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<!DOCTYPE html>
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<title>Automatic differentiation package - torch.autograd — PyTorch 0.1.12_2 documentation</title>
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<p class="caption"><span class="caption-text">Notes</span></p>
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<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a><ul>
<li class="toctree-l2"><a class="reference internal" href="notes/autograd.html#excluding-subgraphs-from-backward">Excluding subgraphs from backward</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#nllloss2d"><span class="hidden-section">NLLLoss2d</span></a></li>
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<div class="section" id="module-torch.autograd">
<span id="automatic-differentiation-package-torch-autograd"></span><h1>Automatic differentiation package - torch.autograd<a class="headerlink" href="#module-torch.autograd" title="Permalink to this headline">¶</a></h1>
<p>torch.autograd provides classes and functions implementing automatic
differentiation of arbitrary scalar valued functions. It requires minimal
changes to the existing code - you only need to wrap all tensors in
<a class="reference internal" href="#torch.autograd.Variable" title="torch.autograd.Variable"><code class="xref py py-class docutils literal"><span class="pre">Variable</span></code></a> objects.</p>
<dl class="function">
<dt id="torch.autograd.backward">
<code class="descclassname">torch.autograd.</code><code class="descname">backward</code><span class="sig-paren">(</span><em>variables</em>, <em>grad_variables</em>, <em>retain_variables=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd.html#backward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.backward" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the sum of gradients of given variables w.r.t. graph leaves.</p>
<p>The graph is differentiated using the chain rule. If any of <code class="docutils literal"><span class="pre">variables</span></code>
are non-scalar (i.e. their data has more than one element) and require
gradient, the function additionaly requires specifying <code class="docutils literal"><span class="pre">grad_variables</span></code>.
It should be a sequence of matching length, that containins gradient of
the differentiated function w.r.t. corresponding variables (<code class="docutils literal"><span class="pre">None</span></code> is an
acceptable value for all variables that don’t need gradient tensors).</p>
<p>This function accumulates gradients in the leaves - you might need to zero
them before calling it.</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>variables</strong> (<em>sequence of Variable</em>) – Variables of which the derivative will be
computed.</li>
<li><strong>grad_variables</strong> (<em>sequence of Tensor</em>) – Gradients w.r.t. each element of
corresponding variables. Required only for non-scalar variables that
require gradient.</li>
<li><strong>retain_variables</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – If <code class="docutils literal"><span class="pre">True</span></code>, buffers necessary for computing
gradients won’t be freed after use. It is only necessary to
specify <code class="docutils literal"><span class="pre">True</span></code> if you want to differentiate some subgraph multiple
times.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<div class="section" id="variable">
<h2>Variable<a class="headerlink" href="#variable" title="Permalink to this headline">¶</a></h2>
<div class="section" id="api-compatibility">
<h3>API compatibility<a class="headerlink" href="#api-compatibility" title="Permalink to this headline">¶</a></h3>
<p>Variable API is nearly the same as regular Tensor API (with the exception
of a couple in-place methods, that would overwrite inputs required for
gradient computation). In most cases Tensors can be safely replaced with
Variables and the code will remain to work just fine. Because of this,
we’re not documenting all the operations on variables, and you should
refer to <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal"><span class="pre">torch.Tensor</span></code></a> docs for this purpose.</p>
</div>
<div class="section" id="in-place-operations-on-variables">
<h3>In-place operations on Variables<a class="headerlink" href="#in-place-operations-on-variables" title="Permalink to this headline">¶</a></h3>
<p>Supporting in-place operations in autograd is a hard matter, and we discourage
their use in most cases. Autograd’s aggressive buffer freeing and reuse makes
it very efficient and there are very few occasions when in-place operations
actually lower memory usage by any significant amount. Unless you’re operating
under heavy memory pressure, you might never need to use them.</p>
</div>
<div class="section" id="in-place-correctness-checks">
<h3>In-place correctness checks<a class="headerlink" href="#in-place-correctness-checks" title="Permalink to this headline">¶</a></h3>
<p>All <a class="reference internal" href="#torch.autograd.Variable" title="torch.autograd.Variable"><code class="xref py py-class docutils literal"><span class="pre">Variable</span></code></a> s keep track of in-place operations applied to them, and
if the implementation detects that a variable was saved for backward in one of
the functions, but it was modified in-place afterwards, an error will be raised
once backward pass is started. This ensures that if you’re using in-place
functions and not seing any errors, you can be sure that the computed gradients
are correct.</p>
<dl class="class">
<dt id="torch.autograd.Variable">
<em class="property">class </em><code class="descclassname">torch.autograd.</code><code class="descname">Variable</code><a class="reference internal" href="_modules/torch/autograd/variable.html#Variable"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.Variable" title="Permalink to this definition">¶</a></dt>
<dd><p>Wraps a tensor and records the operations applied to it.</p>
<p>Variable is a thin wrapper around a Tensor object, that also holds
the gradient w.r.t. to it, and a reference to a function that created it.
This reference allows retracing the whole chain of operations that
created the data. If the Variable has been created by the user, its creator
will be <code class="docutils literal"><span class="pre">None</span></code> and we call such objects <em>leaf</em> Variables.</p>
<p>Since autograd only supports scalar valued function differentiation, grad
size always matches the data size. Also, grad is normally only allocated
for leaf variables, and will be always zero otherwise.</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">Variables:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – Wrapped tensor of any type.</li>
<li><strong>grad</strong> – Variable holding the gradient of type and location matching
the <code class="docutils literal"><span class="pre">.data</span></code>. This attribute is lazily allocated and can’t
be reassigned.</li>
<li><strong>requires_grad</strong> – Boolean indicating whether the Variable has been
created by a subgraph containing any Variable, that requires it.
See <a class="reference internal" href="notes/autograd.html#excluding-subgraphs"><span class="std std-ref">Excluding subgraphs from backward</span></a> for more details.
Can be changed only on leaf Variables.</li>
<li><strong>volatile</strong> – Boolean indicating that the Variable should be used in
inference mode, i.e. don’t save the history. See
<a class="reference internal" href="notes/autograd.html#excluding-subgraphs"><span class="std std-ref">Excluding subgraphs from backward</span></a> for more details.
Can be changed only on leaf Variables.</li>
<li><strong>creator</strong> – Function of which the variable was an output. For leaf
(user created) variables it’s <code class="docutils literal"><span class="pre">None</span></code>. Read-only attribute.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data</strong> (<em>any tensor class</em>) – Tensor to wrap.</li>
<li><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – Value of the requires_grad flag. <strong>Keyword only.</strong></li>
<li><strong>volatile</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – Value of the volatile flag. <strong>Keyword only.</strong></li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torch.autograd.Variable.backward">
<code class="descname">backward</code><span class="sig-paren">(</span><em>gradient=None</em>, <em>retain_variables=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/variable.html#Variable.backward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.Variable.backward" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the gradient of current variable w.r.t. graph leaves.</p>
<p>The graph is differentiated using the chain rule. If the variable is
non-scalar (i.e. its data has more than one element) and requires
gradient, the function additionaly requires specifying <code class="docutils literal"><span class="pre">gradient</span></code>.
It should be a tensor of matching type and location, that contains
the gradient of the differentiated function w.r.t. <code class="docutils literal"><span class="pre">self</span></code>.</p>
<p>This function accumulates gradients in the leaves - you might need to zero
them before calling it.</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>gradient</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Gradient of the differentiated function
w.r.t. the data. Required only if the data has more than one
element. Type and location should match these of <code class="docutils literal"><span class="pre">self.data</span></code>.</li>
<li><strong>retain_variables</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – If <code class="docutils literal"><span class="pre">True</span></code>, buffers necessary for computing
gradients won’t be freed after use. It is only necessary to
specify <code class="docutils literal"><span class="pre">True</span></code> if you want to differentiate some subgraph multiple
times (in some cases it will be much more efficient to use
<cite>autograd.backward</cite>).</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.autograd.Variable.detach">
<code class="descname">detach</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/variable.html#Variable.detach"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.Variable.detach" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a new Variable, detached from the current graph.</p>
<p>Result will never require gradient. If the input is volatile, the output
will be volatile too.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Returned Variable uses the same data tensor, as the original one, and
in-place modifications on either of them will be seen, and may trigger
errors in correctness checks.</p>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.autograd.Variable.detach_">
<code class="descname">detach_</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/variable.html#Variable.detach_"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.Variable.detach_" title="Permalink to this definition">¶</a></dt>
<dd><p>Detaches the Variable from the graph that created it, making it a leaf.</p>
</dd></dl>
<dl class="method">
<dt id="torch.autograd.Variable.register_hook">
<code class="descname">register_hook</code><span class="sig-paren">(</span><em>hook</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/variable.html#Variable.register_hook"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.Variable.register_hook" title="Permalink to this definition">¶</a></dt>
<dd><p>Registers a backward hook.</p>
<p>The hook will be called every time a gradient with respect to the
variable is computed. The hook should have the following signature:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">grad</span><span class="p">)</span> <span class="o">-></span> <span class="n">Variable</span> <span class="ow">or</span> <span class="kc">None</span>
</pre></div>
</div>
<p>The hook should not modify its argument, but it can optionally return
a new gradient which will be used in place of <code class="xref py py-attr docutils literal"><span class="pre">grad</span></code>.</p>
<p>This function returns a handle with a method <code class="docutils literal"><span class="pre">handle.remove()</span></code>
that removes the hook from the module.</p>
<p class="rubric">Example</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">v</span> <span class="o">=</span> <span class="n">Variable</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="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</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">h</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">register_hook</span><span class="p">(</span><span class="k">lambda</span> <span class="n">grad</span><span class="p">:</span> <span class="n">grad</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)</span> <span class="c1"># double the gradient</span>
<span class="gp">>>> </span><span class="n">v</span><span class="o">.</span><span class="n">backward</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="mi">1</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="gp">>>> </span><span class="n">v</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">data</span>
<span class="go"> 2</span>
<span class="go"> 2</span>
<span class="go"> 2</span>
<span class="go">[torch.FloatTensor of size 3]</span>
<span class="gp">>>> </span><span class="n">h</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span> <span class="c1"># removes the hook</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.autograd.Variable.reinforce">
<code class="descname">reinforce</code><span class="sig-paren">(</span><em>reward</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/variable.html#Variable.reinforce"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.Variable.reinforce" title="Permalink to this definition">¶</a></dt>
<dd><p>Registers a reward obtained as a result of a stochastic process.</p>
<p>Differentiating stochastic nodes requires providing them with reward
value. If your graph contains any stochastic operations, you should
call this function on their outputs. Otherwise an error will be raised.</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"><strong>reward</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Tensor with per-element rewards. It has to match
the device location and shape of Variable’s data.</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
</div>
<div class="section" id="function">
<h2><span class="hidden-section">Function</span><a class="headerlink" href="#function" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.autograd.Function">
<em class="property">class </em><code class="descclassname">torch.autograd.</code><code class="descname">Function</code><a class="reference internal" href="_modules/torch/autograd/function.html#Function"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.Function" title="Permalink to this definition">¶</a></dt>
<dd><p>Records operation history and defines formulas for differentiating ops.</p>
<p>Every operation performed on <a class="reference internal" href="#torch.autograd.Variable" title="torch.autograd.Variable"><code class="xref py py-class docutils literal"><span class="pre">Variable</span></code></a> s creates a new function
object, that performs the computation, and records that it happened.
The history is retained in the form of a DAG of functions, with edges
denoting data dependencies (<code class="docutils literal"><span class="pre">input</span> <span class="pre"><-</span> <span class="pre">output</span></code>). Then, when backward is
called, the graph is processed in the topological ordering, by calling
<a class="reference internal" href="#torch.autograd.backward" title="torch.autograd.backward"><code class="xref py py-func docutils literal"><span class="pre">backward()</span></code></a> methods of each <a class="reference internal" href="#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal"><span class="pre">Function</span></code></a> object, and passing
returned gradients on to next <a class="reference internal" href="#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal"><span class="pre">Function</span></code></a> s.</p>
<p>Normally, the only way users interact with functions is by creating
subclasses and defining new operations. This is a recommended way of
extending torch.autograd.</p>
<p>Since Function logic is a hotspot in most scripts, almost all of it
was moved to our C backend, to ensure that the framework overhead is
minimal.</p>
<p>Each function is meant to be used only once (in the forward pass).</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">Variables:</th><td class="field-body"><ul class="first last simple">
<li><strong>saved_tensors</strong> – Tuple of Tensors that were saved in the call to
<a class="reference internal" href="#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-func docutils literal"><span class="pre">forward()</span></code></a>.</li>
<li><strong>needs_input_grad</strong> – Tuple of booleans of length <code class="xref py py-attr docutils literal"><span class="pre">num_inputs</span></code>,
indicating whether a given input requires gradient. This can be
used to optimize buffers saved for backward, and ignoring gradient
computation in <a class="reference internal" href="#torch.autograd.Function.backward" title="torch.autograd.Function.backward"><code class="xref py py-func docutils literal"><span class="pre">backward()</span></code></a>.</li>
<li><strong>num_inputs</strong> – Number of inputs given to <a class="reference internal" href="#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-func docutils literal"><span class="pre">forward()</span></code></a>.</li>
<li><strong>num_outputs</strong> – Number of tensors returned by <a class="reference internal" href="#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-func docutils literal"><span class="pre">forward()</span></code></a>.</li>
<li><strong>requires_grad</strong> – Boolean indicating whether the <a class="reference internal" href="#torch.autograd.backward" title="torch.autograd.backward"><code class="xref py py-func docutils literal"><span class="pre">backward()</span></code></a> will
ever need to be called.</li>
<li><strong>previous_functions</strong> – Tuple of (int, Function) pairs of length
<code class="xref py py-attr docutils literal"><span class="pre">num_inputs</span></code>. Each entry contains a reference to a
<a class="reference internal" href="#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal"><span class="pre">Function</span></code></a> that created corresponding input, and an index
of the previous function output that’s been used.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torch.autograd.Function.backward">
<code class="descname">backward</code><span class="sig-paren">(</span><em>*grad_output</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/function.html#Function.backward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.Function.backward" title="Permalink to this definition">¶</a></dt>
<dd><p>Defines a formula for differentiating the operation.</p>
<p>This function is to be overriden by all subclasses.</p>
<p>All arguments are tensors. It has to accept exactly as many arguments,
as many outputs did <a class="reference internal" href="#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-func docutils literal"><span class="pre">forward()</span></code></a> return, and it should return as
many tensors, as there were inputs to <a class="reference internal" href="#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-func docutils literal"><span class="pre">forward()</span></code></a>. Each argument
is the gradient w.r.t the given output, and each returned value should
be the gradient w.r.t. the corresponding input.</p>
</dd></dl>
<dl class="method">
<dt id="torch.autograd.Function.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>*input</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/function.html#Function.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.Function.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs the operation.</p>
<p>This function is to be overriden by all subclasses.</p>
<p>It can take and return an arbitrary number of tensors.</p>
</dd></dl>
<dl class="method">
<dt id="torch.autograd.Function.mark_dirty">
<code class="descname">mark_dirty</code><span class="sig-paren">(</span><em>*args</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/function.html#Function.mark_dirty"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.Function.mark_dirty" title="Permalink to this definition">¶</a></dt>
<dd><p>Marks given tensors as modified in an in-place operation.</p>
<p><strong>This should be called at most once, only from inside the</strong>
<a class="reference internal" href="#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-func docutils literal"><span class="pre">forward()</span></code></a> <strong>method, and all arguments should be inputs.</strong></p>
<p>Every tensor that’s been modified in-place in a call to <a class="reference internal" href="#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-func docutils literal"><span class="pre">forward()</span></code></a>
should be given to this function, to ensure correcness of our checks.
It doesn’t matter wheter the function is called before or after
modification.</p>
</dd></dl>
<dl class="method">
<dt id="torch.autograd.Function.mark_non_differentiable">
<code class="descname">mark_non_differentiable</code><span class="sig-paren">(</span><em>*args</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/function.html#Function.mark_non_differentiable"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.Function.mark_non_differentiable" title="Permalink to this definition">¶</a></dt>
<dd><p>Marks outputs as non-differentiable.</p>
<p><strong>This should be called at most once, only from inside the</strong>
<a class="reference internal" href="#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-func docutils literal"><span class="pre">forward()</span></code></a> <strong>method, and all arguments should be outputs.</strong></p>
<p>This will mark outputs as not requiring gradients, increasing the
efficiency of backward computation. You still need to accept a gradient
for each output in <a class="reference internal" href="#torch.autograd.Function.backward" title="torch.autograd.Function.backward"><code class="xref py py-meth docutils literal"><span class="pre">backward()</span></code></a>, but it’s always going to
be <code class="docutils literal"><span class="pre">None</span></code>.</p>
<p>This is used e.g. for indices returned from a max <a class="reference internal" href="#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal"><span class="pre">Function</span></code></a>.</p>
</dd></dl>
<dl class="method">
<dt id="torch.autograd.Function.mark_shared_storage">
<code class="descname">mark_shared_storage</code><span class="sig-paren">(</span><em>*pairs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/function.html#Function.mark_shared_storage"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.Function.mark_shared_storage" title="Permalink to this definition">¶</a></dt>
<dd><p>Marks that given pairs of distinct tensors are sharing storage.</p>
<p><strong>This should be called at most once, only from inside the</strong>
<a class="reference internal" href="#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-func docutils literal"><span class="pre">forward()</span></code></a> <strong>method, and all arguments should be pairs of
(input, output).</strong></p>
<p>If some of the outputs are going to be tensors sharing storage with
some of the inputs, all pairs of (input_arg, output_arg) should be
given to this function, to ensure correctness checking of in-place
modification. The only exception is when an output is exactly the same
tensor as input (e.g. in-place ops). In such case it’s easy to conclude
that they’re sharing data, so we don’t require specifying such
dependencies.</p>
<p>This function is not needed in most functions. It’s primarily used in
indexing and transpose ops.</p>
</dd></dl>
<dl class="method">
<dt id="torch.autograd.Function.save_for_backward">
<code class="descname">save_for_backward</code><span class="sig-paren">(</span><em>*tensors</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/function.html#Function.save_for_backward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.Function.save_for_backward" title="Permalink to this definition">¶</a></dt>
<dd><p>Saves given tensors for a future call to <a class="reference internal" href="#torch.autograd.Function.backward" title="torch.autograd.Function.backward"><code class="xref py py-func docutils literal"><span class="pre">backward()</span></code></a>.</p>
<p><strong>This should be called at most once, and only from inside the</strong>
<a class="reference internal" href="#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-func docutils literal"><span class="pre">forward()</span></code></a> <strong>method.</strong></p>
<p>Later, saved tensors can be accessed through the <code class="xref py py-attr docutils literal"><span class="pre">saved_tensors</span></code>
attribute. Before returning them to the user, a check is made, to
ensure they weren’t used in any in-place operation that modified
their content.</p>
<p>Arguments can also be <code class="docutils literal"><span class="pre">None</span></code>.</p>
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