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<div class="section" id="torch-sparse">
<span id="sparse-docs"></span><h1>torch.sparse<a class="headerlink" href="#torch-sparse" title="Permalink to this headline">¶</a></h1>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">This API is currently experimental and may change in the near future.</p>
</div>
<p>Torch supports sparse tensors in COO(rdinate) format, which can
efficiently store and process tensors for which the majority of elements
are zeros.</p>
<p>A sparse tensor is represented as a pair of dense tensors: a tensor
of values and a 2D tensor of indices. A sparse tensor can be constructed
by providing these two tensors, as well as the size of the sparse tensor
(which cannot be inferred from these tensors!) Suppose we want to define
a sparse tensor with the entry 3 at location (0, 2), entry 4 at
location (1, 0), and entry 5 at location (1, 2). We would then write:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">i</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</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="mi">1</span><span class="p">],</span>
<span class="go"> [2, 0, 2]])</span>
<span class="gp">>>> </span><span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">v</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="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]))</span><span class="o">.</span><span class="n">to_dense</span><span class="p">()</span>
<span class="go"> 0 0 3</span>
<span class="go"> 4 0 5</span>
<span class="go">[torch.FloatTensor of size 2x3]</span>
</pre></div>
</div>
<p>Note that the input to LongTensor is NOT a list of index tuples. If you want
to write your indices this way, you should transpose before passing them to
the sparse constructor:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">i</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span> <span class="p">])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">t</span><span class="p">(),</span> <span class="n">v</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="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]))</span><span class="o">.</span><span class="n">to_dense</span><span class="p">()</span>
<span class="go"> 0 0 3</span>
<span class="go"> 4 0 5</span>
<span class="go">[torch.FloatTensor of size 2x3]</span>
</pre></div>
</div>
<p>You can also construct hybrid sparse tensors, where only the first n
dimensions are sparse, and the rest of the dimensions are dense.</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">i</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span><span class="o">.</span><span class="n">to_dense</span><span class="p">()</span>
<span class="go"> 0 0</span>
<span class="go"> 0 0</span>
<span class="go"> 1 3</span>
<span class="go"> 0 0</span>
<span class="go"> 5 7</span>
<span class="go">[torch.FloatTensor of size 5x2]</span>
</pre></div>
</div>
<p>An empty sparse tensor can be constructed by specifying its size:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="go">SparseFloatTensor of size 2x3 with indices:</span>
<span class="go">[torch.LongTensor with no dimension]</span>
<span class="go">and values:</span>
<span class="go">[torch.FloatTensor with no dimension]</span>
</pre></div>
</div>
<dl class="docutils">
<dt>SparseTensor has the following invariants:</dt>
<dd><ol class="first last arabic simple">
<li>sparse_dim + dense_dim = len(SparseTensor.shape)</li>
<li>SparseTensor._indices().shape = (sparse_dim, nnz)</li>
<li>SparseTensor._values().shape = (nnz, SparseTensor.shape[sparse_dim:])</li>
</ol>
</dd>
</dl>
<p>Since SparseTensor._indices() is always a 2D tensor, the smallest sparse_dim = 1.
Therefore, representation of a SparseTensor of sparse_dim = 0 is simply a dense tensor.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p>Our sparse tensor format permits <em>uncoalesced</em> sparse tensors, where
there may be duplicate coordinates in the indices; in this case,
the interpretation is that the value at that index is the sum of all
duplicate value entries. Uncoalesced tensors permit us to implement
certain operators more efficiently.</p>
<p>For the most part, you shouldn’t have to care whether or not a
sparse tensor is coalesced or not, as most operations will work
identically given a coalesced or uncoalesced sparse tensor.
However, there are two cases in which you may need to care.</p>
<p>First, if you repeatedly perform an operation that can produce
duplicate entries (e.g., <a class="reference internal" href="#torch.sparse.FloatTensor.add" title="torch.sparse.FloatTensor.add"><code class="xref py py-func docutils literal"><span class="pre">torch.sparse.FloatTensor.add()</span></code></a>), you
should occasionally coalesce your sparse tensors to prevent
them from growing too large.</p>
<p>Second, some operators will produce different values depending on
whether or not they are coalesced or not (e.g.,
<a class="reference internal" href="#torch.sparse.FloatTensor._values" title="torch.sparse.FloatTensor._values"><code class="xref py py-func docutils literal"><span class="pre">torch.sparse.FloatTensor._values()</span></code></a> and
<a class="reference internal" href="#torch.sparse.FloatTensor._indices" title="torch.sparse.FloatTensor._indices"><code class="xref py py-func docutils literal"><span class="pre">torch.sparse.FloatTensor._indices()</span></code></a>, as well as
<a class="reference internal" href="tensors.html#torch.Tensor.sparse_mask" title="torch.Tensor.sparse_mask"><code class="xref py py-func docutils literal"><span class="pre">torch.Tensor.sparse_mask()</span></code></a>). These operators are
prefixed by an underscore to indicate that they reveal internal
implementation details and should be used with care, since code
that works with coalesced sparse tensors may not work with
uncoalesced sparse tensors; generally speaking, it is safest
to explicitly coalesce before working with these operators.</p>
<p class="last">For example, suppose that we wanted to implement an operator
by operating directly on <a class="reference internal" href="#torch.sparse.FloatTensor._values" title="torch.sparse.FloatTensor._values"><code class="xref py py-func docutils literal"><span class="pre">torch.sparse.FloatTensor._values()</span></code></a>.
Multiplication by a scalar can be implemented in the obvious way,
as multiplication distributes over addition; however, square root
cannot be implemented directly, since <code class="docutils literal"><span class="pre">sqrt(a</span> <span class="pre">+</span> <span class="pre">b)</span> <span class="pre">!=</span> <span class="pre">sqrt(a)</span> <span class="pre">+</span>
<span class="pre">sqrt(b)</span></code> (which is what would be computed if you were given an
uncoalesced tensor.)</p>
</div>
<dl class="class">
<dt id="torch.sparse.FloatTensor">
<em class="property">class </em><code class="descclassname">torch.sparse.</code><code class="descname">FloatTensor</code><a class="headerlink" href="#torch.sparse.FloatTensor" title="Permalink to this definition">¶</a></dt>
<dd><dl class="method">
<dt id="torch.sparse.FloatTensor.add">
<code class="descname">add</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.add" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.add_">
<code class="descname">add_</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.add_" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.clone">
<code class="descname">clone</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.clone" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.dim">
<code class="descname">dim</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.dim" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.div">
<code class="descname">div</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.div" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.div_">
<code class="descname">div_</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.div_" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.get_device">
<code class="descname">get_device</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.get_device" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.hspmm">
<code class="descname">hspmm</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.hspmm" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.mm">
<code class="descname">mm</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.mm" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.mul">
<code class="descname">mul</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.mul" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.mul_">
<code class="descname">mul_</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.mul_" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.narrow_copy">
<code class="descname">narrow_copy</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.narrow_copy" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.resizeAs_">
<code class="descname">resizeAs_</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.resizeAs_" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.size">
<code class="descname">size</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.size" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.spadd">
<code class="descname">spadd</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.spadd" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.spmm">
<code class="descname">spmm</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.spmm" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.sspaddmm">
<code class="descname">sspaddmm</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.sspaddmm" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.sspmm">
<code class="descname">sspmm</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.sspmm" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.sub">
<code class="descname">sub</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.sub" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.sub_">
<code class="descname">sub_</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.sub_" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.t_">
<code class="descname">t_</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.t_" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.toDense">
<code class="descname">toDense</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.toDense" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.transpose">
<code class="descname">transpose</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.transpose" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.transpose_">
<code class="descname">transpose_</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.transpose_" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.zero_">
<code class="descname">zero_</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.zero_" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.coalesce">
<code class="descname">coalesce</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.coalesce" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor.is_coalesced">
<code class="descname">is_coalesced</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor.is_coalesced" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor._indices">
<code class="descname">_indices</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor._indices" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor._values">
<code class="descname">_values</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor._values" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.sparse.FloatTensor._nnz">
<code class="descname">_nnz</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.sparse.FloatTensor._nnz" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
<div class="section" id="functions">
<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torch.sparse.addmm">
<code class="descclassname">torch.sparse.</code><code class="descname">addmm</code><span class="sig-paren">(</span><em>mat</em>, <em>mat1</em>, <em>mat2</em>, <em>beta=1</em>, <em>alpha=1</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/sparse.html#addmm"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.sparse.addmm" title="Permalink to this definition">¶</a></dt>
<dd><p>This function does exact same thing as <a class="reference internal" href="torch.html#torch.addmm" title="torch.addmm"><code class="xref py py-func docutils literal"><span class="pre">torch.addmm()</span></code></a> in the forward,
except that it supports backward for sparse matrix <code class="xref py py-attr docutils literal"><span class="pre">mat1</span></code>. <code class="xref py py-attr docutils literal"><span class="pre">mat1</span></code>
need to have <cite>sparse_dim = 2</cite>. Note that the gradients of <code class="xref py py-attr docutils literal"><span class="pre">mat1</span></code> is a
coalesced sparse tensor.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>mat</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – a dense matrix to be added</li>
<li><strong>mat1</strong> (<em>SparseTensor</em>) – a sparse matrix to be multiplied</li>
<li><strong>mat2</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – a dense matrix be multiplied</li>
<li><strong>beta</strong> (<em>Number</em><em>, </em><em>optional</em>) – multiplier for <code class="xref py py-attr docutils literal"><span class="pre">mat</span></code> (<span class="math">\(\beta\)</span>)</li>
<li><strong>alpha</strong> (<em>Number</em><em>, </em><em>optional</em>) – multiplier for <span class="math">\(mat1 @ mat2\)</span> (<span class="math">\(\alpha\)</span>)</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.sparse.mm">
<code class="descclassname">torch.sparse.</code><code class="descname">mm</code><span class="sig-paren">(</span><em>mat1</em>, <em>mat2</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/sparse.html#mm"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.sparse.mm" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs a matrix multiplication of the sparse matrix <code class="xref py py-attr docutils literal"><span class="pre">mat1</span></code>
and dense matrix <code class="xref py py-attr docutils literal"><span class="pre">mat2</span></code>. Similar to <a class="reference internal" href="torch.html#torch.mm" title="torch.mm"><code class="xref py py-func docutils literal"><span class="pre">torch.mm()</span></code></a>, If <code class="xref py py-attr docutils literal"><span class="pre">mat1</span></code> is a
<span class="math">\((n \times m)\)</span> tensor, <code class="xref py py-attr docutils literal"><span class="pre">mat2</span></code> is a <span class="math">\((m \times p)\)</span> tensor, out will be a
<span class="math">\((n \times p)\)</span> dense tensor. <code class="xref py py-attr docutils literal"><span class="pre">mat1</span></code> need to have <cite>sparse_dim = 2</cite>.
This function also supports backward for both matrices. Note that the gradients of
<code class="xref py py-attr docutils literal"><span class="pre">mat1</span></code> is a coalesced sparse tensor.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>mat1</strong> (<em>SparseTensor</em>) – the first sparse matrix to be multiplied</li>
<li><strong>mat2</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the second dense matrix to be multiplied</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">to_sparse</span><span class="p">()</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">a</span>
<span class="go">tensor(indices=tensor([[0, 0, 0, 1, 1, 1],</span>
<span class="go"> [0, 1, 2, 0, 1, 2]]),</span>
<span class="go"> values=tensor([ 1.5901, 0.0183, -0.6146, 1.8061, -0.0112, 0.6302]),</span>
<span class="go"> size=(2, 3), nnz=6, layout=torch.sparse_coo, requires_grad=True)</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">b</span>
<span class="go">tensor([[-0.6479, 0.7874],</span>
<span class="go"> [-1.2056, 0.5641],</span>
<span class="go"> [-1.1716, -0.9923]], requires_grad=True)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span>
<span class="go">tensor([[-0.3323, 1.8723],</span>
<span class="go"> [-1.8951, 0.7904]], grad_fn=<SparseAddmmBackward>)</span>
<span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">a</span><span class="o">.</span><span class="n">grad</span>
<span class="go">tensor(indices=tensor([[0, 0, 0, 1, 1, 1],</span>
<span class="go"> [0, 1, 2, 0, 1, 2]]),</span>
<span class="go"> values=tensor([ 0.1394, -0.6415, -2.1639, 0.1394, -0.6415, -2.1639]),</span>
<span class="go"> size=(2, 3), nnz=6, layout=torch.sparse_coo)</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.sparse.sum">
<code class="descclassname">torch.sparse.</code><code class="descname">sum</code><span class="sig-paren">(</span><em>input</em>, <em>dim=None</em>, <em>dtype=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/sparse.html#sum"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.sparse.sum" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the sum of each row of SparseTensor <code class="xref py py-attr docutils literal"><span class="pre">input</span></code> in the given
dimensions <code class="xref py py-attr docutils literal"><span class="pre">dim</span></code>. If :attr::<cite>dim</cite> is a list of dimensions,
reduce over all of them. When sum over all <code class="docutils literal"><span class="pre">sparse_dim</span></code>, this method
returns a Tensor instead of SparseTensor.</p>
<p>All summed <code class="xref py py-attr docutils literal"><span class="pre">dim</span></code> are squeezed (see <a class="reference internal" href="torch.html#torch.squeeze" title="torch.squeeze"><code class="xref py py-func docutils literal"><span class="pre">torch.squeeze()</span></code></a>), resulting an output
tensor having :attr::<cite>dim</cite> fewer dimensions than <code class="xref py py-attr docutils literal"><span class="pre">input</span></code>.</p>
<p>During backward, only gradients at <code class="docutils literal"><span class="pre">nnz</span></code> locations of <code class="xref py py-attr docutils literal"><span class="pre">input</span></code>
will propagate back. Note that the gradients of <code class="xref py py-attr docutils literal"><span class="pre">input</span></code> is coalesced.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>input</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the input SparseTensor</li>
<li><strong>dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em> or </em><em>tuple of python:ints</em>) – a dimension or a list of dimensions to reduce. Default: reduce
over all dims.</li>
<li><strong>dtype</strong> (<code class="xref py py-class docutils literal"><span class="pre">torch.dtype</span></code>, optional) – the desired data type of returned Tensor.
Default: dtype of <code class="xref py py-attr docutils literal"><span class="pre">input</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">nnz</span> <span class="o">=</span> <span class="mi">3</span>
<span class="gp">>>> </span><span class="n">dims</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">I</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dims</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">nnz</span><span class="p">,)),</span>
<span class="go"> torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz)</span>
<span class="gp">>>> </span><span class="n">V</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">nnz</span><span class="p">,</span> <span class="n">dims</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">dims</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
<span class="gp">>>> </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">dims</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">S</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sparse_coo_tensor</span><span class="p">(</span><span class="n">I</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">S</span>
<span class="go">tensor(indices=tensor([[2, 0, 3],</span>
<span class="go"> [2, 4, 1]]),</span>
<span class="go"> values=tensor([[[-0.6438, -1.6467, 1.4004],</span>
<span class="go"> [ 0.3411, 0.0918, -0.2312]],</span>
<span class="go"> [[ 0.5348, 0.0634, -2.0494],</span>
<span class="go"> [-0.7125, -1.0646, 2.1844]],</span>
<span class="go"> [[ 0.1276, 0.1874, -0.6334],</span>
<span class="go"> [-1.9682, -0.5340, 0.7483]]]),</span>
<span class="go"> size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo)</span>
<span class="go"># when sum over only part of sparse_dims, return a SparseTensor</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">S</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
<span class="go">tensor(indices=tensor([[0, 2, 3]]),</span>
<span class="go"> values=tensor([[-1.4512, 0.4073],</span>
<span class="go"> [-0.8901, 0.2017],</span>
<span class="go"> [-0.3183, -1.7539]]),</span>
<span class="go"> size=(5, 2), nnz=3, layout=torch.sparse_coo)</span>
<span class="go"># when sum over all sparse dim, return a dense Tensor</span>
<span class="go"># with summed dims squeezed</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">S</span><span class="p">,</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="mi">3</span><span class="p">])</span>
<span class="go">tensor([-2.6596, -1.1450])</span>
</pre></div>
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