368 lines
13 KiB
Python
368 lines
13 KiB
Python
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#
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# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# OpenAI chat usage accounting: backport MiniMax reasoning token accounting.
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#
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from __future__ import annotations
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import ast
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import textwrap
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from collections.abc import Sequence
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from pathlib import Path
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from typing import Any
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from vllm.entrypoints.openai.chat_completion import protocol as chat_protocol
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from vllm.entrypoints.openai.chat_completion import serving as chat_serving
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from vllm.entrypoints.openai.chat_completion.serving import OpenAIServingChat
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from vllm.entrypoints.openai.engine import protocol as engine_protocol
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from vllm.reasoning import minimax_m2_reasoning_parser as minimax_parser
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def _extract_class_method_source(
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module_path: str,
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class_name: str,
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method_name: str,
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) -> str:
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source = Path(module_path).read_text(encoding="utf-8")
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tree = ast.parse(source)
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for node in tree.body:
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if isinstance(node, ast.ClassDef) and node.name == class_name:
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for item in node.body:
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if isinstance(item, (ast.FunctionDef, ast.AsyncFunctionDef)) and item.name == method_name:
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method_source = ast.get_source_segment(source, item)
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if method_source is None:
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break
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return textwrap.dedent(method_source)
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raise RuntimeError(f"Unable to extract {class_name}.{method_name} from {module_path}.")
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def _install_method(method_name: str, method_source: str) -> None:
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namespace: dict[str, Any] = {}
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exec(method_source, chat_serving.__dict__, namespace)
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method = namespace[method_name]
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method.__module__ = OpenAIServingChat.__module__
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method.__qualname__ = f"{OpenAIServingChat.__qualname__}.{method_name}"
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setattr(OpenAIServingChat, method_name, method)
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def _replace_block(
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source: str,
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old: str,
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new: str,
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*,
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count: int = 1,
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) -> str:
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if source.count(old) < count:
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raise RuntimeError("Failed to locate expected block while patching OpenAIServingChat usage accounting.")
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return source.replace(old, new, count)
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class CompletionTokenUsageInfo(engine_protocol.OpenAIBaseModel):
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reasoning_tokens: int | None = None
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audio_tokens: int | None = None
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accepted_prediction_tokens: int | None = None
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rejected_prediction_tokens: int | None = None
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class UsageInfo(engine_protocol.UsageInfo):
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completion_tokens_details: CompletionTokenUsageInfo | None = None
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CompletionTokenUsageInfo.__module__ = engine_protocol.__name__
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UsageInfo.__module__ = engine_protocol.__name__
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engine_protocol.CompletionTokenUsageInfo = CompletionTokenUsageInfo
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engine_protocol.UsageInfo = UsageInfo
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chat_protocol.UsageInfo = UsageInfo
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chat_serving.CompletionTokenUsageInfo = CompletionTokenUsageInfo
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chat_serving.UsageInfo = UsageInfo
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def _rebuild_model_field(model_cls, field_name: str, annotation) -> None:
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model_cls.__annotations__[field_name] = annotation
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model_cls.model_fields[field_name].annotation = annotation
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model_cls.model_rebuild(force=True)
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_rebuild_model_field(chat_protocol.ChatCompletionResponse, "usage", UsageInfo)
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_rebuild_model_field(chat_protocol.ChatCompletionStreamResponse, "usage", UsageInfo | None)
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_rebuild_model_field(engine_protocol.RequestResponseMetadata, "final_usage_info", UsageInfo | None)
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def _count_minimax_reasoning_tokens(
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token_ids: Sequence[int],
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end_token_id: int | None,
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) -> int:
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if end_token_id is None:
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return 0
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for idx, token_id in enumerate(token_ids):
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if token_id == end_token_id:
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return idx
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return len(token_ids)
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def _patched_count_reasoning_tokens(self, token_ids: Sequence[int]) -> int:
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return _count_minimax_reasoning_tokens(token_ids, self.end_token_id)
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minimax_parser.MiniMaxM2ReasoningParser.count_reasoning_tokens = _patched_count_reasoning_tokens
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minimax_parser.MiniMaxM2AppendThinkReasoningParser.count_reasoning_tokens = _patched_count_reasoning_tokens
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def _count_reasoning_tokens_for_usage(
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token_ids: Sequence[int],
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reasoning_parser,
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) -> int | None:
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if reasoning_parser is None:
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return None
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return reasoning_parser.count_reasoning_tokens(token_ids)
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def _make_usage_info(
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self,
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*,
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prompt_tokens: int,
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completion_tokens: int,
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num_cached_tokens: int | None = None,
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reasoning_tokens: int | None = None,
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) -> UsageInfo:
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usage = UsageInfo(
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=prompt_tokens + completion_tokens,
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)
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if reasoning_tokens is not None:
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usage.completion_tokens_details = CompletionTokenUsageInfo(
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reasoning_tokens=max(0, min(reasoning_tokens, completion_tokens))
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)
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if self.enable_prompt_tokens_details and num_cached_tokens:
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usage.prompt_tokens_details = chat_serving.PromptTokenUsageInfo(cached_tokens=num_cached_tokens)
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return usage
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OpenAIServingChat._count_reasoning_tokens_for_usage = staticmethod(_count_reasoning_tokens_for_usage)
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OpenAIServingChat._make_usage_info = _make_usage_info
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def _patch_chat_completion_stream_generator() -> None:
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method_source = _extract_class_method_source(
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chat_serving.__file__,
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"OpenAIServingChat",
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"chat_completion_stream_generator",
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)
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method_source = _replace_block(
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method_source,
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"""\
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previous_num_tokens = [0] * num_choices
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finish_reason_sent = [False] * num_choices
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""",
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"""\
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previous_num_tokens = [0] * num_choices
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raw_output_token_ids = [[] for _ in range(num_choices)]
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finish_reason_sent = [False] * num_choices
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""",
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)
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method_source = _replace_block(
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method_source,
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"""\
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if include_continuous_usage:
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chunk.usage = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=0,
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total_tokens=num_prompt_tokens,
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)
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""",
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"""\
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if include_continuous_usage:
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chunk.usage = self._make_usage_info(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=0,
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reasoning_tokens=self._count_reasoning_tokens_for_usage(
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raw_output_token_ids[i], reasoning_parser
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),
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)
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""",
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)
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method_source = _replace_block(
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method_source,
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"""\
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if include_continuous_usage:
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chunk.usage = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=0,
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total_tokens=num_prompt_tokens,
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)
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""",
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"""\
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if include_continuous_usage:
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chunk.usage = self._make_usage_info(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=0,
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reasoning_tokens=self._count_reasoning_tokens_for_usage(
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raw_output_token_ids[i], reasoning_parser
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),
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)
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""",
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)
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method_source = _replace_block(
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method_source,
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"""\
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previous_num_tokens[i] += len(output.token_ids)
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""",
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"""\
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previous_num_tokens[i] += len(output.token_ids)
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raw_output_token_ids[i].extend(as_list(output.token_ids))
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""",
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)
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method_source = _replace_block(
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method_source,
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"""\
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if include_continuous_usage:
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completion_tokens = previous_num_tokens[i]
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chunk.usage = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=num_prompt_tokens + completion_tokens,
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)
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""",
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"""\
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if include_continuous_usage:
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completion_tokens = previous_num_tokens[i]
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chunk.usage = self._make_usage_info(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=completion_tokens,
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reasoning_tokens=self._count_reasoning_tokens_for_usage(
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raw_output_token_ids[i], reasoning_parser
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),
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)
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""",
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)
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method_source = _replace_block(
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method_source,
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"""\
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final_usage = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=num_prompt_tokens + completion_tokens,
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)
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if self.enable_prompt_tokens_details and num_cached_tokens:
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final_usage.prompt_tokens_details = PromptTokenUsageInfo(
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cached_tokens=num_cached_tokens
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)
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""",
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"""\
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reasoning_tokens = None
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if reasoning_parser is not None:
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reasoning_tokens = sum(
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self._count_reasoning_tokens_for_usage(
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token_ids, reasoning_parser
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)
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or 0
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for token_ids in raw_output_token_ids
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)
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final_usage = self._make_usage_info(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=completion_tokens,
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num_cached_tokens=num_cached_tokens,
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reasoning_tokens=reasoning_tokens,
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)
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""",
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)
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method_source = _replace_block(
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method_source,
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"""\
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request_metadata.final_usage_info = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=num_completion_tokens,
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total_tokens=num_prompt_tokens + num_completion_tokens,
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)
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""",
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"""\
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reasoning_tokens = None
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if reasoning_parser is not None:
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reasoning_tokens = sum(
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self._count_reasoning_tokens_for_usage(
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token_ids, reasoning_parser
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)
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or 0
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for token_ids in raw_output_token_ids
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)
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request_metadata.final_usage_info = self._make_usage_info(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=num_completion_tokens,
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reasoning_tokens=reasoning_tokens,
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)
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""",
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)
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_install_method("chat_completion_stream_generator", method_source)
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def _patch_chat_completion_full_generator() -> None:
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method_source = _extract_class_method_source(
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chat_serving.__file__,
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"OpenAIServingChat",
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"chat_completion_full_generator",
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)
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method_source = _replace_block(
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method_source,
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"""\
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usage = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=num_generated_tokens,
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total_tokens=num_prompt_tokens + num_generated_tokens,
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)
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if self.enable_prompt_tokens_details and final_res.num_cached_tokens:
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usage.prompt_tokens_details = PromptTokenUsageInfo(
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cached_tokens=final_res.num_cached_tokens
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)
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""",
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"""\
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reasoning_tokens = None
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if reasoning_parser is not None:
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reasoning_tokens = sum(
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self._count_reasoning_tokens_for_usage(
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as_list(output.token_ids), reasoning_parser
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)
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or 0
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for output in final_res.outputs
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)
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usage = self._make_usage_info(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=num_generated_tokens,
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num_cached_tokens=final_res.num_cached_tokens,
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reasoning_tokens=reasoning_tokens,
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)
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""",
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)
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_install_method("chat_completion_full_generator", method_source)
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_patch_chat_completion_stream_generator()
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_patch_chat_completion_full_generator()
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