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bcdf9fa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 | # 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]
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