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<li class="navelem"><a class="el" href="dir_20697b8f204bdfcab31e6b1a416f3ab8.html">caffe2</a></li><li class="navelem"><a class="el" href="dir_05c92498e25fa977790f551ff793f1b1.html">mkl</a></li><li class="navelem"><a class="el" href="dir_9a8f178644e12033403db01c04c605e4.html">utils</a></li> </ul>
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<div class="title">mkl_dnn_cppwrapper.h</div> </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">// Do not directl include this file. Include caffe2/mkl/mkl_utils.h instead.</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="preprocessor">#ifndef CAFFE2_UTILS_MKL_MKL_DNN_CPPWRAPPER_H</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="preprocessor">#define CAFFE2_UTILS_MKL_MKL_DNN_CPPWRAPPER_H</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> </div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="preprocessor">#include <stdarg.h></span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include <stddef.h></span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <mkl.h></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> </div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#define C2_MKL_TEMPLATE_PREFIX \</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor"> template <typename T> \</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor"> inline</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#define C2_MKL_SPEC_PREFIX \</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor"> template <> \</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor"> inline</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="keyword">namespace </span><a class="code" href="namespacecaffe2.html">caffe2</a> {</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="keyword">namespace </span>mkl {</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> </div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnLayoutCreate(</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  dnnLayout_t* pLayout,</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> size[],</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> strides[]);</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> C2_MKL_SPEC_PREFIX dnnError_t dnnLayoutCreate<float>(</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  dnnLayout_t* pLayout,</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> size[],</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> strides[]) {</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keywordflow">return</span> dnnLayoutCreate_F32(pLayout, dimension, size, strides);</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> }</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> C2_MKL_SPEC_PREFIX dnnError_t dnnLayoutCreate<double>(</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  dnnLayout_t* pLayout,</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> size[],</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> strides[]) {</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keywordflow">return</span> dnnLayoutCreate_F64(pLayout, dimension, size, strides);</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> }</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnLayoutCreateFromPrimitive(</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  dnnLayout_t* pLayout,</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keyword">const</span> dnnPrimitive_t primitive,</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  dnnResourceType_t type);</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> C2_MKL_SPEC_PREFIX dnnError_t dnnLayoutCreateFromPrimitive<float>(</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  dnnLayout_t* pLayout,</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keyword">const</span> dnnPrimitive_t primitive,</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  dnnResourceType_t type) {</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keywordflow">return</span> dnnLayoutCreateFromPrimitive_F32(pLayout, primitive, type);</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> }</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> C2_MKL_SPEC_PREFIX dnnError_t dnnLayoutCreateFromPrimitive<double>(</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  dnnLayout_t* pLayout,</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keyword">const</span> dnnPrimitive_t primitive,</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  dnnResourceType_t type) {</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordflow">return</span> dnnLayoutCreateFromPrimitive_F64(pLayout, primitive, type);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> }</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> </div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> C2_MKL_TEMPLATE_PREFIX <span class="keywordtype">size_t</span> dnnLayoutGetMemorySize(<span class="keyword">const</span> dnnLayout_t layout);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> C2_MKL_SPEC_PREFIX <span class="keywordtype">size_t</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> dnnLayoutGetMemorySize<float>(<span class="keyword">const</span> dnnLayout_t layout) {</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordflow">return</span> dnnLayoutGetMemorySize_F32(layout);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> }</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> C2_MKL_SPEC_PREFIX <span class="keywordtype">size_t</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> dnnLayoutGetMemorySize<double>(<span class="keyword">const</span> dnnLayout_t layout) {</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keywordflow">return</span> dnnLayoutGetMemorySize_F64(layout);</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> }</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> C2_MKL_TEMPLATE_PREFIX <span class="keywordtype">int</span> dnnLayoutCompare(</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keyword">const</span> dnnLayout_t l1,</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keyword">const</span> dnnLayout_t l2);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> C2_MKL_SPEC_PREFIX <span class="keywordtype">int</span> dnnLayoutCompare<float>(</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keyword">const</span> dnnLayout_t l1,</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keyword">const</span> dnnLayout_t l2) {</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keywordflow">return</span> dnnLayoutCompare_F32(l1, l2);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> }</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> C2_MKL_SPEC_PREFIX <span class="keywordtype">int</span> dnnLayoutCompare<double>(</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keyword">const</span> dnnLayout_t l1,</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keyword">const</span> dnnLayout_t l2) {</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keywordflow">return</span> dnnLayoutCompare_F64(l1, l2);</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> }</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> </div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> C2_MKL_TEMPLATE_PREFIX dnnError_t</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> dnnAllocateBuffer(<span class="keywordtype">void</span>** pPtr, dnnLayout_t layout);</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> C2_MKL_SPEC_PREFIX dnnError_t</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> dnnAllocateBuffer<float>(<span class="keywordtype">void</span>** pPtr, dnnLayout_t layout) {</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordflow">return</span> dnnAllocateBuffer_F32(pPtr, layout);</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> }</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> C2_MKL_SPEC_PREFIX dnnError_t</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> dnnAllocateBuffer<double>(<span class="keywordtype">void</span>** pPtr, dnnLayout_t layout) {</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordflow">return</span> dnnAllocateBuffer_F64(pPtr, layout);</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> }</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> </div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnReleaseBuffer(<span class="keywordtype">void</span>* ptr);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> C2_MKL_SPEC_PREFIX dnnError_t dnnReleaseBuffer<float>(<span class="keywordtype">void</span>* ptr) {</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keywordflow">return</span> dnnReleaseBuffer_F32(ptr);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> }</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> C2_MKL_SPEC_PREFIX dnnError_t dnnReleaseBuffer<double>(<span class="keywordtype">void</span>* ptr) {</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordflow">return</span> dnnReleaseBuffer_F64(ptr);</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> }</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> </div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnLayoutDelete(dnnLayout_t layout);</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> C2_MKL_SPEC_PREFIX dnnError_t dnnLayoutDelete<float>(dnnLayout_t layout) {</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keywordflow">return</span> dnnLayoutDelete_F32(layout);</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> }</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> C2_MKL_SPEC_PREFIX dnnError_t dnnLayoutDelete<double>(dnnLayout_t layout) {</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keywordflow">return</span> dnnLayoutDelete_F64(layout);</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> }</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> </div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> C2_MKL_TEMPLATE_PREFIX dnnError_t</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> dnnPrimitiveAttributesCreate(dnnPrimitiveAttributes_t* attributes);</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> C2_MKL_SPEC_PREFIX dnnError_t</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> dnnPrimitiveAttributesCreate<float>(dnnPrimitiveAttributes_t* attributes) {</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keywordflow">return</span> dnnPrimitiveAttributesCreate_F32(attributes);</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> }</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> C2_MKL_SPEC_PREFIX dnnError_t</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> dnnPrimitiveAttributesCreate<double>(dnnPrimitiveAttributes_t* attributes) {</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keywordflow">return</span> dnnPrimitiveAttributesCreate_F64(attributes);</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> }</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> C2_MKL_TEMPLATE_PREFIX dnnError_t</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> dnnPrimitiveAttributesDestroy(dnnPrimitiveAttributes_t attributes);</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> C2_MKL_SPEC_PREFIX dnnError_t</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> dnnPrimitiveAttributesDestroy<float>(dnnPrimitiveAttributes_t attributes) {</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keywordflow">return</span> dnnPrimitiveAttributesDestroy_F32(attributes);</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> }</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> C2_MKL_SPEC_PREFIX dnnError_t</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> dnnPrimitiveAttributesDestroy<double>(dnnPrimitiveAttributes_t attributes) {</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keywordflow">return</span> dnnPrimitiveAttributesDestroy_F64(attributes);</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> }</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> </div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnPrimitiveGetAttributes(</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  dnnPrimitive_t primitive,</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  dnnPrimitiveAttributes_t* attributes);</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> C2_MKL_SPEC_PREFIX dnnError_t dnnPrimitiveGetAttributes<float>(</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  dnnPrimitive_t primitive,</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  dnnPrimitiveAttributes_t* attributes) {</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="keywordflow">return</span> dnnPrimitiveGetAttributes_F32(primitive, attributes);</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> }</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> C2_MKL_SPEC_PREFIX dnnError_t dnnPrimitiveGetAttributes<double>(</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  dnnPrimitive_t primitive,</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  dnnPrimitiveAttributes_t* attributes) {</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordflow">return</span> dnnPrimitiveGetAttributes_F64(primitive, attributes);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> }</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> </div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> C2_MKL_TEMPLATE_PREFIX dnnError_t</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> dnnExecute(dnnPrimitive_t primitive, <span class="keywordtype">void</span>* resources[]);</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> C2_MKL_SPEC_PREFIX dnnError_t</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> dnnExecute<float>(dnnPrimitive_t primitive, <span class="keywordtype">void</span>* resources[]) {</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keywordflow">return</span> dnnExecute_F32(primitive, resources);</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> }</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> C2_MKL_SPEC_PREFIX dnnError_t</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> dnnExecute<double>(dnnPrimitive_t primitive, <span class="keywordtype">void</span>* resources[]) {</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keywordflow">return</span> dnnExecute_F64(primitive, resources);</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> }</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> C2_MKL_TEMPLATE_PREFIX dnnError_t</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> dnnExecuteAsync(dnnPrimitive_t primitive, <span class="keywordtype">void</span>* resources[]);</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> C2_MKL_SPEC_PREFIX dnnError_t</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> dnnExecuteAsync<float>(dnnPrimitive_t primitive, <span class="keywordtype">void</span>* resources[]) {</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keywordflow">return</span> dnnExecuteAsync_F32(primitive, resources);</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> }</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> C2_MKL_SPEC_PREFIX dnnError_t</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> dnnExecuteAsync<double>(dnnPrimitive_t primitive, <span class="keywordtype">void</span>* resources[]) {</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keywordflow">return</span> dnnExecuteAsync_F64(primitive, resources);</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> }</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> </div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnWaitFor(dnnPrimitive_t primitive);</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> C2_MKL_SPEC_PREFIX dnnError_t dnnWaitFor<float>(dnnPrimitive_t primitive) {</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keywordflow">return</span> dnnWaitFor_F32(primitive);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> }</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> C2_MKL_SPEC_PREFIX dnnError_t dnnWaitFor<double>(dnnPrimitive_t primitive) {</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keywordflow">return</span> dnnWaitFor_F64(primitive);</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> }</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> </div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnDelete(dnnPrimitive_t primitive);</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> C2_MKL_SPEC_PREFIX dnnError_t dnnDelete<float>(dnnPrimitive_t primitive) {</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keywordflow">return</span> dnnDelete_F32(primitive);</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> }</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> C2_MKL_SPEC_PREFIX dnnError_t dnnDelete<double>(dnnPrimitive_t primitive) {</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keywordflow">return</span> dnnDelete_F64(primitive);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> }</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> </div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnConversionCreate(</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  dnnPrimitive_t* pConversion,</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keyword">const</span> dnnLayout_t from,</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keyword">const</span> dnnLayout_t to);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> C2_MKL_SPEC_PREFIX dnnError_t dnnConversionCreate<float>(</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  dnnPrimitive_t* pConversion,</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keyword">const</span> dnnLayout_t from,</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="keyword">const</span> dnnLayout_t to) {</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keywordflow">return</span> dnnConversionCreate_F32(pConversion, from, to);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> }</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> C2_MKL_SPEC_PREFIX dnnError_t dnnConversionCreate<double>(</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  dnnPrimitive_t* pConversion,</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="keyword">const</span> dnnLayout_t from,</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="keyword">const</span> dnnLayout_t to) {</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keywordflow">return</span> dnnConversionCreate_F64(pConversion, from, to);</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> }</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> </div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> C2_MKL_TEMPLATE_PREFIX dnnError_t</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span> dnnConversionExecute(dnnPrimitive_t conversion, <span class="keywordtype">void</span>* from, <span class="keywordtype">void</span>* to);</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> C2_MKL_SPEC_PREFIX dnnError_t</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> dnnConversionExecute<float>(dnnPrimitive_t conversion, <span class="keywordtype">void</span>* from, <span class="keywordtype">void</span>* to) {</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keywordflow">return</span> dnnConversionExecute_F32(conversion, from, to);</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span> }</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> C2_MKL_SPEC_PREFIX dnnError_t</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> dnnConversionExecute<double>(dnnPrimitive_t conversion, <span class="keywordtype">void</span>* from, <span class="keywordtype">void</span>* to) {</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keywordflow">return</span> dnnConversionExecute_F64(conversion, from, to);</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span> }</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> </div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnConvolutionCreateForward(</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keyword">const</span> dnnBorder_t border_type);</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span> C2_MKL_SPEC_PREFIX dnnError_t dnnConvolutionCreateForward<float>(</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keywordflow">return</span> dnnConvolutionCreateForward_F32(</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  pConvolution,</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  attributes,</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  algorithm,</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  dimension,</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  srcSize,</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  dstSize,</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  filterSize,</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  convolutionStrides,</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  inputOffset,</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  border_type);</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span> }</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span> </div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span> C2_MKL_SPEC_PREFIX dnnError_t dnnConvolutionCreateForward<double>(</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keywordflow">return</span> dnnConvolutionCreateForward_F64(</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  pConvolution,</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  attributes,</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  algorithm,</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  dimension,</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  srcSize,</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  dstSize,</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  filterSize,</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  convolutionStrides,</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  inputOffset,</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  border_type);</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> }</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span> </div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnConvolutionCreateForwardBias(</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keyword">const</span> dnnBorder_t border_type);</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span> C2_MKL_SPEC_PREFIX dnnError_t dnnConvolutionCreateForwardBias<float>(</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="keywordflow">return</span> dnnConvolutionCreateForwardBias_F32(</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  pConvolution,</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  attributes,</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  algorithm,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  dimension,</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  srcSize,</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  dstSize,</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  filterSize,</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  convolutionStrides,</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  inputOffset,</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  border_type);</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span> }</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> C2_MKL_SPEC_PREFIX dnnError_t dnnConvolutionCreateForwardBias<double>(</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="keywordflow">return</span> dnnConvolutionCreateForwardBias_F64(</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  pConvolution,</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  attributes,</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  algorithm,</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  dimension,</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  srcSize,</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  dstSize,</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  filterSize,</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  convolutionStrides,</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  inputOffset,</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  border_type);</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span> }</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> </div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnConvolutionCreateBackwardData(</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="keyword">const</span> dnnBorder_t border_type);</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span> C2_MKL_SPEC_PREFIX dnnError_t dnnConvolutionCreateBackwardData<float>(</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="keywordflow">return</span> dnnConvolutionCreateBackwardData_F32(</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  pConvolution,</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  attributes,</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  algorithm,</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  dimension,</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  srcSize,</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  dstSize,</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  filterSize,</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  convolutionStrides,</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  inputOffset,</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  border_type);</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span> }</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span> C2_MKL_SPEC_PREFIX dnnError_t dnnConvolutionCreateBackwardData<double>(</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="keywordflow">return</span> dnnConvolutionCreateBackwardData_F64(</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  pConvolution,</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  attributes,</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  algorithm,</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  dimension,</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  srcSize,</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  dstSize,</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  filterSize,</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  convolutionStrides,</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  inputOffset,</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  border_type);</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> }</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span> </div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnConvolutionCreateBackwardFilter(</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <span class="keyword">const</span> dnnBorder_t border_type);</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span> C2_MKL_SPEC_PREFIX dnnError_t dnnConvolutionCreateBackwardFilter<float>(</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keywordflow">return</span> dnnConvolutionCreateBackwardFilter_F32(</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  pConvolution,</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  attributes,</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  algorithm,</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  dimension,</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  srcSize,</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  dstSize,</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  filterSize,</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  convolutionStrides,</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  inputOffset,</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  border_type);</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span> }</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> C2_MKL_SPEC_PREFIX dnnError_t dnnConvolutionCreateBackwardFilter<double>(</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <span class="keywordflow">return</span> dnnConvolutionCreateBackwardFilter_F64(</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  pConvolution,</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  attributes,</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  algorithm,</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  dimension,</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  srcSize,</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  dstSize,</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  filterSize,</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  convolutionStrides,</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  inputOffset,</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  border_type);</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span> }</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span> </div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnConvolutionCreateBackwardBias(</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[]);</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span> C2_MKL_SPEC_PREFIX dnnError_t dnnConvolutionCreateBackwardBias<float>(</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[]) {</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="keywordflow">return</span> dnnConvolutionCreateBackwardBias_F32(</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  pConvolution, attributes, algorithm, dimension, dstSize);</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> }</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span> C2_MKL_SPEC_PREFIX dnnError_t dnnConvolutionCreateBackwardBias<double>(</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[]) {</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <span class="keywordflow">return</span> dnnConvolutionCreateBackwardBias_F64(</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  pConvolution, attributes, algorithm, dimension, dstSize);</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span> }</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span> </div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnGroupsConvolutionCreateForward(</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <span class="keywordtype">size_t</span> groups,</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="keyword">const</span> dnnBorder_t border_type);</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span> C2_MKL_SPEC_PREFIX dnnError_t dnnGroupsConvolutionCreateForward<float>(</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="keywordtype">size_t</span> groups,</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  <span class="keywordflow">return</span> dnnGroupsConvolutionCreateForward_F32(</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  pConvolution,</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  attributes,</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  algorithm,</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  groups,</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  dimension,</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  srcSize,</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  dstSize,</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  filterSize,</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  convolutionStrides,</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  inputOffset,</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  border_type);</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span> }</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span> C2_MKL_SPEC_PREFIX dnnError_t dnnGroupsConvolutionCreateForward<double>(</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <span class="keywordtype">size_t</span> groups,</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  <span class="keywordflow">return</span> dnnGroupsConvolutionCreateForward_F64(</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  pConvolution,</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  attributes,</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  algorithm,</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  groups,</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  dimension,</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  srcSize,</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  dstSize,</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  filterSize,</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  convolutionStrides,</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  inputOffset,</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  border_type);</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span> }</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span> </div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnGroupsConvolutionCreateForwardBias(</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <span class="keywordtype">size_t</span> groups,</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keyword">const</span> dnnBorder_t border_type);</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span> C2_MKL_SPEC_PREFIX dnnError_t dnnGroupsConvolutionCreateForwardBias<float>(</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <span class="keywordtype">size_t</span> groups,</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  <span class="keywordflow">return</span> dnnGroupsConvolutionCreateForwardBias_F32(</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  pConvolution,</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  attributes,</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  algorithm,</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  groups,</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  dimension,</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  srcSize,</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  dstSize,</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  filterSize,</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  convolutionStrides,</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  inputOffset,</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  border_type);</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span> }</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span> C2_MKL_SPEC_PREFIX dnnError_t dnnGroupsConvolutionCreateForwardBias<double>(</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <span class="keywordtype">size_t</span> groups,</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  <span class="keywordflow">return</span> dnnGroupsConvolutionCreateForwardBias_F64(</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  pConvolution,</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  attributes,</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  algorithm,</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  groups,</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  dimension,</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  srcSize,</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  dstSize,</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  filterSize,</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  convolutionStrides,</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  inputOffset,</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  border_type);</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span> }</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span> </div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnGroupsConvolutionCreateBackwardData(</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  <span class="keywordtype">size_t</span> groups,</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <span class="keyword">const</span> dnnBorder_t border_type);</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span> C2_MKL_SPEC_PREFIX dnnError_t dnnGroupsConvolutionCreateBackwardData<float>(</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  <span class="keywordtype">size_t</span> groups,</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  <span class="keywordflow">return</span> dnnGroupsConvolutionCreateBackwardData_F32(</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  pConvolution,</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  attributes,</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  algorithm,</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  groups,</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  dimension,</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  srcSize,</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  dstSize,</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  filterSize,</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  convolutionStrides,</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  inputOffset,</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  border_type);</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span> }</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span> C2_MKL_SPEC_PREFIX dnnError_t dnnGroupsConvolutionCreateBackwardData<double>(</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  <span class="keywordtype">size_t</span> groups,</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  <span class="keywordflow">return</span> dnnGroupsConvolutionCreateBackwardData_F64(</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  pConvolution,</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  attributes,</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  algorithm,</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  groups,</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  dimension,</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  srcSize,</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  dstSize,</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  filterSize,</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  convolutionStrides,</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  inputOffset,</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  border_type);</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span> }</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span> </div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnGroupsConvolutionCreateBackwardFilter(</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  <span class="keywordtype">size_t</span> groups,</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  <span class="keyword">const</span> dnnBorder_t border_type);</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span> C2_MKL_SPEC_PREFIX dnnError_t dnnGroupsConvolutionCreateBackwardFilter<float>(</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <span class="keywordtype">size_t</span> groups,</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  <span class="keywordflow">return</span> dnnGroupsConvolutionCreateBackwardFilter_F32(</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  pConvolution,</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  attributes,</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  algorithm,</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  groups,</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  dimension,</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  srcSize,</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  dstSize,</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  filterSize,</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  convolutionStrides,</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  inputOffset,</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  border_type);</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span> }</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span> C2_MKL_SPEC_PREFIX dnnError_t dnnGroupsConvolutionCreateBackwardFilter<double>(</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  <span class="keywordtype">size_t</span> groups,</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[],</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> filterSize[],</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> convolutionStrides[],</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  <span class="keywordflow">return</span> dnnGroupsConvolutionCreateBackwardFilter_F64(</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  pConvolution,</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  attributes,</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  algorithm,</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  groups,</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  dimension,</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  srcSize,</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  dstSize,</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  filterSize,</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  convolutionStrides,</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  inputOffset,</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  border_type);</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span> }</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span> </div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnGroupsConvolutionCreateBackwardBias(</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  <span class="keywordtype">size_t</span> groups,</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[]);</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span> C2_MKL_SPEC_PREFIX dnnError_t dnnGroupsConvolutionCreateBackwardBias<float>(</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  <span class="keywordtype">size_t</span> groups,</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[]) {</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  <span class="keywordflow">return</span> dnnGroupsConvolutionCreateBackwardBias_F32(</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  pConvolution, attributes, algorithm, groups, dimension, dstSize);</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span> }</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span> C2_MKL_SPEC_PREFIX dnnError_t dnnGroupsConvolutionCreateBackwardBias<double>(</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  dnnPrimitive_t* pConvolution,</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  dnnAlgorithm_t algorithm,</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  <span class="keywordtype">size_t</span> groups,</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  <span class="keywordtype">size_t</span> dimension,</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> dstSize[]) {</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  <span class="keywordflow">return</span> dnnGroupsConvolutionCreateBackwardBias_F64(</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  pConvolution, attributes, algorithm, groups, dimension, dstSize);</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span> }</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span> </div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnReLUCreateForward(</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  dnnPrimitive_t* pRelu,</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  <span class="keywordtype">float</span> negativeSlope);</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span> C2_MKL_SPEC_PREFIX dnnError_t dnnReLUCreateForward<float>(</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  dnnPrimitive_t* pRelu,</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  <span class="keywordtype">float</span> negativeSlope) {</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  <span class="keywordflow">return</span> dnnReLUCreateForward_F32(pRelu, attributes, dataLayout, negativeSlope);</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span> }</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span> C2_MKL_SPEC_PREFIX dnnError_t dnnReLUCreateForward<double>(</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  dnnPrimitive_t* pRelu,</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  <span class="keywordtype">float</span> negativeSlope) {</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  <span class="keywordflow">return</span> dnnReLUCreateForward_F64(pRelu, attributes, dataLayout, negativeSlope);</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span> }</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span> </div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnReLUCreateBackward(</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  dnnPrimitive_t* pRelu,</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  <span class="keyword">const</span> dnnLayout_t diffLayout,</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  <span class="keywordtype">float</span> negativeSlope);</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span> C2_MKL_SPEC_PREFIX dnnError_t dnnReLUCreateBackward<float>(</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  dnnPrimitive_t* pRelu,</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  <span class="keyword">const</span> dnnLayout_t diffLayout,</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  <span class="keywordtype">float</span> negativeSlope) {</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  <span class="keywordflow">return</span> dnnReLUCreateBackward_F32(</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  pRelu, attributes, diffLayout, dataLayout, negativeSlope);</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span> }</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span> C2_MKL_SPEC_PREFIX dnnError_t dnnReLUCreateBackward<double>(</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  dnnPrimitive_t* pRelu,</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  <span class="keyword">const</span> dnnLayout_t diffLayout,</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  <span class="keywordtype">float</span> negativeSlope) {</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  <span class="keywordflow">return</span> dnnReLUCreateBackward_F64(</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  pRelu, attributes, diffLayout, dataLayout, negativeSlope);</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span> }</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span> </div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnLRNCreateForward(</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  dnnPrimitive_t* pLrn,</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  <span class="keywordtype">size_t</span> kernel_size,</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  <span class="keywordtype">float</span> alpha,</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  <span class="keywordtype">float</span> beta,</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  <span class="keywordtype">float</span> k);</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span> C2_MKL_SPEC_PREFIX dnnError_t dnnLRNCreateForward<float>(</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  dnnPrimitive_t* pLrn,</div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  <span class="keywordtype">size_t</span> kernel_size,</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  <span class="keywordtype">float</span> alpha,</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  <span class="keywordtype">float</span> beta,</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  <span class="keywordtype">float</span> k) {</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  <span class="keywordflow">return</span> dnnLRNCreateForward_F32(</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  pLrn, attributes, dataLayout, kernel_size, alpha, beta, k);</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span> }</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span> C2_MKL_SPEC_PREFIX dnnError_t dnnLRNCreateForward<double>(</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  dnnPrimitive_t* pLrn,</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  <span class="keywordtype">size_t</span> kernel_size,</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  <span class="keywordtype">float</span> alpha,</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  <span class="keywordtype">float</span> beta,</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  <span class="keywordtype">float</span> k) {</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  <span class="keywordflow">return</span> dnnLRNCreateForward_F64(</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  pLrn, attributes, dataLayout, kernel_size, alpha, beta, k);</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span> }</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span> </div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnLRNCreateBackward(</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  dnnPrimitive_t* pLrn,</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  <span class="keyword">const</span> dnnLayout_t diffLayout,</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  <span class="keywordtype">size_t</span> kernel_size,</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  <span class="keywordtype">float</span> alpha,</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  <span class="keywordtype">float</span> beta,</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  <span class="keywordtype">float</span> k);</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span> C2_MKL_SPEC_PREFIX dnnError_t dnnLRNCreateBackward<float>(</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  dnnPrimitive_t* pLrn,</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  <span class="keyword">const</span> dnnLayout_t diffLayout,</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  <span class="keywordtype">size_t</span> kernel_size,</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  <span class="keywordtype">float</span> alpha,</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  <span class="keywordtype">float</span> beta,</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  <span class="keywordtype">float</span> k) {</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  <span class="keywordflow">return</span> dnnLRNCreateBackward_F32(</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  pLrn, attributes, diffLayout, dataLayout, kernel_size, alpha, beta, k);</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span> }</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span> C2_MKL_SPEC_PREFIX dnnError_t dnnLRNCreateBackward<double>(</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  dnnPrimitive_t* pLrn,</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>  <span class="keyword">const</span> dnnLayout_t diffLayout,</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  <span class="keywordtype">size_t</span> kernel_size,</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  <span class="keywordtype">float</span> alpha,</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  <span class="keywordtype">float</span> beta,</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  <span class="keywordtype">float</span> k) {</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  <span class="keywordflow">return</span> dnnLRNCreateBackward_F64(</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  pLrn, attributes, diffLayout, dataLayout, kernel_size, alpha, beta, k);</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span> }</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span> </div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnPoolingCreateForward(</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  dnnPrimitive_t* pPooling,</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  dnnAlgorithm_t op,</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  <span class="keyword">const</span> dnnLayout_t srcLayout,</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> kernelSize[],</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> kernelStride[],</div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  <span class="keyword">const</span> dnnBorder_t border_type);</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span> C2_MKL_SPEC_PREFIX dnnError_t dnnPoolingCreateForward<float>(</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  dnnPrimitive_t* pPooling,</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  dnnAlgorithm_t op,</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  <span class="keyword">const</span> dnnLayout_t srcLayout,</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> kernelSize[],</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> kernelStride[],</div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  <span class="keywordflow">return</span> dnnPoolingCreateForward_F32(</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  pPooling,</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  attributes,</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  op,</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  srcLayout,</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  kernelSize,</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  kernelStride,</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  inputOffset,</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  border_type);</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span> }</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span> C2_MKL_SPEC_PREFIX dnnError_t dnnPoolingCreateForward<double>(</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  dnnPrimitive_t* pPooling,</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  dnnAlgorithm_t op,</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span>  <span class="keyword">const</span> dnnLayout_t srcLayout,</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> kernelSize[],</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> kernelStride[],</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  <span class="keywordflow">return</span> dnnPoolingCreateForward_F64(</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  pPooling,</div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  attributes,</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>  op,</div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  srcLayout,</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  kernelSize,</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  kernelStride,</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  inputOffset,</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  border_type);</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span> }</div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span> </div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnPoolingCreateBackward(</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  dnnPrimitive_t* pPooling,</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  dnnAlgorithm_t op,</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  <span class="keyword">const</span> dnnLayout_t srcLayout,</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> kernelSize[],</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> kernelStride[],</div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  <span class="keyword">const</span> dnnBorder_t border_type);</div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span> C2_MKL_SPEC_PREFIX dnnError_t dnnPoolingCreateBackward<float>(</div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span>  dnnPrimitive_t* pPooling,</div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  dnnAlgorithm_t op,</div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  <span class="keyword">const</span> dnnLayout_t srcLayout,</div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> kernelSize[],</div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> kernelStride[],</div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  <span class="keywordflow">return</span> dnnPoolingCreateBackward_F32(</div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  pPooling,</div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  attributes,</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  op,</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  srcLayout,</div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  kernelSize,</div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span>  kernelStride,</div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  inputOffset,</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span>  border_type);</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span> }</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span> C2_MKL_SPEC_PREFIX dnnError_t dnnPoolingCreateBackward<double>(</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  dnnPrimitive_t* pPooling,</div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  dnnAlgorithm_t op,</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  <span class="keyword">const</span> dnnLayout_t srcLayout,</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> kernelSize[],</div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> kernelStride[],</div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputOffset[],</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  <span class="keyword">const</span> dnnBorder_t border_type) {</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  <span class="keywordflow">return</span> dnnPoolingCreateBackward_F64(</div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  pPooling,</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  attributes,</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  op,</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  srcLayout,</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  kernelSize,</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  kernelStride,</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  inputOffset,</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  border_type);</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span> }</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span> </div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnConcatCreate(</div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  dnnPrimitive_t* pConcat,</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> N,</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  dnnLayout_t src[]);</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span> C2_MKL_SPEC_PREFIX dnnError_t dnnConcatCreate<float>(</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  dnnPrimitive_t* pConcat,</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> N,</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  dnnLayout_t src[]) {</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  <span class="keywordflow">return</span> dnnConcatCreate_F32(pConcat, attributes, N, src);</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span> }</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span> C2_MKL_SPEC_PREFIX dnnError_t dnnConcatCreate<double>(</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  dnnPrimitive_t* pConcat,</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> N,</div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  dnnLayout_t src[]) {</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  <span class="keywordflow">return</span> dnnConcatCreate_F64(pConcat, attributes, N, src);</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span> }</div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span> </div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnSplitCreate(</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  dnnPrimitive_t* pSplit,</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> N,</div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span>  dnnLayout_t src,</div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  <span class="keywordtype">size_t</span> dst[]);</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span> C2_MKL_SPEC_PREFIX dnnError_t dnnSplitCreate<float>(</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  dnnPrimitive_t* pSplit,</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> N,</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  dnnLayout_t src,</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  <span class="keywordtype">size_t</span> dst[]) {</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  <span class="keywordflow">return</span> dnnSplitCreate_F32(pSplit, attributes, N, src, dst);</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span> }</div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span> C2_MKL_SPEC_PREFIX dnnError_t dnnSplitCreate<double>(</div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  dnnPrimitive_t* pSplit,</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> N,</div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  dnnLayout_t src,</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span>  <span class="keywordtype">size_t</span> dst[]) {</div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span>  <span class="keywordflow">return</span> dnnSplitCreate_F64(pSplit, attributes, N, src, dst);</div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span> }</div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span> </div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnSumCreate(</div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span>  dnnPrimitive_t* pSum,</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> nSummands,</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  dnnLayout_t layout,</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  T* coefficients);</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span> C2_MKL_SPEC_PREFIX dnnError_t dnnSumCreate<float>(</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>  dnnPrimitive_t* pSum,</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> nSummands,</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>  dnnLayout_t layout,</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  <span class="keywordtype">float</span>* coefficients) {</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  <span class="keywordflow">return</span> dnnSumCreate_F32(pSum, attributes, nSummands, layout, coefficients);</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span> }</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span> C2_MKL_SPEC_PREFIX dnnError_t dnnSumCreate<double>(</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  dnnPrimitive_t* pSum,</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> nSummands,</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>  dnnLayout_t layout,</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>  <span class="keywordtype">double</span>* coefficients) {</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  <span class="keywordflow">return</span> dnnSumCreate_F64(pSum, attributes, nSummands, layout, coefficients);</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span> }</div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span> </div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnBatchNormalizationCreateForward(</div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>  dnnPrimitive_t* pBatchNormalization,</div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>  <span class="keywordtype">float</span> eps);</div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span> C2_MKL_SPEC_PREFIX dnnError_t dnnBatchNormalizationCreateForward<float>(</div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  dnnPrimitive_t* pBatchNormalization,</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  <span class="keywordtype">float</span> eps) {</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>  <span class="keywordflow">return</span> dnnBatchNormalizationCreateForward_F32(</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  pBatchNormalization, attributes, dataLayout, eps);</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span> }</div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span> C2_MKL_SPEC_PREFIX dnnError_t dnnBatchNormalizationCreateForward<double>(</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>  dnnPrimitive_t* pBatchNormalization,</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>  <span class="keywordtype">float</span> eps) {</div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>  <span class="keywordflow">return</span> dnnBatchNormalizationCreateForward_F64(</div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>  pBatchNormalization, attributes, dataLayout, eps);</div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span> }</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span> </div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnBatchNormalizationCreateBackwardData(</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  dnnPrimitive_t* pBatchNormalization,</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>  <span class="keywordtype">float</span> eps);</div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span> C2_MKL_SPEC_PREFIX dnnError_t dnnBatchNormalizationCreateBackwardData<float>(</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>  dnnPrimitive_t* pBatchNormalization,</div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>  <span class="keywordtype">float</span> eps) {</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>  <span class="keywordflow">return</span> dnnBatchNormalizationCreateBackwardData_F32(</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>  pBatchNormalization, attributes, dataLayout, eps);</div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span> }</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span> </div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span> C2_MKL_SPEC_PREFIX dnnError_t dnnBatchNormalizationCreateBackwardData<double>(</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  dnnPrimitive_t* pBatchNormalization,</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  <span class="keywordtype">float</span> eps) {</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>  <span class="keywordflow">return</span> dnnBatchNormalizationCreateBackwardData_F64(</div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>  pBatchNormalization, attributes, dataLayout, eps);</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span> }</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span> </div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnBatchNormalizationCreateBackwardScaleShift(</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span>  dnnPrimitive_t* pBatchNormalization,</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>  <span class="keywordtype">float</span> eps);</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span> C2_MKL_SPEC_PREFIX dnnError_t</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span> dnnBatchNormalizationCreateBackwardScaleShift<float>(</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>  dnnPrimitive_t* pBatchNormalization,</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>  <span class="keywordtype">float</span> eps) {</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>  <span class="keywordflow">return</span> dnnBatchNormalizationCreateBackwardScaleShift_F32(</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>  pBatchNormalization, attributes, dataLayout, eps);</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span> }</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span> C2_MKL_SPEC_PREFIX dnnError_t</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span> dnnBatchNormalizationCreateBackwardScaleShift<double>(</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>  dnnPrimitive_t* pBatchNormalization,</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>  <span class="keywordtype">float</span> eps) {</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>  <span class="keywordflow">return</span> dnnBatchNormalizationCreateBackwardScaleShift_F64(</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  pBatchNormalization, attributes, dataLayout, eps);</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span> }</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span> </div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnBatchNormalizationCreateForward_v2(</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>  dnnPrimitive_t* pBatchNormalization,</div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>  <span class="keywordtype">float</span> eps,</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> flags);</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span> C2_MKL_SPEC_PREFIX dnnError_t dnnBatchNormalizationCreateForward_v2<float>(</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>  dnnPrimitive_t* pBatchNormalization,</div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>  <span class="keywordtype">float</span> eps,</div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> flags) {</div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>  <span class="keywordflow">return</span> dnnBatchNormalizationCreateForward_v2_F32(</div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>  pBatchNormalization, attributes, dataLayout, eps, flags);</div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span> }</div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span> C2_MKL_SPEC_PREFIX dnnError_t dnnBatchNormalizationCreateForward_v2<double>(</div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>  dnnPrimitive_t* pBatchNormalization,</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>  <span class="keywordtype">float</span> eps,</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> flags) {</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>  <span class="keywordflow">return</span> dnnBatchNormalizationCreateForward_v2_F64(</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span>  pBatchNormalization, attributes, dataLayout, eps, flags);</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span> }</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span> </div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnBatchNormalizationCreateBackward_v2(</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>  dnnPrimitive_t* pBatchNormalization,</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>  <span class="keywordtype">float</span> eps,</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> flags);</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span> C2_MKL_SPEC_PREFIX dnnError_t dnnBatchNormalizationCreateBackward_v2<float>(</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>  dnnPrimitive_t* pBatchNormalization,</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>  <span class="keywordtype">float</span> eps,</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> flags) {</div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>  <span class="keywordflow">return</span> dnnBatchNormalizationCreateBackward_v2_F32(</div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  pBatchNormalization, attributes, dataLayout, eps, flags);</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span> }</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span> </div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span> C2_MKL_SPEC_PREFIX dnnError_t dnnBatchNormalizationCreateBackward_v2<double>(</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  dnnPrimitive_t* pBatchNormalization,</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>  <span class="keyword">const</span> dnnLayout_t dataLayout,</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>  <span class="keywordtype">float</span> eps,</div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> flags) {</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  <span class="keywordflow">return</span> dnnBatchNormalizationCreateBackward_v2_F64(</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>  pBatchNormalization, attributes, dataLayout, eps, flags);</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span> }</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span> </div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnInnerProductCreateForward(</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>  dnnPrimitive_t* pInnerProduct,</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>  <span class="keywordtype">size_t</span> dimensions,</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>  <span class="keywordtype">size_t</span> outputChannels);</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span> C2_MKL_SPEC_PREFIX dnnError_t dnnInnerProductCreateForward<float>(</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>  dnnPrimitive_t* pInnerProduct,</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>  <span class="keywordtype">size_t</span> dimensions,</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>  <span class="keywordtype">size_t</span> outputChannels) {</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>  <span class="keywordflow">return</span> dnnInnerProductCreateForward_F32(</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>  pInnerProduct, attributes, dimensions, srcSize, outputChannels);</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span> }</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span> C2_MKL_SPEC_PREFIX dnnError_t dnnInnerProductCreateForward<double>(</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  dnnPrimitive_t* pInnerProduct,</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  <span class="keywordtype">size_t</span> dimensions,</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>  <span class="keywordtype">size_t</span> outputChannels) {</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>  <span class="keywordflow">return</span> dnnInnerProductCreateForward_F64(</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>  pInnerProduct, attributes, dimensions, srcSize, outputChannels);</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span> }</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span> </div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnInnerProductCreateForwardBias(</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  dnnPrimitive_t* pInnerProduct,</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>  <span class="keywordtype">size_t</span> dimensions,</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  <span class="keywordtype">size_t</span> outputChannels);</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span> C2_MKL_SPEC_PREFIX dnnError_t dnnInnerProductCreateForwardBias<float>(</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>  dnnPrimitive_t* pInnerProduct,</div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  <span class="keywordtype">size_t</span> dimensions,</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>  <span class="keywordtype">size_t</span> outputChannels) {</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>  <span class="keywordflow">return</span> dnnInnerProductCreateForwardBias_F32(</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  pInnerProduct, attributes, dimensions, srcSize, outputChannels);</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span> }</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span> C2_MKL_SPEC_PREFIX dnnError_t dnnInnerProductCreateForwardBias<double>(</div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  dnnPrimitive_t* pInnerProduct,</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  <span class="keywordtype">size_t</span> dimensions,</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  <span class="keywordtype">size_t</span> outputChannels) {</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>  <span class="keywordflow">return</span> dnnInnerProductCreateForwardBias_F64(</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>  pInnerProduct, attributes, dimensions, srcSize, outputChannels);</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span> }</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span> </div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnInnerProductCreateBackwardData(</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>  dnnPrimitive_t* pInnerProduct,</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>  <span class="keywordtype">size_t</span> dimensions,</div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>  <span class="keywordtype">size_t</span> outputChannels);</div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span> C2_MKL_SPEC_PREFIX dnnError_t dnnInnerProductCreateBackwardData<float>(</div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  dnnPrimitive_t* pInnerProduct,</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  <span class="keywordtype">size_t</span> dimensions,</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>  <span class="keywordtype">size_t</span> outputChannels) {</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>  <span class="keywordflow">return</span> dnnInnerProductCreateBackwardData_F32(</div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  pInnerProduct, attributes, dimensions, srcSize, outputChannels);</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span> }</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span> C2_MKL_SPEC_PREFIX dnnError_t dnnInnerProductCreateBackwardData<double>(</div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>  dnnPrimitive_t* pInnerProduct,</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>  <span class="keywordtype">size_t</span> dimensions,</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>  <span class="keywordtype">size_t</span> outputChannels) {</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span>  <span class="keywordflow">return</span> dnnInnerProductCreateBackwardData_F64(</div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  pInnerProduct, attributes, dimensions, srcSize, outputChannels);</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span> }</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span> </div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnInnerProductCreateBackwardFilter(</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>  dnnPrimitive_t* pInnerProduct,</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span>  <span class="keywordtype">size_t</span> dimensions,</div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>  <span class="keywordtype">size_t</span> outputChannels);</div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span> C2_MKL_SPEC_PREFIX dnnError_t dnnInnerProductCreateBackwardFilter<float>(</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span>  dnnPrimitive_t* pInnerProduct,</div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>  <span class="keywordtype">size_t</span> dimensions,</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>  <span class="keywordtype">size_t</span> outputChannels) {</div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>  <span class="keywordflow">return</span> dnnInnerProductCreateBackwardFilter_F32(</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>  pInnerProduct, attributes, dimensions, srcSize, outputChannels);</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span> }</div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span> C2_MKL_SPEC_PREFIX dnnError_t dnnInnerProductCreateBackwardFilter<double>(</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>  dnnPrimitive_t* pInnerProduct,</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>  <span class="keywordtype">size_t</span> dimensions,</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[],</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>  <span class="keywordtype">size_t</span> outputChannels) {</div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>  <span class="keywordflow">return</span> dnnInnerProductCreateBackwardFilter_F64(</div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>  pInnerProduct, attributes, dimensions, srcSize, outputChannels);</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span> }</div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span> </div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span> C2_MKL_TEMPLATE_PREFIX dnnError_t dnnInnerProductCreateBackwardBias(</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>  dnnPrimitive_t* pInnerProduct,</div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  <span class="keywordtype">size_t</span> dimensions,</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[]);</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span> C2_MKL_SPEC_PREFIX dnnError_t dnnInnerProductCreateBackwardBias<float>(</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>  dnnPrimitive_t* pInnerProduct,</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>  <span class="keywordtype">size_t</span> dimensions,</div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[]) {</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>  <span class="keywordflow">return</span> dnnInnerProductCreateBackwardBias_F32(</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>  pInnerProduct, attributes, dimensions, srcSize);</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span> }</div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span> C2_MKL_SPEC_PREFIX dnnError_t dnnInnerProductCreateBackwardBias<double>(</div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  dnnPrimitive_t* pInnerProduct,</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>  dnnPrimitiveAttributes_t attributes,</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>  <span class="keywordtype">size_t</span> dimensions,</div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> srcSize[]) {</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>  <span class="keywordflow">return</span> dnnInnerProductCreateBackwardBias_F64(</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>  pInnerProduct, attributes, dimensions, srcSize);</div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span> }</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span> </div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span> } <span class="comment">// namespace mkl</span></div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span> } <span class="comment">// namespace caffe2</span></div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span> </div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span> <span class="comment">// Undef macros to make sure that things are clean.</span></div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span> <span class="preprocessor">#undef C2_MKL_TEMPLATE_PREFIX</span></div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span> <span class="preprocessor">#undef C2_MKL_SPEC_PREFIX</span></div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span> </div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span> <span class="preprocessor">#endif // CAFFE2_UTILS_MKL_MKL_DNN_CPPWRAPPER_H</span></div><div class="ttc" id="namespacecaffe2_html"><div class="ttname"><a href="namespacecaffe2.html">caffe2</a></div><div class="ttdoc">A global dictionary that holds information about what Caffe2 modules have been loaded in the current ...</div><div class="ttdef"><b>Definition:</b> <a href="convert__encoded__to__raw__leveldb_8cc_source.html#l00047">convert_encoded_to_raw_leveldb.cc:47</a></div></div>
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