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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_a88b8b93dde245e9b5ee872e77180f4c.html">operators</a></li> </ul>
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<div class="title">sparse_to_dense_mask_op.h</div> </div>
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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_SPARSE_TO_DENSE_MASK_OP_H_</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="preprocessor">#define CAFFE2_OPERATORS_SPARSE_TO_DENSE_MASK_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 <algorithm></span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="preprocessor">#include <unordered_map></span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include <vector></span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include "caffe2/core/context.h"</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include "caffe2/core/operator.h"</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include "caffe2/core/tensor.h"</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include "caffe2/utils/math.h"</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> </div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="keyword">namespace </span><a class="code" href="namespacecaffe2.html">caffe2</a> {</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> <span class="keyword">template</span> <<span class="keyword">class</span> Context></div><div class="line"><a name="l00015"></a><span class="lineno"><a class="line" href="classcaffe2_1_1_sparse_to_dense_mask_base.html"> 15</a></span> <span class="keyword">class </span><a class="code" href="classcaffe2_1_1_sparse_to_dense_mask_base.html">SparseToDenseMaskBase</a> : <span class="keyword">public</span> <a class="code" href="classcaffe2_1_1_operator.html">Operator</a><Context> {</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  USE_OPERATOR_CONTEXT_FUNCTIONS;</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  <a class="code" href="classcaffe2_1_1_sparse_to_dense_mask_base.html">SparseToDenseMaskBase</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="l00019"></a><span class="lineno"> 19</span>  : <a class="code" href="classcaffe2_1_1_operator.html">Operator<Context></a>(operator_def, ws) {</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  std::vector<int64_t> mask =</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  OperatorBase::template GetRepeatedArgument<int64_t>(<span class="stringliteral">"mask"</span>);</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  featuresCount_ = mask.size();</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> </div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  CAFFE_ENFORCE(!mask.empty(), <span class="stringliteral">"mask can't be empty"</span>);</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keyword">auto</span> biggest = *std::max_element(mask.begin(), mask.end());</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  dense_.assign(std::min(kMaxDenseSize, biggest + 1), -1);</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < mask.size(); i++) {</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  int64_t <span class="keywordtype">id</span> = mask[i];</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  CAFFE_ENFORCE_GE(<span class="keywordtype">id</span>, 0, <span class="stringliteral">"Only positive IDs are allowed."</span>);</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keywordflow">if</span> (<span class="keywordtype">id</span> >= kMaxDenseSize) {</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  CAFFE_ENFORCE(sparse_.count(<span class="keywordtype">id</span>) == 0, <span class="stringliteral">"Duplicated id: "</span>, id);</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  sparse_[id] = i;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  CAFFE_ENFORCE(dense_[<span class="keywordtype">id</span>] == -1, <span class="stringliteral">"Duplicated id: "</span>, <span class="keywordtype">id</span>);</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  dense_[id] = i;</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>  }</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>  <span class="keyword">protected</span>:</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keyword">const</span> int64_t kMaxDenseSize = 1024 * 128;</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> </div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  std::unordered_map<int64_t, int> sparse_;</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  std::vector<int> dense_;</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keywordtype">int</span> featuresCount_;</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">inline</span> <span class="keywordtype">int</span> getFeatureIdx(int64_t <span class="keywordtype">id</span>)<span class="keyword"> const </span>{</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keywordflow">if</span> (<span class="keywordtype">id</span> >= kMaxDenseSize) {</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& iter = sparse_.find(<span class="keywordtype">id</span>);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keywordflow">if</span> (iter == sparse_.end()) {</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keywordflow">return</span> -1;</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordflow">return</span> iter->second;</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  }</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordflow">return</span> (<span class="keywordtype">id</span> >= dense_.size()) ? -1 : dense_[<span class="keywordtype">id</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>  }</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> };</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> <span class="keyword">template</span> <<span class="keyword">class</span> Context></div><div class="line"><a name="l00062"></a><span class="lineno"><a class="line" href="classcaffe2_1_1_sparse_to_dense_mask_op.html"> 62</a></span> <span class="keyword">class </span><a class="code" href="classcaffe2_1_1_sparse_to_dense_mask_op.html">SparseToDenseMaskOp</a> : <span class="keyword">public</span> <a class="code" href="classcaffe2_1_1_sparse_to_dense_mask_base.html">SparseToDenseMaskBase</a><Context> {</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  USE_OPERATOR_CONTEXT_FUNCTIONS;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <a class="code" href="classcaffe2_1_1_sparse_to_dense_mask_op.html">SparseToDenseMaskOp</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="l00066"></a><span class="lineno"> 66</span>  : <a class="code" href="classcaffe2_1_1_sparse_to_dense_mask_base.html">SparseToDenseMaskBase<Context></a>(operator_def, ws) {</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  returnPresenceMask_ = OperatorBase::template GetSingleArgument<bool>(</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="stringliteral">"return_presence_mask"</span>, <span class="keyword">false</span>);</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  maxSkippedSparseIndices_ =</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  OperatorBase::template GetSingleArgument<int32_t>(</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="stringliteral">"max_skipped_indices"</span>, kMaxSkippedSparseIndices);</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  }</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keywordtype">bool</span> RunOnDevice()<span class="keyword"> override </span>{</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordflow">return</span> <a class="code" href="structcaffe2_1_1_dispatch_helper.html">DispatchHelper<TensorTypes<int32_t, int64_t></a>>::call(</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keyword">this</span>, Input(INDICES));</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  }</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> TInd></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keywordtype">bool</span> DoRunWithType() {</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keyword">auto</span>& sparse_indices = Input(INDICES);</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  CAFFE_ENFORCE_EQ(sparse_indices.ndim(), 1);</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keyword">auto</span>& sparse_values = Input(VALUES);</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  CAFFE_ENFORCE_GE(sparse_values.ndim(), 1);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  CAFFE_ENFORCE_EQ(sparse_indices.size(), sparse_values.dim(0));</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keyword">auto</span>& default_value = Input(DEFAULT);</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  CAFFE_ENFORCE_EQ(default_value.ndim() + 1, sparse_values.ndim());</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  CAFFE_ENFORCE_EQ(default_value.size(), sparse_values.size_from_dim(1));</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  CAFFE_ENFORCE(sparse_values.meta() == default_value.meta());</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keyword">const</span> TInd* sparse_indices_vec = sparse_indices.template data<TInd>();</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* sparse_values_vec =</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span><span class="keywordtype">char</span>*<span class="keyword">></span>(sparse_values.raw_data());</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keyword">const</span> <span class="keywordtype">void</span>* default_val = default_value.raw_data();</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>  TIndex block_size = default_value.size();</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordtype">size_t</span> block_nbytes = default_value.nbytes();</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>  <span class="keyword">const</span> <span class="keywordtype">int</span> cols = this->featuresCount_;</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keywordtype">int</span> rows = -1;</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  int32_t sparse_indices_length = sparse_indices.dim32(0);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keyword">const</span> int32_t* lengths_vec = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keyword">auto</span>* output = Output(OUTPUTVALUE);</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <a class="code" href="classcaffe2_1_1_tensor.html">Tensor<Context></a>* presence_mask = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keywordflow">if</span> (returnPresenceMask_) {</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  presence_mask = Output(PRESENCEMASK);</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>  vector<TIndex> shape;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="keywordflow">if</span> (InputSize() == 4) {</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keyword">auto</span>& lengths = Input(LENGTHS);</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  CAFFE_ENFORCE_EQ(lengths.ndim(), 1);</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  lengths_vec = lengths.template data<int32_t>();</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  rows = lengths.dim32(0);</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  }</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="keywordflow">if</span> (rows == -1) {</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="comment">// if the LENGTHS is not set, the output will be a vector</span></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  rows = 1;</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  lengths_vec = &sparse_indices_length;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  shape.push_back(rows);</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  }</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  shape.push_back(cols);</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keywordflow">if</span> (returnPresenceMask_) {</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  presence_mask-><a class="code" href="classcaffe2_1_1_tensor.html#a359b5ed5cfd9beaf7f62a5561d939c3b">Resize</a>(shape);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  }</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  shape.insert(</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  shape.end(), default_value.dims().begin(), default_value.dims().end());</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  output->Resize(shape);</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>  <span class="comment">// init</span></div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="comment">// TODO: consider unrolling CopyItems to make elemental types copy faster</span></div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keywordtype">char</span>* output_data =</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keyword">static_cast<</span><span class="keywordtype">char</span>*<span class="keyword">></span>(output->raw_mutable_data(sparse_values.meta()));</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < cols * rows; i++) {</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  context_.template CopyItems<Context, Context>(</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  default_value.meta(),</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  block_size,</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  default_val,</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  output_data + i * block_nbytes);</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  }</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordtype">bool</span>* presence_mask_data = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keywordflow">if</span> (returnPresenceMask_) {</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  presence_mask_data = presence_mask->template mutable_data<bool>();</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  math::Set<bool, Context>(</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  rows * cols, <span class="keyword">false</span>, presence_mask_data, &context_);</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  }</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> </div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  int64_t offset = 0;</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> r = 0; r < rows; r++) {</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> c = 0; c < lengths_vec[r]; c++) {</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keyword">const</span> <span class="keyword">auto</span> sparse_index = sparse_indices_vec[offset + c];</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keywordflow">if</span> (sparse_index < 0 ||</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  sparse_index >= std::numeric_limits<TInd>::max()) {</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  CAFFE_ENFORCE_LT(</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  ++skippedSparseIndices_,</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  maxSkippedSparseIndices_,</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="stringliteral">"Too many sparse indices skipped"</span>);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keywordflow">continue</span>;</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="keywordtype">int</span> idx = this->getFeatureIdx(sparse_index);</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keywordflow">if</span> (idx != -1) {</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  context_.template CopyItems<Context, Context>(</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  sparse_values.meta(),</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  block_size,</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  sparse_values_vec + (offset + c) * block_nbytes,</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  output_data + (r * cols + idx) * block_nbytes);</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="keywordflow">if</span> (returnPresenceMask_) {</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  presence_mask_data[r * cols + idx] = <span class="keyword">true</span>;</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>  }</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>  offset += lengths_vec[r];</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> </div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</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">private</span>:</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keyword">static</span> <span class="keyword">const</span> uint32_t kMaxSkippedSparseIndices = 5;</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>  <span class="keywordtype">bool</span> returnPresenceMask_;</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  uint32_t maxSkippedSparseIndices_ = 0;</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  uint32_t skippedSparseIndices_ = 0;</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span> </div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  INPUT_TAGS(INDICES, VALUES, DEFAULT, LENGTHS);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  OUTPUT_TAGS(OUTPUTVALUE, PRESENCEMASK);</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> };</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> </div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> <span class="keyword">template</span> <<span class="keyword">class</span> Context></div><div class="line"><a name="l00190"></a><span class="lineno"><a class="line" href="classcaffe2_1_1_sparse_to_dense_mask_gradient_op.html"> 190</a></span> <span class="keyword">class </span><a class="code" href="classcaffe2_1_1_sparse_to_dense_mask_gradient_op.html">SparseToDenseMaskGradientOp</a> : <span class="keyword">public</span> <a class="code" href="classcaffe2_1_1_sparse_to_dense_mask_base.html">SparseToDenseMaskBase</a><Context> {</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  USE_OPERATOR_CONTEXT_FUNCTIONS;</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <a class="code" href="classcaffe2_1_1_sparse_to_dense_mask_gradient_op.html">SparseToDenseMaskGradientOp</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="l00194"></a><span class="lineno"> 194</span>  : <a class="code" href="classcaffe2_1_1_sparse_to_dense_mask_base.html">SparseToDenseMaskBase<Context></a>(operator_def, ws) {}</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> </div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keywordtype">bool</span> RunOnDevice()<span class="keyword"> override </span>{</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keywordflow">return</span> <a class="code" href="structcaffe2_1_1_dispatch_helper.html">DispatchHelper<TensorTypes<int32_t, int64_t></a>>::call(</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keyword">this</span>, Input(INDICES));</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="keyword">template</span> <<span class="keyword">typename</span> TInd></div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="keywordtype">bool</span> DoRunWithType() {</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keyword">auto</span>& sparse_indices = Input(INDICES);</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  CAFFE_ENFORCE_EQ(sparse_indices.ndim(), 1);</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keyword">auto</span>& gradient_output = Input(GOUTPUT);</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> </div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  TIndex block_size = gradient_output.size_from_dim(1);</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordtype">size_t</span> block_nbytes = gradient_output.itemsize() * block_size;</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>  <span class="keyword">const</span> <span class="keywordtype">int</span> cols = this->featuresCount_;</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keywordtype">int</span> rows = -1;</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="keywordtype">int</span> iter_offset = 1;</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  int32_t default_length = sparse_indices.dim32(0);</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keyword">const</span> int32_t* lengths_vec = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keyword">auto</span>* output = Output(GVALUES);</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  vector<TIndex> shape;</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keywordflow">if</span> (InputSize() > LENGTHS) {</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="comment">// if the LENGTHS is set, the gradient_output has dim:</span></div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="comment">// lengths * mask.size() * feature_dim</span></div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keyword">auto</span>& lengths = Input(LENGTHS);</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  lengths_vec = lengths.template data<int32_t>();</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  rows = lengths.dim32(0);</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  CAFFE_ENFORCE_EQ(lengths.ndim(), 1);</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  CAFFE_ENFORCE_GE(gradient_output.ndim(), 2);</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  CAFFE_ENFORCE_EQ(gradient_output.dim(0), rows);</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  CAFFE_ENFORCE_EQ(gradient_output.dim(1), cols);</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  block_nbytes /= gradient_output.dim(1);</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  block_size /= gradient_output.dim(1);</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  iter_offset += 1;</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  }</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keywordflow">if</span> (rows == -1) {</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="comment">// if the LENGTHS is not set, the gradient_output has dim:</span></div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="comment">// mask.size() * feature_dim</span></div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  rows = 1;</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  lengths_vec = &default_length;</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  CAFFE_ENFORCE_GE(gradient_output.ndim(), 1);</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  CAFFE_ENFORCE_EQ(gradient_output.dim(0), cols);</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  }</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  shape.push_back(default_length);</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="comment">// insert feature_dim</span></div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  shape.insert(</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  shape.end(),</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  gradient_output.dims().begin() + iter_offset,</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  gradient_output.dims().end());</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  output->Resize(shape);</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span> </div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="keyword">const</span> TInd* sparse_indices_vec = sparse_indices.template data<TInd>();</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* gradient_output_vec =</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span><span class="keywordtype">char</span>*<span class="keyword">></span>(gradient_output.raw_data());</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> </div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keywordtype">char</span>* output_data =</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="keyword">static_cast<</span><span class="keywordtype">char</span>*<span class="keyword">></span>(output->raw_mutable_data(gradient_output.meta()));</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  math::Set<char, Context>(</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  default_length * gradient_output.itemsize(), 0, output_data, &context_);</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> </div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  int32_t offset = 0;</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="comment">// SparseToDenseMask is not injective; gradient_used records</span></div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="comment">// if the gradient is used for other input value from the same row</span></div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  vector<bool> gradient_used(cols, <span class="keyword">false</span>);</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> r = 0; r < rows; r++) {</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  std::fill(gradient_used.begin(), gradient_used.end(), <span class="keyword">false</span>);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> c = lengths_vec[r] - 1; c >= 0; c--) {</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="keywordtype">int</span> idx = this->getFeatureIdx(sparse_indices_vec[offset + c]);</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keywordflow">if</span> (idx != -1 && !gradient_used[idx]) {</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  gradient_used[idx] = <span class="keyword">true</span>;</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  context_.template CopyItems<Context, Context>(</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  gradient_output.meta(),</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  block_size,</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  gradient_output_vec + (r * cols + idx) * block_nbytes,</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  output_data + (offset + c) * block_nbytes);</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  }</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  }</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  offset += lengths_vec[r];</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  }</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  }</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span> </div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  INPUT_TAGS(INDICES, GOUTPUT, LENGTHS);</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  OUTPUT_TAGS(GVALUES);</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> };</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> </div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> } <span class="comment">// namespace caffe2</span></div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span> </div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> <span class="preprocessor">#endif // CAFFE2_OPERATORS_SPARSE_TO_DENSE_MASK_OP_H_</span></div><div class="ttc" id="classcaffe2_1_1_sparse_to_dense_mask_base_html"><div class="ttname"><a href="classcaffe2_1_1_sparse_to_dense_mask_base.html">caffe2::SparseToDenseMaskBase</a></div><div class="ttdef"><b>Definition:</b> <a href="sparse__to__dense__mask__op_8h_source.html#l00015">sparse_to_dense_mask_op.h:15</a></div></div>
<div class="ttc" id="classcaffe2_1_1_tensor_html"><div class="ttname"><a href="classcaffe2_1_1_tensor.html">caffe2::Tensor</a></div><div class="ttdoc">Tensor is the basic class in Caffe2 that stores a contiguous memory with its shape information...</div><div class="ttdef"><b>Definition:</b> <a href="tensor_8h_source.html#l00093">tensor.h:93</a></div></div>
<div class="ttc" id="classcaffe2_1_1_workspace_html"><div class="ttname"><a href="classcaffe2_1_1_workspace.html">caffe2::Workspace</a></div><div class="ttdoc">Workspace is a class that holds all the related objects created during runtime: (1) all blobs...</div><div class="ttdef"><b>Definition:</b> <a href="workspace_8h_source.html#l00047">workspace.h:47</a></div></div>
<div class="ttc" id="classcaffe2_1_1_sparse_to_dense_mask_gradient_op_html"><div class="ttname"><a href="classcaffe2_1_1_sparse_to_dense_mask_gradient_op.html">caffe2::SparseToDenseMaskGradientOp</a></div><div class="ttdef"><b>Definition:</b> <a href="sparse__to__dense__mask__op_8h_source.html#l00190">sparse_to_dense_mask_op.h:190</a></div></div>
<div class="ttc" id="classcaffe2_1_1_tensor_html_a359b5ed5cfd9beaf7f62a5561d939c3b"><div class="ttname"><a href="classcaffe2_1_1_tensor.html#a359b5ed5cfd9beaf7f62a5561d939c3b">caffe2::Tensor::Resize</a></div><div class="ttdeci">void Resize(Ts...dim_source)</div><div class="ttdoc">Resizes a tensor. </div><div class="ttdef"><b>Definition:</b> <a href="tensor_8h_source.html#l00288">tensor.h:288</a></div></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>
<div class="ttc" id="structcaffe2_1_1_dispatch_helper_html"><div class="ttname"><a href="structcaffe2_1_1_dispatch_helper.html">caffe2::DispatchHelper</a></div><div class="ttdef"><b>Definition:</b> <a href="core_2operator_8h_source.html#l00560">operator.h:560</a></div></div>
<div class="ttc" id="classcaffe2_1_1_operator_html"><div class="ttname"><a href="classcaffe2_1_1_operator.html">caffe2::Operator</a></div><div class="ttdef"><b>Definition:</b> <a href="core_2operator_8h_source.html#l00325">operator.h:325</a></div></div>
<div class="ttc" id="classcaffe2_1_1_sparse_to_dense_mask_op_html"><div class="ttname"><a href="classcaffe2_1_1_sparse_to_dense_mask_op.html">caffe2::SparseToDenseMaskOp</a></div><div class="ttdef"><b>Definition:</b> <a href="sparse__to__dense__mask__op_8h_source.html#l00062">sparse_to_dense_mask_op.h:62</a></div></div>
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