[WIP] Run eagle with full cudagraph

[zixi-qi]

WIP change to support running v1 eagle speculative decoding with full cudagraph. Currently there is a numerical gap when full cudagraph is turned on:

• piecewise

VLLM_USE_V1=1 python examples/offline_inference/eagle.py  --num_spec_tokens 7 --max_num_seqs 1 --num_prompts 1

--------------------------------------------------
mean acceptance length: 2.82
--------------------------------------------------
acceptance at token 0:0.77
acceptance at token 1:0.51
acceptance at token 2:0.28
acceptance at token 3:0.13
acceptance at token 4:0.05
acceptance at token 5:0.03
acceptance at token 6:0.03

• full cudagraph

VLLM_USE_V1=1 python examples/offline_inference/eagle.py  --num_spec_tokens 7 --max_num_seqs 1 --num_prompts 1 --compilation_config '{"full_cuda_graph": true}'

--------------------------------------------------
mean acceptance length: 2.08
--------------------------------------------------
acceptance at token 0:0.63
acceptance at token 1:0.28
acceptance at token 2:0.11
acceptance at token 3:0.05
acceptance at token 4:0.02
acceptance at token 5:0.00
acceptance at token 6:0.00

[gemini-code-assist]

Summary of Changes

Hello @zixi-qi, 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 aims to enable the full CUDA graph for the Eagle speculative decoding in vLLM. It introduces a new command-line argument for specifying compilation configurations and modifies the dummy run methods to accommodate attention metadata. The PR is marked as a work in progress, as there's a numerical gap observed when the full CUDA graph is enabled.

Highlights

• Eagle Speculative Decoding: This PR introduces changes to support running v1 Eagle speculative decoding with full CUDA graph.
• Command-line Argument: Added a --compilation_config argument to examples/offline_inference/eagle.py to allow specifying compilation configurations, including enabling full CUDA graph.
• Dummy Run: Modified the dummy_run method in vllm/v1/spec_decode/eagle.py to accept attn_metadata.
• Drafter Dummy Run: Modified the _dummy_run method in vllm/v1/worker/gpu_model_runner.py to pass attn_metadata to self.drafter.dummy_run.

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[gemini-code-assist]

Code Review

This pull request enables full CUDA graph support for Eagle speculative decoding by plumbing attn_metadata to the drafter's dummy_run method during CUDA graph capture. This ensures the model's state is consistent between eager execution and a graphed run, resolving numerical discrepancies. The changes in vllm/v1/spec_decode/eagle.py and vllm/v1/worker/gpu_model_runner.py are well-targeted, and the modifications to the example script in examples/offline_inference/eagle.py are appropriate for testing this new functionality.

[gemini-code-assist]

The direct call to json.loads can cause the script to crash with a json.JSONDecodeError if an invalid JSON string is passed to the --compilation_config argument. Consider adding a try-except block to handle potential parsing errors gracefully.

compilation_config = None
if args.compilation_config:
    try:
        compilation_config = json.loads(args.compilation_config)
    except json.JSONDecodeError as e:
        raise ValueError(f"Invalid JSON for --compilation_config: {e}") from e

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[houseroad]

yeah, there could be some case the CUDA graph handle things incorrectly. @yinghai hit similar problems before.