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<div class="section" id="automatic-mixed-precision-package-torch-amp">
<h1>Automatic Mixed Precision package - torch.amp<a class="headerlink" href="#automatic-mixed-precision-package-torch-amp" title="Permalink to this heading">¶</a></h1>
<span class="target" id="module-torch.cpu.amp"></span><span class="target" id="module-torch.cuda.amp"></span><span class="target" id="module-torch.amp"></span><p><a class="reference internal" href="#module-torch.amp" title="torch.amp"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.amp</span></code></a> provides convenience methods for mixed precision,
where some operations use the <code class="docutils literal notranslate"><span class="pre">torch.float32</span></code> (<code class="docutils literal notranslate"><span class="pre">float</span></code>) datatype and other operations
use lower precision floating point datatype (<code class="docutils literal notranslate"><span class="pre">lower_precision_fp</span></code>): <code class="docutils literal notranslate"><span class="pre">torch.float16</span></code> (<code class="docutils literal notranslate"><span class="pre">half</span></code>) or <code class="docutils literal notranslate"><span class="pre">torch.bfloat16</span></code>. Some ops, like linear layers and convolutions,
are much faster in <code class="docutils literal notranslate"><span class="pre">lower_precision_fp</span></code>. Other ops, like reductions, often require the dynamic
range of <code class="docutils literal notranslate"><span class="pre">float32</span></code>. Mixed precision tries to match each op to its appropriate datatype.</p>
<p>Ordinarily, “automatic mixed precision training” with datatype of <code class="docutils literal notranslate"><span class="pre">torch.float16</span></code> uses <a class="reference internal" href="#torch.autocast" title="torch.autocast"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autocast</span></code></a> and
<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.amp.GradScaler</span></code> together, as shown in the <a class="reference internal" href="notes/amp_examples.html#amp-examples"><span class="std std-ref">Automatic Mixed Precision examples</span></a>
and <a class="reference external" href="https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html">Automatic Mixed Precision recipe</a>.
However, <a class="reference internal" href="#torch.autocast" title="torch.autocast"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autocast</span></code></a> and <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.GradScaler</span></code> are modular, and may be used separately if desired.
As shown in the CPU example section of <a class="reference internal" href="#torch.autocast" title="torch.autocast"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autocast</span></code></a>, “automatic mixed precision training/inference” on CPU with
datatype of <code class="docutils literal notranslate"><span class="pre">torch.bfloat16</span></code> only uses <a class="reference internal" href="#torch.autocast" title="torch.autocast"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autocast</span></code></a>.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p><code class="docutils literal notranslate"><span class="pre">torch.cuda.amp.autocast(args...)</span></code> and <code class="docutils literal notranslate"><span class="pre">torch.cpu.amp.autocast(args...)</span></code> will be deprecated. Please use <code class="docutils literal notranslate"><span class="pre">torch.autocast("cuda",</span> <span class="pre">args...)</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.autocast("cpu",</span> <span class="pre">args...)</span></code> instead.
<code class="docutils literal notranslate"><span class="pre">torch.cuda.amp.GradScaler(args...)</span></code> and <code class="docutils literal notranslate"><span class="pre">torch.cpu.amp.GradScaler(args...)</span></code> will be deprecated. Please use <code class="docutils literal notranslate"><span class="pre">torch.GradScaler("cuda",</span> <span class="pre">args...)</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.GradScaler("cpu",</span> <span class="pre">args...)</span></code> instead.</p>
</div>
<p><a class="reference internal" href="#torch.autocast" title="torch.autocast"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autocast</span></code></a> and <a class="reference internal" href="#torch.cpu.amp.autocast" title="torch.cpu.amp.autocast"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cpu.amp.autocast</span></code></a> are new in version <cite>1.10</cite>.</p>
<div class="contents local topic" id="contents">
<ul class="simple">
<li><p><a class="reference internal" href="#autocasting" id="id4">Autocasting</a></p></li>
<li><p><a class="reference internal" href="#gradient-scaling" id="id5">Gradient Scaling</a></p></li>
<li><p><a class="reference internal" href="#autocast-op-reference" id="id6">Autocast Op Reference</a></p>
<ul>
<li><p><a class="reference internal" href="#op-eligibility" id="id7">Op Eligibility</a></p></li>
<li><p><a class="reference internal" href="#cuda-op-specific-behavior" id="id8">CUDA Op-Specific Behavior</a></p>
<ul>
<li><p><a class="reference internal" href="#cuda-ops-that-can-autocast-to-float16" id="id9">CUDA Ops that can autocast to <code class="docutils literal notranslate"><span class="pre">float16</span></code></a></p></li>
<li><p><a class="reference internal" href="#cuda-ops-that-can-autocast-to-float32" id="id10">CUDA Ops that can autocast to <code class="docutils literal notranslate"><span class="pre">float32</span></code></a></p></li>
<li><p><a class="reference internal" href="#cuda-ops-that-promote-to-the-widest-input-type" id="id11">CUDA Ops that promote to the widest input type</a></p></li>
<li><p><a class="reference internal" href="#prefer-binary-cross-entropy-with-logits-over-binary-cross-entropy" id="id12">Prefer <code class="docutils literal notranslate"><span class="pre">binary_cross_entropy_with_logits</span></code> over <code class="docutils literal notranslate"><span class="pre">binary_cross_entropy</span></code></a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#xpu-op-specific-behavior-experimental" id="id13">XPU Op-Specific Behavior (Experimental)</a></p>
<ul>
<li><p><a class="reference internal" href="#xpu-ops-that-can-autocast-to-float16" id="id14">XPU Ops that can autocast to <code class="docutils literal notranslate"><span class="pre">float16</span></code></a></p></li>
<li><p><a class="reference internal" href="#xpu-ops-that-can-autocast-to-float32" id="id15">XPU Ops that can autocast to <code class="docutils literal notranslate"><span class="pre">float32</span></code></a></p></li>
<li><p><a class="reference internal" href="#xpu-ops-that-promote-to-the-widest-input-type" id="id16">XPU Ops that promote to the widest input type</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#cpu-op-specific-behavior" id="id17">CPU Op-Specific Behavior</a></p>
<ul>
<li><p><a class="reference internal" href="#cpu-ops-that-can-autocast-to-bfloat16" id="id18">CPU Ops that can autocast to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code></a></p></li>
<li><p><a class="reference internal" href="#cpu-ops-that-can-autocast-to-float32" id="id19">CPU Ops that can autocast to <code class="docutils literal notranslate"><span class="pre">float32</span></code></a></p></li>
<li><p><a class="reference internal" href="#cpu-ops-that-promote-to-the-widest-input-type" id="id20">CPU Ops that promote to the widest input type</a></p></li>
</ul>
</li>
</ul>
</li>
</ul>
</div>
<div class="section" id="autocasting">
<span id="id1"></span><h2><a class="toc-backref" href="#id4">Autocasting</a><a class="headerlink" href="#autocasting" title="Permalink to this heading">¶</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="torch.amp.autocast_mode.is_autocast_available">
<span class="sig-prename descclassname"><span class="pre">torch.amp.autocast_mode.</span></span><span class="sig-name descname"><span class="pre">is_autocast_available</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/amp/autocast_mode.html#is_autocast_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.amp.autocast_mode.is_autocast_available" title="Permalink to this definition">¶</a></dt>
<dd><p>Return a bool indicating if autocast is available on <code class="xref py py-attr docutils literal notranslate"><span class="pre">device_type</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>device_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – Device type to use. Possible values are: ‘cuda’, ‘cpu’, ‘xpu’ and so on.
The type is the same as the <cite>type</cite> attribute of a <a class="reference internal" href="tensor_attributes.html#torch.device" title="torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>.
Thus, you may obtain the device type of a tensor using <cite>Tensor.device.type</cite>.</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.autocast">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">autocast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</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">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_enabled</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/amp/autocast_mode.html#autocast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autocast" title="Permalink to this definition">¶</a></dt>
<dd><p>Instances of <a class="reference internal" href="#torch.autocast" title="torch.autocast"><code class="xref py py-class docutils literal notranslate"><span class="pre">autocast</span></code></a> serve as context managers or decorators that
allow regions of your script to run in mixed precision.</p>
<p>In these regions, ops run in an op-specific dtype chosen by autocast
to improve performance while maintaining accuracy.
See the <a class="reference internal" href="#autocast-op-reference"><span class="std std-ref">Autocast Op Reference</span></a> for details.</p>
<p>When entering an autocast-enabled region, Tensors may be any type.
You should not call <code class="docutils literal notranslate"><span class="pre">half()</span></code> or <code class="docutils literal notranslate"><span class="pre">bfloat16()</span></code> on your model(s) or inputs when using autocasting.</p>
<p><a class="reference internal" href="#torch.autocast" title="torch.autocast"><code class="xref py py-class docutils literal notranslate"><span class="pre">autocast</span></code></a> should wrap only the forward pass(es) of your network, including the loss
computation(s). Backward passes under autocast are not recommended.
Backward ops run in the same type that autocast used for corresponding forward ops.</p>
<p>Example for CUDA Devices:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Creates model and optimizer in default precision</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">Net</span><span class="p">()</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="o">...</span><span class="p">)</span>
<span class="k">for</span> <span class="nb">input</span><span class="p">,</span> <span class="n">target</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="c1"># Enables autocasting for the forward pass (model + loss)</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">autocast</span><span class="p">(</span><span class="n">device_type</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">):</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fn</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="c1"># Exits the context manager before backward()</span>
<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
</pre></div>
</div>
<p>See the <a class="reference internal" href="notes/amp_examples.html#amp-examples"><span class="std std-ref">Automatic Mixed Precision examples</span></a> for usage (along with gradient scaling)
in more complex scenarios (e.g., gradient penalty, multiple models/losses, custom autograd functions).</p>
<p><a class="reference internal" href="#torch.autocast" title="torch.autocast"><code class="xref py py-class docutils literal notranslate"><span class="pre">autocast</span></code></a> can also be used as a decorator, e.g., on the <code class="docutils literal notranslate"><span class="pre">forward</span></code> method of your model:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">AutocastModel</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="o">...</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">autocast</span><span class="p">(</span><span class="n">device_type</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="o">...</span>
</pre></div>
</div>
<p>Floating-point Tensors produced in an autocast-enabled region may be <code class="docutils literal notranslate"><span class="pre">float16</span></code>.
After returning to an autocast-disabled region, using them with floating-point
Tensors of different dtypes may cause type mismatch errors. If so, cast the Tensor(s)
produced in the autocast region back to <code class="docutils literal notranslate"><span class="pre">float32</span></code> (or other dtype if desired).
If a Tensor from the autocast region is already <code class="docutils literal notranslate"><span class="pre">float32</span></code>, the cast is a no-op,
and incurs no additional overhead.
CUDA Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Creates some tensors in default dtype (here assumed to be float32)</span>
<span class="n">a_float32</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">8</span><span class="p">,</span> <span class="mi">8</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="n">b_float32</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">8</span><span class="p">,</span> <span class="mi">8</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="n">c_float32</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">8</span><span class="p">,</span> <span class="mi">8</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="n">d_float32</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">8</span><span class="p">,</span> <span class="mi">8</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="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">autocast</span><span class="p">(</span><span class="n">device_type</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">):</span>
<span class="c1"># torch.mm is on autocast's list of ops that should run in float16.</span>
<span class="c1"># Inputs are float32, but the op runs in float16 and produces float16 output.</span>
<span class="c1"># No manual casts are required.</span>
<span class="n">e_float16</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">a_float32</span><span class="p">,</span> <span class="n">b_float32</span><span class="p">)</span>
<span class="c1"># Also handles mixed input types</span>
<span class="n">f_float16</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">d_float32</span><span class="p">,</span> <span class="n">e_float16</span><span class="p">)</span>
<span class="c1"># After exiting autocast, calls f_float16.float() to use with d_float32</span>
<span class="n">g_float32</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">d_float32</span><span class="p">,</span> <span class="n">f_float16</span><span class="o">.</span><span class="n">float</span><span class="p">())</span>
</pre></div>
</div>
<p>CPU Training Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Creates model and optimizer in default precision</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">Net</span><span class="p">()</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="o">...</span><span class="p">)</span>
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="n">epochs</span><span class="p">:</span>
<span class="k">for</span> <span class="nb">input</span><span class="p">,</span> <span class="n">target</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="c1"># Runs the forward pass with autocasting.</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">autocast</span><span class="p">(</span><span class="n">device_type</span><span class="o">=</span><span class="s2">"cpu"</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">):</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fn</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
</pre></div>
</div>
<p>CPU Inference Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Creates model in default precision</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">Net</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">autocast</span><span class="p">(</span><span class="n">device_type</span><span class="o">=</span><span class="s2">"cpu"</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">):</span>
<span class="k">for</span> <span class="nb">input</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
<span class="c1"># Runs the forward pass with autocasting.</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
</pre></div>
</div>
<p>CPU Inference Example with Jit Trace:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">TestModel</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_size</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">input_size</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">num_classes</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">TestModel</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="c1"># For now, we suggest to disable the Jit Autocast Pass,</span>
<span class="c1"># As the issue: https://github.com/pytorch/pytorch/issues/75956</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_jit_set_autocast_mode</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cpu</span><span class="o">.</span><span class="n">amp</span><span class="o">.</span><span class="n">autocast</span><span class="p">(</span><span class="n">cache_enabled</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">model</span><span class="p">,</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="n">input_size</span><span class="p">))</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">freeze</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<span class="c1"># Models Run</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
<span class="n">model</span><span class="p">(</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="n">input_size</span><span class="p">))</span>
</pre></div>
</div>
<p>Type mismatch errors <em>in</em> an autocast-enabled region are a bug; if this is what you observe,
please file an issue.</p>
<p><code class="docutils literal notranslate"><span class="pre">autocast(enabled=False)</span></code> subregions can be nested in autocast-enabled regions.
Locally disabling autocast can be useful, for example, if you want to force a subregion
to run in a particular <code class="docutils literal notranslate"><span class="pre">dtype</span></code>. Disabling autocast gives you explicit control over
the execution type. In the subregion, inputs from the surrounding region
should be cast to <code class="docutils literal notranslate"><span class="pre">dtype</span></code> before use:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Creates some tensors in default dtype (here assumed to be float32)</span>
<span class="n">a_float32</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">8</span><span class="p">,</span> <span class="mi">8</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="n">b_float32</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">8</span><span class="p">,</span> <span class="mi">8</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="n">c_float32</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">8</span><span class="p">,</span> <span class="mi">8</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="n">d_float32</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">8</span><span class="p">,</span> <span class="mi">8</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="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">autocast</span><span class="p">(</span><span class="n">device_type</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">):</span>
<span class="n">e_float16</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">a_float32</span><span class="p">,</span> <span class="n">b_float32</span><span class="p">)</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">autocast</span><span class="p">(</span><span class="n">device_type</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">,</span> <span class="n">enabled</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="c1"># Calls e_float16.float() to ensure float32 execution</span>
<span class="c1"># (necessary because e_float16 was created in an autocasted region)</span>
<span class="n">f_float32</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">c_float32</span><span class="p">,</span> <span class="n">e_float16</span><span class="o">.</span><span class="n">float</span><span class="p">())</span>
<span class="c1"># No manual casts are required when re-entering the autocast-enabled region.</span>
<span class="c1"># torch.mm again runs in float16 and produces float16 output, regardless of input types.</span>
<span class="n">g_float16</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">d_float32</span><span class="p">,</span> <span class="n">f_float32</span><span class="p">)</span>
</pre></div>
</div>
<p>The autocast state is thread-local. If you want it enabled in a new thread, the context manager or decorator
must be invoked in that thread. This affects <a class="reference internal" href="generated/torch.nn.DataParallel.html#torch.nn.DataParallel" title="torch.nn.DataParallel"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.DataParallel</span></code></a> and
<a class="reference internal" href="generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel" title="torch.nn.parallel.DistributedDataParallel"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.parallel.DistributedDataParallel</span></code></a> when used with more than one GPU per process
(see <a class="reference internal" href="notes/amp_examples.html#amp-multigpu"><span class="std std-ref">Working with Multiple GPUs</span></a>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>device_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><em>required</em>) – Device type to use. Possible values are: ‘cuda’, ‘cpu’, ‘xpu’ and ‘hpu’.
The type is the same as the <cite>type</cite> attribute of a <a class="reference internal" href="tensor_attributes.html#torch.device" title="torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>.
Thus, you may obtain the device type of a tensor using <cite>Tensor.device.type</cite>.</p></li>
<li><p><strong>enabled</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether autocasting should be enabled in the region.
Default: <code class="docutils literal notranslate"><span class="pre">True</span></code></p></li>
<li><p><strong>dtype</strong> (<em>torch_dtype</em><em>, </em><em>optional</em>) – Data type for ops run in autocast. It uses the default value
(<code class="docutils literal notranslate"><span class="pre">torch.float16</span></code> for CUDA and <code class="docutils literal notranslate"><span class="pre">torch.bfloat16</span></code> for CPU), given by
<code class="xref py py-func docutils literal notranslate"><span class="pre">get_autocast_dtype()</span></code>, if <a class="reference internal" href="tensor_attributes.html#torch.dtype" title="torch.dtype"><code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code></a> is <code class="docutils literal notranslate"><span class="pre">None</span></code>.
Default: <code class="docutils literal notranslate"><span class="pre">None</span></code></p></li>
<li><p><strong>cache_enabled</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether the weight cache inside autocast should be enabled.
Default: <code class="docutils literal notranslate"><span class="pre">True</span></code></p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.amp.custom_fwd">
<span class="sig-prename descclassname"><span class="pre">torch.amp.</span></span><span class="sig-name descname"><span class="pre">custom_fwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fwd</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="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_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/amp/autocast_mode.html#custom_fwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.amp.custom_fwd" title="Permalink to this definition">¶</a></dt>
<dd><p>Create a helper decorator for <code class="docutils literal notranslate"><span class="pre">forward</span></code> methods of custom autograd functions.</p>
<p>Autograd functions are subclasses of <a class="reference internal" href="autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.Function</span></code></a>.
See the <a class="reference internal" href="notes/amp_examples.html#amp-custom-examples"><span class="std std-ref">example page</span></a> for more detail.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>device_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – Device type to use. ‘cuda’, ‘cpu’, ‘xpu’ and so on.
The type is the same as the <cite>type</cite> attribute of a <a class="reference internal" href="tensor_attributes.html#torch.device" title="torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>.
Thus, you may obtain the device type of a tensor using <cite>Tensor.device.type</cite>.</p></li>
<li><p><strong>cast_inputs</strong> (<a class="reference internal" href="tensor_attributes.html#torch.dtype" title="torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a> or None, optional, default=None) – If not <code class="docutils literal notranslate"><span class="pre">None</span></code>,
when <code class="docutils literal notranslate"><span class="pre">forward</span></code> runs in an autocast-enabled region, casts incoming
floating-point Tensors to the target dtype (non-floating-point Tensors are not affected),
then executes <code class="docutils literal notranslate"><span class="pre">forward</span></code> with autocast disabled.
If <code class="docutils literal notranslate"><span class="pre">None</span></code>, <code class="docutils literal notranslate"><span class="pre">forward</span></code>’s internal ops execute with the current autocast state.</p></li>
</ul>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>If the decorated <code class="docutils literal notranslate"><span class="pre">forward</span></code> is called outside an autocast-enabled region,
<a class="reference internal" href="#torch.amp.custom_fwd" title="torch.amp.custom_fwd"><code class="xref py py-func docutils literal notranslate"><span class="pre">custom_fwd</span></code></a> is a no-op and <code class="docutils literal notranslate"><span class="pre">cast_inputs</span></code> has no effect.</p>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.amp.custom_bwd">
<span class="sig-prename descclassname"><span class="pre">torch.amp.</span></span><span class="sig-name descname"><span class="pre">custom_bwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bwd</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="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/amp/autocast_mode.html#custom_bwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.amp.custom_bwd" title="Permalink to this definition">¶</a></dt>
<dd><p>Create a helper decorator for backward methods of custom autograd functions.</p>
<p>Autograd functions are subclasses of <a class="reference internal" href="autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.Function</span></code></a>.
Ensures that <code class="docutils literal notranslate"><span class="pre">backward</span></code> executes with the same autocast state as <code class="docutils literal notranslate"><span class="pre">forward</span></code>.
See the <a class="reference internal" href="notes/amp_examples.html#amp-custom-examples"><span class="std std-ref">example page</span></a> for more detail.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>device_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – Device type to use. ‘cuda’, ‘cpu’, ‘xpu’ and so on.
The type is the same as the <cite>type</cite> attribute of a <a class="reference internal" href="tensor_attributes.html#torch.device" title="torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>.
Thus, you may obtain the device type of a tensor using <cite>Tensor.device.type</cite>.</p>
</dd>
</dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.cuda.amp.autocast">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">autocast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.float16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/amp/autocast_mode.html#autocast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.autocast" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="#torch.autocast" title="torch.autocast"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autocast</span></code></a>.</p>
<p><code class="docutils literal notranslate"><span class="pre">torch.cuda.amp.autocast(args...)</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">torch.amp.autocast("cuda",</span> <span class="pre">args...)</span></code> instead.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.cuda.amp.custom_fwd">
<span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">custom_fwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fwd</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="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_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/cuda/amp/autocast_mode.html#custom_fwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.custom_fwd" title="Permalink to this definition">¶</a></dt>
<dd><p><code class="docutils literal notranslate"><span class="pre">torch.cuda.amp.custom_fwd(args...)</span></code> is deprecated. Please use
<code class="docutils literal notranslate"><span class="pre">torch.amp.custom_fwd(args...,</span> <span class="pre">device_type='cuda')</span></code> instead.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.cuda.amp.custom_bwd">
<span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">custom_bwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bwd</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/amp/autocast_mode.html#custom_bwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.custom_bwd" title="Permalink to this definition">¶</a></dt>
<dd><p><code class="docutils literal notranslate"><span class="pre">torch.cuda.amp.custom_bwd(args...)</span></code> is deprecated. Please use
<code class="docutils literal notranslate"><span class="pre">torch.amp.custom_bwd(args...,</span> <span class="pre">device_type='cuda')</span></code> instead.</p>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.cpu.amp.autocast">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cpu.amp.</span></span><span class="sig-name descname"><span class="pre">autocast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.bfloat16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cpu/amp/autocast_mode.html#autocast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.amp.autocast" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="#torch.autocast" title="torch.autocast"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autocast</span></code></a>.
<code class="docutils literal notranslate"><span class="pre">torch.cpu.amp.autocast(args...)</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">torch.amp.autocast("cpu",</span> <span class="pre">args...)</span></code> instead.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
</div>
<div class="section" id="gradient-scaling">
<span id="id2"></span><h2><a class="toc-backref" href="#id5">Gradient Scaling</a><a class="headerlink" href="#gradient-scaling" title="Permalink to this heading">¶</a></h2>
<p>If the forward pass for a particular op has <code class="docutils literal notranslate"><span class="pre">float16</span></code> inputs, the backward pass for
that op will produce <code class="docutils literal notranslate"><span class="pre">float16</span></code> gradients.
Gradient values with small magnitudes may not be representable in <code class="docutils literal notranslate"><span class="pre">float16</span></code>.
These values will flush to zero (“underflow”), so the update for the corresponding parameters will be lost.</p>
<p>To prevent underflow, “gradient scaling” multiplies the network’s loss(es) by a scale factor and
invokes a backward pass on the scaled loss(es). Gradients flowing backward through the network are
then scaled by the same factor. In other words, gradient values have a larger magnitude,
so they don’t flush to zero.</p>
<p>Each parameter’s gradient (<code class="docutils literal notranslate"><span class="pre">.grad</span></code> attribute) should be unscaled before the optimizer
updates the parameters, so the scale factor does not interfere with the learning rate.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>AMP/fp16 may not work for every model! For example, most bf16-pretrained models cannot operate in
the fp16 numerical range of max 65504 and will cause gradients to overflow instead of underflow. In
this case, the scale factor may decrease under 1 as an attempt to bring gradients to a number
representable in the fp16 dynamic range. While one may expect the scale to always be above 1, our
GradScaler does NOT make this guarantee to maintain performance. If you encounter NaNs in your loss
or gradients when running with AMP/fp16, verify your model is compatible.</p>
</div>
<dl class="py class">
<dt class="sig sig-object py" id="torch.cuda.amp.GradScaler">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">GradScaler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">init_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">65536.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backoff_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/amp/grad_scaler.html#GradScaler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.GradScaler" title="Permalink to this definition">¶</a></dt>
<dd><p>See <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.amp.GradScaler</span></code>.
<code class="docutils literal notranslate"><span class="pre">torch.cuda.amp.GradScaler(args...)</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">torch.amp.GradScaler("cuda",</span> <span class="pre">args...)</span></code> instead.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.cpu.amp.GradScaler">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cpu.amp.</span></span><span class="sig-name descname"><span class="pre">GradScaler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">init_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">65536.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backoff_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cpu/amp/grad_scaler.html#GradScaler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.amp.GradScaler" title="Permalink to this definition">¶</a></dt>
<dd><p>See <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.amp.GradScaler</span></code>.
<code class="docutils literal notranslate"><span class="pre">torch.cpu.amp.GradScaler(args...)</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">torch.amp.GradScaler("cpu",</span> <span class="pre">args...)</span></code> instead.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
</div>
<div class="section" id="autocast-op-reference">
<span id="id3"></span><h2><a class="toc-backref" href="#id6">Autocast Op Reference</a><a class="headerlink" href="#autocast-op-reference" title="Permalink to this heading">¶</a></h2>
<div class="section" id="op-eligibility">
<span id="autocast-eligibility"></span><h3><a class="toc-backref" href="#id7">Op Eligibility</a><a class="headerlink" href="#op-eligibility" title="Permalink to this heading">¶</a></h3>
<p>Ops that run in <code class="docutils literal notranslate"><span class="pre">float64</span></code> or non-floating-point dtypes are not eligible, and will
run in these types whether or not autocast is enabled.</p>
<p>Only out-of-place ops and Tensor methods are eligible.
In-place variants and calls that explicitly supply an <code class="docutils literal notranslate"><span class="pre">out=...</span></code> Tensor
are allowed in autocast-enabled regions, but won’t go through autocasting.
For example, in an autocast-enabled region <code class="docutils literal notranslate"><span class="pre">a.addmm(b,</span> <span class="pre">c)</span></code> can autocast,
but <code class="docutils literal notranslate"><span class="pre">a.addmm_(b,</span> <span class="pre">c)</span></code> and <code class="docutils literal notranslate"><span class="pre">a.addmm(b,</span> <span class="pre">c,</span> <span class="pre">out=d)</span></code> cannot.
For best performance and stability, prefer out-of-place ops in autocast-enabled
regions.</p>
<p>Ops called with an explicit <code class="docutils literal notranslate"><span class="pre">dtype=...</span></code> argument are not eligible,
and will produce output that respects the <code class="docutils literal notranslate"><span class="pre">dtype</span></code> argument.</p>
</div>
<div class="section" id="cuda-op-specific-behavior">
<span id="autocast-cuda-op-reference"></span><h3><a class="toc-backref" href="#id8">CUDA Op-Specific Behavior</a><a class="headerlink" href="#cuda-op-specific-behavior" title="Permalink to this heading">¶</a></h3>
<p>The following lists describe the behavior of eligible ops in autocast-enabled regions.
These ops always go through autocasting whether they are invoked as part of a <a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>,
as a function, or as a <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> method. If functions are exposed in multiple namespaces,
they go through autocasting regardless of the namespace.</p>
<p>Ops not listed below do not go through autocasting. They run in the type
defined by their inputs. However, autocasting may still change the type
in which unlisted ops run if they’re downstream from autocasted ops.</p>
<p>If an op is unlisted, we assume it’s numerically stable in <code class="docutils literal notranslate"><span class="pre">float16</span></code>.
If you believe an unlisted op is numerically unstable in <code class="docutils literal notranslate"><span class="pre">float16</span></code>,
please file an issue.</p>
<div class="section" id="cuda-ops-that-can-autocast-to-float16">
<h4><a class="toc-backref" href="#id9">CUDA Ops that can autocast to <code class="docutils literal notranslate"><span class="pre">float16</span></code></a><a class="headerlink" href="#cuda-ops-that-can-autocast-to-float16" title="Permalink to this heading">¶</a></h4>
<p><code class="docutils literal notranslate"><span class="pre">__matmul__</span></code>,
<code class="docutils literal notranslate"><span class="pre">addbmm</span></code>,
<code class="docutils literal notranslate"><span class="pre">addmm</span></code>,
<code class="docutils literal notranslate"><span class="pre">addmv</span></code>,
<code class="docutils literal notranslate"><span class="pre">addr</span></code>,
<code class="docutils literal notranslate"><span class="pre">baddbmm</span></code>,
<code class="docutils literal notranslate"><span class="pre">bmm</span></code>,
<code class="docutils literal notranslate"><span class="pre">chain_matmul</span></code>,
<code class="docutils literal notranslate"><span class="pre">multi_dot</span></code>,
<code class="docutils literal notranslate"><span class="pre">conv1d</span></code>,
<code class="docutils literal notranslate"><span class="pre">conv2d</span></code>,
<code class="docutils literal notranslate"><span class="pre">conv3d</span></code>,
<code class="docutils literal notranslate"><span class="pre">conv_transpose1d</span></code>,
<code class="docutils literal notranslate"><span class="pre">conv_transpose2d</span></code>,
<code class="docutils literal notranslate"><span class="pre">conv_transpose3d</span></code>,
<code class="docutils literal notranslate"><span class="pre">GRUCell</span></code>,
<code class="docutils literal notranslate"><span class="pre">linear</span></code>,
<code class="docutils literal notranslate"><span class="pre">LSTMCell</span></code>,
<code class="docutils literal notranslate"><span class="pre">matmul</span></code>,
<code class="docutils literal notranslate"><span class="pre">mm</span></code>,
<code class="docutils literal notranslate"><span class="pre">mv</span></code>,
<code class="docutils literal notranslate"><span class="pre">prelu</span></code>,
<code class="docutils literal notranslate"><span class="pre">RNNCell</span></code></p>
</div>
<div class="section" id="cuda-ops-that-can-autocast-to-float32">
<h4><a class="toc-backref" href="#id10">CUDA Ops that can autocast to <code class="docutils literal notranslate"><span class="pre">float32</span></code></a><a class="headerlink" href="#cuda-ops-that-can-autocast-to-float32" title="Permalink to this heading">¶</a></h4>
<p><code class="docutils literal notranslate"><span class="pre">__pow__</span></code>,
<code class="docutils literal notranslate"><span class="pre">__rdiv__</span></code>,
<code class="docutils literal notranslate"><span class="pre">__rpow__</span></code>,
<code class="docutils literal notranslate"><span class="pre">__rtruediv__</span></code>,
<code class="docutils literal notranslate"><span class="pre">acos</span></code>,
<code class="docutils literal notranslate"><span class="pre">asin</span></code>,
<code class="docutils literal notranslate"><span class="pre">binary_cross_entropy_with_logits</span></code>,
<code class="docutils literal notranslate"><span class="pre">cosh</span></code>,
<code class="docutils literal notranslate"><span class="pre">cosine_embedding_loss</span></code>,
<code class="docutils literal notranslate"><span class="pre">cdist</span></code>,
<code class="docutils literal notranslate"><span class="pre">cosine_similarity</span></code>,
<code class="docutils literal notranslate"><span class="pre">cross_entropy</span></code>,
<code class="docutils literal notranslate"><span class="pre">cumprod</span></code>,
<code class="docutils literal notranslate"><span class="pre">cumsum</span></code>,
<code class="docutils literal notranslate"><span class="pre">dist</span></code>,
<code class="docutils literal notranslate"><span class="pre">erfinv</span></code>,
<code class="docutils literal notranslate"><span class="pre">exp</span></code>,
<code class="docutils literal notranslate"><span class="pre">expm1</span></code>,
<code class="docutils literal notranslate"><span class="pre">group_norm</span></code>,
<code class="docutils literal notranslate"><span class="pre">hinge_embedding_loss</span></code>,
<code class="docutils literal notranslate"><span class="pre">kl_div</span></code>,
<code class="docutils literal notranslate"><span class="pre">l1_loss</span></code>,
<code class="docutils literal notranslate"><span class="pre">layer_norm</span></code>,
<code class="docutils literal notranslate"><span class="pre">log</span></code>,
<code class="docutils literal notranslate"><span class="pre">log_softmax</span></code>,
<code class="docutils literal notranslate"><span class="pre">log10</span></code>,
<code class="docutils literal notranslate"><span class="pre">log1p</span></code>,
<code class="docutils literal notranslate"><span class="pre">log2</span></code>,
<code class="docutils literal notranslate"><span class="pre">margin_ranking_loss</span></code>,
<code class="docutils literal notranslate"><span class="pre">mse_loss</span></code>,
<code class="docutils literal notranslate"><span class="pre">multilabel_margin_loss</span></code>,
<code class="docutils literal notranslate"><span class="pre">multi_margin_loss</span></code>,
<code class="docutils literal notranslate"><span class="pre">nll_loss</span></code>,
<code class="docutils literal notranslate"><span class="pre">norm</span></code>,
<code class="docutils literal notranslate"><span class="pre">normalize</span></code>,
<code class="docutils literal notranslate"><span class="pre">pdist</span></code>,
<code class="docutils literal notranslate"><span class="pre">poisson_nll_loss</span></code>,
<code class="docutils literal notranslate"><span class="pre">pow</span></code>,
<code class="docutils literal notranslate"><span class="pre">prod</span></code>,
<code class="docutils literal notranslate"><span class="pre">reciprocal</span></code>,
<code class="docutils literal notranslate"><span class="pre">rsqrt</span></code>,
<code class="docutils literal notranslate"><span class="pre">sinh</span></code>,
<code class="docutils literal notranslate"><span class="pre">smooth_l1_loss</span></code>,
<code class="docutils literal notranslate"><span class="pre">soft_margin_loss</span></code>,
<code class="docutils literal notranslate"><span class="pre">softmax</span></code>,
<code class="docutils literal notranslate"><span class="pre">softmin</span></code>,
<code class="docutils literal notranslate"><span class="pre">softplus</span></code>,
<code class="docutils literal notranslate"><span class="pre">sum</span></code>,
<code class="docutils literal notranslate"><span class="pre">renorm</span></code>,
<code class="docutils literal notranslate"><span class="pre">tan</span></code>,
<code class="docutils literal notranslate"><span class="pre">triplet_margin_loss</span></code></p>
</div>
<div class="section" id="cuda-ops-that-promote-to-the-widest-input-type">
<h4><a class="toc-backref" href="#id11">CUDA Ops that promote to the widest input type</a><a class="headerlink" href="#cuda-ops-that-promote-to-the-widest-input-type" title="Permalink to this heading">¶</a></h4>
<p>These ops don’t require a particular dtype for stability, but take multiple inputs
and require that the inputs’ dtypes match. If all of the inputs are
<code class="docutils literal notranslate"><span class="pre">float16</span></code>, the op runs in <code class="docutils literal notranslate"><span class="pre">float16</span></code>. If any of the inputs is <code class="docutils literal notranslate"><span class="pre">float32</span></code>,
autocast casts all inputs to <code class="docutils literal notranslate"><span class="pre">float32</span></code> and runs the op in <code class="docutils literal notranslate"><span class="pre">float32</span></code>.</p>
<p><code class="docutils literal notranslate"><span class="pre">addcdiv</span></code>,
<code class="docutils literal notranslate"><span class="pre">addcmul</span></code>,
<code class="docutils literal notranslate"><span class="pre">atan2</span></code>,
<code class="docutils literal notranslate"><span class="pre">bilinear</span></code>,
<code class="docutils literal notranslate"><span class="pre">cross</span></code>,
<code class="docutils literal notranslate"><span class="pre">dot</span></code>,
<code class="docutils literal notranslate"><span class="pre">grid_sample</span></code>,
<code class="docutils literal notranslate"><span class="pre">index_put</span></code>,
<code class="docutils literal notranslate"><span class="pre">scatter_add</span></code>,
<code class="docutils literal notranslate"><span class="pre">tensordot</span></code></p>
<p>Some ops not listed here (e.g., binary ops like <code class="docutils literal notranslate"><span class="pre">add</span></code>) natively promote
inputs without autocasting’s intervention. If inputs are a mixture of <code class="docutils literal notranslate"><span class="pre">float16</span></code>
and <code class="docutils literal notranslate"><span class="pre">float32</span></code>, these ops run in <code class="docutils literal notranslate"><span class="pre">float32</span></code> and produce <code class="docutils literal notranslate"><span class="pre">float32</span></code> output,
regardless of whether autocast is enabled.</p>
</div>
<div class="section" id="prefer-binary-cross-entropy-with-logits-over-binary-cross-entropy">
<h4><a class="toc-backref" href="#id12">Prefer <code class="docutils literal notranslate"><span class="pre">binary_cross_entropy_with_logits</span></code> over <code class="docutils literal notranslate"><span class="pre">binary_cross_entropy</span></code></a><a class="headerlink" href="#prefer-binary-cross-entropy-with-logits-over-binary-cross-entropy" title="Permalink to this heading">¶</a></h4>
<p>The backward passes of <a class="reference internal" href="generated/torch.nn.functional.binary_cross_entropy.html#torch.nn.functional.binary_cross_entropy" title="torch.nn.functional.binary_cross_entropy"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.binary_cross_entropy()</span></code></a> (and <a class="reference internal" href="generated/torch.nn.BCELoss.html#torch.nn.BCELoss" title="torch.nn.BCELoss"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.nn.BCELoss</span></code></a>, which wraps it)
can produce gradients that aren’t representable in <code class="docutils literal notranslate"><span class="pre">float16</span></code>. In autocast-enabled regions, the forward input
may be <code class="docutils literal notranslate"><span class="pre">float16</span></code>, which means the backward gradient must be representable in <code class="docutils literal notranslate"><span class="pre">float16</span></code> (autocasting <code class="docutils literal notranslate"><span class="pre">float16</span></code>