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<div class="title">WorkloadData.cpp</div>  </div>
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<a href="_workload_data_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>&#160;<span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;</div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_workload_data_8hpp.xhtml">backendsCommon/WorkloadData.hpp</a>&gt;</span></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_cpu_tensor_handle_8hpp.xhtml">backendsCommon/CpuTensorHandle.hpp</a>&gt;</span></div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_data_layout_indexed_8hpp.xhtml">armnnUtils/DataLayoutIndexed.hpp</a>&gt;</span></div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_tensor_utils_8hpp.xhtml">armnnUtils/TensorUtils.hpp</a>&gt;</span></div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_numeric_cast_8hpp.xhtml">armnn/utility/NumericCast.hpp</a>&gt;</span></div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;</div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="preprocessor">#include &lt;algorithm&gt;</span></div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="preprocessor">#include &lt;iomanip&gt;</span></div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="preprocessor">#include &lt;string&gt;</span></div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="preprocessor">#include &lt;sstream&gt;</span></div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;</div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="preprocessor">#include &lt;fmt/format.h&gt;</span></div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;</div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearmnn_utils.xhtml">armnnUtils</a>;</div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;</div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;{</div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;</div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00025"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">   25</a></span>&#160;<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">GetBiasDataType</a>(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> inputDataType)</div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;{</div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;    <span class="keywordflow">switch</span> (inputDataType)</div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;    {</div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;        <span class="keywordflow">case</span> DataType::Float16:</div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;            <span class="keywordflow">return</span> DataType::Float16;</div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>:</div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;        <span class="keywordflow">case</span> DataType::Float32:</div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;            <span class="keywordflow">return</span> DataType::Float32;</div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;        <span class="keywordflow">case</span> DataType::QAsymmS8:</div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;            <span class="keywordflow">return</span> DataType::Signed32;</div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;        <span class="keywordflow">case</span> DataType::QAsymmU8:</div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;            <span class="keywordflow">return</span> DataType::Signed32;</div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;        <span class="keywordflow">case</span> DataType::QSymmS8:</div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;            <span class="keywordflow">return</span> DataType::Signed32;</div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;        <span class="keywordflow">case</span> DataType::QSymmS16:</div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;            <span class="keywordflow">return</span> DataType::Signed32;</div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;        <span class="keywordflow">default</span>:</div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;            <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(<span class="keyword">false</span>, <span class="stringliteral">&quot;Invalid input data type&quot;</span>);</div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;            <span class="keywordflow">return</span> DataType::Float32;</div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;    }</div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;}</div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;</div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;<span class="keyword">namespace</span></div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;{</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;</div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;<span class="comment">//android ndk does not support std::to_string function.</span></div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;std::string to_string(T value)</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;{</div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;    std::ostringstream os;</div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;    os &lt;&lt; value;</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;    <span class="keywordflow">return</span> os.str();</div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;}</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;<span class="keywordtype">void</span> ValidatePointer(<span class="keyword">const</span> <span class="keywordtype">void</span>* ptr, std::string <span class="keyword">const</span>&amp; descName, std::string <span class="keyword">const</span>&amp; paramName)</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;{</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;    <span class="keywordflow">if</span> (!ptr)</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;    {</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName +  <span class="stringliteral">&quot;: Invalid null pointer. The &quot;</span> +</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;                                      paramName + <span class="stringliteral">&quot; parameter must be set.&quot;</span>);</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;    }</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;}</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;<span class="keywordtype">void</span> ValidateTensorShapesMatch(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; first,</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;                               <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; second,</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;                               std::string <span class="keyword">const</span>&amp; descName,</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;                               std::string <span class="keyword">const</span>&amp; firstName,</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;                               std::string <span class="keyword">const</span>&amp; secondName)</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;{</div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;    <span class="keywordflow">if</span> (first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>() != second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>())</div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;    {</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">&quot;: &quot;</span></div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;                                       + firstName + <span class="stringliteral">&quot; &amp; &quot;</span> + secondName + <span class="stringliteral">&quot; must have identical shapes&quot;</span>);</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;    }</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;}</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;<span class="keywordtype">void</span> ValidateNumInputs(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo, std::string <span class="keyword">const</span>&amp; descName, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> expectedSize)</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;{</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;    <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size() != expectedSize)</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    {</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName +</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;                                       <span class="stringliteral">&quot;: Requires exactly &quot;</span> + to_string(expectedSize) + <span class="stringliteral">&quot;input(s). &quot;</span> +</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;                                       to_string(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size()) + <span class="stringliteral">&quot; have been provided.&quot;</span>);</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;    }</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;}</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;<span class="keywordtype">void</span> ValidateNumOutputs(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo, std::string <span class="keyword">const</span>&amp; descName, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> expectedSize)</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;{</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;    <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size() != expectedSize)</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;    {</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName +</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;                                       <span class="stringliteral">&quot;: Requires exactly &quot;</span> + to_string(expectedSize) + <span class="stringliteral">&quot; output(s). &quot;</span> +</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;                                       to_string(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size()) + <span class="stringliteral">&quot; has been provided.&quot;</span>);</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;    }</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;}</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;<span class="keywordtype">void</span> ValidateTensorNumDimensions(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; tensor,</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;                                 std::string <span class="keyword">const</span>&amp; descName,</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;                                 <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDimensions,</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;                                 std::string <span class="keyword">const</span>&amp; tensorName)</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;{</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;    <span class="keywordflow">if</span> (tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() != numDimensions)</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;    {</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">&quot;: Expected &quot;</span> + to_string(numDimensions) + <span class="stringliteral">&quot; but got &quot;</span> +</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;            to_string(tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()) + <span class="stringliteral">&quot; dimensions for &quot;</span> +</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;            tensorName + <span class="stringliteral">&quot; tensor.&quot;</span>);</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;    }</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;}</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;<span class="keywordtype">void</span> ValidateTensorNumElements(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; tensor,</div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;                               std::string <span class="keyword">const</span>&amp; descName,</div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;                               <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numElements,</div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;                               std::string <span class="keyword">const</span>&amp; tensorName)</div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;{</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    <span class="keywordflow">if</span> (tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() != numElements)</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    {</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">&quot;: Expected &quot;</span> + to_string(numElements) + <span class="stringliteral">&quot; but got &quot;</span> +</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;                                       to_string(tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>()) + <span class="stringliteral">&quot; elements for &quot;</span> +</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;                                       tensorName + <span class="stringliteral">&quot; tensor.&quot;</span>);</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;    }</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;}</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;<span class="keywordtype">void</span> ValidateTensorNumDimNumElem(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; tensorInfo,</div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;                                 <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDimension,</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;                                 <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numElements,</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;                                 std::string <span class="keyword">const</span>&amp; tensorName)</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;{</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;    <span class="keyword">const</span> std::string functionName{<span class="stringliteral">&quot;ValidateTensorNumDimNumElem&quot;</span>};</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;    ValidateTensorNumDimensions(tensorInfo, functionName, numDimension, tensorName);</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;    ValidateTensorNumElements(tensorInfo, functionName, numElements, tensorName);</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;}</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;<span class="keywordtype">void</span> ValidateTensorDataType(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; tensor, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dataType,</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;    <span class="keyword">const</span> std::string&amp; descName, std::string <span class="keyword">const</span>&amp; tensorName)</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;{</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;    <span class="keywordflow">if</span> (tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != dataType)</div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;    {</div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">&quot;: Expected data type &quot;</span> + <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(dataType) + <span class="stringliteral">&quot; but got &quot;</span> +</div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;            <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()) + <span class="stringliteral">&quot; for &quot;</span> + tensorName + <span class="stringliteral">&quot; tensor.&quot;</span>);</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;    }</div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;}</div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;</div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;<span class="keywordtype">void</span> ValidPerAxisQuantizedDataType(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; tensor, <span class="keyword">const</span> std::string&amp; descName, <span class="keyword">const</span> std::string&amp; tensorName)</div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;{</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;    <a class="code" href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;    <span class="keywordflow">if</span> (tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != DataType::QSymmS8 &amp;&amp;</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;        tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != DataType::QuantizedSymm8PerAxis)</div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;    {</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName +</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;            <span class="stringliteral">&quot;: Expected data type which supports per-axis quantization scheme but got &quot;</span> +</div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;            <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()) + <span class="stringliteral">&quot; for &quot;</span> + tensorName + <span class="stringliteral">&quot; tensor.&quot;</span>);</div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;    }</div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;    <a class="code" href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;}</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;</div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;<span class="keywordtype">void</span> ValidateTensorQuantizationSpace(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; first,</div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;                                     <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; second,</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;                                     <span class="keyword">const</span> std::string&amp; descName,</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;                                     std::string <span class="keyword">const</span>&amp; firstName,</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;                                     std::string <span class="keyword">const</span>&amp; secondName)</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;{</div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;    <span class="keywordflow">if</span> (!first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a7c00efeb540198b33b8558c76e5cc2dd">IsQuantized</a>() ||</div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;        !second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a7c00efeb540198b33b8558c76e5cc2dd">IsQuantized</a>())</div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;    {</div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;        <span class="comment">// Not a quantized type, ignore the validation</span></div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;        <span class="keywordflow">return</span>;</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    }</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> firstDataType  = first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>();</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> secondDataType = second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>();</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;</div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;    <span class="keywordflow">if</span> (firstDataType != secondDataType)</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;    {</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">&quot;: &quot;</span> + firstName + <span class="stringliteral">&quot; and &quot;</span> + secondName +</div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;                                       <span class="stringliteral">&quot; must be of the same quantized type, &quot;</span> +</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;                                       firstName + <span class="stringliteral">&quot; is &quot;</span> + <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(firstDataType) + <span class="stringliteral">&quot;, &quot;</span> +</div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;                                       secondName + <span class="stringliteral">&quot; is &quot;</span> + <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(secondDataType));</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;    }</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;</div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;    <span class="keywordflow">if</span> (!first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a22f377fc4e10dc1773a3f979061e85f1">IsTypeSpaceMatch</a>(second))</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;    {</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">&quot;: &quot;</span> + firstName + <span class="stringliteral">&quot; and &quot;</span> + secondName +</div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;                                       <span class="stringliteral">&quot; must have the same quantization space, &quot;</span> +</div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;                                       firstName + <span class="stringliteral">&quot; has offset &quot;</span> + to_string(first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>()) +</div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;                                       <span class="stringliteral">&quot; and scale &quot;</span> + to_string(first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>()) + <span class="stringliteral">&quot;, &quot;</span> +</div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;                                       secondName + <span class="stringliteral">&quot; has offset &quot;</span> + to_string(second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>()) +</div><div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;                                       <span class="stringliteral">&quot; and scale &quot;</span> + to_string(second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>()));</div><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;    }</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;}</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;<span class="keywordtype">void</span> ValidateBiasTensorQuantization(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; biasTensor,</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;                                    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo,</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;                                    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; weightsTensorInfo,</div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;                                    <span class="keyword">const</span> std::string&amp; descName)</div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;{</div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;    <span class="comment">// Helper lambda function to validate a single bias quantization scale value</span></div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;    <span class="keyword">auto</span> VerifyBiasQuantizationScale = [&amp;descName](<span class="keywordtype">float</span> biasScale, <span class="keywordtype">float</span> expectedScale) -&gt; <span class="keywordtype">void</span></div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;    {</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;        constexpr <span class="keywordtype">float</span> tolerance = 0.000001f;</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;        <span class="keywordflow">if</span> (std::abs(biasScale - expectedScale) &gt; tolerance)</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;        {</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;            <span class="comment">// Print the float values with extra precision to see very small differences</span></div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;            std::stringstream msg;</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;            msg &lt;&lt; std::setprecision(10) &lt;&lt; descName &lt;&lt; <span class="stringliteral">&quot;: Expected &quot;</span> &lt;&lt; expectedScale &lt;&lt;</div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;                <span class="stringliteral">&quot; quantization scale for bias tensor (the product of the input and weight scales), but got &quot;</span> &lt;&lt;</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;                biasScale;</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(msg.str(), <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>());</div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;        }</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;    };</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;    <span class="keywordflow">if</span> (biasTensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>() != 0)</div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;    {</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">&quot;: Expected zero quantization offset for bias tensor but got &quot;</span> +</div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;            to_string(biasTensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>()));</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;    }</div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;    <span class="keywordflow">if</span> (biasTensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#af672d1c9e2a120a18926cb645981fbb7">HasMultipleQuantizationScales</a>() || weightsTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#af672d1c9e2a120a18926cb645981fbb7">HasMultipleQuantizationScales</a>())</div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;    {</div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;        <span class="comment">// Validate per-axis quantization scales</span></div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;        <span class="keyword">const</span> std::vector&lt;float&gt;&amp; weightScales = weightsTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8bc11f1fa23ef42532f9fdd04d355270">GetQuantizationScales</a>();</div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;        <span class="keyword">const</span> std::vector&lt;float&gt;&amp; biasScales   = biasTensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8bc11f1fa23ef42532f9fdd04d355270">GetQuantizationScales</a>();</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;</div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;        <span class="keywordflow">if</span> (weightScales.size() != biasScales.size())</div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;        {</div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;            std::stringstream msg;</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;            msg &lt;&lt; descName &lt;&lt; <span class="stringliteral">&quot;: Expected matching number of per-axis quantization scales for weights and bias, &quot;</span></div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;                &lt;&lt; <span class="stringliteral">&quot;but got different values. This is currently unsupported: weights=&quot;</span> &lt;&lt; weightScales.size()</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;                &lt;&lt; <span class="stringliteral">&quot;, biases=&quot;</span> &lt;&lt; biasScales.size();</div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(msg.str(), <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>());</div><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;        }</div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0ul; i &lt; biasScales.size(); ++i)</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;        {</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;            <span class="keyword">const</span> <span class="keywordtype">float</span> expectedScale = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>() * weightScales[i];</div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;            VerifyBiasQuantizationScale(biasScales[i], expectedScale);</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;        }</div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;    }</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;    {</div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;        <span class="comment">// Validate per-tensor quantization scale</span></div><div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">float</span> expectedScale = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>() * weightsTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>();</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;        VerifyBiasQuantizationScale(biasTensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(), expectedScale);</div><div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;    }</div><div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;}</div><div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;</div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;<span class="keywordtype">void</span> ValidateTensors(<span class="keyword">const</span> std::vector&lt;ITensorHandle*&gt;&amp; vec,</div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numExpected,</div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;    <span class="keyword">const</span> std::string&amp; descName,</div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;    <span class="keyword">const</span> std::string&amp; varName)</div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;{</div><div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;    <span class="keywordflow">if</span> (vec.empty() &amp;&amp; numExpected &gt; 0)</div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;    {</div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">&quot;: Invalid empty &quot;</span> + varName + <span class="stringliteral">&quot; array.&quot;</span>);</div><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;    }</div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;</div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; numExpected; ++i)</div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;    {</div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;        <span class="keywordflow">if</span> (!vec[i])</div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;        {</div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">&quot;: Invalid NULL for &quot;</span> + varName + to_string(i));</div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;        }</div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;    }</div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;}</div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;</div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;<span class="keywordtype">void</span> ValidateBroadcastTensorShapesMatch(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; first,</div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;                                        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; second,</div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;                                        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; output,</div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;                                        std::string <span class="keyword">const</span>&amp; descName,</div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;                                        std::string <span class="keyword">const</span>&amp; firstName,</div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;                                        std::string <span class="keyword">const</span>&amp; secondName)</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;{</div><div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;    <span class="comment">// Tensors must have the same number of dimensions in order to be explicit about which dimensions will get</span></div><div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;    <span class="comment">// broadcasted.</span></div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;    <span class="keywordflow">if</span> (first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() != second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;    {</div><div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName  + <span class="stringliteral">&quot;: Tensors &quot;</span></div><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;            + firstName + <span class="stringliteral">&quot; &amp; &quot;</span> + secondName</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;            + <span class="stringliteral">&quot; must have the same number of dimensions in order to be broadcasted&quot;</span>);</div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;    }</div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;    uint32_t numDims = first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;    std::vector&lt;uint32_t&gt; outputDims(numDims, 0u);</div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;    <span class="keywordflow">for</span> (uint32_t i = 0; i &lt; numDims; i++)</div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;    {</div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">bool</span> dimsNotEqual = first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i] != second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i];</div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">bool</span> dimsNotOne = (first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i] != 1) &amp;&amp; (second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i] != 1);</div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;        <span class="keywordflow">if</span> (dimsNotEqual &amp;&amp; dimsNotOne)</div><div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;        {</div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">&quot;Broadcasting is not possible for incompatible shapes&quot;</span>);</div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;        }</div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;        outputDims[i] = std::max(first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i], second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i]);</div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;    }</div><div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> broadcastShape = <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(armnn::numeric_cast&lt;unsigned int&gt;(outputDims.size()), outputDims.data());</div><div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;    <span class="keywordflow">if</span> (broadcastShape != output.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>())</div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;    {</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">&quot;: The tensor shape resulting from adding &quot;</span></div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;                                       + firstName + <span class="stringliteral">&quot; &amp; &quot;</span> + secondName</div><div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;                                       + <span class="stringliteral">&quot; does not match the output shape&quot;</span>);</div><div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;    }</div><div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;}</div><div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;</div><div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;<span class="keywordtype">void</span> ValidateDataTypes(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>,</div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;                       <span class="keyword">const</span> std::vector&lt;armnn::DataType&gt;&amp; supportedTypes,</div><div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;                       std::string <span class="keyword">const</span>&amp; descName)</div><div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;{</div><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;    <span class="keyword">auto</span> iterator = std::find(supportedTypes.begin(), supportedTypes.end(), info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>());</div><div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;    <span class="keywordflow">if</span> (iterator == supportedTypes.end())</div><div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;    {</div><div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName  + <span class="stringliteral">&quot;: &quot;</span> + <span class="stringliteral">&quot; Tensor type is not supported.&quot;</span>);</div><div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;    }</div><div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;}</div><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;</div><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;<span class="keywordtype">void</span> ValidateTensorDataTypesMatch(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; first,</div><div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;                                  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; second,</div><div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;                                  std::string <span class="keyword">const</span>&amp; descName,</div><div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;                                  std::string <span class="keyword">const</span>&amp; firstName,</div><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;                                  std::string <span class="keyword">const</span>&amp; secondName)</div><div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;{</div><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;    <span class="keywordflow">if</span> (first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>())</div><div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;    {</div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">&quot;: &quot;</span> + firstName + <span class="stringliteral">&quot; &amp; &quot;</span> + secondName +</div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;                                       <span class="stringliteral">&quot; must have identical data types.&quot;</span>);</div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;    }</div><div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;}</div><div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;</div><div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;<span class="keywordtype">void</span> ValidateTensorNumElementsMatch(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; first,</div><div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;                                    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; second,</div><div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;                                    std::string <span class="keyword">const</span>&amp; descName,</div><div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;                                    std::string <span class="keyword">const</span>&amp; firstName,</div><div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;                                    std::string <span class="keyword">const</span>&amp; secondName)</div><div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;{</div><div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;    <span class="keywordflow">if</span> (first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() != second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>())</div><div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;    {</div><div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">&quot;: &quot;</span> + firstName + <span class="stringliteral">&quot; &amp; &quot;</span> + secondName +</div><div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;                                       <span class="stringliteral">&quot; must have the same number of elements.&quot;</span>);</div><div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;    }</div><div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;}</div><div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;</div><div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;<span class="keywordtype">void</span> ValidateWeightDataType(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputInfo,</div><div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;                            <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; weightInfo,</div><div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;                            <span class="keyword">const</span> std::string&amp; descName)</div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;{</div><div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> inputType = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>();</div><div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;    <span class="keywordflow">if</span> (<a class="code" href="namespacearmnn.xhtml#ad91bc7bfe29186f5d78c28386c6c5309">IsQuantized8BitType</a>(inputType))</div><div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;    {</div><div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;        <a class="code" href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;        <span class="keyword">const</span> std::vector&lt;DataType&gt; validTypes =</div><div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;        {</div><div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;            DataType::QAsymmS8,</div><div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;            DataType::QAsymmU8,</div><div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;            DataType::QSymmS8,</div><div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;            DataType::QuantizedSymm8PerAxis <span class="comment">// deprecated</span></div><div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;        };</div><div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;        <a class="code" href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;</div><div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;        ValidateDataTypes(weightInfo, validTypes, descName);</div><div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;    }</div><div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;    {</div><div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;        ValidateTensorDataTypesMatch(inputInfo, weightInfo, descName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;weight&quot;</span>);</div><div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;    }</div><div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;}</div><div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;</div><div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;<span class="keywordtype">void</span> ValidatePerAxisQuantizationDimension(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; tensorInfo,</div><div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;                                          <span class="keyword">const</span> std::string&amp; descName,</div><div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;                                          <span class="keyword">const</span> std::string&amp; tensorName)</div><div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;{</div><div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;unsigned int&gt;</a>&amp; quantizationDim = tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b8fc85ce966c035d789cf22db5088a1">GetQuantizationDim</a>();</div><div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;    <span class="keywordflow">if</span> (!quantizationDim.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>())</div><div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;    {</div><div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">&quot;{0}: Quantization dimension for per-axis quantization &quot;</span></div><div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;                                                   <span class="stringliteral">&quot;not set on tensor {1}.&quot;</span>, descName, tensorName));</div><div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;    }</div><div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;</div><div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;    <span class="keywordflow">if</span> (quantizationDim.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>() != 0)</div><div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;    {</div><div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(</div><div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;            <span class="stringliteral">&quot;{0}: Quantization dimension for per-axis quantization expected to be 0 on tensor {1}, &quot;</span></div><div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;            <span class="stringliteral">&quot;but got: {2}&quot;</span>, descName, tensorName, quantizationDim.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>()));</div><div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;    }</div><div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;}</div><div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;</div><div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;<span class="keywordtype">void</span> ValidatePerAxisQuantizationOffset(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; tensorInfo,</div><div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;                                       <span class="keyword">const</span> std::string&amp; descName,</div><div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;                                       <span class="keyword">const</span> std::string&amp; tensorName)</div><div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;{</div><div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;    int32_t quantizationOffset = tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>();</div><div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;    <span class="keywordflow">if</span> (quantizationOffset != 0)</div><div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;    {</div><div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(</div><div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;            <span class="stringliteral">&quot;{0}: Quantization offset for per-axis quantization expected to be 0 on tensor {1}, but got: {2}&quot;</span>,</div><div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;            descName, tensorName, quantizationOffset));</div><div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;    }</div><div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;}</div><div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;</div><div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;<span class="keywordtype">void</span> ValidatePerAxisQuantization(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputInfo,</div><div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;                                 <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputInfo,</div><div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;                                 <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; weightInfo,</div><div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;                                 <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;TensorInfo&gt;</a>&amp; optionalBiasInfo,</div><div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;                                 <span class="keyword">const</span> std::string&amp; descName)</div><div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;{</div><div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;    <span class="keywordflow">if</span> (weightInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#ab85cd8cc10c96a7c99c14042c251fc48">HasPerAxisQuantization</a>())</div><div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;    {</div><div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;        <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> inputDataType  = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>();</div><div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;        <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> outputDataType = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>();</div><div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;</div><div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">bool</span> canHavePerAxisQuantization = (<a class="code" href="namespacearmnn.xhtml#ad91bc7bfe29186f5d78c28386c6c5309">IsQuantized8BitType</a>(inputDataType)) &amp;&amp; inputDataType == outputDataType;</div><div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;</div><div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;        <span class="keywordflow">if</span> (!canHavePerAxisQuantization)</div><div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;        {</div><div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(</div><div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;                <span class="stringliteral">&quot;{0}: Per-axis quantization parameters set on tensor {1}, but data type does not support &quot;</span></div><div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;                <span class="stringliteral">&quot;per-axis quantization.&quot;</span>, descName, <span class="stringliteral">&quot;weight&quot;</span>));</div><div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;        }</div><div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;</div><div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;</div><div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;        ValidPerAxisQuantizedDataType(weightInfo, descName, <span class="stringliteral">&quot;weight&quot;</span>);</div><div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;        ValidatePerAxisQuantizationDimension(weightInfo, descName, <span class="stringliteral">&quot;weight&quot;</span>);</div><div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;        ValidatePerAxisQuantizationOffset(weightInfo, descName, <span class="stringliteral">&quot;weight&quot;</span>);</div><div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;</div><div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;        <span class="keywordflow">if</span> (optionalBiasInfo.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>())</div><div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;        {</div><div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;            <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; biasInfo = optionalBiasInfo.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>();</div><div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;            <span class="keywordflow">if</span> (!biasInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#ab85cd8cc10c96a7c99c14042c251fc48">HasPerAxisQuantization</a>())</div><div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;            {</div><div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;                <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(</div><div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;                        <span class="stringliteral">&quot;{}: Per-axis quantization parameters not set on bias tensor, &quot;</span></div><div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;                        <span class="stringliteral">&quot;despite being set on weight tensor.&quot;</span>, descName));</div><div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;            }</div><div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;</div><div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;            ValidateTensorDataType(biasInfo, DataType::Signed32, descName, <span class="stringliteral">&quot;bias&quot;</span>);</div><div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;            ValidatePerAxisQuantizationDimension(biasInfo, descName, <span class="stringliteral">&quot;bias&quot;</span>);</div><div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;            ValidatePerAxisQuantizationOffset(biasInfo, descName, <span class="stringliteral">&quot;bias&quot;</span>);</div><div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;        }</div><div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;    }</div><div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;}</div><div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;</div><div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;} <span class="comment">// anonymous namespace</span></div><div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;</div><div class="line"><a name="l00460"></a><span class="lineno"><a class="line" href="structarmnn_1_1_queue_descriptor.xhtml#a765d2cee4ccce5b9467e0c2b6d25b84a">  460</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a765d2cee4ccce5b9467e0c2b6d25b84a">QueueDescriptor::ValidateInputsOutputs</a>(<span class="keyword">const</span> std::string&amp; descName,</div><div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numExpectedIn, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numExpectedOut)<span class="keyword"> const</span></div><div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;    ValidateTensors(<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>, numExpectedIn, descName, <span class="stringliteral">&quot;input&quot;</span>);</div><div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;    ValidateTensors(<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>, numExpectedOut, descName, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;}</div><div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;</div><div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00468"></a><span class="lineno"><a class="line" href="structarmnn_1_1_map_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">  468</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_map_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">MapQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;MapQueueDescriptor&quot;</span>};</div><div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;</div><div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 0);</div><div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;</div><div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size(); ++i)</div><div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;    {</div><div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;        <span class="keywordflow">if</span> (!<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[i])</div><div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;        {</div><div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;                fmt::format(<span class="stringliteral">&quot;{}: Invalid NULL input {}.&quot;</span>, descriptorName, static_cast&lt;int&gt;(i)));</div><div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;        }</div><div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;    }</div><div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;}</div><div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;</div><div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00486"></a><span class="lineno"><a class="line" href="structarmnn_1_1_unmap_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">  486</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_unmap_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">UnmapQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;UnmapQueueDescriptor&quot;</span>};</div><div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;</div><div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 0);</div><div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;</div><div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size(); ++i)</div><div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160;    {</div><div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;        <span class="keywordflow">if</span> (!<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[i])</div><div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;        {</div><div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;                fmt::format(<span class="stringliteral">&quot;{}: Invalid NULL input {}.&quot;</span>, descriptorName, static_cast&lt;int&gt;(i)));</div><div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;        }</div><div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;    }</div><div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;}</div><div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;</div><div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00504"></a><span class="lineno"><a class="line" href="structarmnn_1_1_mem_copy_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">  504</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_mem_copy_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">MemCopyQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;MemCopyQueueDescriptor&quot;</span>};</div><div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;</div><div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName , 1);</div><div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;</div><div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;</div><div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;    ValidateTensorNumElementsMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;    ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;</div><div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160; 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   }</div><div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;</div><div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size(); ++i)</div><div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;    {</div><div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;        <span class="keywordflow">if</span> (!<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[i])</div><div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;        {</div><div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(</div><div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;                <span class="stringliteral">&quot;{0}: Invalid NULL input {1}.&quot;</span>, descriptorName, i));</div><div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;        }</div><div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;</div><div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;        <span class="keywordflow">if</span> (!<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[i])</div><div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;        {</div><div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">&quot;{0}: Invalid NULL output {1}&quot;</span>, descriptorName, i));</div><div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;        }</div><div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;    }</div><div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;}</div><div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;</div><div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00540"></a><span class="lineno"><a class="line" href="structarmnn_1_1_mem_import_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">  540</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_mem_import_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">MemImportQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;    ValidateNumInputs(workloadInfo, <span class="stringliteral">&quot;MemImportQueueDescriptor&quot;</span>, 1);</div><div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;    ValidateNumOutputs(workloadInfo, <span class="stringliteral">&quot;MemImportQueueDescriptor&quot;</span> , 1);</div><div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;</div><div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;    <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size() != 1)</div><div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;    {</div><div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">&quot;Number of input infos ({}) is not 1.&quot;</span>,</div><div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;                                                   workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size()));</div><div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160;</div><div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160;    }</div><div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160;</div><div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;    <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size() != workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size())</div><div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;    {</div><div class="line"><a name="l00554"></a><span class="lineno">  554</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(</div><div class="line"><a name="l00555"></a><span class="lineno">  555</span>&#160;            <span class="stringliteral">&quot;Number of input infos ({0}) does not match the number of output infos ({1})&quot;</span>,</div><div class="line"><a name="l00556"></a><span class="lineno">  556</span>&#160;            workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size(), workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size()));</div><div class="line"><a name="l00557"></a><span class="lineno">  557</span>&#160;    }</div><div class="line"><a name="l00558"></a><span class="lineno">  558</span>&#160;</div><div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160;    <span class="keywordflow">for</span> (std::size_t i = 0; i &lt; workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size(); ++i)</div><div class="line"><a name="l00560"></a><span class="lineno">  560</span>&#160;    {</div><div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160;        <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[i].GetNumElements() !=</div><div class="line"><a name="l00562"></a><span class="lineno">  562</span>&#160;            workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[i].GetNumElements())</div><div class="line"><a name="l00563"></a><span class="lineno">  563</span>&#160;        {</div><div class="line"><a name="l00564"></a><span class="lineno">  564</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(</div><div class="line"><a name="l00565"></a><span class="lineno">  565</span>&#160;                <span class="stringliteral">&quot;Number of elements for tensor input and output {} does not match&quot;</span>, i ));</div><div class="line"><a name="l00566"></a><span class="lineno">  566</span>&#160;        }</div><div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160;    }</div><div class="line"><a name="l00568"></a><span class="lineno">  568</span>&#160;</div><div class="line"><a name="l00569"></a><span class="lineno">  569</span>&#160;    <span class="keywordflow">if</span> (<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size() != 1)</div><div class="line"><a name="l00570"></a><span class="lineno">  570</span>&#160;    {</div><div class="line"><a name="l00571"></a><span class="lineno">  571</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">&quot;Number of inputs ({}) is not 1.&quot;</span>, <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size()));</div><div class="line"><a name="l00572"></a><span class="lineno">  572</span>&#160;    }</div><div class="line"><a name="l00573"></a><span class="lineno">  573</span>&#160;</div><div class="line"><a name="l00574"></a><span class="lineno">  574</span>&#160;    <span class="keywordflow">if</span> (<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size() != <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>.size())</div><div class="line"><a name="l00575"></a><span class="lineno">  575</span>&#160;    {</div><div class="line"><a name="l00576"></a><span class="lineno">  576</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(</div><div class="line"><a name="l00577"></a><span class="lineno">  577</span>&#160;            <span class="stringliteral">&quot;Number of inputs ({0}) does not match the number of outputs ({1})&quot;</span>,</div><div class="line"><a name="l00578"></a><span class="lineno">  578</span>&#160;            <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size(), <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>.size()));</div><div class="line"><a name="l00579"></a><span class="lineno">  579</span>&#160;    }</div><div class="line"><a name="l00580"></a><span class="lineno">  580</span>&#160;</div><div class="line"><a name="l00581"></a><span class="lineno">  581</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size(); ++i)</div><div class="line"><a name="l00582"></a><span class="lineno">  582</span>&#160;    {</div><div class="line"><a name="l00583"></a><span class="lineno">  583</span>&#160;        <span class="keywordflow">if</span> (!<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[i])</div><div class="line"><a name="l00584"></a><span class="lineno">  584</span>&#160;        {</div><div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">&quot;Invalid null input {}&quot;</span>, i));</div><div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160;        }</div><div class="line"><a name="l00587"></a><span class="lineno">  587</span>&#160;</div><div class="line"><a name="l00588"></a><span class="lineno">  588</span>&#160;        <span class="keywordflow">if</span> (!<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[i])</div><div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160;        {</div><div class="line"><a name="l00590"></a><span class="lineno">  590</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">&quot;Invalid null output {}&quot;</span>, i));</div><div class="line"><a name="l00591"></a><span class="lineno">  591</span>&#160;        }</div><div class="line"><a name="l00592"></a><span class="lineno">  592</span>&#160;    }</div><div class="line"><a name="l00593"></a><span class="lineno">  593</span>&#160;}</div><div class="line"><a name="l00594"></a><span class="lineno">  594</span>&#160;</div><div class="line"><a name="l00595"></a><span class="lineno">  595</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00596"></a><span class="lineno"><a class="line" href="structarmnn_1_1_mem_sync_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">  596</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_mem_sync_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">MemSyncQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00597"></a><span class="lineno">  597</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00598"></a><span class="lineno">  598</span>&#160;    ValidateNumInputs(workloadInfo, <span class="stringliteral">&quot;MemSyncQueueDescriptor&quot;</span>, 1);</div><div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160;    ValidateNumOutputs(workloadInfo, <span class="stringliteral">&quot;MemSyncQueueDescriptor&quot;</span> , 1);</div><div class="line"><a name="l00600"></a><span class="lineno">  600</span>&#160;</div><div class="line"><a name="l00601"></a><span class="lineno">  601</span>&#160;    <span class="keywordflow">if</span> (<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size() != 1)</div><div class="line"><a name="l00602"></a><span class="lineno">  602</span>&#160;    {</div><div class="line"><a name="l00603"></a><span class="lineno">  603</span>&#160; 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   }</div><div class="line"><a name="l00610"></a><span class="lineno">  610</span>&#160;</div><div class="line"><a name="l00611"></a><span class="lineno">  611</span>&#160;    <span class="keywordflow">if</span> (!<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0])</div><div class="line"><a name="l00612"></a><span class="lineno">  612</span>&#160;    {</div><div class="line"><a name="l00613"></a><span class="lineno">  613</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">&quot;Invalid null input 0&quot;</span>));</div><div class="line"><a name="l00614"></a><span class="lineno">  614</span>&#160;    }</div><div class="line"><a name="l00615"></a><span class="lineno">  615</span>&#160;}</div><div class="line"><a name="l00616"></a><span class="lineno">  616</span>&#160;</div><div class="line"><a name="l00617"></a><span class="lineno">  617</span>&#160;<span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00618"></a><span class="lineno"><a class="line" href="structarmnn_1_1_activation_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">  618</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_activation_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ActivationQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00619"></a><span class="lineno">  619</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00620"></a><span class="lineno">  620</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;ActivationQueueDescriptor&quot;</span>};</div><div class="line"><a name="l00621"></a><span class="lineno">  621</span>&#160;</div><div class="line"><a name="l00622"></a><span class="lineno">  622</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l00623"></a><span class="lineno">  623</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l00624"></a><span class="lineno">  624</span>&#160;</div><div class="line"><a name="l00625"></a><span class="lineno">  625</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l00626"></a><span class="lineno">  626</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l00627"></a><span class="lineno">  627</span>&#160;</div><div class="line"><a name="l00628"></a><span class="lineno">  628</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l00629"></a><span class="lineno">  629</span>&#160;    {</div><div class="line"><a name="l00630"></a><span class="lineno">  630</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l00631"></a><span class="lineno">  631</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l00632"></a><span class="lineno">  632</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l00633"></a><span class="lineno">  633</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l00634"></a><span class="lineno">  634</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l00635"></a><span class="lineno">  635</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l00636"></a><span class="lineno">  636</span>&#160;    };</div><div class="line"><a name="l00637"></a><span class="lineno">  637</span>&#160;</div><div class="line"><a name="l00638"></a><span class="lineno">  638</span>&#160;    ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l00639"></a><span class="lineno">  639</span>&#160;    ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00640"></a><span class="lineno">  640</span>&#160;    ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00641"></a><span class="lineno">  641</span>&#160;}</div><div class="line"><a name="l00642"></a><span class="lineno">  642</span>&#160;</div><div class="line"><a name="l00643"></a><span class="lineno"><a class="line" href="structarmnn_1_1_arg_min_max_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">  643</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_arg_min_max_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ArgMinMaxQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00644"></a><span class="lineno">  644</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00645"></a><span class="lineno">  645</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;ArgMinMaxQueueDescriptor&quot;</span>};</div><div class="line"><a name="l00646"></a><span class="lineno">  646</span>&#160;</div><div class="line"><a name="l00647"></a><span class="lineno">  647</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l00648"></a><span class="lineno">  648</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l00649"></a><span class="lineno">  649</span>&#160;</div><div class="line"><a name="l00650"></a><span class="lineno">  650</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; 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       outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">DataType::Signed64</a>)</div><div class="line"><a name="l00655"></a><span class="lineno">  655</span>&#160;    {</div><div class="line"><a name="l00656"></a><span class="lineno">  656</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Output of ArgMinMax layer must be Int32 or Int64.&quot;</span>);</div><div class="line"><a name="l00657"></a><span class="lineno">  657</span>&#160;    }</div><div class="line"><a name="l00658"></a><span class="lineno">  658</span>&#160;</div><div class="line"><a name="l00659"></a><span class="lineno">  659</span>&#160;    std::vector&lt;DataType&gt; supportedInputTypes =</div><div class="line"><a name="l00660"></a><span class="lineno">  660</span>&#160; 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       <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>,</div><div class="line"><a name="l00667"></a><span class="lineno">  667</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>,</div><div class="line"><a name="l00668"></a><span class="lineno">  668</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">DataType::Signed64</a></div><div class="line"><a name="l00669"></a><span class="lineno">  669</span>&#160;    };</div><div class="line"><a name="l00670"></a><span class="lineno">  670</span>&#160;</div><div class="line"><a name="l00671"></a><span class="lineno">  671</span>&#160;    ValidateDataTypes(inputTensorInfo, supportedInputTypes, descriptorName);</div><div class="line"><a name="l00672"></a><span class="lineno">  672</span>&#160;</div><div class="line"><a name="l00673"></a><span class="lineno">  673</span>&#160;    <span class="keyword">auto</span> inputShape = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00674"></a><span class="lineno">  674</span>&#160;    <span class="keyword">auto</span> outputShape = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00675"></a><span class="lineno">  675</span>&#160;</div><div class="line"><a name="l00676"></a><span class="lineno">  676</span>&#160;    <span class="keyword">auto</span> inputNumDimensions = inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00677"></a><span class="lineno">  677</span>&#160;    <span class="keyword">auto</span> unsignedAxis = <a class="code" href="namespacearmnn_utils.xhtml#ac93cb1365b4bcb67df2a3164606096c5">armnnUtils::GetUnsignedAxis</a>(inputNumDimensions, m_Parameters.m_Axis);</div><div class="line"><a name="l00678"></a><span class="lineno">  678</span>&#160;</div><div class="line"><a name="l00679"></a><span class="lineno">  679</span>&#160; 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           <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + outputShapeError);</div><div class="line"><a name="l00687"></a><span class="lineno">  687</span>&#160;        }</div><div class="line"><a name="l00688"></a><span class="lineno">  688</span>&#160;    }</div><div class="line"><a name="l00689"></a><span class="lineno">  689</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l00690"></a><span class="lineno">  690</span>&#160;    {</div><div class="line"><a name="l00691"></a><span class="lineno">  691</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; unsignedAxis; ++i)</div><div class="line"><a name="l00692"></a><span class="lineno">  692</span>&#160;        {</div><div class="line"><a name="l00693"></a><span class="lineno">  693</span>&#160;            <span class="keywordflow">if</span> (outputShape[i] != inputShape[i])</div><div class="line"><a name="l00694"></a><span class="lineno">  694</span>&#160;            {</div><div class="line"><a name="l00695"></a><span class="lineno">  695</span>&#160;                <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + outputShapeError);</div><div class="line"><a name="l00696"></a><span class="lineno">  696</span>&#160;            }</div><div class="line"><a name="l00697"></a><span class="lineno">  697</span>&#160;        }</div><div class="line"><a name="l00698"></a><span class="lineno">  698</span>&#160;</div><div class="line"><a name="l00699"></a><span class="lineno">  699</span>&#160;        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = unsignedAxis + 1; i &lt; inputNumDimensions; ++i)</div><div class="line"><a name="l00700"></a><span class="lineno">  700</span>&#160;        {</div><div class="line"><a name="l00701"></a><span class="lineno">  701</span>&#160;            <span class="keywordflow">if</span> (outputShape[i - 1] != inputShape[i])</div><div class="line"><a name="l00702"></a><span class="lineno">  702</span>&#160;            {</div><div class="line"><a name="l00703"></a><span class="lineno">  703</span>&#160;                <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + outputShapeError);</div><div class="line"><a name="l00704"></a><span class="lineno">  704</span>&#160;            }</div><div class="line"><a name="l00705"></a><span class="lineno">  705</span>&#160;        }</div><div class="line"><a name="l00706"></a><span class="lineno">  706</span>&#160;    }</div><div class="line"><a name="l00707"></a><span class="lineno">  707</span>&#160;}</div><div class="line"><a name="l00708"></a><span class="lineno">  708</span>&#160;</div><div class="line"><a name="l00709"></a><span class="lineno"><a class="line" href="structarmnn_1_1_softmax_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">  709</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_softmax_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">SoftmaxQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00710"></a><span class="lineno">  710</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00711"></a><span class="lineno">  711</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;SoftmaxQueueDescriptor&quot;</span>};</div><div class="line"><a name="l00712"></a><span class="lineno">  712</span>&#160;</div><div class="line"><a name="l00713"></a><span class="lineno">  713</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l00714"></a><span class="lineno">  714</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l00715"></a><span class="lineno">  715</span>&#160;</div><div class="line"><a name="l00716"></a><span class="lineno">  716</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l00717"></a><span class="lineno">  717</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l00718"></a><span class="lineno">  718</span>&#160;</div><div class="line"><a name="l00719"></a><span class="lineno">  719</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l00720"></a><span class="lineno">  720</span>&#160;    {</div><div class="line"><a name="l00721"></a><span class="lineno">  721</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l00722"></a><span class="lineno">  722</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l00723"></a><span class="lineno">  723</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l00724"></a><span class="lineno">  724</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l00725"></a><span class="lineno">  725</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l00726"></a><span class="lineno">  726</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l00727"></a><span class="lineno">  727</span>&#160;    };</div><div class="line"><a name="l00728"></a><span class="lineno">  728</span>&#160;</div><div class="line"><a name="l00729"></a><span class="lineno">  729</span>&#160;    ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l00730"></a><span class="lineno">  730</span>&#160;    ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00731"></a><span class="lineno">  731</span>&#160;    ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00732"></a><span class="lineno">  732</span>&#160;}</div><div class="line"><a name="l00733"></a><span class="lineno">  733</span>&#160;</div><div class="line"><a name="l00734"></a><span class="lineno"><a class="line" href="structarmnn_1_1_splitter_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">  734</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_splitter_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">SplitterQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00735"></a><span class="lineno">  735</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00736"></a><span class="lineno">  736</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;SplitterQueueDescriptor&quot;</span>};</div><div class="line"><a name="l00737"></a><span class="lineno">  737</span>&#160;</div><div class="line"><a name="l00738"></a><span class="lineno">  738</span>&#160;    ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l00739"></a><span class="lineno">  739</span>&#160;</div><div class="line"><a name="l00740"></a><span class="lineno">  740</span>&#160;    <span class="comment">// Check the supported data types</span></div><div class="line"><a name="l00741"></a><span class="lineno">  741</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l00742"></a><span class="lineno">  742</span>&#160;    {</div><div class="line"><a name="l00743"></a><span class="lineno">  743</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l00744"></a><span class="lineno">  744</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l00745"></a><span class="lineno">  745</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l00746"></a><span class="lineno">  746</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">DataType::Boolean</a>,</div><div class="line"><a name="l00747"></a><span class="lineno">  747</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>,</div><div class="line"><a name="l00748"></a><span class="lineno">  748</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l00749"></a><span class="lineno">  749</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l00750"></a><span class="lineno">  750</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l00751"></a><span class="lineno">  751</span>&#160;    };</div><div class="line"><a name="l00752"></a><span class="lineno">  752</span>&#160;</div><div class="line"><a name="l00753"></a><span class="lineno">  753</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l00754"></a><span class="lineno">  754</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">long</span> i = 0ul; i &lt; workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size(); ++i)</div><div class="line"><a name="l00755"></a><span class="lineno">  755</span>&#160;    {</div><div class="line"><a name="l00756"></a><span class="lineno">  756</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[i];</div><div class="line"><a name="l00757"></a><span class="lineno">  757</span>&#160;        ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l00758"></a><span class="lineno">  758</span>&#160;</div><div class="line"><a name="l00759"></a><span class="lineno">  759</span>&#160;        <span class="keyword">const</span> std::string outputName = <span class="stringliteral">&quot;output_&quot;</span> + std::to_string(i);</div><div class="line"><a name="l00760"></a><span class="lineno">  760</span>&#160;        ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, outputName);</div><div class="line"><a name="l00761"></a><span class="lineno">  761</span>&#160;    }</div><div class="line"><a name="l00762"></a><span class="lineno">  762</span>&#160;</div><div class="line"><a name="l00763"></a><span class="lineno">  763</span>&#160;    <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size() &lt;= 0)</div><div class="line"><a name="l00764"></a><span class="lineno">  764</span>&#160;    {</div><div class="line"><a name="l00765"></a><span class="lineno">  765</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: At least one output needs to be provided.&quot;</span>);</div><div class="line"><a name="l00766"></a><span class="lineno">  766</span>&#160; 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           <span class="stringliteral">&quot;Number of windows: &quot;</span> +</div><div class="line"><a name="l00774"></a><span class="lineno">  774</span>&#160;            to_string(m_ViewOrigins.size()) +</div><div class="line"><a name="l00775"></a><span class="lineno">  775</span>&#160;            <span class="stringliteral">&quot;. Number of workloadInfo.m_OutputTensorInfos: &quot;</span> + to_string(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size()));</div><div class="line"><a name="l00776"></a><span class="lineno">  776</span>&#160;    }</div><div class="line"><a name="l00777"></a><span class="lineno">  777</span>&#160;</div><div class="line"><a name="l00778"></a><span class="lineno">  778</span>&#160;    <span class="comment">//The dimensionality of all the windows has to match the dimensionality (not shape) of the input.</span></div><div class="line"><a name="l00779"></a><span class="lineno">  779</span>&#160;    std::size_t inputDims = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0].GetNumDimensions();</div><div class="line"><a name="l00780"></a><span class="lineno">  780</span>&#160;    <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> w = 0; w &lt; m_ViewOrigins.size(); ++w )</div><div class="line"><a name="l00781"></a><span class="lineno">  781</span>&#160;    {</div><div class="line"><a name="l00782"></a><span class="lineno">  782</span>&#160;        <span class="comment">//Checks that the dimensionality of input is same as the split windows.</span></div><div class="line"><a name="l00783"></a><span class="lineno">  783</span>&#160;        <a class="code" href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml">ViewOrigin</a> <span class="keyword">const</span>&amp; e = m_ViewOrigins[w];</div><div class="line"><a name="l00784"></a><span class="lineno">  784</span>&#160;        <span class="keywordflow">if</span> (e.<a class="code" href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml#af2f0a8c9eb32861711c0ce30b7986c44">m_Origin</a>.size() != inputDims)</div><div class="line"><a name="l00785"></a><span class="lineno">  785</span>&#160;        {</div><div class="line"><a name="l00786"></a><span class="lineno">  786</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Window origin have to &quot;</span></div><div class="line"><a name="l00787"></a><span class="lineno">  787</span>&#160;                                           <span class="stringliteral">&quot;have the same dimensionality as the input tensor. &quot;</span></div><div class="line"><a name="l00788"></a><span class="lineno">  788</span>&#160;                                           <span class="stringliteral">&quot;Window origin (index: &quot;</span> +</div><div class="line"><a name="l00789"></a><span class="lineno">  789</span>&#160;                                           to_string(w) + <span class="stringliteral">&quot;) has &quot;</span> + to_string(e.<a class="code" href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml#af2f0a8c9eb32861711c0ce30b7986c44">m_Origin</a>.size()) +</div><div class="line"><a name="l00790"></a><span class="lineno">  790</span>&#160;                                           <span class="stringliteral">&quot; dimensions, the input &quot;</span></div><div class="line"><a name="l00791"></a><span class="lineno">  791</span>&#160;                                           <span class="stringliteral">&quot;tensor has &quot;</span> +</div><div class="line"><a name="l00792"></a><span class="lineno">  792</span>&#160;                                           to_string(inputDims) + <span class="stringliteral">&quot; dimensions.&quot;</span>);</div><div class="line"><a name="l00793"></a><span class="lineno">  793</span>&#160;        }</div><div class="line"><a name="l00794"></a><span class="lineno">  794</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; e.<a class="code" href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml#af2f0a8c9eb32861711c0ce30b7986c44">m_Origin</a>.size(); ++i)</div><div class="line"><a name="l00795"></a><span class="lineno">  795</span>&#160;        {</div><div class="line"><a name="l00796"></a><span class="lineno">  796</span>&#160;            <span class="keywordflow">if</span> (e.<a class="code" href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml#af2f0a8c9eb32861711c0ce30b7986c44">m_Origin</a>[i] + workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[w].GetShape()[i] &gt;</div><div class="line"><a name="l00797"></a><span class="lineno">  797</span>&#160;                workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0].GetShape()[i])</div><div class="line"><a name="l00798"></a><span class="lineno">  798</span>&#160;            {</div><div class="line"><a name="l00799"></a><span class="lineno">  799</span>&#160;                <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Window extent coordinates have to &quot;</span></div><div class="line"><a name="l00800"></a><span class="lineno">  800</span>&#160;                                               <span class="stringliteral">&quot;be smaller or equal than the size of the input in that coord.&quot;</span>);</div><div class="line"><a name="l00801"></a><span class="lineno">  801</span>&#160;            }</div><div class="line"><a name="l00802"></a><span class="lineno">  802</span>&#160;        }</div><div class="line"><a name="l00803"></a><span class="lineno">  803</span>&#160;    }</div><div class="line"><a name="l00804"></a><span class="lineno">  804</span>&#160;}</div><div class="line"><a name="l00805"></a><span class="lineno">  805</span>&#160;</div><div class="line"><a name="l00806"></a><span class="lineno"><a class="line" href="structarmnn_1_1_concat_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">  806</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ConcatQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00807"></a><span class="lineno">  807</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00808"></a><span class="lineno">  808</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;ConcatQueueDescriptor&quot;</span>};</div><div class="line"><a name="l00809"></a><span class="lineno">  809</span>&#160;</div><div class="line"><a name="l00810"></a><span class="lineno">  810</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l00811"></a><span class="lineno">  811</span>&#160;</div><div class="line"><a name="l00812"></a><span class="lineno">  812</span>&#160;    <span class="keywordflow">if</span> (<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size() &lt;= 0)</div><div class="line"><a name="l00813"></a><span class="lineno">  813</span>&#160;    {</div><div class="line"><a name="l00814"></a><span class="lineno">  814</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: At least one input needs to be provided.&quot;</span>);</div><div class="line"><a name="l00815"></a><span class="lineno">  815</span>&#160;    }</div><div class="line"><a name="l00816"></a><span class="lineno">  816</span>&#160;    <span class="keywordflow">if</span> (<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>.size() &lt;= 0)</div><div class="line"><a name="l00817"></a><span class="lineno">  817</span>&#160;    {</div><div class="line"><a name="l00818"></a><span class="lineno">  818</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: At least one output needs to be provided.&quot;</span>);</div><div class="line"><a name="l00819"></a><span class="lineno">  819</span>&#160;    }</div><div class="line"><a name="l00820"></a><span class="lineno">  820</span>&#160;</div><div class="line"><a name="l00821"></a><span class="lineno">  821</span>&#160;    <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size() &lt;= 0)</div><div class="line"><a name="l00822"></a><span class="lineno">  822</span>&#160;    {</div><div class="line"><a name="l00823"></a><span class="lineno">  823</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: At least one TensorInfo input needs to be provided.&quot;</span>);</div><div class="line"><a name="l00824"></a><span class="lineno">  824</span>&#160;    }</div><div class="line"><a name="l00825"></a><span class="lineno">  825</span>&#160;    <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size() &lt;= 0)</div><div class="line"><a name="l00826"></a><span class="lineno">  826</span>&#160;    {</div><div class="line"><a name="l00827"></a><span class="lineno">  827</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: At least one TensorInfo output needs to be provided.&quot;</span>);</div><div class="line"><a name="l00828"></a><span class="lineno">  828</span>&#160;    }</div><div class="line"><a name="l00829"></a><span class="lineno">  829</span>&#160;</div><div class="line"><a name="l00830"></a><span class="lineno">  830</span>&#160;    <span class="keywordflow">if</span>(m_Parameters.GetConcatAxis() &gt; workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0].GetShape().GetNumDimensions())</div><div class="line"><a name="l00831"></a><span class="lineno">  831</span>&#160;    {</div><div class="line"><a name="l00832"></a><span class="lineno">  832</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Invalid concatenation axis provided.&quot;</span>);</div><div class="line"><a name="l00833"></a><span class="lineno">  833</span>&#160;    }</div><div class="line"><a name="l00834"></a><span class="lineno">  834</span>&#160;</div><div class="line"><a name="l00835"></a><span class="lineno">  835</span>&#160;    <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0].GetShape().GetNumDimensions() - m_Parameters.GetConcatAxis() == 1)</div><div class="line"><a name="l00836"></a><span class="lineno">  836</span>&#160;    {</div><div class="line"><a name="l00837"></a><span class="lineno">  837</span>&#160;        <span class="keywordflow">return</span>;</div><div class="line"><a name="l00838"></a><span class="lineno">  838</span>&#160;    }</div><div class="line"><a name="l00839"></a><span class="lineno">  839</span>&#160;</div><div class="line"><a name="l00840"></a><span class="lineno">  840</span>&#160;    <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size() != m_ViewOrigins.size())</div><div class="line"><a name="l00841"></a><span class="lineno">  841</span>&#160;    {</div><div class="line"><a name="l00842"></a><span class="lineno">  842</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l00843"></a><span class="lineno">  843</span>&#160;            descriptorName + <span class="stringliteral">&quot;: Number of split windows &quot;</span></div><div class="line"><a name="l00844"></a><span class="lineno">  844</span>&#160;            <span class="stringliteral">&quot;has to match number of workloadInfo.m_InputTensorInfos. &quot;</span></div><div class="line"><a name="l00845"></a><span class="lineno">  845</span>&#160;            <span class="stringliteral">&quot;Number of windows: &quot;</span> +</div><div class="line"><a name="l00846"></a><span class="lineno">  846</span>&#160;            to_string(m_ViewOrigins.size()) +</div><div class="line"><a name="l00847"></a><span class="lineno">  847</span>&#160;            <span class="stringliteral">&quot;. Number of workloadInfo.m_InputTensorInfos: &quot;</span> + to_string(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size()));</div><div class="line"><a name="l00848"></a><span class="lineno">  848</span>&#160;    }</div><div class="line"><a name="l00849"></a><span class="lineno">  849</span>&#160;</div><div class="line"><a name="l00850"></a><span class="lineno">  850</span>&#160;    <span class="comment">//The dimensionality of all the windows has to match the dimensionality (not shape) of the output.</span></div><div class="line"><a name="l00851"></a><span class="lineno">  851</span>&#160;    std::size_t outputDims = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0].GetNumDimensions();</div><div class="line"><a name="l00852"></a><span class="lineno">  852</span>&#160;    <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> w = 0; w &lt; m_ViewOrigins.size(); ++w )</div><div class="line"><a name="l00853"></a><span class="lineno">  853</span>&#160;    {</div><div class="line"><a name="l00854"></a><span class="lineno">  854</span>&#160;        <span class="comment">//Checks that the dimensionality of output is same as the split windows.</span></div><div class="line"><a name="l00855"></a><span class="lineno">  855</span>&#160;        <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ViewOrigin</a> <span class="keyword">const</span>&amp; e = m_ViewOrigins[w];</div><div class="line"><a name="l00856"></a><span class="lineno">  856</span>&#160;        <span class="keywordflow">if</span> (e.<a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml#af2f0a8c9eb32861711c0ce30b7986c44">m_Origin</a>.size() != outputDims)</div><div class="line"><a name="l00857"></a><span class="lineno">  857</span>&#160;        {</div><div class="line"><a name="l00858"></a><span class="lineno">  858</span>&#160; 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                                          <span class="stringliteral">&quot; dimensions, the output &quot;</span></div><div class="line"><a name="l00863"></a><span class="lineno">  863</span>&#160;                                           <span class="stringliteral">&quot;tensor has &quot;</span> +</div><div class="line"><a name="l00864"></a><span class="lineno">  864</span>&#160;                                           to_string(outputDims) + <span class="stringliteral">&quot; dimensions.&quot;</span>);</div><div class="line"><a name="l00865"></a><span class="lineno">  865</span>&#160;        }</div><div class="line"><a name="l00866"></a><span class="lineno">  866</span>&#160;        <span class="comment">//Checks that the merge windows are within the output tensor.</span></div><div class="line"><a name="l00867"></a><span class="lineno">  867</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; e.<a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml#af2f0a8c9eb32861711c0ce30b7986c44">m_Origin</a>.size(); ++i)</div><div class="line"><a name="l00868"></a><span class="lineno">  868</span>&#160;        {</div><div class="line"><a name="l00869"></a><span class="lineno">  869</span>&#160;            <span class="keywordflow">if</span> (e.<a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml#af2f0a8c9eb32861711c0ce30b7986c44">m_Origin</a>[i] + workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[w].GetShape()[i]</div><div class="line"><a name="l00870"></a><span class="lineno">  870</span>&#160;                &gt; workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0].GetShape()[i])</div><div class="line"><a name="l00871"></a><span class="lineno">  871</span>&#160; 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   <span class="comment">// Check the supported data types</span></div><div class="line"><a name="l00879"></a><span class="lineno">  879</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l00880"></a><span class="lineno">  880</span>&#160;    {</div><div class="line"><a name="l00881"></a><span class="lineno">  881</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l00882"></a><span class="lineno">  882</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l00883"></a><span class="lineno">  883</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l00884"></a><span class="lineno">  884</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">DataType::Boolean</a>,</div><div class="line"><a name="l00885"></a><span class="lineno">  885</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>,</div><div class="line"><a name="l00886"></a><span class="lineno">  886</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l00887"></a><span class="lineno">  887</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l00888"></a><span class="lineno">  888</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l00889"></a><span class="lineno">  889</span>&#160;    };</div><div class="line"><a name="l00890"></a><span class="lineno">  890</span>&#160;</div><div class="line"><a name="l00891"></a><span class="lineno">  891</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l00892"></a><span class="lineno">  892</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">long</span> i = 0ul; i &lt; workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size(); ++i)</div><div class="line"><a name="l00893"></a><span class="lineno">  893</span>&#160;    {</div><div class="line"><a name="l00894"></a><span class="lineno">  894</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[i];</div><div class="line"><a name="l00895"></a><span class="lineno">  895</span>&#160;        ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l00896"></a><span class="lineno">  896</span>&#160;</div><div class="line"><a name="l00897"></a><span class="lineno">  897</span>&#160;        <span class="keyword">const</span> std::string inputName = <span class="stringliteral">&quot;input_&quot;</span> + std::to_string(i);</div><div class="line"><a name="l00898"></a><span class="lineno">  898</span>&#160;        ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, inputName, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00899"></a><span class="lineno">  899</span>&#160;    }</div><div class="line"><a name="l00900"></a><span class="lineno">  900</span>&#160;}</div><div class="line"><a name="l00901"></a><span class="lineno">  901</span>&#160;</div><div class="line"><a name="l00902"></a><span class="lineno"><a class="line" href="structarmnn_1_1_stack_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">  902</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_stack_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">StackQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00903"></a><span class="lineno">  903</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00904"></a><span class="lineno">  904</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;StackQueueDescriptor&quot;</span>};</div><div class="line"><a name="l00905"></a><span class="lineno">  905</span>&#160;</div><div class="line"><a name="l00906"></a><span class="lineno">  906</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l00907"></a><span class="lineno">  907</span>&#160;</div><div class="line"><a name="l00908"></a><span class="lineno">  908</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_NumInputs != workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size())</div><div class="line"><a name="l00909"></a><span class="lineno">  909</span>&#160;    {</div><div class="line"><a name="l00910"></a><span class="lineno">  910</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Must have the defined number of input tensors.&quot;</span>);</div><div class="line"><a name="l00911"></a><span class="lineno">  911</span>&#160;    }</div><div class="line"><a name="l00912"></a><span class="lineno">  912</span>&#160;</div><div class="line"><a name="l00913"></a><span class="lineno">  913</span>&#160;    <span class="comment">// All inputs must have the same shape, which is defined in parameters</span></div><div class="line"><a name="l00914"></a><span class="lineno">  914</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>&amp; inputShape = m_Parameters.m_InputShape;</div><div class="line"><a name="l00915"></a><span class="lineno">  915</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size(); ++i)</div><div class="line"><a name="l00916"></a><span class="lineno">  916</span>&#160;    {</div><div class="line"><a name="l00917"></a><span class="lineno">  917</span>&#160;        <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[i].GetShape() != inputShape)</div><div class="line"><a name="l00918"></a><span class="lineno">  918</span>&#160;        {</div><div class="line"><a name="l00919"></a><span class="lineno">  919</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: All input tensor shapes must match the defined shape.&quot;</span>);</div><div class="line"><a name="l00920"></a><span class="lineno">  920</span>&#160;        }</div><div class="line"><a name="l00921"></a><span class="lineno">  921</span>&#160;    }</div><div class="line"><a name="l00922"></a><span class="lineno">  922</span>&#160;</div><div class="line"><a name="l00923"></a><span class="lineno">  923</span>&#160;    <span class="keywordflow">if</span> (inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() &gt; 4)</div><div class="line"><a name="l00924"></a><span class="lineno">  924</span>&#160;    {</div><div class="line"><a name="l00925"></a><span class="lineno">  925</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Input tensor may have up to 4 dimensions.&quot;</span>);</div><div class="line"><a name="l00926"></a><span class="lineno">  926</span>&#160;    }</div><div class="line"><a name="l00927"></a><span class="lineno">  927</span>&#160;</div><div class="line"><a name="l00928"></a><span class="lineno">  928</span>&#160; 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                                      <span class="stringliteral">&quot;than the number of input dimensions.&quot;</span>);</div><div class="line"><a name="l00934"></a><span class="lineno">  934</span>&#160;    }</div><div class="line"><a name="l00935"></a><span class="lineno">  935</span>&#160;</div><div class="line"><a name="l00936"></a><span class="lineno">  936</span>&#160;    <span class="comment">// Output shape must be as inferred from the input shape</span></div><div class="line"><a name="l00937"></a><span class="lineno">  937</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>&amp; outputShape = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0].GetShape();</div><div class="line"><a name="l00938"></a><span class="lineno">  938</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; m_Parameters.m_Axis; ++i)</div><div class="line"><a name="l00939"></a><span class="lineno">  939</span>&#160;    {</div><div class="line"><a name="l00940"></a><span class="lineno">  940</span>&#160;        <span class="keywordflow">if</span> (outputShape[i] != inputShape[i])</div><div class="line"><a name="l00941"></a><span class="lineno">  941</span>&#160;        {</div><div class="line"><a name="l00942"></a><span class="lineno">  942</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Output tensor must &quot;</span></div><div class="line"><a name="l00943"></a><span class="lineno">  943</span>&#160;                                           <span class="stringliteral">&quot;match shape inferred from input tensor.&quot;</span>);</div><div class="line"><a name="l00944"></a><span class="lineno">  944</span>&#160;        }</div><div class="line"><a name="l00945"></a><span class="lineno">  945</span>&#160;    }</div><div class="line"><a name="l00946"></a><span class="lineno">  946</span>&#160;</div><div class="line"><a name="l00947"></a><span class="lineno">  947</span>&#160;    <span class="keywordflow">if</span> (outputShape[m_Parameters.m_Axis] != m_Parameters.m_NumInputs)</div><div class="line"><a name="l00948"></a><span class="lineno">  948</span>&#160;    {</div><div class="line"><a name="l00949"></a><span class="lineno">  949</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Output tensor must &quot;</span></div><div class="line"><a name="l00950"></a><span class="lineno">  950</span>&#160;                                       <span class="stringliteral">&quot;match shape inferred from input tensor.&quot;</span>);</div><div class="line"><a name="l00951"></a><span class="lineno">  951</span>&#160;    }</div><div class="line"><a name="l00952"></a><span class="lineno">  952</span>&#160;</div><div class="line"><a name="l00953"></a><span class="lineno">  953</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = m_Parameters.m_Axis + 1; i &lt; inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() + 1; ++i)</div><div class="line"><a name="l00954"></a><span class="lineno">  954</span>&#160;    {</div><div class="line"><a name="l00955"></a><span class="lineno">  955</span>&#160;        <span class="keywordflow">if</span> (outputShape[i] != inputShape[i-1])</div><div class="line"><a name="l00956"></a><span class="lineno">  956</span>&#160;        {</div><div class="line"><a name="l00957"></a><span class="lineno">  957</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Output tensor must &quot;</span></div><div class="line"><a name="l00958"></a><span class="lineno">  958</span>&#160;                                           <span class="stringliteral">&quot;match shape inferred from input tensor.&quot;</span>);</div><div class="line"><a name="l00959"></a><span class="lineno">  959</span>&#160;        }</div><div class="line"><a name="l00960"></a><span class="lineno">  960</span>&#160;    }</div><div class="line"><a name="l00961"></a><span class="lineno">  961</span>&#160;</div><div class="line"><a name="l00962"></a><span class="lineno">  962</span>&#160;    <span class="keywordflow">if</span> (outputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() &gt; 5)</div><div class="line"><a name="l00963"></a><span class="lineno">  963</span>&#160;    {</div><div class="line"><a name="l00964"></a><span class="lineno">  964</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Output tensor may have up to 5 dimensions.&quot;</span>);</div><div class="line"><a name="l00965"></a><span class="lineno">  965</span>&#160;    }</div><div class="line"><a name="l00966"></a><span class="lineno">  966</span>&#160;</div><div class="line"><a name="l00967"></a><span class="lineno">  967</span>&#160;    <span class="comment">// Check the supported data types</span></div><div class="line"><a name="l00968"></a><span class="lineno">  968</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l00969"></a><span class="lineno">  969</span>&#160;    {</div><div class="line"><a name="l00970"></a><span class="lineno">  970</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l00971"></a><span class="lineno">  971</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l00972"></a><span class="lineno">  972</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l00973"></a><span class="lineno">  973</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">DataType::Boolean</a>,</div><div class="line"><a name="l00974"></a><span class="lineno">  974</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>,</div><div class="line"><a name="l00975"></a><span class="lineno">  975</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l00976"></a><span class="lineno">  976</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l00977"></a><span class="lineno">  977</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l00978"></a><span class="lineno">  978</span>&#160; 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                                    workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[i],</div><div class="line"><a name="l00986"></a><span class="lineno">  986</span>&#160;                                     descriptorName,</div><div class="line"><a name="l00987"></a><span class="lineno">  987</span>&#160;                                     <span class="stringliteral">&quot;input_0&quot;</span>,</div><div class="line"><a name="l00988"></a><span class="lineno">  988</span>&#160;                                     <span class="stringliteral">&quot;input_&quot;</span> + std::to_string(i));</div><div class="line"><a name="l00989"></a><span class="lineno">  989</span>&#160;    }</div><div class="line"><a name="l00990"></a><span class="lineno">  990</span>&#160;</div><div class="line"><a name="l00991"></a><span class="lineno">  991</span>&#160;    ValidateTensorDataTypesMatch(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0],</div><div class="line"><a name="l00992"></a><span class="lineno">  992</span>&#160;                                 workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0],</div><div class="line"><a name="l00993"></a><span class="lineno">  993</span>&#160;                                 descriptorName,</div><div class="line"><a name="l00994"></a><span class="lineno">  994</span>&#160;                                 <span class="stringliteral">&quot;input_0&quot;</span>,</div><div class="line"><a name="l00995"></a><span class="lineno">  995</span>&#160;                                 <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00996"></a><span class="lineno">  996</span>&#160;}</div><div class="line"><a name="l00997"></a><span class="lineno">  997</span>&#160;</div><div class="line"><a name="l00998"></a><span class="lineno"><a class="line" href="structarmnn_1_1_fill_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">  998</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_fill_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">FillQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00999"></a><span class="lineno">  999</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;FillQueueDescriptor&quot;</span>};</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>&#160;</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>&#160;</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>&#160;</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>&#160;    ValidateTensorNumDimensions(inputTensorInfo, descriptorName, 1, <span class="stringliteral">&quot;input&quot;</span>);</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>&#160;</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>&#160;    {</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a></div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>&#160;    };</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>&#160;</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>&#160; 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   {</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>&#160; 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workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;AdditionQueueDescriptor&quot;</span>};</div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>&#160;</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 2);</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>&#160;</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; 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       <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>,</div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a></div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>&#160;    };</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>&#160;</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>&#160;    ValidateDataTypes(inputTensorInfo0, supportedTypes, descriptorName);</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>&#160; 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   ValidateBroadcastTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>&#160;                                       inputTensorInfo1,</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>&#160;                                       outputTensorInfo,</div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>&#160;                                       descriptorName,</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>&#160;                                       <span class="stringliteral">&quot;input_0&quot;</span>,</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>&#160;                                       <span class="stringliteral">&quot;input_1&quot;</span>);</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>&#160;}</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>&#160;</div><div class="line"><a name="l01148"></a><span class="lineno"><a class="line" href="structarmnn_1_1_multiplication_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1148</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_multiplication_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">MultiplicationQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;MultiplicationQueueDescriptor&quot;</span>};</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>&#160;</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 2);</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>&#160;</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>&#160;</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>&#160; 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   ValidateBroadcastTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>&#160;                                       inputTensorInfo1,</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>&#160;                                       outputTensorInfo,</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>&#160;                                       descriptorName,</div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>&#160;                                       <span class="stringliteral">&quot;input_0&quot;</span>,</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>&#160;                                       <span class="stringliteral">&quot;input_1&quot;</span>);</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>&#160;}</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>&#160;</div><div class="line"><a name="l01185"></a><span class="lineno"><a class="line" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1185</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">BatchNormalizationQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;BatchNormalizationQueueDescriptor&quot;</span>};</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>&#160;</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>&#160;</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>&#160;</div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>&#160;    {</div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>&#160;    };</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>&#160;</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>&#160;    ValidateDataTypes(inputTensorInfo,  supportedTypes, descriptorName);</div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>&#160;    ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>&#160;</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>&#160;    ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>&#160;    ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>&#160;</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span>&#160; 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   <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; variance = m_Variance-&gt;GetTensorInfo();</div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; beta     = m_Beta-&gt;GetTensorInfo();</div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; gamma    = m_Gamma-&gt;GetTensorInfo();</div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>&#160;</div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span>&#160;    ValidateTensorNumDimensions(mean,     descriptorName, 1, <span class="stringliteral">&quot;mean&quot;</span>);</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span>&#160;    ValidateTensorNumDimensions(variance, descriptorName, 1, <span class="stringliteral">&quot;variance&quot;</span>);</div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>&#160;    ValidateTensorNumDimensions(beta,     descriptorName, 1, <span class="stringliteral">&quot;beta&quot;</span>);</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>&#160;    ValidateTensorNumDimensions(gamma,    descriptorName, 1, <span class="stringliteral">&quot;gamma&quot;</span>);</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>&#160;</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>&#160;    ValidateTensorShapesMatch(mean, variance, descriptorName, <span class="stringliteral">&quot;mean&quot;</span>, <span class="stringliteral">&quot;variance&quot;</span>);</div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>&#160;    ValidateTensorShapesMatch(mean, beta,     descriptorName, <span class="stringliteral">&quot;mean&quot;</span>, <span class="stringliteral">&quot;beta&quot;</span>);</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>&#160;    ValidateTensorShapesMatch(mean, gamma,    descriptorName, <span class="stringliteral">&quot;mean&quot;</span>, <span class="stringliteral">&quot;gamma&quot;</span>);</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>&#160;}</div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>&#160;</div><div class="line"><a name="l01231"></a><span class="lineno"><a class="line" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1231</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">Convolution2dQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;Convolution2dQueueDescriptor&quot;</span>};</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>&#160;</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>&#160;</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>&#160;</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>&#160;    ValidateTensorNumDimensions(inputTensorInfo,  descriptorName, 4, <span class="stringliteral">&quot;input&quot;</span>);</div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>&#160;    ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 4, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>&#160;</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>&#160;    ValidatePointer(m_Weight, descriptorName, <span class="stringliteral">&quot;weight&quot;</span>);</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>&#160;</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; weightTensorInfo = m_Weight-&gt;GetTensorInfo();</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>&#160;    ValidateTensorNumDimensions(weightTensorInfo, descriptorName, 4, <span class="stringliteral">&quot;weight&quot;</span>);</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>&#160;</div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>&#160;    ValidateWeightDataType(inputTensorInfo, weightTensorInfo, descriptorName);</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>&#160;</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>&#160;    <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;TensorInfo&gt;</a> optionalBiasTensorInfo;</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_BiasEnabled)</div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span>&#160;    {</div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>&#160;        ValidatePointer(m_Bias, descriptorName, <span class="stringliteral">&quot;bias&quot;</span>);</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>&#160;</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>&#160;        optionalBiasTensorInfo = MakeOptional&lt;TensorInfo&gt;(m_Bias-&gt;GetTensorInfo());</div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; biasTensorInfo = optionalBiasTensorInfo.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>();</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>&#160;</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>&#160;        ValidateTensorDataType(biasTensorInfo, <a class="code" href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">GetBiasDataType</a>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()), descriptorName, <span class="stringliteral">&quot;bias&quot;</span>);</div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>&#160;        ValidateBiasTensorQuantization(biasTensorInfo, inputTensorInfo, weightTensorInfo, descriptorName);</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span>&#160;    }</div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>&#160;</div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_StrideX &lt;= 0 || m_Parameters.m_StrideY &lt;= 0  )</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>&#160;    {</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span>&#160;            fmt::format(<span class="stringliteral">&quot;{}: strideX (provided {}) and strideY (provided {}) &quot;</span></div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>&#160;                        <span class="stringliteral">&quot;cannot be either negative or 0.&quot;</span>,</div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span>&#160;                        descriptorName, m_Parameters.m_StrideX, m_Parameters.m_StrideY));</div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>&#160;    }</div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>&#160;</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span>&#160;    ValidatePerAxisQuantization(inputTensorInfo,</div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>&#160;                                outputTensorInfo,</div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>&#160;                                weightTensorInfo,</div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span>&#160;                                optionalBiasTensorInfo,</div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>&#160;                                descriptorName);</div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span>&#160;</div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>&#160;    {</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>,</div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a></div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>&#160;    };</div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>&#160;</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>&#160;    ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>&#160;</div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>&#160;    <span class="comment">// For Convolution2d, we allow to have BFloat16 input with Float32 output for optimization.</span></div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>&#160;    <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>)</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>&#160;    {</div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>&#160;        <span class="keywordflow">if</span> (outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a> &amp;&amp; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>)</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span>&#160;        {</div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName  + <span class="stringliteral">&quot;: &quot;</span> + <span class="stringliteral">&quot; Output tensor type must be BFloat16 or Float32 &quot;</span></div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>&#160;                                           <span class="stringliteral">&quot;for BFloat16 input.&quot;</span>);</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span>&#160;        }</div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>&#160;    }</div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>&#160;    {</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>&#160;        ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span>&#160;    }</div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span>&#160;}</div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</span>&#160;</div><div class="line"><a name="l01305"></a><span class="lineno"><a class="line" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1305</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">DepthwiseConvolution2dQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;DepthwiseConvolution2dQueueDescriptor&quot;</span>};</div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span>&#160;</div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>&#160;</div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>&#160;</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span>&#160;    ValidateTensorNumDimensions(inputTensorInfo,  descriptorName, 4, <span class="stringliteral">&quot;input&quot;</span>);</div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>&#160;    ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 4, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>&#160;</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>&#160;    ValidatePointer(m_Weight, descriptorName, <span class="stringliteral">&quot;weight&quot;</span>);</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>&#160;</div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; weightTensorInfo = m_Weight-&gt;GetTensorInfo();</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>&#160;    ValidateTensorNumDimensions(weightTensorInfo, descriptorName, 4, <span class="stringliteral">&quot;weight&quot;</span>);</div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>&#160;</div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_DilationX &lt; 1 || m_Parameters.m_DilationY &lt; 1 )</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>&#160;    {</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>&#160;            fmt::format(<span class="stringliteral">&quot;{}: dilationX (provided {}) and dilationY (provided {}) &quot;</span></div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>&#160;                        <span class="stringliteral">&quot;cannot be smaller than 1.&quot;</span>,</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span>&#160;                        descriptorName, m_Parameters.m_DilationX, m_Parameters.m_DilationX));</div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>&#160;    }</div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>&#160;</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_StrideX &lt;= 0 || m_Parameters.m_StrideY &lt;= 0  )</div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>&#160;    {</div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>&#160;            fmt::format(<span class="stringliteral">&quot;{}: strideX (provided {}) and strideY (provided {}) &quot;</span></div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>&#160;                        <span class="stringliteral">&quot;cannot be either negative or 0.&quot;</span>,</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>&#160;                        descriptorName, m_Parameters.m_StrideX, m_Parameters.m_StrideY));</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>&#160;    }</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>&#160;</div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channelIndex = (m_Parameters.m_DataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>) ? 1 : 3;</div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>&#160;</div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>&#160; 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           <span class="stringliteral">&quot;multiplied by channel_multiplier (provided {3}).&quot;</span>,</div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>&#160;            descriptorName, numWeightOutputChannels, numWeightInputChannels, numWeightChannelMultiplier));</div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>&#160;    }</div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span>&#160;</div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span>&#160;    ValidateWeightDataType(inputTensorInfo, weightTensorInfo, descriptorName);</div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>&#160;</div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>&#160;    <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;TensorInfo&gt;</a> optionalBiasTensorInfo;</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_BiasEnabled)</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>&#160;    {</div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>&#160;        ValidatePointer(m_Bias, descriptorName, <span class="stringliteral">&quot;bias&quot;</span>);</div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>&#160;</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>&#160;        optionalBiasTensorInfo = MakeOptional&lt;TensorInfo&gt;(m_Bias-&gt;GetTensorInfo());</div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; biasTensorInfo = optionalBiasTensorInfo.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>();</div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>&#160;</div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span>&#160;        ValidateBiasTensorQuantization(biasTensorInfo, inputTensorInfo, weightTensorInfo, descriptorName);</div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span>&#160;        ValidateTensorDataType(biasTensorInfo, <a class="code" href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">GetBiasDataType</a>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()), descriptorName, <span class="stringliteral">&quot;bias&quot;</span>);</div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>&#160;    }</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>&#160;    ValidatePerAxisQuantization(inputTensorInfo,</div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span>&#160;                                outputTensorInfo,</div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span>&#160;                                weightTensorInfo,</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>&#160;                                optionalBiasTensorInfo,</div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span>&#160;                                descriptorName);</div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span>&#160;</div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span>&#160;    {</div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span>&#160;    };</div><div class="line"><a name="l01382"></a><span class="lineno"> 1382</span>&#160;</div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span>&#160;    ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span>&#160; 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   <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span>&#160;</div><div class="line"><a name="l01399"></a><span class="lineno"> 1399</span>&#160;    ValidateTensorNumDimensions(inputTensorInfo,  descriptorName, mapping.<a class="code" href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>(), <span class="stringliteral">&quot;input&quot;</span>);</div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span>&#160;    ValidateTensorNumDimensions(outputTensorInfo, descriptorName, mapping.<a class="code" href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>(), <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span>&#160;</div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0u; i &lt; mapping.<a class="code" href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>(); ++i)</div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>&#160;    {</div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span>&#160;        <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i] != outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[mapping[i]])</div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>&#160; 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workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;Pooling2dQueueDescriptor&quot;</span>};</div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>&#160;</div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>&#160;</div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>&#160;</div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>&#160;    ValidateTensorNumDimensions(inputTensorInfo,  descriptorName, 4, <span class="stringliteral">&quot;input&quot;</span>);</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span>&#160;    ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 4, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span>&#160;</div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span>&#160; 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       <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l01464"></a><span class="lineno"> 1464</span>&#160;    };</div><div class="line"><a name="l01465"></a><span class="lineno"> 1465</span>&#160;</div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span>&#160;    ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01467"></a><span class="lineno"> 1467</span>&#160;    ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l01468"></a><span class="lineno"> 1468</span>&#160;</div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span>&#160; 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   {</div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span>&#160;            fmt::format(<span class="stringliteral">&quot;{}: Input batch size ({}) does not match output batch size ({})&quot;</span>,</div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>&#160;                        descriptorName, inputBatchSize, outputBatchSize));</div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span>&#160;    }</div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span>&#160;</div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>&#160;    <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">DataLayoutIndexed</a> dimensionIndices(m_Parameters.m_DataLayout);</div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span>&#160; 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   {</div><div class="line"><a name="l01484"></a><span class="lineno"> 1484</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l01485"></a><span class="lineno"> 1485</span>&#160;            fmt::format(<span class="stringliteral">&quot;{}: Input channel count ({}) does not match output channel count ({})&quot;</span>,</div><div class="line"><a name="l01486"></a><span class="lineno"> 1486</span>&#160;                        descriptorName, inputChannelCount, outputChannelCount));</div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span>&#160;    }</div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span>&#160;}</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span>&#160;</div><div class="line"><a name="l01490"></a><span class="lineno"><a class="line" href="structarmnn_1_1_resize_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1490</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_resize_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ResizeQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;ResizeQueueDescriptor&quot;</span>};</div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span>&#160;</div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span>&#160;</div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; 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       <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span>&#160;    };</div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span>&#160;</div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</span>&#160;    ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span>&#160;    ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</span>&#160;</div><div class="line"><a name="l01516"></a><span class="lineno"> 1516</span>&#160; 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workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;FakeQuantizationQueueDescriptor&quot;</span>};</div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span>&#160;</div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l01542"></a><span class="lineno"> 1542</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span>&#160;</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; 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workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;InstanceNormalizationQueueDescriptor&quot;</span>};</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>&#160;</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>&#160;</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span>&#160;</div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span>&#160;    <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() &gt; 4)</div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span>&#160;    {</div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span>&#160; 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workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l01589"></a><span class="lineno"> 1589</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;L2NormalizationQueueDescriptor&quot;</span>};</div><div class="line"><a name="l01590"></a><span class="lineno"> 1590</span>&#160;</div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span>&#160;</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; 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   <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;ReshapeQueueDescriptor&quot;</span>};</div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span>&#160;</div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l01678"></a><span class="lineno"> 1678</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01679"></a><span class="lineno"> 1679</span>&#160;</div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01681"></a><span class="lineno"> 1681</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01682"></a><span class="lineno"> 1682</span>&#160;</div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span>&#160;    ValidateTensorNumElementsMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>&#160;</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>&#160;    <span class="comment">// Check the supported data types</span></div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span>&#160;    {</div><div class="line"><a name="l01688"></a><span class="lineno"> 1688</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01689"></a><span class="lineno"> 1689</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01691"></a><span class="lineno"> 1691</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>,</div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>,</div><div class="line"><a name="l01695"></a><span class="lineno"> 1695</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">DataType::Boolean</a></div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span>&#160;    };</div><div class="line"><a name="l01697"></a><span class="lineno"> 1697</span>&#160;</div><div class="line"><a name="l01698"></a><span class="lineno"> 1698</span>&#160;    ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span>&#160;    ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l01700"></a><span class="lineno"> 1700</span>&#160;}</div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span>&#160;</div><div class="line"><a name="l01702"></a><span class="lineno"><a class="line" href="structarmnn_1_1_space_to_batch_nd_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1702</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_space_to_batch_nd_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">SpaceToBatchNdQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01703"></a><span class="lineno"> 1703</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;SpaceToBatchNdQueueDescriptor&quot;</span>};</div><div class="line"><a name="l01705"></a><span class="lineno"> 1705</span>&#160;</div><div class="line"><a name="l01706"></a><span class="lineno"> 1706</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l01707"></a><span class="lineno"> 1707</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01708"></a><span class="lineno"> 1708</span>&#160;</div><div class="line"><a name="l01709"></a><span class="lineno"> 1709</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01710"></a><span class="lineno"> 1710</span>&#160; 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       <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>&#160;    };</div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>&#160;</div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span>&#160; 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workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01770"></a><span class="lineno"> 1770</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;SpaceToDepthQueueDescriptor&quot;</span>};</div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span>&#160;</div><div class="line"><a name="l01773"></a><span class="lineno"> 1773</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l01774"></a><span class="lineno"> 1774</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>&#160;</div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>&#160;</div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>&#160;    ValidateTensorNumDimensions(inputTensorInfo,  descriptorName, 4, <span class="stringliteral">&quot;input&quot;</span>);</div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span>&#160;    ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 4, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span>&#160;</div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span>&#160;    {</div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01789"></a><span class="lineno"> 1789</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span>&#160;    };</div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span>&#160;</div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span>&#160;    ValidateDataTypes(inputTensorInfo,  supportedTypes, descriptorName);</div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span>&#160;    ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span>&#160;</div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span>&#160;    ValidateTensorNumElementsMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span>&#160;</div><div class="line"><a name="l01797"></a><span class="lineno"> 1797</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_BlockSize == 0)</div><div class="line"><a name="l01798"></a><span class="lineno"> 1798</span>&#160;    {</div><div class="line"><a name="l01799"></a><span class="lineno"> 1799</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Block size cannot be 0.&quot;</span>);</div><div class="line"><a name="l01800"></a><span class="lineno"> 1800</span>&#160;    }</div><div class="line"><a name="l01801"></a><span class="lineno"> 1801</span>&#160;</div><div class="line"><a name="l01802"></a><span class="lineno"> 1802</span>&#160;    <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">DataLayoutIndexed</a> dimensionIndices(m_Parameters.m_DataLayout);</div><div class="line"><a name="l01803"></a><span class="lineno"> 1803</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> wIndex = dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>();</div><div class="line"><a name="l01804"></a><span class="lineno"> 1804</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> hIndex = dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>();</div><div class="line"><a name="l01805"></a><span class="lineno"> 1805</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> cIndex = dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">GetChannelsIndex</a>();</div><div class="line"><a name="l01806"></a><span class="lineno"> 1806</span>&#160;</div><div class="line"><a name="l01807"></a><span class="lineno"> 1807</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>&amp; inputShape = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l01808"></a><span class="lineno"> 1808</span>&#160;    <span class="keywordflow">if</span> (inputShape[hIndex] % m_Parameters.m_BlockSize != 0 || inputShape[wIndex]  % m_Parameters.m_BlockSize != 0)</div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span>&#160;    {</div><div class="line"><a name="l01810"></a><span class="lineno"> 1810</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Input shape must be divisible &quot;</span></div><div class="line"><a name="l01811"></a><span class="lineno"> 1811</span>&#160;                                       <span class="stringliteral">&quot;by block size in all spatial dimensions&quot;</span>);</div><div class="line"><a name="l01812"></a><span class="lineno"> 1812</span>&#160;    }</div><div class="line"><a name="l01813"></a><span class="lineno"> 1813</span>&#160;</div><div class="line"><a name="l01814"></a><span class="lineno"> 1814</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>&amp; outputShape = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l01815"></a><span class="lineno"> 1815</span>&#160;    <span class="keywordflow">if</span> (outputShape[cIndex] % (m_Parameters.m_BlockSize * m_Parameters.m_BlockSize) != 0)</div><div class="line"><a name="l01816"></a><span class="lineno"> 1816</span>&#160;    {</div><div class="line"><a name="l01817"></a><span class="lineno"> 1817</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: The depth of the output tensor&quot;</span></div><div class="line"><a name="l01818"></a><span class="lineno"> 1818</span>&#160;                                       <span class="stringliteral">&quot;must be divisible by the square of block size.&quot;</span> );</div><div class="line"><a name="l01819"></a><span class="lineno"> 1819</span>&#160;    }</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span>&#160;}</div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span>&#160;</div><div class="line"><a name="l01822"></a><span class="lineno"><a class="line" href="structarmnn_1_1_floor_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1822</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_floor_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">FloorQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01823"></a><span class="lineno"> 1823</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l01824"></a><span class="lineno"> 1824</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;FloorQueueDescriptor&quot;</span>};</div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span>&#160;</div><div class="line"><a name="l01826"></a><span class="lineno"> 1826</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l01827"></a><span class="lineno"> 1827</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01828"></a><span class="lineno"> 1828</span>&#160;</div><div class="line"><a name="l01829"></a><span class="lineno"> 1829</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01830"></a><span class="lineno"> 1830</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01831"></a><span class="lineno"> 1831</span>&#160;</div><div class="line"><a name="l01832"></a><span class="lineno"> 1832</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l01833"></a><span class="lineno"> 1833</span>&#160;    {</div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01835"></a><span class="lineno"> 1835</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01837"></a><span class="lineno"> 1837</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l01838"></a><span class="lineno"> 1838</span>&#160;    };</div><div class="line"><a name="l01839"></a><span class="lineno"> 1839</span>&#160;</div><div class="line"><a name="l01840"></a><span class="lineno"> 1840</span>&#160;    ValidateDataTypes(inputTensorInfo,  supportedTypes, descriptorName);</div><div class="line"><a name="l01841"></a><span class="lineno"> 1841</span>&#160;</div><div class="line"><a name="l01842"></a><span class="lineno"> 1842</span>&#160;    <span class="keywordflow">if</span> (inputTensorInfo != outputTensorInfo)</div><div class="line"><a name="l01843"></a><span class="lineno"> 1843</span>&#160;    {</div><div class="line"><a name="l01844"></a><span class="lineno"> 1844</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Input and output tensor infos do not match.&quot;</span>);</div><div class="line"><a name="l01845"></a><span class="lineno"> 1845</span>&#160;    }</div><div class="line"><a name="l01846"></a><span class="lineno"> 1846</span>&#160;}</div><div class="line"><a name="l01847"></a><span class="lineno"> 1847</span>&#160;</div><div class="line"><a name="l01848"></a><span class="lineno"><a class="line" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1848</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">LstmQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01849"></a><span class="lineno"> 1849</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l01850"></a><span class="lineno"> 1850</span>&#160;    <span class="comment">// ported from android/ml/nn/common/operations/LSTM.cpp CheckInputTensorDimensions()</span></div><div class="line"><a name="l01851"></a><span class="lineno"> 1851</span>&#160;</div><div class="line"><a name="l01852"></a><span class="lineno"> 1852</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;LstmQueueDescriptor&quot;</span>};</div><div class="line"><a name="l01853"></a><span class="lineno"> 1853</span>&#160;</div><div class="line"><a name="l01854"></a><span class="lineno"> 1854</span>&#160;    <span class="comment">// check dimensions of all inputs and outputs</span></div><div class="line"><a name="l01855"></a><span class="lineno"> 1855</span>&#160;    <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size() != 3)</div><div class="line"><a name="l01856"></a><span class="lineno"> 1856</span>&#160;    {</div><div class="line"><a name="l01857"></a><span class="lineno"> 1857</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Invalid number of inputs.&quot;</span>);</div><div class="line"><a name="l01858"></a><span class="lineno"> 1858</span>&#160;    }</div><div class="line"><a name="l01859"></a><span class="lineno"> 1859</span>&#160;    <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size() != 4)</div><div class="line"><a name="l01860"></a><span class="lineno"> 1860</span>&#160;    {</div><div class="line"><a name="l01861"></a><span class="lineno"> 1861</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Invalid number of outputs.&quot;</span>);</div><div class="line"><a name="l01862"></a><span class="lineno"> 1862</span>&#160;    }</div><div class="line"><a name="l01863"></a><span class="lineno"> 1863</span>&#160;</div><div class="line"><a name="l01864"></a><span class="lineno"> 1864</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l01865"></a><span class="lineno"> 1865</span>&#160;    {</div><div class="line"><a name="l01866"></a><span class="lineno"> 1866</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01867"></a><span class="lineno"> 1867</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01868"></a><span class="lineno"> 1868</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01869"></a><span class="lineno"> 1869</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l01870"></a><span class="lineno"> 1870</span>&#160;    };</div><div class="line"><a name="l01871"></a><span class="lineno"> 1871</span>&#160;</div><div class="line"><a name="l01872"></a><span class="lineno"> 1872</span>&#160;    <span class="comment">// check for supported type of one input and match them with all the other input and output</span></div><div class="line"><a name="l01873"></a><span class="lineno"> 1873</span>&#160;    ValidateDataTypes(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0], supportedTypes, descriptorName);</div><div class="line"><a name="l01874"></a><span class="lineno"> 1874</span>&#160;</div><div class="line"><a name="l01875"></a><span class="lineno"> 1875</span>&#160;    <span class="comment">// type matches all other inputs</span></div><div class="line"><a name="l01876"></a><span class="lineno"> 1876</span>&#160; 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                                    <span class="stringliteral">&quot;input_0&quot;</span>,</div><div class="line"><a name="l01882"></a><span class="lineno"> 1882</span>&#160;                                     <span class="stringliteral">&quot;input_&quot;</span> + std::to_string(i));</div><div class="line"><a name="l01883"></a><span class="lineno"> 1883</span>&#160;    }</div><div class="line"><a name="l01884"></a><span class="lineno"> 1884</span>&#160;    <span class="comment">// type matches all other outputs</span></div><div class="line"><a name="l01885"></a><span class="lineno"> 1885</span>&#160;    <span class="keywordflow">for</span> (uint32_t i = 0u; i &lt; workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size(); ++i)</div><div class="line"><a name="l01886"></a><span class="lineno"> 1886</span>&#160;    {</div><div class="line"><a name="l01887"></a><span class="lineno"> 1887</span>&#160;        ValidateTensorDataTypesMatch(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0],</div><div class="line"><a name="l01888"></a><span class="lineno"> 1888</span>&#160;                                     workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[i],</div><div class="line"><a name="l01889"></a><span class="lineno"> 1889</span>&#160;                                     <span class="stringliteral">&quot;LstmQueueDescriptor&quot;</span>,</div><div class="line"><a name="l01890"></a><span class="lineno"> 1890</span>&#160;                                     <span class="stringliteral">&quot;input_0&quot;</span>,</div><div class="line"><a name="l01891"></a><span class="lineno"> 1891</span>&#160;                                     <span class="stringliteral">&quot;output_&quot;</span> + std::to_string(i));</div><div class="line"><a name="l01892"></a><span class="lineno"> 1892</span>&#160;    }</div><div class="line"><a name="l01893"></a><span class="lineno"> 1893</span>&#160;</div><div class="line"><a name="l01894"></a><span class="lineno"> 1894</span>&#160;    <span class="comment">// Making sure clipping parameters have valid values.</span></div><div class="line"><a name="l01895"></a><span class="lineno"> 1895</span>&#160;    <span class="comment">// == 0 means no clipping</span></div><div class="line"><a name="l01896"></a><span class="lineno"> 1896</span>&#160;    <span class="comment">//  &gt; 0 means clipping</span></div><div class="line"><a name="l01897"></a><span class="lineno"> 1897</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_ClippingThresCell &lt; 0.0f)</div><div class="line"><a name="l01898"></a><span class="lineno"> 1898</span>&#160;    {</div><div class="line"><a name="l01899"></a><span class="lineno"> 1899</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: negative cell clipping threshold is invalid&quot;</span>);</div><div class="line"><a name="l01900"></a><span class="lineno"> 1900</span>&#160;    }</div><div class="line"><a name="l01901"></a><span class="lineno"> 1901</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_ClippingThresProj &lt; 0.0f)</div><div class="line"><a name="l01902"></a><span class="lineno"> 1902</span>&#160;    {</div><div class="line"><a name="l01903"></a><span class="lineno"> 1903</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: negative projection clipping threshold is invalid&quot;</span>);</div><div class="line"><a name="l01904"></a><span class="lineno"> 1904</span>&#160;    }</div><div class="line"><a name="l01905"></a><span class="lineno"> 1905</span>&#160;</div><div class="line"><a name="l01906"></a><span class="lineno"> 1906</span>&#160;</div><div class="line"><a name="l01907"></a><span class="lineno"> 1907</span>&#160;    <span class="comment">// Inferring batch size, number of outputs and number of cells from the inputs.</span></div><div class="line"><a name="l01908"></a><span class="lineno"> 1908</span>&#160;    <span class="keyword">const</span> uint32_t n_input = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0].GetShape()[1];</div><div class="line"><a name="l01909"></a><span class="lineno"> 1909</span>&#160;    <span class="keyword">const</span> uint32_t n_batch = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0].GetShape()[0];</div><div class="line"><a name="l01910"></a><span class="lineno"> 1910</span>&#160;    ValidatePointer(m_InputToOutputWeights, <span class="stringliteral">&quot;Null pointer check&quot;</span>, <span class="stringliteral">&quot;InputToOutputWeights&quot;</span>);</div><div class="line"><a name="l01911"></a><span class="lineno"> 1911</span>&#160;    <span class="keyword">const</span> uint32_t n_cell = m_InputToOutputWeights-&gt;GetShape()[0];</div><div class="line"><a name="l01912"></a><span class="lineno"> 1912</span>&#160;    ValidatePointer(m_RecurrentToOutputWeights, <span class="stringliteral">&quot;Null pointer check&quot;</span>, <span class="stringliteral">&quot;RecurrentToOutputWeights&quot;</span>);</div><div class="line"><a name="l01913"></a><span class="lineno"> 1913</span>&#160;    <span class="keyword">const</span> uint32_t n_output = m_RecurrentToOutputWeights-&gt;GetShape()[1];</div><div class="line"><a name="l01914"></a><span class="lineno"> 1914</span>&#160;</div><div class="line"><a name="l01915"></a><span class="lineno"> 1915</span>&#160;    <span class="comment">// input tensor</span></div><div class="line"><a name="l01916"></a><span class="lineno"> 1916</span>&#160;    ValidateTensorNumDimNumElem(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0], 2, (n_batch * n_input),</div><div class="line"><a name="l01917"></a><span class="lineno"> 1917</span>&#160;                                descriptorName + <span class="stringliteral">&quot; input_0&quot;</span>);</div><div class="line"><a name="l01918"></a><span class="lineno"> 1918</span>&#160;    <span class="comment">// outputStateInTensor</span></div><div class="line"><a name="l01919"></a><span class="lineno"> 1919</span>&#160;    ValidateTensorNumDimNumElem(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1], 2, (n_batch * n_output),</div><div class="line"><a name="l01920"></a><span class="lineno"> 1920</span>&#160;                                descriptorName + <span class="stringliteral">&quot; input_1&quot;</span>);</div><div class="line"><a name="l01921"></a><span class="lineno"> 1921</span>&#160;    <span class="comment">// outputStateInTensor</span></div><div class="line"><a name="l01922"></a><span class="lineno"> 1922</span>&#160;    ValidateTensorNumDimNumElem(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[2], 2, (n_batch * n_cell),</div><div class="line"><a name="l01923"></a><span class="lineno"> 1923</span>&#160;                                descriptorName + <span class="stringliteral">&quot; input_2&quot;</span>);</div><div class="line"><a name="l01924"></a><span class="lineno"> 1924</span>&#160;    <span class="comment">// scratchBufferTensor</span></div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> scratchBufferSize = m_Parameters.m_CifgEnabled ? n_cell * 3 : n_cell * 4;</div><div class="line"><a name="l01926"></a><span class="lineno"> 1926</span>&#160;    ValidateTensorNumDimNumElem(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0], 2, (n_batch * scratchBufferSize),</div><div class="line"><a name="l01927"></a><span class="lineno"> 1927</span>&#160;                                descriptorName + <span class="stringliteral">&quot; output_0&quot;</span>);</div><div class="line"><a name="l01928"></a><span class="lineno"> 1928</span>&#160;    <span class="comment">// outputStateOutTensor</span></div><div class="line"><a name="l01929"></a><span class="lineno"> 1929</span>&#160;    ValidateTensorNumDimNumElem(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[1], 2, (n_batch * n_output),</div><div class="line"><a name="l01930"></a><span class="lineno"> 1930</span>&#160;                                descriptorName + <span class="stringliteral">&quot; output_1&quot;</span>);</div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>&#160;    <span class="comment">// cellStateOutTensor</span></div><div class="line"><a name="l01932"></a><span class="lineno"> 1932</span>&#160;    ValidateTensorNumDimNumElem(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[2], 2, (n_batch * n_cell),</div><div class="line"><a name="l01933"></a><span class="lineno"> 1933</span>&#160;                                descriptorName + <span class="stringliteral">&quot; output_2&quot;</span>);</div><div class="line"><a name="l01934"></a><span class="lineno"> 1934</span>&#160;    <span class="comment">// outputTensor</span></div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span>&#160;    ValidateTensorNumDimNumElem(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[3], 2, (n_batch * n_output),</div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span>&#160;                                descriptorName + <span class="stringliteral">&quot; output_3&quot;</span>);</div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>&#160;</div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span>&#160;</div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span>&#160;    <span class="comment">// check that dimensions of inputs/outputs and QueueDescriptor data match with each other</span></div><div class="line"><a name="l01940"></a><span class="lineno"> 1940</span>&#160;    <span class="keywordflow">if</span> ( m_InputToInputWeights )</div><div class="line"><a name="l01941"></a><span class="lineno"> 1941</span>&#160;    {</div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span>&#160;        ValidateTensorNumDimNumElem(m_InputToInputWeights-&gt;GetTensorInfo(), 2,</div><div class="line"><a name="l01943"></a><span class="lineno"> 1943</span>&#160;                                      (n_cell * n_input), <span class="stringliteral">&quot;InputLayerNormWeights&quot;</span>);</div><div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>&#160;    }</div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span>&#160;</div><div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>&#160;    ValidatePointer(m_InputToForgetWeights, <span class="stringliteral">&quot;Null pointer check&quot;</span>, <span class="stringliteral">&quot;InputToForgetWeights&quot;</span>);</div><div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>&#160;    ValidateTensorNumDimNumElem(m_InputToForgetWeights-&gt;GetTensorInfo(), 2,</div><div class="line"><a name="l01948"></a><span class="lineno"> 1948</span>&#160;                                  (n_cell * n_input), <span class="stringliteral">&quot;InputToForgetWeights&quot;</span>);</div><div class="line"><a name="l01949"></a><span class="lineno"> 1949</span>&#160;</div><div class="line"><a name="l01950"></a><span class="lineno"> 1950</span>&#160;    ValidatePointer(m_InputToCellWeights, <span class="stringliteral">&quot;Null pointer check&quot;</span>, <span class="stringliteral">&quot;InputToCellWeights&quot;</span>);</div><div class="line"><a name="l01951"></a><span class="lineno"> 1951</span>&#160;    ValidateTensorNumDimNumElem(m_InputToCellWeights-&gt;GetTensorInfo(), 2,</div><div class="line"><a name="l01952"></a><span class="lineno"> 1952</span>&#160;                                  (n_cell * n_input), <span class="stringliteral">&quot;InputToCellWeights&quot;</span>);</div><div class="line"><a name="l01953"></a><span class="lineno"> 1953</span>&#160;</div><div class="line"><a name="l01954"></a><span class="lineno"> 1954</span>&#160;    <span class="keywordflow">if</span> ( m_RecurrentToInputWeights )</div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span>&#160;    {</div><div class="line"><a name="l01956"></a><span class="lineno"> 1956</span>&#160;        ValidateTensorNumDimNumElem(m_RecurrentToInputWeights-&gt;GetTensorInfo(), 2,</div><div class="line"><a name="l01957"></a><span class="lineno"> 1957</span>&#160;                                      (n_cell * n_output), <span class="stringliteral">&quot;RecurrentToInputWeights&quot;</span>);</div><div class="line"><a name="l01958"></a><span class="lineno"> 1958</span>&#160;    }</div><div class="line"><a name="l01959"></a><span class="lineno"> 1959</span>&#160;</div><div class="line"><a name="l01960"></a><span class="lineno"> 1960</span>&#160;    ValidatePointer(m_RecurrentToForgetWeights, <span class="stringliteral">&quot;Null pointer check&quot;</span>, <span class="stringliteral">&quot;RecurrentToForgetWeights&quot;</span>);</div><div class="line"><a name="l01961"></a><span class="lineno"> 1961</span>&#160;    ValidateTensorNumDimNumElem(m_RecurrentToForgetWeights-&gt;GetTensorInfo(), 2,</div><div class="line"><a name="l01962"></a><span class="lineno"> 1962</span>&#160;                                  (n_cell * n_output), <span class="stringliteral">&quot;RecurrentToForgetWeights&quot;</span>);</div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>&#160;</div><div class="line"><a name="l01964"></a><span class="lineno"> 1964</span>&#160;    ValidatePointer(m_RecurrentToCellWeights, <span class="stringliteral">&quot;Null pointer check&quot;</span>, <span class="stringliteral">&quot;RecurrentToCellWeights&quot;</span>);</div><div class="line"><a name="l01965"></a><span class="lineno"> 1965</span>&#160;    ValidateTensorNumDimNumElem(m_RecurrentToCellWeights-&gt;GetTensorInfo(), 2,</div><div class="line"><a name="l01966"></a><span class="lineno"> 1966</span>&#160;                                  (n_cell * n_output), <span class="stringliteral">&quot;RecurrentToCellWeights&quot;</span>);</div><div class="line"><a name="l01967"></a><span class="lineno"> 1967</span>&#160;</div><div class="line"><a name="l01968"></a><span class="lineno"> 1968</span>&#160;    <span class="comment">// Make sure the input-gate&#39;s parameters are either both present (regular</span></div><div class="line"><a name="l01969"></a><span class="lineno"> 1969</span>&#160;    <span class="comment">// LSTM) or not at all (CIFG-LSTM). And CifgEnable is set accordingly.</span></div><div class="line"><a name="l01970"></a><span class="lineno"> 1970</span>&#160;    <span class="keywordtype">bool</span> cifg_weights_all_or_none = ((m_InputToInputWeights &amp;&amp; m_RecurrentToInputWeights &amp;&amp;</div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</span>&#160;                                     !m_Parameters.m_CifgEnabled) ||</div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>&#160;                                     (!m_InputToInputWeights &amp;&amp; !m_RecurrentToInputWeights &amp;&amp;</div><div class="line"><a name="l01973"></a><span class="lineno"> 1973</span>&#160;                                     m_Parameters.m_CifgEnabled));</div><div class="line"><a name="l01974"></a><span class="lineno"> 1974</span>&#160;    <span class="keywordflow">if</span> (!cifg_weights_all_or_none)</div><div class="line"><a name="l01975"></a><span class="lineno"> 1975</span>&#160;    {</div><div class="line"><a name="l01976"></a><span class="lineno"> 1976</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Input-Gate&#39;s parameters InputToInputWeights and &quot;</span></div><div class="line"><a name="l01977"></a><span class="lineno"> 1977</span>&#160;                                       <span class="stringliteral">&quot;RecurrentToInputWeights must either both be present (regular LSTM) &quot;</span></div><div class="line"><a name="l01978"></a><span class="lineno"> 1978</span>&#160;                                       <span class="stringliteral">&quot;or both not present (CIFG-LSTM). In addition CifgEnable must be set &quot;</span></div><div class="line"><a name="l01979"></a><span class="lineno"> 1979</span>&#160;                                       <span class="stringliteral">&quot;accordingly.&quot;</span>);</div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>&#160;    }</div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span>&#160;</div><div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>&#160;    <span class="keywordflow">if</span> ( m_CellToInputWeights )</div><div class="line"><a name="l01983"></a><span class="lineno"> 1983</span>&#160;    {</div><div class="line"><a name="l01984"></a><span class="lineno"> 1984</span>&#160;        ValidateTensorNumDimNumElem(m_CellToInputWeights-&gt;GetTensorInfo(), 1,</div><div class="line"><a name="l01985"></a><span class="lineno"> 1985</span>&#160;                                      n_cell, <span class="stringliteral">&quot;CellToInputWeights&quot;</span>);</div><div class="line"><a name="l01986"></a><span class="lineno"> 1986</span>&#160;    }</div><div class="line"><a name="l01987"></a><span class="lineno"> 1987</span>&#160;    <span class="keywordflow">if</span> ( m_CellToForgetWeights )</div><div class="line"><a name="l01988"></a><span class="lineno"> 1988</span>&#160;    {</div><div class="line"><a name="l01989"></a><span class="lineno"> 1989</span>&#160;        ValidateTensorNumDimNumElem(m_CellToForgetWeights-&gt;GetTensorInfo(), 1,</div><div class="line"><a name="l01990"></a><span class="lineno"> 1990</span>&#160;                                      n_cell, <span class="stringliteral">&quot;CellToForgetWeights&quot;</span>);</div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>&#160;    }</div><div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>&#160;    <span class="keywordflow">if</span> ( m_CellToOutputWeights )</div><div class="line"><a name="l01993"></a><span class="lineno"> 1993</span>&#160;    {</div><div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>&#160;        ValidateTensorNumDimNumElem(m_CellToOutputWeights-&gt;GetTensorInfo(), 1,</div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span>&#160;                                      n_cell, <span class="stringliteral">&quot;CellToOutputWeights&quot;</span>);</div><div class="line"><a name="l01996"></a><span class="lineno"> 1996</span>&#160;    }</div><div class="line"><a name="l01997"></a><span class="lineno"> 1997</span>&#160;</div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>&#160;    <span class="comment">// Making sure the peephole weights are there all or none. And PeepholeEnable is set accordingly.</span></div><div class="line"><a name="l01999"></a><span class="lineno"> 1999</span>&#160;    <span class="keywordtype">bool</span> peephole_weights_all_or_none =</div><div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>&#160;            (((m_CellToInputWeights || m_Parameters.m_CifgEnabled) &amp;&amp;  m_CellToForgetWeights</div><div class="line"><a name="l02001"></a><span class="lineno"> 2001</span>&#160;            &amp;&amp; m_CellToOutputWeights &amp;&amp; m_Parameters.m_PeepholeEnabled)</div><div class="line"><a name="l02002"></a><span class="lineno"> 2002</span>&#160;            || ( !m_CellToInputWeights &amp;&amp; !m_CellToForgetWeights</div><div class="line"><a name="l02003"></a><span class="lineno"> 2003</span>&#160;            &amp;&amp; !m_CellToOutputWeights &amp;&amp; !m_Parameters.m_PeepholeEnabled));</div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>&#160;    <span class="keywordflow">if</span> (!peephole_weights_all_or_none)</div><div class="line"><a name="l02005"></a><span class="lineno"> 2005</span>&#160;    {</div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Invalid combination of peephole parameters.&quot;</span>);</div><div class="line"><a name="l02007"></a><span class="lineno"> 2007</span>&#160;    }</div><div class="line"><a name="l02008"></a><span class="lineno"> 2008</span>&#160;</div><div class="line"><a name="l02009"></a><span class="lineno"> 2009</span>&#160;    <span class="comment">// Make sure the input gate bias is present only when not a CIFG-LSTM.</span></div><div class="line"><a name="l02010"></a><span class="lineno"> 2010</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_CifgEnabled)</div><div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>&#160;    {</div><div class="line"><a name="l02012"></a><span class="lineno"> 2012</span>&#160;        <span class="keywordflow">if</span> (m_InputGateBias)</div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>&#160;        {</div><div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: InputGateBias is present and CIFG-LSTM is enabled.&quot;</span>);</div><div class="line"><a name="l02015"></a><span class="lineno"> 2015</span>&#160;        }</div><div class="line"><a name="l02016"></a><span class="lineno"> 2016</span>&#160;    }</div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>&#160;    {</div><div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>&#160;        <span class="keywordflow">if</span> (!m_InputGateBias)</div><div class="line"><a name="l02020"></a><span class="lineno"> 2020</span>&#160;        {</div><div class="line"><a name="l02021"></a><span class="lineno"> 2021</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: If CIFG-LSTM is disabled InputGateBias &quot;</span></div><div class="line"><a name="l02022"></a><span class="lineno"> 2022</span>&#160;                                           <span class="stringliteral">&quot;must be present.&quot;</span>);</div><div class="line"><a name="l02023"></a><span class="lineno"> 2023</span>&#160;        }</div><div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>&#160;        ValidateTensorNumDimNumElem(m_InputGateBias-&gt;GetTensorInfo(), 1,</div><div class="line"><a name="l02025"></a><span class="lineno"> 2025</span>&#160;                                      n_cell, <span class="stringliteral">&quot;InputGateBias&quot;</span>);</div><div class="line"><a name="l02026"></a><span class="lineno"> 2026</span>&#160;    }</div><div class="line"><a name="l02027"></a><span class="lineno"> 2027</span>&#160;</div><div class="line"><a name="l02028"></a><span class="lineno"> 2028</span>&#160;    ValidatePointer(m_ForgetGateBias, <span class="stringliteral">&quot;Null pointer check&quot;</span>, <span class="stringliteral">&quot;ForgetGateBias&quot;</span>);</div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>&#160;    ValidateTensorNumDimNumElem(m_ForgetGateBias-&gt;GetTensorInfo(), 1, n_cell, <span class="stringliteral">&quot;ForgetGateBias&quot;</span>);</div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>&#160;</div><div class="line"><a name="l02031"></a><span class="lineno"> 2031</span>&#160;    ValidatePointer(m_CellBias, <span class="stringliteral">&quot;Null pointer check&quot;</span>, <span class="stringliteral">&quot;CellBias&quot;</span>);</div><div class="line"><a name="l02032"></a><span class="lineno"> 2032</span>&#160;    ValidateTensorNumDimNumElem(m_CellBias-&gt;GetTensorInfo(), 1, n_cell, <span class="stringliteral">&quot;CellBias&quot;</span>);</div><div class="line"><a name="l02033"></a><span class="lineno"> 2033</span>&#160;</div><div class="line"><a name="l02034"></a><span class="lineno"> 2034</span>&#160;    ValidatePointer(m_OutputGateBias, <span class="stringliteral">&quot;Null pointer check&quot;</span>, <span class="stringliteral">&quot;OutputGateBias&quot;</span>);</div><div class="line"><a name="l02035"></a><span class="lineno"> 2035</span>&#160;    ValidateTensorNumDimNumElem(m_OutputGateBias-&gt;GetTensorInfo(), 1, n_cell, <span class="stringliteral">&quot;OutputGateBias&quot;</span>);</div><div class="line"><a name="l02036"></a><span class="lineno"> 2036</span>&#160;</div><div class="line"><a name="l02037"></a><span class="lineno"> 2037</span>&#160;    <span class="keywordflow">if</span> (m_ProjectionWeights)</div><div class="line"><a name="l02038"></a><span class="lineno"> 2038</span>&#160;    {</div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</span>&#160;        ValidateTensorNumDimNumElem(m_ProjectionWeights-&gt;GetTensorInfo(), 2,</div><div class="line"><a name="l02040"></a><span class="lineno"> 2040</span>&#160;                                      (n_cell * n_output), <span class="stringliteral">&quot;ProjectionWeights&quot;</span>);</div><div class="line"><a name="l02041"></a><span class="lineno"> 2041</span>&#160;    }</div><div class="line"><a name="l02042"></a><span class="lineno"> 2042</span>&#160;    <span class="keywordflow">if</span> (m_ProjectionBias)</div><div class="line"><a name="l02043"></a><span class="lineno"> 2043</span>&#160;    {</div><div class="line"><a name="l02044"></a><span class="lineno"> 2044</span>&#160;        ValidateTensorNumDimNumElem(m_ProjectionBias-&gt;GetTensorInfo(), 1, n_output, <span class="stringliteral">&quot;ProjectionBias&quot;</span>);</div><div class="line"><a name="l02045"></a><span class="lineno"> 2045</span>&#160;    }</div><div class="line"><a name="l02046"></a><span class="lineno"> 2046</span>&#160;</div><div class="line"><a name="l02047"></a><span class="lineno"> 2047</span>&#160;    <span class="comment">// Making sure the projection tensors are consistent:</span></div><div class="line"><a name="l02048"></a><span class="lineno"> 2048</span>&#160;    <span class="comment">// 1) If projection weight is not present, then projection bias should not be</span></div><div class="line"><a name="l02049"></a><span class="lineno"> 2049</span>&#160;    <span class="comment">// present.</span></div><div class="line"><a name="l02050"></a><span class="lineno"> 2050</span>&#160;    <span class="comment">// 2) If projection weight is present, then projection bias is optional.</span></div><div class="line"><a name="l02051"></a><span class="lineno"> 2051</span>&#160;    <span class="keywordtype">bool</span> projecton_tensors_consistent = ((!m_ProjectionWeights &amp;&amp; !m_ProjectionBias &amp;&amp;</div><div class="line"><a name="l02052"></a><span class="lineno"> 2052</span>&#160;                                        !m_Parameters.m_ProjectionEnabled)</div><div class="line"><a name="l02053"></a><span class="lineno"> 2053</span>&#160;                                        || (m_ProjectionWeights &amp;&amp; !m_ProjectionBias &amp;&amp;</div><div class="line"><a name="l02054"></a><span class="lineno"> 2054</span>&#160;                                        m_Parameters.m_ProjectionEnabled)</div><div class="line"><a name="l02055"></a><span class="lineno"> 2055</span>&#160;                                        || (m_ProjectionWeights &amp;&amp; m_ProjectionBias &amp;&amp;</div><div class="line"><a name="l02056"></a><span class="lineno"> 2056</span>&#160;                                        m_Parameters.m_ProjectionEnabled));</div><div class="line"><a name="l02057"></a><span class="lineno"> 2057</span>&#160;    <span class="keywordflow">if</span> (!projecton_tensors_consistent)</div><div class="line"><a name="l02058"></a><span class="lineno"> 2058</span>&#160;    {</div><div class="line"><a name="l02059"></a><span class="lineno"> 2059</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Projection tensors are inconsistent.&quot;</span>);</div><div class="line"><a name="l02060"></a><span class="lineno"> 2060</span>&#160;    }</div><div class="line"><a name="l02061"></a><span class="lineno"> 2061</span>&#160;</div><div class="line"><a name="l02062"></a><span class="lineno"> 2062</span>&#160;    <span class="comment">// The four layer normalization weights either all have values or none of them have values. Additionally, if</span></div><div class="line"><a name="l02063"></a><span class="lineno"> 2063</span>&#160;    <span class="comment">// CIFG is used, input layer normalization weights tensor is omitted and the other layer normalization weights</span></div><div class="line"><a name="l02064"></a><span class="lineno"> 2064</span>&#160;    <span class="comment">// either all have values or none of them have values. Layer normalization is used when the values of all the</span></div><div class="line"><a name="l02065"></a><span class="lineno"> 2065</span>&#160;    <span class="comment">// layer normalization weights are present</span></div><div class="line"><a name="l02066"></a><span class="lineno"> 2066</span>&#160;    <span class="keywordflow">if</span> (m_InputLayerNormWeights)</div><div class="line"><a name="l02067"></a><span class="lineno"> 2067</span>&#160;    {</div><div class="line"><a name="l02068"></a><span class="lineno"> 2068</span>&#160;        ValidateTensorNumDimNumElem(m_InputLayerNormWeights-&gt;GetTensorInfo(), 1, n_cell, <span class="stringliteral">&quot;InputLayerNormWeights&quot;</span>);</div><div class="line"><a name="l02069"></a><span class="lineno"> 2069</span>&#160;    }</div><div class="line"><a name="l02070"></a><span class="lineno"> 2070</span>&#160;    <span class="keywordflow">if</span> (m_ForgetLayerNormWeights)</div><div class="line"><a name="l02071"></a><span class="lineno"> 2071</span>&#160;    {</div><div class="line"><a name="l02072"></a><span class="lineno"> 2072</span>&#160;        ValidateTensorNumDimNumElem(m_ForgetLayerNormWeights-&gt;GetTensorInfo(), 1, n_cell, <span class="stringliteral">&quot;ForgetLayerNormWeights&quot;</span>);</div><div class="line"><a name="l02073"></a><span class="lineno"> 2073</span>&#160;    }</div><div class="line"><a name="l02074"></a><span class="lineno"> 2074</span>&#160;    <span class="keywordflow">if</span> (m_CellLayerNormWeights)</div><div class="line"><a name="l02075"></a><span class="lineno"> 2075</span>&#160;    {</div><div class="line"><a name="l02076"></a><span class="lineno"> 2076</span>&#160;        ValidateTensorNumDimNumElem(m_CellLayerNormWeights-&gt;GetTensorInfo(), 1, n_cell, <span class="stringliteral">&quot;CellLayerNormWeights&quot;</span>);</div><div class="line"><a name="l02077"></a><span class="lineno"> 2077</span>&#160;    }</div><div class="line"><a name="l02078"></a><span class="lineno"> 2078</span>&#160;    <span class="keywordflow">if</span> (m_OutputLayerNormWeights)</div><div class="line"><a name="l02079"></a><span class="lineno"> 2079</span>&#160;    {</div><div class="line"><a name="l02080"></a><span class="lineno"> 2080</span>&#160;        ValidateTensorNumDimNumElem(m_OutputLayerNormWeights-&gt;GetTensorInfo(), 1, n_cell, <span class="stringliteral">&quot;OutputLayerNormWeights&quot;</span>);</div><div class="line"><a name="l02081"></a><span class="lineno"> 2081</span>&#160;    }</div><div class="line"><a name="l02082"></a><span class="lineno"> 2082</span>&#160;</div><div class="line"><a name="l02083"></a><span class="lineno"> 2083</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_LayerNormEnabled)</div><div class="line"><a name="l02084"></a><span class="lineno"> 2084</span>&#160;    {</div><div class="line"><a name="l02085"></a><span class="lineno"> 2085</span>&#160;        <span class="keywordflow">if</span> (!m_Parameters.m_CifgEnabled)</div><div class="line"><a name="l02086"></a><span class="lineno"> 2086</span>&#160;        {</div><div class="line"><a name="l02087"></a><span class="lineno"> 2087</span>&#160;            <span class="keywordflow">if</span> (!m_InputLayerNormWeights)</div><div class="line"><a name="l02088"></a><span class="lineno"> 2088</span>&#160;            {</div><div class="line"><a name="l02089"></a><span class="lineno"> 2089</span>&#160;                <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Layer normalisation is enabled and CIFG-LSTM is &quot;</span></div><div class="line"><a name="l02090"></a><span class="lineno"> 2090</span>&#160;                                               <span class="stringliteral">&quot;disabled but InputLayerNormWeights are not present&quot;</span>);</div><div class="line"><a name="l02091"></a><span class="lineno"> 2091</span>&#160;            }</div><div class="line"><a name="l02092"></a><span class="lineno"> 2092</span>&#160;            ValidateTensorNumDimNumElem(m_InputLayerNormWeights-&gt;GetTensorInfo(),</div><div class="line"><a name="l02093"></a><span class="lineno"> 2093</span>&#160;                                          1, n_cell, <span class="stringliteral">&quot;InputLayerNormWeights&quot;</span>);</div><div class="line"><a name="l02094"></a><span class="lineno"> 2094</span>&#160;        }</div><div class="line"><a name="l02095"></a><span class="lineno"> 2095</span>&#160;        <span class="keywordflow">else</span> <span class="keywordflow">if</span> (m_InputLayerNormWeights)</div><div class="line"><a name="l02096"></a><span class="lineno"> 2096</span>&#160;        {</div><div class="line"><a name="l02097"></a><span class="lineno"> 2097</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;:InputLayerNormWeights are present while CIFG is &quot;</span></div><div class="line"><a name="l02098"></a><span class="lineno"> 2098</span>&#160;                                           <span class="stringliteral">&quot;enabled&quot;</span>);</div><div class="line"><a name="l02099"></a><span class="lineno"> 2099</span>&#160;        }</div><div class="line"><a name="l02100"></a><span class="lineno"> 2100</span>&#160;</div><div class="line"><a name="l02101"></a><span class="lineno"> 2101</span>&#160;        ValidatePointer(m_ForgetLayerNormWeights, <span class="stringliteral">&quot;Null pointer check layer normalisation enabled&quot;</span>,</div><div class="line"><a name="l02102"></a><span class="lineno"> 2102</span>&#160;                        <span class="stringliteral">&quot;ForgetLayerNormWeights&quot;</span>);</div><div class="line"><a name="l02103"></a><span class="lineno"> 2103</span>&#160;        ValidateTensorNumDimNumElem(m_ForgetLayerNormWeights-&gt;GetTensorInfo(), 1, n_cell, <span class="stringliteral">&quot;ForgetLayerNormWeights&quot;</span>);</div><div class="line"><a name="l02104"></a><span class="lineno"> 2104</span>&#160;</div><div class="line"><a name="l02105"></a><span class="lineno"> 2105</span>&#160;        ValidatePointer(m_OutputLayerNormWeights, <span class="stringliteral">&quot;Null pointer check layer normalisation enabled&quot;</span>,</div><div class="line"><a name="l02106"></a><span class="lineno"> 2106</span>&#160;                        <span class="stringliteral">&quot;OutputLayerNormWeights&quot;</span>);</div><div class="line"><a name="l02107"></a><span class="lineno"> 2107</span>&#160;        ValidateTensorNumDimNumElem(m_OutputLayerNormWeights-&gt;GetTensorInfo(), 1, n_cell, <span class="stringliteral">&quot;OutputLayerNormWeights&quot;</span>);</div><div class="line"><a name="l02108"></a><span class="lineno"> 2108</span>&#160;</div><div class="line"><a name="l02109"></a><span class="lineno"> 2109</span>&#160;        ValidatePointer(m_CellLayerNormWeights, <span class="stringliteral">&quot;Null pointer check layer normalisation enabled&quot;</span>,</div><div class="line"><a name="l02110"></a><span class="lineno"> 2110</span>&#160;                        <span class="stringliteral">&quot;CellLayerNormWeights&quot;</span>);</div><div class="line"><a name="l02111"></a><span class="lineno"> 2111</span>&#160;        ValidateTensorNumDimNumElem(m_CellLayerNormWeights-&gt;GetTensorInfo(), 1, n_cell, <span class="stringliteral">&quot;CellLayerNormWeights&quot;</span>);</div><div class="line"><a name="l02112"></a><span class="lineno"> 2112</span>&#160;    }</div><div class="line"><a name="l02113"></a><span class="lineno"> 2113</span>&#160;    <span class="keywordflow">else</span> <span class="keywordflow">if</span> (m_InputLayerNormWeights || m_ForgetLayerNormWeights || m_OutputLayerNormWeights || m_CellLayerNormWeights)</div><div class="line"><a name="l02114"></a><span class="lineno"> 2114</span>&#160;    {</div><div class="line"><a name="l02115"></a><span class="lineno"> 2115</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Layer normalisation is disabled but one or more layer &quot;</span></div><div class="line"><a name="l02116"></a><span class="lineno"> 2116</span>&#160;                                       <span class="stringliteral">&quot;normalisation weights are present.&quot;</span>);</div><div class="line"><a name="l02117"></a><span class="lineno"> 2117</span>&#160;    }</div><div class="line"><a name="l02118"></a><span class="lineno"> 2118</span>&#160;}</div><div class="line"><a name="l02119"></a><span class="lineno"> 2119</span>&#160;</div><div class="line"><a name="l02120"></a><span class="lineno"><a class="line" href="structarmnn_1_1_convert_bf16_to_fp32_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2120</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_convert_bf16_to_fp32_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ConvertBf16ToFp32QueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02121"></a><span class="lineno"> 2121</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02122"></a><span class="lineno"> 2122</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;ConvertBf16ToFp32QueueDescriptor&quot;</span>};</div><div class="line"><a name="l02123"></a><span class="lineno"> 2123</span>&#160;</div><div class="line"><a name="l02124"></a><span class="lineno"> 2124</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l02125"></a><span class="lineno"> 2125</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02126"></a><span class="lineno"> 2126</span>&#160;</div><div class="line"><a name="l02127"></a><span class="lineno"> 2127</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02128"></a><span class="lineno"> 2128</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02129"></a><span class="lineno"> 2129</span>&#160;</div><div class="line"><a name="l02130"></a><span class="lineno"> 2130</span>&#160;    <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>)</div><div class="line"><a name="l02131"></a><span class="lineno"> 2131</span>&#160;    {</div><div class="line"><a name="l02132"></a><span class="lineno"> 2132</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Input tensor type must be BFloat16.&quot;</span>);</div><div class="line"><a name="l02133"></a><span class="lineno"> 2133</span>&#160;    }</div><div class="line"><a name="l02134"></a><span class="lineno"> 2134</span>&#160;</div><div class="line"><a name="l02135"></a><span class="lineno"> 2135</span>&#160;    <span class="keywordflow">if</span> (outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>)</div><div class="line"><a name="l02136"></a><span class="lineno"> 2136</span>&#160;    {</div><div class="line"><a name="l02137"></a><span class="lineno"> 2137</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Output tensor type must be Float32.&quot;</span>);</div><div class="line"><a name="l02138"></a><span class="lineno"> 2138</span>&#160;    }</div><div class="line"><a name="l02139"></a><span class="lineno"> 2139</span>&#160;</div><div class="line"><a name="l02140"></a><span class="lineno"> 2140</span>&#160;    ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l02141"></a><span class="lineno"> 2141</span>&#160;}</div><div class="line"><a name="l02142"></a><span class="lineno"> 2142</span>&#160;</div><div class="line"><a name="l02143"></a><span class="lineno"><a class="line" href="structarmnn_1_1_convert_fp32_to_bf16_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2143</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_convert_fp32_to_bf16_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ConvertFp32ToBf16QueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02144"></a><span class="lineno"> 2144</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02145"></a><span class="lineno"> 2145</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;ConvertFp32ToBf16QueueDescriptor&quot;</span>};</div><div class="line"><a name="l02146"></a><span class="lineno"> 2146</span>&#160;</div><div class="line"><a name="l02147"></a><span class="lineno"> 2147</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l02148"></a><span class="lineno"> 2148</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02149"></a><span class="lineno"> 2149</span>&#160;</div><div class="line"><a name="l02150"></a><span class="lineno"> 2150</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02151"></a><span class="lineno"> 2151</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02152"></a><span class="lineno"> 2152</span>&#160;</div><div class="line"><a name="l02153"></a><span class="lineno"> 2153</span>&#160;    <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>)</div><div class="line"><a name="l02154"></a><span class="lineno"> 2154</span>&#160;    {</div><div class="line"><a name="l02155"></a><span class="lineno"> 2155</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Input tensor type must be Float32.&quot;</span>);</div><div class="line"><a name="l02156"></a><span class="lineno"> 2156</span>&#160;    }</div><div class="line"><a name="l02157"></a><span class="lineno"> 2157</span>&#160;</div><div class="line"><a name="l02158"></a><span class="lineno"> 2158</span>&#160;    <span class="keywordflow">if</span> (outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>)</div><div class="line"><a name="l02159"></a><span class="lineno"> 2159</span>&#160;    {</div><div class="line"><a name="l02160"></a><span class="lineno"> 2160</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Output tensor type must be BFloat16.&quot;</span>);</div><div class="line"><a name="l02161"></a><span class="lineno"> 2161</span>&#160;    }</div><div class="line"><a name="l02162"></a><span class="lineno"> 2162</span>&#160;</div><div class="line"><a name="l02163"></a><span class="lineno"> 2163</span>&#160;    ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l02164"></a><span class="lineno"> 2164</span>&#160;}</div><div class="line"><a name="l02165"></a><span class="lineno"> 2165</span>&#160;</div><div class="line"><a name="l02166"></a><span class="lineno"><a class="line" href="structarmnn_1_1_convert_fp32_to_fp16_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2166</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_convert_fp32_to_fp16_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ConvertFp32ToFp16QueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02167"></a><span class="lineno"> 2167</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02168"></a><span class="lineno"> 2168</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;ConvertFp32ToFp16QueueDescriptor&quot;</span>};</div><div class="line"><a name="l02169"></a><span class="lineno"> 2169</span>&#160;</div><div class="line"><a name="l02170"></a><span class="lineno"> 2170</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l02171"></a><span class="lineno"> 2171</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02172"></a><span class="lineno"> 2172</span>&#160;</div><div class="line"><a name="l02173"></a><span class="lineno"> 2173</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02174"></a><span class="lineno"> 2174</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02175"></a><span class="lineno"> 2175</span>&#160;</div><div class="line"><a name="l02176"></a><span class="lineno"> 2176</span>&#160;    <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>)</div><div class="line"><a name="l02177"></a><span class="lineno"> 2177</span>&#160;    {</div><div class="line"><a name="l02178"></a><span class="lineno"> 2178</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Input tensor type must be Float32.&quot;</span>);</div><div class="line"><a name="l02179"></a><span class="lineno"> 2179</span>&#160;    }</div><div class="line"><a name="l02180"></a><span class="lineno"> 2180</span>&#160;</div><div class="line"><a name="l02181"></a><span class="lineno"> 2181</span>&#160;    <span class="keywordflow">if</span> (outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>)</div><div class="line"><a name="l02182"></a><span class="lineno"> 2182</span>&#160;    {</div><div class="line"><a name="l02183"></a><span class="lineno"> 2183</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Output tensor type must be Float16.&quot;</span>);</div><div class="line"><a name="l02184"></a><span class="lineno"> 2184</span>&#160;    }</div><div class="line"><a name="l02185"></a><span class="lineno"> 2185</span>&#160;</div><div class="line"><a name="l02186"></a><span class="lineno"> 2186</span>&#160;    ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l02187"></a><span class="lineno"> 2187</span>&#160;}</div><div class="line"><a name="l02188"></a><span class="lineno"> 2188</span>&#160;</div><div class="line"><a name="l02189"></a><span class="lineno"><a class="line" href="structarmnn_1_1_convert_fp16_to_fp32_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2189</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_convert_fp16_to_fp32_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ConvertFp16ToFp32QueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02190"></a><span class="lineno"> 2190</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02191"></a><span class="lineno"> 2191</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;ConvertFp16ToFp32QueueDescriptor&quot;</span>};</div><div class="line"><a name="l02192"></a><span class="lineno"> 2192</span>&#160;</div><div class="line"><a name="l02193"></a><span class="lineno"> 2193</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l02194"></a><span class="lineno"> 2194</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02195"></a><span class="lineno"> 2195</span>&#160;</div><div class="line"><a name="l02196"></a><span class="lineno"> 2196</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02197"></a><span class="lineno"> 2197</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02198"></a><span class="lineno"> 2198</span>&#160;</div><div class="line"><a name="l02199"></a><span class="lineno"> 2199</span>&#160;    <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>)</div><div class="line"><a name="l02200"></a><span class="lineno"> 2200</span>&#160; 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workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02213"></a><span class="lineno"> 2213</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02214"></a><span class="lineno"> 2214</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;DivisionQueueDescriptor&quot;</span>};</div><div class="line"><a name="l02215"></a><span class="lineno"> 2215</span>&#160;</div><div class="line"><a name="l02216"></a><span class="lineno"> 2216</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 2);</div><div class="line"><a name="l02217"></a><span class="lineno"> 2217</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02218"></a><span class="lineno"> 2218</span>&#160;</div><div class="line"><a name="l02219"></a><span class="lineno"> 2219</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; 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       <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02230"></a><span class="lineno"> 2230</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>,</div><div class="line"><a name="l02231"></a><span class="lineno"> 2231</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a></div><div class="line"><a name="l02232"></a><span class="lineno"> 2232</span>&#160;    };</div><div class="line"><a name="l02233"></a><span class="lineno"> 2233</span>&#160;</div><div class="line"><a name="l02234"></a><span class="lineno"> 2234</span>&#160;    ValidateDataTypes(inputTensorInfo0, supportedTypes, descriptorName);</div><div class="line"><a name="l02235"></a><span class="lineno"> 2235</span>&#160; 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                                      <span class="stringliteral">&quot;input_1&quot;</span>);</div><div class="line"><a name="l02244"></a><span class="lineno"> 2244</span>&#160;}</div><div class="line"><a name="l02245"></a><span class="lineno"> 2245</span>&#160;</div><div class="line"><a name="l02246"></a><span class="lineno"><a class="line" href="structarmnn_1_1_subtraction_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2246</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_subtraction_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">SubtractionQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02247"></a><span class="lineno"> 2247</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02248"></a><span class="lineno"> 2248</span>&#160; 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       <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02260"></a><span class="lineno"> 2260</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02261"></a><span class="lineno"> 2261</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02262"></a><span class="lineno"> 2262</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l02263"></a><span class="lineno"> 2263</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02264"></a><span class="lineno"> 2264</span>&#160; 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workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02359"></a><span class="lineno"> 2359</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02360"></a><span class="lineno"> 2360</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;PadQueueDescriptor&quot;</span>};</div><div class="line"><a name="l02361"></a><span class="lineno"> 2361</span>&#160;</div><div class="line"><a name="l02362"></a><span class="lineno"> 2362</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l02363"></a><span class="lineno"> 2363</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02364"></a><span class="lineno"> 2364</span>&#160;</div><div class="line"><a name="l02365"></a><span class="lineno"> 2365</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; 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                                      std::to_string(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()) + <span class="stringliteral">&quot; entries &quot;</span> +</div><div class="line"><a name="l02376"></a><span class="lineno"> 2376</span>&#160;                                       <span class="stringliteral">&quot; not &quot;</span> + std::to_string(m_Parameters.m_PadList.size()) + <span class="stringliteral">&quot; entries.&quot;</span>);</div><div class="line"><a name="l02377"></a><span class="lineno"> 2377</span>&#160;    }</div><div class="line"><a name="l02378"></a><span class="lineno"> 2378</span>&#160;}</div><div class="line"><a name="l02379"></a><span class="lineno"> 2379</span>&#160;</div><div class="line"><a name="l02380"></a><span class="lineno"><a class="line" href="structarmnn_1_1_quantize_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2380</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_quantize_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">QuantizeQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02381"></a><span class="lineno"> 2381</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02382"></a><span class="lineno"> 2382</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;QuantizeQueueDescriptor&quot;</span>};</div><div class="line"><a name="l02383"></a><span class="lineno"> 2383</span>&#160;</div><div class="line"><a name="l02384"></a><span class="lineno"> 2384</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l02385"></a><span class="lineno"> 2385</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02386"></a><span class="lineno"> 2386</span>&#160;</div><div class="line"><a name="l02387"></a><span class="lineno"> 2387</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02388"></a><span class="lineno"> 2388</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02389"></a><span class="lineno"> 2389</span>&#160;</div><div class="line"><a name="l02390"></a><span class="lineno"> 2390</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l02391"></a><span class="lineno"> 2391</span>&#160;    {</div><div class="line"><a name="l02392"></a><span class="lineno"> 2392</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02393"></a><span class="lineno"> 2393</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02394"></a><span class="lineno"> 2394</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02395"></a><span class="lineno"> 2395</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>,</div><div class="line"><a name="l02396"></a><span class="lineno"> 2396</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l02397"></a><span class="lineno"> 2397</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02398"></a><span class="lineno"> 2398</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02399"></a><span class="lineno"> 2399</span>&#160;    };</div><div class="line"><a name="l02400"></a><span class="lineno"> 2400</span>&#160;</div><div class="line"><a name="l02401"></a><span class="lineno"> 2401</span>&#160;    ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02402"></a><span class="lineno"> 2402</span>&#160;</div><div class="line"><a name="l02403"></a><span class="lineno"> 2403</span>&#160;    <span class="keywordflow">if</span> (!<a class="code" href="namespacearmnn.xhtml#ad44c007f21af2d0375e3ef9400a1b275">IsQuantizedType</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()))</div><div class="line"><a name="l02404"></a><span class="lineno"> 2404</span>&#160;    {</div><div class="line"><a name="l02405"></a><span class="lineno"> 2405</span>&#160; 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       <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02423"></a><span class="lineno"> 2423</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02424"></a><span class="lineno"> 2424</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l02425"></a><span class="lineno"> 2425</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02426"></a><span class="lineno"> 2426</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02427"></a><span class="lineno"> 2427</span>&#160; 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       <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02447"></a><span class="lineno"> 2447</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02448"></a><span class="lineno"> 2448</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l02449"></a><span class="lineno"> 2449</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02450"></a><span class="lineno"> 2450</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02451"></a><span class="lineno"> 2451</span>&#160; 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   }</div><div class="line"><a name="l02479"></a><span class="lineno"> 2479</span>&#160;</div><div class="line"><a name="l02480"></a><span class="lineno"> 2480</span>&#160;    <span class="comment">// Stride entries must be non-zero</span></div><div class="line"><a name="l02481"></a><span class="lineno"> 2481</span>&#160;    <span class="keywordflow">for</span> (<span class="keyword">auto</span>&amp; stride : m_Parameters.m_Stride)</div><div class="line"><a name="l02482"></a><span class="lineno"> 2482</span>&#160;    {</div><div class="line"><a name="l02483"></a><span class="lineno"> 2483</span>&#160;        <span class="keywordflow">if</span> (stride == 0)</div><div class="line"><a name="l02484"></a><span class="lineno"> 2484</span>&#160;        {</div><div class="line"><a name="l02485"></a><span class="lineno"> 2485</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Stride entries must be non-zero.&quot;</span>);</div><div class="line"><a name="l02486"></a><span class="lineno"> 2486</span>&#160;        }</div><div class="line"><a name="l02487"></a><span class="lineno"> 2487</span>&#160;    }</div><div class="line"><a name="l02488"></a><span class="lineno"> 2488</span>&#160;}</div><div class="line"><a name="l02489"></a><span class="lineno"> 2489</span>&#160;</div><div class="line"><a name="l02490"></a><span class="lineno"><a class="line" href="structarmnn_1_1_minimum_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2490</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_minimum_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">MinimumQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02491"></a><span class="lineno"> 2491</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02492"></a><span class="lineno"> 2492</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;MinimumQueueDescriptor&quot;</span>};</div><div class="line"><a name="l02493"></a><span class="lineno"> 2493</span>&#160;</div><div class="line"><a name="l02494"></a><span class="lineno"> 2494</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 2);</div><div class="line"><a name="l02495"></a><span class="lineno"> 2495</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02496"></a><span class="lineno"> 2496</span>&#160;</div><div class="line"><a name="l02497"></a><span class="lineno"> 2497</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02498"></a><span class="lineno"> 2498</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02499"></a><span class="lineno"> 2499</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02500"></a><span class="lineno"> 2500</span>&#160;</div><div class="line"><a name="l02501"></a><span class="lineno"> 2501</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l02502"></a><span class="lineno"> 2502</span>&#160;    {</div><div class="line"><a name="l02503"></a><span class="lineno"> 2503</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02504"></a><span class="lineno"> 2504</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02505"></a><span class="lineno"> 2505</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02506"></a><span class="lineno"> 2506</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l02507"></a><span class="lineno"> 2507</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02508"></a><span class="lineno"> 2508</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>,</div><div class="line"><a name="l02509"></a><span class="lineno"> 2509</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a></div><div class="line"><a name="l02510"></a><span class="lineno"> 2510</span>&#160;    };</div><div class="line"><a name="l02511"></a><span class="lineno"> 2511</span>&#160;</div><div class="line"><a name="l02512"></a><span class="lineno"> 2512</span>&#160;    ValidateDataTypes(inputTensorInfo0, supportedTypes, descriptorName);</div><div class="line"><a name="l02513"></a><span class="lineno"> 2513</span>&#160;    ValidateDataTypes(inputTensorInfo1, supportedTypes, descriptorName);</div><div class="line"><a name="l02514"></a><span class="lineno"> 2514</span>&#160;    ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02515"></a><span class="lineno"> 2515</span>&#160;</div><div class="line"><a name="l02516"></a><span class="lineno"> 2516</span>&#160;    ValidateBroadcastTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l02517"></a><span class="lineno"> 2517</span>&#160;                                       inputTensorInfo1,</div><div class="line"><a name="l02518"></a><span class="lineno"> 2518</span>&#160;                                       outputTensorInfo,</div><div class="line"><a name="l02519"></a><span class="lineno"> 2519</span>&#160;                                       descriptorName,</div><div class="line"><a name="l02520"></a><span class="lineno"> 2520</span>&#160;                                       <span class="stringliteral">&quot;input_0&quot;</span>,</div><div class="line"><a name="l02521"></a><span class="lineno"> 2521</span>&#160;                                       <span class="stringliteral">&quot;input_1&quot;</span>);</div><div class="line"><a name="l02522"></a><span class="lineno"> 2522</span>&#160;}</div><div class="line"><a name="l02523"></a><span class="lineno"> 2523</span>&#160;</div><div class="line"><a name="l02524"></a><span class="lineno"><a class="line" href="structarmnn_1_1_debug_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2524</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_debug_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">DebugQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02525"></a><span class="lineno"> 2525</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02526"></a><span class="lineno"> 2526</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;DebugQueueDescriptor&quot;</span>};</div><div class="line"><a name="l02527"></a><span class="lineno"> 2527</span>&#160;</div><div class="line"><a name="l02528"></a><span class="lineno"> 2528</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l02529"></a><span class="lineno"> 2529</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02530"></a><span class="lineno"> 2530</span>&#160;}</div><div class="line"><a name="l02531"></a><span class="lineno"> 2531</span>&#160;</div><div class="line"><a name="l02532"></a><span class="lineno"><a class="line" href="structarmnn_1_1_equal_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2532</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_equal_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">EqualQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02533"></a><span class="lineno"> 2533</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02534"></a><span class="lineno"> 2534</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;EqualQueueDescriptor&quot;</span>};</div><div class="line"><a name="l02535"></a><span class="lineno"> 2535</span>&#160;</div><div class="line"><a name="l02536"></a><span class="lineno"> 2536</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 2);</div><div class="line"><a name="l02537"></a><span class="lineno"> 2537</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02538"></a><span class="lineno"> 2538</span>&#160;</div><div class="line"><a name="l02539"></a><span class="lineno"> 2539</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02540"></a><span class="lineno"> 2540</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02541"></a><span class="lineno"> 2541</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02542"></a><span class="lineno"> 2542</span>&#160;</div><div class="line"><a name="l02543"></a><span class="lineno"> 2543</span>&#160;    ValidateBroadcastTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l02544"></a><span class="lineno"> 2544</span>&#160;                                       inputTensorInfo1,</div><div class="line"><a name="l02545"></a><span class="lineno"> 2545</span>&#160;                                       outputTensorInfo,</div><div class="line"><a name="l02546"></a><span class="lineno"> 2546</span>&#160;                                       descriptorName,</div><div class="line"><a name="l02547"></a><span class="lineno"> 2547</span>&#160;                                       <span class="stringliteral">&quot;input_0&quot;</span>,</div><div class="line"><a name="l02548"></a><span class="lineno"> 2548</span>&#160;                                       <span class="stringliteral">&quot;input_1&quot;</span>);</div><div class="line"><a name="l02549"></a><span class="lineno"> 2549</span>&#160;</div><div class="line"><a name="l02550"></a><span class="lineno"> 2550</span>&#160;    <span class="keywordflow">if</span> (outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">DataType::Boolean</a>)</div><div class="line"><a name="l02551"></a><span class="lineno"> 2551</span>&#160;    {</div><div class="line"><a name="l02552"></a><span class="lineno"> 2552</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Output tensor type must be Boolean.&quot;</span>);</div><div class="line"><a name="l02553"></a><span class="lineno"> 2553</span>&#160;    }</div><div class="line"><a name="l02554"></a><span class="lineno"> 2554</span>&#160;}</div><div class="line"><a name="l02555"></a><span class="lineno"> 2555</span>&#160;</div><div class="line"><a name="l02556"></a><span class="lineno"><a class="line" href="structarmnn_1_1_greater_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2556</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_greater_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">GreaterQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02557"></a><span class="lineno"> 2557</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02558"></a><span class="lineno"> 2558</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;GreaterQueueDescriptor&quot;</span>};</div><div class="line"><a name="l02559"></a><span class="lineno"> 2559</span>&#160;</div><div class="line"><a name="l02560"></a><span class="lineno"> 2560</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 2);</div><div class="line"><a name="l02561"></a><span class="lineno"> 2561</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02562"></a><span class="lineno"> 2562</span>&#160;</div><div class="line"><a name="l02563"></a><span class="lineno"> 2563</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02564"></a><span class="lineno"> 2564</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02565"></a><span class="lineno"> 2565</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02566"></a><span class="lineno"> 2566</span>&#160;</div><div class="line"><a name="l02567"></a><span class="lineno"> 2567</span>&#160;    ValidateBroadcastTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l02568"></a><span class="lineno"> 2568</span>&#160;                                       inputTensorInfo1,</div><div class="line"><a name="l02569"></a><span class="lineno"> 2569</span>&#160;                                       outputTensorInfo,</div><div class="line"><a name="l02570"></a><span class="lineno"> 2570</span>&#160;                                       descriptorName,</div><div class="line"><a name="l02571"></a><span class="lineno"> 2571</span>&#160;                                       <span class="stringliteral">&quot;input_0&quot;</span>,</div><div class="line"><a name="l02572"></a><span class="lineno"> 2572</span>&#160;                                       <span class="stringliteral">&quot;input_1&quot;</span>);</div><div class="line"><a name="l02573"></a><span class="lineno"> 2573</span>&#160;</div><div class="line"><a name="l02574"></a><span class="lineno"> 2574</span>&#160;    <span class="keywordflow">if</span> (outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">DataType::Boolean</a>)</div><div class="line"><a name="l02575"></a><span class="lineno"> 2575</span>&#160;    {</div><div class="line"><a name="l02576"></a><span class="lineno"> 2576</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Output tensor type must be Boolean.&quot;</span>);</div><div class="line"><a name="l02577"></a><span class="lineno"> 2577</span>&#160;    }</div><div class="line"><a name="l02578"></a><span class="lineno"> 2578</span>&#160;}</div><div class="line"><a name="l02579"></a><span class="lineno"> 2579</span>&#160;</div><div class="line"><a name="l02580"></a><span class="lineno"><a class="line" href="structarmnn_1_1_rsqrt_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2580</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_rsqrt_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">RsqrtQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02581"></a><span class="lineno"> 2581</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02582"></a><span class="lineno"> 2582</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;RsqrtQueueDescriptor&quot;</span>};</div><div class="line"><a name="l02583"></a><span class="lineno"> 2583</span>&#160;</div><div class="line"><a name="l02584"></a><span class="lineno"> 2584</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l02585"></a><span class="lineno"> 2585</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02586"></a><span class="lineno"> 2586</span>&#160;</div><div class="line"><a name="l02587"></a><span class="lineno"> 2587</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02588"></a><span class="lineno"> 2588</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02589"></a><span class="lineno"> 2589</span>&#160;</div><div class="line"><a name="l02590"></a><span class="lineno"> 2590</span>&#160;    ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l02591"></a><span class="lineno"> 2591</span>&#160;</div><div class="line"><a name="l02592"></a><span class="lineno"> 2592</span>&#160; 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       <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02599"></a><span class="lineno"> 2599</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02600"></a><span class="lineno"> 2600</span>&#160;    };</div><div class="line"><a name="l02601"></a><span class="lineno"> 2601</span>&#160;</div><div class="line"><a name="l02602"></a><span class="lineno"> 2602</span>&#160;    ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02603"></a><span class="lineno"> 2603</span>&#160;    ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l02604"></a><span class="lineno"> 2604</span>&#160;}</div><div class="line"><a name="l02605"></a><span class="lineno"> 2605</span>&#160;</div><div class="line"><a name="l02606"></a><span class="lineno"><a class="line" href="structarmnn_1_1_gather_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2606</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_gather_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">GatherQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02607"></a><span class="lineno"> 2607</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02608"></a><span class="lineno"> 2608</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;GatherQueueDescriptor&quot;</span>};</div><div class="line"><a name="l02609"></a><span class="lineno"> 2609</span>&#160;</div><div class="line"><a name="l02610"></a><span class="lineno"> 2610</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 2);</div><div class="line"><a name="l02611"></a><span class="lineno"> 2611</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02612"></a><span class="lineno"> 2612</span>&#160;</div><div class="line"><a name="l02613"></a><span class="lineno"> 2613</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; indicesTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02614"></a><span class="lineno"> 2614</span>&#160;    <span class="keywordflow">if</span> (indicesTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>)</div><div class="line"><a name="l02615"></a><span class="lineno"> 2615</span>&#160;    {</div><div class="line"><a name="l02616"></a><span class="lineno"> 2616</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Indices tensor type must be Int32.&quot;</span>);</div><div class="line"><a name="l02617"></a><span class="lineno"> 2617</span>&#160;    }</div><div class="line"><a name="l02618"></a><span class="lineno"> 2618</span>&#160;</div><div class="line"><a name="l02619"></a><span class="lineno"> 2619</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02620"></a><span class="lineno"> 2620</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02621"></a><span class="lineno"> 2621</span>&#160;</div><div class="line"><a name="l02622"></a><span class="lineno"> 2622</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l02623"></a><span class="lineno"> 2623</span>&#160;    {</div><div class="line"><a name="l02624"></a><span class="lineno"> 2624</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02625"></a><span class="lineno"> 2625</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02626"></a><span class="lineno"> 2626</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02627"></a><span class="lineno"> 2627</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l02628"></a><span class="lineno"> 2628</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02629"></a><span class="lineno"> 2629</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>,</div><div class="line"><a name="l02630"></a><span class="lineno"> 2630</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>,</div><div class="line"><a name="l02631"></a><span class="lineno"> 2631</span>&#160;    };</div><div class="line"><a name="l02632"></a><span class="lineno"> 2632</span>&#160;</div><div class="line"><a name="l02633"></a><span class="lineno"> 2633</span>&#160;    ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02634"></a><span class="lineno"> 2634</span>&#160;</div><div class="line"><a name="l02635"></a><span class="lineno"> 2635</span>&#160;    ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l02636"></a><span class="lineno"> 2636</span>&#160;</div><div class="line"><a name="l02637"></a><span class="lineno"> 2637</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputDim  = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() + indicesTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - 1;</div><div class="line"><a name="l02638"></a><span class="lineno"> 2638</span>&#160;    ValidateTensorNumDimensions(outputTensorInfo, descriptorName, outputDim, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l02639"></a><span class="lineno"> 2639</span>&#160;}</div><div class="line"><a name="l02640"></a><span class="lineno"> 2640</span>&#160;</div><div class="line"><a name="l02641"></a><span class="lineno"><a class="line" href="structarmnn_1_1_detection_post_process_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2641</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_detection_post_process_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">DetectionPostProcessQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02642"></a><span class="lineno"> 2642</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02643"></a><span class="lineno"> 2643</span>&#160;    <span class="keyword">const</span> std::string&amp; descriptorName{<span class="stringliteral">&quot;DetectionPostProcessQueueDescriptor&quot;</span>};</div><div class="line"><a name="l02644"></a><span class="lineno"> 2644</span>&#160;</div><div class="line"><a name="l02645"></a><span class="lineno"> 2645</span>&#160;    ValidateNumInputs(workloadInfo, descriptorName, 2);</div><div class="line"><a name="l02646"></a><span class="lineno"> 2646</span>&#160;</div><div class="line"><a name="l02647"></a><span class="lineno"> 2647</span>&#160;    <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size() != 4)</div><div class="line"><a name="l02648"></a><span class="lineno"> 2648</span>&#160;    {</div><div class="line"><a name="l02649"></a><span class="lineno"> 2649</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Requires exactly four outputs. &quot;</span> +</div><div class="line"><a name="l02650"></a><span class="lineno"> 2650</span>&#160;                                       to_string(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size()) + <span class="stringliteral">&quot; has been provided.&quot;</span>);</div><div class="line"><a name="l02651"></a><span class="lineno"> 2651</span>&#160;    }</div><div class="line"><a name="l02652"></a><span class="lineno"> 2652</span>&#160;</div><div class="line"><a name="l02653"></a><span class="lineno"> 2653</span>&#160;    <span class="keywordflow">if</span> (m_Anchors == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l02654"></a><span class="lineno"> 2654</span>&#160;    {</div><div class="line"><a name="l02655"></a><span class="lineno"> 2655</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Anchors tensor descriptor is missing.&quot;</span>);</div><div class="line"><a name="l02656"></a><span class="lineno"> 2656</span>&#160;    }</div><div class="line"><a name="l02657"></a><span class="lineno"> 2657</span>&#160;</div><div class="line"><a name="l02658"></a><span class="lineno"> 2658</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; boxEncodingsInfo =  workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02659"></a><span class="lineno"> 2659</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; <a class="code" href="_neon_end_to_end_tests_8cpp.xhtml#a64c1dd1b6dd60be9f4a16db9c8f427a5">scoresInfo</a>       =  workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02660"></a><span class="lineno"> 2660</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; <a class="code" href="_neon_end_to_end_tests_8cpp.xhtml#a358cb7cd3c0647b25be049fd734b8c22">anchorsInfo</a>      = m_Anchors-&gt;GetTensorInfo();</div><div class="line"><a name="l02661"></a><span class="lineno"> 2661</span>&#160;</div><div class="line"><a name="l02662"></a><span class="lineno"> 2662</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; detectionBoxesInfo   = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02663"></a><span class="lineno"> 2663</span>&#160; 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       <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02675"></a><span class="lineno"> 2675</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02676"></a><span class="lineno"> 2676</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l02677"></a><span class="lineno"> 2677</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02678"></a><span class="lineno"> 2678</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02679"></a><span class="lineno"> 2679</span>&#160;    };</div><div class="line"><a name="l02680"></a><span class="lineno"> 2680</span>&#160;</div><div class="line"><a name="l02681"></a><span class="lineno"> 2681</span>&#160;    ValidateDataTypes(boxEncodingsInfo, supportedInputTypes, descriptorName);</div><div class="line"><a name="l02682"></a><span class="lineno"> 2682</span>&#160;    ValidateDataTypes(scoresInfo, supportedInputTypes, descriptorName);</div><div class="line"><a name="l02683"></a><span class="lineno"> 2683</span>&#160;    ValidateDataTypes(anchorsInfo, supportedInputTypes, descriptorName);</div><div class="line"><a name="l02684"></a><span class="lineno"> 2684</span>&#160;</div><div class="line"><a name="l02685"></a><span class="lineno"> 2685</span>&#160;    ValidateTensorNumDimensions(detectionBoxesInfo, descriptorName, 3, <span class="stringliteral">&quot;detection boxes&quot;</span>);</div><div class="line"><a name="l02686"></a><span class="lineno"> 2686</span>&#160;    ValidateTensorNumDimensions(detectionScoresInfo, descriptorName, 2, <span class="stringliteral">&quot;detection scores&quot;</span>);</div><div class="line"><a name="l02687"></a><span class="lineno"> 2687</span>&#160;    ValidateTensorNumDimensions(detectionClassesInfo, descriptorName, 2, <span class="stringliteral">&quot;detection classes&quot;</span>);</div><div class="line"><a name="l02688"></a><span class="lineno"> 2688</span>&#160;    ValidateTensorNumDimensions(numDetectionsInfo, descriptorName, 1, <span class="stringliteral">&quot;num detections&quot;</span>);</div><div class="line"><a name="l02689"></a><span class="lineno"> 2689</span>&#160;</div><div class="line"><a name="l02690"></a><span class="lineno"> 2690</span>&#160;    <span class="comment">// NOTE: Output is always Float32 regardless of input type</span></div><div class="line"><a name="l02691"></a><span class="lineno"> 2691</span>&#160;    ValidateTensorDataType(detectionBoxesInfo, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>, descriptorName, <span class="stringliteral">&quot;detection boxes&quot;</span>);</div><div class="line"><a name="l02692"></a><span class="lineno"> 2692</span>&#160;    ValidateTensorDataType(detectionScoresInfo, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>, descriptorName, <span class="stringliteral">&quot;detection scores&quot;</span>);</div><div class="line"><a name="l02693"></a><span class="lineno"> 2693</span>&#160;    ValidateTensorDataType(detectionClassesInfo, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>, descriptorName, <span class="stringliteral">&quot;detection classes&quot;</span>);</div><div class="line"><a name="l02694"></a><span class="lineno"> 2694</span>&#160;    ValidateTensorDataType(numDetectionsInfo, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>, descriptorName, <span class="stringliteral">&quot;num detections&quot;</span>);</div><div class="line"><a name="l02695"></a><span class="lineno"> 2695</span>&#160;</div><div class="line"><a name="l02696"></a><span class="lineno"> 2696</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_NmsIouThreshold &lt;= 0.0f || m_Parameters.m_NmsIouThreshold &gt; 1.0f)</div><div class="line"><a name="l02697"></a><span class="lineno"> 2697</span>&#160;    {</div><div class="line"><a name="l02698"></a><span class="lineno"> 2698</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Intersection over union threshold &quot;</span></div><div class="line"><a name="l02699"></a><span class="lineno"> 2699</span>&#160;                                       <span class="stringliteral">&quot;must be positive and less than or equal to 1.&quot;</span>);</div><div class="line"><a name="l02700"></a><span class="lineno"> 2700</span>&#160;    }</div><div class="line"><a name="l02701"></a><span class="lineno"> 2701</span>&#160;</div><div class="line"><a name="l02702"></a><span class="lineno"> 2702</span>&#160;    <span class="keywordflow">if</span> (scoresInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2] != m_Parameters.m_NumClasses + 1)</div><div class="line"><a name="l02703"></a><span class="lineno"> 2703</span>&#160;    {</div><div class="line"><a name="l02704"></a><span class="lineno"> 2704</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Number of classes with background &quot;</span></div><div class="line"><a name="l02705"></a><span class="lineno"> 2705</span>&#160;                                       <span class="stringliteral">&quot;should be equal to number of classes + 1.&quot;</span>);</div><div class="line"><a name="l02706"></a><span class="lineno"> 2706</span>&#160;    }</div><div class="line"><a name="l02707"></a><span class="lineno"> 2707</span>&#160;}</div><div class="line"><a name="l02708"></a><span class="lineno"> 2708</span>&#160;</div><div class="line"><a name="l02709"></a><span class="lineno"><a class="line" href="structarmnn_1_1_dequantize_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2709</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_dequantize_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">DequantizeQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02710"></a><span class="lineno"> 2710</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02711"></a><span class="lineno"> 2711</span>&#160;    <span class="keyword">const</span> std::string&amp; descriptorName{<span class="stringliteral">&quot;DequantizeQueueDescriptor&quot;</span>};</div><div class="line"><a name="l02712"></a><span class="lineno"> 2712</span>&#160;</div><div class="line"><a name="l02713"></a><span class="lineno"> 2713</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l02714"></a><span class="lineno"> 2714</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02715"></a><span class="lineno"> 2715</span>&#160;</div><div class="line"><a name="l02716"></a><span class="lineno"> 2716</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02717"></a><span class="lineno"> 2717</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02718"></a><span class="lineno"> 2718</span>&#160;</div><div class="line"><a name="l02719"></a><span class="lineno"> 2719</span>&#160;    <span class="keywordflow">if</span> (!<a class="code" href="namespacearmnn.xhtml#ad44c007f21af2d0375e3ef9400a1b275">IsQuantizedType</a>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()))</div><div class="line"><a name="l02720"></a><span class="lineno"> 2720</span>&#160;    {</div><div class="line"><a name="l02721"></a><span class="lineno"> 2721</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Input to dequantize layer must be quantized type.&quot;</span>);</div><div class="line"><a name="l02722"></a><span class="lineno"> 2722</span>&#160;    }</div><div class="line"><a name="l02723"></a><span class="lineno"> 2723</span>&#160;</div><div class="line"><a name="l02724"></a><span class="lineno"> 2724</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l02725"></a><span class="lineno"> 2725</span>&#160;    {</div><div class="line"><a name="l02726"></a><span class="lineno"> 2726</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02727"></a><span class="lineno"> 2727</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02728"></a><span class="lineno"> 2728</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a></div><div class="line"><a name="l02729"></a><span class="lineno"> 2729</span>&#160;    };</div><div class="line"><a name="l02730"></a><span class="lineno"> 2730</span>&#160;</div><div class="line"><a name="l02731"></a><span class="lineno"> 2731</span>&#160;    ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02732"></a><span class="lineno"> 2732</span>&#160;}</div><div class="line"><a name="l02733"></a><span class="lineno"> 2733</span>&#160;</div><div class="line"><a name="l02734"></a><span class="lineno"><a class="line" href="structarmnn_1_1_merge_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2734</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_merge_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">MergeQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02735"></a><span class="lineno"> 2735</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02736"></a><span class="lineno"> 2736</span>&#160;    <span class="keyword">const</span> std::string&amp; descriptorName{<span class="stringliteral">&quot;MergeQueueDescriptor&quot;</span>};</div><div class="line"><a name="l02737"></a><span class="lineno"> 2737</span>&#160;</div><div class="line"><a name="l02738"></a><span class="lineno"> 2738</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 2);</div><div class="line"><a name="l02739"></a><span class="lineno"> 2739</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02740"></a><span class="lineno"> 2740</span>&#160;</div><div class="line"><a name="l02741"></a><span class="lineno"> 2741</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02742"></a><span class="lineno"> 2742</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02743"></a><span class="lineno"> 2743</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02744"></a><span class="lineno"> 2744</span>&#160;</div><div class="line"><a name="l02745"></a><span class="lineno"> 2745</span>&#160;    ValidateTensorShapesMatch(inputTensorInfo0, inputTensorInfo1, descriptorName, <span class="stringliteral">&quot;input_0&quot;</span>, <span class="stringliteral">&quot;input_1&quot;</span>);</div><div class="line"><a name="l02746"></a><span class="lineno"> 2746</span>&#160;    ValidateTensorShapesMatch(inputTensorInfo0, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input_0&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l02747"></a><span class="lineno"> 2747</span>&#160;</div><div class="line"><a name="l02748"></a><span class="lineno"> 2748</span>&#160;    ValidateTensorDataTypesMatch(inputTensorInfo0, inputTensorInfo1, descriptorName, <span class="stringliteral">&quot;input_0&quot;</span>, <span class="stringliteral">&quot;input_1&quot;</span>);</div><div class="line"><a name="l02749"></a><span class="lineno"> 2749</span>&#160;    ValidateTensorDataTypesMatch(inputTensorInfo0, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input_0&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l02750"></a><span class="lineno"> 2750</span>&#160;}</div><div class="line"><a name="l02751"></a><span class="lineno"> 2751</span>&#160;</div><div class="line"><a name="l02752"></a><span class="lineno"><a class="line" href="structarmnn_1_1_switch_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2752</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_switch_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">SwitchQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02753"></a><span class="lineno"> 2753</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02754"></a><span class="lineno"> 2754</span>&#160;    <span class="keyword">const</span> std::string&amp; descriptorName{<span class="stringliteral">&quot;SwitchQueueDescriptor&quot;</span>};</div><div class="line"><a name="l02755"></a><span class="lineno"> 2755</span>&#160;</div><div class="line"><a name="l02756"></a><span class="lineno"> 2756</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 2);</div><div class="line"><a name="l02757"></a><span class="lineno"> 2757</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 2);</div><div class="line"><a name="l02758"></a><span class="lineno"> 2758</span>&#160;</div><div class="line"><a name="l02759"></a><span class="lineno"> 2759</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02760"></a><span class="lineno"> 2760</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02761"></a><span class="lineno"> 2761</span>&#160;</div><div class="line"><a name="l02762"></a><span class="lineno"> 2762</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02763"></a><span class="lineno"> 2763</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[1];</div><div class="line"><a name="l02764"></a><span class="lineno"> 2764</span>&#160;</div><div class="line"><a name="l02765"></a><span class="lineno"> 2765</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l02766"></a><span class="lineno"> 2766</span>&#160;    {</div><div class="line"><a name="l02767"></a><span class="lineno"> 2767</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02768"></a><span class="lineno"> 2768</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02769"></a><span class="lineno"> 2769</span>&#160; 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   ValidateDataTypes(inputTensorInfo1, supportedTypes, descriptorName);</div><div class="line"><a name="l02776"></a><span class="lineno"> 2776</span>&#160;</div><div class="line"><a name="l02777"></a><span class="lineno"> 2777</span>&#160;    ValidateDataTypes(outputTensorInfo0, supportedTypes, descriptorName);</div><div class="line"><a name="l02778"></a><span class="lineno"> 2778</span>&#160;    ValidateDataTypes(outputTensorInfo1, supportedTypes, descriptorName);</div><div class="line"><a name="l02779"></a><span class="lineno"> 2779</span>&#160;</div><div class="line"><a name="l02780"></a><span class="lineno"> 2780</span>&#160;    ValidateTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l02781"></a><span class="lineno"> 2781</span>&#160;                              outputTensorInfo0,</div><div class="line"><a name="l02782"></a><span class="lineno"> 2782</span>&#160;                              descriptorName,</div><div class="line"><a name="l02783"></a><span class="lineno"> 2783</span>&#160;                              <span class="stringliteral">&quot;input_0&quot;</span>,</div><div class="line"><a name="l02784"></a><span class="lineno"> 2784</span>&#160;                              <span class="stringliteral">&quot;output_0&quot;</span>);</div><div class="line"><a name="l02785"></a><span class="lineno"> 2785</span>&#160;</div><div class="line"><a name="l02786"></a><span class="lineno"> 2786</span>&#160;    ValidateTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l02787"></a><span class="lineno"> 2787</span>&#160;                              outputTensorInfo1,</div><div class="line"><a name="l02788"></a><span class="lineno"> 2788</span>&#160;                              descriptorName,</div><div class="line"><a name="l02789"></a><span class="lineno"> 2789</span>&#160;                              <span class="stringliteral">&quot;input_0&quot;</span>,</div><div class="line"><a name="l02790"></a><span class="lineno"> 2790</span>&#160;                              <span class="stringliteral">&quot;output_1&quot;</span>);</div><div class="line"><a name="l02791"></a><span class="lineno"> 2791</span>&#160;}</div><div class="line"><a name="l02792"></a><span class="lineno"> 2792</span>&#160;</div><div class="line"><a name="l02793"></a><span class="lineno"><a class="line" href="structarmnn_1_1_pre_compiled_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2793</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_pre_compiled_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">PreCompiledQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; <span class="comment">/*workloadInfo*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02794"></a><span class="lineno"> 2794</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02795"></a><span class="lineno"> 2795</span>&#160;    <span class="comment">// This is internally generated so it should not need validation.</span></div><div class="line"><a name="l02796"></a><span class="lineno"> 2796</span>&#160;}</div><div class="line"><a name="l02797"></a><span class="lineno"> 2797</span>&#160;</div><div class="line"><a name="l02798"></a><span class="lineno"><a class="line" href="structarmnn_1_1_prelu_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2798</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_prelu_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">PreluQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02799"></a><span class="lineno"> 2799</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02800"></a><span class="lineno"> 2800</span>&#160;    <span class="keyword">const</span> std::string&amp; descriptorName{<span class="stringliteral">&quot;PreluQueueDescriptor&quot;</span>};</div><div class="line"><a name="l02801"></a><span class="lineno"> 2801</span>&#160;</div><div class="line"><a name="l02802"></a><span class="lineno"> 2802</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 2);</div><div class="line"><a name="l02803"></a><span class="lineno"> 2803</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02804"></a><span class="lineno"> 2804</span>&#160;</div><div class="line"><a name="l02805"></a><span class="lineno"> 2805</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02806"></a><span class="lineno"> 2806</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; alphaTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02807"></a><span class="lineno"> 2807</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02808"></a><span class="lineno"> 2808</span>&#160;</div><div class="line"><a name="l02809"></a><span class="lineno"> 2809</span>&#160;    std::vector&lt;DataType&gt; supportedTypes</div><div class="line"><a name="l02810"></a><span class="lineno"> 2810</span>&#160;    {</div><div class="line"><a name="l02811"></a><span class="lineno"> 2811</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02812"></a><span class="lineno"> 2812</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02813"></a><span class="lineno"> 2813</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02814"></a><span class="lineno"> 2814</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l02815"></a><span class="lineno"> 2815</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02816"></a><span class="lineno"> 2816</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02817"></a><span class="lineno"> 2817</span>&#160;    };</div><div class="line"><a name="l02818"></a><span class="lineno"> 2818</span>&#160;</div><div class="line"><a name="l02819"></a><span class="lineno"> 2819</span>&#160;    ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02820"></a><span class="lineno"> 2820</span>&#160;    ValidateDataTypes(alphaTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02821"></a><span class="lineno"> 2821</span>&#160;</div><div class="line"><a name="l02822"></a><span class="lineno"> 2822</span>&#160;    ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02823"></a><span class="lineno"> 2823</span>&#160;</div><div class="line"><a name="l02824"></a><span class="lineno"> 2824</span>&#160;    ValidateTensorDataTypesMatch(inputTensorInfo, alphaTensorInfo,  descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;alpha&quot;</span>);</div><div class="line"><a name="l02825"></a><span class="lineno"> 2825</span>&#160;    ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;ouptut&quot;</span>);</div><div class="line"><a name="l02826"></a><span class="lineno"> 2826</span>&#160;</div><div class="line"><a name="l02827"></a><span class="lineno"> 2827</span>&#160;    ValidateBroadcastTensorShapesMatch(inputTensorInfo,</div><div class="line"><a name="l02828"></a><span class="lineno"> 2828</span>&#160;                                       alphaTensorInfo,</div><div class="line"><a name="l02829"></a><span class="lineno"> 2829</span>&#160;                                       outputTensorInfo,</div><div class="line"><a name="l02830"></a><span class="lineno"> 2830</span>&#160;                                       descriptorName,</div><div class="line"><a name="l02831"></a><span class="lineno"> 2831</span>&#160;                                       <span class="stringliteral">&quot;input&quot;</span>,</div><div class="line"><a name="l02832"></a><span class="lineno"> 2832</span>&#160;                                       <span class="stringliteral">&quot;alpha&quot;</span>);</div><div class="line"><a name="l02833"></a><span class="lineno"> 2833</span>&#160;}</div><div class="line"><a name="l02834"></a><span class="lineno"> 2834</span>&#160;</div><div class="line"><a name="l02835"></a><span class="lineno"><a class="line" href="structarmnn_1_1_transpose_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2835</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_transpose_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">TransposeConvolution2dQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02836"></a><span class="lineno"> 2836</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02837"></a><span class="lineno"> 2837</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;TransposeConvolution2dQueueDescriptor&quot;</span>};</div><div class="line"><a name="l02838"></a><span class="lineno"> 2838</span>&#160;</div><div class="line"><a name="l02839"></a><span class="lineno"> 2839</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l02840"></a><span class="lineno"> 2840</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02841"></a><span class="lineno"> 2841</span>&#160;</div><div class="line"><a name="l02842"></a><span class="lineno"> 2842</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02843"></a><span class="lineno"> 2843</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02844"></a><span class="lineno"> 2844</span>&#160;</div><div class="line"><a name="l02845"></a><span class="lineno"> 2845</span>&#160;    ValidateTensorNumDimensions(inputTensorInfo,  descriptorName, 4, <span class="stringliteral">&quot;input&quot;</span>);</div><div class="line"><a name="l02846"></a><span class="lineno"> 2846</span>&#160;    ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 4, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l02847"></a><span class="lineno"> 2847</span>&#160;</div><div class="line"><a name="l02848"></a><span class="lineno"> 2848</span>&#160;    ValidatePointer(m_Weight, descriptorName, <span class="stringliteral">&quot;weight&quot;</span>);</div><div class="line"><a name="l02849"></a><span class="lineno"> 2849</span>&#160;</div><div class="line"><a name="l02850"></a><span class="lineno"> 2850</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; weightTensorInfo = m_Weight-&gt;GetTensorInfo();</div><div class="line"><a name="l02851"></a><span class="lineno"> 2851</span>&#160;    ValidateTensorNumDimensions(weightTensorInfo, descriptorName, 4, <span class="stringliteral">&quot;weight&quot;</span>);</div><div class="line"><a name="l02852"></a><span class="lineno"> 2852</span>&#160;</div><div class="line"><a name="l02853"></a><span class="lineno"> 2853</span>&#160;    ValidateWeightDataType(inputTensorInfo, weightTensorInfo, descriptorName);</div><div class="line"><a name="l02854"></a><span class="lineno"> 2854</span>&#160;</div><div class="line"><a name="l02855"></a><span class="lineno"> 2855</span>&#160;    <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;TensorInfo&gt;</a> optionalBiasTensorInfo;</div><div class="line"><a name="l02856"></a><span class="lineno"> 2856</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_BiasEnabled)</div><div class="line"><a name="l02857"></a><span class="lineno"> 2857</span>&#160;    {</div><div class="line"><a name="l02858"></a><span class="lineno"> 2858</span>&#160;        ValidatePointer(m_Bias, descriptorName, <span class="stringliteral">&quot;bias&quot;</span>);</div><div class="line"><a name="l02859"></a><span class="lineno"> 2859</span>&#160;</div><div class="line"><a name="l02860"></a><span class="lineno"> 2860</span>&#160;        optionalBiasTensorInfo = MakeOptional&lt;TensorInfo&gt;(m_Bias-&gt;GetTensorInfo());</div><div class="line"><a name="l02861"></a><span class="lineno"> 2861</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; biasTensorInfo = optionalBiasTensorInfo.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>();</div><div class="line"><a name="l02862"></a><span class="lineno"> 2862</span>&#160;</div><div class="line"><a name="l02863"></a><span class="lineno"> 2863</span>&#160;        ValidateTensorDataType(biasTensorInfo, <a class="code" href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">GetBiasDataType</a>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()), descriptorName, <span class="stringliteral">&quot;bias&quot;</span>);</div><div class="line"><a name="l02864"></a><span class="lineno"> 2864</span>&#160;        ValidateBiasTensorQuantization(biasTensorInfo, inputTensorInfo, weightTensorInfo, descriptorName);</div><div class="line"><a name="l02865"></a><span class="lineno"> 2865</span>&#160;    }</div><div class="line"><a name="l02866"></a><span class="lineno"> 2866</span>&#160;</div><div class="line"><a name="l02867"></a><span class="lineno"> 2867</span>&#160;    ValidatePerAxisQuantization(inputTensorInfo,</div><div class="line"><a name="l02868"></a><span class="lineno"> 2868</span>&#160;                                outputTensorInfo,</div><div class="line"><a name="l02869"></a><span class="lineno"> 2869</span>&#160;                                weightTensorInfo,</div><div class="line"><a name="l02870"></a><span class="lineno"> 2870</span>&#160;                                optionalBiasTensorInfo,</div><div class="line"><a name="l02871"></a><span class="lineno"> 2871</span>&#160;                                descriptorName);</div><div class="line"><a name="l02872"></a><span class="lineno"> 2872</span>&#160;</div><div class="line"><a name="l02873"></a><span class="lineno"> 2873</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l02874"></a><span class="lineno"> 2874</span>&#160;    {</div><div class="line"><a name="l02875"></a><span class="lineno"> 2875</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02876"></a><span class="lineno"> 2876</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02877"></a><span class="lineno"> 2877</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02878"></a><span class="lineno"> 2878</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l02879"></a><span class="lineno"> 2879</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02880"></a><span class="lineno"> 2880</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02881"></a><span class="lineno"> 2881</span>&#160;    };</div><div class="line"><a name="l02882"></a><span class="lineno"> 2882</span>&#160;</div><div class="line"><a name="l02883"></a><span class="lineno"> 2883</span>&#160; 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workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02888"></a><span class="lineno"> 2888</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02889"></a><span class="lineno"> 2889</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;TransposeQueueDescriptor&quot;</span>};</div><div class="line"><a name="l02890"></a><span class="lineno"> 2890</span>&#160;</div><div class="line"><a name="l02891"></a><span class="lineno"> 2891</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l02892"></a><span class="lineno"> 2892</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02893"></a><span class="lineno"> 2893</span>&#160;</div><div class="line"><a name="l02894"></a><span class="lineno"> 2894</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a>&amp; mapping = m_Parameters.m_DimMappings;</div><div class="line"><a name="l02895"></a><span class="lineno"> 2895</span>&#160;</div><div class="line"><a name="l02896"></a><span class="lineno"> 2896</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02897"></a><span class="lineno"> 2897</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02898"></a><span class="lineno"> 2898</span>&#160;</div><div class="line"><a name="l02899"></a><span class="lineno"> 2899</span>&#160;    ValidateTensorNumDimensions(inputTensorInfo,  descriptorName, mapping.<a class="code" href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>(), <span class="stringliteral">&quot;input&quot;</span>);</div><div class="line"><a name="l02900"></a><span class="lineno"> 2900</span>&#160; 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       {</div><div class="line"><a name="l02906"></a><span class="lineno"> 2906</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: src dimension &quot;</span> + to_string(mapping[i]) +</div><div class="line"><a name="l02907"></a><span class="lineno"> 2907</span>&#160;                                           <span class="stringliteral">&quot; (=&quot;</span> + to_string(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[mapping[i]]) + <span class="stringliteral">&quot;) &quot;</span> +</div><div class="line"><a name="l02908"></a><span class="lineno"> 2908</span>&#160;                                           <span class="stringliteral">&quot;must match dst dimension &quot;</span> + to_string(i) +</div><div class="line"><a name="l02909"></a><span class="lineno"> 2909</span>&#160;                                           <span class="stringliteral">&quot; (=&quot;</span> + to_string(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i]) + <span class="stringliteral">&quot;)&quot;</span>);</div><div class="line"><a name="l02910"></a><span class="lineno"> 2910</span>&#160;        }</div><div class="line"><a name="l02911"></a><span class="lineno"> 2911</span>&#160;    }</div><div class="line"><a name="l02912"></a><span class="lineno"> 2912</span>&#160;</div><div class="line"><a name="l02913"></a><span class="lineno"> 2913</span>&#160;    ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l02914"></a><span class="lineno"> 2914</span>&#160;}</div><div class="line"><a name="l02915"></a><span class="lineno"> 2915</span>&#160;</div><div class="line"><a name="l02916"></a><span class="lineno"><a class="line" href="structarmnn_1_1_q_lstm_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2916</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_q_lstm_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">QLstmQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02917"></a><span class="lineno"> 2917</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02918"></a><span class="lineno"> 2918</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;QLstmQueueDescriptor&quot;</span>};</div><div class="line"><a name="l02919"></a><span class="lineno"> 2919</span>&#160;</div><div class="line"><a name="l02920"></a><span class="lineno"> 2920</span>&#160;    <span class="comment">// Validate number of inputs/outputs</span></div><div class="line"><a name="l02921"></a><span class="lineno"> 2921</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 3);</div><div class="line"><a name="l02922"></a><span class="lineno"> 2922</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 3);</div><div class="line"><a name="l02923"></a><span class="lineno"> 2923</span>&#160;</div><div class="line"><a name="l02924"></a><span class="lineno"> 2924</span>&#160;    <span class="comment">// Input/output tensor info</span></div><div class="line"><a name="l02925"></a><span class="lineno"> 2925</span>&#160;    <span class="keyword">auto</span> inputInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02926"></a><span class="lineno"> 2926</span>&#160;    <span class="keyword">auto</span> outputStateInInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02927"></a><span class="lineno"> 2927</span>&#160;    <span class="keyword">auto</span> cellStateInInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[2];</div><div class="line"><a name="l02928"></a><span class="lineno"> 2928</span>&#160;</div><div class="line"><a name="l02929"></a><span class="lineno"> 2929</span>&#160;    <span class="keyword">auto</span> outputStateOutInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02930"></a><span class="lineno"> 2930</span>&#160;    <span class="keyword">auto</span> cellStateOutInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[1];</div><div class="line"><a name="l02931"></a><span class="lineno"> 2931</span>&#160;    <span class="keyword">auto</span> outputInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[2];</div><div class="line"><a name="l02932"></a><span class="lineno"> 2932</span>&#160;</div><div class="line"><a name="l02933"></a><span class="lineno"> 2933</span>&#160;    <span class="comment">// Supported types for various tensors in QLSTM</span></div><div class="line"><a name="l02934"></a><span class="lineno"> 2934</span>&#160;    std::vector&lt;DataType&gt; inputOutputSupportedTypes =</div><div class="line"><a name="l02935"></a><span class="lineno"> 2935</span>&#160;    {</div><div class="line"><a name="l02936"></a><span class="lineno"> 2936</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a></div><div class="line"><a name="l02937"></a><span class="lineno"> 2937</span>&#160;    };</div><div class="line"><a name="l02938"></a><span class="lineno"> 2938</span>&#160;</div><div class="line"><a name="l02939"></a><span class="lineno"> 2939</span>&#160;    std::vector&lt;DataType&gt; cellStateSupportedTypes =</div><div class="line"><a name="l02940"></a><span class="lineno"> 2940</span>&#160;    {</div><div class="line"><a name="l02941"></a><span class="lineno"> 2941</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02942"></a><span class="lineno"> 2942</span>&#160;    };</div><div class="line"><a name="l02943"></a><span class="lineno"> 2943</span>&#160;</div><div class="line"><a name="l02944"></a><span class="lineno"> 2944</span>&#160;    std::vector&lt;DataType&gt; weightsSupportedTypes =</div><div class="line"><a name="l02945"></a><span class="lineno"> 2945</span>&#160;    {</div><div class="line"><a name="l02946"></a><span class="lineno"> 2946</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a></div><div class="line"><a name="l02947"></a><span class="lineno"> 2947</span>&#160;    };</div><div class="line"><a name="l02948"></a><span class="lineno"> 2948</span>&#160;</div><div class="line"><a name="l02949"></a><span class="lineno"> 2949</span>&#160;    std::vector&lt;DataType&gt; layerNormPeepholeWeightsSupportedTypes =</div><div class="line"><a name="l02950"></a><span class="lineno"> 2950</span>&#160;    {</div><div class="line"><a name="l02951"></a><span class="lineno"> 2951</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02952"></a><span class="lineno"> 2952</span>&#160;    };</div><div class="line"><a name="l02953"></a><span class="lineno"> 2953</span>&#160;</div><div class="line"><a name="l02954"></a><span class="lineno"> 2954</span>&#160;    std::vector&lt;DataType&gt; biasSupportedTypes =</div><div class="line"><a name="l02955"></a><span class="lineno"> 2955</span>&#160;    {</div><div class="line"><a name="l02956"></a><span class="lineno"> 2956</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a></div><div class="line"><a name="l02957"></a><span class="lineno"> 2957</span>&#160;    };</div><div class="line"><a name="l02958"></a><span class="lineno"> 2958</span>&#160;</div><div class="line"><a name="l02959"></a><span class="lineno"> 2959</span>&#160;    <span class="comment">// Validate types of input/output tensors</span></div><div class="line"><a name="l02960"></a><span class="lineno"> 2960</span>&#160;    ValidateDataTypes(inputInfo, inputOutputSupportedTypes, descriptorName);</div><div class="line"><a name="l02961"></a><span class="lineno"> 2961</span>&#160;    ValidateDataTypes(outputStateInInfo, inputOutputSupportedTypes, descriptorName);</div><div class="line"><a name="l02962"></a><span class="lineno"> 2962</span>&#160;    ValidateDataTypes(cellStateInInfo, cellStateSupportedTypes, descriptorName);</div><div class="line"><a name="l02963"></a><span class="lineno"> 2963</span>&#160;</div><div class="line"><a name="l02964"></a><span class="lineno"> 2964</span>&#160;    ValidateDataTypes(outputStateOutInfo, inputOutputSupportedTypes, descriptorName);</div><div class="line"><a name="l02965"></a><span class="lineno"> 2965</span>&#160;    ValidateDataTypes(cellStateOutInfo, cellStateSupportedTypes, descriptorName);</div><div class="line"><a name="l02966"></a><span class="lineno"> 2966</span>&#160;    ValidateDataTypes(outputInfo, inputOutputSupportedTypes, descriptorName);</div><div class="line"><a name="l02967"></a><span class="lineno"> 2967</span>&#160;</div><div class="line"><a name="l02968"></a><span class="lineno"> 2968</span>&#160;    <span class="comment">// Validate matching types of input/output tensors</span></div><div class="line"><a name="l02969"></a><span class="lineno"> 2969</span>&#160;    ValidateTensorDataTypesMatch(inputInfo, outputStateInInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;outputStateIn&quot;</span>);</div><div class="line"><a name="l02970"></a><span class="lineno"> 2970</span>&#160;    ValidateTensorDataTypesMatch(outputStateInInfo, outputStateOutInfo, descriptorName,</div><div class="line"><a name="l02971"></a><span class="lineno"> 2971</span>&#160;                                 <span class="stringliteral">&quot;outputStateIn&quot;</span>, <span class="stringliteral">&quot;outputStateOut&quot;</span>);</div><div class="line"><a name="l02972"></a><span class="lineno"> 2972</span>&#160;    ValidateTensorDataTypesMatch(cellStateInInfo, cellStateOutInfo, descriptorName, <span class="stringliteral">&quot;cellStateIn&quot;</span>, <span class="stringliteral">&quot;cellStateOut&quot;</span>);</div><div class="line"><a name="l02973"></a><span class="lineno"> 2973</span>&#160;</div><div class="line"><a name="l02974"></a><span class="lineno"> 2974</span>&#160;    <span class="comment">// Infer number of batches, number of units, input size and output size from tensor dimensions</span></div><div class="line"><a name="l02975"></a><span class="lineno"> 2975</span>&#160;    <span class="keyword">const</span> uint32_t numBatches = inputInfo.GetShape()[0];</div><div class="line"><a name="l02976"></a><span class="lineno"> 2976</span>&#160;    <span class="keyword">const</span> uint32_t inputSize  = inputInfo.GetShape()[1];</div><div class="line"><a name="l02977"></a><span class="lineno"> 2977</span>&#160;    <span class="keyword">const</span> uint32_t outputSize = outputStateInInfo.GetShape()[1];</div><div class="line"><a name="l02978"></a><span class="lineno"> 2978</span>&#160;    <span class="keyword">const</span> uint32_t numUnits = cellStateInInfo.GetShape()[1];</div><div class="line"><a name="l02979"></a><span class="lineno"> 2979</span>&#160;</div><div class="line"><a name="l02980"></a><span class="lineno"> 2980</span>&#160;    <span class="comment">// Validate number of dimensions and number of elements for input/output tensors</span></div><div class="line"><a name="l02981"></a><span class="lineno"> 2981</span>&#160;    ValidateTensorNumDimNumElem(inputInfo, 2, (numBatches * inputSize), descriptorName + <span class="stringliteral">&quot; input&quot;</span>);</div><div class="line"><a name="l02982"></a><span class="lineno"> 2982</span>&#160;    ValidateTensorNumDimNumElem(outputStateInInfo, 2, (numBatches * outputSize), descriptorName + <span class="stringliteral">&quot; outputStateIn&quot;</span>);</div><div class="line"><a name="l02983"></a><span class="lineno"> 2983</span>&#160;    ValidateTensorNumDimNumElem(cellStateInInfo, 2, (numBatches * numUnits), descriptorName + <span class="stringliteral">&quot; cellStateIn&quot;</span>);</div><div class="line"><a name="l02984"></a><span class="lineno"> 2984</span>&#160;</div><div class="line"><a name="l02985"></a><span class="lineno"> 2985</span>&#160;    ValidateTensorNumDimNumElem(outputStateOutInfo, 2, (numBatches * outputSize), descriptorName + <span class="stringliteral">&quot; outputStateOut&quot;</span>);</div><div class="line"><a name="l02986"></a><span class="lineno"> 2986</span>&#160;    ValidateTensorNumDimNumElem(cellStateOutInfo, 2, (numBatches * numUnits), descriptorName + <span class="stringliteral">&quot; cellStateOut&quot;</span>);</div><div class="line"><a name="l02987"></a><span class="lineno"> 2987</span>&#160;    ValidateTensorNumDimNumElem(outputInfo, 2, (numBatches * outputSize), descriptorName + <span class="stringliteral">&quot; output&quot;</span>);</div><div class="line"><a name="l02988"></a><span class="lineno"> 2988</span>&#160;</div><div class="line"><a name="l02989"></a><span class="lineno"> 2989</span>&#160;    <span class="comment">// Validate number of dimensions and number of elements for MANDATORY weight tensors</span></div><div class="line"><a name="l02990"></a><span class="lineno"> 2990</span>&#160;    ValidatePointer(m_InputToForgetWeights, descriptorName, <span class="stringliteral">&quot;InputToForgetWeights&quot;</span>);</div><div class="line"><a name="l02991"></a><span class="lineno"> 2991</span>&#160;    <span class="keyword">auto</span> inputToForgetWeightsInfo = m_InputToForgetWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l02992"></a><span class="lineno"> 2992</span>&#160;    ValidateTensorNumDimNumElem(inputToForgetWeightsInfo, 2, (numUnits * inputSize), <span class="stringliteral">&quot; InputToForgetWeights&quot;</span>);</div><div class="line"><a name="l02993"></a><span class="lineno"> 2993</span>&#160;</div><div class="line"><a name="l02994"></a><span class="lineno"> 2994</span>&#160;    ValidatePointer(m_InputToCellWeights, descriptorName, <span class="stringliteral">&quot;InputToCellWeights&quot;</span>);</div><div class="line"><a name="l02995"></a><span class="lineno"> 2995</span>&#160;    <span class="keyword">auto</span> inputToCellWeightsInfo = m_InputToCellWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l02996"></a><span class="lineno"> 2996</span>&#160;    ValidateTensorNumDimNumElem(inputToCellWeightsInfo, 2, (numUnits * inputSize), <span class="stringliteral">&quot; InputToCellWeights&quot;</span>);</div><div class="line"><a name="l02997"></a><span class="lineno"> 2997</span>&#160;</div><div class="line"><a name="l02998"></a><span class="lineno"> 2998</span>&#160;    ValidatePointer(m_InputToOutputWeights, descriptorName, <span class="stringliteral">&quot;InputToOutputWeights&quot;</span>);</div><div class="line"><a name="l02999"></a><span class="lineno"> 2999</span>&#160;    <span class="keyword">auto</span> inputToOutputWeightsInfo = m_InputToOutputWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03000"></a><span class="lineno"> 3000</span>&#160;    ValidateTensorNumDimNumElem(inputToOutputWeightsInfo, 2, (numUnits * inputSize), <span class="stringliteral">&quot; InputToOutputWeights&quot;</span>);</div><div class="line"><a name="l03001"></a><span class="lineno"> 3001</span>&#160;</div><div class="line"><a name="l03002"></a><span class="lineno"> 3002</span>&#160;    ValidatePointer(m_RecurrentToForgetWeights, descriptorName, <span class="stringliteral">&quot;RecurrentToForgetWeights&quot;</span>);</div><div class="line"><a name="l03003"></a><span class="lineno"> 3003</span>&#160;    <span class="keyword">auto</span> recurrentToForgetWeightsInfo = m_RecurrentToForgetWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03004"></a><span class="lineno"> 3004</span>&#160;    ValidateTensorNumDimNumElem(recurrentToForgetWeightsInfo, 2, (numUnits * outputSize),</div><div class="line"><a name="l03005"></a><span class="lineno"> 3005</span>&#160;                                <span class="stringliteral">&quot; RecurrentToForgetWeights&quot;</span>);</div><div class="line"><a name="l03006"></a><span class="lineno"> 3006</span>&#160;</div><div class="line"><a name="l03007"></a><span class="lineno"> 3007</span>&#160;    ValidatePointer(m_RecurrentToCellWeights, descriptorName, <span class="stringliteral">&quot;RecurrentToCellWeights&quot;</span>);</div><div class="line"><a name="l03008"></a><span class="lineno"> 3008</span>&#160;    <span class="keyword">auto</span> recurrentToCellWeightsInfo = m_RecurrentToCellWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03009"></a><span class="lineno"> 3009</span>&#160;    ValidateTensorNumDimNumElem(recurrentToCellWeightsInfo, 2, (numUnits * outputSize), <span class="stringliteral">&quot; RecurrentToCellWeights&quot;</span>);</div><div class="line"><a name="l03010"></a><span class="lineno"> 3010</span>&#160;</div><div class="line"><a name="l03011"></a><span class="lineno"> 3011</span>&#160;    ValidatePointer(m_RecurrentToOutputWeights, descriptorName, <span class="stringliteral">&quot;RecurrentToOutputWeights&quot;</span>);</div><div class="line"><a name="l03012"></a><span class="lineno"> 3012</span>&#160;    <span class="keyword">auto</span> recurrentToOutputWeightsInfo = m_RecurrentToOutputWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03013"></a><span class="lineno"> 3013</span>&#160;    ValidateTensorNumDimNumElem(recurrentToOutputWeightsInfo, 2, (numUnits * outputSize), <span class="stringliteral">&quot; RecurrentToCellWeights&quot;</span>);</div><div class="line"><a name="l03014"></a><span class="lineno"> 3014</span>&#160;</div><div class="line"><a name="l03015"></a><span class="lineno"> 3015</span>&#160;    <span class="comment">// Validate data types for MANDATORY weights tensors (all should match each other)</span></div><div class="line"><a name="l03016"></a><span class="lineno"> 3016</span>&#160;    ValidateDataTypes(inputToForgetWeightsInfo, weightsSupportedTypes, descriptorName);</div><div class="line"><a name="l03017"></a><span class="lineno"> 3017</span>&#160;</div><div class="line"><a name="l03018"></a><span class="lineno"> 3018</span>&#160;    ValidateTensorDataTypesMatch(inputToForgetWeightsInfo, inputToCellWeightsInfo, descriptorName,</div><div class="line"><a name="l03019"></a><span class="lineno"> 3019</span>&#160;                                 <span class="stringliteral">&quot;inputToForgetWeights&quot;</span>, <span class="stringliteral">&quot;inputToCellWeights&quot;</span>);</div><div class="line"><a name="l03020"></a><span class="lineno"> 3020</span>&#160;    ValidateTensorDataTypesMatch(inputToForgetWeightsInfo, inputToOutputWeightsInfo, descriptorName,</div><div class="line"><a name="l03021"></a><span class="lineno"> 3021</span>&#160;                                 <span class="stringliteral">&quot;inputToForgetWeights&quot;</span>, <span class="stringliteral">&quot;inputToOutputWeights&quot;</span>);</div><div class="line"><a name="l03022"></a><span class="lineno"> 3022</span>&#160;</div><div class="line"><a name="l03023"></a><span class="lineno"> 3023</span>&#160;    ValidateTensorDataTypesMatch(inputToForgetWeightsInfo, recurrentToForgetWeightsInfo, descriptorName,</div><div class="line"><a name="l03024"></a><span class="lineno"> 3024</span>&#160;                                 <span class="stringliteral">&quot;inputToForgetWeights&quot;</span>, <span class="stringliteral">&quot;recurrentToForgeteights&quot;</span>);</div><div class="line"><a name="l03025"></a><span class="lineno"> 3025</span>&#160;    ValidateTensorDataTypesMatch(inputToForgetWeightsInfo, recurrentToCellWeightsInfo, descriptorName,</div><div class="line"><a name="l03026"></a><span class="lineno"> 3026</span>&#160;                                 <span class="stringliteral">&quot;inputToForgetWeights&quot;</span>, <span class="stringliteral">&quot;recurrentToCellWeights&quot;</span>);</div><div class="line"><a name="l03027"></a><span class="lineno"> 3027</span>&#160;    ValidateTensorDataTypesMatch(inputToForgetWeightsInfo, recurrentToOutputWeightsInfo, descriptorName,</div><div class="line"><a name="l03028"></a><span class="lineno"> 3028</span>&#160;                                 <span class="stringliteral">&quot;inputToForgetWeights&quot;</span>, <span class="stringliteral">&quot;recurrentToOutputWeights&quot;</span>);</div><div class="line"><a name="l03029"></a><span class="lineno"> 3029</span>&#160;</div><div class="line"><a name="l03030"></a><span class="lineno"> 3030</span>&#160;    <span class="comment">// Validate number of dimensions and number of elements for MANDATORY bias tensors</span></div><div class="line"><a name="l03031"></a><span class="lineno"> 3031</span>&#160;    ValidatePointer(m_ForgetGateBias, descriptorName, <span class="stringliteral">&quot;ForgetGateBias&quot;</span>);</div><div class="line"><a name="l03032"></a><span class="lineno"> 3032</span>&#160;    <span class="keyword">auto</span> forgetGateBiasInfo = m_ForgetGateBias-&gt;GetTensorInfo();</div><div class="line"><a name="l03033"></a><span class="lineno"> 3033</span>&#160;    ValidateTensorNumDimNumElem(forgetGateBiasInfo, 1, numUnits, <span class="stringliteral">&quot; ForgetGateBias&quot;</span>);</div><div class="line"><a name="l03034"></a><span class="lineno"> 3034</span>&#160;</div><div class="line"><a name="l03035"></a><span class="lineno"> 3035</span>&#160;    ValidatePointer(m_CellBias, descriptorName, <span class="stringliteral">&quot;CellBias&quot;</span>);</div><div class="line"><a name="l03036"></a><span class="lineno"> 3036</span>&#160;    <span class="keyword">auto</span> cellBiasInfo = m_CellBias-&gt;GetTensorInfo();</div><div class="line"><a name="l03037"></a><span class="lineno"> 3037</span>&#160;    ValidateTensorNumDimNumElem(cellBiasInfo, 1, numUnits, <span class="stringliteral">&quot; CellBias&quot;</span>);</div><div class="line"><a name="l03038"></a><span class="lineno"> 3038</span>&#160;</div><div class="line"><a name="l03039"></a><span class="lineno"> 3039</span>&#160;    ValidatePointer(m_OutputGateBias, descriptorName, <span class="stringliteral">&quot;OutputGateBias&quot;</span>);</div><div class="line"><a name="l03040"></a><span class="lineno"> 3040</span>&#160;    <span class="keyword">auto</span> outputGateBiasInfo = m_OutputGateBias-&gt;GetTensorInfo();</div><div class="line"><a name="l03041"></a><span class="lineno"> 3041</span>&#160;    ValidateTensorNumDimNumElem(outputGateBiasInfo, 1, numUnits, <span class="stringliteral">&quot; OutputGateBias&quot;</span>);</div><div class="line"><a name="l03042"></a><span class="lineno"> 3042</span>&#160;</div><div class="line"><a name="l03043"></a><span class="lineno"> 3043</span>&#160;    <span class="comment">// Validate data types for MANDATORY bias tensors</span></div><div class="line"><a name="l03044"></a><span class="lineno"> 3044</span>&#160;    ValidateDataTypes(forgetGateBiasInfo, biasSupportedTypes, descriptorName);</div><div class="line"><a name="l03045"></a><span class="lineno"> 3045</span>&#160;</div><div class="line"><a name="l03046"></a><span class="lineno"> 3046</span>&#160;    ValidateTensorDataTypesMatch(forgetGateBiasInfo, cellBiasInfo, descriptorName,</div><div class="line"><a name="l03047"></a><span class="lineno"> 3047</span>&#160;                                 <span class="stringliteral">&quot;forgetGateBias&quot;</span>, <span class="stringliteral">&quot;cellBias&quot;</span>);</div><div class="line"><a name="l03048"></a><span class="lineno"> 3048</span>&#160;    ValidateTensorDataTypesMatch(forgetGateBiasInfo, outputGateBiasInfo, descriptorName,</div><div class="line"><a name="l03049"></a><span class="lineno"> 3049</span>&#160;                                 <span class="stringliteral">&quot;forgetGateBias&quot;</span>, <span class="stringliteral">&quot;outputGateBias&quot;</span>);</div><div class="line"><a name="l03050"></a><span class="lineno"> 3050</span>&#160;</div><div class="line"><a name="l03051"></a><span class="lineno"> 3051</span>&#160;    <span class="comment">// Validate OPTIONAL params: CIFG (inputToInputWeights, recurrentToInputWeights, inputGateBias)</span></div><div class="line"><a name="l03052"></a><span class="lineno"> 3052</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span> allCifgParamsPresentOrNot = ((m_InputToInputWeights &amp;&amp; m_RecurrentToInputWeights &amp;&amp; m_InputGateBias &amp;&amp;</div><div class="line"><a name="l03053"></a><span class="lineno"> 3053</span>&#160;                                             !m_Parameters.m_CifgEnabled) ||</div><div class="line"><a name="l03054"></a><span class="lineno"> 3054</span>&#160;                                            (!m_InputToInputWeights &amp;&amp; !m_RecurrentToInputWeights &amp;&amp;</div><div class="line"><a name="l03055"></a><span class="lineno"> 3055</span>&#160;                                             !m_InputGateBias &amp;&amp; m_Parameters.m_CifgEnabled));</div><div class="line"><a name="l03056"></a><span class="lineno"> 3056</span>&#160;</div><div class="line"><a name="l03057"></a><span class="lineno"> 3057</span>&#160;    <span class="keywordflow">if</span> (!allCifgParamsPresentOrNot)</div><div class="line"><a name="l03058"></a><span class="lineno"> 3058</span>&#160;    {</div><div class="line"><a name="l03059"></a><span class="lineno"> 3059</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName +</div><div class="line"><a name="l03060"></a><span class="lineno"> 3060</span>&#160;                <span class="stringliteral">&quot;: InputToInputWeights, RecurrentToInputWeights and InputGateBias must either all be present &quot;</span></div><div class="line"><a name="l03061"></a><span class="lineno"> 3061</span>&#160;                <span class="stringliteral">&quot;(CIFG disabled) or not be present at all (CIFG enabled). m_Parameters.m_CifgEnabled should be &quot;</span></div><div class="line"><a name="l03062"></a><span class="lineno"> 3062</span>&#160;                <span class="stringliteral">&quot;set appropriately.&quot;</span>);</div><div class="line"><a name="l03063"></a><span class="lineno"> 3063</span>&#160;    }</div><div class="line"><a name="l03064"></a><span class="lineno"> 3064</span>&#160;</div><div class="line"><a name="l03065"></a><span class="lineno"> 3065</span>&#160;    <span class="keywordflow">if</span> (!m_Parameters.m_CifgEnabled)</div><div class="line"><a name="l03066"></a><span class="lineno"> 3066</span>&#160;    {</div><div class="line"><a name="l03067"></a><span class="lineno"> 3067</span>&#160;        <span class="comment">// Validate number of dimensions and number of elements</span></div><div class="line"><a name="l03068"></a><span class="lineno"> 3068</span>&#160;        <span class="keyword">auto</span> inputToInputWeightsInfo = m_InputToInputWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03069"></a><span class="lineno"> 3069</span>&#160;        ValidateTensorNumDimNumElem(inputToInputWeightsInfo, 2, (numUnits * inputSize), <span class="stringliteral">&quot; InputToInputWeights&quot;</span>);</div><div class="line"><a name="l03070"></a><span class="lineno"> 3070</span>&#160;</div><div class="line"><a name="l03071"></a><span class="lineno"> 3071</span>&#160;        <span class="keyword">auto</span> recurrentToInputWeightsInfo = m_RecurrentToInputWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03072"></a><span class="lineno"> 3072</span>&#160;        ValidateTensorNumDimNumElem(recurrentToInputWeightsInfo, 2, (numUnits * outputSize),</div><div class="line"><a name="l03073"></a><span class="lineno"> 3073</span>&#160;                                    <span class="stringliteral">&quot; RecurrentToInputWeights&quot;</span>);</div><div class="line"><a name="l03074"></a><span class="lineno"> 3074</span>&#160;</div><div class="line"><a name="l03075"></a><span class="lineno"> 3075</span>&#160;        <span class="keyword">auto</span> inputGateBiasInfo = m_InputGateBias-&gt;GetTensorInfo();</div><div class="line"><a name="l03076"></a><span class="lineno"> 3076</span>&#160;        ValidateTensorNumDimNumElem(inputGateBiasInfo, 1, numUnits, <span class="stringliteral">&quot; InputGateBias&quot;</span>);</div><div class="line"><a name="l03077"></a><span class="lineno"> 3077</span>&#160;</div><div class="line"><a name="l03078"></a><span class="lineno"> 3078</span>&#160;        <span class="comment">// Validate data types</span></div><div class="line"><a name="l03079"></a><span class="lineno"> 3079</span>&#160;        ValidateTensorDataTypesMatch(inputToForgetWeightsInfo, inputToInputWeightsInfo, descriptorName,</div><div class="line"><a name="l03080"></a><span class="lineno"> 3080</span>&#160;                                     <span class="stringliteral">&quot;inputToForgetWeights&quot;</span>, <span class="stringliteral">&quot;inputToInputWeights&quot;</span>);</div><div class="line"><a name="l03081"></a><span class="lineno"> 3081</span>&#160;        ValidateTensorDataTypesMatch(inputToForgetWeightsInfo, recurrentToInputWeightsInfo, descriptorName,</div><div class="line"><a name="l03082"></a><span class="lineno"> 3082</span>&#160;                                     <span class="stringliteral">&quot;inputToForgetWeights&quot;</span>, <span class="stringliteral">&quot;recurrentToInputWeights&quot;</span>);</div><div class="line"><a name="l03083"></a><span class="lineno"> 3083</span>&#160;        ValidateTensorDataTypesMatch(forgetGateBiasInfo, inputGateBiasInfo, descriptorName,</div><div class="line"><a name="l03084"></a><span class="lineno"> 3084</span>&#160;                                     <span class="stringliteral">&quot;forgetGateBias&quot;</span>, <span class="stringliteral">&quot;inputGateBias&quot;</span>);</div><div class="line"><a name="l03085"></a><span class="lineno"> 3085</span>&#160;    }</div><div class="line"><a name="l03086"></a><span class="lineno"> 3086</span>&#160;</div><div class="line"><a name="l03087"></a><span class="lineno"> 3087</span>&#160;    <span class="comment">// Validate OPTIONAL params: Peephole (cellToInputWeights, cellToForgetWeights, cellToOutputWeights)</span></div><div class="line"><a name="l03088"></a><span class="lineno"> 3088</span>&#160;    <span class="keywordtype">bool</span> allPeepholeWeightsPresentOrNot =</div><div class="line"><a name="l03089"></a><span class="lineno"> 3089</span>&#160;            (((m_CellToInputWeights || m_Parameters.m_CifgEnabled) &amp;&amp; m_CellToForgetWeights</div><div class="line"><a name="l03090"></a><span class="lineno"> 3090</span>&#160;              &amp;&amp; m_CellToOutputWeights &amp;&amp; m_Parameters.m_PeepholeEnabled)</div><div class="line"><a name="l03091"></a><span class="lineno"> 3091</span>&#160;             || (!m_CellToInputWeights &amp;&amp; !m_CellToForgetWeights</div><div class="line"><a name="l03092"></a><span class="lineno"> 3092</span>&#160;                 &amp;&amp; !m_CellToOutputWeights &amp;&amp; !m_Parameters.m_PeepholeEnabled));</div><div class="line"><a name="l03093"></a><span class="lineno"> 3093</span>&#160;</div><div class="line"><a name="l03094"></a><span class="lineno"> 3094</span>&#160;    <span class="keywordflow">if</span> (!allPeepholeWeightsPresentOrNot)</div><div class="line"><a name="l03095"></a><span class="lineno"> 3095</span>&#160;    {</div><div class="line"><a name="l03096"></a><span class="lineno"> 3096</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName +</div><div class="line"><a name="l03097"></a><span class="lineno"> 3097</span>&#160;                <span class="stringliteral">&quot;: CellToInputWeights, CellToForgetWeights and CellToOutputWeights should all be present (Peephole &quot;</span></div><div class="line"><a name="l03098"></a><span class="lineno"> 3098</span>&#160;                <span class="stringliteral">&quot;enabled) or not be present at all (Peephole disabled). CellToInputWeights should only be present &quot;</span></div><div class="line"><a name="l03099"></a><span class="lineno"> 3099</span>&#160;                <span class="stringliteral">&quot;when Peephole is enabled and CIFG is disabled. m_Parameters.m_PeepholeEnabled should be set &quot;</span></div><div class="line"><a name="l03100"></a><span class="lineno"> 3100</span>&#160;                <span class="stringliteral">&quot;appropriately.&quot;</span>);</div><div class="line"><a name="l03101"></a><span class="lineno"> 3101</span>&#160;    }</div><div class="line"><a name="l03102"></a><span class="lineno"> 3102</span>&#160;</div><div class="line"><a name="l03103"></a><span class="lineno"> 3103</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_PeepholeEnabled)</div><div class="line"><a name="l03104"></a><span class="lineno"> 3104</span>&#160;    {</div><div class="line"><a name="l03105"></a><span class="lineno"> 3105</span>&#160;        <span class="keyword">auto</span> cellToForgetWeightsInfo = m_CellToForgetWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03106"></a><span class="lineno"> 3106</span>&#160;        ValidateTensorNumDimNumElem(cellToForgetWeightsInfo, 1, numUnits, <span class="stringliteral">&quot; cellToForgetWeights&quot;</span>);</div><div class="line"><a name="l03107"></a><span class="lineno"> 3107</span>&#160;        ValidateDataTypes(cellToForgetWeightsInfo, layerNormPeepholeWeightsSupportedTypes, descriptorName);</div><div class="line"><a name="l03108"></a><span class="lineno"> 3108</span>&#160;</div><div class="line"><a name="l03109"></a><span class="lineno"> 3109</span>&#160;        <span class="keyword">auto</span> cellToOutputWeightsInfo = m_CellToOutputWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03110"></a><span class="lineno"> 3110</span>&#160;        ValidateTensorNumDimNumElem(cellToOutputWeightsInfo, 1, numUnits, <span class="stringliteral">&quot; cellToOutputWeights&quot;</span>);</div><div class="line"><a name="l03111"></a><span class="lineno"> 3111</span>&#160;        ValidateTensorDataTypesMatch(cellToForgetWeightsInfo, cellToOutputWeightsInfo, descriptorName,</div><div class="line"><a name="l03112"></a><span class="lineno"> 3112</span>&#160;                                     <span class="stringliteral">&quot;cellToForgetWeight&quot;</span>, <span class="stringliteral">&quot;cellToOutputWeights&quot;</span>);</div><div class="line"><a name="l03113"></a><span class="lineno"> 3113</span>&#160;</div><div class="line"><a name="l03114"></a><span class="lineno"> 3114</span>&#160;        <span class="keywordflow">if</span> (!m_Parameters.m_CifgEnabled)</div><div class="line"><a name="l03115"></a><span class="lineno"> 3115</span>&#160;        {</div><div class="line"><a name="l03116"></a><span class="lineno"> 3116</span>&#160;            <span class="keyword">auto</span> cellToInputWeightsInfo = m_CellToInputWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03117"></a><span class="lineno"> 3117</span>&#160;            ValidateTensorNumDimNumElem(cellToInputWeightsInfo, 1, numUnits, <span class="stringliteral">&quot; cellToInputWeights&quot;</span>);</div><div class="line"><a name="l03118"></a><span class="lineno"> 3118</span>&#160;            ValidateTensorDataTypesMatch(cellToForgetWeightsInfo, cellToInputWeightsInfo, descriptorName,</div><div class="line"><a name="l03119"></a><span class="lineno"> 3119</span>&#160;                                         <span class="stringliteral">&quot;cellToForgetWeights&quot;</span>, <span class="stringliteral">&quot;cellToInputWeights&quot;</span>);</div><div class="line"><a name="l03120"></a><span class="lineno"> 3120</span>&#160;        }</div><div class="line"><a name="l03121"></a><span class="lineno"> 3121</span>&#160;    }</div><div class="line"><a name="l03122"></a><span class="lineno"> 3122</span>&#160;</div><div class="line"><a name="l03123"></a><span class="lineno"> 3123</span>&#160;    <span class="comment">// Validate OPTIONAL params: Layer Norm Weights</span></div><div class="line"><a name="l03124"></a><span class="lineno"> 3124</span>&#160;    <span class="keywordtype">bool</span> allLayerNormWeightsPresentOrNot =</div><div class="line"><a name="l03125"></a><span class="lineno"> 3125</span>&#160;            (((m_InputLayerNormWeights || m_Parameters.m_CifgEnabled) &amp;&amp; m_ForgetLayerNormWeights</div><div class="line"><a name="l03126"></a><span class="lineno"> 3126</span>&#160;              &amp;&amp; m_CellLayerNormWeights &amp;&amp; m_OutputLayerNormWeights &amp;&amp; m_Parameters.m_LayerNormEnabled)</div><div class="line"><a name="l03127"></a><span class="lineno"> 3127</span>&#160;             || (!m_InputLayerNormWeights &amp;&amp; !m_ForgetLayerNormWeights &amp;&amp; !m_CellLayerNormWeights</div><div class="line"><a name="l03128"></a><span class="lineno"> 3128</span>&#160;                 &amp;&amp; !m_OutputLayerNormWeights &amp;&amp; !m_Parameters.m_LayerNormEnabled));</div><div class="line"><a name="l03129"></a><span class="lineno"> 3129</span>&#160;</div><div class="line"><a name="l03130"></a><span class="lineno"> 3130</span>&#160;    <span class="keywordflow">if</span> (!allLayerNormWeightsPresentOrNot)</div><div class="line"><a name="l03131"></a><span class="lineno"> 3131</span>&#160;    {</div><div class="line"><a name="l03132"></a><span class="lineno"> 3132</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName +</div><div class="line"><a name="l03133"></a><span class="lineno"> 3133</span>&#160;                                       <span class="stringliteral">&quot;: InputLayerNormWeights, ForgetLayerNormWeights, m_OutputLayerNormWeights &quot;</span></div><div class="line"><a name="l03134"></a><span class="lineno"> 3134</span>&#160;                                       <span class="stringliteral">&quot;and CellLayerNormWeights should all be present (Layer Norm enabled) or not &quot;</span></div><div class="line"><a name="l03135"></a><span class="lineno"> 3135</span>&#160;                                       <span class="stringliteral">&quot;be present at all (Layer Norm disabled). InputLayerNormWeights should &quot;</span></div><div class="line"><a name="l03136"></a><span class="lineno"> 3136</span>&#160;                                       <span class="stringliteral">&quot;only be present when Layer Norm is enabled and CIFG is disabled. &quot;</span></div><div class="line"><a name="l03137"></a><span class="lineno"> 3137</span>&#160;                                       <span class="stringliteral">&quot;m_Parameters.m_LayerNormEnabled should be set appropriately.&quot;</span>);</div><div class="line"><a name="l03138"></a><span class="lineno"> 3138</span>&#160;    }</div><div class="line"><a name="l03139"></a><span class="lineno"> 3139</span>&#160;</div><div class="line"><a name="l03140"></a><span class="lineno"> 3140</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_LayerNormEnabled)</div><div class="line"><a name="l03141"></a><span class="lineno"> 3141</span>&#160;    {</div><div class="line"><a name="l03142"></a><span class="lineno"> 3142</span>&#160;        <span class="keyword">auto</span> forgetLayerNormWeightsInfo = m_ForgetLayerNormWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03143"></a><span class="lineno"> 3143</span>&#160;        ValidateTensorNumDimNumElem(forgetLayerNormWeightsInfo, 1, numUnits, <span class="stringliteral">&quot; forgetLayerNormWeights&quot;</span>);</div><div class="line"><a name="l03144"></a><span class="lineno"> 3144</span>&#160;        ValidateDataTypes(forgetLayerNormWeightsInfo, layerNormPeepholeWeightsSupportedTypes, descriptorName);</div><div class="line"><a name="l03145"></a><span class="lineno"> 3145</span>&#160;</div><div class="line"><a name="l03146"></a><span class="lineno"> 3146</span>&#160;        <span class="keyword">auto</span> cellLayerNormWeightsInfo = m_CellLayerNormWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03147"></a><span class="lineno"> 3147</span>&#160;        ValidateTensorNumDimNumElem(cellLayerNormWeightsInfo, 1, numUnits, <span class="stringliteral">&quot; cellLayerNormWeights&quot;</span>);</div><div class="line"><a name="l03148"></a><span class="lineno"> 3148</span>&#160;        ValidateTensorDataTypesMatch(forgetLayerNormWeightsInfo, cellLayerNormWeightsInfo, descriptorName,</div><div class="line"><a name="l03149"></a><span class="lineno"> 3149</span>&#160;                                     <span class="stringliteral">&quot;forgetLayerNormWeights&quot;</span>, <span class="stringliteral">&quot;cellLayerNormWeights&quot;</span>);</div><div class="line"><a name="l03150"></a><span class="lineno"> 3150</span>&#160;</div><div class="line"><a name="l03151"></a><span class="lineno"> 3151</span>&#160;        <span class="keyword">auto</span> outputLayerNormWeightsInfo = m_OutputLayerNormWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03152"></a><span class="lineno"> 3152</span>&#160;        ValidateTensorNumDimNumElem(outputLayerNormWeightsInfo, 1, numUnits, <span class="stringliteral">&quot; outputLayerNormWeights&quot;</span>);</div><div class="line"><a name="l03153"></a><span class="lineno"> 3153</span>&#160;        ValidateTensorDataTypesMatch(forgetLayerNormWeightsInfo, outputLayerNormWeightsInfo, descriptorName,</div><div class="line"><a name="l03154"></a><span class="lineno"> 3154</span>&#160;                                     <span class="stringliteral">&quot;forgetLayerNormWeights&quot;</span>, <span class="stringliteral">&quot;outputLayerNormWeights&quot;</span>);</div><div class="line"><a name="l03155"></a><span class="lineno"> 3155</span>&#160;</div><div class="line"><a name="l03156"></a><span class="lineno"> 3156</span>&#160;        <span class="keywordflow">if</span> (!m_Parameters.m_CifgEnabled)</div><div class="line"><a name="l03157"></a><span class="lineno"> 3157</span>&#160;        {</div><div class="line"><a name="l03158"></a><span class="lineno"> 3158</span>&#160;            <span class="keyword">auto</span> inputLayerNormWeightsInfo = m_InputLayerNormWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03159"></a><span class="lineno"> 3159</span>&#160;            ValidateTensorNumDimNumElem(inputLayerNormWeightsInfo, 1, numUnits, <span class="stringliteral">&quot; inputLayerNormWeights&quot;</span>);</div><div class="line"><a name="l03160"></a><span class="lineno"> 3160</span>&#160;            ValidateTensorDataTypesMatch(forgetLayerNormWeightsInfo, inputLayerNormWeightsInfo, descriptorName,</div><div class="line"><a name="l03161"></a><span class="lineno"> 3161</span>&#160;                                         <span class="stringliteral">&quot;forgetLayerNormWeights&quot;</span>, <span class="stringliteral">&quot;inputLayerNormWeights&quot;</span>);</div><div class="line"><a name="l03162"></a><span class="lineno"> 3162</span>&#160;        }</div><div class="line"><a name="l03163"></a><span class="lineno"> 3163</span>&#160;    }</div><div class="line"><a name="l03164"></a><span class="lineno"> 3164</span>&#160;</div><div class="line"><a name="l03165"></a><span class="lineno"> 3165</span>&#160;    <span class="comment">// Validate OPTIONAL params: Projection (projectionWeights, projectionBias)</span></div><div class="line"><a name="l03166"></a><span class="lineno"> 3166</span>&#160;    <span class="keywordtype">bool</span> correctProjectionTensorsPresent =</div><div class="line"><a name="l03167"></a><span class="lineno"> 3167</span>&#160;            ((!m_ProjectionWeights &amp;&amp; !m_ProjectionBias &amp;&amp; !m_Parameters.m_ProjectionEnabled) ||</div><div class="line"><a name="l03168"></a><span class="lineno"> 3168</span>&#160;            (m_ProjectionWeights &amp;&amp; !m_ProjectionBias &amp;&amp; m_Parameters.m_ProjectionEnabled) ||</div><div class="line"><a name="l03169"></a><span class="lineno"> 3169</span>&#160;            (m_ProjectionWeights &amp;&amp; m_ProjectionBias &amp;&amp; m_Parameters.m_ProjectionEnabled));</div><div class="line"><a name="l03170"></a><span class="lineno"> 3170</span>&#160;</div><div class="line"><a name="l03171"></a><span class="lineno"> 3171</span>&#160;    <span class="keywordflow">if</span> (!correctProjectionTensorsPresent)</div><div class="line"><a name="l03172"></a><span class="lineno"> 3172</span>&#160;    {</div><div class="line"><a name="l03173"></a><span class="lineno"> 3173</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName +</div><div class="line"><a name="l03174"></a><span class="lineno"> 3174</span>&#160;                                       <span class="stringliteral">&quot;: If projection is enabled, ProjectionWeights should be present and &quot;</span></div><div class="line"><a name="l03175"></a><span class="lineno"> 3175</span>&#160;                                       <span class="stringliteral">&quot;ProjectionBias is optional. If projection is disabled, neither &quot;</span></div><div class="line"><a name="l03176"></a><span class="lineno"> 3176</span>&#160;                                       <span class="stringliteral">&quot;ProjectionWeights nor ProjectionBias should be present.&quot;</span>);</div><div class="line"><a name="l03177"></a><span class="lineno"> 3177</span>&#160;    }</div><div class="line"><a name="l03178"></a><span class="lineno"> 3178</span>&#160;</div><div class="line"><a name="l03179"></a><span class="lineno"> 3179</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_ProjectionEnabled)</div><div class="line"><a name="l03180"></a><span class="lineno"> 3180</span>&#160;    {</div><div class="line"><a name="l03181"></a><span class="lineno"> 3181</span>&#160;        <span class="keyword">auto</span> projectionWeightsInfo = m_ProjectionWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03182"></a><span class="lineno"> 3182</span>&#160;        ValidateTensorNumDimNumElem(projectionWeightsInfo, 2, (numUnits * outputSize), <span class="stringliteral">&quot;ProjectionWeights&quot;</span>);</div><div class="line"><a name="l03183"></a><span class="lineno"> 3183</span>&#160;        ValidateDataTypes(projectionWeightsInfo, weightsSupportedTypes, descriptorName);</div><div class="line"><a name="l03184"></a><span class="lineno"> 3184</span>&#160;</div><div class="line"><a name="l03185"></a><span class="lineno"> 3185</span>&#160;        <span class="keywordflow">if</span> (m_ProjectionBias)</div><div class="line"><a name="l03186"></a><span class="lineno"> 3186</span>&#160;        {</div><div class="line"><a name="l03187"></a><span class="lineno"> 3187</span>&#160;            <span class="keyword">auto</span> projectionBiasInfo = m_ProjectionBias-&gt;GetTensorInfo();</div><div class="line"><a name="l03188"></a><span class="lineno"> 3188</span>&#160;            ValidateTensorNumDimNumElem(projectionBiasInfo, 1, outputSize, <span class="stringliteral">&quot;ProjectionBias&quot;</span>);</div><div class="line"><a name="l03189"></a><span class="lineno"> 3189</span>&#160;            ValidateDataTypes(projectionBiasInfo, biasSupportedTypes, descriptorName);</div><div class="line"><a name="l03190"></a><span class="lineno"> 3190</span>&#160;        }</div><div class="line"><a name="l03191"></a><span class="lineno"> 3191</span>&#160;</div><div class="line"><a name="l03192"></a><span class="lineno"> 3192</span>&#160;    }</div><div class="line"><a name="l03193"></a><span class="lineno"> 3193</span>&#160;    <span class="keywordflow">else</span> <span class="keywordflow">if</span> ((outputInfo.GetQuantizationScale() != m_Parameters.m_HiddenStateScale) &amp;&amp;</div><div class="line"><a name="l03194"></a><span class="lineno"> 3194</span>&#160;              outputInfo.GetQuantizationOffset() != m_Parameters.m_HiddenStateZeroPoint) {</div><div class="line"><a name="l03195"></a><span class="lineno"> 3195</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName +</div><div class="line"><a name="l03196"></a><span class="lineno"> 3196</span>&#160;                                       <span class="stringliteral">&quot;: If projection is disabled, output quantization info (scale, offset) &quot;</span></div><div class="line"><a name="l03197"></a><span class="lineno"> 3197</span>&#160;                                       <span class="stringliteral">&quot;should match HiddenStateScale and HiddenStateZeroPoint.&quot;</span>);</div><div class="line"><a name="l03198"></a><span class="lineno"> 3198</span>&#160;    }</div><div class="line"><a name="l03199"></a><span class="lineno"> 3199</span>&#160;</div><div class="line"><a name="l03200"></a><span class="lineno"> 3200</span>&#160;}</div><div class="line"><a name="l03201"></a><span class="lineno"> 3201</span>&#160;</div><div class="line"><a name="l03202"></a><span class="lineno"><a class="line" href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 3202</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">QuantizedLstmQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l03203"></a><span class="lineno"> 3203</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l03204"></a><span class="lineno"> 3204</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;QuantizedLstmQueueDescriptor&quot;</span>};</div><div class="line"><a name="l03205"></a><span class="lineno"> 3205</span>&#160;</div><div class="line"><a name="l03206"></a><span class="lineno"> 3206</span>&#160;    <span class="comment">// Validate number of inputs/outputs</span></div><div class="line"><a name="l03207"></a><span class="lineno"> 3207</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 3);</div><div class="line"><a name="l03208"></a><span class="lineno"> 3208</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 2);</div><div class="line"><a name="l03209"></a><span class="lineno"> 3209</span>&#160;</div><div class="line"><a name="l03210"></a><span class="lineno"> 3210</span>&#160;    <span class="comment">// Input/output tensor infos</span></div><div class="line"><a name="l03211"></a><span class="lineno"> 3211</span>&#160;    <span class="keyword">auto</span> inputInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l03212"></a><span class="lineno"> 3212</span>&#160;    <span class="keyword">auto</span> cellStateInInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l03213"></a><span class="lineno"> 3213</span>&#160;    <span class="keyword">auto</span> outputStateInInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[2];</div><div class="line"><a name="l03214"></a><span class="lineno"> 3214</span>&#160;</div><div class="line"><a name="l03215"></a><span class="lineno"> 3215</span>&#160;    <span class="keyword">auto</span> cellStateOutInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l03216"></a><span class="lineno"> 3216</span>&#160;    <span class="keyword">auto</span> outputStateOutInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[1];</div><div class="line"><a name="l03217"></a><span class="lineno"> 3217</span>&#160;</div><div class="line"><a name="l03218"></a><span class="lineno"> 3218</span>&#160;    std::vector&lt;DataType&gt; inputOutputSupportedTypes =</div><div class="line"><a name="l03219"></a><span class="lineno"> 3219</span>&#160;    {</div><div class="line"><a name="l03220"></a><span class="lineno"> 3220</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a></div><div class="line"><a name="l03221"></a><span class="lineno"> 3221</span>&#160;    };</div><div class="line"><a name="l03222"></a><span class="lineno"> 3222</span>&#160;</div><div class="line"><a name="l03223"></a><span class="lineno"> 3223</span>&#160;    std::vector&lt;DataType&gt; cellStateSupportedTypes =</div><div class="line"><a name="l03224"></a><span class="lineno"> 3224</span>&#160;    {</div><div class="line"><a name="l03225"></a><span class="lineno"> 3225</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l03226"></a><span class="lineno"> 3226</span>&#160;    };</div><div class="line"><a name="l03227"></a><span class="lineno"> 3227</span>&#160;</div><div class="line"><a name="l03228"></a><span class="lineno"> 3228</span>&#160;    std::vector&lt;DataType&gt; weightsSupportedTypes =</div><div class="line"><a name="l03229"></a><span class="lineno"> 3229</span>&#160;    {</div><div class="line"><a name="l03230"></a><span class="lineno"> 3230</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a></div><div class="line"><a name="l03231"></a><span class="lineno"> 3231</span>&#160;    };</div><div class="line"><a name="l03232"></a><span class="lineno"> 3232</span>&#160;</div><div class="line"><a name="l03233"></a><span class="lineno"> 3233</span>&#160;    std::vector&lt;DataType&gt; biasSupportedTypes =</div><div class="line"><a name="l03234"></a><span class="lineno"> 3234</span>&#160;    {</div><div class="line"><a name="l03235"></a><span class="lineno"> 3235</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a></div><div class="line"><a name="l03236"></a><span class="lineno"> 3236</span>&#160;    };</div><div class="line"><a name="l03237"></a><span class="lineno"> 3237</span>&#160;</div><div class="line"><a name="l03238"></a><span class="lineno"> 3238</span>&#160;    <span class="comment">// Validate types of input/output tensors</span></div><div class="line"><a name="l03239"></a><span class="lineno"> 3239</span>&#160;    ValidateDataTypes(inputInfo, inputOutputSupportedTypes, descriptorName);</div><div class="line"><a name="l03240"></a><span class="lineno"> 3240</span>&#160;    ValidateDataTypes(cellStateInInfo, cellStateSupportedTypes, descriptorName);</div><div class="line"><a name="l03241"></a><span class="lineno"> 3241</span>&#160;    ValidateDataTypes(outputStateInInfo, inputOutputSupportedTypes, descriptorName);</div><div class="line"><a name="l03242"></a><span class="lineno"> 3242</span>&#160;</div><div class="line"><a name="l03243"></a><span class="lineno"> 3243</span>&#160;    ValidateDataTypes(cellStateOutInfo, cellStateSupportedTypes, descriptorName);</div><div class="line"><a name="l03244"></a><span class="lineno"> 3244</span>&#160;    ValidateDataTypes(outputStateOutInfo, inputOutputSupportedTypes, descriptorName);</div><div class="line"><a name="l03245"></a><span class="lineno"> 3245</span>&#160;</div><div class="line"><a name="l03246"></a><span class="lineno"> 3246</span>&#160;    <span class="comment">// Validate matching types of input/output tensors</span></div><div class="line"><a name="l03247"></a><span class="lineno"> 3247</span>&#160;    ValidateTensorDataTypesMatch(inputInfo, outputStateInInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;outputStateIn&quot;</span>);</div><div class="line"><a name="l03248"></a><span class="lineno"> 3248</span>&#160;    ValidateTensorDataTypesMatch(outputStateInInfo, outputStateOutInfo, descriptorName,</div><div class="line"><a name="l03249"></a><span class="lineno"> 3249</span>&#160;                                 <span class="stringliteral">&quot;outputStateIn&quot;</span>, <span class="stringliteral">&quot;outputStateOut&quot;</span>);</div><div class="line"><a name="l03250"></a><span class="lineno"> 3250</span>&#160;    ValidateTensorDataTypesMatch(cellStateInInfo, cellStateOutInfo, descriptorName, <span class="stringliteral">&quot;cellStateIn&quot;</span>, <span class="stringliteral">&quot;cellStateOut&quot;</span>);</div><div class="line"><a name="l03251"></a><span class="lineno"> 3251</span>&#160;</div><div class="line"><a name="l03252"></a><span class="lineno"> 3252</span>&#160;    <span class="comment">// Validate matching quantization info for input/output tensors</span></div><div class="line"><a name="l03253"></a><span class="lineno"> 3253</span>&#160;    ValidateTensorQuantizationSpace(inputInfo, outputStateInInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;outputStateIn&quot;</span>);</div><div class="line"><a name="l03254"></a><span class="lineno"> 3254</span>&#160;    ValidateTensorQuantizationSpace(inputInfo, outputStateOutInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;outputStateOut&quot;</span>);</div><div class="line"><a name="l03255"></a><span class="lineno"> 3255</span>&#160;    ValidateTensorQuantizationSpace(cellStateInInfo, cellStateOutInfo, descriptorName, <span class="stringliteral">&quot;cellStateIn&quot;</span>, <span class="stringliteral">&quot;cellStateOut&quot;</span>);</div><div class="line"><a name="l03256"></a><span class="lineno"> 3256</span>&#160;</div><div class="line"><a name="l03257"></a><span class="lineno"> 3257</span>&#160;    <span class="comment">// Infer number of batches, input size and output size from tensor dimensions</span></div><div class="line"><a name="l03258"></a><span class="lineno"> 3258</span>&#160;    <span class="keyword">const</span> uint32_t numBatches = inputInfo.GetShape()[0];</div><div class="line"><a name="l03259"></a><span class="lineno"> 3259</span>&#160;    <span class="keyword">const</span> uint32_t inputSize  = inputInfo.GetShape()[1];</div><div class="line"><a name="l03260"></a><span class="lineno"> 3260</span>&#160;    <span class="keyword">const</span> uint32_t outputSize = cellStateInInfo.GetShape()[1];</div><div class="line"><a name="l03261"></a><span class="lineno"> 3261</span>&#160;</div><div class="line"><a name="l03262"></a><span class="lineno"> 3262</span>&#160;    <span class="comment">// Validate number of dimensions and number of elements for input/output tensors</span></div><div class="line"><a name="l03263"></a><span class="lineno"> 3263</span>&#160;    ValidateTensorNumDimNumElem(inputInfo, 2, (numBatches * inputSize), descriptorName + <span class="stringliteral">&quot; input&quot;</span>);</div><div class="line"><a name="l03264"></a><span class="lineno"> 3264</span>&#160;    ValidateTensorNumDimNumElem(cellStateInInfo, 2, (numBatches * outputSize), descriptorName + <span class="stringliteral">&quot; cellStateIn&quot;</span>);</div><div class="line"><a name="l03265"></a><span class="lineno"> 3265</span>&#160;    ValidateTensorNumDimNumElem(outputStateInInfo, 2, (numBatches * outputSize), descriptorName + <span class="stringliteral">&quot; outputStateIn&quot;</span>);</div><div class="line"><a name="l03266"></a><span class="lineno"> 3266</span>&#160;    ValidateTensorNumDimNumElem(cellStateOutInfo, 2, (numBatches * outputSize), descriptorName + <span class="stringliteral">&quot; cellStateOut&quot;</span>);</div><div class="line"><a name="l03267"></a><span class="lineno"> 3267</span>&#160;    ValidateTensorNumDimNumElem(outputStateOutInfo, 2, (numBatches * outputSize), descriptorName + <span class="stringliteral">&quot; outputStateOut&quot;</span>);</div><div class="line"><a name="l03268"></a><span class="lineno"> 3268</span>&#160;</div><div class="line"><a name="l03269"></a><span class="lineno"> 3269</span>&#160;    <span class="comment">// Validate number of dimensions and number of elements for weights tensors</span></div><div class="line"><a name="l03270"></a><span class="lineno"> 3270</span>&#160;    ValidatePointer(m_InputToInputWeights, descriptorName, <span class="stringliteral">&quot;InputToInputWeights&quot;</span>);</div><div class="line"><a name="l03271"></a><span class="lineno"> 3271</span>&#160;    <span class="keyword">auto</span> inputToInputWeightsInfo = m_InputToInputWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03272"></a><span class="lineno"> 3272</span>&#160;    ValidateTensorNumDimNumElem(inputToInputWeightsInfo, 2, (outputSize * inputSize), <span class="stringliteral">&quot; InputToInputWeights&quot;</span>);</div><div class="line"><a name="l03273"></a><span class="lineno"> 3273</span>&#160;</div><div class="line"><a name="l03274"></a><span class="lineno"> 3274</span>&#160;    ValidatePointer(m_InputToForgetWeights, descriptorName, <span class="stringliteral">&quot;InputToForgetWeights&quot;</span>);</div><div class="line"><a name="l03275"></a><span class="lineno"> 3275</span>&#160;    <span class="keyword">auto</span> inputToForgetWeightsInfo = m_InputToForgetWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03276"></a><span class="lineno"> 3276</span>&#160;    ValidateTensorNumDimNumElem(inputToForgetWeightsInfo, 2, (outputSize * inputSize), <span class="stringliteral">&quot; InputToForgetWeights&quot;</span>);</div><div class="line"><a name="l03277"></a><span class="lineno"> 3277</span>&#160;</div><div class="line"><a name="l03278"></a><span class="lineno"> 3278</span>&#160;    ValidatePointer(m_InputToCellWeights, descriptorName, <span class="stringliteral">&quot;InputToCellWeights&quot;</span>);</div><div class="line"><a name="l03279"></a><span class="lineno"> 3279</span>&#160;    <span class="keyword">auto</span> inputToCellWeightsInfo = m_InputToCellWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03280"></a><span class="lineno"> 3280</span>&#160;    ValidateTensorNumDimNumElem(inputToCellWeightsInfo, 2, (outputSize * inputSize), <span class="stringliteral">&quot; InputToCellWeights&quot;</span>);</div><div class="line"><a name="l03281"></a><span class="lineno"> 3281</span>&#160;</div><div class="line"><a name="l03282"></a><span class="lineno"> 3282</span>&#160;    ValidatePointer(m_InputToOutputWeights, descriptorName, <span class="stringliteral">&quot;InputToOutputWeights&quot;</span>);</div><div class="line"><a name="l03283"></a><span class="lineno"> 3283</span>&#160;    <span class="keyword">auto</span> inputToOutputWeightsInfo = m_InputToOutputWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03284"></a><span class="lineno"> 3284</span>&#160;    ValidateTensorNumDimNumElem(inputToOutputWeightsInfo, 2, (outputSize * inputSize), <span class="stringliteral">&quot; InputToOutputWeights&quot;</span>);</div><div class="line"><a name="l03285"></a><span class="lineno"> 3285</span>&#160;</div><div class="line"><a name="l03286"></a><span class="lineno"> 3286</span>&#160;    ValidatePointer(m_RecurrentToInputWeights, descriptorName, <span class="stringliteral">&quot;RecurrentToInputWeights&quot;</span>);</div><div class="line"><a name="l03287"></a><span class="lineno"> 3287</span>&#160;    <span class="keyword">auto</span> recurrentToInputWeightsInfo = m_RecurrentToInputWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03288"></a><span class="lineno"> 3288</span>&#160;    ValidateTensorNumDimNumElem(recurrentToInputWeightsInfo, 2, (outputSize * outputSize), <span class="stringliteral">&quot; RecurrentToInputWeights&quot;</span>);</div><div class="line"><a name="l03289"></a><span class="lineno"> 3289</span>&#160;</div><div class="line"><a name="l03290"></a><span class="lineno"> 3290</span>&#160;    ValidatePointer(m_RecurrentToForgetWeights, descriptorName, <span class="stringliteral">&quot;RecurrentToForgetWeights&quot;</span>);</div><div class="line"><a name="l03291"></a><span class="lineno"> 3291</span>&#160;    <span class="keyword">auto</span> recurrentToForgetWeightsInfo = m_RecurrentToForgetWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03292"></a><span class="lineno"> 3292</span>&#160;    ValidateTensorNumDimNumElem(recurrentToForgetWeightsInfo, 2, (outputSize * outputSize),</div><div class="line"><a name="l03293"></a><span class="lineno"> 3293</span>&#160;                                <span class="stringliteral">&quot; RecurrentToForgetWeights&quot;</span>);</div><div class="line"><a name="l03294"></a><span class="lineno"> 3294</span>&#160;</div><div class="line"><a name="l03295"></a><span class="lineno"> 3295</span>&#160;    ValidatePointer(m_RecurrentToCellWeights, descriptorName, <span class="stringliteral">&quot;RecurrentToCellWeights&quot;</span>);</div><div class="line"><a name="l03296"></a><span class="lineno"> 3296</span>&#160;    <span class="keyword">auto</span> recurrentToCellWeightsInfo = m_RecurrentToCellWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03297"></a><span class="lineno"> 3297</span>&#160;    ValidateTensorNumDimNumElem(recurrentToCellWeightsInfo, 2, (outputSize * outputSize), <span class="stringliteral">&quot; RecurrentToCellWeights&quot;</span>);</div><div class="line"><a name="l03298"></a><span class="lineno"> 3298</span>&#160;</div><div class="line"><a name="l03299"></a><span class="lineno"> 3299</span>&#160;    ValidatePointer(m_RecurrentToOutputWeights, descriptorName, <span class="stringliteral">&quot;RecurrentToOutputWeights&quot;</span>);</div><div class="line"><a name="l03300"></a><span class="lineno"> 3300</span>&#160;    <span class="keyword">auto</span> recurrentToOutputWeightsInfo = m_RecurrentToOutputWeights-&gt;GetTensorInfo();</div><div class="line"><a name="l03301"></a><span class="lineno"> 3301</span>&#160;    ValidateTensorNumDimNumElem(recurrentToOutputWeightsInfo, 2, (outputSize * outputSize), <span class="stringliteral">&quot; RecurrentToCellWeights&quot;</span>);</div><div class="line"><a name="l03302"></a><span class="lineno"> 3302</span>&#160;</div><div class="line"><a name="l03303"></a><span class="lineno"> 3303</span>&#160;    <span class="comment">// Validate data types for weights tensors (all should match each other)</span></div><div class="line"><a name="l03304"></a><span class="lineno"> 3304</span>&#160;    ValidateDataTypes(inputToInputWeightsInfo, weightsSupportedTypes, descriptorName);</div><div class="line"><a name="l03305"></a><span class="lineno"> 3305</span>&#160;</div><div class="line"><a name="l03306"></a><span class="lineno"> 3306</span>&#160;    ValidateTensorDataTypesMatch(inputToInputWeightsInfo, inputToForgetWeightsInfo, descriptorName,</div><div class="line"><a name="l03307"></a><span class="lineno"> 3307</span>&#160;                                 <span class="stringliteral">&quot;inputToInputWeights&quot;</span>, <span class="stringliteral">&quot;inputToForgetWeights&quot;</span>);</div><div class="line"><a name="l03308"></a><span class="lineno"> 3308</span>&#160;    ValidateTensorDataTypesMatch(inputToInputWeightsInfo, inputToCellWeightsInfo, descriptorName,</div><div class="line"><a name="l03309"></a><span class="lineno"> 3309</span>&#160;                                 <span class="stringliteral">&quot;inputToInputWeights&quot;</span>, <span class="stringliteral">&quot;inputToCellWeights&quot;</span>);</div><div class="line"><a name="l03310"></a><span class="lineno"> 3310</span>&#160;    ValidateTensorDataTypesMatch(inputToInputWeightsInfo, inputToOutputWeightsInfo, descriptorName,</div><div class="line"><a name="l03311"></a><span class="lineno"> 3311</span>&#160;                                 <span class="stringliteral">&quot;inputToInputWeights&quot;</span>, <span class="stringliteral">&quot;inputToOutputWeights&quot;</span>);</div><div class="line"><a name="l03312"></a><span class="lineno"> 3312</span>&#160;</div><div class="line"><a name="l03313"></a><span class="lineno"> 3313</span>&#160;    ValidateTensorDataTypesMatch(inputToInputWeightsInfo, recurrentToInputWeightsInfo, descriptorName,</div><div class="line"><a name="l03314"></a><span class="lineno"> 3314</span>&#160;                                 <span class="stringliteral">&quot;inputToInputWeights&quot;</span>, <span class="stringliteral">&quot;recurrentToInputWeights&quot;</span>);</div><div class="line"><a name="l03315"></a><span class="lineno"> 3315</span>&#160;    ValidateTensorDataTypesMatch(inputToInputWeightsInfo, recurrentToForgetWeightsInfo, descriptorName,</div><div class="line"><a name="l03316"></a><span class="lineno"> 3316</span>&#160;                                 <span class="stringliteral">&quot;inputToInputWeights&quot;</span>, <span class="stringliteral">&quot;recurrentToForgeteights&quot;</span>);</div><div class="line"><a name="l03317"></a><span class="lineno"> 3317</span>&#160;    ValidateTensorDataTypesMatch(inputToInputWeightsInfo, recurrentToCellWeightsInfo, descriptorName,</div><div class="line"><a name="l03318"></a><span class="lineno"> 3318</span>&#160;                                 <span class="stringliteral">&quot;inputToInputWeights&quot;</span>, <span class="stringliteral">&quot;recurrentToCellWeights&quot;</span>);</div><div class="line"><a name="l03319"></a><span class="lineno"> 3319</span>&#160;    ValidateTensorDataTypesMatch(inputToInputWeightsInfo, recurrentToOutputWeightsInfo, descriptorName,</div><div class="line"><a name="l03320"></a><span class="lineno"> 3320</span>&#160;                                 <span class="stringliteral">&quot;inputToInputWeights&quot;</span>, <span class="stringliteral">&quot;recurrentToOutputWeights&quot;</span>);</div><div class="line"><a name="l03321"></a><span class="lineno"> 3321</span>&#160;</div><div class="line"><a name="l03322"></a><span class="lineno"> 3322</span>&#160;    <span class="comment">// Validate matching quantization info for weight tensors (all should match each other)</span></div><div class="line"><a name="l03323"></a><span class="lineno"> 3323</span>&#160;    ValidateTensorQuantizationSpace(inputToInputWeightsInfo, inputToForgetWeightsInfo,</div><div class="line"><a name="l03324"></a><span class="lineno"> 3324</span>&#160;                                    descriptorName, <span class="stringliteral">&quot;inputToInputWeights&quot;</span>, <span class="stringliteral">&quot;inputToForgetWeights&quot;</span>);</div><div class="line"><a name="l03325"></a><span class="lineno"> 3325</span>&#160;    ValidateTensorQuantizationSpace(inputToInputWeightsInfo, inputToCellWeightsInfo,</div><div class="line"><a name="l03326"></a><span class="lineno"> 3326</span>&#160;                                    descriptorName, <span class="stringliteral">&quot;inputToInputWeights&quot;</span>, <span class="stringliteral">&quot;inputToCellWeights&quot;</span>);</div><div class="line"><a name="l03327"></a><span class="lineno"> 3327</span>&#160;    ValidateTensorQuantizationSpace(inputToInputWeightsInfo, inputToOutputWeightsInfo,</div><div class="line"><a name="l03328"></a><span class="lineno"> 3328</span>&#160;                                    descriptorName, <span class="stringliteral">&quot;inputToInputWeights&quot;</span>, <span class="stringliteral">&quot;inputToOutputWeights&quot;</span>);</div><div class="line"><a name="l03329"></a><span class="lineno"> 3329</span>&#160;</div><div class="line"><a name="l03330"></a><span class="lineno"> 3330</span>&#160;    ValidateTensorQuantizationSpace(inputToInputWeightsInfo, recurrentToInputWeightsInfo,</div><div class="line"><a name="l03331"></a><span class="lineno"> 3331</span>&#160;                                    descriptorName, <span class="stringliteral">&quot;inputToInputWeights&quot;</span>, <span class="stringliteral">&quot;recurrentToInputWeights&quot;</span>);</div><div class="line"><a name="l03332"></a><span class="lineno"> 3332</span>&#160;    ValidateTensorQuantizationSpace(inputToInputWeightsInfo, recurrentToForgetWeightsInfo,</div><div class="line"><a name="l03333"></a><span class="lineno"> 3333</span>&#160;                                    descriptorName, <span class="stringliteral">&quot;inputToInputWeights&quot;</span>, <span class="stringliteral">&quot;recurrentToForgetWeights&quot;</span>);</div><div class="line"><a name="l03334"></a><span class="lineno"> 3334</span>&#160;    ValidateTensorQuantizationSpace(inputToInputWeightsInfo, recurrentToCellWeightsInfo,</div><div class="line"><a name="l03335"></a><span class="lineno"> 3335</span>&#160;                                    descriptorName, <span class="stringliteral">&quot;inputToInputWeights&quot;</span>, <span class="stringliteral">&quot;recurrentToCellWeights&quot;</span>);</div><div class="line"><a name="l03336"></a><span class="lineno"> 3336</span>&#160;    ValidateTensorQuantizationSpace(inputToInputWeightsInfo, recurrentToOutputWeightsInfo,</div><div class="line"><a name="l03337"></a><span class="lineno"> 3337</span>&#160;                                    descriptorName, <span class="stringliteral">&quot;inputToInputWeights&quot;</span>, <span class="stringliteral">&quot;recurrentToOutputWeights&quot;</span>);</div><div class="line"><a name="l03338"></a><span class="lineno"> 3338</span>&#160;</div><div class="line"><a name="l03339"></a><span class="lineno"> 3339</span>&#160;    <span class="comment">// Validate number of dimensions and number of elements in bias tensors</span></div><div class="line"><a name="l03340"></a><span class="lineno"> 3340</span>&#160;    ValidatePointer(m_InputGateBias, descriptorName, <span class="stringliteral">&quot;InputGateBias&quot;</span>);</div><div class="line"><a name="l03341"></a><span class="lineno"> 3341</span>&#160;    <span class="keyword">auto</span> inputGateBiasInfo = m_InputGateBias-&gt;GetTensorInfo();</div><div class="line"><a name="l03342"></a><span class="lineno"> 3342</span>&#160;    ValidateTensorNumDimNumElem(inputGateBiasInfo, 1, outputSize, <span class="stringliteral">&quot; InputGateBias&quot;</span>);</div><div class="line"><a name="l03343"></a><span class="lineno"> 3343</span>&#160;</div><div class="line"><a name="l03344"></a><span class="lineno"> 3344</span>&#160;    ValidatePointer(m_ForgetGateBias, descriptorName, <span class="stringliteral">&quot;ForgetGateBias&quot;</span>);</div><div class="line"><a name="l03345"></a><span class="lineno"> 3345</span>&#160;    <span class="keyword">auto</span> forgetGateBiasInfo = m_ForgetGateBias-&gt;GetTensorInfo();</div><div class="line"><a name="l03346"></a><span class="lineno"> 3346</span>&#160;    ValidateTensorNumDimNumElem(forgetGateBiasInfo, 1, outputSize, <span class="stringliteral">&quot; ForgetGateBias&quot;</span>);</div><div class="line"><a name="l03347"></a><span class="lineno"> 3347</span>&#160;</div><div class="line"><a name="l03348"></a><span class="lineno"> 3348</span>&#160;    ValidatePointer(m_CellBias, descriptorName, <span class="stringliteral">&quot;CellBias&quot;</span>);</div><div class="line"><a name="l03349"></a><span class="lineno"> 3349</span>&#160;    <span class="keyword">auto</span> cellBiasInfo = m_CellBias-&gt;GetTensorInfo();</div><div class="line"><a name="l03350"></a><span class="lineno"> 3350</span>&#160;    ValidateTensorNumDimNumElem(cellBiasInfo, 1, outputSize, <span class="stringliteral">&quot; CellBias&quot;</span>);</div><div class="line"><a name="l03351"></a><span class="lineno"> 3351</span>&#160;</div><div class="line"><a name="l03352"></a><span class="lineno"> 3352</span>&#160;    ValidatePointer(m_OutputGateBias, descriptorName, <span class="stringliteral">&quot;OutputGateBias&quot;</span>);</div><div class="line"><a name="l03353"></a><span class="lineno"> 3353</span>&#160;    <span class="keyword">auto</span> outputGateBiasInfo = m_OutputGateBias-&gt;GetTensorInfo();</div><div class="line"><a name="l03354"></a><span class="lineno"> 3354</span>&#160;    ValidateTensorNumDimNumElem(outputGateBiasInfo, 1, outputSize, <span class="stringliteral">&quot; OutputGateBias&quot;</span>);</div><div class="line"><a name="l03355"></a><span class="lineno"> 3355</span>&#160;</div><div class="line"><a name="l03356"></a><span class="lineno"> 3356</span>&#160;    <span class="comment">// Validate data types for bias tensors (all should match each other)</span></div><div class="line"><a name="l03357"></a><span class="lineno"> 3357</span>&#160;    ValidateDataTypes(inputGateBiasInfo, biasSupportedTypes, descriptorName);</div><div class="line"><a name="l03358"></a><span class="lineno"> 3358</span>&#160;</div><div class="line"><a name="l03359"></a><span class="lineno"> 3359</span>&#160;    ValidateTensorDataTypesMatch(inputGateBiasInfo, forgetGateBiasInfo, descriptorName,</div><div class="line"><a name="l03360"></a><span class="lineno"> 3360</span>&#160;                                 <span class="stringliteral">&quot;inputGateBias&quot;</span>, <span class="stringliteral">&quot;forgetGateBias&quot;</span>);</div><div class="line"><a name="l03361"></a><span class="lineno"> 3361</span>&#160;    ValidateTensorDataTypesMatch(inputGateBiasInfo, cellBiasInfo, descriptorName,</div><div class="line"><a name="l03362"></a><span class="lineno"> 3362</span>&#160;                                 <span class="stringliteral">&quot;inputGateBias&quot;</span>, <span class="stringliteral">&quot;cellBias&quot;</span>);</div><div class="line"><a name="l03363"></a><span class="lineno"> 3363</span>&#160;    ValidateTensorDataTypesMatch(inputGateBiasInfo, outputGateBiasInfo, descriptorName,</div><div class="line"><a name="l03364"></a><span class="lineno"> 3364</span>&#160;                                 <span class="stringliteral">&quot;inputGateBias&quot;</span>, <span class="stringliteral">&quot;outputGateBias&quot;</span>);</div><div class="line"><a name="l03365"></a><span class="lineno"> 3365</span>&#160;</div><div class="line"><a name="l03366"></a><span class="lineno"> 3366</span>&#160;    <span class="comment">// Validate bias tensor quantization info</span></div><div class="line"><a name="l03367"></a><span class="lineno"> 3367</span>&#160;    ValidateBiasTensorQuantization(inputGateBiasInfo, inputInfo, inputToInputWeightsInfo, descriptorName);</div><div class="line"><a name="l03368"></a><span class="lineno"> 3368</span>&#160;    ValidateBiasTensorQuantization(forgetGateBiasInfo, inputInfo, inputToInputWeightsInfo, descriptorName);</div><div class="line"><a name="l03369"></a><span class="lineno"> 3369</span>&#160;    ValidateBiasTensorQuantization(cellBiasInfo, inputInfo, inputToInputWeightsInfo, descriptorName);</div><div class="line"><a name="l03370"></a><span class="lineno"> 3370</span>&#160;    ValidateBiasTensorQuantization(outputGateBiasInfo, inputInfo, inputToInputWeightsInfo, descriptorName);</div><div class="line"><a name="l03371"></a><span class="lineno"> 3371</span>&#160;}</div><div class="line"><a name="l03372"></a><span class="lineno"> 3372</span>&#160;</div><div class="line"><a name="l03373"></a><span class="lineno"><a class="line" href="structarmnn_1_1_abs_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 3373</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_abs_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">AbsQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l03374"></a><span class="lineno"> 3374</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l03375"></a><span class="lineno"> 3375</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;AbsQueueDescriptor&quot;</span>};</div><div class="line"><a name="l03376"></a><span class="lineno"> 3376</span>&#160;</div><div class="line"><a name="l03377"></a><span class="lineno"> 3377</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l03378"></a><span class="lineno"> 3378</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l03379"></a><span class="lineno"> 3379</span>&#160;</div><div class="line"><a name="l03380"></a><span class="lineno"> 3380</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l03381"></a><span class="lineno"> 3381</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l03382"></a><span class="lineno"> 3382</span>&#160;</div><div class="line"><a name="l03383"></a><span class="lineno"> 3383</span>&#160;    ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l03384"></a><span class="lineno"> 3384</span>&#160;</div><div class="line"><a name="l03385"></a><span class="lineno"> 3385</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l03386"></a><span class="lineno"> 3386</span>&#160;    {</div><div class="line"><a name="l03387"></a><span class="lineno"> 3387</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l03388"></a><span class="lineno"> 3388</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l03389"></a><span class="lineno"> 3389</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l03390"></a><span class="lineno"> 3390</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l03391"></a><span class="lineno"> 3391</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l03392"></a><span class="lineno"> 3392</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>,</div><div class="line"><a name="l03393"></a><span class="lineno"> 3393</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a></div><div class="line"><a name="l03394"></a><span class="lineno"> 3394</span>&#160;    };</div><div class="line"><a name="l03395"></a><span class="lineno"> 3395</span>&#160;</div><div class="line"><a name="l03396"></a><span class="lineno"> 3396</span>&#160;    ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l03397"></a><span class="lineno"> 3397</span>&#160;    ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l03398"></a><span class="lineno"> 3398</span>&#160;}</div><div class="line"><a name="l03399"></a><span class="lineno"> 3399</span>&#160;</div><div class="line"><a name="l03400"></a><span class="lineno"><a class="line" href="structarmnn_1_1_slice_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 3400</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_slice_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">SliceQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l03401"></a><span class="lineno"> 3401</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l03402"></a><span class="lineno"> 3402</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;SliceQueueDescriptor&quot;</span>};</div><div class="line"><a name="l03403"></a><span class="lineno"> 3403</span>&#160;</div><div class="line"><a name="l03404"></a><span class="lineno"> 3404</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l03405"></a><span class="lineno"> 3405</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l03406"></a><span class="lineno"> 3406</span>&#160;</div><div class="line"><a name="l03407"></a><span class="lineno"> 3407</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l03408"></a><span class="lineno"> 3408</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l03409"></a><span class="lineno"> 3409</span>&#160;</div><div class="line"><a name="l03410"></a><span class="lineno"> 3410</span>&#160;    ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l03411"></a><span class="lineno"> 3411</span>&#160;</div><div class="line"><a name="l03412"></a><span class="lineno"> 3412</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> rank = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l03413"></a><span class="lineno"> 3413</span>&#160;    <span class="keywordflow">if</span> (rank &gt; 4)</div><div class="line"><a name="l03414"></a><span class="lineno"> 3414</span>&#160;    {</div><div class="line"><a name="l03415"></a><span class="lineno"> 3415</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Input tensors with rank greater than 4 are not supported.&quot;</span>);</div><div class="line"><a name="l03416"></a><span class="lineno"> 3416</span>&#160;    }</div><div class="line"><a name="l03417"></a><span class="lineno"> 3417</span>&#160;</div><div class="line"><a name="l03418"></a><span class="lineno"> 3418</span>&#160;    ValidateTensorNumDimensions(outputTensorInfo, descriptorName, rank, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l03419"></a><span class="lineno"> 3419</span>&#160;</div><div class="line"><a name="l03420"></a><span class="lineno"> 3420</span>&#160;    <span class="comment">// Check if m_Begin and m_Size have the expected length</span></div><div class="line"><a name="l03421"></a><span class="lineno"> 3421</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_Begin.size() != rank)</div><div class="line"><a name="l03422"></a><span class="lineno"> 3422</span>&#160;    {</div><div class="line"><a name="l03423"></a><span class="lineno"> 3423</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName +</div><div class="line"><a name="l03424"></a><span class="lineno"> 3424</span>&#160;            <span class="stringliteral">&quot;: Length of begin offset descriptor must equal rank &quot;</span> + std::to_string(rank));</div><div class="line"><a name="l03425"></a><span class="lineno"> 3425</span>&#160;    }</div><div class="line"><a name="l03426"></a><span class="lineno"> 3426</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_Size.size() != rank)</div><div class="line"><a name="l03427"></a><span class="lineno"> 3427</span>&#160;    {</div><div class="line"><a name="l03428"></a><span class="lineno"> 3428</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName +</div><div class="line"><a name="l03429"></a><span class="lineno"> 3429</span>&#160;            <span class="stringliteral">&quot;: Length of size descriptor must equal rank &quot;</span> + std::to_string(rank));</div><div class="line"><a name="l03430"></a><span class="lineno"> 3430</span>&#160;    }</div><div class="line"><a name="l03431"></a><span class="lineno"> 3431</span>&#160;</div><div class="line"><a name="l03432"></a><span class="lineno"> 3432</span>&#160;    <span class="comment">// Check if the shape of the output tensor matches m_Size</span></div><div class="line"><a name="l03433"></a><span class="lineno"> 3433</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>&amp; outputShape = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l03434"></a><span class="lineno"> 3434</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0u; i &lt; rank; ++i)</div><div class="line"><a name="l03435"></a><span class="lineno"> 3435</span>&#160;    {</div><div class="line"><a name="l03436"></a><span class="lineno"> 3436</span>&#160;        <span class="keywordflow">if</span> (m_Parameters.m_Size[i] != outputShape[i])</div><div class="line"><a name="l03437"></a><span class="lineno"> 3437</span>&#160;        {</div><div class="line"><a name="l03438"></a><span class="lineno"> 3438</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Size descriptor does not match output tensor.&quot;</span>);</div><div class="line"><a name="l03439"></a><span class="lineno"> 3439</span>&#160;        }</div><div class="line"><a name="l03440"></a><span class="lineno"> 3440</span>&#160;    }</div><div class="line"><a name="l03441"></a><span class="lineno"> 3441</span>&#160;</div><div class="line"><a name="l03442"></a><span class="lineno"> 3442</span>&#160;    <span class="comment">// Check if the sum of begin offset and size in a given dimension</span></div><div class="line"><a name="l03443"></a><span class="lineno"> 3443</span>&#160;    <span class="comment">// does not exceed the size of corresponding input</span></div><div class="line"><a name="l03444"></a><span class="lineno"> 3444</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>&amp; inputShape  = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l03445"></a><span class="lineno"> 3445</span>&#160;    <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0u; i &lt; rank; ++i)</div><div class="line"><a name="l03446"></a><span class="lineno"> 3446</span>&#160;    {</div><div class="line"><a name="l03447"></a><span class="lineno"> 3447</span>&#160;        <span class="keywordflow">if</span> (m_Parameters.m_Begin[i] + m_Parameters.m_Size[i] &gt; inputShape[i])</div><div class="line"><a name="l03448"></a><span class="lineno"> 3448</span>&#160;        {</div><div class="line"><a name="l03449"></a><span class="lineno"> 3449</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Sum of begin offset and size for dimension &quot;</span> +</div><div class="line"><a name="l03450"></a><span class="lineno"> 3450</span>&#160;                std::to_string(i) + <span class="stringliteral">&quot; exceeds input size.&quot;</span>);</div><div class="line"><a name="l03451"></a><span class="lineno"> 3451</span>&#160;        }</div><div class="line"><a name="l03452"></a><span class="lineno"> 3452</span>&#160;    }</div><div class="line"><a name="l03453"></a><span class="lineno"> 3453</span>&#160;}</div><div class="line"><a name="l03454"></a><span class="lineno"> 3454</span>&#160;</div><div class="line"><a name="l03455"></a><span class="lineno"><a class="line" href="structarmnn_1_1_depth_to_space_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 3455</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_depth_to_space_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">DepthToSpaceQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l03456"></a><span class="lineno"> 3456</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l03457"></a><span class="lineno"> 3457</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;DepthToSpaceQueueDescriptor&quot;</span>};</div><div class="line"><a name="l03458"></a><span class="lineno"> 3458</span>&#160;</div><div class="line"><a name="l03459"></a><span class="lineno"> 3459</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l03460"></a><span class="lineno"> 3460</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l03461"></a><span class="lineno"> 3461</span>&#160;</div><div class="line"><a name="l03462"></a><span class="lineno"> 3462</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l03463"></a><span class="lineno"> 3463</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l03464"></a><span class="lineno"> 3464</span>&#160;</div><div class="line"><a name="l03465"></a><span class="lineno"> 3465</span>&#160;    ValidateTensorNumDimensions(inputInfo,  descriptorName, 4, <span class="stringliteral">&quot;input&quot;</span>);</div><div class="line"><a name="l03466"></a><span class="lineno"> 3466</span>&#160;    ValidateTensorNumDimensions(outputInfo, descriptorName, 4, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l03467"></a><span class="lineno"> 3467</span>&#160;</div><div class="line"><a name="l03468"></a><span class="lineno"> 3468</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l03469"></a><span class="lineno"> 3469</span>&#160;    {</div><div class="line"><a name="l03470"></a><span class="lineno"> 3470</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l03471"></a><span class="lineno"> 3471</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l03472"></a><span class="lineno"> 3472</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l03473"></a><span class="lineno"> 3473</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l03474"></a><span class="lineno"> 3474</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l03475"></a><span class="lineno"> 3475</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l03476"></a><span class="lineno"> 3476</span>&#160;    };</div><div class="line"><a name="l03477"></a><span class="lineno"> 3477</span>&#160;</div><div class="line"><a name="l03478"></a><span class="lineno"> 3478</span>&#160;    ValidateDataTypes(inputInfo,  supportedTypes, descriptorName);</div><div class="line"><a name="l03479"></a><span class="lineno"> 3479</span>&#160;    ValidateDataTypes(outputInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l03480"></a><span class="lineno"> 3480</span>&#160;</div><div class="line"><a name="l03481"></a><span class="lineno"> 3481</span>&#160;    ValidateTensorNumElementsMatch(inputInfo, outputInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l03482"></a><span class="lineno"> 3482</span>&#160;</div><div class="line"><a name="l03483"></a><span class="lineno"> 3483</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_BlockSize == 0)</div><div class="line"><a name="l03484"></a><span class="lineno"> 3484</span>&#160;    {</div><div class="line"><a name="l03485"></a><span class="lineno"> 3485</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Block size cannot be 0.&quot;</span>);</div><div class="line"><a name="l03486"></a><span class="lineno"> 3486</span>&#160;    }</div><div class="line"><a name="l03487"></a><span class="lineno"> 3487</span>&#160;</div><div class="line"><a name="l03488"></a><span class="lineno"> 3488</span>&#160;    <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">DataLayoutIndexed</a> dimensionIndices(m_Parameters.m_DataLayout);</div><div class="line"><a name="l03489"></a><span class="lineno"> 3489</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> wIndex = dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>();</div><div class="line"><a name="l03490"></a><span class="lineno"> 3490</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> hIndex = dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>();</div><div class="line"><a name="l03491"></a><span class="lineno"> 3491</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> cIndex = dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">GetChannelsIndex</a>();</div><div class="line"><a name="l03492"></a><span class="lineno"> 3492</span>&#160;</div><div class="line"><a name="l03493"></a><span class="lineno"> 3493</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>&amp; outputShape = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l03494"></a><span class="lineno"> 3494</span>&#160;    <span class="keywordflow">if</span> (outputShape[hIndex] % m_Parameters.m_BlockSize != 0 || outputShape[wIndex]  % m_Parameters.m_BlockSize != 0)</div><div class="line"><a name="l03495"></a><span class="lineno"> 3495</span>&#160;    {</div><div class="line"><a name="l03496"></a><span class="lineno"> 3496</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Output width and height shape&quot;</span></div><div class="line"><a name="l03497"></a><span class="lineno"> 3497</span>&#160;                                       <span class="stringliteral">&quot;must be divisible by block size.&quot;</span>);</div><div class="line"><a name="l03498"></a><span class="lineno"> 3498</span>&#160;    }</div><div class="line"><a name="l03499"></a><span class="lineno"> 3499</span>&#160;</div><div class="line"><a name="l03500"></a><span class="lineno"> 3500</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>&amp; inputShape = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l03501"></a><span class="lineno"> 3501</span>&#160;    <span class="keywordflow">if</span> (inputShape[cIndex] % (m_Parameters.m_BlockSize * m_Parameters.m_BlockSize) != 0)</div><div class="line"><a name="l03502"></a><span class="lineno"> 3502</span>&#160;    {</div><div class="line"><a name="l03503"></a><span class="lineno"> 3503</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: The depth of the input tensor&quot;</span></div><div class="line"><a name="l03504"></a><span class="lineno"> 3504</span>&#160;                                       <span class="stringliteral">&quot;must be divisible by the square of block size.&quot;</span> );</div><div class="line"><a name="l03505"></a><span class="lineno"> 3505</span>&#160;    }</div><div class="line"><a name="l03506"></a><span class="lineno"> 3506</span>&#160;}</div><div class="line"><a name="l03507"></a><span class="lineno"> 3507</span>&#160;</div><div class="line"><a name="l03508"></a><span class="lineno"><a class="line" href="structarmnn_1_1_comparison_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 3508</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_comparison_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ComparisonQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l03509"></a><span class="lineno"> 3509</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l03510"></a><span class="lineno"> 3510</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;ComparisonQueueDescriptor&quot;</span>};</div><div class="line"><a name="l03511"></a><span class="lineno"> 3511</span>&#160;</div><div class="line"><a name="l03512"></a><span class="lineno"> 3512</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 2);</div><div class="line"><a name="l03513"></a><span class="lineno"> 3513</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l03514"></a><span class="lineno"> 3514</span>&#160;</div><div class="line"><a name="l03515"></a><span class="lineno"> 3515</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l03516"></a><span class="lineno"> 3516</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l03517"></a><span class="lineno"> 3517</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l03518"></a><span class="lineno"> 3518</span>&#160;</div><div class="line"><a name="l03519"></a><span class="lineno"> 3519</span>&#160;    ValidateBroadcastTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l03520"></a><span class="lineno"> 3520</span>&#160;                                       inputTensorInfo1,</div><div class="line"><a name="l03521"></a><span class="lineno"> 3521</span>&#160;                                       outputTensorInfo,</div><div class="line"><a name="l03522"></a><span class="lineno"> 3522</span>&#160;                                       descriptorName,</div><div class="line"><a name="l03523"></a><span class="lineno"> 3523</span>&#160;                                       <span class="stringliteral">&quot;input_0&quot;</span>,</div><div class="line"><a name="l03524"></a><span class="lineno"> 3524</span>&#160;                                       <span class="stringliteral">&quot;input_1&quot;</span>);</div><div class="line"><a name="l03525"></a><span class="lineno"> 3525</span>&#160;</div><div class="line"><a name="l03526"></a><span class="lineno"> 3526</span>&#160;    <span class="keywordflow">if</span> (outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">DataType::Boolean</a>)</div><div class="line"><a name="l03527"></a><span class="lineno"> 3527</span>&#160;    {</div><div class="line"><a name="l03528"></a><span class="lineno"> 3528</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Output tensor type must be Boolean.&quot;</span>);</div><div class="line"><a name="l03529"></a><span class="lineno"> 3529</span>&#160;    }</div><div class="line"><a name="l03530"></a><span class="lineno"> 3530</span>&#160;}</div><div class="line"><a name="l03531"></a><span class="lineno"> 3531</span>&#160;</div><div class="line"><a name="l03532"></a><span class="lineno"><a class="line" href="structarmnn_1_1_elementwise_unary_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 3532</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_elementwise_unary_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ElementwiseUnaryQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l03533"></a><span class="lineno"> 3533</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l03534"></a><span class="lineno"> 3534</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;ElementwiseUnaryQueueDescriptor&quot;</span>};</div><div class="line"><a name="l03535"></a><span class="lineno"> 3535</span>&#160;</div><div class="line"><a name="l03536"></a><span class="lineno"> 3536</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l03537"></a><span class="lineno"> 3537</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l03538"></a><span class="lineno"> 3538</span>&#160;</div><div class="line"><a name="l03539"></a><span class="lineno"> 3539</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l03540"></a><span class="lineno"> 3540</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l03541"></a><span class="lineno"> 3541</span>&#160;</div><div class="line"><a name="l03542"></a><span class="lineno"> 3542</span>&#160;    ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l03543"></a><span class="lineno"> 3543</span>&#160;</div><div class="line"><a name="l03544"></a><span class="lineno"> 3544</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l03545"></a><span class="lineno"> 3545</span>&#160;    {</div><div class="line"><a name="l03546"></a><span class="lineno"> 3546</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l03547"></a><span class="lineno"> 3547</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l03548"></a><span class="lineno"> 3548</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l03549"></a><span class="lineno"> 3549</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l03550"></a><span class="lineno"> 3550</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l03551"></a><span class="lineno"> 3551</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>,</div><div class="line"><a name="l03552"></a><span class="lineno"> 3552</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a></div><div class="line"><a name="l03553"></a><span class="lineno"> 3553</span>&#160;    };</div><div class="line"><a name="l03554"></a><span class="lineno"> 3554</span>&#160;</div><div class="line"><a name="l03555"></a><span class="lineno"> 3555</span>&#160;    std::vector&lt;DataType&gt; logicalSupportedTypes =</div><div class="line"><a name="l03556"></a><span class="lineno"> 3556</span>&#160;    {</div><div class="line"><a name="l03557"></a><span class="lineno"> 3557</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">DataType::Boolean</a></div><div class="line"><a name="l03558"></a><span class="lineno"> 3558</span>&#160;    };</div><div class="line"><a name="l03559"></a><span class="lineno"> 3559</span>&#160;</div><div class="line"><a name="l03560"></a><span class="lineno"> 3560</span>&#160;    <span class="keywordflow">if</span> (m_Parameters.m_Operation == <a class="code" href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8a2a25ebd8c909241e3f7818389b804ecc">UnaryOperation::LogicalNot</a>)</div><div class="line"><a name="l03561"></a><span class="lineno"> 3561</span>&#160;    {</div><div class="line"><a name="l03562"></a><span class="lineno"> 3562</span>&#160;        ValidateDataTypes(inputTensorInfo, logicalSupportedTypes, descriptorName);</div><div class="line"><a name="l03563"></a><span class="lineno"> 3563</span>&#160;    }</div><div class="line"><a name="l03564"></a><span class="lineno"> 3564</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l03565"></a><span class="lineno"> 3565</span>&#160;    {</div><div class="line"><a name="l03566"></a><span class="lineno"> 3566</span>&#160;        ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l03567"></a><span class="lineno"> 3567</span>&#160;    }</div><div class="line"><a name="l03568"></a><span class="lineno"> 3568</span>&#160;</div><div class="line"><a name="l03569"></a><span class="lineno"> 3569</span>&#160;</div><div class="line"><a name="l03570"></a><span class="lineno"> 3570</span>&#160;    ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l03571"></a><span class="lineno"> 3571</span>&#160;}</div><div class="line"><a name="l03572"></a><span class="lineno"> 3572</span>&#160;</div><div class="line"><a name="l03573"></a><span class="lineno"><a class="line" href="structarmnn_1_1_rank_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 3573</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_rank_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">RankQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l03574"></a><span class="lineno"> 3574</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l03575"></a><span class="lineno"> 3575</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;RankQueueDescriptor&quot;</span>};</div><div class="line"><a name="l03576"></a><span class="lineno"> 3576</span>&#160;</div><div class="line"><a name="l03577"></a><span class="lineno"> 3577</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l03578"></a><span class="lineno"> 3578</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l03579"></a><span class="lineno"> 3579</span>&#160;</div><div class="line"><a name="l03580"></a><span class="lineno"> 3580</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l03581"></a><span class="lineno"> 3581</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l03582"></a><span class="lineno"> 3582</span>&#160;</div><div class="line"><a name="l03583"></a><span class="lineno"> 3583</span>&#160;    ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 1, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l03584"></a><span class="lineno"> 3584</span>&#160;    ValidateTensorNumElements(outputTensorInfo, descriptorName, 1, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l03585"></a><span class="lineno"> 3585</span>&#160;</div><div class="line"><a name="l03586"></a><span class="lineno"> 3586</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l03587"></a><span class="lineno"> 3587</span>&#160;    {</div><div class="line"><a name="l03588"></a><span class="lineno"> 3588</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l03589"></a><span class="lineno"> 3589</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l03590"></a><span class="lineno"> 3590</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l03591"></a><span class="lineno"> 3591</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l03592"></a><span class="lineno"> 3592</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l03593"></a><span class="lineno"> 3593</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>,</div><div class="line"><a name="l03594"></a><span class="lineno"> 3594</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>,</div><div class="line"><a name="l03595"></a><span class="lineno"> 3595</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a></div><div class="line"><a name="l03596"></a><span class="lineno"> 3596</span>&#160;    };</div><div class="line"><a name="l03597"></a><span class="lineno"> 3597</span>&#160;</div><div class="line"><a name="l03598"></a><span class="lineno"> 3598</span>&#160;    ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l03599"></a><span class="lineno"> 3599</span>&#160;    ValidateDataTypes(outputTensorInfo, { DataType::Signed32 }, descriptorName);</div><div class="line"><a name="l03600"></a><span class="lineno"> 3600</span>&#160;}</div><div class="line"><a name="l03601"></a><span class="lineno"> 3601</span>&#160;</div><div class="line"><a name="l03602"></a><span class="lineno"><a class="line" href="structarmnn_1_1_logical_binary_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 3602</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_logical_binary_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">LogicalBinaryQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>&amp; workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l03603"></a><span class="lineno"> 3603</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l03604"></a><span class="lineno"> 3604</span>&#160;    <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">&quot;LogicalBinaryQueueDescriptor&quot;</span>};</div><div class="line"><a name="l03605"></a><span class="lineno"> 3605</span>&#160;</div><div class="line"><a name="l03606"></a><span class="lineno"> 3606</span>&#160;    ValidateNumInputs(workloadInfo,  descriptorName, 2);</div><div class="line"><a name="l03607"></a><span class="lineno"> 3607</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l03608"></a><span class="lineno"> 3608</span>&#160;</div><div class="line"><a name="l03609"></a><span class="lineno"> 3609</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l03610"></a><span class="lineno"> 3610</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l03611"></a><span class="lineno"> 3611</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l03612"></a><span class="lineno"> 3612</span>&#160;</div><div class="line"><a name="l03613"></a><span class="lineno"> 3613</span>&#160;    ValidateBroadcastTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l03614"></a><span class="lineno"> 3614</span>&#160;                                       inputTensorInfo1,</div><div class="line"><a name="l03615"></a><span class="lineno"> 3615</span>&#160; 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   {</div><div class="line"><a name="l03622"></a><span class="lineno"> 3622</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">&quot;: Input tensor 0 type must be Boolean.&quot;</span>);</div><div class="line"><a name="l03623"></a><span class="lineno"> 3623</span>&#160;    }</div><div class="line"><a name="l03624"></a><span class="lineno"> 3624</span>&#160;</div><div class="line"><a name="l03625"></a><span class="lineno"> 3625</span>&#160;    <span class="keywordflow">if</span> (inputTensorInfo1.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">DataType::Boolean</a>)</div><div class="line"><a name="l03626"></a><span class="lineno"> 3626</span>&#160;    {</div><div class="line"><a name="l03627"></a><span class="lineno"> 3627</span>&#160; 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   ValidateNumInputs(workloadInfo,  descriptorName, 1);</div><div class="line"><a name="l03641"></a><span class="lineno"> 3641</span>&#160;    ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l03642"></a><span class="lineno"> 3642</span>&#160;</div><div class="line"><a name="l03643"></a><span class="lineno"> 3643</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo  = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l03644"></a><span class="lineno"> 3644</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l03645"></a><span class="lineno"> 3645</span>&#160;</div><div class="line"><a name="l03646"></a><span class="lineno"> 3646</span>&#160;    std::vector&lt;DataType&gt; supportedTypes =</div><div class="line"><a name="l03647"></a><span class="lineno"> 3647</span>&#160;    {</div><div class="line"><a name="l03648"></a><span class="lineno"> 3648</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l03649"></a><span class="lineno"> 3649</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l03650"></a><span class="lineno"> 3650</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l03651"></a><span class="lineno"> 3651</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l03652"></a><span class="lineno"> 3652</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l03653"></a><span class="lineno"> 3653</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>,</div><div class="line"><a name="l03654"></a><span class="lineno"> 3654</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a></div><div class="line"><a name="l03655"></a><span class="lineno"> 3655</span>&#160;    };</div><div class="line"><a name="l03656"></a><span class="lineno"> 3656</span>&#160;</div><div class="line"><a name="l03657"></a><span class="lineno"> 3657</span>&#160;    ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l03658"></a><span class="lineno"> 3658</span>&#160;    ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">&quot;input&quot;</span>, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l03659"></a><span class="lineno"> 3659</span>&#160;}</div><div class="line"><a name="l03660"></a><span class="lineno"> 3660</span>&#160;</div><div class="line"><a name="l03661"></a><span class="lineno"> 3661</span>&#160;} <span class="comment">// namespace armnn</span></div><div class="ttc" id="structarmnn_1_1_permute_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_permute_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::PermuteQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01387">WorkloadData.cpp:1387</a></div></div>
<div class="ttc" id="structarmnn_1_1_division_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_division_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::DivisionQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02212">WorkloadData.cpp:2212</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_q_lstm_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::QLstmQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02916">WorkloadData.cpp:2916</a></div></div>
<div class="ttc" id="structarmnn_1_1_merge_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_merge_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::MergeQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02734">WorkloadData.cpp:2734</a></div></div>
<div class="ttc" id="_workload_data_8hpp_xhtml"><div class="ttname"><a href="_workload_data_8hpp.xhtml">WorkloadData.hpp</a></div></div>
<div class="ttc" id="_data_layout_indexed_8hpp_xhtml"><div class="ttname"><a href="_data_layout_indexed_8hpp.xhtml">DataLayoutIndexed.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_subtraction_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_subtraction_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::SubtractionQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02246">WorkloadData.cpp:2246</a></div></div>
<div class="ttc" id="structarmnn_1_1_log_softmax_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_log_softmax_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::LogSoftmaxQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01619">WorkloadData.cpp:1619</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a22f377fc4e10dc1773a3f979061e85f1"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a22f377fc4e10dc1773a3f979061e85f1">armnn::TensorInfo::IsTypeSpaceMatch</a></div><div class="ttdeci">bool IsTypeSpaceMatch(const TensorInfo &amp;other) const</div><div class="ttdoc">Check that the types are the same and, if quantize, that the quantization parameters are the same...</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00423">Tensor.cpp:423</a></div></div>
<div class="ttc" id="structarmnn_1_1_instance_normalization_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_instance_normalization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::InstanceNormalizationQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01558">WorkloadData.cpp:1558</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a></div></div>
<div class="ttc" id="structarmnn_1_1_mem_sync_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_mem_sync_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::MemSyncQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00596">WorkloadData.cpp:596</a></div></div>
<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a414e6f95548e6f7a01d5028b55ad3941"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">armnnUtils::DataLayoutIndexed::GetWidthIndex</a></div><div class="ttdeci">unsigned int GetWidthIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00025">DataLayoutIndexed.hpp:25</a></div></div>
<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin_xhtml_af2f0a8c9eb32861711c0ce30b7986c44"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml#af2f0a8c9eb32861711c0ce30b7986c44">armnn::ConcatQueueDescriptor::ViewOrigin::m_Origin</a></div><div class="ttdeci">std::vector&lt; unsigned int &gt; m_Origin</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00128">WorkloadData.hpp:128</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape &amp; GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00187">Tensor.hpp:187</a></div></div>
<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin_xhtml"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">armnn::ConcatQueueDescriptor::ViewOrigin</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00122">WorkloadData.hpp:122</a></div></div>
<div class="ttc" id="classarmnn_1_1_optional_xhtml"><div class="ttname"><a href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt; unsigned int &gt;</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad44c007f21af2d0375e3ef9400a1b275"><div class="ttname"><a href="namespacearmnn.xhtml#ad44c007f21af2d0375e3ef9400a1b275">armnn::IsQuantizedType</a></div><div class="ttdeci">constexpr bool IsQuantizedType()</div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8hpp_source.xhtml#l00249">TypesUtils.hpp:249</a></div></div>
<div class="ttc" id="_deprecated_8hpp_xhtml_ab66a241a0ed3ee89c866e777b035d0ed"><div class="ttname"><a href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_BEGIN</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.xhtml#l00033">Deprecated.hpp:33</a></div></div>
<div class="ttc" id="structarmnn_1_1_mean_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_mean_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::MeanQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02314">WorkloadData.cpp:2314</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div>
<div class="ttc" id="structarmnn_1_1_abs_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_abs_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::AbsQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l03373">WorkloadData.cpp:3373</a></div></div>
<div class="ttc" id="structarmnn_1_1_normalization_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_normalization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::NormalizationQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01083">WorkloadData.cpp:1083</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_transpose_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::TransposeQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02887">WorkloadData.cpp:2887</a></div></div>
<div class="ttc" id="structarmnn_1_1_space_to_depth_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_space_to_depth_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::SpaceToDepthQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01769">WorkloadData.cpp:1769</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_ab85cd8cc10c96a7c99c14042c251fc48"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#ab85cd8cc10c96a7c99c14042c251fc48">armnn::TensorInfo::HasPerAxisQuantization</a></div><div class="ttdeci">bool HasPerAxisQuantization() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00437">Tensor.cpp:437</a></div></div>
<div class="ttc" id="structarmnn_1_1_minimum_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_minimum_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::MinimumQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02490">WorkloadData.cpp:2490</a></div></div>
<div class="ttc" id="structarmnn_1_1_floor_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_floor_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::FloorQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01822">WorkloadData.cpp:1822</a></div></div>
<div class="ttc" id="structarmnn_1_1_gather_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_gather_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::GatherQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02606">WorkloadData.cpp:2606</a></div></div>
<div class="ttc" id="structarmnn_1_1_fake_quantization_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_fake_quantization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::FakeQuantizationQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01537">WorkloadData.cpp:1537</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div>
<div class="ttc" id="structarmnn_1_1_space_to_batch_nd_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_space_to_batch_nd_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::SpaceToBatchNdQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01702">WorkloadData.cpp:1702</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::BatchNormalizationQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01185">WorkloadData.cpp:1185</a></div></div>
<div class="ttc" id="structarmnn_1_1_detection_post_process_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::DetectionPostProcessQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02641">WorkloadData.cpp:2641</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b8fc85ce966c035d789cf22db5088a1"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b8fc85ce966c035d789cf22db5088a1">armnn::TensorInfo::GetQuantizationDim</a></div><div class="ttdeci">Optional&lt; unsigned int &gt; GetQuantizationDim() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00485">Tensor.cpp:485</a></div></div>
<div class="ttc" id="structarmnn_1_1_maximum_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_maximum_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::MaximumQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02280">WorkloadData.cpp:2280</a></div></div>
<div class="ttc" id="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin_xhtml"><div class="ttname"><a href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml">armnn::SplitterQueueDescriptor::ViewOrigin</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00103">WorkloadData.hpp:103</a></div></div>
<div class="ttc" id="structarmnn_1_1_equal_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_equal_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::EqualQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02532">WorkloadData.cpp:2532</a></div></div>
<div class="ttc" id="structarmnn_1_1_logical_binary_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_logical_binary_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::LogicalBinaryQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l03602">WorkloadData.cpp:3602</a></div></div>
<div class="ttc" id="structarmnn_1_1_mem_import_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_mem_import_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::MemImportQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00540">WorkloadData.cpp:540</a></div></div>
<div class="ttc" id="_neon_end_to_end_tests_8cpp_xhtml_a358cb7cd3c0647b25be049fd734b8c22"><div class="ttname"><a href="_neon_end_to_end_tests_8cpp.xhtml#a358cb7cd3c0647b25be049fd734b8c22">anchorsInfo</a></div><div class="ttdeci">armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::Float32)</div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">armnn::DataType::Signed64</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div></div>
<div class="ttc" id="structarmnn_1_1_queue_descriptor_xhtml_a765d2cee4ccce5b9467e0c2b6d25b84a"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a765d2cee4ccce5b9467e0c2b6d25b84a">armnn::QueueDescriptor::ValidateInputsOutputs</a></div><div class="ttdeci">void ValidateInputsOutputs(const std::string &amp;descName, unsigned int numExpectedIn, unsigned int numExpectedOut) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00460">WorkloadData.cpp:460</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__software__tools_8dox_source.xhtml#l00006">01_00_software_tools.dox:6</a></div></div>
<div class="ttc" id="structarmnn_1_1_strided_slice_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_strided_slice_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::StridedSliceQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02433">WorkloadData.cpp:2433</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div>
<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml_a490ec6b59006d1fe1ec2ea30e69fb97c"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">armnn::PermutationVector::GetSize</a></div><div class="ttdeci">SizeType GetSize() const</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00241">Types.hpp:241</a></div></div>
<div class="ttc" id="classarmnn_1_1_optional_reference_switch_xhtml_a77c7d528ac063d870b8c8426ec81c1c3"><div class="ttname"><a href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">armnn::OptionalReferenceSwitch::value</a></div><div class="ttdeci">const T &amp; value() const</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00146">Optional.hpp:146</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8bc11f1fa23ef42532f9fdd04d355270"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8bc11f1fa23ef42532f9fdd04d355270">armnn::TensorInfo::GetQuantizationScales</a></div><div class="ttdeci">std::vector&lt; float &gt; GetQuantizationScales() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00442">Tensor.cpp:442</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_af672d1c9e2a120a18926cb645981fbb7"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#af672d1c9e2a120a18926cb645981fbb7">armnn::TensorInfo::HasMultipleQuantizationScales</a></div><div class="ttdeci">bool HasMultipleQuantizationScales() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00197">Tensor.hpp:197</a></div></div>
<div class="ttc" id="structarmnn_1_1_debug_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_debug_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::DebugQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02524">WorkloadData.cpp:2524</a></div></div>
<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a61c00316c443adc233c24e85c6c5b740"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">armnnUtils::DataLayoutIndexed::GetHeightIndex</a></div><div class="ttdeci">unsigned int GetHeightIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00024">DataLayoutIndexed.hpp:24</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a1cfaa710db2a54673b21d2ea2da757c8a2a25ebd8c909241e3f7818389b804ecc"><div class="ttname"><a href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8a2a25ebd8c909241e3f7818389b804ecc">armnn::UnaryOperation::LogicalNot</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::LstmQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01848">WorkloadData.cpp:1848</a></div></div>
<div class="ttc" id="_numeric_cast_8hpp_xhtml"><div class="ttname"><a href="_numeric_cast_8hpp.xhtml">NumericCast.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_splitter_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_splitter_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::SplitterQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00734">WorkloadData.cpp:734</a></div></div>
<div class="ttc" id="structarmnn_1_1_convert_fp16_to_fp32_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_convert_fp16_to_fp32_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ConvertFp16ToFp32QueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02189">WorkloadData.cpp:2189</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a81b5ff8545adad19a1c9d4ca076d552c"><div class="ttname"><a href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">armnn::GetDataTypeName</a></div><div class="ttdeci">constexpr const char * GetDataTypeName(DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8hpp_source.xhtml#l00180">TypesUtils.hpp:180</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad91bc7bfe29186f5d78c28386c6c5309"><div class="ttname"><a href="namespacearmnn.xhtml#ad91bc7bfe29186f5d78c28386c6c5309">armnn::IsQuantized8BitType</a></div><div class="ttdeci">constexpr bool IsQuantized8BitType(DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8hpp_source.xhtml#l00254">TypesUtils.hpp:254</a></div></div>
<div class="ttc" id="structarmnn_1_1_activation_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_activation_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ActivationQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00618">WorkloadData.cpp:618</a></div></div>
<div class="ttc" id="structarmnn_1_1_workload_info_xhtml_ac97905bfa0daab357b91df1347600309"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">armnn::WorkloadInfo::m_InputTensorInfos</a></div><div class="ttdeci">std::vector&lt; TensorInfo &gt; m_InputTensorInfos</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00018">WorkloadInfo.hpp:18</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::TransposeConvolution2dQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02835">WorkloadData.cpp:2835</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00032">Types.hpp:32</a></div></div>
<div class="ttc" id="_deprecated_8hpp_xhtml_ad762b11b48e5c1d1c1743f529485728a"><div class="ttname"><a href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_END</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.xhtml#l00034">Deprecated.hpp:34</a></div></div>
<div class="ttc" id="structarmnn_1_1_slice_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_slice_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::SliceQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l03400">WorkloadData.cpp:3400</a></div></div>
<div class="ttc" id="_assert_8hpp_xhtml_a91c4dfde57907d7698c7531785690a7f"><div class="ttname"><a href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a></div><div class="ttdeci">#define ARMNN_ASSERT_MSG(COND, MSG)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.xhtml#l00015">Assert.hpp:15</a></div></div>
<div class="ttc" id="structarmnn_1_1_multiplication_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_multiplication_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::MultiplicationQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01148">WorkloadData.cpp:1148</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a770b51078da02f44a819e9f95d8058b5"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">armnn::TensorInfo::GetQuantizationOffset</a></div><div class="ttdeci">int32_t GetQuantizationOffset() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00469">Tensor.cpp:469</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a047ca888c43bd7fb5702853bf72410d0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">armnn::TensorInfo::GetQuantizationScale</a></div><div class="ttdeci">float GetQuantizationScale() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00452">Tensor.cpp:452</a></div></div>
<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a></div><div class="ttdoc">Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout...</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00017">DataLayoutIndexed.hpp:17</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">armnn::TensorInfo::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00194">Tensor.hpp:194</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div>
<div class="ttc" id="classarmnn_1_1_optional_base_xhtml_a86b749ce2c4bc627fa8a1fcfaf0e314f"><div class="ttname"><a href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">armnn::OptionalBase::has_value</a></div><div class="ttdeci">bool has_value() const noexcept</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00053">Optional.hpp:53</a></div></div>
<div class="ttc" id="structarmnn_1_1_convert_bf16_to_fp32_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_convert_bf16_to_fp32_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ConvertBf16ToFp32QueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02120">WorkloadData.cpp:2120</a></div></div>
<div class="ttc" id="structarmnn_1_1_rsqrt_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_rsqrt_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::RsqrtQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02580">WorkloadData.cpp:2580</a></div></div>
<div class="ttc" id="structarmnn_1_1_map_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_map_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::MapQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00468">WorkloadData.cpp:468</a></div></div>
<div class="ttc" id="namespacearmnn_utils_xhtml_ac93cb1365b4bcb67df2a3164606096c5"><div class="ttname"><a href="namespacearmnn_utils.xhtml#ac93cb1365b4bcb67df2a3164606096c5">armnnUtils::GetUnsignedAxis</a></div><div class="ttdeci">unsigned int GetUnsignedAxis(const unsigned int inputDimension, const int axis)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.xhtml#l00127">TensorUtils.cpp:127</a></div></div>
<div class="ttc" id="structarmnn_1_1_l2_normalization_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::L2NormalizationQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01587">WorkloadData.cpp:1587</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::BatchToSpaceNdQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02409">WorkloadData.cpp:2409</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a></div></div>
<div class="ttc" id="structarmnn_1_1_unmap_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_unmap_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::UnmapQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00486">WorkloadData.cpp:486</a></div></div>
<div class="ttc" id="structarmnn_1_1_addition_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_addition_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::AdditionQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01111">WorkloadData.cpp:1111</a></div></div>
<div class="ttc" id="structarmnn_1_1_workload_info_xhtml_a67b178f8a836bc1e52b8de109760adfd"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">armnn::WorkloadInfo::m_OutputTensorInfos</a></div><div class="ttdeci">std::vector&lt; TensorInfo &gt; m_OutputTensorInfos</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00019">WorkloadInfo.hpp:19</a></div></div>
<div class="ttc" id="structarmnn_1_1_mem_copy_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_mem_copy_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::MemCopyQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00504">WorkloadData.cpp:504</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::Convolution2dQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01231">WorkloadData.cpp:1231</a></div></div>
<div class="ttc" id="structarmnn_1_1_elementwise_unary_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_elementwise_unary_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ElementwiseUnaryQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l03532">WorkloadData.cpp:3532</a></div></div>
<div class="ttc" id="structarmnn_1_1_convert_fp32_to_bf16_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_convert_fp32_to_bf16_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ConvertFp32ToBf16QueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02143">WorkloadData.cpp:2143</a></div></div>
<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a></div></div>
<div class="ttc" id="_exceptions_8hpp_xhtml_aa3be76aec4ce713822a5ea1ecbb7bc61"><div class="ttname"><a href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a></div><div class="ttdeci">#define CHECK_LOCATION()</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00197">Exceptions.hpp:197</a></div></div>
<div class="ttc" id="structarmnn_1_1_dequantize_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_dequantize_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::DequantizeQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02709">WorkloadData.cpp:2709</a></div></div>
<div class="ttc" id="structarmnn_1_1_stack_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_stack_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::StackQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00902">WorkloadData.cpp:902</a></div></div>
<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00212">Types.hpp:212</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantize_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_quantize_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::QuantizeQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02380">WorkloadData.cpp:2380</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a872803f5667392efc3c8e5607bd453ad"><div class="ttname"><a href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">armnn::GetBiasDataType</a></div><div class="ttdeci">DataType GetBiasDataType(DataType inputDataType)</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00025">WorkloadData.cpp:25</a></div></div>
<div class="ttc" id="structarmnn_1_1_comparison_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_comparison_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ComparisonQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l03508">WorkloadData.cpp:3508</a></div></div>
<div class="ttc" id="_cpu_tensor_handle_8hpp_xhtml"><div class="ttname"><a href="_cpu_tensor_handle_8hpp.xhtml">CpuTensorHandle.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_constant_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_constant_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ConstantQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01642">WorkloadData.cpp:1642</a></div></div>
<div class="ttc" id="structarmnn_1_1_convert_fp32_to_fp16_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_convert_fp32_to_fp16_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ConvertFp32ToFp16QueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02166">WorkloadData.cpp:2166</a></div></div>
<div class="ttc" id="structarmnn_1_1_rank_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_rank_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::RankQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l03573">WorkloadData.cpp:3573</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::DepthwiseConvolution2dQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01305">WorkloadData.cpp:1305</a></div></div>
<div class="ttc" id="structarmnn_1_1_pad_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_pad_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::PadQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02358">WorkloadData.cpp:2358</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::QuantizedLstmQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l03202">WorkloadData.cpp:3202</a></div></div>
<div class="ttc" id="structarmnn_1_1_pre_compiled_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_pre_compiled_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::PreCompiledQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02793">WorkloadData.cpp:2793</a></div></div>
<div class="ttc" id="_tensor_utils_8hpp_xhtml"><div class="ttname"><a href="_tensor_utils_8hpp.xhtml">TensorUtils.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_reduce_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_reduce_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ReduceQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l03636">WorkloadData.cpp:3636</a></div></div>
<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ConcatQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00806">WorkloadData.cpp:806</a></div></div>
<div class="ttc" id="structarmnn_1_1_queue_descriptor_xhtml_a6abd491bb99ffe88bd472c1ae5a1ed1a"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">armnn::QueueDescriptor::m_Outputs</a></div><div class="ttdeci">std::vector&lt; ITensorHandle * &gt; m_Outputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00031">WorkloadData.hpp:31</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_pooling2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::Pooling2dQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01416">WorkloadData.cpp:1416</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
<div class="ttc" id="structarmnn_1_1_reshape_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_reshape_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ReshapeQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01673">WorkloadData.cpp:1673</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorShape::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdoc">Function that returns the tensor rank. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00174">Tensor.cpp:174</a></div></div>
<div class="ttc" id="structarmnn_1_1_fill_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_fill_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::FillQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00998">WorkloadData.cpp:998</a></div></div>
<div class="ttc" id="structarmnn_1_1_switch_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_switch_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::SwitchQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02752">WorkloadData.cpp:2752</a></div></div>
<div class="ttc" id="structarmnn_1_1_fully_connected_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::FullyConnectedQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01021">WorkloadData.cpp:1021</a></div></div>
<div class="ttc" id="structarmnn_1_1_greater_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_greater_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::GreaterQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02556">WorkloadData.cpp:2556</a></div></div>
<div class="ttc" id="_neon_end_to_end_tests_8cpp_xhtml_a64c1dd1b6dd60be9f4a16db9c8f427a5"><div class="ttname"><a href="_neon_end_to_end_tests_8cpp.xhtml#a64c1dd1b6dd60be9f4a16db9c8f427a5">scoresInfo</a></div><div class="ttdeci">armnn::TensorInfo scoresInfo({ 1, 6, 3 }, armnn::DataType::Float32)</div></div>
<div class="ttc" id="namespacearmnn_xhtml_a375ca3cff9f1b005d1412dc5f3cf5b6e"><div class="ttname"><a href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a></div><div class="ttdeci">std::enable_if_t&lt; std::is_unsigned&lt; Source &gt;::value &amp;&amp;std::is_unsigned&lt; Dest &gt;::value, Dest &gt; numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00035">NumericCast.hpp:35</a></div></div>
<div class="ttc" id="namespacearmnn_utils_xhtml"><div class="ttname"><a href="namespacearmnn_utils.xhtml">armnnUtils</a></div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00013">DataLayoutIndexed.hpp:13</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
<div class="ttc" id="structarmnn_1_1_arg_min_max_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ArgMinMaxQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00643">WorkloadData.cpp:643</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a></div></div>
<div class="ttc" id="structarmnn_1_1_queue_descriptor_xhtml_a4b50e46a6810018f3edecfb68b2a76b3"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">armnn::QueueDescriptor::m_Inputs</a></div><div class="ttdeci">std::vector&lt; ITensorHandle * &gt; m_Inputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00030">WorkloadData.hpp:30</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div></div>
<div class="ttc" id="structarmnn_1_1_prelu_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_prelu_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::PreluQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02798">WorkloadData.cpp:2798</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_bilinear_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_resize_bilinear_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ResizeBilinearQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01443">WorkloadData.cpp:1443</a></div></div>
<div class="ttc" id="structarmnn_1_1_depth_to_space_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_depth_to_space_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::DepthToSpaceQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l03455">WorkloadData.cpp:3455</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorInfo::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00191">Tensor.hpp:191</a></div></div>
<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a861b2621ee46e4b63379988b360b8cd9"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">armnnUtils::DataLayoutIndexed::GetChannelsIndex</a></div><div class="ttdeci">unsigned int GetChannelsIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00023">DataLayoutIndexed.hpp:23</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a7c00efeb540198b33b8558c76e5cc2dd"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a7c00efeb540198b33b8558c76e5cc2dd">armnn::TensorInfo::IsQuantized</a></div><div class="ttdeci">bool IsQuantized() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00495">Tensor.cpp:495</a></div></div>
<div class="ttc" id="structarmnn_1_1_softmax_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_softmax_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::SoftmaxQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00709">WorkloadData.cpp:709</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorInfo::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00192">Tensor.hpp:192</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_resize_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ResizeQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01490">WorkloadData.cpp:1490</a></div></div>
<div class="ttc" id="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin_xhtml_af2f0a8c9eb32861711c0ce30b7986c44"><div class="ttname"><a href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml#af2f0a8c9eb32861711c0ce30b7986c44">armnn::SplitterQueueDescriptor::ViewOrigin::m_Origin</a></div><div class="ttdeci">std::vector&lt; unsigned int &gt; m_Origin</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00109">WorkloadData.hpp:109</a></div></div>
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