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<div class="section" id="pytorch-2-0-performance-dashboard">
<h1>PyTorch 2.0 Performance Dashboard<a class="headerlink" href="#pytorch-2-0-performance-dashboard" title="Permalink to this heading">¶</a></h1>
<p><strong>Author:</strong> <a class="reference external" href="https://github.com/desertfire">Bin Bao</a> and <a class="reference external" href="https://github.com/huydhn">Huy Do</a></p>
<p>PyTorch 2.0’s performance is tracked nightly on this <a class="reference external" href="https://hud.pytorch.org/benchmark/compilers">dashboard</a>.
The performance collection runs on 12 GCP A100 nodes every night. Each node contains a 40GB A100 Nvidia GPU and
a 6-core 2.2GHz Intel Xeon CPU. The corresponding CI workflow file can be found
<a class="reference external" href="https://github.com/pytorch/pytorch/blob/main/.github/workflows/inductor-perf-test-nightly.yml">here</a>.</p>
<div class="section" id="how-to-read-the-dashboard">
<h2>How to read the dashboard?<a class="headerlink" href="#how-to-read-the-dashboard" title="Permalink to this heading">¶</a></h2>
<p>The landing page shows tables for all three benchmark suites we measure, <code class="docutils literal notranslate"><span class="pre">TorchBench</span></code>, <code class="docutils literal notranslate"><span class="pre">Huggingface</span></code>, and <code class="docutils literal notranslate"><span class="pre">TIMM</span></code>,
and graphs for one benchmark suite with the default setting. For example, the default graphs currently show the AMP
training performance trend in the past 7 days for <code class="docutils literal notranslate"><span class="pre">TorchBench</span></code>. Droplists on the top of that page can be
selected to view tables and graphs with different options. In addition to the pass rate, there are 3 key
performance metrics reported there: <code class="docutils literal notranslate"><span class="pre">Geometric</span> <span class="pre">mean</span> <span class="pre">speedup</span></code>, <code class="docutils literal notranslate"><span class="pre">Mean</span> <span class="pre">compilation</span> <span class="pre">time</span></code>, and
<code class="docutils literal notranslate"><span class="pre">Peak</span> <span class="pre">memory</span> <span class="pre">footprint</span> <span class="pre">compression</span> <span class="pre">ratio</span></code>.
Both <code class="docutils literal notranslate"><span class="pre">Geometric</span> <span class="pre">mean</span> <span class="pre">speedup</span></code> and <code class="docutils literal notranslate"><span class="pre">Peak</span> <span class="pre">memory</span> <span class="pre">footprint</span> <span class="pre">compression</span> <span class="pre">ratio</span></code> are compared against
the PyTorch eager performance, and the larger the better. Each individual performance number on those tables can be clicked,
which will bring you to a view with detailed numbers for all the tests in that specific benchmark suite.</p>
</div>
<div class="section" id="what-is-measured-on-the-dashboard">
<h2>What is measured on the dashboard?<a class="headerlink" href="#what-is-measured-on-the-dashboard" title="Permalink to this heading">¶</a></h2>
<p>All the dashboard tests are defined in this
<a class="reference external" href="https://github.com/pytorch/pytorch/blob/3e18d3958be3dfcc36d3ef3c481f064f98ebeaf6/.ci/pytorch/test.sh#L305">function</a>.
The exact test configurations are subject to change, but at the moment, we measure both inference and training
performance with AMP precision on the three benchmark suites. We also measure different settings of TorchInductor,
including <code class="docutils literal notranslate"><span class="pre">default</span></code>, <code class="docutils literal notranslate"><span class="pre">with_cudagraphs</span> <span class="pre">(default</span> <span class="pre">+</span> <span class="pre">cudagraphs)</span></code>, and <code class="docutils literal notranslate"><span class="pre">dynamic</span> <span class="pre">(default</span> <span class="pre">+</span> <span class="pre">dynamic_shapes)</span></code>.</p>
</div>
<div class="section" id="can-i-check-if-my-pr-affects-torchinductor-s-performance-on-the-dashboard-before-merging">
<h2>Can I check if my PR affects TorchInductor’s performance on the dashboard before merging?<a class="headerlink" href="#can-i-check-if-my-pr-affects-torchinductor-s-performance-on-the-dashboard-before-merging" title="Permalink to this heading">¶</a></h2>
<p>Individual dashboard runs can be triggered manually by clicking the <code class="docutils literal notranslate"><span class="pre">Run</span> <span class="pre">workflow</span></code> button
<a class="reference external" href="https://github.com/pytorch/pytorch/actions/workflows/inductor-perf-test-nightly.yml">here</a>
and submitting with your PR’s branch selected. This will kick off a whole dashboard run with your PR’s changes.
Once it is done, you can check the results by selecting the corresponding branch name and commit ID
on the performance dashboard UI. Be aware that this is an expensive CI run. With the limited
resources, please use this functionality wisely.</p>
</div>
<div class="section" id="how-can-i-run-any-performance-test-locally">
<h2>How can I run any performance test locally?<a class="headerlink" href="#how-can-i-run-any-performance-test-locally" title="Permalink to this heading">¶</a></h2>
<p>The exact command lines used during a complete dashboard run can be found in any recent CI run logs.
The <a class="reference external" href="https://github.com/pytorch/pytorch/actions/workflows/inductor-perf-test-nightly.yml">workflow page</a>
is a good place to look for logs from some of the recent runs.
In those logs, you can search for lines like
<code class="docutils literal notranslate"><span class="pre">python</span> <span class="pre">benchmarks/dynamo/huggingface.py</span> <span class="pre">--performance</span> <span class="pre">--cold-start-latency</span> <span class="pre">--inference</span> <span class="pre">--amp</span> <span class="pre">--backend</span> <span class="pre">inductor</span> <span class="pre">--disable-cudagraphs</span> <span class="pre">--device</span> <span class="pre">cuda</span></code>
and run them locally if you have a GPU working with PyTorch 2.0.
<code class="docutils literal notranslate"><span class="pre">python</span> <span class="pre">benchmarks/dynamo/huggingface.py</span> <span class="pre">-h</span></code> will give you a detailed explanation on options of the benchmarking script.</p>
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