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[SGLang Async Rollout] Validate prompt_len + max_resp_len <= max_mode… #1627

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Merged
vermouth1992 merged 1 commit into from
May 24, 2025
Merged

[SGLang Async Rollout] Validate prompt_len + max_resp_len <= max_mode… #1627

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4 changes: 2 additions & 2 deletions verl/workers/rollout/schemas.py
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Original file line number Diff line number Diff line change
Expand Up @@ -103,12 +103,12 @@ class AsyncRolloutRequest(BaseModel):
}
}

def get_generation_prompt(self, tokenizer: PreTrainedTokenizer) -> str:
def get_generation_prompt(self, tokenizer: PreTrainedTokenizer) -> list[int]:
return tokenizer.apply_chat_template( # type: ignore
conversation=[msg.model_dump() for msg in self.messages],
tools=[tool.model_dump() for tool in self.tools] if self.tools else None,
add_generation_prompt=True,
tokenize=False,
tokenize=True,
)

def add_assistant_message(
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10 changes: 7 additions & 3 deletions verl/workers/rollout/sglang_rollout/async_sglang_rollout.py
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Original file line number Diff line number Diff line change
Expand Up @@ -482,7 +482,11 @@ async def _async_rollout_a_request(self, req: AsyncRolloutRequest, do_sample: bo
else:
raise ValueError(f"Unexpected tool calling last message state: {_req.messages[-1]}")
elif _req.state == AsyncRolloutRequestStateEnum.RUNNING:
generation_prompt = _req.get_generation_prompt(self.tokenizer)
generation_prompt_ids = _req.get_generation_prompt(self.tokenizer)
max_new_tokens = min(self.config.response_length, self.config.max_model_len - len(generation_prompt_ids) - 1)
if max_new_tokens <= 0:
finish_reason_type = FinishReasonTypeEnum.STOP
break
if not do_sample:
kwargs = dict(
n=1,
Expand All @@ -494,7 +498,6 @@ async def _async_rollout_a_request(self, req: AsyncRolloutRequest, do_sample: bo
top_k=-1,
ignore_eos=False,
min_new_tokens=0,
max_new_tokens=self.config.response_length,
skip_special_tokens=True,
spaces_between_special_tokens=True,
)
Expand All @@ -506,12 +509,13 @@ async def _async_rollout_a_request(self, req: AsyncRolloutRequest, do_sample: bo
"temperature": self.config.val_kwargs.temperature,
"n": 1, # if validate, already repeat in ray_trainer
}
kwargs["max_new_tokens"] = max_new_tokens
if "n" not in kwargs or kwargs["n"] > 1: # group size is supported in preprocess
kwargs["n"] = 1
# users can customize different sampling_params at different run
with self.update_sampling_params(**kwargs):
output = await self._engine.async_generate(
prompt=generation_prompt,
input_ids=generation_prompt_ids,
sampling_params=self.sampling_params,
return_logprob=False,
)
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