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<p class="caption"><span class="caption-text">Notes</span></p>
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<li class="toctree-l1"><a class="reference internal" href="amp_examples.html">Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="autograd.html">Autograd mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="broadcasting.html">Broadcasting semantics</a></li>
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<div class="section" id="cuda-semantics">
<span id="id1"></span><h1>CUDA semantics<a class="headerlink" href="#cuda-semantics" title="Permalink to this headline">¶</a></h1>
<p><a class="reference internal" href="../cuda.html#module-torch.cuda" title="torch.cuda"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.cuda</span></code></a> is used to set up and run CUDA operations. It keeps track of
the currently selected GPU, and all CUDA tensors you allocate will by default be
created on that device. The selected device can be changed with a
<a class="reference internal" href="../generated/torch.cuda.device.html#torch.cuda.device" title="torch.cuda.device"><code class="xref any py py-class docutils literal notranslate"><span class="pre">torch.cuda.device</span></code></a> context manager.</p>
<p>However, once a tensor is allocated, you can do operations on it irrespective
of the selected device, and the results will be always placed in on the same
device as the tensor.</p>
<p>Cross-GPU operations are not allowed by default, with the exception of
<a class="reference internal" href="../generated/torch.Tensor.copy_.html#torch.Tensor.copy_" title="torch.Tensor.copy_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">copy_()</span></code></a> and other methods with copy-like functionality
such as <a class="reference internal" href="../generated/torch.Tensor.to.html#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">to()</span></code></a> and <a class="reference internal" href="../generated/torch.Tensor.cuda.html#torch.Tensor.cuda" title="torch.Tensor.cuda"><code class="xref py py-meth docutils literal notranslate"><span class="pre">cuda()</span></code></a>.
Unless you enable peer-to-peer memory access, any attempts to launch ops on
tensors spread across different devices will raise an error.</p>
<p>Below you can find a small example showcasing this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cuda</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda'</span><span class="p">)</span> <span class="c1"># Default CUDA device</span>
<span class="n">cuda0</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda:0'</span><span class="p">)</span>
<span class="n">cuda2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda:2'</span><span class="p">)</span> <span class="c1"># GPU 2 (these are 0-indexed)</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="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda0</span><span class="p">)</span>
<span class="c1"># x.device is device(type='cuda', index=0)</span>
<span class="n">y</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">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">])</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="c1"># y.device is device(type='cuda', index=0)</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">device</span><span class="p">(</span><span class="mi">1</span><span class="p">):</span>
<span class="c1"># allocates a tensor on GPU 1</span>
<span class="n">a</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">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda</span><span class="p">)</span>
<span class="c1"># transfers a tensor from CPU to GPU 1</span>
<span class="n">b</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">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">])</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="c1"># a.device and b.device are device(type='cuda', index=1)</span>
<span class="c1"># You can also use ``Tensor.to`` to transfer a tensor:</span>
<span class="n">b2</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">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">])</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">cuda</span><span class="p">)</span>
<span class="c1"># b.device and b2.device are device(type='cuda', index=1)</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="c1"># c.device is device(type='cuda', index=1)</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="c1"># z.device is device(type='cuda', index=0)</span>
<span class="c1"># even within a context, you can specify the device</span>
<span class="c1"># (or give a GPU index to the .cuda call)</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda2</span><span class="p">)</span>
<span class="n">e</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">cuda2</span><span class="p">)</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">(</span><span class="n">cuda2</span><span class="p">)</span>
<span class="c1"># d.device, e.device, and f.device are all device(type='cuda', index=2)</span>
</pre></div>
</div>
<div class="section" id="tensorfloat-32-tf32-on-ampere-devices">
<span id="tf32-on-ampere"></span><h2>TensorFloat-32(TF32) on Ampere devices<a class="headerlink" href="#tensorfloat-32-tf32-on-ampere-devices" title="Permalink to this headline">¶</a></h2>
<p>Starting in PyTorch 1.7, there is a new flag called <cite>allow_tf32</cite> which defaults to true.
This flag controls whether PyTorch is allowed to use the TensorFloat32 (TF32) tensor cores,
available on new NVIDIA GPUs since Ampere, internally to compute matmul (matrix multiplies
and batched matrix multiplies) and convolutions.</p>
<p>TF32 tensor cores are designed to achieve better performance on matmul and convolutions on
<cite>torch.float32</cite> tensors by rounding input data to have 10 bits of mantissa, and accumulating
results with FP32 precision, maintaining FP32 dynamic range.</p>
<p>matmuls and convolutions are controlled separately, and their corresponding flags can be accessed at:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># The flag below controls whether to allow TF32 on matmul. This flag defaults to True.</span>
<span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">matmul</span><span class="o">.</span><span class="n">allow_tf32</span> <span class="o">=</span> <span class="kc">True</span>
<span class="c1"># The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.</span>
<span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">allow_tf32</span> <span class="o">=</span> <span class="kc">True</span>
</pre></div>
</div>
<p>Note that besides matmuls and convolutions themselves, functions and nn modules that internally uses
matmuls or convolutions are also affected. These include <cite>nn.Linear</cite>, <cite>nn.Conv*</cite>, cdist, tensordot,
affine grid and grid sample, adaptive log softmax, GRU and LSTM.</p>
<p>To get an idea of the precision and speed, see the example code below:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">a_full</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">10240</span><span class="p">,</span> <span class="mi">10240</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">double</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
<span class="n">b_full</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">10240</span><span class="p">,</span> <span class="mi">10240</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">double</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
<span class="n">ab_full</span> <span class="o">=</span> <span class="n">a_full</span> <span class="o">@</span> <span class="n">b_full</span>
<span class="n">mean</span> <span class="o">=</span> <span class="n">ab_full</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="c1"># 80.7277</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">a_full</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">b_full</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
<span class="c1"># Do matmul at TF32 mode.</span>
<span class="n">ab_tf32</span> <span class="o">=</span> <span class="n">a</span> <span class="o">@</span> <span class="n">b</span> <span class="c1"># takes 0.016s on GA100</span>
<span class="n">error</span> <span class="o">=</span> <span class="p">(</span><span class="n">ab_tf32</span> <span class="o">-</span> <span class="n">ab_full</span><span class="p">)</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="c1"># 0.1747</span>
<span class="n">relative_error</span> <span class="o">=</span> <span class="n">error</span> <span class="o">/</span> <span class="n">mean</span> <span class="c1"># 0.0022</span>
<span class="c1"># Do matmul with TF32 disabled.</span>
<span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">matmul</span><span class="o">.</span><span class="n">allow_tf32</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">ab_fp32</span> <span class="o">=</span> <span class="n">a</span> <span class="o">@</span> <span class="n">b</span> <span class="c1"># takes 0.11s on GA100</span>
<span class="n">error</span> <span class="o">=</span> <span class="p">(</span><span class="n">ab_fp32</span> <span class="o">-</span> <span class="n">ab_full</span><span class="p">)</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="c1"># 0.0031</span>
<span class="n">relative_error</span> <span class="o">=</span> <span class="n">error</span> <span class="o">/</span> <span class="n">mean</span> <span class="c1"># 0.000039</span>
</pre></div>
</div>
<p>From the above example, we can see that with TF32 enabled, the speed is ~7x faster, relative error
compared to double precision is approximately 2 orders of magnitude larger. If the full FP32 precision
is needed, users can disable TF32 by:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">matmul</span><span class="o">.</span><span class="n">allow_tf32</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">allow_tf32</span> <span class="o">=</span> <span class="kc">False</span>
</pre></div>
</div>
<p>To toggle the TF32 flags off in C++, you can do</p>
<div class="highlight-C++ notranslate"><div class="highlight"><pre><span></span><span class="n">at</span><span class="o">::</span><span class="n">globalContext</span><span class="p">().</span><span class="n">setAllowTF32CuBLAS</span><span class="p">(</span><span class="nb">false</span><span class="p">);</span><span class="w"></span>
<span class="n">at</span><span class="o">::</span><span class="n">globalContext</span><span class="p">().</span><span class="n">setAllowTF32CuDNN</span><span class="p">(</span><span class="nb">false</span><span class="p">);</span><span class="w"></span>
</pre></div>
</div>
<p>For more information about TF32, see:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/">TensorFloat-32</a></p></li>
<li><p><a class="reference external" href="https://devblogs.nvidia.com/cuda-11-features-revealed/">CUDA 11</a></p></li>
<li><p><a class="reference external" href="https://devblogs.nvidia.com/nvidia-ampere-architecture-in-depth/">Ampere architecture</a></p></li>
</ul>
</div>
<div class="section" id="asynchronous-execution">
<h2>Asynchronous execution<a class="headerlink" href="#asynchronous-execution" title="Permalink to this headline">¶</a></h2>
<p>By default, GPU operations are asynchronous. When you call a function that
uses the GPU, the operations are <em>enqueued</em> to the particular device, but not
necessarily executed until later. This allows us to execute more computations
in parallel, including operations on CPU or other GPUs.</p>
<p>In general, the effect of asynchronous computation is invisible to the caller,
because (1) each device executes operations in the order they are queued, and
(2) PyTorch automatically performs necessary synchronization when copying data
between CPU and GPU or between two GPUs. Hence, computation will proceed as if
every operation was executed synchronously.</p>
<p>You can force synchronous computation by setting environment variable
<code class="docutils literal notranslate"><span class="pre">CUDA_LAUNCH_BLOCKING=1</span></code>. This can be handy when an error occurs on the GPU.
(With asynchronous execution, such an error isn’t reported until after the
operation is actually executed, so the stack trace does not show where it was
requested.)</p>
<p>A consequence of the asynchronous computation is that time measurements without
synchronizations are not accurate. To get precise measurements, one should either
call <a class="reference internal" href="../generated/torch.cuda.synchronize.html#torch.cuda.synchronize" title="torch.cuda.synchronize"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cuda.synchronize()</span></code></a> before measuring, or use <a class="reference internal" href="../generated/torch.cuda.Event.html#torch.cuda.Event" title="torch.cuda.Event"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.Event</span></code></a>
to record times as following:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">start_event</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Event</span><span class="p">(</span><span class="n">enable_timing</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">end_event</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Event</span><span class="p">(</span><span class="n">enable_timing</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">start_event</span><span class="o">.</span><span class="n">record</span><span class="p">()</span>
<span class="c1"># Run some things here</span>
<span class="n">end_event</span><span class="o">.</span><span class="n">record</span><span class="p">()</span>
<span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span> <span class="c1"># Wait for the events to be recorded!</span>
<span class="n">elapsed_time_ms</span> <span class="o">=</span> <span class="n">start_event</span><span class="o">.</span><span class="n">elapsed_time</span><span class="p">(</span><span class="n">end_event</span><span class="p">)</span>
</pre></div>
</div>
<p>As an exception, several functions such as <a class="reference internal" href="../generated/torch.Tensor.to.html#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">to()</span></code></a> and
<a class="reference internal" href="../generated/torch.Tensor.copy_.html#torch.Tensor.copy_" title="torch.Tensor.copy_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">copy_()</span></code></a> admit an explicit <code class="xref py py-attr docutils literal notranslate"><span class="pre">non_blocking</span></code> argument,
which lets the caller bypass synchronization when it is unnecessary.
Another exception is CUDA streams, explained below.</p>
<div class="section" id="cuda-streams">
<h3>CUDA streams<a class="headerlink" href="#cuda-streams" title="Permalink to this headline">¶</a></h3>
<p>A <a class="reference external" href="https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#streams">CUDA stream</a> is a linear sequence of execution that belongs to a specific
device. You normally do not need to create one explicitly: by default, each
device uses its own “default” stream.</p>
<p>Operations inside each stream are serialized in the order they are created,
but operations from different streams can execute concurrently in any
relative order, unless explicit synchronization functions (such as
<a class="reference internal" href="../generated/torch.cuda.synchronize.html#torch.cuda.synchronize" title="torch.cuda.synchronize"><code class="xref py py-meth docutils literal notranslate"><span class="pre">synchronize()</span></code></a> or <a class="reference internal" href="../generated/torch.cuda.Stream.html#torch.cuda.Stream.wait_stream" title="torch.cuda.Stream.wait_stream"><code class="xref py py-meth docutils literal notranslate"><span class="pre">wait_stream()</span></code></a>) are
used. For example, the following code is incorrect:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cuda</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda'</span><span class="p">)</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Stream</span><span class="p">()</span> <span class="c1"># Create a new stream.</span>
<span class="n">A</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda</span><span class="p">)</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</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">stream</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
<span class="c1"># sum() may start execution before normal_() finishes!</span>
<span class="n">B</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
</pre></div>
</div>
<p>When the “current stream” is the default stream, PyTorch automatically performs
necessary synchronization when data is moved around, as explained above.
However, when using non-default streams, it is the user’s responsibility to
ensure proper synchronization.</p>
</div>
<div class="section" id="stream-semantics-of-backward-passes">
<span id="bwd-cuda-stream-semantics"></span><h3>Stream semantics of backward passes<a class="headerlink" href="#stream-semantics-of-backward-passes" title="Permalink to this headline">¶</a></h3>
<p>Each backward CUDA op runs on the same stream that was used for its corresponding forward op.
If your forward pass runs independent ops in parallel on different streams,
this helps the backward pass exploit that same parallelism.</p>
<p>The stream semantics of a backward call with respect to surrounding ops are the same
as for any other call. The backward pass inserts internal syncs to ensure this even when
backward ops run on multiple streams as described in the previous paragraph.
More concretely, when calling
<a class="reference internal" href="../generated/torch.autograd.backward.html#torch.autograd.backward" title="torch.autograd.backward"><code class="xref py py-func docutils literal notranslate"><span class="pre">autograd.backward</span></code></a>,
<a class="reference internal" href="../generated/torch.autograd.grad.html#torch.autograd.grad" title="torch.autograd.grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">autograd.grad</span></code></a>, or
<a class="reference internal" href="../generated/torch.Tensor.backward.html#torch.Tensor.backward" title="torch.Tensor.backward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">tensor.backward</span></code></a>,
and optionally supplying CUDA tensor(s) as the initial gradient(s) (e.g.,
<a class="reference internal" href="../generated/torch.autograd.backward.html#torch.autograd.backward" title="torch.autograd.backward"><code class="xref py py-func docutils literal notranslate"><span class="pre">autograd.backward(...,</span> <span class="pre">grad_tensors=initial_grads)</span></code></a>,
<a class="reference internal" href="../generated/torch.autograd.grad.html#torch.autograd.grad" title="torch.autograd.grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">autograd.grad(...,</span> <span class="pre">grad_outputs=initial_grads)</span></code></a>, or
<a class="reference internal" href="../generated/torch.Tensor.backward.html#torch.Tensor.backward" title="torch.Tensor.backward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">tensor.backward(...,</span> <span class="pre">gradient=initial_grad)</span></code></a>),
the acts of</p>
<ol class="arabic simple">
<li><p>optionally populating initial gradient(s),</p></li>
<li><p>invoking the backward pass, and</p></li>
<li><p>using the gradients</p></li>
</ol>
<p>have the same stream-semantics relationship as any group of ops:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Stream</span><span class="p">()</span>
<span class="c1"># Safe, grads are used in the same stream context as backward()</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">stream</span><span class="p">(</span><span class="n">s</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">use</span> <span class="n">grads</span>
<span class="c1"># Unsafe</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">stream</span><span class="p">(</span><span class="n">s</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">use</span> <span class="n">grads</span>
<span class="c1"># Safe, with synchronization</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">stream</span><span class="p">(</span><span class="n">s</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">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">()</span><span class="o">.</span><span class="n">wait_stream</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
<span class="n">use</span> <span class="n">grads</span>
<span class="c1"># Safe, populating initial grad and invoking backward are in the same stream context</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">stream</span><span class="p">(</span><span class="n">s</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">gradient</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">loss</span><span class="p">))</span>
<span class="c1"># Unsafe, populating initial_grad and invoking backward are in different stream contexts,</span>
<span class="c1"># without synchronization</span>
<span class="n">initial_grad</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">loss</span><span class="p">)</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">stream</span><span class="p">(</span><span class="n">s</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">gradient</span><span class="o">=</span><span class="n">initial_grad</span><span class="p">)</span>
<span class="c1"># Safe, with synchronization</span>
<span class="n">initial_grad</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
<span class="n">s</span><span class="o">.</span><span class="n">wait_stream</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">())</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">stream</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
<span class="n">initial_grad</span><span class="o">.</span><span class="n">record_stream</span><span class="p">(</span><span class="n">s</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">gradient</span><span class="o">=</span><span class="n">initial_grad</span><span class="p">)</span>
</pre></div>
</div>
<div class="section" id="bc-note-using-grads-on-the-default-stream">
<h4>BC note: Using grads on the default stream<a class="headerlink" href="#bc-note-using-grads-on-the-default-stream" title="Permalink to this headline">¶</a></h4>
<p>In prior versions of PyTorch (1.9 and earlier), the autograd engine always synced
the default stream with all backward ops, so the following pattern:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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">stream</span><span class="p">(</span><span class="n">s</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">use</span> <span class="n">grads</span>
</pre></div>
</div>
<p>was safe as long as <code class="docutils literal notranslate"><span class="pre">use</span> <span class="pre">grads</span></code> happened on the default stream.
In present PyTorch, that pattern is no longer safe. If <code class="docutils literal notranslate"><span class="pre">backward()</span></code>
and <code class="docutils literal notranslate"><span class="pre">use</span> <span class="pre">grads</span></code> are in different stream contexts, you must sync the streams:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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">stream</span><span class="p">(</span><span class="n">s</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">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">()</span><span class="o">.</span><span class="n">wait_stream</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
<span class="n">use</span> <span class="n">grads</span>
</pre></div>
</div>
<p>even if <code class="docutils literal notranslate"><span class="pre">use</span> <span class="pre">grads</span></code> is on the default stream.</p>
</div>
</div>
</div>
<div class="section" id="memory-management">
<span id="cuda-memory-management"></span><h2>Memory management<a class="headerlink" href="#memory-management" title="Permalink to this headline">¶</a></h2>
<p>PyTorch uses a caching memory allocator to speed up memory allocations. This
allows fast memory deallocation without device synchronizations. However, the
unused memory managed by the allocator will still show as if used in
<code class="docutils literal notranslate"><span class="pre">nvidia-smi</span></code>. You can use <a class="reference internal" href="../generated/torch.cuda.memory_allocated.html#torch.cuda.memory_allocated" title="torch.cuda.memory_allocated"><code class="xref py py-meth docutils literal notranslate"><span class="pre">memory_allocated()</span></code></a> and
<a class="reference internal" href="../generated/torch.cuda.max_memory_allocated.html#torch.cuda.max_memory_allocated" title="torch.cuda.max_memory_allocated"><code class="xref py py-meth docutils literal notranslate"><span class="pre">max_memory_allocated()</span></code></a> to monitor memory occupied by
tensors, and use <a class="reference internal" href="../generated/torch.cuda.memory_reserved.html#torch.cuda.memory_reserved" title="torch.cuda.memory_reserved"><code class="xref py py-meth docutils literal notranslate"><span class="pre">memory_reserved()</span></code></a> and
<a class="reference internal" href="../generated/torch.cuda.max_memory_reserved.html#torch.cuda.max_memory_reserved" title="torch.cuda.max_memory_reserved"><code class="xref py py-meth docutils literal notranslate"><span class="pre">max_memory_reserved()</span></code></a> to monitor the total amount of memory
managed by the caching allocator. Calling <a class="reference internal" href="../generated/torch.cuda.empty_cache.html#torch.cuda.empty_cache" title="torch.cuda.empty_cache"><code class="xref py py-meth docutils literal notranslate"><span class="pre">empty_cache()</span></code></a>
releases all <strong>unused</strong> cached memory from PyTorch so that those can be used
by other GPU applications. However, the occupied GPU memory by tensors will not
be freed so it can not increase the amount of GPU memory available for PyTorch.</p>
<p>For more advanced users, we offer more comprehensive memory benchmarking via
<a class="reference internal" href="../generated/torch.cuda.memory_stats.html#torch.cuda.memory_stats" title="torch.cuda.memory_stats"><code class="xref py py-meth docutils literal notranslate"><span class="pre">memory_stats()</span></code></a>. We also offer the capability to capture a
complete snapshot of the memory allocator state via
<a class="reference internal" href="../generated/torch.cuda.memory_snapshot.html#torch.cuda.memory_snapshot" title="torch.cuda.memory_snapshot"><code class="xref py py-meth docutils literal notranslate"><span class="pre">memory_snapshot()</span></code></a>, which can help you understand the
underlying allocation patterns produced by your code.</p>
<p>Use of a caching allocator can interfere with memory checking tools such as
<code class="docutils literal notranslate"><span class="pre">cuda-memcheck</span></code>. To debug memory errors using <code class="docutils literal notranslate"><span class="pre">cuda-memcheck</span></code>, set
<code class="docutils literal notranslate"><span class="pre">PYTORCH_NO_CUDA_MEMORY_CACHING=1</span></code> in your environment to disable caching.</p>
<p>The behavior of caching allocator can be controlled via environment variable
<code class="docutils literal notranslate"><span class="pre">PYTORCH_CUDA_ALLOC_CONF</span></code>.
The format is <code class="docutils literal notranslate"><span class="pre">PYTORCH_CUDA_ALLOC_CONF=<option>:<value>,<option2><value2>...</span></code>
Available options:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">max_split_size_mb</span></code> prevents the allocator from splitting blocks larger
than this size (in MB). This can help prevent fragmentation and may allow
some borderline workloads to complete without running out of memory.
Performance cost can range from ‘zero’ to ‘substatial’ depending on
allocation patterns. Default value is unlimited, i.e. all blocks can be
split. The <a class="reference internal" href="../generated/torch.cuda.memory_stats.html#torch.cuda.memory_stats" title="torch.cuda.memory_stats"><code class="xref py py-meth docutils literal notranslate"><span class="pre">memory_stats()</span></code></a> and
<a class="reference internal" href="../generated/torch.cuda.memory_summary.html#torch.cuda.memory_summary" title="torch.cuda.memory_summary"><code class="xref py py-meth docutils literal notranslate"><span class="pre">memory_summary()</span></code></a> methods are useful for tuning. This
option should be used as a last resort for a workload that is aborting
due to ‘out of memory’ and showing a large amount of inactive split blocks.</p></li>
</ul>
</div>
<div class="section" id="cufft-plan-cache">
<span id="id2"></span><h2>cuFFT plan cache<a class="headerlink" href="#cufft-plan-cache" title="Permalink to this headline">¶</a></h2>
<p>For each CUDA device, an LRU cache of cuFFT plans is used to speed up repeatedly
running FFT methods (e.g., <a class="reference internal" href="../generated/torch.fft.fft.html#torch.fft.fft" title="torch.fft.fft"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.fft.fft()</span></code></a>) on CUDA tensors of same geometry
with same configuration. Because some cuFFT plans may allocate GPU memory,
these caches have a maximum capacity.</p>
<p>You may control and query the properties of the cache of current device with
the following APIs:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.cuda.cufft_plan_cache.max_size</span></code> gives the capacity of the
cache (default is 4096 on CUDA 10 and newer, and 1023 on older CUDA versions).
Setting this value directly modifies the capacity.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.cuda.cufft_plan_cache.size</span></code> gives the number of plans
currently residing in the cache.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.cuda.cufft_plan_cache.clear()</span></code> clears the cache.</p></li>
</ul>
<p>To control and query plan caches of a non-default device, you can index the
<code class="docutils literal notranslate"><span class="pre">torch.backends.cuda.cufft_plan_cache</span></code> object with either a <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code>
object or a device index, and access one of the above attributes. E.g., to set
the capacity of the cache for device <code class="docutils literal notranslate"><span class="pre">1</span></code>, one can write
<code class="docutils literal notranslate"><span class="pre">torch.backends.cuda.cufft_plan_cache[1].max_size</span> <span class="pre">=</span> <span class="pre">10</span></code>.</p>
</div>
<div class="section" id="best-practices">
<h2>Best practices<a class="headerlink" href="#best-practices" title="Permalink to this headline">¶</a></h2>
<div class="section" id="device-agnostic-code">
<h3>Device-agnostic code<a class="headerlink" href="#device-agnostic-code" title="Permalink to this headline">¶</a></h3>
<p>Due to the structure of PyTorch, you may need to explicitly write
device-agnostic (CPU or GPU) code; an example may be creating a new tensor as
the initial hidden state of a recurrent neural network.</p>
<p>The first step is to determine whether the GPU should be used or not. A common
pattern is to use Python’s <code class="docutils literal notranslate"><span class="pre">argparse</span></code> module to read in user arguments, and
have a flag that can be used to disable CUDA, in combination with
<a class="reference internal" href="../generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available"><code class="xref py py-meth docutils literal notranslate"><span class="pre">is_available()</span></code></a>. In the following, <code class="docutils literal notranslate"><span class="pre">args.device</span></code> results in a
<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code> object that can be used to move tensors to CPU or CUDA.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">argparse</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="n">parser</span> <span class="o">=</span> <span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentParser</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s1">'PyTorch Example'</span><span class="p">)</span>
<span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">'--disable-cuda'</span><span class="p">,</span> <span class="n">action</span><span class="o">=</span><span class="s1">'store_true'</span><span class="p">,</span>
<span class="n">help</span><span class="o">=</span><span class="s1">'Disable CUDA'</span><span class="p">)</span>
<span class="n">args</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
<span class="n">args</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">args</span><span class="o">.</span><span class="n">disable_cuda</span> <span class="ow">and</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">():</span>
<span class="n">args</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda'</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">args</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cpu'</span><span class="p">)</span>
</pre></div>
</div>
<p>Now that we have <code class="docutils literal notranslate"><span class="pre">args.device</span></code>, we can use it to create a Tensor on the
desired device.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">8</span><span class="p">,</span> <span class="mi">42</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
<span class="n">net</span> <span class="o">=</span> <span class="n">Network</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
</pre></div>
</div>
<p>This can be used in a number of cases to produce device agnostic code. Below
is an example when using a dataloader:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cuda0</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda:0'</span><span class="p">)</span> <span class="c1"># CUDA GPU 0</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">train_loader</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">cuda0</span><span class="p">)</span>
</pre></div>
</div>
<p>When working with multiple GPUs on a system, you can use the
<code class="docutils literal notranslate"><span class="pre">CUDA_VISIBLE_DEVICES</span></code> environment flag to manage which GPUs are available to
PyTorch. As mentioned above, to manually control which GPU a tensor is created
on, the best practice is to use a <a class="reference internal" href="../generated/torch.cuda.device.html#torch.cuda.device" title="torch.cuda.device"><code class="xref any py py-class docutils literal notranslate"><span class="pre">torch.cuda.device</span></code></a> context manager.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"Outside device is 0"</span><span class="p">)</span> <span class="c1"># On device 0 (default in most scenarios)</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">device</span><span class="p">(</span><span class="mi">1</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Inside device is 1"</span><span class="p">)</span> <span class="c1"># On device 1</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Outside device is still 0"</span><span class="p">)</span> <span class="c1"># On device 0</span>
</pre></div>
</div>
<p>If you have a tensor and would like to create a new tensor of the same type on
the same device, then you can use a <code class="docutils literal notranslate"><span class="pre">torch.Tensor.new_*</span></code> method
(see <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>).
Whilst the previously mentioned <code class="docutils literal notranslate"><span class="pre">torch.*</span></code> factory functions
(<a class="reference internal" href="../torch.html#tensor-creation-ops"><span class="std std-ref">Creation Ops</span></a>) depend on the current GPU context and
the attributes arguments you pass in, <code class="docutils literal notranslate"><span class="pre">torch.Tensor.new_*</span></code> methods preserve
the device and other attributes of the tensor.</p>
<p>This is the recommended practice when creating modules in which new
tensors need to be created internally during the forward pass.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cuda</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda'</span><span class="p">)</span>
<span class="n">x_cpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="n">x_gpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda</span><span class="p">)</span>
<span class="n">x_cpu_long</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">2</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">int64</span><span class="p">)</span>
<span class="n">y_cpu</span> <span class="o">=</span> <span class="n">x_cpu</span><span class="o">.</span><span class="n">new_full</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">fill_value</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">y_cpu</span><span class="p">)</span>
<span class="n">tensor</span><span class="p">([[</span> <span class="mf">0.3000</span><span class="p">,</span> <span class="mf">0.3000</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">0.3000</span><span class="p">,</span> <span class="mf">0.3000</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">0.3000</span><span class="p">,</span> <span class="mf">0.3000</span><span class="p">]])</span>
<span class="n">y_gpu</span> <span class="o">=</span> <span class="n">x_gpu</span><span class="o">.</span><span class="n">new_full</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">fill_value</span><span class="o">=-</span><span class="mi">5</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">y_gpu</span><span class="p">)</span>
<span class="n">tensor</span><span class="p">([[</span><span class="o">-</span><span class="mf">5.0000</span><span class="p">,</span> <span class="o">-</span><span class="mf">5.0000</span><span class="p">],</span>
<span class="p">[</span><span class="o">-</span><span class="mf">5.0000</span><span class="p">,</span> <span class="o">-</span><span class="mf">5.0000</span><span class="p">],</span>
<span class="p">[</span><span class="o">-</span><span class="mf">5.0000</span><span class="p">,</span> <span class="o">-</span><span class="mf">5.0000</span><span class="p">]],</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda:0'</span><span class="p">)</span>
<span class="n">y_cpu_long</span> <span class="o">=</span> <span class="n">x_cpu_long</span><span class="o">.</span><span class="n">new_tensor</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">y_cpu_long</span><span class="p">)</span>
<span class="n">tensor</span><span class="p">([[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
</pre></div>
</div>
<p>If you want to create a tensor of the same type and size of another tensor, and
fill it with either ones or zeros, <a class="reference internal" href="../generated/torch.ones_like.html#torch.ones_like" title="torch.ones_like"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ones_like()</span></code></a> or
<a class="reference internal" href="../generated/torch.zeros_like.html#torch.zeros_like" title="torch.zeros_like"><code class="xref py py-meth docutils literal notranslate"><span class="pre">zeros_like()</span></code></a> are provided as convenient helper functions (which
also preserve <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code> of a Tensor).</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x_cpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">x_gpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">y_cpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">x_cpu</span><span class="p">)</span>
<span class="n">y_gpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">x_gpu</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="use-pinned-memory-buffers">
<span id="cuda-memory-pinning"></span><h3>Use pinned memory buffers<a class="headerlink" href="#use-pinned-memory-buffers" title="Permalink to this headline">¶</a></h3>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This is an advanced tip. If you overuse pinned memory, it can cause serious
problems when running low on RAM, and you should be aware that pinning is
often an expensive operation.</p>
</div>
<p>Host to GPU copies are much faster when they originate from pinned (page-locked)
memory. CPU tensors and storages expose a <a class="reference internal" href="../generated/torch.Tensor.pin_memory.html#torch.Tensor.pin_memory" title="torch.Tensor.pin_memory"><code class="xref py py-meth docutils literal notranslate"><span class="pre">pin_memory()</span></code></a>
method, that returns a copy of the object, with data put in a pinned region.</p>
<p>Also, once you pin a tensor or storage, you can use asynchronous GPU copies.
Just pass an additional <code class="docutils literal notranslate"><span class="pre">non_blocking=True</span></code> argument to a
<a class="reference internal" href="../generated/torch.Tensor.to.html#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">to()</span></code></a> or a <a class="reference internal" href="../generated/torch.Tensor.cuda.html#torch.Tensor.cuda" title="torch.Tensor.cuda"><code class="xref py py-meth docutils literal notranslate"><span class="pre">cuda()</span></code></a> call. This can be used
to overlap data transfers with computation.</p>
<p>You can make the <a class="reference internal" href="../data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> return batches placed in
pinned memory by passing <code class="docutils literal notranslate"><span class="pre">pin_memory=True</span></code> to its constructor.</p>
</div>
<div class="section" id="use-nn-parallel-distributeddataparallel-instead-of-multiprocessing-or-nn-dataparallel">
<span id="cuda-nn-ddp-instead"></span><h3>Use nn.parallel.DistributedDataParallel instead of multiprocessing or nn.DataParallel<a class="headerlink" href="#use-nn-parallel-distributeddataparallel-instead-of-multiprocessing-or-nn-dataparallel" title="Permalink to this headline">¶</a></h3>
<p>Most use cases involving batched inputs and multiple GPUs should default to
using <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">DistributedDataParallel</span></code></a> to utilize more
than one GPU.</p>
<p>There are significant caveats to using CUDA models with
<a class="reference internal" href="../multiprocessing.html#module-torch.multiprocessing" title="torch.multiprocessing"><code class="xref py py-mod docutils literal notranslate"><span class="pre">multiprocessing</span></code></a>; unless care is taken to meet the data handling
requirements exactly, it is likely that your program will have incorrect or
undefined behavior.</p>
<p>It is recommended to use <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">DistributedDataParallel</span></code></a>,
instead of <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">DataParallel</span></code></a> to do multi-GPU training, even if
there is only a single node.</p>
<p>The difference between <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">DistributedDataParallel</span></code></a> and
<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">DataParallel</span></code></a> is: <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">DistributedDataParallel</span></code></a>
uses multiprocessing where a process is created for each GPU, while
<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">DataParallel</span></code></a> uses multithreading. By using multiprocessing,
each GPU has its dedicated process, this avoids the performance overhead caused
by GIL of Python interpreter.</p>
<p>If you use <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">DistributedDataParallel</span></code></a>, you could use
<cite>torch.distributed.launch</cite> utility to launch your program, see <a class="reference internal" href="../distributed.html#distributed-launch"><span class="std std-ref">Third-party backends</span></a>.</p>
</div>
</div>
<div class="section" id="cuda-graphs">
<span id="cuda-graph-semantics"></span><h2>CUDA Graphs<a class="headerlink" href="#cuda-graphs" title="Permalink to this headline">¶</a></h2>
<p>A CUDA graph is a record of the work (mostly kernels and their arguments) that a
CUDA stream and its dependent streams perform.
For general principles and details on the underlying CUDA API, see
<a class="reference external" href="https://developer.nvidia.com/blog/cuda-graphs/">Getting Started with CUDA Graphs</a> and the
<a class="reference external" href="https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#cuda-graphs">Graphs section</a> of the CUDA C Programming Guide.</p>
<p>PyTorch supports the construction of CUDA graphs using <a class="reference external" href="https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#creating-a-graph-using-stream-capture">stream capture</a>, which puts a
CUDA stream in <em>capture mode</em>. CUDA work issued to a capturing stream doesn’t actually
run on the GPU. Instead, the work is recorded in a graph.</p>
<p>After capture, the graph can be <em>launched</em> to run the GPU work as many times as needed.
Each replay runs the same kernels with the same arguments. For pointer arguments this
means the same memory addresses are used.
By filling input memory with new data (e.g., from a new batch) before each replay,
you can rerun the same work on new data.</p>
<div class="section" id="why-cuda-graphs">
<h3>Why CUDA Graphs?<a class="headerlink" href="#why-cuda-graphs" title="Permalink to this headline">¶</a></h3>
<p>Replaying a graph sacrifices the dynamic flexibility of typical eager execution in exchange for
<strong>greatly reduced CPU overhead</strong>. A graph’s arguments and kernels are fixed, so a graph replay
skips all layers of argument setup and kernel dispatch, including Python, C++, and CUDA driver
overheads. Under the hood, a replay submits the entire graph’s work to the GPU with
a single call to <a class="reference external" href="https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__GRAPH.html#group__CUDART__GRAPH_1g1accfe1da0c605a577c22d9751a09597">cudaGraphLaunch</a>. Kernels in a replay also execute slightly faster
on the GPU, but eliding CPU overhead is the main benefit.</p>
<p>You should try CUDA graphs if all or part of your network is graph-safe (usually this means
static shapes and static control flow, but see the other <a class="reference internal" href="#capture-constraints"><span class="std std-ref">constraints</span></a>)
and you suspect its runtime is at least somewhat CPU-limited.</p>
</div>
<div class="section" id="pytorch-api">
<h3>PyTorch API<a class="headerlink" href="#pytorch-api" title="Permalink to this headline">¶</a></h3>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This API is in beta and may change in future releases.</p>
</div>
<p>PyTorch exposes graphs via a raw <a class="reference internal" href="../generated/torch.cuda.CUDAGraph.html#torch.cuda.CUDAGraph" title="torch.cuda.CUDAGraph"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.CUDAGraph</span></code></a> class
and two convenience wrappers,
<a class="reference internal" href="../generated/torch.cuda.graph.html#torch.cuda.graph" title="torch.cuda.graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.graph</span></code></a> and
<a class="reference internal" href="../generated/torch.cuda.make_graphed_callables.html#torch.cuda.make_graphed_callables" title="torch.cuda.make_graphed_callables"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.make_graphed_callables</span></code></a>.</p>
<p><a class="reference internal" href="../generated/torch.cuda.graph.html#torch.cuda.graph" title="torch.cuda.graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.graph</span></code></a> is a simple, versatile context manager that
captures CUDA work in its context.
Before capture, warm up the workload to be captured by running
a few eager iterations. Warmup must occur on a side stream.
Because the graph reads from and writes to the same memory addresses in every
replay, you must maintain long-lived references to tensors that hold
input and output data during capture.
To run the graph on new input data, copy new data to the capture’s input tensor(s),
replay the graph, then read the new output from the capture’s output tensor(s).
Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">g</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">CUDAGraph</span><span class="p">()</span>
<span class="c1"># Placeholder input used for capture</span>
<span class="n">static_input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">5</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="c1"># Warmup before capture</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Stream</span><span class="p">()</span>
<span class="n">s</span><span class="o">.</span><span class="n">wait_stream</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">())</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">stream</span><span class="p">(</span><span class="n">s</span><span class="p">):</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">static_output</span> <span class="o">=</span> <span class="n">static_input</span> <span class="o">*</span> <span class="mi">2</span>
<span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">()</span><span class="o">.</span><span class="n">wait_stream</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
<span class="c1"># Captures the graph</span>
<span class="c1"># To allow capture, automatically sets a side stream as the current stream in the context</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">graph</span><span class="p">(</span><span class="n">g</span><span class="p">):</span>
<span class="n">static_output</span> <span class="o">=</span> <span class="n">static_input</span> <span class="o">*</span> <span class="mi">2</span>
<span class="c1"># Fills the graph's input memory with new data to compute on</span>
<span class="n">static_input</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="mi">5</span><span class="p">,),</span> <span class="mi">3</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">g</span><span class="o">.</span><span class="n">replay</span><span class="p">()</span>
<span class="c1"># static_output holds the results</span>
<span class="nb">print</span><span class="p">(</span><span class="n">static_output</span><span class="p">)</span> <span class="c1"># full of 3 * 2 = 6</span>
<span class="c1"># Fills the graph's input memory with more data to compute on</span>
<span class="n">static_input</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="mi">5</span><span class="p">,),</span> <span class="mi">4</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">g</span><span class="o">.</span><span class="n">replay</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">static_output</span><span class="p">)</span> <span class="c1"># full of 4 * 2 = 8</span>
</pre></div>
</div>
<p>See
<a class="reference internal" href="#whole-network-capture"><span class="std std-ref">Whole-network capture</span></a>,
<a class="reference internal" href="#graphs-with-amp"><span class="std std-ref">Usage with torch.cuda.amp</span></a>, and
<a class="reference internal" href="#multistream-capture"><span class="std std-ref">Usage with multiple streams</span></a>
for realistic and advanced patterns.</p>
<p><a class="reference internal" href="../generated/torch.cuda.make_graphed_callables.html#torch.cuda.make_graphed_callables" title="torch.cuda.make_graphed_callables"><code class="xref py py-class docutils literal notranslate"><span class="pre">make_graphed_callables</span></code></a> is more sophisticated.
<a class="reference internal" href="../generated/torch.cuda.make_graphed_callables.html#torch.cuda.make_graphed_callables" title="torch.cuda.make_graphed_callables"><code class="xref py py-class docutils literal notranslate"><span class="pre">make_graphed_callables</span></code></a> accepts Python functions and
<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>s. For each passed function or Module,
it creates separate graphs of the forward-pass and backward-pass work. See
<a class="reference internal" href="#partial-network-capture"><span class="std std-ref">Partial-network capture</span></a>.</p>
<div class="section" id="constraints">
<span id="capture-constraints"></span><h4>Constraints<a class="headerlink" href="#constraints" title="Permalink to this headline">¶</a></h4>
<p>A set of ops is <em>capturable</em> if it doesn’t violate any of the following constraints.</p>
<p>Constraints apply to all work in a
<a class="reference internal" href="../generated/torch.cuda.graph.html#torch.cuda.graph" title="torch.cuda.graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.graph</span></code></a> context and all work in the forward and backward passes
of any callable you pass to <a class="reference internal" href="../generated/torch.cuda.make_graphed_callables.html#torch.cuda.make_graphed_callables" title="torch.cuda.make_graphed_callables"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cuda.make_graphed_callables()</span></code></a>.</p>
<p>Violating any of these will likely cause a runtime error:</p>
<ul class="simple">
<li><p>Capture must occur on a non-default stream. (This is only a concern if you use the raw
<a class="reference internal" href="../generated/torch.cuda.CUDAGraph.html#torch.cuda.CUDAGraph.capture_begin" title="torch.cuda.CUDAGraph.capture_begin"><code class="xref py py-meth docutils literal notranslate"><span class="pre">CUDAGraph.capture_begin</span></code></a> and
<a class="reference internal" href="../generated/torch.cuda.CUDAGraph.html#torch.cuda.CUDAGraph.capture_end" title="torch.cuda.CUDAGraph.capture_end"><code class="xref py py-meth docutils literal notranslate"><span class="pre">CUDAGraph.capture_end</span></code></a> calls.
<a class="reference internal" href="../generated/torch.cuda.graph.html#torch.cuda.graph" title="torch.cuda.graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">graph</span></code></a> and
<a class="reference internal" href="../generated/torch.cuda.make_graphed_callables.html#torch.cuda.make_graphed_callables" title="torch.cuda.make_graphed_callables"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_graphed_callables()</span></code></a> set a side stream for you.)</p></li>
<li><p>Ops that sychronize the CPU with the GPU (e.g., <code class="docutils literal notranslate"><span class="pre">.item()</span></code> calls) are prohibited.</p></li>
<li><p>CUDA RNG ops are allowed, but must use default generators. For example, explicitly constructing a
new <a class="reference internal" href="../generated/torch.Generator.html#torch.Generator" title="torch.Generator"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Generator</span></code></a> instance and passing it as the <code class="docutils literal notranslate"><span class="pre">generator</span></code> argument to an RNG function
is prohibited.</p></li>
</ul>
<p>Violating any of these will likely cause silent numerical errors or undefined behavior:</p>
<ul class="simple">
<li><p>Within a process, only one capture may be underway at a time.</p></li>
<li><p>No non-captured CUDA work may run in this process (on any thread) while capture is underway.</p></li>
<li><p>CPU work is not captured. If the captured ops include CPU work, that work will be elided during replay.</p></li>
<li><p>Every replay reads from and writes to the same (virtual) memory addresses.</p></li>
<li><p>Dynamic control flow (based on CPU or GPU data) is prohibited.</p></li>
<li><p>Dynamic shapes are prohibited. The graph assumes every tensor in the captured op sequence
has the same size and layout in every replay.</p></li>
<li><p>Using multiple streams in a capture is allowed, but there are <a class="reference internal" href="#multistream-capture"><span class="std std-ref">restrictions</span></a>.</p></li>
</ul>
</div>
<div class="section" id="non-constraints">
<h4>Non-constraints<a class="headerlink" href="#non-constraints" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li><p>Once captured, the graph may be replayed on any stream.</p></li>
</ul>
</div>
</div>
<div class="section" id="whole-network-capture">
<span id="id3"></span><h3>Whole-network capture<a class="headerlink" href="#whole-network-capture" title="Permalink to this headline">¶</a></h3>
<p>If your entire network is capturable, you can capture and replay an entire iteration:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">N</span><span class="p">,</span> <span class="n">D_in</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">D_out</span> <span class="o">=</span> <span class="mi">640</span><span class="p">,</span> <span class="mi">4096</span><span class="p">,</span> <span class="mi">2048</span><span class="p">,</span> <span class="mi">1024</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">torch</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">D_in</span><span class="p">,</span> <span class="n">H</span><span class="p">),</span>
<span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="mf">0.2</span><span class="p">),</span>
<span class="n">torch</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">H</span><span class="p">,</span> <span class="n">D_out</span><span class="p">),</span>
<span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="n">loss_fn</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">MSELoss</span><span class="p">()</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</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="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="c1"># Placeholders used for capture</span>
<span class="n">static_input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">D_in</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
<span class="n">static_target</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">D_out</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
<span class="c1"># warmup</span>
<span class="c1"># Uses static_input and static_target here for convenience,</span>
<span class="c1"># but in a real setting, because the warmup includes optimizer.step()</span>
<span class="c1"># you must use a few batches of real data.</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Stream</span><span class="p">()</span>
<span class="n">s</span><span class="o">.</span><span class="n">wait_stream</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">())</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">stream</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
<span class="k">for</span> <span class="n">i</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">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">(</span><span class="n">set_to_none</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">static_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">y_pred</span><span class="p">,</span> <span class="n">static_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>
<span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">()</span><span class="o">.</span><span class="n">wait_stream</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
<span class="c1"># capture</span>
<span class="n">g</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">CUDAGraph</span><span class="p">()</span>
<span class="c1"># Sets grads to None before capture, so backward() will create</span>
<span class="c1"># .grad attributes with allocations from the graph's private pool</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">(</span><span class="n">set_to_none</span><span class="o">=</span><span class="kc">True</span><span class="p">)</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">graph</span><span class="p">(</span><span class="n">g</span><span class="p">):</span>
<span class="n">static_y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">static_input</span><span class="p">)</span>
<span class="n">static_loss</span> <span class="o">=</span> <span class="n">loss_fn</span><span class="p">(</span><span class="n">static_y_pred</span><span class="p">,</span> <span class="n">static_target</span><span class="p">)</span>
<span class="n">static_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>
<span class="n">real_inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">rand_like</span><span class="p">(</span><span class="n">static_input</span><span class="p">)</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">10</span><span class="p">)]</span>