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<a href="default__mma__tensor__op_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="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#pragma once</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="preprocessor">#include "<a class="code" href="cutlass_8h.html">cutlass/cutlass.h</a>"</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "<a class="code" href="mma__tensor__op_8h.html">cutlass/gemm/warp/mma_tensor_op.h</a>"</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="keyword">namespace </span><a class="code" href="namespacecutlass.html">cutlass</a> {</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="keyword">namespace </span>gemm {</div><div class="line"><a name="l00036"></a><span class="lineno"><a class="line" href="namespacecutlass_1_1gemm_1_1warp.html"> 36</a></span> <span class="keyword">namespace </span>warp {</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="keyword">template</span> <</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keyword">typename</span> WarpShape_,</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keyword">typename</span> InstructionShape_,</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keyword">typename</span> ElementA_,</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keyword">typename</span> LayoutA_,</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keyword">typename</span> ElementB_,</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keyword">typename</span> LayoutB_,</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keyword">typename</span> ElementC_,</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keyword">typename</span> LayoutC_,</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keyword">typename</span> Operator_ = arch::OpMultiplyAdd,</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordtype">int</span> PartitionsK = 1,</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordtype">bool</span> AccumulatorsInRowMajor = <span class="keyword">false</span>,</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordtype">int</span> PartitionsN = 1</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> ></div><div class="line"><a name="l00067"></a><span class="lineno"><a class="line" href="structcutlass_1_1gemm_1_1warp_1_1DefaultMmaTensorOp.html"> 67</a></span> <span class="keyword">struct </span><a class="code" href="structcutlass_1_1gemm_1_1warp_1_1DefaultMmaTensorOp.html">DefaultMmaTensorOp</a>;</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> </div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> <span class="keyword">template</span> <</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keyword">typename</span> WarpShape_,</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keyword">typename</span> InstructionShape_,</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keyword">typename</span> ElementA,</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keyword">typename</span> LayoutA,</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keyword">typename</span> ElementB,</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keyword">typename</span> LayoutB,</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keyword">typename</span> ElementC,</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keyword">typename</span> LayoutC,</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keyword">typename</span> Operator_,</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keywordtype">int</span> PartitionsK,</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordtype">bool</span> AccumulatorsInRowMajor,</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordtype">int</span> PartitionsN></div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> <span class="keyword">struct </span><a class="code" href="structcutlass_1_1gemm_1_1warp_1_1DefaultMmaTensorOp.html">DefaultMmaTensorOp</a> {</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keyword">using</span> <a class="code" href="structcutlass_1_1gemm_1_1warp_1_1MmaTensorOpPolicy.html">Policy</a> = <a class="code" href="structcutlass_1_1gemm_1_1warp_1_1MmaTensorOpPolicy.html">cutlass::gemm::warp::MmaTensorOpPolicy</a><</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <a class="code" href="structcutlass_1_1arch_1_1Mma.html">cutlass::arch::Mma</a><InstructionShape_, 32, ElementA,</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <a class="code" href="classcutlass_1_1layout_1_1RowMajor.html">cutlass::layout::RowMajor</a>, ElementB,</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <a class="code" href="classcutlass_1_1layout_1_1ColumnMajor.html">cutlass::layout::ColumnMajor</a>, ElementC,</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  cutlass::layout::RowMajor, Operator_>,</div><div class="line"><a name="l00104"></a><span class="lineno"><a class="line" href="structcutlass_1_1gemm_1_1warp_1_1DefaultMmaTensorOp.html#a9e8b45cc517276a2e5d7f8d2c0839295"> 104</a></span>  <a class="code" href="structcutlass_1_1MatrixShape.html">cutlass::MatrixShape<1, 1></a> >;</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> </div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="comment">// Define the warp-level tensor op</span></div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="keyword">using</span> <a class="code" href="classcutlass_1_1gemm_1_1warp_1_1MmaTensorOp.html">Type</a> = <a class="code" href="classcutlass_1_1gemm_1_1warp_1_1MmaTensorOp.html">cutlass::gemm::warp::MmaTensorOp</a><</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  WarpShape_, ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC,</div><div class="line"><a name="l00109"></a><span class="lineno"><a class="line" href="structcutlass_1_1gemm_1_1warp_1_1DefaultMmaTensorOp.html#ace849803d830e4ff1c655fa934ad86fa"> 109</a></span>  <a class="code" href="structcutlass_1_1gemm_1_1warp_1_1DefaultMmaTensorOp.html#a9e8b45cc517276a2e5d7f8d2c0839295">Policy</a>, PartitionsK, AccumulatorsInRowMajor, PartitionsN>;</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> };</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</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">// namespace warp</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> } <span class="comment">// namespace gemm</span></div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> } <span class="comment">// namespace cutlass</span></div><div class="ttc" id="structcutlass_1_1MatrixShape_html"><div class="ttname"><a href="structcutlass_1_1MatrixShape.html">cutlass::MatrixShape</a></div><div class="ttdoc">Describes the size of a matrix tile. </div><div class="ttdef"><b>Definition:</b> matrix_shape.h:42</div></div>
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<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_1gemm_1_1warp_1_1DefaultMmaTensorOp_html"><div class="ttname"><a href="structcutlass_1_1gemm_1_1warp_1_1DefaultMmaTensorOp.html">cutlass::gemm::warp::DefaultMmaTensorOp</a></div><div class="ttdoc">Partial specialization for m-by-n-by-kgroup. </div><div class="ttdef"><b>Definition:</b> default_mma_tensor_op.h:67</div></div>
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<div class="ttc" id="classcutlass_1_1gemm_1_1warp_1_1MmaTensorOp_html"><div class="ttname"><a href="classcutlass_1_1gemm_1_1warp_1_1MmaTensorOp.html">cutlass::gemm::warp::MmaTensorOp</a></div><div class="ttdoc">Structure to compute the matrix product targeting CUDA cores and SIMT math instructions. </div><div class="ttdef"><b>Definition:</b> mma_tensor_op.h:82</div></div>
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<div class="ttc" id="classcutlass_1_1layout_1_1ColumnMajor_html"><div class="ttname"><a href="classcutlass_1_1layout_1_1ColumnMajor.html">cutlass::layout::ColumnMajor</a></div><div class="ttdoc">Mapping function for column-major matrices. </div><div class="ttdef"><b>Definition:</b> layout/matrix.h:142</div></div>
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<div class="ttc" id="structcutlass_1_1gemm_1_1warp_1_1MmaTensorOpPolicy_html"><div class="ttname"><a href="structcutlass_1_1gemm_1_1warp_1_1MmaTensorOpPolicy.html">cutlass::gemm::warp::MmaTensorOpPolicy</a></div><div class="ttdoc">Policy. </div><div class="ttdef"><b>Definition:</b> mma_tensor_op_policy.h:48</div></div>
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<div class="ttc" id="classcutlass_1_1layout_1_1RowMajor_html"><div class="ttname"><a href="classcutlass_1_1layout_1_1RowMajor.html">cutlass::layout::RowMajor</a></div><div class="ttdoc">Mapping function for row-major matrices. </div><div class="ttdef"><b>Definition:</b> layout/matrix.h:50</div></div>
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<div class="ttc" id="structcutlass_1_1gemm_1_1warp_1_1DefaultMmaTensorOp_html_a9e8b45cc517276a2e5d7f8d2c0839295"><div class="ttname"><a href="structcutlass_1_1gemm_1_1warp_1_1DefaultMmaTensorOp.html#a9e8b45cc517276a2e5d7f8d2c0839295">cutlass::gemm::warp::DefaultMmaTensorOp::Policy</a></div><div class="ttdeci">cutlass::gemm::warp::MmaTensorOpPolicy< cutlass::arch::Mma< InstructionShape_, 32, ElementA, cutlass::layout::RowMajor, ElementB, cutlass::layout::ColumnMajor, ElementC, cutlass::layout::RowMajor, Operator_ >, cutlass::MatrixShape< 1, 1 > > Policy</div><div class="ttdef"><b>Definition:</b> default_mma_tensor_op.h:104</div></div>
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<div class="ttc" id="structcutlass_1_1arch_1_1Mma_html"><div class="ttname"><a href="structcutlass_1_1arch_1_1Mma.html">cutlass::arch::Mma</a></div><div class="ttdoc">Matrix multiply-add operation. </div><div class="ttdef"><b>Definition:</b> arch/mma.h:92</div></div>
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<div class="ttc" id="mma__tensor__op_8h_html"><div class="ttname"><a href="mma__tensor__op_8h.html">mma_tensor_op.h</a></div><div class="ttdoc">Templates implementing warp-level matrix multiply-accumulate operations targeting Tensor Cores...</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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