174 lines
6.6 KiB
Python
174 lines
6.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import json
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from collections.abc import Sequence
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from transformers import PreTrainedTokenizerBase
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from vllm.entrypoints.harmony_utils import parse_chat_output
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from vllm.entrypoints.openai.protocol import ChatCompletionRequest, DeltaMessage
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from vllm.entrypoints.tool_server import ToolServer
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from vllm.logger import init_logger
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from vllm.reasoning import ReasoningParser
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logger = init_logger(__name__)
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no_func_reaonsing_tag = {
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"type": "structural_tag",
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"format": {
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"type": "triggered_tags",
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"tags": [
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{
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"begin": "<|channel|>analysis<|message|>",
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"content": {"type": "any_text"},
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"end": "<|end|>",
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}
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],
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"triggers": ["<|channel|>analysis"],
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"stop_after_first": False,
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},
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}
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def from_builtin_tool_to_tag(tool: str) -> list[dict]:
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tag = [
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{
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"begin": f"<|channel|>commentary to={tool}",
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"content": {"type": "any_text"},
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"end": "<|end|>",
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},
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{
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"begin": f"<|channel|>analysis to={tool}",
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"content": {"type": "any_text"},
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"end": "<|end|>",
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},
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]
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return tag
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def tag_with_builtin_funcs(no_func_reaonsing_tag, builtin_tool_list: list[str]) -> dict:
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import copy
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new_tag = copy.deepcopy(no_func_reaonsing_tag)
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new_tag["format"]["triggers"].append("<|channel|>commentary to=")
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for tool in builtin_tool_list:
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new_tag["format"]["tags"].extend(from_builtin_tool_to_tag(tool))
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return new_tag
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class GptOssReasoningParser(ReasoningParser):
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"""
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Reasoning parser for GptOss model.
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The GptOss model uses harmony to extract reasoning content and this parser
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is only used for detecting the end of the reasoning content.
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"""
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def __init__(self, tokenizer: PreTrainedTokenizerBase, *args, **kwargs):
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super().__init__(tokenizer, *args, **kwargs)
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# The model can output some special tokens between "final" and "<|message|>"
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# So we need to look for both sequences to determine the end of reasoning.
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self.reasoning_end_token_ids_prefix = self.model_tokenizer.encode(
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"<|channel|>final"
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)
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self.reasoning_end_token_ids_suffix = self.model_tokenizer.encode("<|message|>")
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self.reasoning_max_num_between_tokens = 20
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def is_reasoning_end(self, input_ids: list[int]) -> bool:
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end_token_ids_prefix = self.reasoning_end_token_ids_prefix
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end_token_ids_suffix = self.reasoning_end_token_ids_suffix
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assert len(end_token_ids_prefix) > 0, "reasoning_end_token_ids_prefix is empty"
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assert len(end_token_ids_suffix) > 0, "reasoning_end_token_ids_suffix is empty"
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# Check if the end sequence is present in the input_ids.
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# We search from the end of input_ids to find the last match.
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for i in range(len(input_ids) - len(end_token_ids_prefix), -1, -1):
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if input_ids[i : i + len(end_token_ids_prefix)] == end_token_ids_prefix:
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# We have found the prefix, now we look for the suffix after the prefix.
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suffix_start = i + len(end_token_ids_prefix)
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for j in range(
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suffix_start, len(input_ids) - len(end_token_ids_suffix) + 1
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):
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if j - suffix_start >= self.reasoning_max_num_between_tokens:
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break
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if (
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input_ids[j : j + len(end_token_ids_suffix)]
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== end_token_ids_suffix
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):
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return True
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return False
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def extract_content_ids(self, input_ids: list[int]) -> list[int]:
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_, content, _ = parse_chat_output(input_ids)
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if content is None:
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return []
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return self.model_tokenizer.encode(content)
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def extract_reasoning_streaming(
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self,
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previous_text: str,
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current_text: str,
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delta_text: str,
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previous_token_ids: Sequence[int],
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current_token_ids: Sequence[int],
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delta_token_ids: Sequence[int],
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) -> DeltaMessage | None:
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prev_reasoning, prev_content, _ = parse_chat_output(list(previous_token_ids))
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cur_reasoning, cur_content, _ = parse_chat_output(list(current_token_ids))
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reasoning_delta = None
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content_delta = None
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if cur_reasoning is not None:
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prev_r = prev_reasoning or ""
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if cur_reasoning.startswith(prev_r):
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reasoning_delta = cur_reasoning[len(prev_r) :] or None
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else:
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reasoning_delta = cur_reasoning
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if cur_content is not None:
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prev_c = prev_content or ""
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if cur_content.startswith(prev_c):
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content_delta = cur_content[len(prev_c) :] or None
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else:
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content_delta = cur_content
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if reasoning_delta is None and content_delta is None:
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return None
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return DeltaMessage(reasoning=reasoning_delta, content=content_delta)
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def extract_reasoning(
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self,
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model_output: str,
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request: ChatCompletionRequest,
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) -> tuple[str | None, str | None]:
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raise NotImplementedError(
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"gpt-oss has a special branch for parsing reasoning in non-streaming mode. This method shouldn't be used." # noqa: E501
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)
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# This function prepares the structural tag to format reasoning output
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def prepare_structured_tag(
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self, original_tag: str | None, tool_server: ToolServer | None
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) -> str:
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if original_tag is None:
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if tool_server is None:
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return json.dumps(no_func_reaonsing_tag)
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else:
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builtin_tool_list: list[str] = []
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if tool_server.has_tool("browser"):
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builtin_tool_list.append("browser")
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if tool_server.has_tool("python"):
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builtin_tool_list.append("python")
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if tool_server.has_tool("container"):
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builtin_tool_list.append("container")
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if len(builtin_tool_list) > 0:
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logger.info("Builtin_tool_list: %s", builtin_tool_list)
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func_tag = json.dumps(
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tag_with_builtin_funcs(no_func_reaonsing_tag, builtin_tool_list)
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)
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else:
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logger.info("Builtin_tool_list is empty")
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func_tag = json.dumps(no_func_reaonsing_tag)
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return func_tag
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else:
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# There is potential risk for appending the tag to the original tag
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return original_tag
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