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<div class="title">device/tensor_foreach.h</div> </div>
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<a href="device_2tensor__foreach_8h.html">Go to the documentation of this file.</a><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) 2017-2019, NVIDIA CORPORATION. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * Redistribution and use in source and binary forms, with or without modification, are permitted</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> * provided that the following conditions are met:</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * * Redistributions of source code must retain the above copyright notice, this list of</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * conditions and the following disclaimer.</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * * Redistributions in binary form must reproduce the above copyright notice, this list of</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * conditions and the following disclaimer in the documentation and/or other materials</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * provided with the distribution.</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> * to endorse or promote products derived from this software without specific prior written</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * permission.</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> *</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> *</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="comment"> **************************************************************************************************/</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#pragma once</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include <stdexcept></span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="cutlass_8h.html">cutlass/cutlass.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="device_2kernel_2tensor__foreach_8h.html">cutlass/util/reference/device/kernel/tensor_foreach.h</a>"</span></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="keyword">namespace </span><a class="code" href="namespacecutlass.html">cutlass</a> {</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="keyword">namespace </span>reference {</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="keyword">namespace </span>device {</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> </div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="keyword">template</span> <<span class="keyword">typename</span> Func, <span class="keywordtype">int</span> Rank, <span class="keyword">typename</span> Params></div><div class="line"><a name="l00039"></a><span class="lineno"><a class="line" href="structcutlass_1_1reference_1_1device_1_1TensorForEach.html"> 39</a></span> <span class="keyword">struct </span><a class="code" href="structcutlass_1_1reference_1_1device_1_1TensorForEach.html">TensorForEach</a> {</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div><div class="line"><a name="l00042"></a><span class="lineno"><a class="line" href="structcutlass_1_1reference_1_1device_1_1TensorForEach.html#ad693751cf94aea83a14235a5ec7c7e92"> 42</a></span>  <a class="code" href="structcutlass_1_1reference_1_1device_1_1TensorForEach.html#ad693751cf94aea83a14235a5ec7c7e92">TensorForEach</a>(<a class="code" href="structcutlass_1_1Coord.html">Coord<Rank></a> size, Params params = Params(), <span class="keywordtype">int</span> grid_size = 0, <span class="keywordtype">int</span> block_size = 0) {</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> </div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keywordflow">if</span> (!grid_size || !block_size) {</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="comment">// if grid_size or block_size are zero, query occupancy using the CUDA Occupancy API</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  cudaError_t result = cudaOccupancyMaxPotentialBlockSize(</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  &grid_size,</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  &block_size,</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  reinterpret_cast<void const *>(kernel::TensorForEach<Func, Rank, Params>));</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordflow">if</span> (result != cudaSuccess) {</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Failed to query occupancy."</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> </div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="comment">// Limit block size. This has the effect of increasing the number of items processed by a</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="comment">// single thread and reduces the impact of initialization overhead.</span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  block_size = (block_size < 128 ? block_size : 128);</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  }</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  dim3 grid(grid_size, 1, 1);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  dim3 block(block_size, 1, 1);</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>  kernel::TensorForEach<Func, Rank, Params><<< grid, block >>>(size, params);</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  }</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> };</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> </div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> <span class="keyword">template</span> <<span class="keyword">typename</span> Func, <span class="keywordtype">int</span> Rank, <span class="keyword">typename</span> Params></div><div class="line"><a name="l00072"></a><span class="lineno"><a class="line" href="structcutlass_1_1reference_1_1device_1_1TensorDiagonalForEach.html"> 72</a></span> <span class="keyword">struct </span><a class="code" href="structcutlass_1_1reference_1_1device_1_1TensorDiagonalForEach.html">TensorDiagonalForEach</a> {</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00075"></a><span class="lineno"><a class="line" href="structcutlass_1_1reference_1_1device_1_1TensorDiagonalForEach.html#adab64f903f234d0266400bd2416134ee"> 75</a></span>  <a class="code" href="structcutlass_1_1reference_1_1device_1_1TensorDiagonalForEach.html#adab64f903f234d0266400bd2416134ee">TensorDiagonalForEach</a>(<a class="code" href="structcutlass_1_1Coord.html">Coord<Rank></a> size, Params params = Params(), <span class="keywordtype">int</span> start = 0, <span class="keywordtype">int</span> end = -1, <span class="keywordtype">int</span> block_size = 128) { </div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> </div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keywordflow">if</span> (end < 0) {</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  end = size.min();</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>  dim3 block(block_size, 1, 1);</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  dim3 grid((end - start + block_size - 1) / block_size, 1, 1);</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> </div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  kernel::TensorDiagonalForEach<Func, Rank, Params><<< grid, block >>>(size, params, start, end);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  }</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> };</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> </div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> </div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> <span class="keyword">template</span> <<span class="keyword">typename</span> Element, <span class="keyword">typename</span> Func></div><div class="line"><a name="l00092"></a><span class="lineno"><a class="line" href="structcutlass_1_1reference_1_1device_1_1BlockForEach.html"> 92</a></span> <span class="keyword">struct </span><a class="code" href="structcutlass_1_1reference_1_1device_1_1BlockForEach.html">BlockForEach</a> {</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> </div><div class="line"><a name="l00095"></a><span class="lineno"><a class="line" href="structcutlass_1_1reference_1_1device_1_1BlockForEach.html#a161e212b9b7ddbac36888de97538e106"> 95</a></span>  <a class="code" href="structcutlass_1_1reference_1_1device_1_1BlockForEach.html#a161e212b9b7ddbac36888de97538e106">BlockForEach</a>(</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  Element *ptr, </div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordtype">size_t</span> capacity,</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keyword">typename</span> Func::Params params = <span class="keyword">typename</span> Func::Params(), </div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keywordtype">int</span> grid_size = 0, </div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keywordtype">int</span> block_size = 0) {</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> </div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keywordflow">if</span> (!grid_size || !block_size) {</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> </div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="comment">// if grid_size or block_size are zero, query occupancy using the CUDA Occupancy API</span></div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  cudaError_t result = cudaOccupancyMaxPotentialBlockSize(</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  &grid_size,</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  &block_size,</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  reinterpret_cast<void const *>(kernel::BlockForEach<Element, Func>));</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> </div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keywordflow">if</span> (result != cudaSuccess) {</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Failed to query occupancy."</span>);</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  }</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> </div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="comment">// Limit block size. This has the effect of increasing the number of items processed by a</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="comment">// single thread and reduces the impact of initialization overhead.</span></div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  block_size = (block_size < 128 ? block_size : 128);</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  }</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  dim3 grid(grid_size, 1, 1);</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  dim3 block(block_size, 1, 1);</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> </div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  kernel::BlockForEach<Element, Func><<< grid, block >>>(ptr, capacity, params);</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  }</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> };</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> </div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> } <span class="comment">// namespace device</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> } <span class="comment">// namespace reference</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> } <span class="comment">// namespace cutlass</span></div><div class="ttc" id="namespacecutlass_html"><div class="ttname"><a href="namespacecutlass.html">cutlass</a></div><div class="ttdef"><b>Definition:</b> aligned_buffer.h:35</div></div>
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<div class="ttc" id="structcutlass_1_1reference_1_1device_1_1TensorDiagonalForEach_html_adab64f903f234d0266400bd2416134ee"><div class="ttname"><a href="structcutlass_1_1reference_1_1device_1_1TensorDiagonalForEach.html#adab64f903f234d0266400bd2416134ee">cutlass::reference::device::TensorDiagonalForEach::TensorDiagonalForEach</a></div><div class="ttdeci">TensorDiagonalForEach(Coord< Rank > size, Params params=Params(), int start=0, int end=-1, int block_size=128)</div><div class="ttdoc">Constructor performs the operation. </div><div class="ttdef"><b>Definition:</b> device/tensor_foreach.h:75</div></div>
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<div class="ttc" id="structcutlass_1_1reference_1_1device_1_1TensorForEach_html_ad693751cf94aea83a14235a5ec7c7e92"><div class="ttname"><a href="structcutlass_1_1reference_1_1device_1_1TensorForEach.html#ad693751cf94aea83a14235a5ec7c7e92">cutlass::reference::device::TensorForEach::TensorForEach</a></div><div class="ttdeci">TensorForEach(Coord< Rank > size, Params params=Params(), int grid_size=0, int block_size=0)</div><div class="ttdoc">Constructor performs the operation. </div><div class="ttdef"><b>Definition:</b> device/tensor_foreach.h:42</div></div>
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<div class="ttc" id="structcutlass_1_1reference_1_1device_1_1TensorDiagonalForEach_html"><div class="ttname"><a href="structcutlass_1_1reference_1_1device_1_1TensorDiagonalForEach.html">cutlass::reference::device::TensorDiagonalForEach</a></div><div class="ttdoc">Launches a kernel calling a functor for each element along a tensor&#39;s diagonal. </div><div class="ttdef"><b>Definition:</b> device/tensor_foreach.h:72</div></div>
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<div class="ttc" id="structcutlass_1_1reference_1_1device_1_1BlockForEach_html_a161e212b9b7ddbac36888de97538e106"><div class="ttname"><a href="structcutlass_1_1reference_1_1device_1_1BlockForEach.html#a161e212b9b7ddbac36888de97538e106">cutlass::reference::device::BlockForEach::BlockForEach</a></div><div class="ttdeci">BlockForEach(Element *ptr, size_t capacity, typename Func::Params params=typename Func::Params(), int grid_size=0, int block_size=0)</div><div class="ttdoc">Constructor performs the operation. </div><div class="ttdef"><b>Definition:</b> device/tensor_foreach.h:95</div></div>
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<div class="ttc" id="structcutlass_1_1reference_1_1device_1_1TensorForEach_html"><div class="ttname"><a href="structcutlass_1_1reference_1_1device_1_1TensorForEach.html">cutlass::reference::device::TensorForEach</a></div><div class="ttdoc">Launches a kernel calling a functor for each element in a tensor&#39;s index space. </div><div class="ttdef"><b>Definition:</b> device/tensor_foreach.h:39</div></div>
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<div class="ttc" id="structcutlass_1_1Coord_html"><div class="ttname"><a href="structcutlass_1_1Coord.html">cutlass::Coord</a></div><div class="ttdoc">Statically-sized array specifying Coords within a tensor. </div><div class="ttdef"><b>Definition:</b> coord.h:43</div></div>
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<div class="ttc" id="structcutlass_1_1reference_1_1device_1_1BlockForEach_html"><div class="ttname"><a href="structcutlass_1_1reference_1_1device_1_1BlockForEach.html">cutlass::reference::device::BlockForEach</a></div><div class="ttdef"><b>Definition:</b> device/tensor_foreach.h:92</div></div>
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<div class="ttc" id="device_2kernel_2tensor__foreach_8h_html"><div class="ttname"><a href="device_2kernel_2tensor__foreach_8h.html">tensor_foreach.h</a></div></div>
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<div class="ttc" id="cutlass_8h_html"><div class="ttname"><a href="cutlass_8h.html">cutlass.h</a></div><div class="ttdoc">Basic include for CUTLASS. </div></div>
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