[Performance] Improve the inference performance of Eagle3.

[liumain1122]

vLLM version: v0.11.0
vLLM main: vllm-project/vllm

What this PR does / why we need it?

Does this PR introduce any user-facing change?

How was this patch tested?

• vLLM version: v0.11.0
• vLLM main: vllm-project/vllm@2918c1b

[github-actions]

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[gemini-code-assist]

Code Review

This pull request introduces significant performance improvements to the Eagle3 inference process by refactoring tensor operations from NumPy to native PyTorch. This change effectively reduces CPU-GPU data transfer overhead and leverages GPU acceleration, which is crucial for optimizing inference speed. The updated _prepare_inputs method now has a cleaner interface, directly accepting necessary tensor inputs, and a new helper function _prepare_eagle_input_sequential has been added for efficient token index generation. Overall, these changes align well with the goal of enhancing performance.

[gemini-code-assist]

The BLOCK_SIZE is hardcoded to 1024. While this might be an optimal value for certain scenarios, hardcoding it can limit the flexibility and potential performance of the _prepare_eagle_input_sequential function across different hardware, model configurations, or varying batch sizes. Consider making this value configurable or dynamically determining it based on runtime parameters to ensure broader applicability and optimal performance.

Suggested change

	BLOCK_SIZE = 1024
	BLOCK_SIZE = 1024 # Consider making this configurable or dynamic