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<!DOCTYPE html>
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<h1>Source code for torch.nn.modules.sparse</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">torch.nn.parameter</span> <span class="k">import</span> <span class="n">Parameter</span>
<span class="kn">from</span> <span class="nn">.module</span> <span class="k">import</span> <span class="n">Module</span>
<div class="viewcode-block" id="Embedding"><a class="viewcode-back" href="../../../../nn.html#torch.nn.Embedding">[docs]</a><span class="k">class</span> <span class="nc">Embedding</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
<span class="sd">r"""A simple lookup table that stores embeddings of a fixed dictionary and size.</span>
<span class="sd"> This module is often used to store word embeddings and retrieve them using indices.</span>
<span class="sd"> The input to the module is a list of indices, and the output is the corresponding</span>
<span class="sd"> word embeddings.</span>
<span class="sd"> Args:</span>
<span class="sd"> num_embeddings (int): size of the dictionary of embeddings</span>
<span class="sd"> embedding_dim (int): the size of each embedding vector</span>
<span class="sd"> padding_idx (int, optional): If given, pads the output with zeros whenever it encounters the index.</span>
<span class="sd"> max_norm (float, optional): If given, will renormalize the embeddings to always have a norm lesser than this</span>
<span class="sd"> norm_type (float, optional): The p of the p-norm to compute for the max_norm option</span>
<span class="sd"> scale_grad_by_freq (boolean, optional): if given, this will scale gradients by the frequency of</span>
<span class="sd"> the words in the dictionary.</span>
<span class="sd"> Attributes:</span>
<span class="sd"> weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)</span>
<span class="sd"> Shape:</span>
<span class="sd"> - Input: LongTensor `(N, W)`, N = mini-batch, W = number of indices to extract per mini-batch</span>
<span class="sd"> - Output: `(N, W, embedding_dim)`</span>
<span class="sd"> Examples::</span>
<span class="sd"> >>> # an Embedding module containing 10 tensors of size 3</span>
<span class="sd"> >>> embedding = nn.Embedding(10, 3)</span>
<span class="sd"> >>> # a batch of 2 samples of 4 indices each</span>
<span class="sd"> >>> input = Variable(torch.LongTensor([[1,2,4,5],[4,3,2,9]]))</span>
<span class="sd"> >>> embedding(input)</span>
<span class="sd"> Variable containing:</span>
<span class="sd"> (0 ,.,.) =</span>
<span class="sd"> -1.0822 1.2522 0.2434</span>
<span class="sd"> 0.8393 -0.6062 -0.3348</span>
<span class="sd"> 0.6597 0.0350 0.0837</span>
<span class="sd"> 0.5521 0.9447 0.0498</span>
<span class="sd"> (1 ,.,.) =</span>
<span class="sd"> 0.6597 0.0350 0.0837</span>
<span class="sd"> -0.1527 0.0877 0.4260</span>
<span class="sd"> 0.8393 -0.6062 -0.3348</span>
<span class="sd"> -0.8738 -0.9054 0.4281</span>
<span class="sd"> [torch.FloatTensor of size 2x4x3]</span>
<span class="sd"> >>> # example with padding_idx</span>
<span class="sd"> >>> embedding = nn.Embedding(10, 3, padding_idx=0)</span>
<span class="sd"> >>> input = Variable(torch.LongTensor([[0,2,0,5]]))</span>
<span class="sd"> >>> embedding(input)</span>
<span class="sd"> Variable containing:</span>
<span class="sd"> (0 ,.,.) =</span>
<span class="sd"> 0.0000 0.0000 0.0000</span>
<span class="sd"> 0.3452 0.4937 -0.9361</span>
<span class="sd"> 0.0000 0.0000 0.0000</span>
<span class="sd"> 0.0706 -2.1962 -0.6276</span>
<span class="sd"> [torch.FloatTensor of size 1x4x3]</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_embeddings</span><span class="p">,</span> <span class="n">embedding_dim</span><span class="p">,</span> <span class="n">padding_idx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">max_norm</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">norm_type</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">scale_grad_by_freq</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">sparse</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Embedding</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_embeddings</span> <span class="o">=</span> <span class="n">num_embeddings</span>
<span class="bp">self</span><span class="o">.</span><span class="n">embedding_dim</span> <span class="o">=</span> <span class="n">embedding_dim</span>
<span class="bp">self</span><span class="o">.</span><span class="n">padding_idx</span> <span class="o">=</span> <span class="n">padding_idx</span>
<span class="bp">self</span><span class="o">.</span><span class="n">max_norm</span> <span class="o">=</span> <span class="n">max_norm</span>
<span class="bp">self</span><span class="o">.</span><span class="n">norm_type</span> <span class="o">=</span> <span class="n">norm_type</span>
<span class="bp">self</span><span class="o">.</span><span class="n">scale_grad_by_freq</span> <span class="o">=</span> <span class="n">scale_grad_by_freq</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">Parameter</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="n">num_embeddings</span><span class="p">,</span> <span class="n">embedding_dim</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sparse</span> <span class="o">=</span> <span class="n">sparse</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reset_parameters</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">reset_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">padding_idx</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">weight</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">padding_idx</span><span class="p">]</span><span class="o">.</span><span class="n">fill_</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="n">padding_idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">padding_idx</span>
<span class="k">if</span> <span class="n">padding_idx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">padding_idx</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_backend</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span>
<span class="n">padding_idx</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_norm</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm_type</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">scale_grad_by_freq</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span>
<span class="p">)(</span><span class="nb">input</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">s</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{name}</span><span class="s1">(</span><span class="si">{num_embeddings}</span><span class="s1">, </span><span class="si">{embedding_dim}</span><span class="s1">'</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">padding_idx</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">s</span> <span class="o">+=</span> <span class="s1">', padding_idx=</span><span class="si">{padding_idx}</span><span class="s1">'</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_norm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">s</span> <span class="o">+=</span> <span class="s1">', max_norm=</span><span class="si">{max_norm}</span><span class="s1">'</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm_type</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">s</span> <span class="o">+=</span> <span class="s1">', norm_type=</span><span class="si">{norm_type}</span><span class="s1">'</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_grad_by_freq</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">False</span><span class="p">:</span>
<span class="n">s</span> <span class="o">+=</span> <span class="s1">', scale_grad_by_freq=</span><span class="si">{scale_grad_by_freq}</span><span class="s1">'</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">False</span><span class="p">:</span>
<span class="n">s</span> <span class="o">+=</span> <span class="s1">', sparse=True'</span>
<span class="n">s</span> <span class="o">+=</span> <span class="s1">')'</span>
<span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">__class__</span><span class="o">.</span><span class="n">__name__</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">__dict__</span><span class="p">)</span></div>
<span class="c1"># TODO: SparseLinear</span>
</pre></div>
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