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<li class="toctree-l1"><a class="reference internal" href="../../../notes/amp_examples.html">CUDA Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../notes/autograd.html">Autograd mechanics</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../../notes/extending.func.html">Extending torch.func with autograd.Function</a></li>
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<h1>Source code for torch.distributions.distribution</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch.distributions</span> <span class="kn">import</span> <span class="n">constraints</span>
<span class="kn">from</span> <span class="nn">torch.distributions.utils</span> <span class="kn">import</span> <span class="n">lazy_property</span>
<span class="kn">from</span> <span class="nn">torch.types</span> <span class="kn">import</span> <span class="n">_size</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"Distribution"</span><span class="p">]</span>
<div class="viewcode-block" id="Distribution"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution">[docs]</a><span class="k">class</span> <span class="nc">Distribution</span><span class="p">:</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Distribution is the abstract base class for probability distributions.</span>
<span class="sd"> """</span>
<span class="n">has_rsample</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">has_enumerate_support</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">_validate_args</span> <span class="o">=</span> <span class="n">__debug__</span>
<div class="viewcode-block" id="Distribution.set_default_validate_args"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.set_default_validate_args">[docs]</a> <span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">set_default_validate_args</span><span class="p">(</span><span class="n">value</span><span class="p">:</span> <span class="nb">bool</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Sets whether validation is enabled or disabled.</span>
<span class="sd"> The default behavior mimics Python's ``assert`` statement: validation</span>
<span class="sd"> is on by default, but is disabled if Python is run in optimized mode</span>
<span class="sd"> (via ``python -O``). Validation may be expensive, so you may want to</span>
<span class="sd"> disable it once a model is working.</span>
<span class="sd"> Args:</span>
<span class="sd"> value (bool): Whether to enable validation.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">value</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">]:</span>
<span class="k">raise</span> <span class="ne">ValueError</span>
<span class="n">Distribution</span><span class="o">.</span><span class="n">_validate_args</span> <span class="o">=</span> <span class="n">value</span></div>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">batch_shape</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(),</span>
<span class="n">event_shape</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(),</span>
<span class="n">validate_args</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_batch_shape</span> <span class="o">=</span> <span class="n">batch_shape</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_event_shape</span> <span class="o">=</span> <span class="n">event_shape</span>
<span class="k">if</span> <span class="n">validate_args</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_validate_args</span> <span class="o">=</span> <span class="n">validate_args</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_args</span><span class="p">:</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">arg_constraints</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_constraints</span>
<span class="k">except</span> <span class="ne">NotImplementedError</span><span class="p">:</span>
<span class="n">arg_constraints</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="si">}</span><span class="s2"> does not define `arg_constraints`. "</span>
<span class="o">+</span> <span class="s2">"Please set `arg_constraints = </span><span class="si">{}</span><span class="s2">` or initialize the distribution "</span>
<span class="o">+</span> <span class="s2">"with `validate_args=False` to turn off validation."</span>
<span class="p">)</span>
<span class="k">for</span> <span class="n">param</span><span class="p">,</span> <span class="n">constraint</span> <span class="ow">in</span> <span class="n">arg_constraints</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="k">if</span> <span class="n">constraints</span><span class="o">.</span><span class="n">is_dependent</span><span class="p">(</span><span class="n">constraint</span><span class="p">):</span>
<span class="k">continue</span> <span class="c1"># skip constraints that cannot be checked</span>
<span class="k">if</span> <span class="n">param</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span>
<span class="nb">getattr</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">),</span> <span class="n">param</span><span class="p">),</span> <span class="n">lazy_property</span>
<span class="p">):</span>
<span class="k">continue</span> <span class="c1"># skip checking lazily-constructed args</span>
<span class="n">value</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">param</span><span class="p">)</span>
<span class="n">valid</span> <span class="o">=</span> <span class="n">constraint</span><span class="o">.</span><span class="n">check</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">valid</span><span class="o">.</span><span class="n">all</span><span class="p">():</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"Expected parameter </span><span class="si">{</span><span class="n">param</span><span class="si">}</span><span class="s2"> "</span>
<span class="sa">f</span><span class="s2">"(</span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">value</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2"> of shape </span><span class="si">{</span><span class="nb">tuple</span><span class="p">(</span><span class="n">value</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="si">}</span><span class="s2">) "</span>
<span class="sa">f</span><span class="s2">"of distribution </span><span class="si">{</span><span class="nb">repr</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="si">}</span><span class="s2"> "</span>
<span class="sa">f</span><span class="s2">"to satisfy the constraint </span><span class="si">{</span><span class="nb">repr</span><span class="p">(</span><span class="n">constraint</span><span class="p">)</span><span class="si">}</span><span class="s2">, "</span>
<span class="sa">f</span><span class="s2">"but found invalid values:</span><span class="se">\n</span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s2">"</span>
<span class="p">)</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<div class="viewcode-block" id="Distribution.expand"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.expand">[docs]</a> <span class="k">def</span> <span class="nf">expand</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_shape</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">,</span> <span class="n">_instance</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Returns a new distribution instance (or populates an existing instance</span>
<span class="sd"> provided by a derived class) with batch dimensions expanded to</span>
<span class="sd"> `batch_shape`. This method calls :class:`~torch.Tensor.expand` on</span>
<span class="sd"> the distribution's parameters. As such, this does not allocate new</span>
<span class="sd"> memory for the expanded distribution instance. Additionally,</span>
<span class="sd"> this does not repeat any args checking or parameter broadcasting in</span>
<span class="sd"> `__init__.py`, when an instance is first created.</span>
<span class="sd"> Args:</span>
<span class="sd"> batch_shape (torch.Size): the desired expanded size.</span>
<span class="sd"> _instance: new instance provided by subclasses that</span>
<span class="sd"> need to override `.expand`.</span>
<span class="sd"> Returns:</span>
<span class="sd"> New distribution instance with batch dimensions expanded to</span>
<span class="sd"> `batch_size`.</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">batch_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Returns the shape over which parameters are batched.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_shape</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">event_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Returns the shape of a single sample (without batching).</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_event_shape</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">arg_constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">constraints</span><span class="o">.</span><span class="n">Constraint</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Returns a dictionary from argument names to</span>
<span class="sd"> :class:`~torch.distributions.constraints.Constraint` objects that</span>
<span class="sd"> should be satisfied by each argument of this distribution. Args that</span>
<span class="sd"> are not tensors need not appear in this dict.</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">support</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">Optional</span><span class="p">[</span><span class="n">Any</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Returns a :class:`~torch.distributions.constraints.Constraint` object</span>
<span class="sd"> representing this distribution's support.</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">mean</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Returns the mean of the distribution.</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">mode</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Returns the mode of the distribution.</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="si">}</span><span class="s2"> does not implement mode"</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">variance</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Returns the variance of the distribution.</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">stddev</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Returns the standard deviation of the distribution.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">variance</span><span class="o">.</span><span class="n">sqrt</span><span class="p">()</span>
<div class="viewcode-block" id="Distribution.sample"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.sample">[docs]</a> <span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_shape</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">())</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Generates a sample_shape shaped sample or sample_shape shaped batch of</span>
<span class="sd"> samples if the distribution parameters are batched.</span>
<span class="sd"> """</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">rsample</span><span class="p">(</span><span class="n">sample_shape</span><span class="p">)</span></div>
<div class="viewcode-block" id="Distribution.rsample"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.rsample">[docs]</a> <span class="k">def</span> <span class="nf">rsample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_shape</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">())</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Generates a sample_shape shaped reparameterized sample or sample_shape</span>
<span class="sd"> shaped batch of reparameterized samples if the distribution parameters</span>
<span class="sd"> are batched.</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
<div class="viewcode-block" id="Distribution.sample_n"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.sample_n">[docs]</a> <span class="k">def</span> <span class="nf">sample_n</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Generates n samples or n batches of samples if the distribution</span>
<span class="sd"> parameters are batched.</span>
<span class="sd"> """</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
<span class="s2">"sample_n will be deprecated. Use .sample((n,)) instead"</span><span class="p">,</span> <span class="ne">UserWarning</span>
<span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">((</span><span class="n">n</span><span class="p">,)))</span></div>
<div class="viewcode-block" id="Distribution.log_prob"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.log_prob">[docs]</a> <span class="k">def</span> <span class="nf">log_prob</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Returns the log of the probability density/mass function evaluated at</span>
<span class="sd"> `value`.</span>
<span class="sd"> Args:</span>
<span class="sd"> value (Tensor):</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
<div class="viewcode-block" id="Distribution.cdf"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.cdf">[docs]</a> <span class="k">def</span> <span class="nf">cdf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Returns the cumulative density/mass function evaluated at</span>
<span class="sd"> `value`.</span>
<span class="sd"> Args:</span>
<span class="sd"> value (Tensor):</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
<div class="viewcode-block" id="Distribution.icdf"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.icdf">[docs]</a> <span class="k">def</span> <span class="nf">icdf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Returns the inverse cumulative density/mass function evaluated at</span>
<span class="sd"> `value`.</span>
<span class="sd"> Args:</span>
<span class="sd"> value (Tensor):</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
<div class="viewcode-block" id="Distribution.enumerate_support"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.enumerate_support">[docs]</a> <span class="k">def</span> <span class="nf">enumerate_support</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">expand</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Returns tensor containing all values supported by a discrete</span>
<span class="sd"> distribution. The result will enumerate over dimension 0, so the shape</span>
<span class="sd"> of the result will be `(cardinality,) + batch_shape + event_shape`</span>
<span class="sd"> (where `event_shape = ()` for univariate distributions).</span>
<span class="sd"> Note that this enumerates over all batched tensors in lock-step</span>
<span class="sd"> `[[0, 0], [1, 1], ...]`. With `expand=False`, enumeration happens</span>
<span class="sd"> along dim 0, but with the remaining batch dimensions being</span>
<span class="sd"> singleton dimensions, `[[0], [1], ..`.</span>
<span class="sd"> To iterate over the full Cartesian product use</span>
<span class="sd"> `itertools.product(m.enumerate_support())`.</span>
<span class="sd"> Args:</span>
<span class="sd"> expand (bool): whether to expand the support over the</span>
<span class="sd"> batch dims to match the distribution's `batch_shape`.</span>
<span class="sd"> Returns:</span>
<span class="sd"> Tensor iterating over dimension 0.</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
<div class="viewcode-block" id="Distribution.entropy"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.entropy">[docs]</a> <span class="k">def</span> <span class="nf">entropy</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Returns entropy of distribution, batched over batch_shape.</span>
<span class="sd"> Returns:</span>
<span class="sd"> Tensor of shape batch_shape.</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
<div class="viewcode-block" id="Distribution.perplexity"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.perplexity">[docs]</a> <span class="k">def</span> <span class="nf">perplexity</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Returns perplexity of distribution, batched over batch_shape.</span>
<span class="sd"> Returns:</span>
<span class="sd"> Tensor of shape batch_shape.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">entropy</span><span class="p">())</span></div>
<span class="k">def</span> <span class="nf">_extended_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_shape</span><span class="p">:</span> <span class="n">_size</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">())</span> <span class="o">-></span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Returns the size of the sample returned by the distribution, given</span>
<span class="sd"> a `sample_shape`. Note, that the batch and event shapes of a distribution</span>
<span class="sd"> instance are fixed at the time of construction. If this is empty, the</span>
<span class="sd"> returned shape is upcast to (1,).</span>
<span class="sd"> Args:</span>
<span class="sd"> sample_shape (torch.Size): the size of the sample to be drawn.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sample_shape</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">):</span>
<span class="n">sample_shape</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(</span><span class="n">sample_shape</span><span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(</span><span class="n">sample_shape</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_shape</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_event_shape</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_validate_sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Argument validation for distribution methods such as `log_prob`,</span>
<span class="sd"> `cdf` and `icdf`. The rightmost dimensions of a value to be</span>
<span class="sd"> scored via these methods must agree with the distribution's batch</span>
<span class="sd"> and event shapes.</span>
<span class="sd"> Args:</span>
<span class="sd"> value (Tensor): the tensor whose log probability is to be</span>
<span class="sd"> computed by the `log_prob` method.</span>
<span class="sd"> Raises</span>
<span class="sd"> ValueError: when the rightmost dimensions of `value` do not match the</span>
<span class="sd"> distribution's batch and event shapes.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The value argument to log_prob must be a Tensor"</span><span class="p">)</span>
<span class="n">event_dim_start</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">value</span><span class="o">.</span><span class="n">size</span><span class="p">())</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_event_shape</span><span class="p">)</span>
<span class="k">if</span> <span class="n">value</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="n">event_dim_start</span><span class="p">:]</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_event_shape</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"The right-most size of value must match event_shape: </span><span class="si">{</span><span class="n">value</span><span class="o">.</span><span class="n">size</span><span class="p">()</span><span class="si">}</span><span class="s2"> vs </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_event_shape</span><span class="si">}</span><span class="s2">."</span>
<span class="p">)</span>
<span class="n">actual_shape</span> <span class="o">=</span> <span class="n">value</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
<span class="n">expected_shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_shape</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_event_shape</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">reversed</span><span class="p">(</span><span class="n">actual_shape</span><span class="p">),</span> <span class="nb">reversed</span><span class="p">(</span><span class="n">expected_shape</span><span class="p">)):</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">j</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"Value is not broadcastable with batch_shape+event_shape: </span><span class="si">{</span><span class="n">actual_shape</span><span class="si">}</span><span class="s2"> vs </span><span class="si">{</span><span class="n">expected_shape</span><span class="si">}</span><span class="s2">."</span>
<span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">support</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">support</span>
<span class="k">except</span> <span class="ne">NotImplementedError</span><span class="p">:</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="si">}</span><span class="s2"> does not define `support` to enable "</span>
<span class="o">+</span> <span class="s2">"sample validation. Please initialize the distribution with "</span>
<span class="o">+</span> <span class="s2">"`validate_args=False` to turn off validation."</span>
<span class="p">)</span>
<span class="k">return</span>
<span class="k">assert</span> <span class="n">support</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">valid</span> <span class="o">=</span> <span class="n">support</span><span class="o">.</span><span class="n">check</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">valid</span><span class="o">.</span><span class="n">all</span><span class="p">():</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s2">"Expected value argument "</span>
<span class="sa">f</span><span class="s2">"(</span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">value</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2"> of shape </span><span class="si">{</span><span class="nb">tuple</span><span class="p">(</span><span class="n">value</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="si">}</span><span class="s2">) "</span>
<span class="sa">f</span><span class="s2">"to be within the support (</span><span class="si">{</span><span class="nb">repr</span><span class="p">(</span><span class="n">support</span><span class="p">)</span><span class="si">}</span><span class="s2">) "</span>
<span class="sa">f</span><span class="s2">"of the distribution </span><span class="si">{</span><span class="nb">repr</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="si">}</span><span class="s2">, "</span>
<span class="sa">f</span><span class="s2">"but found invalid values:</span><span class="se">\n</span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s2">"</span>
<span class="p">)</span>
<span class="k">def</span> <span class="nf">_get_checked_instance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="bp">cls</span><span class="p">,</span> <span class="n">_instance</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">if</span> <span class="n">_instance</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span> <span class="o">!=</span> <span class="bp">cls</span><span class="o">.</span><span class="fm">__init__</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"Subclass </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2"> of </span><span class="si">{</span><span class="bp">cls</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2"> that defines a custom __init__ method "</span>
<span class="s2">"must also define a custom .expand() method."</span>
<span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span> <span class="k">if</span> <span class="n">_instance</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">_instance</span>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="nb">str</span><span class="p">:</span>
<span class="n">param_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_constraints</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">]</span>
<span class="n">args_string</span> <span class="o">=</span> <span class="s2">", "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
<span class="p">[</span>
<span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">p</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="n">p</span><span class="p">]</span><span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="n">p</span><span class="p">]</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="mi">1</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="n">p</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span>
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">param_names</span>
<span class="p">]</span>
<span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">+</span> <span class="s2">"("</span> <span class="o">+</span> <span class="n">args_string</span> <span class="o">+</span> <span class="s2">")"</span></div>
</pre></div>
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