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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/**</span></div>
<div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) 2015-present, Facebook, Inc.</span></div>
<div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> * All rights reserved.</span></div>
<div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> *</span></div>
<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>
<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>
<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> */</span></div>
<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div>
<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment">// Copyright 2004-present Facebook. All Rights Reserved.</span></div>
<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div>
<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#pragma once</span></div>
<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor"></span></div>
<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include "DeviceTensor.cuh"</span></div>
<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include "HostTensor.cuh"</span></div>
<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> </div>
<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="keyword">namespace </span>faiss { <span class="keyword">namespace </span>gpu {</div>
<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"></span></div>
<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment">/// Ensure the memory at `p` is either on the given device, or copy it</span></div>
<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment">/// to the device in a new allocation.</span></div>
<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment">/// If `resources` is provided, then we will perform a temporary</span></div>
<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment">/// memory allocation if needed. Otherwise, we will call cudaMalloc if</span></div>
<div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment">/// needed.</span></div>
<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"></span><span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim></div>
<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> DeviceTensor<T, Dim, true> toDevice(GpuResources* resources,</div>
<div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keywordtype">int</span> dstDevice,</div>
<div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  T* src,</div>
<div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  cudaStream_t stream,</div>
<div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  std::initializer_list<int> sizes) {</div>
<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keywordtype">int</span> dev = getDeviceForAddress(src);</div>
<div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div>
<div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="keywordflow">if</span> (dev == dstDevice) {</div>
<div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="comment">// On device we expect</span></div>
<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordflow">return</span> DeviceTensor<T, Dim, true>(src, sizes);</div>
<div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="comment">// On different device or on host</span></div>
<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  DeviceScope scope(dstDevice);</div>
<div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div>
<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  Tensor<T, Dim, true> oldT(src, sizes);</div>
<div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div>
<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="keywordflow">if</span> (resources) {</div>
<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  DeviceTensor<T, Dim, true> newT(resources->getMemoryManager(dstDevice),</div>
<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  sizes,</div>
<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  stream);</div>
<div class="line"><a name="l00044"></a><span class="lineno"> 44</span> </div>
<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  newT.copyFrom(oldT, stream);</div>
<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keywordflow">return</span> newT;</div>
<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  DeviceTensor<T, Dim, true> newT(sizes);</div>
<div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div>
<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  newT.copyFrom(oldT, stream);</div>
<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keywordflow">return</span> newT;</div>
<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  }</div>
<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  }</div>
<div class="line"><a name="l00054"></a><span class="lineno"> 54</span> }</div>
<div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <span class="comment"></span></div>
<div class="line"><a name="l00056"></a><span class="lineno"> 56</span> <span class="comment">/// Copies a device array's allocation to an address, if necessary</span></div>
<div class="line"><a name="l00057"></a><span class="lineno"> 57</span> <span class="comment"></span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
<div class="line"><a name="l00058"></a><span class="lineno"> 58</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> fromDevice(T* src, T* dst, <span class="keywordtype">size_t</span> num, cudaStream_t stream) {</div>
<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="comment">// It is possible that the array already represents memory at `p`,</span></div>
<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="comment">// in which case no copy is needed</span></div>
<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keywordflow">if</span> (src == dst) {</div>
<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  }</div>
<div class="line"><a name="l00064"></a><span class="lineno"> 64</span> </div>
<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordtype">int</span> dev = getDeviceForAddress(dst);</div>
<div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div>
<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keywordflow">if</span> (dev == -1) {</div>
<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  CUDA_VERIFY(cudaMemcpyAsync(dst,</div>
<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  src,</div>
<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  num * <span class="keyword">sizeof</span>(T),</div>
<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  cudaMemcpyDeviceToHost,</div>
<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  stream));</div>
<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  CUDA_VERIFY(cudaMemcpyAsync(dst,</div>
<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  src,</div>
<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  num * <span class="keyword">sizeof</span>(T),</div>
<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  cudaMemcpyDeviceToDevice,</div>
<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  stream));</div>
<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  }</div>
<div class="line"><a name="l00080"></a><span class="lineno"> 80</span> }</div>
<div class="line"><a name="l00081"></a><span class="lineno"> 81</span> <span class="comment"></span></div>
<div class="line"><a name="l00082"></a><span class="lineno"> 82</span> <span class="comment">/// Copies a device array's allocation to an address, if necessary</span></div>
<div class="line"><a name="l00083"></a><span class="lineno"> 83</span> <span class="comment"></span><span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> Dim></div>
<div class="line"><a name="l00084"></a><span class="lineno"> 84</span> <span class="keywordtype">void</span> fromDevice(Tensor<T, Dim, true>& src, T* dst, cudaStream_t stream) {</div>
<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  FAISS_ASSERT(src.isContiguous());</div>
<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  fromDevice(src.data(), dst, src.numElements(), stream);</div>
<div class="line"><a name="l00087"></a><span class="lineno"> 87</span> }</div>
<div class="line"><a name="l00088"></a><span class="lineno"> 88</span> </div>
<div class="line"><a name="l00089"></a><span class="lineno"> 89</span> } } <span class="comment">// namespace</span></div>
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