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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="preprocessor">#ifndef CAFFE2_OPERATORS_LSTM_UNIT_OP_H_</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="preprocessor">#define CAFFE2_OPERATORS_LSTM_UNIT_OP_H_</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> </div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="preprocessor">#include "caffe2/core/context.h"</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="preprocessor">#include "caffe2/core/operator.h"</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "caffe2/utils/conversions.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="keyword">namespace </span><a class="code" href="namespacecaffe2.html">caffe2</a> {</div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="keyword">namespace </span>detail {</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="keyword">inline</span> T sigmoid(T x) {</div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span>  <span class="keywordflow">return</span> 1. / (1. + exp(-x));</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> }</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="keyword">inline</span> T host_tanh(T x) {</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  <span class="keywordflow">return</span> 2. * sigmoid(2. * x) - 1.;</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> }</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> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> Context></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="keywordtype">void</span> LSTMUnit(</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  <span class="keywordtype">int</span> N,</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <span class="keywordtype">int</span> D,</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <span class="keywordtype">int</span> t,</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keyword">const</span> T* H_prev,</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keyword">const</span> T* C_prev,</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keyword">const</span> T* X,</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keyword">const</span> int32_t* seqLengths,</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keywordtype">bool</span> drop_states,</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  T* C,</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  T* H,</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> forget_bias,</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  Context* <span class="comment">/*context*/</span>) {</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> n = 0; n < N; ++n) {</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> valid = seqLengths == <span class="keyword">nullptr</span> || t < seqLengths[n];</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> </div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> d = 0; d < D; ++d) {</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordflow">if</span> (!valid) {</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="keywordflow">if</span> (drop_states) {</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  H[d] = 0;</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  C[d] = 0;</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  H[d] = H_prev[d];</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  C[d] = C_prev[d];</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  }</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">const</span> T i = sigmoid(X[d]);</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keyword">const</span> T f = sigmoid(X[1 * D + d] + convert::To<float, T>(forget_bias));</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keyword">const</span> T o = sigmoid(X[2 * D + d]);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keyword">const</span> T g = host_tanh(X[3 * D + d]);</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keyword">const</span> T c_prev = C_prev[d];</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keyword">const</span> T c = f * c_prev + i * g;</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  C[d] = c;</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keyword">const</span> T host_tanh_c = host_tanh(c);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  H[d] = o * host_tanh_c;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  }</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  }</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  H_prev += D;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  C_prev += D;</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  X += 4 * D;</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  C += D;</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  H += D;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  }</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> }</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> Context></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> <span class="keywordtype">void</span> LSTMUnitGradient(</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keywordtype">int</span> N,</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keywordtype">int</span> D,</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keywordtype">int</span> t,</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keyword">const</span> T* C_prev,</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keyword">const</span> T* X,</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keyword">const</span> int32_t* seqLengths,</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keyword">const</span> T* C,</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keyword">const</span> T* H,</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keyword">const</span> T* C_diff,</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keyword">const</span> T* H_diff,</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keywordtype">bool</span> drop_states,</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  T* H_prev_diff,</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  T* C_prev_diff,</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  T* X_diff,</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> forget_bias,</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  Context* <span class="comment">/*context*/</span>) {</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> n = 0; n < N; ++n) {</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> valid = seqLengths == <span class="keyword">nullptr</span> || t < seqLengths[n];</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>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> d = 0; d < D; ++d) {</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  T* c_prev_diff = C_prev_diff + d;</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  T* h_prev_diff = H_prev_diff + d;</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  T* i_diff = X_diff + d;</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  T* f_diff = X_diff + 1 * D + d;</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  T* o_diff = X_diff + 2 * D + d;</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  T* g_diff = X_diff + 3 * D + d;</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> </div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordflow">if</span> (!valid) {</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keywordflow">if</span> (drop_states) {</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  *h_prev_diff = 0;</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  *c_prev_diff = 0;</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  *h_prev_diff = H_diff[d];</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  *c_prev_diff = C_diff[d];</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  }</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  *i_diff = 0;</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  *f_diff = 0;</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  *o_diff = 0;</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  *g_diff = 0;</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="keyword">const</span> T i = sigmoid(X[d]);</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="keyword">const</span> T f = sigmoid(X[1 * D + d] + convert::To<float, T>(forget_bias));</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keyword">const</span> T o = sigmoid(X[2 * D + d]);</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keyword">const</span> T g = host_tanh(X[3 * D + d]);</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keyword">const</span> T c_prev = C_prev[d];</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="keyword">const</span> T c = C[d];</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="keyword">const</span> T host_tanh_c = host_tanh(c);</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="keyword">const</span> T c_term_diff = C_diff[d] + H_diff[d] * o * (1 - host_tanh_c * host_tanh_c);</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  *c_prev_diff = c_term_diff * f;</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  *h_prev_diff = 0; <span class="comment">// not used in 'valid' case</span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  *i_diff = c_term_diff * g * i * (1 - i);</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  *f_diff = c_term_diff * c_prev * f * (1 - f);</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  *o_diff = H_diff[d] * host_tanh_c * o * (1 - o);</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  *g_diff = c_term_diff * i * (1 - g * g);</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  }</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  }</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  C_prev += D;</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  X += 4 * D;</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  C += D;</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  H += D;</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  C_diff += D;</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  H_diff += D;</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  X_diff += 4 * D;</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  H_prev_diff += D;</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  C_prev_diff += D;</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  }</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> }</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> } <span class="comment">// namespace detail</span></div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> </div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> <span class="keyword">template</span> <<span class="keyword">typename</span> Context></div><div class="line"><a name="l00138"></a><span class="lineno"><a class="line" href="classcaffe2_1_1_l_s_t_m_unit_op.html"> 138</a></span> <span class="keyword">class </span><a class="code" href="classcaffe2_1_1_l_s_t_m_unit_op.html">LSTMUnitOp</a> : <span class="keyword">public</span> <a class="code" href="classcaffe2_1_1_operator.html">Operator</a><Context> {</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <a class="code" href="classcaffe2_1_1_l_s_t_m_unit_op.html">LSTMUnitOp</a>(<span class="keyword">const</span> OperatorDef& operator_def, <a class="code" href="classcaffe2_1_1_workspace.html">Workspace</a>* ws)</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  : <a class="code" href="classcaffe2_1_1_operator.html">Operator<Context></a>(operator_def, ws),</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  forget_bias_(</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  static_cast<float>(OperatorBase::template GetSingleArgument<float>(</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="stringliteral">"forget_bias"</span>,</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  0.0))),</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  sequence_lengths_(OperatorBase::template GetSingleArgument<bool>(</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="stringliteral">"sequence_lengths"</span>,</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keyword">true</span>)),</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  drop_states_(OperatorBase::template GetSingleArgument<bool>(</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="stringliteral">"drop_states"</span>,</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keyword">false</span>)) {}</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  USE_OPERATOR_CONTEXT_FUNCTIONS;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keyword">using</span> <a class="code" href="classcaffe2_1_1_operator.html">Operator<Context>::Operator</a>;</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>  <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keywordtype">bool</span> DoRunWithType() {</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="comment">// handle potentially-missing sequence lengths input</span></div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> TIMESTEP = SEQ_LENGTHS + (sequence_lengths_ ? 1 : 0);</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> </div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="comment">// Extract N</span></div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keyword">const</span> <span class="keyword">auto</span> N = Input(CELL_T_M_1).dim(1);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> </div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="comment">// Gates: 1xNxG</span></div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="keyword">const</span> <span class="keyword">auto</span> G = Input(GATES).dim(2);</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="keyword">const</span> <span class="keyword">auto</span> D = Input(CELL_T_M_1).dim(2);</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> </div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  CAFFE_ENFORCE_EQ(4 * D, G);</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* H_prev = Input(HIDDEN_T_M_1).template data<T>();</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* C_prev = Input(CELL_T_M_1).template data<T>();</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* X = Input(GATES).template data<T>();</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span> </div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keyword">const</span> int32_t* seqLengths = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keywordflow">if</span> (sequence_lengths_) {</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  CAFFE_ENFORCE_EQ(Input(SEQ_LENGTHS).size(), N);</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  seqLengths = Input(SEQ_LENGTHS).template data<int32_t>();</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  }</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>  <span class="keyword">const</span> <span class="keyword">auto</span> t = <span class="keyword">static_cast<</span><a class="code" href="classcaffe2_1_1_operator_base.html">OperatorBase</a>*<span class="keyword">></span>(<span class="keyword">this</span>)</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  ->Input<<a class="code" href="classcaffe2_1_1_tensor.html">Tensor<CPUContext></a>>(TIMESTEP)</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  .template data<int32_t>()[0];</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  Output(CELL_T)->ResizeLike(Input(CELL_T_M_1));</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keyword">auto</span>* C = Output(CELL_T)->template mutable_data<T>();</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  Output(HIDDEN_T)->ResizeLike(Input(CELL_T_M_1));</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keyword">auto</span>* H = Output(HIDDEN_T)->template mutable_data<T>();</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  detail::LSTMUnit<T, Context>(</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  N,</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  D,</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  t,</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  H_prev,</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  C_prev,</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  X,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  seqLengths,</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  drop_states_,</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  C,</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  H,</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  forget_bias_,</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  &context_);</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  }</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span> </div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="keywordtype">bool</span> RunOnDevice()<span class="keyword"> override </span>{</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="keywordflow">return</span> DoRunWithType<float>();</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  }</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span> </div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keyword">protected</span>:</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  INPUT_TAGS(HIDDEN_T_M_1, CELL_T_M_1, GATES, SEQ_LENGTHS);</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="comment">// additional input tags are determined dynamically based on whether</span></div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="comment">// sequence_lengths is present.</span></div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  OUTPUT_TAGS(HIDDEN_T, CELL_T);</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> </div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keywordtype">float</span> forget_bias_;</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="keywordtype">bool</span> sequence_lengths_;</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span> </div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keywordtype">bool</span> drop_states_;</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span> };</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> </div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> <span class="keyword">template</span> <<span class="keyword">typename</span> Context></div><div class="line"><a name="l00219"></a><span class="lineno"><a class="line" href="classcaffe2_1_1_l_s_t_m_unit_gradient_op.html"> 219</a></span> <span class="keyword">class </span><a class="code" href="classcaffe2_1_1_l_s_t_m_unit_gradient_op.html">LSTMUnitGradientOp</a> : <span class="keyword">public</span> <a class="code" href="classcaffe2_1_1_operator.html">Operator</a><Context> {</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <a class="code" href="classcaffe2_1_1_l_s_t_m_unit_gradient_op.html">LSTMUnitGradientOp</a>(<span class="keyword">const</span> OperatorDef& operator_def, <a class="code" href="classcaffe2_1_1_workspace.html">Workspace</a>* ws)</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  : <a class="code" href="classcaffe2_1_1_operator.html">Operator<Context></a>(operator_def, ws),</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  forget_bias_(</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  static_cast<float>(OperatorBase::template GetSingleArgument<float>(</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="stringliteral">"forget_bias"</span>,</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  0.0))),</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  sequence_lengths_(OperatorBase::template GetSingleArgument<bool>(</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="stringliteral">"sequence_lengths"</span>,</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keyword">true</span>)),</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  drop_states_(OperatorBase::template GetSingleArgument<bool>(</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="stringliteral">"drop_states"</span>,</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keyword">false</span>)) {}</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  USE_OPERATOR_CONTEXT_FUNCTIONS;</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span> </div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="keywordtype">bool</span> DoRunWithType() {</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="comment">// handle potentially-missing sequence lengths input</span></div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> inputOffset = SEQ_LENGTHS + (sequence_lengths_ ? 1 : 0);</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> TIMESTEP = inputOffset;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> HIDDEN_T = inputOffset + 1;</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> CELL_T = inputOffset + 2;</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> HIDDEN_T_GRAD = inputOffset + 3;</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> CELL_T_GRAD = inputOffset + 4;</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>  <span class="comment">// Extract N</span></div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keyword">const</span> <span class="keyword">auto</span> N = Input(CELL_T_M_1).dim(1);</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span> </div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="comment">// Gates: 1xNxG</span></div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="keyword">const</span> <span class="keyword">auto</span> G = Input(GATES).dim(2);</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="keyword">const</span> <span class="keyword">auto</span> D = Input(CELL_T_M_1).dim(2);</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> </div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  CAFFE_ENFORCE_EQ(4 * D, G);</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* C_prev = Input(CELL_T_M_1).template data<T>();</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* X = Input(GATES).template data<T>();</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="keyword">const</span> <span class="keyword">auto</span> t = <span class="keyword">static_cast<</span><a class="code" href="classcaffe2_1_1_operator_base.html">OperatorBase</a>*<span class="keyword">></span>(<span class="keyword">this</span>)</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  ->Input<<a class="code" href="classcaffe2_1_1_tensor.html">Tensor<CPUContext></a>>(TIMESTEP)</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  .template data<int32_t>()[0];</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* C = Input(CELL_T).template data<T>();</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* H = Input(HIDDEN_T).template data<T>();</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* C_diff = Input(CELL_T_GRAD).template data<T>();</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* H_diff = Input(HIDDEN_T_GRAD).template data<T>();</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> </div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="keyword">const</span> int32_t* seqLengths = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keywordflow">if</span> (sequence_lengths_) {</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  CAFFE_ENFORCE_EQ(Input(SEQ_LENGTHS).size(), N);</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  seqLengths = Input(SEQ_LENGTHS).template data<int32_t>();</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>  Output(HIDDEN_T_M_1_GRAD)->ResizeLike(Input(HIDDEN_T_M_1));</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keyword">auto</span>* H_prev_diff = Output(HIDDEN_T_M_1_GRAD)->template mutable_data<T>();</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  Output(CELL_T_M_1_GRAD)->ResizeLike(Input(CELL_T_M_1));</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="keyword">auto</span>* C_prev_diff = Output(CELL_T_M_1_GRAD)->template mutable_data<T>();</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  Output(GATES_GRAD)->ResizeLike(Input(GATES));</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="keyword">auto</span>* X_diff = Output(GATES_GRAD)->template mutable_data<T>();</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span> </div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  detail::LSTMUnitGradient<T, Context>(</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  N,</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  D,</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  t,</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  C_prev,</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  X,</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  seqLengths,</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  C,</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  H,</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  C_diff,</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  H_diff,</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  drop_states_,</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  H_prev_diff,</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  C_prev_diff,</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  X_diff,</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  forget_bias_,</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  &context_);</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  }</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span> </div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <span class="keywordtype">bool</span> RunOnDevice()<span class="keyword"> override </span>{</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="keywordflow">return</span> DoRunWithType<float>();</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  }</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span> </div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="keyword">protected</span>:</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  INPUT_TAGS(HIDDEN_T_M_1, CELL_T_M_1, GATES, SEQ_LENGTHS);</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="comment">// additional input tags are determined dynamically based on whether</span></div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="comment">// sequence_lengths is present.</span></div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  OUTPUT_TAGS(HIDDEN_T_M_1_GRAD, CELL_T_M_1_GRAD, GATES_GRAD);</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span> </div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keywordtype">float</span> forget_bias_;</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="keywordtype">bool</span> sequence_lengths_;</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span> </div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="keywordtype">bool</span> drop_states_;</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span> };</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span> } <span class="comment">// namespace caffe2</span></div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span> </div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span> <span class="preprocessor">#endif // CAFFE2_OPERATORS_LSTM_UNIT_OP_H_</span></div><div class="ttc" id="classcaffe2_1_1_l_s_t_m_unit_gradient_op_html"><div class="ttname"><a href="classcaffe2_1_1_l_s_t_m_unit_gradient_op.html">caffe2::LSTMUnitGradientOp</a></div><div class="ttdef"><b>Definition:</b> <a href="lstm__unit__op_8h_source.html#l00219">lstm_unit_op.h:219</a></div></div>
<div class="ttc" id="classcaffe2_1_1_tensor_html"><div class="ttname"><a href="classcaffe2_1_1_tensor.html">caffe2::Tensor< CPUContext ></a></div></div>
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<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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<div class="ttc" id="classcaffe2_1_1_operator_base_html"><div class="ttname"><a href="classcaffe2_1_1_operator_base.html">caffe2::OperatorBase</a></div><div class="ttdef"><b>Definition:</b> <a href="core_2operator_8h_source.html#l00030">operator.h:30</a></div></div>
<div class="ttc" id="classcaffe2_1_1_l_s_t_m_unit_op_html"><div class="ttname"><a href="classcaffe2_1_1_l_s_t_m_unit_op.html">caffe2::LSTMUnitOp</a></div><div class="ttdef"><b>Definition:</b> <a href="lstm__unit__op_8h_source.html#l00138">lstm_unit_op.h:138</a></div></div>
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