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<section id="cuda-stream-sanitizer">
<h1>CUDA Stream Sanitizer<a class="headerlink" href="#cuda-stream-sanitizer" title="Permalink to this heading">¶</a></h1>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This is a prototype feature, which means it is at an early stage
for feedback and testing, and its components are subject to change.</p>
</div>
<section id="module-torch.cuda._sanitizer">
<span id="overview"></span><h2>Overview<a class="headerlink" href="#module-torch.cuda._sanitizer" title="Permalink to this heading">¶</a></h2>
<p>This module introduces CUDA Sanitizer, a tool for detecting synchronization errors
between kernels ran on different streams. It stores information on accesses to tensors
to determine if they are synchronized or not. When enabled in a python program and a
possible data race is detected, a detailed warning will be printed and the program
will exit.</p>
<p>It can be enabled either by importing this module and calling
<a class="reference internal" href="#torch.cuda._sanitizer.enable_cuda_sanitizer" title="torch.cuda._sanitizer.enable_cuda_sanitizer"><code class="xref py py-func docutils literal notranslate"><span class="pre">enable_cuda_sanitizer()</span></code></a> or by exporting the <code class="docutils literal notranslate"><span class="pre">TORCH_CUDA_SANITIZER</span></code>
environment variable.</p>
</section>
<section id="usage">
<h2>Usage<a class="headerlink" href="#usage" title="Permalink to this heading">¶</a></h2>
<p>Here is an example of a simple synchronization error in PyTorch:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">4</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="s2">"cuda"</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">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">torch</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">a</span><span class="p">)</span>
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">a</span></code> tensor is initialized on the default stream and, without any synchronization
methods, modified on a new stream. The two kernels will run concurrently on the same tensor,
which might cause the second kernel to read unitialized data before the first one was able
to write it, or the first kernel might overwrite part of the result of the second.
When this script is run on the commandline with:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">TORCH_CUDA_SANITIZER</span><span class="o">=</span><span class="mi">1</span> <span class="n">python</span> <span class="n">example_error</span><span class="o">.</span><span class="n">py</span>
</pre></div>
</div>
<p>the following output is printed by CSAN:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>============================
CSAN detected a possible data race on tensor with data pointer 139719969079296
Access by stream 94646435460352 during kernel:
aten::mul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
writing to argument(s) self, out, and to the output
With stack trace:
File "example_error.py", line 6, in <module>
torch.mul(a, 5, out=a)
...
File "pytorch/torch/cuda/_sanitizer.py", line 364, in _handle_kernel_launch
stack_trace = traceback.StackSummary.extract(
Previous access by stream 0 during kernel:
aten::rand(int[] size, *, int? dtype=None, Device? device=None) -> Tensor
writing to the output
With stack trace:
File "example_error.py", line 3, in <module>
a = torch.rand(10000, device="cuda")
...
File "pytorch/torch/cuda/_sanitizer.py", line 364, in _handle_kernel_launch
stack_trace = traceback.StackSummary.extract(
Tensor was allocated with stack trace:
File "example_error.py", line 3, in <module>
a = torch.rand(10000, device="cuda")
...
File "pytorch/torch/cuda/_sanitizer.py", line 420, in _handle_memory_allocation
traceback.StackSummary.extract(
</pre></div>
</div>
<p>This gives extensive insight into the origin of the error:</p>
<ul class="simple">
<li><p>A tensor was incorrectly accessed from streams with ids: 0 (default stream) and 94646435460352 (new stream)</p></li>
<li><p>The tensor was allocated by invoking <code class="docutils literal notranslate"><span class="pre">a</span> <span class="pre">=</span> <span class="pre">torch.rand(10000,</span> <span class="pre">device="cuda")</span></code></p></li>
<li><dl class="simple">
<dt>The faulty accesses were caused by operators</dt><dd><ul>
<li><p><code class="docutils literal notranslate"><span class="pre">a</span> <span class="pre">=</span> <span class="pre">torch.rand(10000,</span> <span class="pre">device="cuda")</span></code> on stream 0</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.mul(a,</span> <span class="pre">5,</span> <span class="pre">out=a)</span></code> on stream 94646435460352</p></li>
</ul>
</dd>
</dl>
</li>
<li><p>The error message also displays the schemas of the invoked operators, along with a note
showing which arguments of the operators correspond to the affected tensor.</p>
<ul>
<li><p>In the example, it can be seen that tensor <code class="docutils literal notranslate"><span class="pre">a</span></code> corresponds to arguments <code class="docutils literal notranslate"><span class="pre">self</span></code>, <code class="docutils literal notranslate"><span class="pre">out</span></code>
and the <code class="docutils literal notranslate"><span class="pre">output</span></code> value of the invoked operator <code class="docutils literal notranslate"><span class="pre">torch.mul</span></code>.</p></li>
</ul>
</li>
</ul>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<p>The list of supported torch operators and their schemas can be viewed
<a class="reference internal" href="torch.html"><span class="doc">here</span></a>.</p>
</div>
<p>The bug can be fixed by forcing the new stream to wait for the default stream:</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">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">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">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">default_stream</span><span class="p">())</span>
<span class="n">torch</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">a</span><span class="p">)</span>
</pre></div>
</div>
<p>When the script is run again, there are no errors reported.</p>
</section>
<section id="api-reference">
<h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="torch.cuda._sanitizer.enable_cuda_sanitizer">
<span class="sig-prename descclassname"><span class="pre">torch.cuda._sanitizer.</span></span><span class="sig-name descname"><span class="pre">enable_cuda_sanitizer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/_sanitizer.html#enable_cuda_sanitizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda._sanitizer.enable_cuda_sanitizer" title="Permalink to this definition">¶</a></dt>
<dd><p>Enables CUDA Sanitizer.</p>
<p>The sanitizer will begin to analyze low-level CUDA calls invoked by torch functions
for synchronization errors. All data races found will be printed to the standard
error output along with stack traces of suspected causes. For best results, the
sanitizer should be enabled at the very beginning of the program.</p>
</dd></dl>
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