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[Kernel][Misc] register ops to prevent graph breaks (#6917)

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Co-authored-by: Sage Moore <sage@neuralmagic.com>
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bnellnm
2024-09-11 15:52:19 -04:00
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@@ -39,6 +39,16 @@ FIX #xxxx (*link existing issues this PR will resolve*)
<li>Please add documentation to <code>docs/source/</code> if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.</li>
</ul>
<h3>Adding or changing kernels</h3>
<p>Each custom kernel needs a schema and one or more implementations to be registered with PyTorch.</p>
<ul>
<li>Make sure custom ops are registered following PyTorch guidelines: <a href="https://pytorch.org/tutorials/advanced/cpp_custom_ops.html#cpp-custom-ops-tutorial">Custom C++ and CUDA Operators</a> and <a href="https://docs.google.com/document/d/1_W62p8WJOQQUzPsJYa7s701JXt0qf2OfLub2sbkHOaU">The Custom Operators Manual</a></li>
<li>Custom operations that return <code>Tensors</code> require meta-functions. Meta-functions should be implemented and registered in python so that dynamic dims can be handled automatically. See above documents for a description of meta-functions.</li>
<li>Use <a href="https://pytorch.org/docs/stable/library.html#torch.library.opcheck"><code>torch.libary.opcheck()</code></a> to test the function registration and meta-function for any registered ops. See <code>tests/kernels</code> for examples.</li>
<li>When changing the C++ signature of an existing op, the schema must be updated to reflect the changes.</li>
<li>If a new custom type is needed, see the following document: <a href="https://docs.google.com/document/d/18fBMPuOJ0fY5ZQ6YyrHUppw9FA332CpNtgB6SOIgyuA">Custom Class Support in PT2</a>.
</ul>
<h3>Notes for Large Changes</h3>
<p>Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with <code>rfc-required</code> and might not go through the PR.</p>