Refactor rotary embedding kernels to conform to custom-op design

[maxyanghu]

Added in #16457, MRotaryEmedding currently uses flash_attn.ops.triton.rotary.apply_rotary when the platform is CUDA no matter if it's been enabled as a custom op or not. But by design custom op shouldn't be turned on by default. This could potentially undermine future compiler optimization oppurtunities.

This PR:

1. Added forward_native and forward_cuda functions for MRotaryEmedding so that by default it will use native pytorch implementation
2. Also added forward_native and forward_cuda functions for DualChunkRotaryEmbedding/Llama4VisionRotaryEmbedding/DeepseekScalingRotaryEmbedding and remove their forward functions to follow the custom op design
3. Created a RotaryEmbeddingBase class to achieve the correct MRO chain

[github-actions]

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[gemini-code-assist]

Hello @maxyanghu, 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!

Summary of Changes

Hello! Gemini here, providing a summary of this pull request to help everyone get oriented. This PR focuses on refactoring several Rotary Embedding (RoPE) implementations within vLLM to align them with the custom operator (custom-op) design pattern. The primary motivation is to ensure that custom CUDA kernels (like the Triton one from flash_attn) are not used by default, but rather dispatched correctly when the custom-op feature is enabled. By default, the native PyTorch implementation should be used, allowing for potential future compiler optimizations.

Highlights

• Custom Op Refactoring: The core change refactors the MRotaryEmbedding, DeepseekScalingRotaryEmbedding, Llama4VisionRotaryEmbedding, and DualChunkRotaryEmbedding classes to conform to the custom-op design.
• Native vs. CUDA Dispatch: Introduces forward_native and forward_cuda methods in the affected RoPE classes to explicitly separate the PyTorch implementation from the potential custom CUDA kernel implementation.
• Inheritance Order: Changes the inheritance order for MRotaryEmbedding, DeepseekScalingRotaryEmbedding, and Llama4VisionRotaryEmbedding to inherit from CustomOp before RotaryEmbedding to ensure correct dispatch logic.
• Conditional Kernel Usage: Modifies the internal _apply_rotary_emb helper function to conditionally use the flash_attn Triton kernel based on an enable_custom_op flag.

Changelog

• vllm/model_executor/layers/rotary_embedding.py
  • Modified _apply_rotary_emb helper function to accept enable_custom_op flag and make the CUDA kernel call conditional.
  • Renamed the main forward method to _forward in MRotaryEmbedding and added an enable_custom_op parameter.
  • Added forward_native and forward_cuda methods to MRotaryEmbedding to call _forward with appropriate enable_custom_op values.
  • Renamed the main forward method to forward_native in DeepseekScalingRotaryEmbedding, Llama4VisionRotaryEmbedding, and DualChunkRotaryEmbedding.
  • Added forward_cuda methods to DeepseekScalingRotaryEmbedding, Llama4VisionRotaryEmbedding, and DualChunkRotaryEmbedding (currently aliasing forward_native).
  • Changed the inheritance order for DeepseekScalingRotaryEmbedding, Llama4VisionRotaryEmbedding, and MRotaryEmbedding to CustomOp, RotaryEmbedding.

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[gemini-code-assist]

Code Review

This pull request effectively refactors the rotary embedding kernels to align with the CustomOp design. The changes introduce forward_native and forward_cuda methods, ensuring that MRotaryEmbedding defaults to a native PyTorch implementation and correctly respects custom operator enablement. Other rotary embedding classes are also updated to fit this pattern, enhancing consistency and maintainability. The use of CustomOp as the first parent class correctly ensures the desired dispatch logic. Overall, the changes are well-structured and achieve the stated goals of improving design clarity and enabling better control over custom op usage for future optimizations.

Summary of Findings

• Minor Typo in Docstring: In vllm/model_executor/layers/rotary_embedding.py, line 1042 (RIGHT), the docstring for the _forward method in MRotaryEmbedding has a typo: enalbe_custom_op should be enable_custom_op. This is a low-severity issue and a comment was not added due to review settings.
• Docstrings for forward_cuda methods: For classes DeepseekScalingRotaryEmbedding, Llama4VisionRotaryEmbedding, and DualChunkRotaryEmbedding, their new forward_cuda methods currently call self.forward_native. While this is a valid implementation choice (especially if no specific CUDA kernels are planned for these variants under the custom op path), adding a brief docstring to these forward_cuda methods explaining this behavior (e.g., "Currently uses the native PyTorch implementation for CUDA operations.") would improve clarity for future maintainers. This is a low-severity suggestion and a comment was not added due to review settings.

Merge Readiness

The pull request appears to be in good shape and addresses the outlined objectives effectively. The refactoring aligns the rotary embedding kernels with the custom-op design, which is a positive step for maintainability and future flexibility. No critical or high-severity issues were found. Based on this review, the PR seems ready for merging, pending any other reviews or CI checks. As an AI, I am not authorized to approve pull requests.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @maxyanghu.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork