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<!DOCTYPE html>
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<p class="caption"><span class="caption-text">Notes</span></p>
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<h1>Source code for torch.functional</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">._utils</span> <span class="k">import</span> <span class="n">_range</span>
<div class="viewcode-block" id="split"><a class="viewcode-back" href="../../torch.html#torch.split">[docs]</a><span class="k">def</span> <span class="nf">split</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">split_size</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="sd">"""Splits the tensor into equally sized chunks (if possible).</span>
<span class="sd"> Last chunk will be smaller if the tensor size along a given dimension</span>
<span class="sd"> is not divisible by ``split_size``.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> tensor (Tensor): tensor to split.</span>
<span class="sd"> split_size (int): size of a single chunk.</span>
<span class="sd"> dim (int): dimension along which to split the tensor.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">dim</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">dim</span> <span class="o">+=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span>
<span class="n">dim_size</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span>
<span class="n">num_splits</span> <span class="o">=</span> <span class="p">(</span><span class="n">dim_size</span> <span class="o">+</span> <span class="n">split_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">split_size</span>
<span class="n">last_split_size</span> <span class="o">=</span> <span class="n">split_size</span> <span class="o">-</span> <span class="p">(</span><span class="n">split_size</span> <span class="o">*</span> <span class="n">num_splits</span> <span class="o">-</span> <span class="n">dim_size</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">get_split_size</span><span class="p">(</span><span class="n">i</span><span class="p">):</span>
<span class="k">return</span> <span class="n">split_size</span> <span class="k">if</span> <span class="n">i</span> <span class="o"><</span> <span class="n">num_splits</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">last_split_size</span>
<span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">tensor</span><span class="o">.</span><span class="n">narrow</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">dim</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span> <span class="o">*</span> <span class="n">split_size</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">get_split_size</span><span class="p">(</span><span class="n">i</span><span class="p">)))</span> <span class="k">for</span> <span class="n">i</span>
<span class="ow">in</span> <span class="n">_range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_splits</span><span class="p">))</span></div>
<div class="viewcode-block" id="chunk"><a class="viewcode-back" href="../../torch.html#torch.chunk">[docs]</a><span class="k">def</span> <span class="nf">chunk</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">chunks</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="sd">"""Splits a tensor into a number of chunks along a given dimension.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> tensor (Tensor): tensor to split.</span>
<span class="sd"> chunks (int): number of chunks to return.</span>
<span class="sd"> dim (int): dimension along which to split the tensor.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">dim</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">dim</span> <span class="o">+=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span>
<span class="n">split_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span> <span class="o">+</span> <span class="n">chunks</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">chunks</span>
<span class="k">return</span> <span class="n">split</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">split_size</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span></div>
<div class="viewcode-block" id="stack"><a class="viewcode-back" href="../../torch.html#torch.stack">[docs]</a><span class="k">def</span> <span class="nf">stack</span><span class="p">(</span><span class="n">sequence</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""Concatenates sequence of tensors along a new dimension.</span>
<span class="sd"> All tensors need to be of the same size.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> sequence (Sequence): sequence of tensors to concatenate.</span>
<span class="sd"> dim (int): dimension to insert. Has to be between 0 and the number</span>
<span class="sd"> of dimensions of concatenated tensors (inclusive).</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sequence</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"stack expects a non-empty sequence of tensors"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">dim</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">dim</span> <span class="o">+=</span> <span class="n">sequence</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">t</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">sequence</span><span class="p">]</span>
<span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
<div class="viewcode-block" id="unbind"><a class="viewcode-back" href="../../torch.html#torch.unbind">[docs]</a><span class="k">def</span> <span class="nf">unbind</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="sd">"""Removes a tensor dimension.</span>
<span class="sd"> Returns a tuple of all slices along a given dimension, already without it.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> tensor (Tensor): tensor to unbind.</span>
<span class="sd"> dim (int): dimension to remove.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">tensor</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">_range</span><span class="p">(</span><span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">dim</span><span class="p">)))</span></div>
<span class="k">def</span> <span class="nf">btriunpack</span><span class="p">(</span><span class="n">LU_data</span><span class="p">,</span> <span class="n">LU_pivots</span><span class="p">,</span> <span class="n">unpack_data</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">unpack_pivots</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="sd">"""Unpacks the data and pivots from a batched LU factorization (btrifact) of a tensor.</span>
<span class="sd"> Returns a tuple indexed by:</span>
<span class="sd"> 0: The pivots.</span>
<span class="sd"> 1: The L tensor.</span>
<span class="sd"> 2: The U tensor.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> LU_data (Tensor): The packed LU factorization data.</span>
<span class="sd"> LU_pivots (Tensor): The packed LU factorization pivots.</span>
<span class="sd"> unpack_data (bool): Flag indicating if the data should be unpacked.</span>
<span class="sd"> unpack_pivots (bool): Flag indicating if the pivots should be unpacked.</span>
<span class="sd"> """</span>
<span class="n">nBatch</span><span class="p">,</span> <span class="n">sz</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">LU_data</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
<span class="k">if</span> <span class="n">unpack_data</span><span class="p">:</span>
<span class="n">I_U</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">triu</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">sz</span><span class="p">,</span> <span class="n">sz</span><span class="p">))</span><span class="o">.</span><span class="n">type_as</span><span class="p">(</span><span class="n">LU_data</span><span class="p">)</span><span class="o">.</span><span class="n">byte</span><span class="p">()</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">nBatch</span><span class="p">,</span> <span class="n">sz</span><span class="p">,</span> <span class="n">sz</span><span class="p">)</span>
<span class="n">I_L</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">I_U</span>
<span class="n">L</span> <span class="o">=</span> <span class="n">LU_data</span><span class="o">.</span><span class="n">new</span><span class="p">(</span><span class="n">LU_data</span><span class="o">.</span><span class="n">size</span><span class="p">())</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>
<span class="n">U</span> <span class="o">=</span> <span class="n">LU_data</span><span class="o">.</span><span class="n">new</span><span class="p">(</span><span class="n">LU_data</span><span class="o">.</span><span class="n">size</span><span class="p">())</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>
<span class="n">I_diag</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">sz</span><span class="p">)</span><span class="o">.</span><span class="n">type_as</span><span class="p">(</span><span class="n">LU_data</span><span class="p">)</span><span class="o">.</span><span class="n">byte</span><span class="p">()</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">nBatch</span><span class="p">,</span> <span class="n">sz</span><span class="p">,</span> <span class="n">sz</span><span class="p">)</span>
<span class="n">L</span><span class="p">[</span><span class="n">I_diag</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>
<span class="n">L</span><span class="p">[</span><span class="n">I_L</span><span class="p">]</span> <span class="o">=</span> <span class="n">LU_data</span><span class="p">[</span><span class="n">I_L</span><span class="p">]</span>
<span class="n">U</span><span class="p">[</span><span class="n">I_U</span><span class="p">]</span> <span class="o">=</span> <span class="n">LU_data</span><span class="p">[</span><span class="n">I_U</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">L</span> <span class="o">=</span> <span class="n">U</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">unpack_pivots</span><span class="p">:</span>
<span class="n">P</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">sz</span><span class="p">)</span><span class="o">.</span><span class="n">type_as</span><span class="p">(</span><span class="n">LU_data</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">nBatch</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nBatch</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sz</span><span class="p">):</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">LU_pivots</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">P</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
<span class="n">P</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">P</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="n">k</span><span class="p">]</span>
<span class="n">P</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">t</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">P</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">return</span> <span class="n">P</span><span class="p">,</span> <span class="n">L</span><span class="p">,</span> <span class="n">U</span>
</pre></div>
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