[Bugfix] Fix torch.compile x LoRA for PyTorch 2.8

[zou3519]

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

Purpose

In PyTorch 2.8, we fixed a bug around torch.compile dynamic shapes. This surfaced a latent bug in vLLM's LoRA implementation not being torch.compile-able. In PyTorch 2.7, the LoRA implementation is able to be torch.compile'd due to the bug in torch.compile.

• The problem is that a sequence of 3 operations in the LoRA implementation (torch.narrow followed by an add or mul) causes torch.compile to specialize on the batch size of the input, which causes torch.compile's dynamic shape compilation to fail (since it is unable to trace out a graph that works for dynamic batch size).
• The workaround is to just rewrite it a bit into a form that torch.compile is happy with. This has no perf implications, because taking an additional view on a tensor is free.

Test Plan

Build vLLM with PyTorch 2.8rc.
Run pytest tests/lora/test_quant_model.py::test_quant_model_lora[model0].

I do not have suggestions for how to test this otherwise. We are unable to trigger a failure in PyTorch 2.7 and we do not run the lora tests against PyTorch 2.8 in CI.

Test Result

Test passes.

(Optional) Documentation Update

n/a

[github-actions]

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[gemini-code-assist]

Summary of Changes

Hello @zou3519, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request addresses a critical bug that prevented vLLM's LoRA implementation from being compiled with torch.compile in PyTorch 2.8. The core issue stemmed from torch.narrow causing undesirable dynamic shape specialization. The fix involves refactoring the tensor indexing operations to use direct slicing, thereby resolving the compilation failure without impacting performance.

Highlights

• Bugfix: Resolves a compatibility issue where vLLM's LoRA implementation was not compatible with torch.compile in PyTorch 2.8. This was due to torch.narrow causing dynamic shape specialization that prevented successful compilation.
• Compatibility: Ensures torch.compile can correctly trace and optimize LoRA models by replacing problematic torch.narrow operations with equivalent tensor slicing, which torch.compile handles gracefully for dynamic shapes.
• Performance: The change is stated to have no performance implications, as the replacement of torch.narrow with tensor slicing is functionally equivalent in terms of computational cost, primarily involving view operations.

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[gemini-code-assist]

Code Review

This pull request addresses a bug with torch.compile and LoRA on PyTorch 2.8 by replacing torch.narrow with direct tensor slicing. The change is correct and well-justified. I've provided one suggestion to improve the readability of the new code.