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<section id="torch-tensor-backward">
<h1>torch.Tensor.backward<a class="headerlink" href="#torch-tensor-backward" title="Permalink to this heading">¶</a></h1>
<dl class="py method">
<dt class="sig sig-object py" id="torch.Tensor.backward">
<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.backward"><span class="viewcode-link"><span class="pre">[source]</span></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 notranslate"><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 notranslate"><span class="pre">self</span></code>.</p>
<p>This function accumulates gradients in the leaves - you might need to zero
<code class="docutils literal notranslate"><span class="pre">.grad</span></code> attributes or set them to <code class="docutils literal notranslate"><span class="pre">None</span></code> before calling it.
See <a class="reference internal" href="../autograd.html#default-grad-layouts"><span class="std std-ref">Default gradient layouts</span></a>
for details on the memory layout of accumulated gradients.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>If you run any forward ops, create <code class="docutils literal notranslate"><span class="pre">gradient</span></code>, and/or call <code class="docutils literal notranslate"><span class="pre">backward</span></code>
in a user-specified CUDA stream context, see
<a class="reference internal" href="../notes/cuda.html#bwd-cuda-stream-semantics"><span class="std std-ref">Stream semantics of backward passes</span></a>.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>When <code class="docutils literal notranslate"><span class="pre">inputs</span></code> are provided and a given input is not a leaf,
the current implementation will call its grad_fn (though it is not strictly needed to get this gradients).
It is an implementation detail on which the user should not rely.
See <a class="reference external" href="https://github.com/pytorch/pytorch/pull/60521#issuecomment-867061780">https://github.com/pytorch/pytorch/pull/60521#issuecomment-867061780</a> for more details.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>gradient</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em> or </em><em>None</em>) – 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 notranslate"><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.</p></li>
<li><p><strong>retain_graph</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em>, </em><em>optional</em>) – If <code class="docutils literal notranslate"><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 notranslate"><span class="pre">create_graph</span></code>.</p></li>
<li><p><strong>create_graph</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em>, </em><em>optional</em>) – If <code class="docutils literal notranslate"><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 notranslate"><span class="pre">False</span></code>.</p></li>
<li><p><strong>inputs</strong> (<em>sequence of Tensor</em>) – Inputs w.r.t. which the gradient will be
accumulated into <code class="docutils literal notranslate"><span class="pre">.grad</span></code>. All other Tensors will be ignored. If not
provided, the gradient is accumulated into all the leaf Tensors that were
used to compute the attr::tensors.</p></li>
</ul>
</dd>
</dl>
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