f7b215eea46c818cb7528d9eaa143c8c6f51b9bb893ce71e38702e3cc1">ActivationLayerInfo::ActivationFunction::TANH</a>, 1.0f, 1.0f)));</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">// _mul_output_state_tmp_output_gate</span></div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> output_state_out_symm(output_gate_output.tensor_shape(), 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">DataType::QSYMM16</a>, qsymm_0);</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</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.xhtml#a705182fc799dce8ee017368eea0ca539">CLPixelWiseMultiplication::validate</a>(&output_state_tmp, &output_gate_output, &output_state_out_symm, 1, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>, <a class="code" href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06">RoundingPolicy::TO_ZERO</a>));</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span> </div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <span class="comment">// _dequantize</span></div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> output_state_out_f32(output_state_out_symm.tensor_shape(), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</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_dequantization_layer.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">CLDequantizationLayer::validate</a>(&output_state_out_symm, &output_state_out_f32));</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span> </div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  <span class="comment">// _quantize</span></div><div class="line"><a name="l00446"></a><span class="lineno"> 446</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_quantization_layer.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">CLQuantizationLayer::validate</a>(&output_state_out_f32, output_state_out));</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span> </div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <span class="keywordflow">if</span>(cell_state_out-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a18064e0011c3869d884653e9e7c47b66">total_size</a>() != 0)</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#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(&cell_state_info, cell_state_out);</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <a class="code" href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>(&cell_state_info, cell_state_out);</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <a class="code" href="_validate_8h.xhtml#aba910b683652be1f65437ef37a9da2a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO</a>(&cell_state_info, cell_state_out);</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  }</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span> </div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="keywordflow">if</span>(output_state_out-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a18064e0011c3869d884653e9e7c47b66">total_size</a>() != 0)</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  {</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(&output_state_info, output_state_out);</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <a class="code" href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>(&output_state_info, output_state_out);</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <a class="code" href="_validate_8h.xhtml#aba910b683652be1f65437ef37a9da2a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO</a>(&output_state_info, output_state_out);</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  }</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span> </div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span> }</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span> </div><div class="line"><a name="l00465"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ad1717410afd0be936c6213a63c8005fb"> 465</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ad1717410afd0be936c6213a63c8005fb">CLLSTMLayerQuantized::run</a>()</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>  <a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span> </div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <span class="comment">// Acquire all the temporaries</span></div><div class="line"><a name="l00470"></a><span class="lineno"> 470</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="l00471"></a><span class="lineno"> 471</span> </div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <span class="comment">// Concat and transpose the input</span></div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  _concat_inputs.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span> </div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <span class="comment">// Run gemmlowp</span></div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  _gemmlowp.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  _output_stage.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span> </div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="comment">// Slice the results</span></div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  _slice_input_tensor.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  _slice_forget_tensor.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  _slice_cell_tensor.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  _slice_output_tensor.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span> </div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <span class="comment">// Gates</span></div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <span class="comment">// Forget gate</span></div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  _sigmoid_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="l00488"></a><span class="lineno"> 488</span> </div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <span class="comment">// Input gate</span></div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  _sigmoid_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="l00491"></a><span class="lineno"> 491</span> </div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  <span class="comment">// Input modulation gate</span></div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  _tanh_modulation_gate.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span> </div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <span class="comment">// Output gate</span></div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  _sigmoid_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="l00497"></a><span class="lineno"> 497</span> </div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <span class="comment">// Cell state (long term memory)</span></div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  _mul_forget_gate_cell_state.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  _mul_input_gate_input_mod_gate.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  _add_cell_state_tmps.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</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">// Output state (short term memory)</span></div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  _tanh_output_state.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  _mul_output_state_tmp_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="l00506"></a><span class="lineno"> 506</span> </div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <span class="comment">// Requantize output state from QSYMM16 to QASYMM16</span></div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  _dequantize.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  _quantize.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span> }</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span> </div><div class="line"><a name="l00512"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77"> 512</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">CLLSTMLayerQuantized::prepare</a>()</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span> {</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <span class="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  {</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  _input_weights.<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="l00517"></a><span class="lineno"> 517</span>  _concat_input_weights.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span> </div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  _input_to_input_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  _input_to_forget_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  _input_to_cell_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  _input_to_output_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</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>  _recurrent_weights.<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="l00525"></a><span class="lineno"> 525</span>  _concat_recurrent_weights.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  _recurrent_to_input_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  _recurrent_to_forget_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  _recurrent_to_cell_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  _recurrent_to_output_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span> </div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  _weights.<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="l00532"></a><span class="lineno"> 532</span>  _concat_weights.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span> </div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  _input_weights.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  _input_weights.<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#a1468b0adb6ec3f9d38aa7d60b8a91974">free</a>();</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  _recurrent_weights.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  _recurrent_weights.<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#a1468b0adb6ec3f9d38aa7d60b8a91974">free</a>();</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span> </div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  _weights_transposed.<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="l00540"></a><span class="lineno"> 540</span>  _transpose_weights.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</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>  _weights.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  _weights.<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#a1468b0adb6ec3f9d38aa7d60b8a91974">free</a>();</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span> </div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  _bias.<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="l00546"></a><span class="lineno"> 546</span>  _concat_bias.<a class="code" href="classarm__compute_1_1_c_l_concatenate_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  _input_gate_bias-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  _forget_gate_bias-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  _cell_bias-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  _output_gate_bias-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span> </div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  _is_prepared = <span class="keyword">true</span>;</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> }</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span> </div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span> } <span class="comment">// namespace arm_compute</span></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_c_l_activation_layer_xhtml_aa37e2d0b4cd4f835bfa2a2df4a0bdd2c"><div class="ttname"><a href="classarm__compute_1_1_c_l_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">arm_compute::CLActivationLayer::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 CLActivationLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_activation_layer_8cpp_source.xhtml#l00039">CLActivationLayer.cpp:39</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1quantization_xhtml_a22032f9cf47deae265eafb65ff55b594"><div class="ttname"><a href="namespacearm__compute_1_1quantization.xhtml#a22032f9cf47deae265eafb65ff55b594">arm_compute::quantization::calculate_quantized_multiplier_less_than_one</a></div><div class="ttdeci">arm_compute::Status calculate_quantized_multiplier_less_than_one(float multiplier, int *quant_multiplier, int *right_shift)</div><div class="ttdoc">Calculate quantized representation of multiplier with value less than one.</div><div class="ttdef"><b>Definition:</b> <a href="_asymm_helpers_8cpp_source.xhtml#l00035">AsymmHelpers.cpp:35</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_dequantization_layer_xhtml_a968b23a6ef327fcfb5b99d58e3fbe883"><div class="ttname"><a href="classarm__compute_1_1_c_l_dequantization_layer.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">arm_compute::CLDequantizationLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLDequantizationLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_dequantization_layer_8cpp_source.xhtml#l00038">CLDequantizationLayer.cpp:38</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_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a3ca8a4ea8f992df3b462bc7b24d097c6">arm_compute::DataType::QSYMM16</a></div><div class="ttdoc">quantized, symmetric fixed-point 16-bit number</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_l_s_t_m_layer_quantized_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::CLLSTMLayerQuantized::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_quantized_8cpp_source.xhtml#l00512">CLLSTMLayerQuantized.cpp:512</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="_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_g_e_m_m_lowp_quantize_down_int32_to_int16_scale_by_fixed_point_xhtml_aee63e7671cf04d15be2da1b83d90e61b"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_int16_scale_by_fixed_point.xhtml#aee63e7671cf04d15be2da1b83d90e61b">arm_compute::CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min=0, int max=0)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLGEMMLowpQuantizeDownIn...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_lowp_output_stage_8cpp_source.xhtml#l00086">CLGEMMLowpOutputStage.cpp:86</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_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_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="_c_l_l_s_t_m_layer_quantized_8h_xhtml"><div class="ttname"><a href="_c_l_l_s_t_m_layer_quantized_8h.xhtml">CLLSTMLayerQuantized.h</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_g_e_m_m_lowp_matrix_multiply_core_xhtml_a0ae54876c8c3394f2e207f6b884f2b58"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml#a0ae54876c8c3394f2e207f6b884f2b58">arm_compute::CLGEMMLowpMatrixMultiplyCore::configure</a></div><div class="ttdeci">void configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, const GEMMInfo &gemm_info=GEMMInfo())</div><div class="ttdoc">Initialise the kernel's inputs, output.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00075">CLGEMMLowpMatrixMultiplyCore.cpp:75</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_uniform_quantization_info_xhtml_a1d28dec57cce925ad92342891bd71e7c"><div class="ttname"><a href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">arm_compute::UniformQuantizationInfo::scale</a></div><div class="ttdeci">float scale</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00061">QuantizationInfo.h:61</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_int16_scale_by_fixed_point_xhtml_a5c7abeeecc0f2d6c50cc1dafaadf7c19"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_int16_scale_by_fixed_point.xhtml#a5c7abeeecc0f2d6c50cc1dafaadf7c19">arm_compute::CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, int min=0, int max=0)</div><div class="ttdoc">Initialise the kernel's inputs, output.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_lowp_output_stage_8cpp_source.xhtml#l00077">CLGEMMLowpOutputStage.cpp:77</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="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLGEMMLowpMatrixMultiplyCore::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_lowp_matrix_multiply_core_8cpp_source.xhtml#l00381">CLGEMMLowpMatrixMultiplyCore.cpp:381</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="namespacearm__compute_xhtml_a47be6fa38308d0003c25b60b7dbc45ce"><div class="ttname"><a href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">arm_compute::auto_init_if_empty</a></div><div class="ttdeci">bool auto_init_if_empty(ITensorInfo &info, const TensorShape &shape, int num_channels, DataType data_type, QuantizationInfo quantization_info=QuantizationInfo())</div><div class="ttdoc">Auto initialize the tensor info (shape, number of channels and data type) if the current assignment i...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00201">Helpers.inl:201</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized_xhtml_ab90e9ae19db4dbc4f316851b03402bfa"><div class="ttname"><a href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ab90e9ae19db4dbc4f316851b03402bfa">arm_compute::CLLSTMLayerQuantized::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, const ICLTensor *input_to_input_weights, const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights, const ICLTensor *recurrent_to_input_weights, const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights, const ICLTensor *input_gate_bias, const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias, ICLTensor *cell_state_in, const ICLTensor *output_state_in, ICLTensor *cell_state_out, ICLTensor *output_state_out)</div><div class="ttdoc">Initialize function's tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00058">CLLSTMLayerQuantized.cpp:58</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a78839e7861ba8ffed52ca55da2745761"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a78839e7861ba8ffed52ca55da2745761">arm_compute::TensorInfo::set_quantization_info</a></div><div class="ttdeci">ITensorInfo & set_quantization_info(const QuantizationInfo &quantization_info) override</div><div class="ttdoc">Set the quantization settings (scale and offset) of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00364">TensorInfo.cpp:364</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a9bc00234de9adf8c99a21eb1d7d494c2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">arm_compute::ITensor::mark_as_unused</a></div><div class="ttdeci">void mark_as_unused() const</div><div class="ttdoc">Marks a tensor as unused.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8cpp_source.xhtml#l00167">ITensor.cpp:167</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::Format::S32</a></div><div class="ttdoc">1 channel, 1 S32 per channel</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="classarm__compute_1_1_quantization_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_quantization_info.xhtml">arm_compute::QuantizationInfo</a></div><div class="ttdoc">Quantization information.</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00066">QuantizationInfo.h:66</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core_xhtml_a8c3cf2d65afb288e39909171ada19566"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml#a8c3cf2d65afb288e39909171ada19566">arm_compute::CLGEMMLowpMatrixMultiplyCore::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, const GEMMInfo &gemm_info=GEMMInfo())</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLGEMMLowpMatrixMultiply...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00237">CLGEMMLowpMatrixMultiplyCore.cpp:237</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_c_l_pixel_wise_multiplication_xhtml_a705182fc799dce8ee017368eea0ca539"><div class="ttname"><a href="classarm__compute_1_1_c_l_pixel_wise_multiplication.xhtml#a705182fc799dce8ee017368eea0ca539">arm_compute::CLPixelWiseMultiplication::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_8cpp_source.xhtml#l00052">CLPixelWiseMultiplication.cpp:52</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a6dc630a6ae9cc063b3924bcea8dee9d6"><div class="ttname"><a href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a></div><div class="ttdeci">#define ARM_COMPUTE_UNUSED(...)</div><div class="ttdoc">To avoid unused variables warnings.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00160">Error.h:160</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized_xhtml_aea08b58ba60ca803b947023ce07c5f79"><div class="ttname"><a href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#aea08b58ba60ca803b947023ce07c5f79">arm_compute::CLLSTMLayerQuantized::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *input_to_input_weights, const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights, const ITensorInfo *recurrent_to_input_weights, const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights, const ITensorInfo *input_gate_bias, const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias, const ITensorInfo *cell_state_in, const ITensorInfo *output_state_in, const ITensorInfo *cell_state_out, const ITensorInfo *output_state_out)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLLSTMLayerQuantized.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00257">CLLSTMLayerQuantized.cpp:257</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="_validate_8h_xhtml_a27e4638546c88b8916f967e6e54480a9"><div class="ttname"><a href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00443">Validate.h:443</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="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::DataType::QASYMM8</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a43c38c81ff3058e36ceb9904a944d1ea"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a43c38c81ff3058e36ceb9904a944d1ea">arm_compute::test::validation::bias_info</a></div><div class="ttdeci">bias_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00469">Winograd.cpp:469</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_quantization_info_xhtml_a706fc156bcd4c45441bcaad05884b57d"><div class="ttname"><a href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">arm_compute::QuantizationInfo::uniform</a></div><div class="ttdeci">UniformQuantizationInfo uniform() const</div><div class="ttdoc">Return per layer quantization info.</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00134">QuantizationInfo.h:134</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1misc_1_1_i_cloneable_xhtml_a4d10e5012a872e7f78f2b539b673049d"><div class="ttname"><a href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">arm_compute::misc::ICloneable::clone</a></div><div class="ttdeci">virtual std::unique_ptr< T > clone() const =0</div><div class="ttdoc">Provide a clone of the current object of class T.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_slice_xhtml_afb48914092f4bb2fdf249fa1e3fcd17c"><div class="ttname"><a href="classarm__compute_1_1_c_l_slice.xhtml#afb48914092f4bb2fdf249fa1e3fcd17c">arm_compute::CLSlice::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Coordinates &starts, const Coordinates &ends)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLSlice.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_slice_8cpp_source.xhtml#l00046">CLSlice.cpp:46</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_quantization_layer_xhtml_a074e10cfb217e657b9e81adeca2abc68"><div class="ttname"><a href="classarm__compute_1_1_c_l_quantization_layer.xhtml#a074e10cfb217e657b9e81adeca2abc68">arm_compute::CLQuantizationLayer::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_quantization_layer_8cpp_source.xhtml#l00031">CLQuantizationLayer.cpp:31</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a575d37eaf8a905c8ca3c0250757c2b81"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a575d37eaf8a905c8ca3c0250757c2b81">arm_compute::ITensorInfo::set_quantization_info</a></div><div class="ttdeci">virtual ITensorInfo & set_quantization_info(const QuantizationInfo &quantization_info)=0</div><div class="ttdoc">Set the quantization settings (scale and offset) of the tensor.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_pixel_wise_multiplication_xhtml_ab49eaee7b0a9443f7cbd3a73fd0bf2b2"><div class="ttname"><a href="classarm__compute_1_1_c_l_pixel_wise_multiplication.xhtml#ab49eaee7b0a9443f7cbd3a73fd0bf2b2">arm_compute::CLPixelWiseMultiplication::configure</a></div><div class="ttdeci">void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy)</div><div class="ttdoc">Initialise the kernel's inputs, output and convertion policy.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_pixel_wise_multiplication_8cpp_source.xhtml#l00034">CLPixelWiseMultiplication.cpp:34</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_i_tensor_info_xhtml_a3f3e1a3200223e6a304a533b1016e749"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">arm_compute::ITensorInfo::quantization_info</a></div><div class="ttdeci">virtual QuantizationInfo quantization_info() const =0</div><div class="ttdoc">Get the quantization settings (scale and offset) of the tensor.</div></div> |
| <div class="ttc" id="_validate_8h_xhtml_aba910b683652be1f65437ef37a9da2a9"><div class="ttname"><a href="_validate_8h.xhtml#aba910b683652be1f65437ef37a9da2a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00610">Validate.h:610</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_slice_xhtml_ae883a7cb96f6111b0e8bf3a64842c438"><div class="ttname"><a href="classarm__compute_1_1_c_l_slice.xhtml#ae883a7cb96f6111b0e8bf3a64842c438">arm_compute::CLSlice::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output, const Coordinates &starts, const Coordinates &ends)</div><div class="ttdoc">Configure kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_slice_8cpp_source.xhtml#l00034">CLSlice.cpp:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLLSTMLayerQuantized::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_quantized_8cpp_source.xhtml#l00465">CLLSTMLayerQuantized.cpp:465</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="_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_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_window_xhtml_ad2d402364fa822b0b7775081291eeca9"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">arm_compute::Window::DimY</a></div><div class="ttdeci">static constexpr size_t DimY</div><div class="ttdoc">Alias for dimension 1 also known as Y dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00045">Window.h:45</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="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_transpose_xhtml_a968b23a6ef327fcfb5b99d58e3fbe883"><div class="ttname"><a href="classarm__compute_1_1_c_l_transpose.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">arm_compute::CLTranspose::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLTranspose.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_transpose_8cpp_source.xhtml#l00040">CLTranspose.cpp:40</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a18064e0011c3869d884653e9e7c47b66"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a18064e0011c3869d884653e9e7c47b66">arm_compute::ITensorInfo::total_size</a></div><div class="ttdeci">virtual size_t total_size() const =0</div><div class="ttdoc">Returns the total size of the tensor in bytes.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_dequantization_layer_xhtml_a074e10cfb217e657b9e81adeca2abc68"><div class="ttname"><a href="classarm__compute_1_1_c_l_dequantization_layer.xhtml#a074e10cfb217e657b9e81adeca2abc68">arm_compute::CLDequantizationLayer::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_dequantization_layer_8cpp_source.xhtml#l00031">CLDequantizationLayer.cpp:31</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaa143c8c6f51b9bb893ce71e38702e3cc1"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa143c8c6f51b9bb893ce71e38702e3cc1">arm_compute::ActivationLayerInfo::ActivationFunction::TANH</a></div><div class="ttdoc">Hyperbolic tangent ( )</div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_allocator_xhtml_a1468b0adb6ec3f9d38aa7d60b8a91974"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a1468b0adb6ec3f9d38aa7d60b8a91974">arm_compute::CLTensorAllocator::free</a></div><div class="ttdeci">void free() override</div><div class="ttdoc">Free allocated OpenCL memory.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_allocator_8cpp_source.xhtml#l00152">CLTensorAllocator.cpp:152</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_uniform_quantization_info_xhtml_a97bd6c077f3c7769f575b82988b9b668"><div class="ttname"><a href="structarm__compute_1_1_uniform_quantization_info.xhtml#a97bd6c077f3c7769f575b82988b9b668">arm_compute::UniformQuantizationInfo::offset</a></div><div class="ttdeci">int32_t offset</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00062">QuantizationInfo.h:62</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_activation_layer_xhtml_a239fea32ba46d038ba350dba58026c45"><div class="ttname"><a href="classarm__compute_1_1_c_l_activation_layer.xhtml#a239fea32ba46d038ba350dba58026c45">arm_compute::CLActivationLayer::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_8cpp_source.xhtml#l00032">CLActivationLayer.cpp:32</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00180">ConvolutionLayer.cpp:180</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="classarm__compute_1_1_c_l_transpose_xhtml_a074e10cfb217e657b9e81adeca2abc68"><div class="ttname"><a href="classarm__compute_1_1_c_l_transpose.xhtml#a074e10cfb217e657b9e81adeca2abc68">arm_compute::CLTranspose::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output)</div><div class="ttdoc">Initialise the kernel's inputs and output.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_transpose_8cpp_source.xhtml#l00033">CLTranspose.cpp:33</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="namespacearm__compute_xhtml_add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06"><div class="ttname"><a href="namespacearm__compute.xhtml#add6426cbf2e057a195846d4ba09a50bea5631ad8e27788edfca7e13535d862c06">arm_compute::RoundingPolicy::TO_ZERO</a></div><div class="ttdoc">Truncates the least significant values that are lost in operations.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized_xhtml_a9e382561b8278103570de8550f83e718"><div class="ttname"><a href="classarm__compute_1_1_c_l_l_s_t_m_layer_quantized.xhtml#a9e382561b8278103570de8550f83e718">arm_compute::CLLSTMLayerQuantized::CLLSTMLayerQuantized</a></div><div class="ttdeci">CLLSTMLayerQuantized(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_quantized_8cpp_source.xhtml#l00046">CLLSTMLayerQuantized.cpp:46</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_quantization_layer_xhtml_a968b23a6ef327fcfb5b99d58e3fbe883"><div class="ttname"><a href="classarm__compute_1_1_c_l_quantization_layer.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">arm_compute::CLQuantizationLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLQuantizationLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_quantization_layer_8cpp_source.xhtml#l00038">CLQuantizationLayer.cpp:38</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml"><div class="ttname"><a href="_validate_8h.xhtml">Validate.h</a></div></div> |
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