[BUGFIX] Fix crash in Eagle Speculative Decoding models when exceedin…

[AlonKejzman]

…g draft model length

Purpose

Enable running Eagle Speculative Decoding in environments where the input may exceed the drafter model length but not the verifier's.

Test Plan

Running with inputs that are below the drafter model length, and then between the drafter and verifier.

Test Result

Successful.

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[gemini-code-assist]

Code Review

This pull request introduces a bugfix to prevent crashes in Eagle Speculative Decoding when the input sequence length exceeds the draft model's capacity. The fix correctly adds a check to early-exit from the propose method.

My review identifies a critical issue with the implementation of this early exit. The returned empty tensor has a hardcoded shape and incorrect data type, which can lead to crashes or incorrect behavior in batched scenarios. I've provided a suggestion to fix this by dynamically creating the tensor with the correct shape and dtype. I've also recommended removing the newly introduced constant, as it becomes obsolete with the suggested fix.

[tomasruizt]

Could you provide commands to reproduce the issue?

[AlonKejzman]

Sure!

Serving the model

vllm serve meta-llama/Meta-Llama-3-8B-Instruct --speculative-config '{"method": "eagle", "model": "yuhuili/EAGLE-LLaMA3-Instruct-8B", "num_speculative_tokens": 4, "max_model_len": 2048}'

Request that makes it crash

import requests

url = "http://localhost:8000/v1/chat/completions"

headers = {
    "Authorization": "Bearer EMPTY", 
    "Content-Type": "application/json",
}

payload = {
    "model": "meta-llama/Meta-Llama-3-8B-Instruct",
    "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!" * 2049}
    ],
    "temperature": 0.0,
    "max_tokens": 1,
}

requests.post(url, headers=headers, json=payload, timeout=60)

[tomasruizt]

Is it intended behavior for the draft model to have a shorter model length compared to the target model?

If I understand the use case correctly, it's to use spec decoding only on short sequences and not on longer sequences.

If that is intended, then perhaps we should not be even calling the drafting method in the GPUModelRunner at all, but rather skipping it altogether.

[benchislett]

See also #22935 which is a more restrictive approach that doesn't let the serving engine launch with max_model_len larger than that of the drafter. I think the proper strategy is to simply skip drafting entirely from gpu_model_runner.propose_draft_token_ids() and not in Eagle.propose() since logic like prepare_inputs and other setup is would still run.

[luccafong]

feels more like a general spec decoding issue instead of eagle specific issue, could we add it to gpu_model_runner?

vllm/vllm/v1/worker/gpu_model_runner.py

Line 2152 in 01413e0

self._draft_token_ids = self.propose_draft_token_ids(

[AlonKejzman]

done

[mergify]

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

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

[AlonKejzman]

@tomasruizt @luccafong You are right, I adjusted the fix accordingly
@benchislett WDYT? Maybe this is more flexible than #22935 since it allows for smaller drafter models while bypassing the drafter when needed?