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0df3f646fc
various bugfixes from github issues kmean with some frozen centroids GPU better tiling for large flat datasets default AVX for vector ops
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812 lines
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<li class="navelem"><a class="el" href="dir_6b3ae6988449b0834e9596fad5d75199.html">gpu</a></li><li class="navelem"><a class="el" href="dir_498271007b03b2a0521055e88776887b.html">utils</a></li> </ul>
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<div class="title">Tensor-inl.cuh</div> </div>
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<div class="contents">
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/**</span></div>
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<div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) 2015-present, Facebook, Inc.</span></div>
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<div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> * All rights reserved.</span></div>
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<div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> *</span></div>
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<div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> * This source code is licensed under the BSD+Patents license found in the</span></div>
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<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * LICENSE file in the root directory of this source tree.</span></div>
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<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> */</span></div>
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<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div>
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<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment">// Copyright 2004-present Facebook. All Rights Reserved.</span></div>
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<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div>
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<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include "../GpuFaissAssert.h"</span></div>
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<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include "DeviceUtils.h"</span></div>
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<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <limits></span></div>
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<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div>
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<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="keyword">namespace </span>faiss { <span class="keyword">namespace </span>gpu {</div>
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<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div>
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<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> __host__ __device__</div>
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<div class="line"><a name="l00020"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a8ae7b3f95991125a5648c3b78afd40bd"> 20</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a8ae7b3f95991125a5648c3b78afd40bd">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::Tensor</a>()</div>
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<div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  : data_(nullptr) {</div>
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<div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  static_assert(Dim > 0, <span class="stringliteral">"must have > 0 dimensions"</span>);</div>
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<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> </div>
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<div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < Dim; ++i) {</div>
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<div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#ad96fbf0f5e7c06a1031b8b18f7fc01d7">size_</a>[i] = 0;</div>
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<div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#af4b8fe4b632cdca51ee7972ed93fc3fa">stride_</a>[i] = (IndexT) 1;</div>
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<div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  }</div>
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<div class="line"><a name="l00028"></a><span class="lineno"> 28</span> }</div>
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<div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div>
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<div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00032"></a><span class="lineno"> 32</span> __host__ __device__</div>
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<div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, Dim, InnerContig, IndexT, PtrTraits></a>&</div>
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<div class="line"><a name="l00034"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#ae086a03b8067e18d0c2dda0892dc9e39"> 34</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a503fe45536fb7153fbd18fe61c159304">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::operator=</a>(</div>
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<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, Dim, InnerContig, IndexT, PtrTraits></a>&& t) {</div>
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<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  data_ = t.data_; t.data_ = <span class="keyword">nullptr</span>;</div>
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<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < Dim; ++i) {</div>
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<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  stride_[i] = t.stride_[i]; t.stride_[i] = 0;</div>
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<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  size_[i] = t.size_[i]; t.size_[i] = 0;</div>
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<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  }</div>
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<div class="line"><a name="l00041"></a><span class="lineno"> 41</span> </div>
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<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
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<div class="line"><a name="l00043"></a><span class="lineno"> 43</span> }</div>
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<div class="line"><a name="l00044"></a><span class="lineno"> 44</span> </div>
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<div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00047"></a><span class="lineno"> 47</span> __host__ __device__</div>
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<div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a8ae7b3f95991125a5648c3b78afd40bd">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::</a></div>
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<div class="line"><a name="l00049"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a88210dee96fc97b00f0ab45749528fe9"> 49</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a8ae7b3f95991125a5648c3b78afd40bd">Tensor</a>(DataPtrType data, <span class="keyword">const</span> IndexT sizes[Dim])</div>
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<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  : data_(data) {</div>
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<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  static_assert(Dim > 0, <span class="stringliteral">"must have > 0 dimensions"</span>);</div>
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<div class="line"><a name="l00052"></a><span class="lineno"> 52</span> </div>
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<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < Dim; ++i) {</div>
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<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#ad96fbf0f5e7c06a1031b8b18f7fc01d7">size_</a>[i] = <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#abc0ecc4f882ee09632b5a06be0619adb">sizes</a>[i];</div>
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<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  }</div>
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<div class="line"><a name="l00056"></a><span class="lineno"> 56</span> </div>
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<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#af4b8fe4b632cdca51ee7972ed93fc3fa">stride_</a>[Dim - 1] = (IndexT) 1;</div>
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<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = Dim - 2; i >= 0; --i) {</div>
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<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#af4b8fe4b632cdca51ee7972ed93fc3fa">stride_</a>[i] = <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#af4b8fe4b632cdca51ee7972ed93fc3fa">stride_</a>[i + 1] * <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#abc0ecc4f882ee09632b5a06be0619adb">sizes</a>[i + 1];</div>
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<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  }</div>
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<div class="line"><a name="l00061"></a><span class="lineno"> 61</span> }</div>
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<div class="line"><a name="l00062"></a><span class="lineno"> 62</span> </div>
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<div class="line"><a name="l00063"></a><span class="lineno"> 63</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00065"></a><span class="lineno"> 65</span> __host__ __device__</div>
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<div class="line"><a name="l00066"></a><span class="lineno"> 66</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a8ae7b3f95991125a5648c3b78afd40bd">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::</a></div>
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<div class="line"><a name="l00067"></a><span class="lineno"> 67</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a8ae7b3f95991125a5648c3b78afd40bd">Tensor</a>(DataPtrType data, std::initializer_list<IndexT> sizes)</div>
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<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  : data_(data) {</div>
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<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  GPU_FAISS_ASSERT(sizes.size() == Dim);</div>
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<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  static_assert(Dim > 0, <span class="stringliteral">"must have > 0 dimensions"</span>);</div>
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<div class="line"><a name="l00071"></a><span class="lineno"> 71</span> </div>
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<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keywordtype">int</span> i = 0;</div>
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<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> s : sizes) {</div>
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<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#ad96fbf0f5e7c06a1031b8b18f7fc01d7">size_</a>[i++] = s;</div>
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<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  }</div>
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<div class="line"><a name="l00076"></a><span class="lineno"> 76</span> </div>
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<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#af4b8fe4b632cdca51ee7972ed93fc3fa">stride_</a>[Dim - 1] = (IndexT) 1;</div>
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<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = Dim - 2; j >= 0; --j) {</div>
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<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#af4b8fe4b632cdca51ee7972ed93fc3fa">stride_</a>[j] = <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#af4b8fe4b632cdca51ee7972ed93fc3fa">stride_</a>[j + 1] * <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#ad96fbf0f5e7c06a1031b8b18f7fc01d7">size_</a>[j + 1];</div>
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<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  }</div>
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<div class="line"><a name="l00081"></a><span class="lineno"> 81</span> }</div>
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<div class="line"><a name="l00082"></a><span class="lineno"> 82</span> </div>
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<div class="line"><a name="l00083"></a><span class="lineno"> 83</span> </div>
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<div class="line"><a name="l00084"></a><span class="lineno"> 84</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00086"></a><span class="lineno"> 86</span> __host__ __device__</div>
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<div class="line"><a name="l00087"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#ac651b20d86813b8928f05cde4fb1ff7d"> 87</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a8ae7b3f95991125a5648c3b78afd40bd">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::Tensor</a>(</div>
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<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  DataPtrType data, <span class="keyword">const</span> IndexT sizes[Dim], <span class="keyword">const</span> IndexT strides[Dim])</div>
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<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  : data_(data) {</div>
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<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  static_assert(Dim > 0, <span class="stringliteral">"must have > 0 dimensions"</span>);</div>
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<div class="line"><a name="l00091"></a><span class="lineno"> 91</span> </div>
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<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < Dim; ++i) {</div>
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<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#ad96fbf0f5e7c06a1031b8b18f7fc01d7">size_</a>[i] = sizes[i];</div>
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<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#af4b8fe4b632cdca51ee7972ed93fc3fa">stride_</a>[i] = <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a87a777247486756e99060547a3cc833a">strides</a>[i];</div>
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<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  }</div>
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<div class="line"><a name="l00096"></a><span class="lineno"> 96</span> }</div>
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<div class="line"><a name="l00097"></a><span class="lineno"> 97</span> </div>
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<div class="line"><a name="l00098"></a><span class="lineno"> 98</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00100"></a><span class="lineno"> 100</span> __host__ <span class="keywordtype">void</span></div>
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<div class="line"><a name="l00101"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a6dc00c182a92389b74c89ba7fcab40d3"> 101</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6dc00c182a92389b74c89ba7fcab40d3">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::copyFrom</a>(</div>
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<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, Dim, InnerContig, IndexT, PtrTraits></a>& t,</div>
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<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  cudaStream_t stream) {</div>
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<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="comment">// The tensor must be fully contiguous</span></div>
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<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  GPU_FAISS_ASSERT(this->isContiguous());</div>
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<div class="line"><a name="l00106"></a><span class="lineno"> 106</span> </div>
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<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="comment">// Size must be the same (since dimensions are checked and</span></div>
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<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="comment">// continuity is assumed, we need only check total number of</span></div>
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<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="comment">// elements</span></div>
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<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  GPU_FAISS_ASSERT(this->numElements() == t.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a0ba9ab7c1676b7a41a6e6b2e5a490d2f">numElements</a>());</div>
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<div class="line"><a name="l00111"></a><span class="lineno"> 111</span> </div>
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<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keywordflow">if</span> (t.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a0ba9ab7c1676b7a41a6e6b2e5a490d2f">numElements</a>() > 0) {</div>
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<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  GPU_FAISS_ASSERT(this->data_);</div>
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<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  GPU_FAISS_ASSERT(t.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a50411ce4d0fa32ef715e3321b6e33212">data</a>());</div>
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<div class="line"><a name="l00115"></a><span class="lineno"> 115</span> </div>
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<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keywordtype">int</span> ourDev = getDeviceForAddress(this->data_);</div>
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<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keywordtype">int</span> tDev = getDeviceForAddress(t.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a50411ce4d0fa32ef715e3321b6e33212">data</a>());</div>
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<div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div>
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<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keywordflow">if</span> (tDev == -1) {</div>
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<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  CUDA_VERIFY(cudaMemcpyAsync(this->data_,</div>
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<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  t.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a50411ce4d0fa32ef715e3321b6e33212">data</a>(),</div>
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<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  this->getSizeInBytes(),</div>
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<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  ourDev == -1 ? cudaMemcpyHostToHost :</div>
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<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  cudaMemcpyHostToDevice,</div>
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<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  stream));</div>
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<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  } <span class="keywordflow">else</span> {</div>
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<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  CUDA_VERIFY(cudaMemcpyAsync(this->data_,</div>
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<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  t.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a50411ce4d0fa32ef715e3321b6e33212">data</a>(),</div>
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<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  this->getSizeInBytes(),</div>
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<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  ourDev == -1 ? cudaMemcpyDeviceToHost :</div>
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<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  cudaMemcpyDeviceToDevice,</div>
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<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  stream));</div>
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<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  }</div>
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<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  }</div>
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<div class="line"><a name="l00135"></a><span class="lineno"> 135</span> }</div>
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<div class="line"><a name="l00136"></a><span class="lineno"> 136</span> </div>
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<div class="line"><a name="l00137"></a><span class="lineno"> 137</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00139"></a><span class="lineno"> 139</span> __host__ <span class="keywordtype">void</span></div>
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<div class="line"><a name="l00140"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a6cc21376070a03d77661d6e333972c6a"> 140</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6cc21376070a03d77661d6e333972c6a">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::copyTo</a>(</div>
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<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, Dim, InnerContig, IndexT, PtrTraits></a>& t,</div>
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<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  cudaStream_t stream) {</div>
|
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<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="comment">// The tensor must be fully contiguous</span></div>
|
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<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  GPU_FAISS_ASSERT(this->isContiguous());</div>
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<div class="line"><a name="l00145"></a><span class="lineno"> 145</span> </div>
|
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<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="comment">// Size must be the same (since dimensions are checked and</span></div>
|
|
<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="comment">// continuity is assumed, we need only check total number of</span></div>
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|
<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="comment">// elements</span></div>
|
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<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  GPU_FAISS_ASSERT(this->numElements() == t.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a0ba9ab7c1676b7a41a6e6b2e5a490d2f">numElements</a>());</div>
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<div class="line"><a name="l00150"></a><span class="lineno"> 150</span> </div>
|
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<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordflow">if</span> (t.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a0ba9ab7c1676b7a41a6e6b2e5a490d2f">numElements</a>() > 0) {</div>
|
|
<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  GPU_FAISS_ASSERT(this->data_);</div>
|
|
<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  GPU_FAISS_ASSERT(t.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a50411ce4d0fa32ef715e3321b6e33212">data</a>());</div>
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<div class="line"><a name="l00154"></a><span class="lineno"> 154</span> </div>
|
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<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keywordtype">int</span> ourDev = getDeviceForAddress(this->data_);</div>
|
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<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keywordtype">int</span> tDev = getDeviceForAddress(t.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a50411ce4d0fa32ef715e3321b6e33212">data</a>());</div>
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|
<div class="line"><a name="l00157"></a><span class="lineno"> 157</span> </div>
|
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<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keywordflow">if</span> (tDev == -1) {</div>
|
|
<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  CUDA_VERIFY(cudaMemcpyAsync(t.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a50411ce4d0fa32ef715e3321b6e33212">data</a>(),</div>
|
|
<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  this->data_,</div>
|
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<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  this->getSizeInBytes(),</div>
|
|
<div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  ourDev == -1 ? cudaMemcpyHostToHost :</div>
|
|
<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  cudaMemcpyDeviceToHost,</div>
|
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<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  stream));</div>
|
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<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  CUDA_VERIFY(cudaMemcpyAsync(t.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a50411ce4d0fa32ef715e3321b6e33212">data</a>(),</div>
|
|
<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  this->data_,</div>
|
|
<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  this->getSizeInBytes(),</div>
|
|
<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  ourDev == -1 ? cudaMemcpyHostToDevice :</div>
|
|
<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  cudaMemcpyDeviceToDevice,</div>
|
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<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  stream));</div>
|
|
<div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  }</div>
|
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<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  }</div>
|
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<div class="line"><a name="l00174"></a><span class="lineno"> 174</span> }</div>
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<div class="line"><a name="l00175"></a><span class="lineno"> 175</span> </div>
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<div class="line"><a name="l00176"></a><span class="lineno"> 176</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00178"></a><span class="lineno"> 178</span> <span class="keyword">template</span> <<span class="keyword">typename</span> OtherT, <span class="keywordtype">int</span> OtherDim></div>
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<div class="line"><a name="l00179"></a><span class="lineno"> 179</span> __host__ __device__ <span class="keywordtype">bool</span></div>
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<div class="line"><a name="l00180"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a3067941f8f8f09fc73e2f06243699825"> 180</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a3067941f8f8f09fc73e2f06243699825">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::isSame</a>(</div>
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<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="keyword">const</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<OtherT, OtherDim, InnerContig, IndexT, PtrTraits></a>& rhs)<span class="keyword"> const </span>{</div>
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<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keywordflow">if</span> (Dim != OtherDim) {</div>
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<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
|
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<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  }</div>
|
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<div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div>
|
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<div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < Dim; ++i) {</div>
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<div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keywordflow">if</span> (this->getSize(i) != rhs.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(i)) {</div>
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<div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
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<div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  }</div>
|
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<div class="line"><a name="l00190"></a><span class="lineno"> 190</span> </div>
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<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keywordflow">if</span> (this->getStride(i) != rhs.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a0b8bba630f7a1fa217f90b20d298420a">getStride</a>(i)) {</div>
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<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
|
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<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  }</div>
|
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<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  }</div>
|
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<div class="line"><a name="l00195"></a><span class="lineno"> 195</span> </div>
|
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<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
|
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<div class="line"><a name="l00197"></a><span class="lineno"> 197</span> }</div>
|
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<div class="line"><a name="l00198"></a><span class="lineno"> 198</span> </div>
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<div class="line"><a name="l00199"></a><span class="lineno"> 199</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00201"></a><span class="lineno"> 201</span> <span class="keyword">template</span> <<span class="keyword">typename</span> OtherT, <span class="keywordtype">int</span> OtherDim></div>
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<div class="line"><a name="l00202"></a><span class="lineno"> 202</span> __host__ __device__ <span class="keywordtype">bool</span></div>
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<div class="line"><a name="l00203"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a22c1e45f81f7f9e5427e2eed19f9cd11"> 203</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a22c1e45f81f7f9e5427e2eed19f9cd11">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::isSameSize</a>(</div>
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<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keyword">const</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<OtherT, OtherDim, InnerContig, IndexT, PtrTraits></a>& rhs)<span class="keyword"> const </span>{</div>
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<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keywordflow">if</span> (Dim != OtherDim) {</div>
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<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
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<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  }</div>
|
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<div class="line"><a name="l00208"></a><span class="lineno"> 208</span> </div>
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<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < Dim; ++i) {</div>
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<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keywordflow">if</span> (this->getSize(i) != rhs.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(i)) {</div>
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<div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
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<div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  }</div>
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<div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  }</div>
|
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<div class="line"><a name="l00214"></a><span class="lineno"> 214</span> </div>
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<div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
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<div class="line"><a name="l00216"></a><span class="lineno"> 216</span> }</div>
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<div class="line"><a name="l00217"></a><span class="lineno"> 217</span> </div>
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<div class="line"><a name="l00218"></a><span class="lineno"> 218</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00220"></a><span class="lineno"> 220</span> <span class="keyword">template</span> <<span class="keyword">typename</span> U></div>
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<div class="line"><a name="l00221"></a><span class="lineno"> 221</span> __host__ __device__ <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<U, Dim, InnerContig, IndexT, PtrTraits></a></div>
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<div class="line"><a name="l00222"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a2894f8fdfab8ec3245364a6f9e8a5259"> 222</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a2894f8fdfab8ec3245364a6f9e8a5259">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::cast</a>() {</div>
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<div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  static_assert(<span class="keyword">sizeof</span>(U) == <span class="keyword">sizeof</span>(T), <span class="stringliteral">"cast must be to same size object"</span>);</div>
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<div class="line"><a name="l00224"></a><span class="lineno"> 224</span> </div>
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<div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keywordflow">return</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<U, Dim, InnerContig, IndexT, PtrTraits></a>(</div>
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<div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keyword">reinterpret_cast<</span>U*<span class="keyword">></span>(data_), size_, stride_);</div>
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<div class="line"><a name="l00227"></a><span class="lineno"> 227</span> }</div>
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<div class="line"><a name="l00228"></a><span class="lineno"> 228</span> </div>
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<div class="line"><a name="l00229"></a><span class="lineno"> 229</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00231"></a><span class="lineno"> 231</span> <span class="keyword">template</span> <<span class="keyword">typename</span> U></div>
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<div class="line"><a name="l00232"></a><span class="lineno"> 232</span> __host__ __device__ <span class="keyword">const</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<U, Dim, InnerContig, IndexT, PtrTraits></a></div>
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<div class="line"><a name="l00233"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a79a16bd4300ca8fdba52932c7c97cce9"> 233</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a2894f8fdfab8ec3245364a6f9e8a5259">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::cast</a>()<span class="keyword"> const </span>{</div>
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<div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  static_assert(<span class="keyword">sizeof</span>(U) == <span class="keyword">sizeof</span>(T), <span class="stringliteral">"cast must be to same size object"</span>);</div>
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<div class="line"><a name="l00235"></a><span class="lineno"> 235</span> </div>
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<div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="keywordflow">return</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<U, Dim, InnerContig, IndexT, PtrTraits></a>(</div>
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<div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="keyword">reinterpret_cast<</span>U*<span class="keyword">></span>(data_), size_, stride_);</div>
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<div class="line"><a name="l00238"></a><span class="lineno"> 238</span> }</div>
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<div class="line"><a name="l00239"></a><span class="lineno"> 239</span> </div>
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<div class="line"><a name="l00240"></a><span class="lineno"> 240</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00242"></a><span class="lineno"> 242</span> <span class="keyword">template</span> <<span class="keyword">typename</span> U></div>
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<div class="line"><a name="l00243"></a><span class="lineno"> 243</span> __host__ __device__ <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<U, Dim, InnerContig, IndexT, PtrTraits></a></div>
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<div class="line"><a name="l00244"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a6c9640c365134ccc33cdb2695b016eb3"> 244</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6c9640c365134ccc33cdb2695b016eb3">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::castResize</a>() {</div>
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<div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  static_assert(<span class="keyword">sizeof</span>(U) >= <span class="keyword">sizeof</span>(T), <span class="stringliteral">"only handles greater sizes"</span>);</div>
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<div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  constexpr <span class="keywordtype">int</span> kMultiple = <span class="keyword">sizeof</span>(U) / <span class="keyword">sizeof</span>(T);</div>
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<div class="line"><a name="l00247"></a><span class="lineno"> 247</span> </div>
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<div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  GPU_FAISS_ASSERT(canCastResize<U>());</div>
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<div class="line"><a name="l00249"></a><span class="lineno"> 249</span> </div>
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<div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  IndexT newSize[Dim];</div>
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<div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  IndexT newStride[Dim];</div>
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<div class="line"><a name="l00252"></a><span class="lineno"> 252</span> </div>
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<div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < Dim - 1; ++i) {</div>
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<div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  newSize[i] = size_[i];</div>
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<div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  newStride[i] = stride_[i] / kMultiple;</div>
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<div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  }</div>
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<div class="line"><a name="l00257"></a><span class="lineno"> 257</span> </div>
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<div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  newStride[Dim - 1] = 1; <span class="comment">// this is the same as the old stride</span></div>
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<div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  newSize[Dim - 1] = size_[Dim - 1] / kMultiple;</div>
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<div class="line"><a name="l00260"></a><span class="lineno"> 260</span> </div>
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<div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="keywordflow">return</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<U, Dim, InnerContig, IndexT, PtrTraits></a>(</div>
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<div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="keyword">reinterpret_cast<</span>U*<span class="keyword">></span>(data_), newSize, newStride);</div>
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<div class="line"><a name="l00263"></a><span class="lineno"> 263</span> }</div>
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<div class="line"><a name="l00264"></a><span class="lineno"> 264</span> </div>
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<div class="line"><a name="l00265"></a><span class="lineno"> 265</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00267"></a><span class="lineno"> 267</span> <span class="keyword">template</span> <<span class="keyword">typename</span> U></div>
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<div class="line"><a name="l00268"></a><span class="lineno"> 268</span> __host__ __device__ <span class="keyword">const</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<U, Dim, InnerContig, IndexT, PtrTraits></a></div>
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<div class="line"><a name="l00269"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#ae06338d9b19c62452c2111682447f863"> 269</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6c9640c365134ccc33cdb2695b016eb3">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::castResize</a>()<span class="keyword"> const </span>{</div>
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<div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keywordflow">return</span> <span class="keyword">const_cast<</span><a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, Dim, InnerContig, IndexT, PtrTraits></a>*<span class="keyword">></span>(<span class="keyword">this</span>)-></div>
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<div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  castResize<U>();</div>
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<div class="line"><a name="l00272"></a><span class="lineno"> 272</span> }</div>
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<div class="line"><a name="l00273"></a><span class="lineno"> 273</span> </div>
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<div class="line"><a name="l00274"></a><span class="lineno"> 274</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00276"></a><span class="lineno"> 276</span> <span class="keyword">template</span> <<span class="keyword">typename</span> U></div>
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<div class="line"><a name="l00277"></a><span class="lineno"> 277</span> __host__ __device__ <span class="keywordtype">bool</span></div>
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<div class="line"><a name="l00278"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a7fbbf51f8ef6bea9cc863a86e20d994e"> 278</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a7fbbf51f8ef6bea9cc863a86e20d994e">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::canCastResize</a>()<span class="keyword"> const </span>{</div>
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<div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  static_assert(<span class="keyword">sizeof</span>(U) >= <span class="keyword">sizeof</span>(T), <span class="stringliteral">"only handles greater sizes"</span>);</div>
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<div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  constexpr <span class="keywordtype">int</span> kMultiple = <span class="keyword">sizeof</span>(U) / <span class="keyword">sizeof</span>(T);</div>
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<div class="line"><a name="l00281"></a><span class="lineno"> 281</span> </div>
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<div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <span class="comment">// Check all outer strides</span></div>
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<div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < Dim - 1; ++i) {</div>
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<div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keywordflow">if</span> (stride_[i] % kMultiple != 0) {</div>
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<div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
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<div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  }</div>
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<div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  }</div>
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<div class="line"><a name="l00288"></a><span class="lineno"> 288</span> </div>
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<div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="comment">// Check inner size</span></div>
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<div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="keywordflow">if</span> (size_[Dim - 1] % kMultiple != 0) {</div>
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<div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
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<div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  }</div>
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<div class="line"><a name="l00293"></a><span class="lineno"> 293</span> </div>
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<div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <span class="keywordflow">if</span> (stride_[Dim - 1] != 1) {</div>
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<div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
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<div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  }</div>
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<div class="line"><a name="l00297"></a><span class="lineno"> 297</span> </div>
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<div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
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<div class="line"><a name="l00299"></a><span class="lineno"> 299</span> }</div>
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<div class="line"><a name="l00300"></a><span class="lineno"> 300</span> </div>
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<div class="line"><a name="l00301"></a><span class="lineno"> 301</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00303"></a><span class="lineno"> 303</span> <span class="keyword">template</span> <<span class="keyword">typename</span> NewIndexT></div>
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<div class="line"><a name="l00304"></a><span class="lineno"> 304</span> __host__ <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, Dim, InnerContig, NewIndexT, PtrTraits></a></div>
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<div class="line"><a name="l00305"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a9f0c817e9751fe02926c2346a97f0350"> 305</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a9f0c817e9751fe02926c2346a97f0350">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::castIndexType</a>()<span class="keyword"> const </span>{</div>
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<div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keywordflow">if</span> (<span class="keyword">sizeof</span>(NewIndexT) < <span class="keyword">sizeof</span>(IndexT)) {</div>
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<div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  GPU_FAISS_ASSERT(this->canUseIndexType<NewIndexT>());</div>
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<div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  }</div>
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<div class="line"><a name="l00309"></a><span class="lineno"> 309</span> </div>
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<div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  NewIndexT newSize[Dim];</div>
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<div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  NewIndexT newStride[Dim];</div>
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<div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < Dim; ++i) {</div>
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<div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  newSize[i] = (NewIndexT) size_[i];</div>
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<div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  newStride[i] = (NewIndexT) stride_[i];</div>
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<div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  }</div>
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<div class="line"><a name="l00316"></a><span class="lineno"> 316</span> </div>
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<div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="keywordflow">return</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, Dim, InnerContig, NewIndexT, PtrTraits></a>(</div>
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<div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  data_, newSize, newStride);</div>
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<div class="line"><a name="l00319"></a><span class="lineno"> 319</span> }</div>
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<div class="line"><a name="l00320"></a><span class="lineno"> 320</span> </div>
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<div class="line"><a name="l00321"></a><span class="lineno"> 321</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00323"></a><span class="lineno"> 323</span> <span class="keyword">template</span> <<span class="keyword">typename</span> NewIndexT></div>
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<div class="line"><a name="l00324"></a><span class="lineno"> 324</span> __host__ <span class="keywordtype">bool</span></div>
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<div class="line"><a name="l00325"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a2ac9dc9fa8d81f2651a1be486c14ba62"> 325</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a2ac9dc9fa8d81f2651a1be486c14ba62">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::canUseIndexType</a>()<span class="keyword"> const </span>{</div>
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<div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  static_assert(<span class="keyword">sizeof</span>(<span class="keywordtype">size_t</span>) >= <span class="keyword">sizeof</span>(IndexT),</div>
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<div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <span class="stringliteral">"index size too large"</span>);</div>
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<div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  static_assert(<span class="keyword">sizeof</span>(<span class="keywordtype">size_t</span>) >= <span class="keyword">sizeof</span>(NewIndexT),</div>
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<div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <span class="stringliteral">"new index size too large"</span>);</div>
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<div class="line"><a name="l00330"></a><span class="lineno"> 330</span> </div>
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<div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <span class="comment">// Find maximum offset that can be calculated</span></div>
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<div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="comment">// FIXME: maybe also consider offset in bytes? multiply by sizeof(T)?</span></div>
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<div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <span class="keywordtype">size_t</span> maxOffset = 0;</div>
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<div class="line"><a name="l00334"></a><span class="lineno"> 334</span> </div>
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<div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < Dim; ++i) {</div>
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<div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="keywordtype">size_t</span> curMaxOffset = (size_t) size_[i] * (<span class="keywordtype">size_t</span>) stride_[i];</div>
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<div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="keywordflow">if</span> (curMaxOffset > maxOffset) {</div>
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<div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  maxOffset = curMaxOffset;</div>
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<div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  }</div>
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<div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  }</div>
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<div class="line"><a name="l00341"></a><span class="lineno"> 341</span> </div>
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<div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keywordflow">if</span> (maxOffset > (<span class="keywordtype">size_t</span>) std::numeric_limits<NewIndexT>::max()) {</div>
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<div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
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<div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  }</div>
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<div class="line"><a name="l00345"></a><span class="lineno"> 345</span> </div>
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<div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
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<div class="line"><a name="l00347"></a><span class="lineno"> 347</span> }</div>
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<div class="line"><a name="l00348"></a><span class="lineno"> 348</span> </div>
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<div class="line"><a name="l00349"></a><span class="lineno"> 349</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00351"></a><span class="lineno"> 351</span> __host__ __device__ <span class="keywordtype">size_t</span></div>
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<div class="line"><a name="l00352"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a0ba9ab7c1676b7a41a6e6b2e5a490d2f"> 352</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a0ba9ab7c1676b7a41a6e6b2e5a490d2f">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::numElements</a>()<span class="keyword"> const </span>{</div>
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<div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="keywordtype">size_t</span> size = (size_t) getSize(0);</div>
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<div class="line"><a name="l00354"></a><span class="lineno"> 354</span> </div>
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<div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 1; i < Dim; ++i) {</div>
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<div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  size *= (size_t) getSize(i);</div>
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<div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  }</div>
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<div class="line"><a name="l00358"></a><span class="lineno"> 358</span> </div>
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<div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <span class="keywordflow">return</span> size;</div>
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<div class="line"><a name="l00360"></a><span class="lineno"> 360</span> }</div>
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<div class="line"><a name="l00361"></a><span class="lineno"> 361</span> </div>
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<div class="line"><a name="l00362"></a><span class="lineno"> 362</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00364"></a><span class="lineno"> 364</span> __host__ __device__ <span class="keywordtype">bool</span></div>
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<div class="line"><a name="l00365"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a09019c54911db891c9321fd3b34509c2"> 365</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a09019c54911db891c9321fd3b34509c2">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::isContiguous</a>()<span class="keyword"> const </span>{</div>
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<div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <span class="keywordtype">long</span> prevSize = 1;</div>
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<div class="line"><a name="l00367"></a><span class="lineno"> 367</span> </div>
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<div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = Dim - 1; i >= 0; --i) {</div>
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<div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <span class="keywordflow">if</span> (getSize(i) != (IndexT) 1) {</div>
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<div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="keywordflow">if</span> (getStride(i) == prevSize) {</div>
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<div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  prevSize *= getSize(i);</div>
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<div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  } <span class="keywordflow">else</span> {</div>
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<div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
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<div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  }</div>
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<div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  }</div>
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<div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  }</div>
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<div class="line"><a name="l00377"></a><span class="lineno"> 377</span> </div>
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<div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
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<div class="line"><a name="l00379"></a><span class="lineno"> 379</span> }</div>
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<div class="line"><a name="l00380"></a><span class="lineno"> 380</span> </div>
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<div class="line"><a name="l00381"></a><span class="lineno"> 381</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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|
<div class="line"><a name="l00383"></a><span class="lineno"> 383</span> __host__ __device__ <span class="keywordtype">bool</span></div>
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|
<div class="line"><a name="l00384"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a663e3829c395372acdc8d2e71c0bdabe"> 384</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::isConsistentlySized</a>(<span class="keywordtype">int</span> i)<span class="keyword"> const </span>{</div>
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|
<div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <span class="keywordflow">if</span> (i == 0 && getStride(i) > 0 && getSize(i) > 0) {</div>
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|
<div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
|
|
<div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  } <span class="keywordflow">else</span> <span class="keywordflow">if</span> ((i > 0) && (i < Dim) && (getStride(i) > 0) &&</div>
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|
<div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  ((getStride(i - 1) / getStride(i)) >= getSize(i))) {</div>
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<div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
|
|
<div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  }</div>
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<div class="line"><a name="l00391"></a><span class="lineno"> 391</span> </div>
|
|
<div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
|
|
<div class="line"><a name="l00393"></a><span class="lineno"> 393</span> }</div>
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<div class="line"><a name="l00394"></a><span class="lineno"> 394</span> </div>
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<div class="line"><a name="l00395"></a><span class="lineno"> 395</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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|
<div class="line"><a name="l00397"></a><span class="lineno"> 397</span> __host__ __device__ <span class="keywordtype">bool</span></div>
|
|
<div class="line"><a name="l00398"></a><span class="lineno"> 398</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::isConsistentlySized</a>()<span class="keyword"> const </span>{</div>
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|
<div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < Dim; ++i) {</div>
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|
<div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keywordflow">if</span> (!isConsistentlySized(i)) {</div>
|
|
<div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
|
|
<div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  }</div>
|
|
<div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  }</div>
|
|
<div class="line"><a name="l00404"></a><span class="lineno"> 404</span> </div>
|
|
<div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
|
|
<div class="line"><a name="l00406"></a><span class="lineno"> 406</span> }</div>
|
|
<div class="line"><a name="l00407"></a><span class="lineno"> 407</span> </div>
|
|
<div class="line"><a name="l00408"></a><span class="lineno"> 408</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
|
|
<div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
|
|
<div class="line"><a name="l00410"></a><span class="lineno"> 410</span> __host__ __device__ <span class="keywordtype">bool</span></div>
|
|
<div class="line"><a name="l00411"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a3f4e3c6afdf4a03308756b6ae6462c38"> 411</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a3f4e3c6afdf4a03308756b6ae6462c38">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::isContiguousDim</a>(<span class="keywordtype">int</span> i)<span class="keyword"> const </span>{</div>
|
|
<div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <span class="keywordflow">return</span> (i == Dim - 1) || <span class="comment">// just in case</span></div>
|
|
<div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  ((i < Dim - 1) &&</div>
|
|
<div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  ((getStride(i) / getStride(i + 1)) == getSize(i + 1)));</div>
|
|
<div class="line"><a name="l00415"></a><span class="lineno"> 415</span> }</div>
|
|
<div class="line"><a name="l00416"></a><span class="lineno"> 416</span> </div>
|
|
<div class="line"><a name="l00417"></a><span class="lineno"> 417</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
|
|
<div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
|
|
<div class="line"><a name="l00419"></a><span class="lineno"> 419</span> __host__ __device__ <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, Dim, InnerContig, IndexT, PtrTraits></a></div>
|
|
<div class="line"><a name="l00420"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a82a3484a6458e3e95bb91d320f2c6731"> 420</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a82a3484a6458e3e95bb91d320f2c6731">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::transpose</a>(<span class="keywordtype">int</span> dim1,</div>
|
|
<div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keywordtype">int</span> dim2)<span class="keyword"> const </span>{</div>
|
|
<div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  GPU_FAISS_ASSERT(dim1 >= 0 && dim1 < Dim);</div>
|
|
<div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  GPU_FAISS_ASSERT(dim1 >= 0 && dim2 < Dim);</div>
|
|
<div class="line"><a name="l00424"></a><span class="lineno"> 424</span> </div>
|
|
<div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="comment">// If a tensor is innermost contiguous, one cannot transpose the innermost</span></div>
|
|
<div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="comment">// dimension</span></div>
|
|
<div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <span class="keywordflow">if</span> (InnerContig) {</div>
|
|
<div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  GPU_FAISS_ASSERT(dim1 != Dim - 1 && dim2 != Dim - 1);</div>
|
|
<div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  }</div>
|
|
<div class="line"><a name="l00430"></a><span class="lineno"> 430</span> </div>
|
|
<div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  IndexT newSize[Dim];</div>
|
|
<div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  IndexT newStride[Dim];</div>
|
|
<div class="line"><a name="l00433"></a><span class="lineno"> 433</span> </div>
|
|
<div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < Dim; ++i) {</div>
|
|
<div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  newSize[i] = size_[i];</div>
|
|
<div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  newStride[i] = stride_[i];</div>
|
|
<div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  }</div>
|
|
<div class="line"><a name="l00438"></a><span class="lineno"> 438</span> </div>
|
|
<div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  IndexT tmp = newSize[dim1];</div>
|
|
<div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  newSize[dim1] = newSize[dim2];</div>
|
|
<div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  newSize[dim2] = tmp;</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>  tmp = newStride[dim1];</div>
|
|
<div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  newStride[dim1] = newStride[dim2];</div>
|
|
<div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  newStride[dim2] = tmp;</div>
|
|
<div class="line"><a name="l00446"></a><span class="lineno"> 446</span> </div>
|
|
<div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <span class="keywordflow">return</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, Dim, InnerContig, IndexT, PtrTraits></a>(data_, newSize, newStride);</div>
|
|
<div class="line"><a name="l00448"></a><span class="lineno"> 448</span> }</div>
|
|
<div class="line"><a name="l00449"></a><span class="lineno"> 449</span> </div>
|
|
<div class="line"><a name="l00450"></a><span class="lineno"> 450</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
|
|
<div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
|
|
<div class="line"><a name="l00452"></a><span class="lineno"> 452</span> <span class="keyword">template</span> <<span class="keywordtype">int</span> NewDim></div>
|
|
<div class="line"><a name="l00453"></a><span class="lineno"> 453</span> __host__ __device__ <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, NewDim, InnerContig, IndexT, PtrTraits></a></div>
|
|
<div class="line"><a name="l00454"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a309eb97e9c6dbfdecf383343c072d38c"> 454</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a309eb97e9c6dbfdecf383343c072d38c">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::upcastOuter</a>() {</div>
|
|
<div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="comment">// Can only create tensors of greater dimension</span></div>
|
|
<div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  static_assert(NewDim > Dim, <span class="stringliteral">"Can only upcast to greater dim"</span>);</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>  IndexT newSize[NewDim];</div>
|
|
<div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  IndexT newStride[NewDim];</div>
|
|
<div class="line"><a name="l00460"></a><span class="lineno"> 460</span> </div>
|
|
<div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <span class="keywordtype">int</span> shift = NewDim - Dim;</div>
|
|
<div class="line"><a name="l00462"></a><span class="lineno"> 462</span> </div>
|
|
<div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < NewDim; ++i) {</div>
|
|
<div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <span class="keywordflow">if</span> (i < shift) {</div>
|
|
<div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <span class="comment">// These are the extended dimensions</span></div>
|
|
<div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  newSize[i] = (IndexT) 1;</div>
|
|
<div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  newStride[i] = size_[0] * stride_[0];</div>
|
|
<div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <span class="comment">// Shift the remaining dimensions</span></div>
|
|
<div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  newSize[i] = size_[i - shift];</div>
|
|
<div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  newStride[i] = stride_[i - shift];</div>
|
|
<div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  }</div>
|
|
<div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  }</div>
|
|
<div class="line"><a name="l00474"></a><span class="lineno"> 474</span> </div>
|
|
<div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <span class="keywordflow">return</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, NewDim, InnerContig, IndexT, PtrTraits></a>(</div>
|
|
<div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  data_, newSize, newStride);</div>
|
|
<div class="line"><a name="l00477"></a><span class="lineno"> 477</span> }</div>
|
|
<div class="line"><a name="l00478"></a><span class="lineno"> 478</span> </div>
|
|
<div class="line"><a name="l00479"></a><span class="lineno"> 479</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
|
|
<div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
|
|
<div class="line"><a name="l00481"></a><span class="lineno"> 481</span> <span class="keyword">template</span> <<span class="keywordtype">int</span> NewDim></div>
|
|
<div class="line"><a name="l00482"></a><span class="lineno"> 482</span> __host__ __device__ <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, NewDim, InnerContig, IndexT, PtrTraits></a></div>
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|
<div class="line"><a name="l00483"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#aee5cf46d16344e2a055cf63adb07d24a"> 483</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#aee5cf46d16344e2a055cf63adb07d24a">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::upcastInner</a>() {</div>
|
|
<div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="comment">// Can only create tensors of greater dimension</span></div>
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<div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  static_assert(NewDim > Dim, <span class="stringliteral">"Can only upcast to greater dim"</span>);</div>
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<div class="line"><a name="l00486"></a><span class="lineno"> 486</span> </div>
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<div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  IndexT newSize[NewDim];</div>
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<div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  IndexT newStride[NewDim];</div>
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<div class="line"><a name="l00489"></a><span class="lineno"> 489</span> </div>
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<div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < NewDim; ++i) {</div>
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<div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <span class="keywordflow">if</span> (i < Dim) {</div>
|
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<div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  <span class="comment">// Existing dimensions get copied over</span></div>
|
|
<div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  newSize[i] = size_[i];</div>
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<div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  newStride[i] = stride_[i];</div>
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<div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  } <span class="keywordflow">else</span> {</div>
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<div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <span class="comment">// Extended dimensions</span></div>
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<div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  newSize[i] = (IndexT) 1;</div>
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<div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  newStride[i] = (IndexT) 1;</div>
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<div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  }</div>
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<div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  }</div>
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<div class="line"><a name="l00501"></a><span class="lineno"> 501</span> </div>
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<div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  <span class="keywordflow">return</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, NewDim, InnerContig, IndexT, PtrTraits></a>(</div>
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<div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  data_, newSize, newStride);</div>
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<div class="line"><a name="l00504"></a><span class="lineno"> 504</span> }</div>
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<div class="line"><a name="l00505"></a><span class="lineno"> 505</span> </div>
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<div class="line"><a name="l00506"></a><span class="lineno"> 506</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00508"></a><span class="lineno"> 508</span> <span class="keyword">template</span> <<span class="keywordtype">int</span> NewDim></div>
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<div class="line"><a name="l00509"></a><span class="lineno"> 509</span> __host__ __device__ <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, NewDim, InnerContig, IndexT, PtrTraits></a></div>
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|
<div class="line"><a name="l00510"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a2185b0c1c2c06cc3a4dab6a88eb6d001"> 510</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a2185b0c1c2c06cc3a4dab6a88eb6d001">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::downcastOuter</a>() {</div>
|
|
<div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <span class="comment">// Can only create tensors of lesser dimension</span></div>
|
|
<div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  static_assert(NewDim < Dim, <span class="stringliteral">"Can only downcast to lesser dim"</span>);</div>
|
|
<div class="line"><a name="l00513"></a><span class="lineno"> 513</span> </div>
|
|
<div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <span class="comment">// We can't downcast non-contiguous tensors, since it leaves</span></div>
|
|
<div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  <span class="comment">// garbage data in the tensor. The tensor needs to be contiguous</span></div>
|
|
<div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  <span class="comment">// in all of the dimensions we are collapsing (no padding in</span></div>
|
|
<div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  <span class="comment">// them).</span></div>
|
|
<div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < Dim - NewDim; ++i) {</div>
|
|
<div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  <span class="keywordtype">bool</span> cont = isContiguousDim(i);</div>
|
|
<div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  GPU_FAISS_ASSERT(cont);</div>
|
|
<div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  }</div>
|
|
<div class="line"><a name="l00522"></a><span class="lineno"> 522</span> </div>
|
|
<div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  IndexT newSize[NewDim];</div>
|
|
<div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  IndexT newStride[NewDim];</div>
|
|
<div class="line"><a name="l00525"></a><span class="lineno"> 525</span> </div>
|
|
<div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  <span class="keywordtype">int</span> ignoredDims = Dim - NewDim;</div>
|
|
<div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  IndexT collapsedSize = 1;</div>
|
|
<div class="line"><a name="l00528"></a><span class="lineno"> 528</span> </div>
|
|
<div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < Dim; ++i) {</div>
|
|
<div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  <span class="keywordflow">if</span> (i < ignoredDims) {</div>
|
|
<div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <span class="comment">// Collapse these dimensions</span></div>
|
|
<div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  collapsedSize *= getSize(i);</div>
|
|
<div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <span class="comment">// Non-collapsed dimensions</span></div>
|
|
<div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <span class="keywordflow">if</span> (i == ignoredDims) {</div>
|
|
<div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  <span class="comment">// This is the first non-collapsed dimension</span></div>
|
|
<div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  newSize[i - ignoredDims] = collapsedSize * getSize(i);</div>
|
|
<div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  <span class="comment">// Subsequent non-collapsed dimensions</span></div>
|
|
<div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  newSize[i - ignoredDims] = getSize(i);</div>
|
|
<div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  }</div>
|
|
<div class="line"><a name="l00542"></a><span class="lineno"> 542</span> </div>
|
|
<div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  newStride[i - ignoredDims] = getStride(i);</div>
|
|
<div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  }</div>
|
|
<div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  }</div>
|
|
<div class="line"><a name="l00546"></a><span class="lineno"> 546</span> </div>
|
|
<div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <span class="keywordflow">return</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, NewDim, InnerContig, IndexT, PtrTraits></a>(</div>
|
|
<div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  data_, newSize, newStride);</div>
|
|
<div class="line"><a name="l00549"></a><span class="lineno"> 549</span> }</div>
|
|
<div class="line"><a name="l00550"></a><span class="lineno"> 550</span> </div>
|
|
<div class="line"><a name="l00551"></a><span class="lineno"> 551</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
|
|
<div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
|
|
<div class="line"><a name="l00553"></a><span class="lineno"> 553</span> <span class="keyword">template</span> <<span class="keywordtype">int</span> NewDim></div>
|
|
<div class="line"><a name="l00554"></a><span class="lineno"> 554</span> __host__ __device__ <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, NewDim, InnerContig, IndexT, PtrTraits></a></div>
|
|
<div class="line"><a name="l00555"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a6a43125c6f429f28161d59f19eb8e5c5"> 555</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6a43125c6f429f28161d59f19eb8e5c5">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::downcastInner</a>() {</div>
|
|
<div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <span class="comment">// Can only create tensors of lesser dimension</span></div>
|
|
<div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  static_assert(NewDim < Dim, <span class="stringliteral">"Can only downcast to lesser dim"</span>);</div>
|
|
<div class="line"><a name="l00558"></a><span class="lineno"> 558</span> </div>
|
|
<div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  <span class="comment">// We can't downcast non-contiguous tensors, since it leaves</span></div>
|
|
<div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <span class="comment">// garbage data in the tensor. The tensor needs to be contiguous</span></div>
|
|
<div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  <span class="comment">// in all of the dimensions we are collapsing (no padding in</span></div>
|
|
<div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  <span class="comment">// them).</span></div>
|
|
<div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = NewDim; i < Dim; ++i) {</div>
|
|
<div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  GPU_FAISS_ASSERT(isContiguousDim(i));</div>
|
|
<div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  }</div>
|
|
<div class="line"><a name="l00566"></a><span class="lineno"> 566</span> </div>
|
|
<div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  IndexT newSize[NewDim];</div>
|
|
<div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  IndexT newStride[NewDim];</div>
|
|
<div class="line"><a name="l00569"></a><span class="lineno"> 569</span> </div>
|
|
<div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  IndexT collapsedSize = 1;</div>
|
|
<div class="line"><a name="l00571"></a><span class="lineno"> 571</span> </div>
|
|
<div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = Dim - 1; i >= 0; --i) {</div>
|
|
<div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <span class="keywordflow">if</span> (i >= NewDim) {</div>
|
|
<div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  <span class="comment">// Collapse these dimensions</span></div>
|
|
<div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  collapsedSize *= getSize(i);</div>
|
|
<div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  <span class="comment">// Non-collapsed dimensions</span></div>
|
|
<div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <span class="keywordflow">if</span> (i == NewDim - 1) {</div>
|
|
<div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  <span class="comment">// This is the first non-collapsed dimension</span></div>
|
|
<div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  newSize[i] = collapsedSize * getSize(i);</div>
|
|
<div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  newStride[i] = getStride(Dim - 1);</div>
|
|
<div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  <span class="comment">// Subsequent non-collapsed dimensions</span></div>
|
|
<div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  newSize[i] = getSize(i);</div>
|
|
<div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  newStride[i] = getStride(i);</div>
|
|
<div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  }</div>
|
|
<div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  }</div>
|
|
<div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  }</div>
|
|
<div class="line"><a name="l00589"></a><span class="lineno"> 589</span> </div>
|
|
<div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  <span class="keywordflow">return</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, NewDim, InnerContig, IndexT, PtrTraits></a>(</div>
|
|
<div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  data_, newSize, newStride);</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> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
|
|
<div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
|
|
<div class="line"><a name="l00596"></a><span class="lineno"> 596</span> <span class="keyword">template</span> <<span class="keywordtype">int</span> SubDim></div>
|
|
<div class="line"><a name="l00597"></a><span class="lineno"> 597</span> __host__ __device__ <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, SubDim, InnerContig, IndexT, PtrTraits></a></div>
|
|
<div class="line"><a name="l00598"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a74dbc09519c9c14479b2d18f2e5042e8"> 598</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a35a63cfa4034a8ee14a999132d8a1828">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::view</a>(DataPtrType at) {</div>
|
|
<div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  static_assert(SubDim >= 1 && SubDim < Dim,</div>
|
|
<div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  <span class="stringliteral">"can only create view of lesser dim"</span>);</div>
|
|
<div class="line"><a name="l00601"></a><span class="lineno"> 601</span> </div>
|
|
<div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  IndexT viewSizes[SubDim];</div>
|
|
<div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  IndexT viewStrides[SubDim];</div>
|
|
<div class="line"><a name="l00604"></a><span class="lineno"> 604</span> </div>
|
|
<div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < SubDim; ++i) {</div>
|
|
<div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  viewSizes[i] = size_[Dim - SubDim + i];</div>
|
|
<div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  viewStrides[i] = stride_[Dim - SubDim + i];</div>
|
|
<div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  }</div>
|
|
<div class="line"><a name="l00609"></a><span class="lineno"> 609</span> </div>
|
|
<div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  <span class="keywordflow">return</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, SubDim, InnerContig, IndexT, PtrTraits></a>(</div>
|
|
<div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  at, viewSizes, viewStrides);</div>
|
|
<div class="line"><a name="l00612"></a><span class="lineno"> 612</span> }</div>
|
|
<div class="line"><a name="l00613"></a><span class="lineno"> 613</span> </div>
|
|
<div class="line"><a name="l00614"></a><span class="lineno"> 614</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
|
|
<div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
|
|
<div class="line"><a name="l00616"></a><span class="lineno"> 616</span> <span class="keyword">template</span> <<span class="keywordtype">int</span> SubDim></div>
|
|
<div class="line"><a name="l00617"></a><span class="lineno"> 617</span> __host__ __device__ <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, SubDim, InnerContig, IndexT, PtrTraits></a></div>
|
|
<div class="line"><a name="l00618"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a35a63cfa4034a8ee14a999132d8a1828"> 618</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a35a63cfa4034a8ee14a999132d8a1828">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::view</a>() {</div>
|
|
<div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <span class="keywordflow">return</span> view<SubDim>(data_);</div>
|
|
<div class="line"><a name="l00620"></a><span class="lineno"> 620</span> }</div>
|
|
<div class="line"><a name="l00621"></a><span class="lineno"> 621</span> </div>
|
|
<div class="line"><a name="l00622"></a><span class="lineno"> 622</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
|
|
<div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
|
|
<div class="line"><a name="l00624"></a><span class="lineno"> 624</span> __host__ __device__ <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, Dim, InnerContig, IndexT, PtrTraits></a></div>
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<div class="line"><a name="l00625"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#ac2d0fc7199901a8e0788b58f0970b133"> 625</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#ac2d0fc7199901a8e0788b58f0970b133">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::narrowOutermost</a>(IndexT start,</div>
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<div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  IndexT size) {</div>
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<div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  <span class="keywordflow">return</span> this->narrow(0, start, size);</div>
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<div class="line"><a name="l00628"></a><span class="lineno"> 628</span> }</div>
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<div class="line"><a name="l00629"></a><span class="lineno"> 629</span> </div>
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<div class="line"><a name="l00630"></a><span class="lineno"> 630</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00632"></a><span class="lineno"> 632</span> __host__ __device__ <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, Dim, InnerContig, IndexT, PtrTraits></a></div>
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<div class="line"><a name="l00633"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#ab6db6bf86dd0f7e877af3a6ae2100fe3"> 633</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#ab6db6bf86dd0f7e877af3a6ae2100fe3">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::narrow</a>(<span class="keywordtype">int</span> dim,</div>
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<div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  IndexT start,</div>
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<div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  IndexT size) {</div>
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<div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  DataPtrType newData = data_;</div>
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<div class="line"><a name="l00637"></a><span class="lineno"> 637</span> </div>
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<div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  GPU_FAISS_ASSERT(start >= 0 &&</div>
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<div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  start < size_[dim] &&</div>
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<div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  (start + size) <= size_[dim]);</div>
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<div class="line"><a name="l00641"></a><span class="lineno"> 641</span> </div>
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<div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  <span class="keywordflow">if</span> (start > 0) {</div>
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<div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  newData += (size_t) start * stride_[dim];</div>
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<div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  }</div>
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<div class="line"><a name="l00645"></a><span class="lineno"> 645</span> </div>
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<div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  IndexT newSize[Dim];</div>
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<div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < Dim; ++i) {</div>
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<div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  <span class="keywordflow">if</span> (i == dim) {</div>
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<div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  GPU_FAISS_ASSERT(start + size <= size_[dim]);</div>
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<div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  newSize[i] = size;</div>
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<div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  } <span class="keywordflow">else</span> {</div>
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<div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  newSize[i] = size_[i];</div>
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<div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  }</div>
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<div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  }</div>
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<div class="line"><a name="l00655"></a><span class="lineno"> 655</span> </div>
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<div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  <span class="comment">// If we were innermost contiguous before, we are still innermost contiguous</span></div>
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<div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <span class="keywordflow">return</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, Dim, InnerContig, IndexT, PtrTraits></a>(newData, newSize, stride_);</div>
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<div class="line"><a name="l00658"></a><span class="lineno"> 658</span> }</div>
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<div class="line"><a name="l00659"></a><span class="lineno"> 659</span> </div>
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<div class="line"><a name="l00660"></a><span class="lineno"> 660</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim, <span class="keywordtype">bool</span> InnerContig,</div>
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<div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  <span class="keyword">typename</span> IndexT, <span class="keyword">template</span> <<span class="keyword">typename</span> U> <span class="keyword">class </span>PtrTraits></div>
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<div class="line"><a name="l00662"></a><span class="lineno"> 662</span> <span class="keyword">template</span> <<span class="keywordtype">int</span> NewDim></div>
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<div class="line"><a name="l00663"></a><span class="lineno"> 663</span> __host__ __device__ <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, NewDim, InnerContig, IndexT, PtrTraits></a></div>
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<div class="line"><a name="l00664"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1Tensor.html#a665d97851f0929cad7fc76f945b64c97"> 664</a></span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a35a63cfa4034a8ee14a999132d8a1828">Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::view</a>(</div>
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<div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  std::initializer_list<IndexT> sizes) {</div>
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<div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  GPU_FAISS_ASSERT(this->isContiguous());</div>
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<div class="line"><a name="l00667"></a><span class="lineno"> 667</span> </div>
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<div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  GPU_FAISS_ASSERT(sizes.size() == NewDim);</div>
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<div class="line"><a name="l00669"></a><span class="lineno"> 669</span> </div>
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<div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  <span class="comment">// The total size of the new view must be the same as the total size</span></div>
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<div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  <span class="comment">// of the old view</span></div>
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<div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  <span class="keywordtype">size_t</span> curSize = numElements();</div>
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<div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <span class="keywordtype">size_t</span> newSize = 1;</div>
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<div class="line"><a name="l00674"></a><span class="lineno"> 674</span> </div>
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<div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> s : sizes) {</div>
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<div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  newSize *= s;</div>
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<div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  }</div>
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<div class="line"><a name="l00678"></a><span class="lineno"> 678</span> </div>
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<div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  GPU_FAISS_ASSERT(curSize == newSize);</div>
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<div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  <span class="keywordflow">return</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<T, NewDim, true, IndexT, PtrTraits></a>(data(), sizes);</div>
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<div class="line"><a name="l00681"></a><span class="lineno"> 681</span> }</div>
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<div class="line"><a name="l00682"></a><span class="lineno"> 682</span> </div>
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<div class="line"><a name="l00683"></a><span class="lineno"> 683</span> } } <span class="comment">// namespace</span></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a309eb97e9c6dbfdecf383343c072d38c"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a309eb97e9c6dbfdecf383343c072d38c">faiss::gpu::Tensor::upcastOuter</a></div><div class="ttdeci">__host__ __device__ Tensor< T, NewDim, InnerContig, IndexT, PtrTraits > upcastOuter()</div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00454">Tensor-inl.cuh:454</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a9f0c817e9751fe02926c2346a97f0350"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a9f0c817e9751fe02926c2346a97f0350">faiss::gpu::Tensor::castIndexType</a></div><div class="ttdeci">__host__ Tensor< T, Dim, InnerContig, NewIndexT, PtrTraits > castIndexType() const </div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00305">Tensor-inl.cuh:305</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a3f4e3c6afdf4a03308756b6ae6462c38"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a3f4e3c6afdf4a03308756b6ae6462c38">faiss::gpu::Tensor::isContiguousDim</a></div><div class="ttdeci">__host__ __device__ bool isContiguousDim(int i) const </div><div class="ttdoc">Returns true if the given dimension index has no padding. </div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00411">Tensor-inl.cuh:411</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a2894f8fdfab8ec3245364a6f9e8a5259"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a2894f8fdfab8ec3245364a6f9e8a5259">faiss::gpu::Tensor::cast</a></div><div class="ttdeci">__host__ __device__ Tensor< U, Dim, InnerContig, IndexT, PtrTraits > cast()</div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00222">Tensor-inl.cuh:222</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a0ba9ab7c1676b7a41a6e6b2e5a490d2f"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a0ba9ab7c1676b7a41a6e6b2e5a490d2f">faiss::gpu::Tensor::numElements</a></div><div class="ttdeci">__host__ __device__ size_t numElements() const </div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00352">Tensor-inl.cuh:352</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a2185b0c1c2c06cc3a4dab6a88eb6d001"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a2185b0c1c2c06cc3a4dab6a88eb6d001">faiss::gpu::Tensor::downcastOuter</a></div><div class="ttdeci">__host__ __device__ Tensor< T, NewDim, InnerContig, IndexT, PtrTraits > downcastOuter()</div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00510">Tensor-inl.cuh:510</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a7fbbf51f8ef6bea9cc863a86e20d994e"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a7fbbf51f8ef6bea9cc863a86e20d994e">faiss::gpu::Tensor::canCastResize</a></div><div class="ttdeci">__host__ __device__ bool canCastResize() const </div><div class="ttdoc">Returns true if we can castResize() this tensor to the new type. </div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00278">Tensor-inl.cuh:278</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a8ae7b3f95991125a5648c3b78afd40bd"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a8ae7b3f95991125a5648c3b78afd40bd">faiss::gpu::Tensor::Tensor</a></div><div class="ttdeci">__host__ __device__ Tensor()</div><div class="ttdoc">Default constructor. </div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00020">Tensor-inl.cuh:20</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_aee5cf46d16344e2a055cf63adb07d24a"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#aee5cf46d16344e2a055cf63adb07d24a">faiss::gpu::Tensor::upcastInner</a></div><div class="ttdeci">__host__ __device__ Tensor< T, NewDim, InnerContig, IndexT, PtrTraits > upcastInner()</div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00483">Tensor-inl.cuh:483</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_ac2d0fc7199901a8e0788b58f0970b133"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#ac2d0fc7199901a8e0788b58f0970b133">faiss::gpu::Tensor::narrowOutermost</a></div><div class="ttdeci">__host__ __device__ Tensor< T, Dim, InnerContig, IndexT, PtrTraits > narrowOutermost(IndexT start, IndexT size)</div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00625">Tensor-inl.cuh:625</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_af4b8fe4b632cdca51ee7972ed93fc3fa"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#af4b8fe4b632cdca51ee7972ed93fc3fa">faiss::gpu::Tensor::stride_</a></div><div class="ttdeci">IndexT stride_[Dim]</div><div class="ttdoc">Array of strides (in sizeof(T) terms) per each dimension. </div><div class="ttdef"><b>Definition:</b> <a href="Tensor_8cuh_source.html#l00349">Tensor.cuh:349</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a09019c54911db891c9321fd3b34509c2"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a09019c54911db891c9321fd3b34509c2">faiss::gpu::Tensor::isContiguous</a></div><div class="ttdeci">__host__ __device__ bool isContiguous() const </div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00365">Tensor-inl.cuh:365</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_abc0ecc4f882ee09632b5a06be0619adb"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#abc0ecc4f882ee09632b5a06be0619adb">faiss::gpu::Tensor::sizes</a></div><div class="ttdeci">__host__ __device__ const IndexT * sizes() const </div><div class="ttdoc">Returns the size array. </div><div class="ttdef"><b>Definition:</b> <a href="Tensor_8cuh_source.html#l00247">Tensor.cuh:247</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a6dc00c182a92389b74c89ba7fcab40d3"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a6dc00c182a92389b74c89ba7fcab40d3">faiss::gpu::Tensor::copyFrom</a></div><div class="ttdeci">__host__ void copyFrom(Tensor< T, Dim, InnerContig, IndexT, PtrTraits > &t, cudaStream_t stream)</div><div class="ttdoc">Copies a tensor into ourselves; sizes must match. </div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00101">Tensor-inl.cuh:101</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_ad96fbf0f5e7c06a1031b8b18f7fc01d7"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#ad96fbf0f5e7c06a1031b8b18f7fc01d7">faiss::gpu::Tensor::size_</a></div><div class="ttdeci">IndexT size_[Dim]</div><div class="ttdoc">Size per each dimension. </div><div class="ttdef"><b>Definition:</b> <a href="Tensor_8cuh_source.html#l00352">Tensor.cuh:352</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a87a777247486756e99060547a3cc833a"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a87a777247486756e99060547a3cc833a">faiss::gpu::Tensor::strides</a></div><div class="ttdeci">__host__ __device__ const IndexT * strides() const </div><div class="ttdoc">Returns the stride array. </div><div class="ttdef"><b>Definition:</b> <a href="Tensor_8cuh_source.html#l00252">Tensor.cuh:252</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a6699c311648457f257afa340c61f417c"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">faiss::gpu::Tensor::getSize</a></div><div class="ttdeci">__host__ __device__ IndexT getSize(int i) const </div><div class="ttdef"><b>Definition:</b> <a href="Tensor_8cuh_source.html#l00226">Tensor.cuh:226</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a22c1e45f81f7f9e5427e2eed19f9cd11"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a22c1e45f81f7f9e5427e2eed19f9cd11">faiss::gpu::Tensor::isSameSize</a></div><div class="ttdeci">__host__ __device__ bool isSameSize(const Tensor< OtherT, OtherDim, InnerContig, IndexT, PtrTraits > &rhs) const </div><div class="ttdoc">Returns true if the two tensors are of the same dimensionality and size. </div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00203">Tensor-inl.cuh:203</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a6a43125c6f429f28161d59f19eb8e5c5"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a6a43125c6f429f28161d59f19eb8e5c5">faiss::gpu::Tensor::downcastInner</a></div><div class="ttdeci">__host__ __device__ Tensor< T, NewDim, InnerContig, IndexT, PtrTraits > downcastInner()</div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00555">Tensor-inl.cuh:555</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_ab6db6bf86dd0f7e877af3a6ae2100fe3"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#ab6db6bf86dd0f7e877af3a6ae2100fe3">faiss::gpu::Tensor::narrow</a></div><div class="ttdeci">__host__ __device__ Tensor< T, Dim, InnerContig, IndexT, PtrTraits > narrow(int dim, IndexT start, IndexT size)</div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00633">Tensor-inl.cuh:633</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a50411ce4d0fa32ef715e3321b6e33212"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a50411ce4d0fa32ef715e3321b6e33212">faiss::gpu::Tensor::data</a></div><div class="ttdeci">__host__ __device__ DataPtrType data()</div><div class="ttdoc">Returns a raw pointer to the start of our data. </div><div class="ttdef"><b>Definition:</b> <a href="Tensor_8cuh_source.html#l00178">Tensor.cuh:178</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a6cc21376070a03d77661d6e333972c6a"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a6cc21376070a03d77661d6e333972c6a">faiss::gpu::Tensor::copyTo</a></div><div class="ttdeci">__host__ void copyTo(Tensor< T, Dim, InnerContig, IndexT, PtrTraits > &t, cudaStream_t stream)</div><div class="ttdoc">Copies ourselves into a tensor; sizes must match. </div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00140">Tensor-inl.cuh:140</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html">faiss::gpu::Tensor</a></div><div class="ttdoc">Our tensor type. </div><div class="ttdef"><b>Definition:</b> <a href="Tensor_8cuh_source.html#l00030">Tensor.cuh:30</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a2ac9dc9fa8d81f2651a1be486c14ba62"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a2ac9dc9fa8d81f2651a1be486c14ba62">faiss::gpu::Tensor::canUseIndexType</a></div><div class="ttdeci">__host__ bool canUseIndexType() const </div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00325">Tensor-inl.cuh:325</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a82a3484a6458e3e95bb91d320f2c6731"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a82a3484a6458e3e95bb91d320f2c6731">faiss::gpu::Tensor::transpose</a></div><div class="ttdeci">__host__ __device__ Tensor< T, Dim, InnerContig, IndexT, PtrTraits > transpose(int dim1, int dim2) const </div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00420">Tensor-inl.cuh:420</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a0b8bba630f7a1fa217f90b20d298420a"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a0b8bba630f7a1fa217f90b20d298420a">faiss::gpu::Tensor::getStride</a></div><div class="ttdeci">__host__ __device__ IndexT getStride(int i) const </div><div class="ttdef"><b>Definition:</b> <a href="Tensor_8cuh_source.html#l00232">Tensor.cuh:232</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a503fe45536fb7153fbd18fe61c159304"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a503fe45536fb7153fbd18fe61c159304">faiss::gpu::Tensor::operator=</a></div><div class="ttdeci">__host__ __device__ Tensor< T, Dim, InnerContig, IndexT, PtrTraits > & operator=(Tensor< T, Dim, InnerContig, IndexT, PtrTraits > &t)=default</div><div class="ttdoc">Assignment. </div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a6c9640c365134ccc33cdb2695b016eb3"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a6c9640c365134ccc33cdb2695b016eb3">faiss::gpu::Tensor::castResize</a></div><div class="ttdeci">__host__ __device__ Tensor< U, Dim, InnerContig, IndexT, PtrTraits > castResize()</div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00244">Tensor-inl.cuh:244</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a35a63cfa4034a8ee14a999132d8a1828"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a35a63cfa4034a8ee14a999132d8a1828">faiss::gpu::Tensor::view</a></div><div class="ttdeci">__host__ __device__ Tensor< T, SubDim, InnerContig, IndexT, PtrTraits > view()</div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00618">Tensor-inl.cuh:618</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a3067941f8f8f09fc73e2f06243699825"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a3067941f8f8f09fc73e2f06243699825">faiss::gpu::Tensor::isSame</a></div><div class="ttdeci">__host__ __device__ bool isSame(const Tensor< OtherT, OtherDim, InnerContig, IndexT, PtrTraits > &rhs) const </div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00180">Tensor-inl.cuh:180</a></div></div>
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