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<div class="title">device/kernel/tensor_elementwise.h</div> </div>
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<a href="device_2kernel_2tensor__elementwise_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> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#pragma once</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> </div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include <curand_kernel.h></span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="cutlass_8h.html">cutlass/cutlass.h</a>"</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> </div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="keyword">namespace </span><a class="code" href="namespacecutlass.html">cutlass</a> {</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="keyword">namespace </span>reference {</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="keyword">namespace </span>device {</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="keyword">namespace </span>kernel {</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> </div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00041"></a><span class="lineno"><a class="line" href="namespacecutlass_1_1reference_1_1device_1_1kernel.html#a44bffb16758ab0071aac16d203f2d051"> 41</a></span> __global__ <span class="keywordtype">void</span> <a class="code" href="namespacecutlass_1_1reference_1_1device_1_1kernel.html#a44bffb16758ab0071aac16d203f2d051">TensorInitializeUniform</a>(</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <a class="code" href="structcutlass_1_1Distribution.html">Distribution</a> dist, int64_t seed, <span class="keywordtype">int</span> dim_contiguous, <span class="keywordtype">int</span> dim_strided, T *tensor, <span class="keywordtype">int</span> ldm) {</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  __shared__ curandState_t rng_state[1024];</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>  uint64_t gtid = threadIdx.x + blockIdx.x * blockDim.x + blockIdx.y * gridDim.x * blockDim.x;</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  curand_init(seed, gtid, 0, &rng_state[threadIdx.x]);</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keywordtype">int</span> c_idx = blockIdx.x * blockDim.x + threadIdx.x;</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keywordtype">int</span> s_idx = blockIdx.y * blockDim.x;</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>  tensor += s_idx * ldm + c_idx;</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>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> s_offset = 0; s_offset < blockDim.x; ++s_offset, ++s_idx) {</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordflow">if</span> (s_idx < dim_strided && c_idx < dim_contiguous) {</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordtype">double</span> range = dist.<a class="code" href="structcutlass_1_1Distribution.html#afc30b6976acb39e54f061af1bf2870db">uniform</a>.max - dist.<a class="code" href="structcutlass_1_1Distribution.html#afc30b6976acb39e54f061af1bf2870db">uniform</a>.min;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordtype">double</span> rnd = curand_uniform(&rng_state[threadIdx.x]);</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>  rnd = dist.<a class="code" href="structcutlass_1_1Distribution.html#afc30b6976acb39e54f061af1bf2870db">uniform</a>.min + range * rnd;</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="comment">// Random values are cast to integer after scaling by a power of two to facilitate error</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="comment">// testing</span></div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keywordflow">if</span> (dist.<a class="code" href="structcutlass_1_1Distribution.html#a676b1d8b87691b4218f6ed308e6adfc1">int_scale</a> >= 0) {</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  rnd = double(<span class="keywordtype">int</span>(rnd * <span class="keywordtype">double</span>(1 << dist.<a class="code" href="structcutlass_1_1Distribution.html#a676b1d8b87691b4218f6ed308e6adfc1">int_scale</a>)));</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  *tensor = T(rnd / <span class="keywordtype">double</span>(1 << dist.<a class="code" href="structcutlass_1_1Distribution.html#a676b1d8b87691b4218f6ed308e6adfc1">int_scale</a>));</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  *tensor = T(rnd);</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  }</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  tensor += ldm;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  }</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  }</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> }</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> </div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00080"></a><span class="lineno"><a class="line" href="namespacecutlass_1_1reference_1_1device_1_1kernel.html#a1e8054d6781358c0faeddfe77f28f23b"> 80</a></span> __global__ <span class="keywordtype">void</span> <a class="code" href="namespacecutlass_1_1reference_1_1device_1_1kernel.html#a1e8054d6781358c0faeddfe77f28f23b">TensorInitializeGaussian</a>(</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <a class="code" href="structcutlass_1_1Distribution.html">Distribution</a> dist, int64_t seed, <span class="keywordtype">int</span> dim_contiguous, <span class="keywordtype">int</span> dim_strided, T *tensor, <span class="keywordtype">int</span> ldm) {</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  __shared__ curandState_t rng_state[1024];</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>  uint64_t gtid = threadIdx.x + blockIdx.x * blockDim.x + blockIdx.y * gridDim.x * blockDim.x;</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>  curand_init(seed, gtid, 0, &rng_state[threadIdx.x]);</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>  <span class="keywordtype">int</span> c_idx = blockIdx.x * blockDim.x + threadIdx.x;</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordtype">int</span> s_idx = blockIdx.y * blockDim.x;</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>  tensor += s_idx * ldm + c_idx;</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> </div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> s_offset = 0; s_offset < blockDim.x; ++s_offset, ++s_idx) {</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keywordflow">if</span> (s_idx < dim_strided && c_idx < dim_contiguous) {</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="comment">// Random values are cast to integer after scaling by a power of two to facilitate error</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="comment">// testing</span></div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> </div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keywordtype">double</span> rnd = curand_normal(&rng_state[threadIdx.x]);</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> </div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  rnd = dist.<a class="code" href="structcutlass_1_1Distribution.html#ada9c50671b405fabbb0841a093f809de">gaussian</a>.mean + dist.<a class="code" href="structcutlass_1_1Distribution.html#ada9c50671b405fabbb0841a093f809de">gaussian</a>.stddev * rnd;</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> (dist.<a class="code" href="structcutlass_1_1Distribution.html#a676b1d8b87691b4218f6ed308e6adfc1">int_scale</a> >= 0) {</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  rnd = double(<span class="keywordtype">int</span>(rnd * <span class="keywordtype">double</span>(1 << dist.<a class="code" href="structcutlass_1_1Distribution.html#a676b1d8b87691b4218f6ed308e6adfc1">int_scale</a>)));</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  *tensor = T(rnd / <span class="keywordtype">double</span>(1 << dist.<a class="code" href="structcutlass_1_1Distribution.html#a676b1d8b87691b4218f6ed308e6adfc1">int_scale</a>));</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  *tensor = T(rnd);</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  }</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  }</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> }</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> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00114"></a><span class="lineno"><a class="line" href="namespacecutlass_1_1reference_1_1device_1_1kernel.html#a1c73ae8819459dba630520208038a2ae"> 114</a></span> __global__ <span class="keywordtype">void</span> <a class="code" href="namespacecutlass_1_1reference_1_1device_1_1kernel.html#a1c73ae8819459dba630520208038a2ae">TensorInitializeLinear</a>(</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <a class="code" href="structcutlass_1_1Distribution.html">Distribution</a> dist, int64_t seed, <span class="keywordtype">int</span> dim_contiguous, <span class="keywordtype">int</span> dim_strided, T *tensor, <span class="keywordtype">int</span> ldm) {</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  __shared__ curandState_t rng_state[1024];</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>  uint64_t gtid = threadIdx.x + blockIdx.x * blockDim.x + blockIdx.y * gridDim.x * blockDim.x;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> </div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  curand_init(seed, gtid, 0, &rng_state[threadIdx.x]);</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>  <span class="keywordtype">int</span> c_idx = blockIdx.x * blockDim.x + threadIdx.x;</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keywordtype">int</span> s_idx = blockIdx.y * blockDim.x;</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>  tensor += s_idx * ldm + c_idx;</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> </div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> s_offset = 0; s_offset < blockDim.x; ++s_offset, ++s_idx) {</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordflow">if</span> (s_idx < dim_strided && c_idx < dim_contiguous) {</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  *tensor =</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  dist.linear.offset + dist.linear.delta_row * c_idx + dist.linear.delta_column * s_idx;</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  }</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  }</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> }</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> </div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00137"></a><span class="lineno"><a class="line" href="namespacecutlass_1_1reference_1_1device_1_1kernel.html#a7ac1aaf53d1a16e6d9b050471fa08e2c"> 137</a></span> __global__ <span class="keywordtype">void</span> <a class="code" href="namespacecutlass_1_1reference_1_1device_1_1kernel.html#a7ac1aaf53d1a16e6d9b050471fa08e2c">TensorInitializeIdentity</a>(</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <a class="code" href="structcutlass_1_1Distribution.html">Distribution</a> dist, int64_t seed, <span class="keywordtype">int</span> dim_contiguous, <span class="keywordtype">int</span> dim_strided, T *tensor, <span class="keywordtype">int</span> ldm) {</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  __shared__ curandState_t rng_state[1024];</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  uint64_t gtid = threadIdx.x + blockIdx.x * blockDim.x + blockIdx.y * gridDim.x * blockDim.x;</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> </div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  curand_init(seed, gtid, 0, &rng_state[threadIdx.x]);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keywordtype">int</span> c_idx = blockIdx.x * blockDim.x + threadIdx.x;</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keywordtype">int</span> s_idx = blockIdx.y * blockDim.x;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> </div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  tensor += s_idx * ldm + c_idx;</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> </div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> s_offset = 0; s_offset < blockDim.x; ++s_offset, ++s_idx) {</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordflow">if</span> (s_idx < dim_strided && c_idx < dim_contiguous) {</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  *tensor = (c_idx == s_idx ? T(1) : T(0));</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  }</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  }</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> }</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> </div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> </div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> } <span class="comment">// namespace kernel</span></div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> } <span class="comment">// namespace device</span></div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> } <span class="comment">// namespace reference</span></div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> } <span class="comment">// namespace cutlass</span></div><div class="ttc" id="namespacecutlass_1_1reference_1_1device_1_1kernel_html_a44bffb16758ab0071aac16d203f2d051"><div class="ttname"><a href="namespacecutlass_1_1reference_1_1device_1_1kernel.html#a44bffb16758ab0071aac16d203f2d051">cutlass::reference::device::kernel::TensorInitializeUniform</a></div><div class="ttdeci">__global__ void TensorInitializeUniform(Distribution dist, int64_t seed, int dim_contiguous, int dim_strided, T *tensor, int ldm)</div><div class="ttdoc">Kernel to initialize tensor to uniform random distribution. </div><div class="ttdef"><b>Definition:</b> device/kernel/tensor_elementwise.h:41</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="namespacecutlass_1_1reference_1_1device_1_1kernel_html_a1e8054d6781358c0faeddfe77f28f23b"><div class="ttname"><a href="namespacecutlass_1_1reference_1_1device_1_1kernel.html#a1e8054d6781358c0faeddfe77f28f23b">cutlass::reference::device::kernel::TensorInitializeGaussian</a></div><div class="ttdeci">__global__ void TensorInitializeGaussian(Distribution dist, int64_t seed, int dim_contiguous, int dim_strided, T *tensor, int ldm)</div><div class="ttdoc">Kernel to initialize tensor to uniform distribution. </div><div class="ttdef"><b>Definition:</b> device/kernel/tensor_elementwise.h:80</div></div>
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<div class="ttc" id="structcutlass_1_1Distribution_html_afc30b6976acb39e54f061af1bf2870db"><div class="ttname"><a href="structcutlass_1_1Distribution.html#afc30b6976acb39e54f061af1bf2870db">cutlass::Distribution::uniform</a></div><div class="ttdeci">struct cutlass::Distribution::@18::@20 uniform</div><div class="ttdoc">Uniform distribution. </div></div>
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<div class="ttc" id="namespacecutlass_1_1reference_1_1device_1_1kernel_html_a1c73ae8819459dba630520208038a2ae"><div class="ttname"><a href="namespacecutlass_1_1reference_1_1device_1_1kernel.html#a1c73ae8819459dba630520208038a2ae">cutlass::reference::device::kernel::TensorInitializeLinear</a></div><div class="ttdeci">__global__ void TensorInitializeLinear(Distribution dist, int64_t seed, int dim_contiguous, int dim_strided, T *tensor, int ldm)</div><div class="ttdoc">Kernel to initialize tensor to an identity matrix. </div><div class="ttdef"><b>Definition:</b> device/kernel/tensor_elementwise.h:114</div></div>
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<div class="ttc" id="structcutlass_1_1Distribution_html_ada9c50671b405fabbb0841a093f809de"><div class="ttname"><a href="structcutlass_1_1Distribution.html#ada9c50671b405fabbb0841a093f809de">cutlass::Distribution::gaussian</a></div><div class="ttdeci">struct cutlass::Distribution::@18::@21 gaussian</div><div class="ttdoc">Gaussian distribution. </div></div>
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<div class="ttc" id="namespacecutlass_1_1reference_1_1device_1_1kernel_html_a7ac1aaf53d1a16e6d9b050471fa08e2c"><div class="ttname"><a href="namespacecutlass_1_1reference_1_1device_1_1kernel.html#a7ac1aaf53d1a16e6d9b050471fa08e2c">cutlass::reference::device::kernel::TensorInitializeIdentity</a></div><div class="ttdeci">__global__ void TensorInitializeIdentity(Distribution dist, int64_t seed, int dim_contiguous, int dim_strided, T *tensor, int ldm)</div><div class="ttdoc">Kernel to initialize tensor to an identity matrix. </div><div class="ttdef"><b>Definition:</b> device/kernel/tensor_elementwise.h:137</div></div>
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<div class="ttc" id="structcutlass_1_1Distribution_html"><div class="ttname"><a href="structcutlass_1_1Distribution.html">cutlass::Distribution</a></div><div class="ttdoc">Distribution type. </div><div class="ttdef"><b>Definition:</b> distribution.h:38</div></div>
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<div class="ttc" id="structcutlass_1_1Distribution_html_a676b1d8b87691b4218f6ed308e6adfc1"><div class="ttname"><a href="structcutlass_1_1Distribution.html#a676b1d8b87691b4218f6ed308e6adfc1">cutlass::Distribution::int_scale</a></div><div class="ttdeci">int int_scale</div><div class="ttdoc">Random values are cast to integer after scaling by this power of two. </div><div class="ttdef"><b>Definition:</b> distribution.h:67</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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