feat: add reward model + min_p speculative decode #19968
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Summary of Changes
Hello @jatery55555, 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 introduces the capability to integrate and serve a reward model within the vLLM framework. It primarily focuses on adding a new API endpoint (/v1/score) that enables users to query a loaded reward model for scoring prompt-response pairs. This involves creating a dedicated model class for QWen-based reward models and registering the new API route, enhancing vLLM's utility for reinforcement learning from human feedback (RLHF) applications.
Highlights
- New API Endpoint for Reward Scoring: I've added a new
/v1/scoreAPI endpoint to thevLLMAPI server. This endpoint allows external clients to submit apromptandresponsepair and receive a scalar rewardscorefrom a loaded reward model. This is particularly useful for applications requiring programmatic access to reward model evaluations. - QWen Reward Model Integration: I've introduced a new
QWenRewardModelclass, which extends the existingQWenmodel. This class is specifically designed to load a pre-trained reward head (a small neural network) and compute a reward score. The scoring mechanism involves extracting the hidden state corresponding to theeos_tokenfrom the base QWen model and passing it through the loaded reward head. - Robust Tokenizer Separator Handling: I've improved the logic for constructing prompt strings in cross-encoding scoring within
serving_score.py. The change ensures that the separator token (tokenizer.sep_token) is handled gracefully, defaulting to an empty string iftokenizer.sep_tokenisNone. This makes the system more robust to different tokenizer configurations.
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Code Review
The pull request introduces a reward model and speculative decoding functionality. It adds a new QWenRewardModel class, a /v1/score API endpoint, and related utilities. The code appears well-structured and addresses the intended functionality. However, some improvements can be made regarding error handling, logging, and potential edge cases.
| if not hasattr(model, "score"): | ||
| return {"score": -1.0} # 或抛出错误 |
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Returning a score of -1.0 when the model doesn't have a 'score' attribute might be misleading. Consider raising an exception with a more informative error message, or providing a configuration option to disable the /v1/score endpoint if the model doesn't support it.
if not hasattr(model, "score"):
raise AttributeError("The model does not have a 'score' attribute.")| sep_token = tokenizer.sep_token if tokenizer.sep_token else '' | ||
| request_prompt = f"{t1}{sep_token}{t2}" |
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It's good to handle the case where tokenizer.sep_token might be None. However, consider logging a warning if tokenizer.sep_token is indeed None, as it might indicate an unexpected tokenizer configuration.
| sep_token = tokenizer.sep_token if tokenizer.sep_token else '' | |
| request_prompt = f"{t1}{sep_token}{t2}" | |
| sep_token = tokenizer.sep_token if tokenizer.sep_token else '' | |
| if not tokenizer.sep_token: | |
| logger.warning("tokenizer.sep_token is None, using empty string as separator.") | |
| request_prompt = f"{t1}{sep_token}{t2}" |
| if not os.path.exists(reward_head_path): | ||
| raise FileNotFoundError(f"未找到 reward_head.pt: {reward_head_path}") |
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Consider logging a warning instead of raising a FileNotFoundError if the reward head parameters are not found. This would allow the model to run without the reward head, potentially in a different mode. Also, provide instructions on how to obtain or train the reward head.
| if not os.path.exists(reward_head_path): | |
| raise FileNotFoundError(f"未找到 reward_head.pt: {reward_head_path}") | |
| if not os.path.exists(reward_head_path): | |
| logger.warning(f"Reward head not found at {reward_head_path}. Functionality depending on reward model will be disabled.") | |
| self.reward_head = None # Or some other appropriate default | |
| else: |
| eos_index = eos_mask.int().argmax(dim=1) # [B] | ||
| eos_hidden = hidden[torch.arange(input_ids.size(0)), eos_index] # [B, H] |
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Consider adding a check to ensure that eos_index is not equal to the length of the input IDs. If it is, it means that the EOS token was not found in the input, and taking the hidden state at eos_index will result in accessing the last token's hidden state, which might not be what's intended.
| eos_index = eos_mask.int().argmax(dim=1) # [B] | |
| eos_hidden = hidden[torch.arange(input_ids.size(0)), eos_index] # [B, H] | |
| eos_index = eos_mask.int().argmax(dim=1) # [B] | |
| if torch.any(eos_index == input_ids.size(1)): | |
| logger.warning("EOS token not found in input, using last token's hidden state.") | |
| eos_hidden = hidden[torch.arange(input_ids.size(0)), eos_index] # [B, H] |
[Feat] Support min_p in speculative decoding
wangqia0309
requested review from
WoosukKwon,
robertgshaw2-redhat,
njhill,
ywang96,
comaniac and
alexm-redhat
as code owners
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.Purpose
Test Plan
Test Result
(Optional) Documentation Update