# Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from enum import Enum from typing import Any, Dict, List, Literal, Optional import torch from pydantic import BaseModel from transformers import PreTrainedTokenizer from verl.tools.schemas import OpenAIFunctionToolCall, OpenAIFunctionToolSchema from verl.utils.model import compute_position_id_with_mask class FinishReasonTypeEnum(str, Enum): """The enum for finish reason type.""" LENGTH = "length" STOP = "stop" TOOL_CALL = "tool_calls" @classmethod def from_str(cls, value: str) -> "FinishReasonTypeEnum": if value == "stop": return cls.STOP elif value == "length": return cls.LENGTH elif value == "tool_calls": return cls.TOOL_CALL else: raise ValueError(f"Unsupported finish reason type: {value}") class Message(BaseModel): role: str content: str tool_calls: Optional[List[OpenAIFunctionToolCall]] = None class AsyncRolloutRequestStateEnum(str, Enum): """The enum for async rollout request state.""" PENDING = "pending" RUNNING = "running" COMPLETED = "completed" FAILED = "failed" TOOL_CALLING = "tool_calling" class AsyncRolloutRequest(BaseModel): """The data model for async rollout.""" batch_data_id: int = 0 rollout_offset: int = 0 request_id: str state: AsyncRolloutRequestStateEnum messages: List[Message] tools: Optional[List[OpenAIFunctionToolSchema]] = None tools_kwargs: Dict[str, Any] = {} input_ids: List[int] prompt_ids: List[int] response_ids: List[int] attention_mask: List[int] prompt_attention_mask: List[int] response_attention_mask: List[int] position_ids: List[int] prompt_position_ids: List[int] response_position_ids: List[int] loss_mask: List[int] prompt_loss_mask: List[int] response_loss_mask: List[int] reward_scores: Dict[str, float] max_response_len: int = 8192 max_model_len: int = 32768 format_config: dict = { "chatml": { "assistant_prefix_msg": "\n<|im_start|>assistant\n", "assistant_suffix_msg": "<|im_end|>", "tool_prefix_msg": "\n<|im_start|>tool\n", "tool_suffix_msg": "<|im_end|>", } } def get_generation_prompt(self, tokenizer: PreTrainedTokenizer) -> str: 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, ) def add_assistant_message( self, tokenizer: PreTrainedTokenizer, content: str, tool_calls: Optional[List[OpenAIFunctionToolCall]] = None, format: Literal["chatml"] = "chatml", already_over_long: bool = False, ) -> None: """Currently, we only support chatml format.""" msg = Message(role="assistant", content=content, tool_calls=tool_calls) self.messages.append(msg) if tool_calls is not None: content_with_tool_calls: str = tokenizer.apply_chat_template( # type: ignore conversation=[msg.model_dump()], add_generation_prompt=False, tokenize=False ) else: content_with_tool_calls = content # TODO: support other formats if format in self.format_config: prefix_msg = self.format_config[format]["assistant_prefix_msg"] prefix_token_ids = tokenizer.encode(prefix_msg, add_special_tokens=False) suffix_msg = self.format_config[format]["assistant_suffix_msg"] suffix_token_ids = tokenizer.encode(suffix_msg, add_special_tokens=False) if tool_calls is not None: content = content_with_tool_calls.split(f"{prefix_msg}")[-1].split(f"{suffix_msg}")[0] content_token_ids = tokenizer.encode(content, add_special_tokens=False) if self.input_ids[-len(prefix_token_ids) :] == prefix_token_ids: append_token_ids = content_token_ids _loss_mask = [1] * len(content_token_ids) elif self.input_ids[-len(suffix_token_ids) :] == suffix_token_ids: append_token_ids = prefix_token_ids + content_token_ids _loss_mask = [0] * len(prefix_token_ids) + [1] * len(content_token_ids) else: max_len = max(len(prefix_token_ids), len(suffix_token_ids)) raise ValueError( f"""Unsupported end of message format: {tokenizer.decode(self.input_ids[-max_len:])}, {tokenizer.decode(self.input_ids)=}, {self.messages=}""" ) if not already_over_long: append_token_ids += suffix_token_ids _loss_mask += [1] * len(suffix_token_ids) self.input_ids += append_token_ids _attention_mask = [1] * len(append_token_ids) self.attention_mask += _attention_mask _delta_position_ids = compute_position_id_with_mask(torch.tensor(_attention_mask)).tolist() last_position_id = self.position_ids[-1] _position_ids = [pos_id + last_position_id for pos_id in _delta_position_ids] self.loss_mask += _loss_mask self.position_ids += _position_ids else: raise ValueError(f"Unsupported format: {format}") assert len(self.input_ids) == len(self.attention_mask) == len(self.position_ids) == len(self.loss_mask), f"""Request {self.request_id} has different length of {len(self.input_ids)=}, {len(self.attention_mask)=}, {len(self.position_ids)=}, {len(self.loss_mask)=}""" def add_tool_response_message(self, tokenizer: PreTrainedTokenizer, content: str, format: Literal["chatml"] = "chatml") -> None: """Currently, we only support chatml format.""" msg = Message(role="tool", content=content) self.messages.append(msg) # TODO: support other formats if format in self.format_config: prefix_msg = self.format_config[format]["tool_prefix_msg"] prefix_token_ids = tokenizer.encode(prefix_msg, add_special_tokens=False) suffix_msg = self.format_config[format]["tool_suffix_msg"] suffix_token_ids = tokenizer.encode(suffix_msg, add_special_tokens=False) content_token_ids = tokenizer.encode(content, add_special_tokens=False) if self.input_ids[-len(prefix_token_ids) :] == prefix_token_ids: append_token_ids = content_token_ids + suffix_token_ids elif self.input_ids[-len(suffix_token_ids) :] == suffix_token_ids: append_token_ids = prefix_token_ids + content_token_ids + suffix_token_ids else: raise ValueError(f"Unsupported end of message format: {tokenizer.decode(self.input_ids[-len(prefix_token_ids) :])}") self.input_ids += append_token_ids _attention_mask = [1] * len(append_token_ids) self.attention_mask += _attention_mask _delta_position_ids = compute_position_id_with_mask(torch.tensor(_attention_mask)).tolist() last_position_id = self.position_ids[-1] _position_ids = [pos_id + last_position_id for pos_id in _delta_position_ids] self.loss_mask += [0] * len(append_token_ids) self.position_ids += _position_ids else: raise ValueError(f"Unsupported format: {format}") assert len(self.input_ids) == len(self.attention_mask) == len(self.position_ids) == len(self.loss_mask), f"""Request {self.request_id} has different length of {len(self.input_ids)=}, {len(self.attention_mask)=}, {len(self.position_ids)=}, {len(self.loss_mask)=}""" def finalize( self, tokenizer: PreTrainedTokenizer, reward_scores: Dict[str, float], finish_reason_type: FinishReasonTypeEnum = FinishReasonTypeEnum.STOP, ) -> None: self.state = AsyncRolloutRequestStateEnum.COMPLETED self.reward_scores = reward_scores self.response_ids = self.input_ids[len(self.prompt_ids) :] if finish_reason_type == FinishReasonTypeEnum.STOP: pass elif finish_reason_type == FinishReasonTypeEnum.LENGTH: pass else: raise ValueError(f"Unsupported finalize finish reason type: {finish_reason_type}") self.truncate_output_ids(tokenizer) assert len(self.input_ids) == len(self.attention_mask) == len(self.position_ids) == len(self.loss_mask), f"""Request {self.request_id} has different length of {len(self.input_ids)=}, {len(self.attention_mask)=}, {len(self.position_ids)=}, {len(self.loss_mask)=}""" def truncate_output_ids(self, tokenizer: PreTrainedTokenizer) -> None: self.input_ids = self.input_ids[: self.max_model_len] self.attention_mask = self.attention_mask[: self.max_model_len] self.position_ids = self.position_ids[: self.max_model_len] self.loss_mask = self.loss_mask[: self.max_model_len] self.response_ids = self.input_ids[len(self.prompt_ids) :][: self.max_response_len] self.response_attention_mask = self.attention_mask[len(self.prompt_attention_mask) :][: self.max_response_len] self.response_position_ids = self.position_ids[len(self.prompt_position_ids) :][: self.max_response_len] self.response_loss_mask = self.loss_mask[len(self.prompt_loss_mask) :][: self.max_response_len]