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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><code class="docutils literal"><span class="pre">torch.autograd</span></code> provides classes and functions implementing automatic
differentiation of arbitrary scalar valued functions. It requires minimal
changes to the existing code - you only need to declare <code class="xref py py-class docutils literal"><span class="pre">Tensor</span></code> s
for which gradients should be computed with the <code class="docutils literal"><span class="pre">requires_grad=True</span></code> keyword.</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>tensors</em>, <em>grad_tensors=None</em>, <em>retain_graph=None</em>, <em>create_graph=False</em>, <em>grad_variables=None</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 tensors 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">tensors</span></code>
are non-scalar (i.e. their data has more than one element) and require
gradient, the function additionally requires specifying <code class="docutils literal"><span class="pre">grad_tensors</span></code>.
It should be a sequence of matching length, that contains gradient of
the differentiated function w.r.t. corresponding tensors (<code class="docutils literal"><span class="pre">None</span></code> is an
acceptable value for all tensors 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>tensors</strong> (<em>sequence of Tensor</em>) – Tensors of which the derivative will be
computed.</li>
<li><strong>grad_tensors</strong> (<em>sequence of</em><em> (</em><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em>)</em>) – Gradients w.r.t.
each element of corresponding tensors. None values can be specified for
scalar Tensors or ones that don’t require grad. If a None value would
be acceptable for all grad_tensors, then this argument is optional.</li>
<li><strong>retain_graph</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If <code class="docutils literal"><span class="pre">False</span></code>, the graph used to compute the grad
will be freed. Note that in nearly all cases setting this option to <code class="docutils literal"><span class="pre">True</span></code>
is not needed and often can be worked around in a much more efficient
way. Defaults to the value of <code class="docutils literal"><span class="pre">create_graph</span></code>.</li>
<li><strong>create_graph</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If <code class="docutils literal"><span class="pre">True</span></code>, graph of the derivative will
be constructed, allowing to compute higher order derivative products.
Defaults to <code class="docutils literal"><span class="pre">False</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.autograd.grad">
<code class="descclassname">torch.autograd.</code><code class="descname">grad</code><span class="sig-paren">(</span><em>outputs</em>, <em>inputs</em>, <em>grad_outputs=None</em>, <em>retain_graph=None</em>, <em>create_graph=False</em>, <em>only_inputs=True</em>, <em>allow_unused=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd.html#grad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.grad" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes and returns the sum of gradients of outputs w.r.t. the inputs.</p>
<p><code class="docutils literal"><span class="pre">grad_outputs</span></code> should be a sequence of length matching <code class="docutils literal"><span class="pre">output</span></code>
containing the pre-computed gradients w.r.t. each of the outputs. If an
output doesn’t require_grad, then the gradient can be <code class="docutils literal"><span class="pre">None</span></code>).</p>
<p>If <code class="docutils literal"><span class="pre">only_inputs</span></code> is <code class="docutils literal"><span class="pre">True</span></code>, the function will only return a list of gradients
w.r.t the specified inputs. If it’s <code class="docutils literal"><span class="pre">False</span></code>, then gradient w.r.t. all remaining
leaves will still be computed, and will be accumulated into their <code class="docutils literal"><span class="pre">.grad</span></code>
attribute.</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>outputs</strong> (<em>sequence of Tensor</em>) – outputs of the differentiated function.</li>
<li><strong>inputs</strong> (<em>sequence of Tensor</em>) – Inputs w.r.t. which the gradient will be
returned (and not accumulated into <code class="docutils literal"><span class="pre">.grad</span></code>).</li>
<li><strong>grad_outputs</strong> (<em>sequence of Tensor</em>) – Gradients w.r.t. each output.
None values can be specified for scalar Tensors or ones that don’t require
grad. If a None value would be acceptable for all grad_tensors, then this
argument is optional. Default: None.</li>
<li><strong>retain_graph</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If <code class="docutils literal"><span class="pre">False</span></code>, the graph used to compute the grad
will be freed. Note that in nearly all cases setting this option to <code class="docutils literal"><span class="pre">True</span></code>
is not needed and often can be worked around in a much more efficient
way. Defaults to the value of <code class="docutils literal"><span class="pre">create_graph</span></code>.</li>
<li><strong>create_graph</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If <code class="docutils literal"><span class="pre">True</span></code>, graph of the derivative will
be constructed, allowing to compute higher order derivative products.
Default: <code class="docutils literal"><span class="pre">False</span></code>.</li>
<li><strong>allow_unused</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If <code class="docutils literal"><span class="pre">False</span></code>, specifying inputs that were not
used when computing outputs (and therefore their grad is always zero)
is an error. Defaults to <code class="docutils literal"><span class="pre">False</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<div class="section" id="locally-disabling-gradient-computation">
<span id="locally-disable-grad"></span><h2>Locally disabling gradient computation<a class="headerlink" href="#locally-disabling-gradient-computation" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.autograd.no_grad">
<em class="property">class </em><code class="descclassname">torch.autograd.</code><code class="descname">no_grad</code><a class="reference internal" href="_modules/torch/autograd/grad_mode.html#no_grad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.no_grad" title="Permalink to this definition">¶</a></dt>
<dd><p>Context-manager that disabled gradient calculation.</p>
<p>Disabling gradient calculation is useful for inference, when you are sure
that you will not call <code class="xref py py-meth docutils literal"><span class="pre">Tensor.backward()</span></code>. It will reduce memory
consumption for computations that would otherwise have <cite>requires_grad=True</cite>.
In this mode, the result of every computation will have
<cite>requires_grad=False</cite>, even when the inputs have <cite>requires_grad=True</cite>.</p>
<p>Also functions as a decorator.</p>
<p>Example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</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="mi">1</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="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="gp">... </span> <span class="n">y</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="mi">2</span>
<span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">requires_grad</span>
<span class="go">False</span>
<span class="gp">>>> </span><span class="nd">@torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">()</span>
<span class="gp">... </span><span class="k">def</span> <span class="nf">doubler</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="gp">... </span> <span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="mi">2</span>
<span class="gp">>>> </span><span class="n">z</span> <span class="o">=</span> <span class="n">doubler</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">z</span><span class="o">.</span><span class="n">requires_grad</span>
<span class="go">False</span>
</pre></div>
</div>
</dd></dl>
<dl class="class">
<dt id="torch.autograd.enable_grad">
<em class="property">class </em><code class="descclassname">torch.autograd.</code><code class="descname">enable_grad</code><a class="reference internal" href="_modules/torch/autograd/grad_mode.html#enable_grad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.enable_grad" title="Permalink to this definition">¶</a></dt>
<dd><p>Context-manager that enables gradient calculation.</p>
<p>Enables gradient calculation inside a <a class="reference internal" href="#torch.autograd.no_grad" title="torch.autograd.no_grad"><code class="xref py py-class docutils literal"><span class="pre">no_grad</span></code></a> context. This has
no effect outside of <a class="reference internal" href="#torch.autograd.no_grad" title="torch.autograd.no_grad"><code class="xref py py-class docutils literal"><span class="pre">no_grad</span></code></a>.</p>
<p>Also functions as a decorator.</p>
<p>Example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</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="mi">1</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="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="gp">... </span> <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">enable_grad</span><span class="p">():</span>
<span class="gp">... </span> <span class="n">y</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="mi">2</span>
<span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">requires_grad</span>
<span class="go">True</span>
<span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">grad</span>
<span class="gp">>>> </span><span class="nd">@torch</span><span class="o">.</span><span class="n">enable_grad</span><span class="p">()</span>
<span class="gp">... </span><span class="k">def</span> <span class="nf">doubler</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="gp">... </span> <span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="mi">2</span>
<span class="gp">>>> </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="gp">... </span> <span class="n">z</span> <span class="o">=</span> <span class="n">doubler</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">z</span><span class="o">.</span><span class="n">requires_grad</span>
<span class="go">True</span>
</pre></div>
</div>
</dd></dl>
<dl class="class">
<dt id="torch.autograd.set_grad_enabled">
<em class="property">class </em><code class="descclassname">torch.autograd.</code><code class="descname">set_grad_enabled</code><span class="sig-paren">(</span><em>mode</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/grad_mode.html#set_grad_enabled"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.set_grad_enabled" title="Permalink to this definition">¶</a></dt>
<dd><p>Context-manager that sets gradient calculation to on or off.</p>
<p><code class="docutils literal"><span class="pre">set_grad_enabled</span></code> will enable or disable grads based on its argument <code class="xref py py-attr docutils literal"><span class="pre">mode</span></code>.
It can be used as a context-manager or as a function.</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>mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – Flag whether to enable grad (<code class="docutils literal"><span class="pre">True</span></code>), or disable
(<code class="docutils literal"><span class="pre">False</span></code>). This can be used to conditionally enable
gradients.</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</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="mi">1</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">is_train</span> <span class="o">=</span> <span class="kc">False</span>
<span class="gp">>>> </span><span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">set_grad_enabled</span><span class="p">(</span><span class="n">is_train</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">y</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="mi">2</span>
<span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">requires_grad</span>
<span class="go">False</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">set_grad_enabled</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="mi">2</span>
<span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">requires_grad</span>
<span class="go">True</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">set_grad_enabled</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="mi">2</span>
<span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">requires_grad</span>
<span class="go">False</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="in-place-operations-on-tensors">
<h2>In-place operations on Tensors<a class="headerlink" href="#in-place-operations-on-tensors" title="Permalink to this headline">¶</a></h2>
<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 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 <code class="xref py py-class docutils literal"><span class="pre">Tensor</span></code> s keep track of in-place operations applied to them, and
if the implementation detects that a tensor 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 seeing any errors, you can be sure that the computed
gradients are correct.</p>
</div>
</div>
<div class="section" id="variable-deprecated">
<h2>Variable (deprecated)<a class="headerlink" href="#variable-deprecated" title="Permalink to this headline">¶</a></h2>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p>The Variable API has been deprecated: Variables are no longer necessary to
use autograd with tensors. Autograd automatically supports Tensors with
<code class="docutils literal"><span class="pre">requires_grad</span></code> set to <code class="docutils literal"><span class="pre">True</span></code>. Below please find a quick guide on what
has changed:</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">Variable(tensor)</span></code> and <code class="docutils literal"><span class="pre">Variable(tensor,</span> <span class="pre">requires_grad)</span></code> still work as expected,
but they return Tensors instead of Variables.</li>
<li><code class="docutils literal"><span class="pre">var.data</span></code> is the same thing as <code class="docutils literal"><span class="pre">tensor.data</span></code>.</li>
<li>Methods such as <code class="docutils literal"><span class="pre">var.backward(),</span> <span class="pre">var.detach(),</span> <span class="pre">var.register_hook()</span></code> now work on tensors
with the same method names.</li>
</ul>
<p>In addition, one can now create tensors with <code class="docutils literal"><span class="pre">requires_grad=True</span></code> using factory
methods such as <a class="reference internal" href="torch.html#torch.randn" title="torch.randn"><code class="xref py py-func docutils literal"><span class="pre">torch.randn()</span></code></a>, <a class="reference internal" href="torch.html#torch.zeros" title="torch.zeros"><code class="xref py py-func docutils literal"><span class="pre">torch.zeros()</span></code></a>, <a class="reference internal" href="torch.html#torch.ones" title="torch.ones"><code class="xref py py-func docutils literal"><span class="pre">torch.ones()</span></code></a>, and others
like the following:</p>
<p class="last"><code class="docutils literal"><span class="pre">autograd_tensor</span> <span class="pre">=</span> <span class="pre">torch.randn((2,</span> <span class="pre">3,</span> <span class="pre">4),</span> <span class="pre">requires_grad=True)</span></code></p>
</div>
</div>
<div class="section" id="tensor-autograd-functions">
<h2>Tensor autograd functions<a class="headerlink" href="#tensor-autograd-functions" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.Tensor">
<em class="property">class </em><code class="descclassname">torch.</code><code class="descname">Tensor</code><a class="headerlink" href="#torch.Tensor" title="Permalink to this definition">¶</a></dt>
<dd><dl class="method">
<dt id="torch.Tensor.backward">
<code class="descname">backward</code><span class="sig-paren">(</span><em>gradient=None</em>, <em>retain_graph=None</em>, <em>create_graph=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/tensor.html#Tensor.backward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.Tensor.backward" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the gradient of current tensor w.r.t. graph leaves.</p>
<p>The graph is differentiated using the chain rule. If the tensor is
non-scalar (i.e. its data has more than one element) and requires
gradient, the function additionally 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><em> or </em><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a>) – Gradient w.r.t. the
tensor. If it is a tensor, it will be automatically converted
to a Tensor that does not require grad unless <code class="docutils literal"><span class="pre">create_graph</span></code> is True.
None values can be specified for scalar Tensors or ones that
don’t require grad. If a None value would be acceptable then
this argument is optional.</li>
<li><strong>retain_graph</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If <code class="docutils literal"><span class="pre">False</span></code>, the graph used to compute
the grads will be freed. Note that in nearly all cases setting
this option to True is not needed and often can be worked around
in a much more efficient way. Defaults to the value of
<code class="docutils literal"><span class="pre">create_graph</span></code>.</li>
<li><strong>create_graph</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If <code class="docutils literal"><span class="pre">True</span></code>, graph of the derivative will
be constructed, allowing to compute higher order derivative
products. Defaults to <code class="docutils literal"><span class="pre">False</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.detach">
<code class="descname">detach</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.Tensor.detach" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a new Tensor, detached from the current graph.</p>
<p>The result will never require gradient.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Returned Tensor uses the same data tensor as the original one.
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.Tensor.detach_">
<code class="descname">detach_</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.Tensor.detach_" title="Permalink to this definition">¶</a></dt>
<dd><p>Detaches the Tensor from the graph that created it, making it a leaf.
Views cannot be detached in-place.</p>
</dd></dl>
<dl class="attribute">
<dt id="torch.Tensor.grad">
<code class="descname">grad</code><a class="headerlink" href="#torch.Tensor.grad" title="Permalink to this definition">¶</a></dt>
<dd><p>This attribute is <code class="docutils literal"><span class="pre">None</span></code> by default and becomes a Tensor the first time a call to
<a class="reference internal" href="#torch.Tensor.backward" title="torch.Tensor.backward"><code class="xref py py-func docutils literal"><span class="pre">backward()</span></code></a> computes gradients for <code class="docutils literal"><span class="pre">self</span></code>.
The attribute will then contain the gradients computed and future calls to
<a class="reference internal" href="#torch.Tensor.backward" title="torch.Tensor.backward"><code class="xref py py-func docutils literal"><span class="pre">backward()</span></code></a> will accumulate (add) gradients into it.</p>
</dd></dl>
<dl class="attribute">
<dt id="torch.Tensor.is_leaf">
<code class="descname">is_leaf</code><a class="headerlink" href="#torch.Tensor.is_leaf" title="Permalink to this definition">¶</a></dt>
<dd><p>All Tensors that have <a class="reference internal" href="#torch.Tensor.requires_grad" title="torch.Tensor.requires_grad"><code class="xref py py-attr docutils literal"><span class="pre">requires_grad</span></code></a> which is <code class="docutils literal"><span class="pre">False</span></code> will be leaf Tensors by convention.</p>
<p>For Tensors that have <a class="reference internal" href="#torch.Tensor.requires_grad" title="torch.Tensor.requires_grad"><code class="xref py py-attr docutils literal"><span class="pre">requires_grad</span></code></a> which is <code class="docutils literal"><span class="pre">True</span></code>, they will be leaf Tensors if they were
created by the user. This means that they are not the result of an operation and so
<code class="xref py py-attr docutils literal"><span class="pre">grad_fn</span></code> is None.</p>
<p>Only leaf Tensors will have their <a class="reference internal" href="#torch.Tensor.grad" title="torch.Tensor.grad"><code class="xref py py-attr docutils literal"><span class="pre">grad</span></code></a> populated during a call to <a class="reference internal" href="#torch.Tensor.backward" title="torch.Tensor.backward"><code class="xref py py-func docutils literal"><span class="pre">backward()</span></code></a>.
To get <a class="reference internal" href="#torch.Tensor.grad" title="torch.Tensor.grad"><code class="xref py py-attr docutils literal"><span class="pre">grad</span></code></a> populated for non-leaf Tensors, you can use <a class="reference internal" href="#torch.Tensor.retain_grad" title="torch.Tensor.retain_grad"><code class="xref py py-func docutils literal"><span class="pre">retain_grad()</span></code></a>.</p>
<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">rand</span><span class="p">(</span><span class="mi">10</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">a</span><span class="o">.</span><span class="n">is_leaf</span>
<span class="go">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">rand</span><span class="p">(</span><span class="mi">10</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="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">b</span><span class="o">.</span><span class="n">is_leaf</span>
<span class="go">False</span>
<span class="go"># b was created by the operation that cast a cpu Tensor into a cuda Tensor</span>
<span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">10</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="o">+</span> <span class="mi">2</span>
<span class="gp">>>> </span><span class="n">c</span><span class="o">.</span><span class="n">is_leaf</span>
<span class="go">False</span>
<span class="go"># c was created by the addition operation</span>
<span class="gp">>>> </span><span class="n">d</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">d</span><span class="o">.</span><span class="n">is_leaf</span>
<span class="go">True</span>
<span class="go"># d does not require gradients and so has no operation creating it (that is tracked by the autograd engine)</span>
<span class="gp">>>> </span><span class="n">e</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">e</span><span class="o">.</span><span class="n">is_leaf</span>
<span class="go">True</span>
<span class="go"># e requires gradients and has no operations creating it</span>
<span class="gp">>>> </span><span class="n">f</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">10</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="n">device</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">f</span><span class="o">.</span><span class="n">is_leaf</span>
<span class="go">True</span>
<span class="go"># f requires grad, has not operation creating it</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.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/tensor.html#Tensor.register_hook"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.Tensor.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
Tensor 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">Tensor</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 <a class="reference internal" href="#torch.Tensor.grad" title="torch.Tensor.grad"><code class="xref py py-attr docutils literal"><span class="pre">grad</span></code></a>.</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>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">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">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="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</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="go"> 2</span>
<span class="go"> 4</span>
<span class="go"> 6</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="attribute">
<dt id="torch.Tensor.requires_grad">
<code class="descname">requires_grad</code><a class="headerlink" href="#torch.Tensor.requires_grad" title="Permalink to this definition">¶</a></dt>
<dd><p>Is <code class="docutils literal"><span class="pre">True</span></code> if gradients need to be computed for this Tensor, <code class="docutils literal"><span class="pre">False</span></code> otherwise.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">The fact that gradients need to be computed for a Tensor do not mean that the <a class="reference internal" href="#torch.Tensor.grad" title="torch.Tensor.grad"><code class="xref py py-attr docutils literal"><span class="pre">grad</span></code></a>
attribute will be populated, see <a class="reference internal" href="#torch.Tensor.is_leaf" title="torch.Tensor.is_leaf"><code class="xref py py-attr docutils literal"><span class="pre">is_leaf</span></code></a> for more details.</p>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.retain_grad">
<code class="descname">retain_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/tensor.html#Tensor.retain_grad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.Tensor.retain_grad" title="Permalink to this definition">¶</a></dt>
<dd><p>Enables .grad attribute for non-leaf Tensors.</p>
</dd></dl>
</dd></dl>
</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 <code class="xref py py-class docutils literal"><span class="pre">Tensor</span></code> 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>Each function object is meant to be used only once (in the forward pass).</p>
<p>Examples:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">class</span> <span class="nc">Exp</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
<span class="go">>>></span>
<span class="gp">>>> </span> <span class="nd">@staticmethod</span>
<span class="gp">>>> </span> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="n">result</span> <span class="o">=</span> <span class="n">i</span><span class="o">.</span><span class="n">exp</span><span class="p">()</span>
<span class="gp">>>> </span> <span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
<span class="gp">>>> </span> <span class="k">return</span> <span class="n">result</span>
<span class="go">>>></span>
<span class="gp">>>> </span> <span class="nd">@staticmethod</span>
<span class="gp">>>> </span> <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">grad_output</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="n">result</span><span class="p">,</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span>
<span class="gp">>>> </span> <span class="k">return</span> <span class="n">grad_output</span> <span class="o">*</span> <span class="n">result</span>
</pre></div>
</div>
<dl class="staticmethod">
<dt id="torch.autograd.Function.backward">
<em class="property">static </em><code class="descname">backward</code><span class="sig-paren">(</span><em>ctx</em>, <em>*grad_outputs</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 overridden by all subclasses.</p>
<p>It must accept a context <code class="xref py py-attr docutils literal"><span class="pre">ctx</span></code> as the first argument, followed by
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>
<p>The context can be used to retrieve tensors saved during the forward
pass. It also has an attribute <code class="xref py py-attr docutils literal"><span class="pre">ctx.needs_input_grad</span></code> as a tuple
of booleans representing whether each input needs gradient. E.g.,
<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 have <code class="docutils literal"><span class="pre">ctx.needs_input_grad[0]</span> <span class="pre">=</span> <span class="pre">True</span></code> if the
first input 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> needs gradient computated w.r.t. the
output.</p>
</dd></dl>
<dl class="staticmethod">
<dt id="torch.autograd.Function.forward">
<em class="property">static </em><code class="descname">forward</code><span class="sig-paren">(</span><em>ctx</em>, <em>*args</em>, <em>**kwargs</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 overridden by all subclasses.</p>
<p>It must accept a context ctx as the first argument, followed by any
number of arguments (tensors or other types).</p>
<p>The context can be used to store tensors that can be then retrieved
during the backward pass.</p>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="numerical-gradient-checking">
<span id="grad-check"></span><h2>Numerical gradient checking<a class="headerlink" href="#numerical-gradient-checking" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torch.autograd.gradcheck">
<code class="descclassname">torch.autograd.</code><code class="descname">gradcheck</code><span class="sig-paren">(</span><em>func</em>, <em>inputs</em>, <em>eps=1e-06</em>, <em>atol=1e-05</em>, <em>rtol=0.001</em>, <em>raise_exception=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/gradcheck.html#gradcheck"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.gradcheck" title="Permalink to this definition">¶</a></dt>
<dd><p>Check gradients computed via small finite differences against analytical
gradients w.r.t. tensors in <code class="xref py py-attr docutils literal"><span class="pre">inputs</span></code> that are of floating point type
and with <code class="docutils literal"><span class="pre">requires_grad=True</span></code>.</p>
<p>The check between numerical and analytical gradients uses <a class="reference internal" href="torch.html#torch.allclose" title="torch.allclose"><code class="xref py py-func docutils literal"><span class="pre">allclose()</span></code></a>.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">The default values are designed for <code class="xref py py-attr docutils literal"><span class="pre">input</span></code> of double precision.
This check will likely fail if <code class="xref py py-attr docutils literal"><span class="pre">input</span></code> is of less precision, e.g.,
<code class="docutils literal"><span class="pre">FloatTensor</span></code>.</p>
</div>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">If any checked tensor in <code class="xref py py-attr docutils literal"><span class="pre">input</span></code> has overlapping memory, i.e.,
different indices pointing to the same memory address (e.g., from
<code class="xref py py-func docutils literal"><span class="pre">torch.expand()</span></code>), this check will likely fail because the numerical
gradients computed by point perturbation at such indices will change
values at all other indices that share the same memory address.</p>
</div>
<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 simple">
<li><strong>func</strong> (<em>function</em>) – a Python function that takes Tensor inputs and returns
a Tensor or a tuple of Tensors</li>
<li><strong>inputs</strong> (<em>tuple of Tensor</em><em> or </em><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – inputs to the function</li>
<li><strong>eps</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em>, </em><em>optional</em>) – perturbation for finite differences</li>
<li><strong>atol</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em>, </em><em>optional</em>) – absolute tolerance</li>
<li><strong>rtol</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em>, </em><em>optional</em>) – relative tolerance</li>
<li><strong>raise_exception</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – indicating whether to raise an exception if
the check fails. The exception gives more information about the
exact nature of the failure. This is helpful when debugging gradchecks.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">True if all differences satisfy allclose condition</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.autograd.gradgradcheck">
<code class="descclassname">torch.autograd.</code><code class="descname">gradgradcheck</code><span class="sig-paren">(</span><em>func</em>, <em>inputs</em>, <em>grad_outputs=None</em>, <em>eps=1e-06</em>, <em>atol=1e-05</em>, <em>rtol=0.001</em>, <em>gen_non_contig_grad_outputs=False</em>, <em>raise_exception=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/gradcheck.html#gradgradcheck"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.gradgradcheck" title="Permalink to this definition">¶</a></dt>
<dd><p>Check gradients of gradients computed via small finite differences
against analytical gradients w.r.t. tensors in <code class="xref py py-attr docutils literal"><span class="pre">inputs</span></code> and
<code class="xref py py-attr docutils literal"><span class="pre">grad_outputs</span></code> that are of floating point type and with
<code class="docutils literal"><span class="pre">requires_grad=True</span></code>.</p>
<p>This function checks that backpropagating through the gradients computed
to the given <code class="xref py py-attr docutils literal"><span class="pre">grad_outputs</span></code> are correct.</p>
<p>The check between numerical and analytical gradients uses <a class="reference internal" href="torch.html#torch.allclose" title="torch.allclose"><code class="xref py py-func docutils literal"><span class="pre">allclose()</span></code></a>.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">The default values are designed for <code class="xref py py-attr docutils literal"><span class="pre">input</span></code> and
<code class="xref py py-attr docutils literal"><span class="pre">grad_outputs</span></code> of double precision. This check will likely fail if
they are of less precision, e.g., <code class="docutils literal"><span class="pre">FloatTensor</span></code>.</p>
</div>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">If any checked tensor in <code class="xref py py-attr docutils literal"><span class="pre">input</span></code> and <code class="xref py py-attr docutils literal"><span class="pre">grad_outputs</span></code> has
overlapping memory, i.e., different indices pointing to the same memory
address (e.g., from <code class="xref py py-func docutils literal"><span class="pre">torch.expand()</span></code>), this check will likely fail
because the numerical gradients computed by point perturbation at such
indices will change values at all other indices that share the same
memory address.</p>
</div>
<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 simple">
<li><strong>func</strong> (<em>function</em>) – a Python function that takes Tensor inputs and returns
a Tensor or a tuple of Tensors</li>
<li><strong>inputs</strong> (<em>tuple of Tensor</em><em> or </em><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – inputs to the function</li>
<li><strong>grad_outputs</strong> (<em>tuple of Tensor</em><em> or </em><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – The gradients with
respect to the function’s outputs.</li>
<li><strong>eps</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em>, </em><em>optional</em>) – perturbation for finite differences</li>
<li><strong>atol</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em>, </em><em>optional</em>) – absolute tolerance</li>
<li><strong>rtol</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em>, </em><em>optional</em>) – relative tolerance</li>
<li><strong>gen_non_contig_grad_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – if <code class="xref py py-attr docutils literal"><span class="pre">grad_outputs</span></code> is
<code class="docutils literal"><span class="pre">None</span></code> and <code class="xref py py-attr docutils literal"><span class="pre">gen_non_contig_grad_outputs</span></code> is <code class="docutils literal"><span class="pre">True</span></code>, the
randomly generated gradient outputs are made to be noncontiguous</li>
<li><strong>raise_exception</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – indicating whether to raise an exception if
the check fails. The exception gives more information about the
exact nature of the failure. This is helpful when debugging gradchecks.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">True if all differences satisfy allclose condition</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="profiler">
<h2>Profiler<a class="headerlink" href="#profiler" title="Permalink to this headline">¶</a></h2>
<p>Autograd includes a profiler that lets you inspect the cost of different
operators inside your model - both on the CPU and GPU. There are two modes
implemented at the moment - CPU-only using <a class="reference internal" href="#torch.autograd.profiler.profile" title="torch.autograd.profiler.profile"><code class="xref py py-class docutils literal"><span class="pre">profile</span></code></a>.
and nvprof based (registers both CPU and GPU activity) using
<a class="reference internal" href="#torch.autograd.profiler.emit_nvtx" title="torch.autograd.profiler.emit_nvtx"><code class="xref py py-class docutils literal"><span class="pre">emit_nvtx</span></code></a>.</p>
<dl class="class">
<dt id="torch.autograd.profiler.profile">
<em class="property">class </em><code class="descclassname">torch.autograd.profiler.</code><code class="descname">profile</code><span class="sig-paren">(</span><em>enabled=True</em>, <em>use_cuda=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/profiler.html#profile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.profiler.profile" title="Permalink to this definition">¶</a></dt>
<dd><p>Context manager that manages autograd profiler state and holds a summary of results.</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>enabled</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – Setting this to False makes this context manager a no-op.
Default: <code class="docutils literal"><span class="pre">True</span></code>.</li>
<li><strong>use_cuda</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – Enables timing of CUDA events as well using the cudaEvent API.
Adds approximately 4us of overhead to each tensor operation.
Default: <code class="docutils literal"><span class="pre">False</span></code></li>
</ul>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Example</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</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">1</span><span class="p">,</span> <span class="mi">1</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="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">profile</span><span class="p">()</span> <span class="k">as</span> <span class="n">prof</span><span class="p">:</span>
<span class="gp">... </span> <span class="n">y</span> <span class="o">=</span> <span class="n">x</span> <span class="o">**</span> <span class="mi">2</span>
<span class="gp">... </span> <span class="n">y</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="gp">>>> </span><span class="c1"># NOTE: some columns were removed for brevity</span>
<span class="gp">... </span><span class="nb">print</span><span class="p">(</span><span class="n">prof</span><span class="p">)</span>
<span class="go">------------------------------------- --------------- ---------------</span>
<span class="go">Name CPU time CUDA time</span>
<span class="go">------------------------------------- --------------- ---------------</span>
<span class="go">PowConstant 142.036us 0.000us</span>
<span class="go">N5torch8autograd9GraphRootE 63.524us 0.000us</span>
<span class="go">PowConstantBackward 184.228us 0.000us</span>
<span class="go">MulConstant 50.288us 0.000us</span>
<span class="go">PowConstant 28.439us 0.000us</span>
<span class="go">Mul 20.154us 0.000us</span>
<span class="go">N5torch8autograd14AccumulateGradE 13.790us 0.000us</span>
<span class="go">N5torch8autograd5CloneE 4.088us 0.000us</span>
</pre></div>
</div>
<dl class="method">
<dt id="torch.autograd.profiler.profile.export_chrome_trace">
<code class="descname">export_chrome_trace</code><span class="sig-paren">(</span><em>path</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/profiler.html#profile.export_chrome_trace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.profiler.profile.export_chrome_trace" title="Permalink to this definition">¶</a></dt>
<dd><p>Exports an EventList as a Chrome tracing tools file.</p>
<p>The checkpoint can be later loaded and inspected under <code class="docutils literal"><span class="pre">chrome://tracing</span></code> URL.</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>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a>) – Path where the trace will be written.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.autograd.profiler.profile.key_averages">
<code class="descname">key_averages</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/profiler.html#profile.key_averages"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.profiler.profile.key_averages" title="Permalink to this definition">¶</a></dt>
<dd><p>Averages all function events over their keys.</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">Returns:</th><td class="field-body">An EventList containing FunctionEventAvg objects.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.autograd.profiler.profile.table">
<code class="descname">table</code><span class="sig-paren">(</span><em>sort_by=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/profiler.html#profile.table"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.profiler.profile.table" title="Permalink to this definition">¶</a></dt>
<dd><p>Prints an EventList as a nicely formatted table.</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>sort_by</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a><em>, </em><em>optional</em>) – Attribute used to sort entries. By default
they are printed in the same order as they were registered.
Valid keys include: <code class="docutils literal"><span class="pre">cpu_time</span></code>, <code class="docutils literal"><span class="pre">cuda_time</span></code>, <code class="docutils literal"><span class="pre">cpu_time_total</span></code>,
<code class="docutils literal"><span class="pre">cuda_time_total</span></code>, <code class="docutils literal"><span class="pre">count</span></code>.</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">A string containing the table.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.autograd.profiler.profile.total_average">
<code class="descname">total_average</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/profiler.html#profile.total_average"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.profiler.profile.total_average" title="Permalink to this definition">¶</a></dt>
<dd><p>Averages all events.</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">Returns:</th><td class="field-body">A FunctionEventAvg object.</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torch.autograd.profiler.emit_nvtx">
<em class="property">class </em><code class="descclassname">torch.autograd.profiler.</code><code class="descname">emit_nvtx</code><span class="sig-paren">(</span><em>enabled=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/profiler.html#emit_nvtx"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.profiler.emit_nvtx" title="Permalink to this definition">¶</a></dt>
<dd><p>Context manager that makes every autograd operation emit an NVTX range.</p>
<p>It is useful when running the program under nvprof:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">nvprof</span> <span class="o">--</span><span class="n">profile</span><span class="o">-</span><span class="n">from</span><span class="o">-</span><span class="n">start</span> <span class="n">off</span> <span class="o">-</span><span class="n">o</span> <span class="n">trace_name</span><span class="o">.</span><span class="n">prof</span> <span class="o">--</span> <span class="o"><</span><span class="n">regular</span> <span class="n">command</span> <span class="n">here</span><span class="o">></span>
</pre></div>
</div>
<p>Unfortunately, there’s no way to force nvprof to flush the data it collected
to disk, so for CUDA profiling one has to use this context manager to annotate
nvprof traces and wait for the process to exit before inspecting them.
Then, either NVIDIA Visual Profiler (nvvp) can be used to visualize the timeline, or
<a class="reference internal" href="#torch.autograd.profiler.load_nvprof" title="torch.autograd.profiler.load_nvprof"><code class="xref py py-func docutils literal"><span class="pre">torch.autograd.profiler.load_nvprof()</span></code></a> can load the results for inspection
e.g. in Python REPL.</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>enabled</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – Setting this to False makes this context manager a no-op.
Default: <code class="docutils literal"><span class="pre">True</span></code>.</td>
</tr>
</tbody>
</table>
<p class="rubric">Example</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">profile</span><span class="p">():</span>
<span class="gp">... </span> <span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="c1"># Warmup CUDA memory allocator and profiler</span>
<span class="gp">... </span> <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">emit_nvtx</span><span class="p">():</span>
<span class="gp">... </span> <span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p><strong>Forward-backward correlation</strong></p>
<p>When viewing a profile created using <a class="reference internal" href="#torch.autograd.profiler.emit_nvtx" title="torch.autograd.profiler.emit_nvtx"><code class="xref py py-class docutils literal"><span class="pre">emit_nvtx</span></code></a> in the Nvidia Visual Profiler,
correlating each backward-pass op with the corresponding forward-pass op can be difficult.
To ease this task, <a class="reference internal" href="#torch.autograd.profiler.emit_nvtx" title="torch.autograd.profiler.emit_nvtx"><code class="xref py py-class docutils literal"><span class="pre">emit_nvtx</span></code></a> appends sequence number information to the ranges it
generates.</p>
<p>During the forward pass, each function range is decorated with <code class="docutils literal"><span class="pre">seq=<N></span></code>. <code class="docutils literal"><span class="pre">seq</span></code> is a running
counter, incremented each time a new backward Function object is created and stashed for backward.
Thus, the <cite>seq=<N></cite> annotation associated with each forward function range tells you that
if a backward Function object is created by this forward function,
the backward object will receive sequence number N.
During the backward pass, the top-level range wrapping each C++ backward Function’s
<code class="docutils literal"><span class="pre">apply()</span></code> call is decorated with <code class="docutils literal"><span class="pre">stashed</span> <span class="pre">seq=<M></span></code>. <code class="docutils literal"><span class="pre">M</span></code> is the sequence number that
the backward object was created with. By comparing <code class="docutils literal"><span class="pre">stashed</span> <span class="pre">seq</span></code> numbers in backward with <code class="docutils literal"><span class="pre">seq</span></code>
numbers in forward, you can track down which forward op created each backward Function.</p>
<p>Any functions executed during the backward pass are also decorated with <code class="docutils literal"><span class="pre">seq=<N></span></code>. During
default backward (with <code class="docutils literal"><span class="pre">create_graph=False</span></code>) this information is irrelevant, and in fact,
<code class="docutils literal"><span class="pre">N</span></code> may simply be 0 for all such functions. Only the top-level ranges associated with
backward Function objects’ <code class="docutils literal"><span class="pre">apply()</span></code> methods are useful, as a way to correlate these Function
objects with the earlier forward pass.</p>
<p><strong>Double-backward</strong></p>
<p>If, on the other hand, a backward pass with <code class="docutils literal"><span class="pre">create_graph=True</span></code> is underway (in other words,
if you are setting up for a double-backward), each function’s execution during backward
is given a nonzero, useful <code class="docutils literal"><span class="pre">seq=<N></span></code>. Those functions may themselves create Function objects
to be executed later during double-backward, just as the original functions in the forward pass did.
The relationship between backward and double-backward is conceptually the same as the relationship
between forward and backward: The functions still emit current-sequence-number-tagged ranges,
the Function objects they create still stash those sequence numbers, and during the eventual
double-backward, the Function objects’ <code class="docutils literal"><span class="pre">apply()</span></code> ranges are still tagged with <code class="docutils literal"><span class="pre">stashed</span> <span class="pre">seq</span></code>
numbers, which can be compared to <cite>seq</cite> numbers from the backward pass.</p>
</dd></dl>
<dl class="function">
<dt id="torch.autograd.profiler.load_nvprof">
<code class="descclassname">torch.autograd.profiler.</code><code class="descname">load_nvprof</code><span class="sig-paren">(</span><em>path</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/profiler.html#load_nvprof"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.profiler.load_nvprof" title="Permalink to this definition">¶</a></dt>
<dd><p>Opens an nvprof trace file and parses autograd annotations.</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>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a>) – path to nvprof trace</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="anomaly-detection">
<h2>Anomaly detection<a class="headerlink" href="#anomaly-detection" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.autograd.detect_anomaly">
<em class="property">class </em><code class="descclassname">torch.autograd.</code><code class="descname">detect_anomaly</code><a class="reference internal" href="_modules/torch/autograd/anomaly_mode.html#detect_anomaly"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.autograd.detect_anomaly" title="Permalink to this definition">¶</a></dt>
<dd><p>Context-manager that enable anomaly detection for the autograd engine.</p>
<p>This does two things:
- Running the forward pass with detection enabled will allow the backward
pass to print the traceback of the forward operation that created the failing
backward function.
- Any backward computation that generate “nan” value will raise an error.</p>
<p class="rubric">Example</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch</span> <span class="k">import</span> <span class="n">autograd</span>
<span class="gp">>>> </span><span class="k">class</span> <span class="nc">MyFunc</span><span class="p">(</span><span class="n">autograd</span><span class="o">.</span><span class="n">Function</span><span class="p">):</span>
<span class="gp">... </span> <span class="nd">@staticmethod</span>
<span class="gp">... </span> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">inp</span><span class="p">):</span>
<span class="gp">... </span> <span class="k">return</span> <span class="n">inp</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>