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446912d1cb
Signed-off-by: wangln19 <wanglinian@dev.wanglinian.msh-dev.svc.cluster.local> Signed-off-by: wangln19 <96399074+wangln19@users.noreply.github.com> Co-authored-by: wangln19 <wanglinian@dev.wanglinian.msh-dev.svc.cluster.local> Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
1672 lines
56 KiB
Python
1672 lines
56 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 asyncio
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import inspect
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import json
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from abc import ABC, abstractmethod
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from collections import Counter, defaultdict, deque
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from collections.abc import Awaitable, Callable, Iterable
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from functools import cached_property, lru_cache, partial
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from pathlib import Path
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from typing import Any, Generic, Literal, TypeAlias, TypeVar, cast
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import jinja2
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import jinja2.ext
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import jinja2.meta
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import jinja2.nodes
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import jinja2.parser
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import jinja2.sandbox
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import transformers.utils.chat_template_utils as hf_chat_utils
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from openai.types.chat import (
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ChatCompletionAssistantMessageParam,
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ChatCompletionContentPartImageParam,
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ChatCompletionContentPartInputAudioParam,
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ChatCompletionContentPartRefusalParam,
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ChatCompletionContentPartTextParam,
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ChatCompletionMessageToolCallParam,
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ChatCompletionToolMessageParam,
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)
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from openai.types.chat import (
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ChatCompletionContentPartParam as OpenAIChatCompletionContentPartParam,
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)
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from openai.types.chat import (
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ChatCompletionMessageParam as OpenAIChatCompletionMessageParam,
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)
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from openai.types.chat.chat_completion_content_part_input_audio_param import InputAudio
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from openai.types.responses import ResponseInputImageParam
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from openai_harmony import Message as OpenAIHarmonyMessage
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from PIL import Image
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from pydantic import BaseModel, ConfigDict, TypeAdapter
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from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast, ProcessorMixin
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# pydantic needs the TypedDict from typing_extensions
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from typing_extensions import Required, TypedDict
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from vllm.config import ModelConfig
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from vllm.logger import init_logger
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from vllm.model_executor.models import SupportsMultiModal
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from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalDataDict, MultiModalUUIDDict
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from vllm.multimodal.utils import MediaConnector
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from vllm.transformers_utils.chat_templates import get_chat_template_fallback_path
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from vllm.transformers_utils.processor import cached_get_processor
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from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
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from vllm.utils import random_uuid
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from vllm.utils.func_utils import supports_kw
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logger = init_logger(__name__)
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MODALITY_PLACEHOLDERS_MAP = {
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"image": "<##IMAGE##>",
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"audio": "<##AUDIO##>",
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"video": "<##VIDEO##>",
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}
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class AudioURL(TypedDict, total=False):
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url: Required[str]
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"""
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Either a URL of the audio or a data URL with base64 encoded audio data.
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"""
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class ChatCompletionContentPartAudioParam(TypedDict, total=False):
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audio_url: Required[AudioURL]
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type: Required[Literal["audio_url"]]
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"""The type of the content part."""
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class ChatCompletionContentPartImageEmbedsParam(TypedDict, total=False):
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image_embeds: str | dict[str, str] | None
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"""
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The image embeddings. It can be either:
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- A single base64 string.
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- A dictionary where each value is a base64 string.
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"""
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type: Required[Literal["image_embeds"]]
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"""The type of the content part."""
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uuid: str | None
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"""
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User-provided UUID of a media. User must guarantee that it is properly
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generated and unique for different medias.
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"""
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class VideoURL(TypedDict, total=False):
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url: Required[str]
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"""
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Either a URL of the video or a data URL with base64 encoded video data.
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"""
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class ChatCompletionContentPartVideoParam(TypedDict, total=False):
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video_url: Required[VideoURL]
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type: Required[Literal["video_url"]]
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"""The type of the content part."""
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class PILImage(BaseModel):
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"""
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A PIL.Image.Image object.
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"""
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image_pil: Image.Image
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model_config = ConfigDict(arbitrary_types_allowed=True)
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class CustomChatCompletionContentPILImageParam(TypedDict, total=False):
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"""A simpler version of the param that only accepts a PIL image.
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Example:
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{
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"image_pil": ImageAsset('cherry_blossom').pil_image
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}
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"""
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image_pil: PILImage | None
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uuid: str | None
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"""
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User-provided UUID of a media. User must guarantee that it is properly
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generated and unique for different medias.
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"""
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class CustomChatCompletionContentSimpleImageParam(TypedDict, total=False):
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"""A simpler version of the param that only accepts a plain image_url.
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This is supported by OpenAI API, although it is not documented.
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Example:
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{
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"image_url": "https://example.com/image.jpg"
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}
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"""
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image_url: str | None
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uuid: str | None
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"""
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User-provided UUID of a media. User must guarantee that it is properly
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generated and unique for different medias.
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"""
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class CustomChatCompletionContentSimpleAudioParam(TypedDict, total=False):
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"""A simpler version of the param that only accepts a plain audio_url.
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Example:
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{
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"audio_url": "https://example.com/audio.mp3"
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}
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"""
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audio_url: str | None
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class CustomChatCompletionContentSimpleVideoParam(TypedDict, total=False):
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"""A simpler version of the param that only accepts a plain audio_url.
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Example:
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{
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"video_url": "https://example.com/video.mp4"
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}
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"""
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video_url: str | None
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uuid: str | None
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"""
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User-provided UUID of a media. User must guarantee that it is properly
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generated and unique for different medias.
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"""
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class CustomThinkCompletionContentParam(TypedDict, total=False):
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"""A Think Completion Content Param that accepts a plain text and a boolean.
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Example:
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{
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"thinking": "I am thinking about the answer",
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"closed": True,
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"type": "thinking"
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}
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"""
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thinking: Required[str]
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"""The thinking content."""
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closed: bool
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"""Whether the thinking is closed."""
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type: Required[Literal["thinking"]]
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"""The thinking type."""
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ChatCompletionContentPartParam: TypeAlias = (
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OpenAIChatCompletionContentPartParam
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| ChatCompletionContentPartAudioParam
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| ChatCompletionContentPartInputAudioParam
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| ChatCompletionContentPartVideoParam
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| ChatCompletionContentPartRefusalParam
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| CustomChatCompletionContentPILImageParam
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| CustomChatCompletionContentSimpleImageParam
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| ChatCompletionContentPartImageEmbedsParam
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| CustomChatCompletionContentSimpleAudioParam
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| CustomChatCompletionContentSimpleVideoParam
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| str
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| CustomThinkCompletionContentParam
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)
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class CustomChatCompletionMessageParam(TypedDict, total=False):
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"""Enables custom roles in the Chat Completion API."""
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role: Required[str]
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"""The role of the message's author."""
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content: str | list[ChatCompletionContentPartParam]
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"""The contents of the message."""
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name: str
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"""An optional name for the participant.
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Provides the model information to differentiate between participants of the
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same role.
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"""
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tool_call_id: str | None
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"""Tool call that this message is responding to."""
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tool_calls: Iterable[ChatCompletionMessageToolCallParam] | None
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"""The tool calls generated by the model, such as function calls."""
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ChatCompletionMessageParam: TypeAlias = (
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OpenAIChatCompletionMessageParam
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| CustomChatCompletionMessageParam
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| OpenAIHarmonyMessage
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)
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# TODO: Make fields ReadOnly once mypy supports it
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class ConversationMessage(TypedDict, total=False):
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role: Required[str]
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"""The role of the message's author."""
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content: str | None | list[dict[str, str]]
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"""The contents of the message"""
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tool_call_id: str | None
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"""Tool call that this message is responding to."""
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name: str | None
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"""The name of the function to call"""
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tool_calls: Iterable[ChatCompletionMessageToolCallParam] | None
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"""The tool calls generated by the model, such as function calls."""
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# Passed in by user
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ChatTemplateContentFormatOption = Literal["auto", "string", "openai"]
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# Used internally
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_ChatTemplateContentFormat = Literal["string", "openai"]
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def _is_var_access(node: jinja2.nodes.Node, varname: str) -> bool:
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if isinstance(node, jinja2.nodes.Name):
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return node.ctx == "load" and node.name == varname
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return False
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def _is_attr_access(node: jinja2.nodes.Node, varname: str, key: str) -> bool:
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if isinstance(node, jinja2.nodes.Getitem):
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return (
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_is_var_access(node.node, varname)
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and isinstance(node.arg, jinja2.nodes.Const)
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and node.arg.value == key
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)
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if isinstance(node, jinja2.nodes.Getattr):
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return _is_var_access(node.node, varname) and node.attr == key
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return False
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def _is_var_or_elems_access(
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node: jinja2.nodes.Node,
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varname: str,
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key: str | None = None,
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) -> bool:
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if isinstance(node, jinja2.nodes.Filter):
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return node.node is not None and _is_var_or_elems_access(
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node.node, varname, key
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)
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if isinstance(node, jinja2.nodes.Test):
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return _is_var_or_elems_access(node.node, varname, key)
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if isinstance(node, jinja2.nodes.Getitem) and isinstance(
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node.arg, jinja2.nodes.Slice
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):
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return _is_var_or_elems_access(node.node, varname, key)
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return _is_attr_access(node, varname, key) if key else _is_var_access(node, varname)
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def _iter_nodes_assign_var_or_elems(root: jinja2.nodes.Node, varname: str):
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# Global variable that is implicitly defined at the root
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yield root, varname
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# Iterative BFS
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related_varnames = deque([varname])
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while related_varnames:
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related_varname = related_varnames.popleft()
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for assign_ast in root.find_all(jinja2.nodes.Assign):
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lhs = assign_ast.target
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rhs = assign_ast.node
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if _is_var_or_elems_access(rhs, related_varname):
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assert isinstance(lhs, jinja2.nodes.Name)
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yield assign_ast, lhs.name
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# Avoid infinite looping for self-assignment
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if lhs.name != related_varname:
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related_varnames.append(lhs.name)
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# NOTE: The proper way to handle this is to build a CFG so that we can handle
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# the scope in which each variable is defined, but that is too complicated
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def _iter_nodes_assign_messages_item(root: jinja2.nodes.Node):
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messages_varnames = [
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varname for _, varname in _iter_nodes_assign_var_or_elems(root, "messages")
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]
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# Search for {%- for message in messages -%} loops
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for loop_ast in root.find_all(jinja2.nodes.For):
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loop_iter = loop_ast.iter
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loop_target = loop_ast.target
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for varname in messages_varnames:
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if _is_var_or_elems_access(loop_iter, varname):
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assert isinstance(loop_target, jinja2.nodes.Name)
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yield loop_ast, loop_target.name
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break
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def _iter_nodes_assign_content_item(root: jinja2.nodes.Node):
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message_varnames = [
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varname for _, varname in _iter_nodes_assign_messages_item(root)
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]
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# Search for {%- for content in message['content'] -%} loops
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for loop_ast in root.find_all(jinja2.nodes.For):
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loop_iter = loop_ast.iter
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loop_target = loop_ast.target
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for varname in message_varnames:
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if _is_var_or_elems_access(loop_iter, varname, "content"):
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assert isinstance(loop_target, jinja2.nodes.Name)
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yield loop_ast, loop_target.name
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break
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def _try_extract_ast(chat_template: str) -> jinja2.nodes.Template | None:
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try:
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jinja_compiled = hf_chat_utils._compile_jinja_template(chat_template)
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return jinja_compiled.environment.parse(chat_template)
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except Exception:
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logger.exception("Error when compiling Jinja template")
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return None
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@lru_cache(maxsize=32)
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def _detect_content_format(
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chat_template: str,
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*,
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default: _ChatTemplateContentFormat,
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) -> _ChatTemplateContentFormat:
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jinja_ast = _try_extract_ast(chat_template)
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if jinja_ast is None:
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return default
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try:
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next(_iter_nodes_assign_content_item(jinja_ast))
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except StopIteration:
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return "string"
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except Exception:
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logger.exception("Error when parsing AST of Jinja template")
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return default
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else:
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return "openai"
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def resolve_mistral_chat_template(
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chat_template: str | None,
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**kwargs: Any,
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) -> str | None:
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if chat_template is not None or kwargs.get("chat_template_kwargs") is not None:
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raise ValueError(
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"'chat_template' or 'chat_template_kwargs' cannot be overridden "
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"for mistral tokenizer."
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)
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return None
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_PROCESSOR_CHAT_TEMPLATES = dict[tuple[str, bool], str | None]()
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"""
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Used in `_try_get_processor_chat_template` to avoid calling
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`cached_get_processor` again if the processor fails to be loaded.
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This is needed because `lru_cache` does not cache when an exception happens.
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"""
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def _try_get_processor_chat_template(
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tokenizer: PreTrainedTokenizer | PreTrainedTokenizerFast,
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model_config: ModelConfig,
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) -> str | None:
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cache_key = (tokenizer.name_or_path, model_config.trust_remote_code)
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if cache_key in _PROCESSOR_CHAT_TEMPLATES:
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return _PROCESSOR_CHAT_TEMPLATES[cache_key]
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try:
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processor = cached_get_processor(
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tokenizer.name_or_path,
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processor_cls=(
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PreTrainedTokenizer,
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PreTrainedTokenizerFast,
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ProcessorMixin,
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),
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trust_remote_code=model_config.trust_remote_code,
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)
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if (
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isinstance(processor, ProcessorMixin)
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and hasattr(processor, "chat_template")
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and (chat_template := processor.chat_template) is not None
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):
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_PROCESSOR_CHAT_TEMPLATES[cache_key] = chat_template
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return chat_template
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except Exception:
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logger.debug(
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"Failed to load AutoProcessor chat template for %s",
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tokenizer.name_or_path,
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exc_info=True,
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)
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_PROCESSOR_CHAT_TEMPLATES[cache_key] = None
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return None
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def resolve_hf_chat_template(
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tokenizer: PreTrainedTokenizer | PreTrainedTokenizerFast,
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chat_template: str | None,
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tools: list[dict[str, Any]] | None,
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*,
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model_config: ModelConfig,
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) -> str | None:
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# 1st priority: The given chat template
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if chat_template is not None:
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return chat_template
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# 2nd priority: AutoProcessor chat template, unless tool calling is enabled
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if tools is None:
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chat_template = _try_get_processor_chat_template(tokenizer, model_config)
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if chat_template is not None:
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return chat_template
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# 3rd priority: AutoTokenizer chat template
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try:
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return tokenizer.get_chat_template(chat_template, tools=tools)
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except Exception:
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logger.debug(
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"Failed to load AutoTokenizer chat template for %s",
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tokenizer.name_or_path,
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exc_info=True,
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)
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# 4th priority: Predefined fallbacks
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path = get_chat_template_fallback_path(
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model_type=model_config.hf_config.model_type,
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tokenizer_name_or_path=model_config.tokenizer,
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)
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if path is not None:
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logger.info_once(
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"Loading chat template fallback for %s as there isn't one "
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"defined on HF Hub.",
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tokenizer.name_or_path,
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)
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chat_template = load_chat_template(path)
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else:
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logger.debug_once(
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"There is no chat template fallback for %s", tokenizer.name_or_path
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)
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return chat_template
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|
|
|
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def _resolve_chat_template_content_format(
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chat_template: str | None,
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tools: list[dict[str, Any]] | None,
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tokenizer: AnyTokenizer,
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*,
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model_config: ModelConfig,
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) -> _ChatTemplateContentFormat:
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if isinstance(tokenizer, (PreTrainedTokenizer, PreTrainedTokenizerFast)):
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hf_chat_template = resolve_hf_chat_template(
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tokenizer,
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chat_template=chat_template,
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tools=tools,
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model_config=model_config,
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)
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else:
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hf_chat_template = None
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jinja_text = (
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hf_chat_template
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if isinstance(hf_chat_template, str)
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else load_chat_template(chat_template, is_literal=True)
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)
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detected_format = (
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"string"
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if jinja_text is None
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else _detect_content_format(jinja_text, default="string")
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)
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return detected_format
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|
|
|
|
@lru_cache
|
|
def _log_chat_template_content_format(
|
|
chat_template: str | None,
|
|
given_format: ChatTemplateContentFormatOption,
|
|
detected_format: ChatTemplateContentFormatOption,
|
|
):
|
|
logger.info(
|
|
"Detected the chat template content format to be '%s'. "
|
|
"You can set `--chat-template-content-format` to override this.",
|
|
detected_format,
|
|
)
|
|
|
|
if given_format != "auto" and given_format != detected_format:
|
|
logger.warning(
|
|
"You specified `--chat-template-content-format %s` "
|
|
"which is different from the detected format '%s'. "
|
|
"If our automatic detection is incorrect, please consider "
|
|
"opening a GitHub issue so that we can improve it: "
|
|
"https://github.com/vllm-project/vllm/issues/new/choose",
|
|
given_format,
|
|
detected_format,
|
|
)
|
|
|
|
|
|
def resolve_chat_template_content_format(
|
|
chat_template: str | None,
|
|
tools: list[dict[str, Any]] | None,
|
|
given_format: ChatTemplateContentFormatOption,
|
|
tokenizer: AnyTokenizer,
|
|
*,
|
|
model_config: ModelConfig,
|
|
) -> _ChatTemplateContentFormat:
|
|
if given_format != "auto":
|
|
return given_format
|
|
|
|
detected_format = _resolve_chat_template_content_format(
|
|
chat_template,
|
|
tools,
|
|
tokenizer,
|
|
model_config=model_config,
|
|
)
|
|
|
|
_log_chat_template_content_format(
|
|
chat_template,
|
|
given_format=given_format,
|
|
detected_format=detected_format,
|
|
)
|
|
|
|
return detected_format
|
|
|
|
|
|
ModalityStr = Literal["image", "audio", "video", "image_embeds"]
|
|
_T = TypeVar("_T")
|
|
|
|
|
|
class BaseMultiModalItemTracker(ABC, Generic[_T]):
|
|
"""
|
|
Tracks multi-modal items in a given request and ensures that the number
|
|
of multi-modal items in a given request does not exceed the configured
|
|
maximum per prompt.
|
|
"""
|
|
|
|
def __init__(self, model_config: ModelConfig, tokenizer: AnyTokenizer):
|
|
super().__init__()
|
|
|
|
self._model_config = model_config
|
|
self._tokenizer = tokenizer
|
|
|
|
self._items_by_modality = defaultdict[str, list[_T | None]](list)
|
|
self._uuids_by_modality = defaultdict[str, list[str | None]](list)
|
|
|
|
@property
|
|
def model_config(self) -> ModelConfig:
|
|
return self._model_config
|
|
|
|
@cached_property
|
|
def model_cls(self) -> type[SupportsMultiModal]:
|
|
from vllm.model_executor.model_loader import get_model_cls
|
|
|
|
model_cls = get_model_cls(self.model_config)
|
|
return cast(type[SupportsMultiModal], model_cls)
|
|
|
|
@property
|
|
def allowed_local_media_path(self):
|
|
return self._model_config.allowed_local_media_path
|
|
|
|
@property
|
|
def allowed_media_domains(self):
|
|
return self._model_config.allowed_media_domains
|
|
|
|
@property
|
|
def mm_registry(self):
|
|
return MULTIMODAL_REGISTRY
|
|
|
|
@cached_property
|
|
def mm_processor(self):
|
|
return self.mm_registry.create_processor(self.model_config)
|
|
|
|
def add(
|
|
self,
|
|
modality: ModalityStr,
|
|
item: _T | None,
|
|
uuid: str | None = None,
|
|
) -> str | None:
|
|
"""
|
|
Add a multi-modal item to the current prompt and returns the
|
|
placeholder string to use, if any.
|
|
|
|
An optional uuid can be added which serves as a unique identifier of the
|
|
media.
|
|
"""
|
|
input_modality = modality.replace("_embeds", "")
|
|
num_items = len(self._items_by_modality[modality]) + 1
|
|
|
|
self.mm_processor.validate_num_items(input_modality, num_items)
|
|
|
|
self._items_by_modality[modality].append(item)
|
|
self._uuids_by_modality[modality].append(uuid)
|
|
|
|
return self.model_cls.get_placeholder_str(modality, num_items)
|
|
|
|
def all_mm_uuids(self) -> MultiModalUUIDDict | None:
|
|
if not self._items_by_modality:
|
|
return None
|
|
mm_uuids = {}
|
|
uuids_by_modality = dict(self._uuids_by_modality)
|
|
if "image" in uuids_by_modality and "image_embeds" in uuids_by_modality:
|
|
raise ValueError("Mixing raw image and embedding inputs is not allowed")
|
|
|
|
if "image_embeds" in uuids_by_modality:
|
|
image_embeds_uuids = uuids_by_modality["image_embeds"]
|
|
if len(image_embeds_uuids) > 1:
|
|
raise ValueError("Only one message can have {'type': 'image_embeds'}")
|
|
mm_uuids["image"] = uuids_by_modality["image_embeds"]
|
|
if "image" in uuids_by_modality:
|
|
mm_uuids["image"] = uuids_by_modality["image"] # UUIDs of images
|
|
if "audio" in uuids_by_modality:
|
|
mm_uuids["audio"] = uuids_by_modality["audio"] # UUIDs of audios
|
|
if "video" in uuids_by_modality:
|
|
mm_uuids["video"] = uuids_by_modality["video"] # UUIDs of videos
|
|
return mm_uuids
|
|
|
|
@abstractmethod
|
|
def create_parser(self) -> "BaseMultiModalContentParser":
|
|
raise NotImplementedError
|
|
|
|
|
|
class MultiModalItemTracker(BaseMultiModalItemTracker[object]):
|
|
def all_mm_data(self) -> MultiModalDataDict | None:
|
|
if not self._items_by_modality:
|
|
return None
|
|
mm_inputs = {}
|
|
items_by_modality = dict(self._items_by_modality)
|
|
if "image" in items_by_modality and "image_embeds" in items_by_modality:
|
|
raise ValueError("Mixing raw image and embedding inputs is not allowed")
|
|
|
|
if "image_embeds" in items_by_modality:
|
|
image_embeds_lst = items_by_modality["image_embeds"]
|
|
if len(image_embeds_lst) > 1:
|
|
raise ValueError("Only one message can have {'type': 'image_embeds'}")
|
|
mm_inputs["image"] = image_embeds_lst[0]
|
|
if "image" in items_by_modality:
|
|
mm_inputs["image"] = items_by_modality["image"] # A list of images
|
|
if "audio" in items_by_modality:
|
|
mm_inputs["audio"] = items_by_modality["audio"] # A list of audios
|
|
if "video" in items_by_modality:
|
|
mm_inputs["video"] = items_by_modality["video"] # A list of videos
|
|
return mm_inputs
|
|
|
|
def create_parser(self) -> "BaseMultiModalContentParser":
|
|
return MultiModalContentParser(self)
|
|
|
|
|
|
class AsyncMultiModalItemTracker(BaseMultiModalItemTracker[Awaitable[object]]):
|
|
async def all_mm_data(self) -> MultiModalDataDict | None:
|
|
if not self._items_by_modality:
|
|
return None
|
|
mm_inputs = {}
|
|
items_by_modality = {}
|
|
for modality, items in self._items_by_modality.items():
|
|
coros = []
|
|
for item in items:
|
|
if item is not None:
|
|
coros.append(item)
|
|
else:
|
|
coros.append(asyncio.sleep(0))
|
|
items_by_modality[modality] = await asyncio.gather(*coros)
|
|
|
|
if "image" in items_by_modality and "image_embeds" in items_by_modality:
|
|
raise ValueError("Mixing raw image and embedding inputs is not allowed")
|
|
|
|
if "image_embeds" in items_by_modality:
|
|
image_embeds_lst = items_by_modality["image_embeds"]
|
|
if len(image_embeds_lst) > 1:
|
|
raise ValueError("Only one message can have {'type': 'image_embeds'}")
|
|
mm_inputs["image"] = image_embeds_lst[0]
|
|
if "image" in items_by_modality:
|
|
mm_inputs["image"] = items_by_modality["image"] # A list of images
|
|
if "audio" in items_by_modality:
|
|
mm_inputs["audio"] = items_by_modality["audio"] # A list of audios
|
|
if "video" in items_by_modality:
|
|
mm_inputs["video"] = items_by_modality["video"] # A list of videos
|
|
return mm_inputs
|
|
|
|
def create_parser(self) -> "BaseMultiModalContentParser":
|
|
return AsyncMultiModalContentParser(self)
|
|
|
|
|
|
class BaseMultiModalContentParser(ABC):
|
|
def __init__(self) -> None:
|
|
super().__init__()
|
|
|
|
# stores model placeholders list with corresponding
|
|
# general MM placeholder:
|
|
# {
|
|
# "<##IMAGE##>": ["<image>", "<image>", "<image>"],
|
|
# "<##AUDIO##>": ["<audio>", "<audio>"]
|
|
# }
|
|
self._placeholder_storage: dict[str, list] = defaultdict(list)
|
|
|
|
def _add_placeholder(self, modality: ModalityStr, placeholder: str | None):
|
|
mod_placeholder = MODALITY_PLACEHOLDERS_MAP[modality]
|
|
if placeholder:
|
|
self._placeholder_storage[mod_placeholder].append(placeholder)
|
|
|
|
def mm_placeholder_storage(self) -> dict[str, list]:
|
|
return dict(self._placeholder_storage)
|
|
|
|
@abstractmethod
|
|
def parse_image(self, image_url: str | None, uuid: str | None = None) -> None:
|
|
raise NotImplementedError
|
|
|
|
@abstractmethod
|
|
def parse_image_embeds(
|
|
self,
|
|
image_embeds: str | dict[str, str] | None,
|
|
uuid: str | None = None,
|
|
) -> None:
|
|
raise NotImplementedError
|
|
|
|
@abstractmethod
|
|
def parse_image_pil(
|
|
self, image_pil: Image.Image | None, uuid: str | None = None
|
|
) -> None:
|
|
raise NotImplementedError
|
|
|
|
@abstractmethod
|
|
def parse_audio(self, audio_url: str | None, uuid: str | None = None) -> None:
|
|
raise NotImplementedError
|
|
|
|
@abstractmethod
|
|
def parse_input_audio(
|
|
self, input_audio: InputAudio | None, uuid: str | None = None
|
|
) -> None:
|
|
raise NotImplementedError
|
|
|
|
@abstractmethod
|
|
def parse_video(self, video_url: str | None, uuid: str | None = None) -> None:
|
|
raise NotImplementedError
|
|
|
|
|
|
class MultiModalContentParser(BaseMultiModalContentParser):
|
|
def __init__(self, tracker: MultiModalItemTracker) -> None:
|
|
super().__init__()
|
|
|
|
self._tracker = tracker
|
|
multimodal_config = self._tracker.model_config.multimodal_config
|
|
media_io_kwargs = getattr(multimodal_config, "media_io_kwargs", None)
|
|
self._connector = MediaConnector(
|
|
media_io_kwargs=media_io_kwargs,
|
|
allowed_local_media_path=tracker.allowed_local_media_path,
|
|
allowed_media_domains=tracker.allowed_media_domains,
|
|
)
|
|
|
|
@property
|
|
def model_config(self) -> ModelConfig:
|
|
return self._tracker.model_config
|
|
|
|
def parse_image(self, image_url: str | None, uuid: str | None = None) -> None:
|
|
image = self._connector.fetch_image(image_url) if image_url else None
|
|
|
|
placeholder = self._tracker.add("image", image, uuid)
|
|
self._add_placeholder("image", placeholder)
|
|
|
|
def parse_image_embeds(
|
|
self,
|
|
image_embeds: str | dict[str, str] | None,
|
|
uuid: str | None = None,
|
|
) -> None:
|
|
mm_config = self.model_config.get_multimodal_config()
|
|
if not mm_config.enable_mm_embeds:
|
|
raise ValueError(
|
|
"You must set `--enable-mm-embeds` to input `image_embeds`"
|
|
)
|
|
|
|
if isinstance(image_embeds, dict):
|
|
embeds = {
|
|
k: self._connector.fetch_image_embedding(v)
|
|
for k, v in image_embeds.items()
|
|
}
|
|
placeholder = self._tracker.add("image_embeds", embeds, uuid)
|
|
|
|
if isinstance(image_embeds, str):
|
|
embedding = self._connector.fetch_image_embedding(image_embeds)
|
|
placeholder = self._tracker.add("image_embeds", embedding, uuid)
|
|
|
|
if image_embeds is None:
|
|
placeholder = self._tracker.add("image_embeds", None, uuid)
|
|
|
|
self._add_placeholder("image", placeholder)
|
|
|
|
def parse_image_pil(
|
|
self, image_pil: Image.Image | None, uuid: str | None = None
|
|
) -> None:
|
|
placeholder = self._tracker.add("image", image_pil, uuid)
|
|
self._add_placeholder("image", placeholder)
|
|
|
|
def parse_audio(self, audio_url: str | None, uuid: str | None = None) -> None:
|
|
audio = self._connector.fetch_audio(audio_url) if audio_url else None
|
|
|
|
placeholder = self._tracker.add("audio", audio, uuid)
|
|
self._add_placeholder("audio", placeholder)
|
|
|
|
def parse_input_audio(
|
|
self, input_audio: InputAudio | None, uuid: str | None = None
|
|
) -> None:
|
|
if input_audio:
|
|
audio_data = input_audio.get("data", "")
|
|
audio_format = input_audio.get("format", "")
|
|
if audio_data:
|
|
audio_url = f"data:audio/{audio_format};base64,{audio_data}"
|
|
else:
|
|
# If a UUID is provided, audio data may be empty.
|
|
audio_url = None
|
|
else:
|
|
audio_url = None
|
|
|
|
return self.parse_audio(audio_url, uuid)
|
|
|
|
def parse_video(self, video_url: str | None, uuid: str | None = None) -> None:
|
|
video = self._connector.fetch_video(video_url=video_url) if video_url else None
|
|
|
|
placeholder = self._tracker.add("video", video, uuid)
|
|
self._add_placeholder("video", placeholder)
|
|
|
|
|
|
class AsyncMultiModalContentParser(BaseMultiModalContentParser):
|
|
def __init__(self, tracker: AsyncMultiModalItemTracker) -> None:
|
|
super().__init__()
|
|
|
|
self._tracker = tracker
|
|
multimodal_config = self._tracker.model_config.multimodal_config
|
|
media_io_kwargs = getattr(multimodal_config, "media_io_kwargs", None)
|
|
self._connector = MediaConnector(
|
|
media_io_kwargs=media_io_kwargs,
|
|
allowed_local_media_path=tracker.allowed_local_media_path,
|
|
allowed_media_domains=tracker.allowed_media_domains,
|
|
)
|
|
|
|
@property
|
|
def model_config(self) -> ModelConfig:
|
|
return self._tracker.model_config
|
|
|
|
def parse_image(self, image_url: str | None, uuid: str | None = None) -> None:
|
|
image_coro = self._connector.fetch_image_async(image_url) if image_url else None
|
|
|
|
placeholder = self._tracker.add("image", image_coro, uuid)
|
|
self._add_placeholder("image", placeholder)
|
|
|
|
def parse_image_embeds(
|
|
self,
|
|
image_embeds: str | dict[str, str] | None,
|
|
uuid: str | None = None,
|
|
) -> None:
|
|
mm_config = self.model_config.get_multimodal_config()
|
|
if not mm_config.enable_mm_embeds:
|
|
raise ValueError(
|
|
"You must set `--enable-mm-embeds` to input `image_embeds`"
|
|
)
|
|
|
|
future: asyncio.Future[str | dict[str, str] | None] = asyncio.Future()
|
|
|
|
if isinstance(image_embeds, dict):
|
|
embeds = {
|
|
k: self._connector.fetch_image_embedding(v)
|
|
for k, v in image_embeds.items()
|
|
}
|
|
future.set_result(embeds)
|
|
|
|
if isinstance(image_embeds, str):
|
|
embedding = self._connector.fetch_image_embedding(image_embeds)
|
|
future.set_result(embedding)
|
|
|
|
if image_embeds is None:
|
|
future.set_result(None)
|
|
|
|
placeholder = self._tracker.add("image_embeds", future, uuid)
|
|
self._add_placeholder("image", placeholder)
|
|
|
|
def parse_image_pil(
|
|
self, image_pil: Image.Image | None, uuid: str | None = None
|
|
) -> None:
|
|
future: asyncio.Future[Image.Image | None] = asyncio.Future()
|
|
if image_pil:
|
|
future.set_result(image_pil)
|
|
else:
|
|
future.set_result(None)
|
|
|
|
placeholder = self._tracker.add("image", future, uuid)
|
|
self._add_placeholder("image", placeholder)
|
|
|
|
def parse_audio(self, audio_url: str | None, uuid: str | None = None) -> None:
|
|
audio_coro = self._connector.fetch_audio_async(audio_url) if audio_url else None
|
|
|
|
placeholder = self._tracker.add("audio", audio_coro, uuid)
|
|
self._add_placeholder("audio", placeholder)
|
|
|
|
def parse_input_audio(
|
|
self, input_audio: InputAudio | None, uuid: str | None = None
|
|
) -> None:
|
|
if input_audio:
|
|
audio_data = input_audio.get("data", "")
|
|
audio_format = input_audio.get("format", "")
|
|
if audio_data:
|
|
audio_url = f"data:audio/{audio_format};base64,{audio_data}"
|
|
else:
|
|
# If a UUID is provided, audio data may be empty.
|
|
audio_url = None
|
|
else:
|
|
audio_url = None
|
|
|
|
return self.parse_audio(audio_url, uuid)
|
|
|
|
def parse_video(self, video_url: str | None, uuid: str | None = None) -> None:
|
|
video = (
|
|
self._connector.fetch_video_async(video_url=video_url)
|
|
if video_url
|
|
else None
|
|
)
|
|
|
|
placeholder = self._tracker.add("video", video, uuid)
|
|
self._add_placeholder("video", placeholder)
|
|
|
|
|
|
def validate_chat_template(chat_template: Path | str | None):
|
|
"""Raises if the provided chat template appears invalid."""
|
|
if chat_template is None:
|
|
return
|
|
|
|
elif isinstance(chat_template, Path) and not chat_template.exists():
|
|
raise FileNotFoundError("the supplied chat template path doesn't exist")
|
|
|
|
elif isinstance(chat_template, str):
|
|
JINJA_CHARS = "{}\n"
|
|
if (
|
|
not any(c in chat_template for c in JINJA_CHARS)
|
|
and not Path(chat_template).exists()
|
|
):
|
|
raise ValueError(
|
|
f"The supplied chat template string ({chat_template}) "
|
|
f"appears path-like, but doesn't exist!"
|
|
)
|
|
|
|
else:
|
|
raise TypeError(f"{type(chat_template)} is not a valid chat template type")
|
|
|
|
|
|
def _load_chat_template(
|
|
chat_template: Path | str | None,
|
|
*,
|
|
is_literal: bool = False,
|
|
) -> str | None:
|
|
if chat_template is None:
|
|
return None
|
|
|
|
if is_literal:
|
|
if isinstance(chat_template, Path):
|
|
raise TypeError(
|
|
"chat_template is expected to be read directly from its value"
|
|
)
|
|
|
|
return chat_template
|
|
|
|
try:
|
|
with open(chat_template) as f:
|
|
return f.read()
|
|
except OSError as e:
|
|
if isinstance(chat_template, Path):
|
|
raise
|
|
|
|
JINJA_CHARS = "{}\n"
|
|
if not any(c in chat_template for c in JINJA_CHARS):
|
|
msg = (
|
|
f"The supplied chat template ({chat_template}) "
|
|
f"looks like a file path, but it failed to be "
|
|
f"opened. Reason: {e}"
|
|
)
|
|
raise ValueError(msg) from e
|
|
|
|
# If opening a file fails, set chat template to be args to
|
|
# ensure we decode so our escape are interpreted correctly
|
|
return _load_chat_template(chat_template, is_literal=True)
|
|
|
|
|
|
_cached_load_chat_template = lru_cache(_load_chat_template)
|
|
|
|
|
|
def load_chat_template(
|
|
chat_template: Path | str | None,
|
|
*,
|
|
is_literal: bool = False,
|
|
) -> str | None:
|
|
return _cached_load_chat_template(chat_template, is_literal=is_literal)
|
|
|
|
|
|
def _get_interleaved_text_prompt(
|
|
placeholder_storage: dict[str, list], texts: list[str]
|
|
) -> str:
|
|
for idx, elem in enumerate(texts):
|
|
if elem in placeholder_storage:
|
|
texts[idx] = placeholder_storage[elem].pop(0)
|
|
|
|
return "\n".join(texts)
|
|
|
|
|
|
# TODO: Let user specify how to insert multimodal tokens into prompt
|
|
# (similar to chat template)
|
|
def _get_full_multimodal_text_prompt(
|
|
placeholder_storage: dict[str, list],
|
|
texts: list[str],
|
|
interleave_strings: bool,
|
|
) -> str:
|
|
"""Combine multimodal prompts for a multimodal language model."""
|
|
|
|
# flatten storage to make it looks like
|
|
# {
|
|
# "<|image|>": 2,
|
|
# "<|audio|>": 1
|
|
# }
|
|
placeholder_counts = Counter(
|
|
[v for elem in placeholder_storage.values() for v in elem]
|
|
)
|
|
|
|
if interleave_strings:
|
|
text_prompt = _get_interleaved_text_prompt(placeholder_storage, texts)
|
|
else:
|
|
text_prompt = "\n".join(texts)
|
|
|
|
# Pass interleaved text further in case the user used image placeholders
|
|
# himself, but forgot to disable the 'interleave_strings' flag
|
|
|
|
# Look through the text prompt to check for missing placeholders
|
|
missing_placeholders: list[str] = []
|
|
for placeholder in placeholder_counts:
|
|
# For any existing placeholder in the text prompt, we leave it as is
|
|
placeholder_counts[placeholder] -= text_prompt.count(placeholder)
|
|
|
|
if placeholder_counts[placeholder] < 0:
|
|
logger.error(
|
|
"Placeholder count is negative! "
|
|
"Ensure that the 'interleave_strings' flag is disabled "
|
|
"(current value: %s) "
|
|
"when manually placing image placeholders.",
|
|
interleave_strings,
|
|
)
|
|
logger.debug("Input prompt: %s", text_prompt)
|
|
raise ValueError(
|
|
f"Found more '{placeholder}' placeholders in input prompt than "
|
|
"actual multimodal data items."
|
|
)
|
|
|
|
missing_placeholders.extend([placeholder] * placeholder_counts[placeholder])
|
|
|
|
# NOTE: Default behaviour: we always add missing placeholders
|
|
# at the front of the prompt, if interleave_strings=False
|
|
return "\n".join(missing_placeholders + [text_prompt])
|
|
|
|
|
|
# No need to validate using Pydantic again
|
|
_TextParser = partial(cast, ChatCompletionContentPartTextParam)
|
|
_ImageEmbedsParser = partial(cast, ChatCompletionContentPartImageEmbedsParam)
|
|
_InputAudioParser = partial(cast, ChatCompletionContentPartInputAudioParam)
|
|
_RefusalParser = partial(cast, ChatCompletionContentPartRefusalParam)
|
|
_PILImageParser = partial(cast, CustomChatCompletionContentPILImageParam)
|
|
_ThinkParser = partial(cast, CustomThinkCompletionContentParam)
|
|
# Need to validate url objects
|
|
_ImageParser = TypeAdapter(ChatCompletionContentPartImageParam).validate_python
|
|
_AudioParser = TypeAdapter(ChatCompletionContentPartAudioParam).validate_python
|
|
_VideoParser = TypeAdapter(ChatCompletionContentPartVideoParam).validate_python
|
|
|
|
_ResponsesInputImageParser = TypeAdapter(ResponseInputImageParam).validate_python
|
|
_ContentPart: TypeAlias = str | dict[str, str] | InputAudio | PILImage
|
|
|
|
# Define a mapping from part types to their corresponding parsing functions.
|
|
MM_PARSER_MAP: dict[
|
|
str,
|
|
Callable[[ChatCompletionContentPartParam], _ContentPart],
|
|
] = {
|
|
"text": lambda part: _TextParser(part).get("text", None),
|
|
"thinking": lambda part: _ThinkParser(part).get("thinking", None),
|
|
"input_text": lambda part: _TextParser(part).get("text", None),
|
|
"input_image": lambda part: _ResponsesInputImageParser(part).get("image_url", None),
|
|
"image_url": lambda part: _ImageParser(part).get("image_url", {}).get("url", None),
|
|
"image_embeds": lambda part: _ImageEmbedsParser(part).get("image_embeds", None),
|
|
"image_pil": lambda part: _PILImageParser(part).get("image_pil", None),
|
|
"audio_url": lambda part: _AudioParser(part).get("audio_url", {}).get("url", None),
|
|
"input_audio": lambda part: _InputAudioParser(part).get("input_audio", None),
|
|
"refusal": lambda part: _RefusalParser(part).get("refusal", None),
|
|
"video_url": lambda part: _VideoParser(part).get("video_url", {}).get("url", None),
|
|
}
|
|
|
|
|
|
def _parse_chat_message_content_mm_part(
|
|
part: ChatCompletionContentPartParam,
|
|
) -> tuple[str, _ContentPart]:
|
|
"""
|
|
Parses a given multi-modal content part based on its type.
|
|
|
|
Args:
|
|
part: A dict containing the content part, with a potential 'type' field.
|
|
|
|
Returns:
|
|
A tuple (part_type, content) where:
|
|
- part_type: Type of the part (e.g., 'text', 'image_url').
|
|
- content: Parsed content (e.g., text, image URL).
|
|
|
|
Raises:
|
|
ValueError: If the 'type' field is missing and no direct URL is found.
|
|
"""
|
|
assert isinstance(
|
|
part, dict
|
|
) # This is needed to avoid mypy errors: part.get() from str
|
|
part_type = part.get("type", None)
|
|
uuid = part.get("uuid", None)
|
|
|
|
if isinstance(part_type, str) and part_type in MM_PARSER_MAP and uuid is None: # noqa: E501
|
|
content = MM_PARSER_MAP[part_type](part)
|
|
|
|
# Special case for 'image_url.detail'
|
|
# We only support 'auto', which is the default
|
|
if part_type == "image_url" and part.get("detail", "auto") != "auto":
|
|
logger.warning(
|
|
"'image_url.detail' is currently not supported and will be ignored."
|
|
)
|
|
|
|
return part_type, content
|
|
|
|
# Handle missing 'type' but provided direct URL fields.
|
|
# 'type' is required field by pydantic
|
|
if part_type is None or uuid is not None:
|
|
if "image_url" in part:
|
|
image_params = cast(CustomChatCompletionContentSimpleImageParam, part)
|
|
image_url = image_params.get("image_url", None)
|
|
if isinstance(image_url, dict):
|
|
# Can potentially happen if user provides a uuid
|
|
# with url as a dict of {"url": url}
|
|
image_url = image_url.get("url", None)
|
|
return "image_url", image_url
|
|
if "image_pil" in part:
|
|
# "image_pil" could be None if UUID is provided.
|
|
image_params = cast( # type: ignore
|
|
CustomChatCompletionContentPILImageParam, part
|
|
)
|
|
image_pil = image_params.get("image_pil", None)
|
|
return "image_pil", image_pil
|
|
if "image_embeds" in part:
|
|
# "image_embeds" could be None if UUID is provided.
|
|
image_params = cast( # type: ignore
|
|
ChatCompletionContentPartImageEmbedsParam, part
|
|
)
|
|
image_embeds = image_params.get("image_embeds", None)
|
|
return "image_embeds", image_embeds
|
|
if "audio_url" in part:
|
|
audio_params = cast(CustomChatCompletionContentSimpleAudioParam, part)
|
|
audio_url = audio_params.get("audio_url", None)
|
|
if isinstance(audio_url, dict):
|
|
# Can potentially happen if user provides a uuid
|
|
# with url as a dict of {"url": url}
|
|
audio_url = audio_url.get("url", None)
|
|
return "audio_url", audio_url
|
|
if part.get("input_audio") is not None:
|
|
input_audio_params = cast(dict[str, str], part)
|
|
return "input_audio", input_audio_params
|
|
if "video_url" in part:
|
|
video_params = cast(CustomChatCompletionContentSimpleVideoParam, part)
|
|
video_url = video_params.get("video_url", None)
|
|
if isinstance(video_url, dict):
|
|
# Can potentially happen if user provides a uuid
|
|
# with url as a dict of {"url": url}
|
|
video_url = video_url.get("url", None)
|
|
return "video_url", video_url
|
|
# Raise an error if no 'type' or direct URL is found.
|
|
raise ValueError("Missing 'type' field in multimodal part.")
|
|
|
|
if not isinstance(part_type, str):
|
|
raise ValueError("Invalid 'type' field in multimodal part.")
|
|
return part_type, "unknown part_type content"
|
|
|
|
|
|
PART_TYPES_TO_SKIP_NONE_CONTENT = (
|
|
"text",
|
|
"refusal",
|
|
)
|
|
|
|
|
|
def _parse_chat_message_content_parts(
|
|
role: str,
|
|
parts: Iterable[ChatCompletionContentPartParam],
|
|
mm_tracker: BaseMultiModalItemTracker,
|
|
*,
|
|
wrap_dicts: bool,
|
|
interleave_strings: bool,
|
|
) -> list[ConversationMessage]:
|
|
content = list[_ContentPart]()
|
|
|
|
mm_parser = mm_tracker.create_parser()
|
|
|
|
for part in parts:
|
|
parse_res = _parse_chat_message_content_part(
|
|
part,
|
|
mm_parser,
|
|
wrap_dicts=wrap_dicts,
|
|
interleave_strings=interleave_strings,
|
|
)
|
|
if parse_res:
|
|
content.append(parse_res)
|
|
|
|
if wrap_dicts:
|
|
# Parsing wraps images and texts as interleaved dictionaries
|
|
return [ConversationMessage(role=role, content=content)] # type: ignore
|
|
texts = cast(list[str], content)
|
|
mm_placeholder_storage = mm_parser.mm_placeholder_storage()
|
|
if mm_placeholder_storage:
|
|
text_prompt = _get_full_multimodal_text_prompt(
|
|
mm_placeholder_storage, texts, interleave_strings
|
|
)
|
|
else:
|
|
text_prompt = "\n".join(texts)
|
|
|
|
return [ConversationMessage(role=role, content=text_prompt)]
|
|
|
|
|
|
def _parse_chat_message_content_part(
|
|
part: ChatCompletionContentPartParam,
|
|
mm_parser: BaseMultiModalContentParser,
|
|
*,
|
|
wrap_dicts: bool,
|
|
interleave_strings: bool,
|
|
) -> _ContentPart | None:
|
|
"""Parses a single part of a conversation. If wrap_dicts is True,
|
|
structured dictionary pieces for texts and images will be
|
|
wrapped in dictionaries, i.e., {"type": "text", "text", ...} and
|
|
{"type": "image"}, respectively. Otherwise multimodal data will be
|
|
handled by mm_parser, and texts will be returned as strings to be joined
|
|
with multimodal placeholders.
|
|
"""
|
|
if isinstance(part, str): # Handle plain text parts
|
|
return part
|
|
# Handle structured dictionary parts
|
|
part_type, content = _parse_chat_message_content_mm_part(part)
|
|
# if part_type is text/refusal/image_url/audio_url/video_url/input_audio but
|
|
# content is None, log a warning and skip
|
|
if part_type in PART_TYPES_TO_SKIP_NONE_CONTENT and content is None:
|
|
logger.warning(
|
|
"Skipping multimodal part '%s' (type: '%s') "
|
|
"with empty / unparsable content.",
|
|
part,
|
|
part_type,
|
|
)
|
|
return None
|
|
|
|
if part_type in ("text", "input_text", "refusal", "thinking"):
|
|
str_content = cast(str, content)
|
|
if wrap_dicts:
|
|
return {"type": "text", "text": str_content}
|
|
else:
|
|
return str_content
|
|
|
|
# For media items, if a user has provided one, use it. Otherwise, insert
|
|
# a placeholder empty uuid.
|
|
uuid = part.get("uuid", None)
|
|
if uuid is not None:
|
|
uuid = str(uuid)
|
|
|
|
modality = None
|
|
if part_type == "image_pil":
|
|
image_content = cast(Image.Image, content) if content is not None else None
|
|
mm_parser.parse_image_pil(image_content, uuid)
|
|
modality = "image"
|
|
elif part_type in ("image_url", "input_image"):
|
|
str_content = cast(str, content)
|
|
mm_parser.parse_image(str_content, uuid)
|
|
modality = "image"
|
|
elif part_type == "image_embeds":
|
|
content = cast(str | dict[str, str], content) if content is not None else None
|
|
mm_parser.parse_image_embeds(content, uuid)
|
|
modality = "image"
|
|
elif part_type == "audio_url":
|
|
str_content = cast(str, content)
|
|
mm_parser.parse_audio(str_content, uuid)
|
|
modality = "audio"
|
|
elif part_type == "input_audio":
|
|
dict_content = cast(InputAudio, content)
|
|
mm_parser.parse_input_audio(dict_content, uuid)
|
|
modality = "audio"
|
|
elif part_type == "video_url":
|
|
str_content = cast(str, content)
|
|
mm_parser.parse_video(str_content, uuid)
|
|
modality = "video"
|
|
else:
|
|
raise NotImplementedError(f"Unknown part type: {part_type}")
|
|
|
|
return (
|
|
{"type": modality}
|
|
if wrap_dicts
|
|
else (MODALITY_PLACEHOLDERS_MAP[modality] if interleave_strings else None)
|
|
)
|
|
|
|
|
|
# No need to validate using Pydantic again
|
|
_AssistantParser = partial(cast, ChatCompletionAssistantMessageParam)
|
|
_ToolParser = partial(cast, ChatCompletionToolMessageParam)
|
|
|
|
|
|
def _parse_chat_message_content(
|
|
message: ChatCompletionMessageParam,
|
|
mm_tracker: BaseMultiModalItemTracker,
|
|
content_format: _ChatTemplateContentFormat,
|
|
interleave_strings: bool,
|
|
) -> list[ConversationMessage]:
|
|
role = message["role"]
|
|
content = message.get("content")
|
|
|
|
if content is None:
|
|
content = []
|
|
elif isinstance(content, str):
|
|
content = [ChatCompletionContentPartTextParam(type="text", text=content)]
|
|
result = _parse_chat_message_content_parts(
|
|
role,
|
|
content, # type: ignore
|
|
mm_tracker,
|
|
wrap_dicts=(content_format == "openai"),
|
|
interleave_strings=interleave_strings,
|
|
)
|
|
|
|
for result_msg in result:
|
|
if role == "assistant":
|
|
parsed_msg = _AssistantParser(message)
|
|
|
|
# The 'tool_calls' is not None check ensures compatibility.
|
|
# It's needed only if downstream code doesn't strictly
|
|
# follow the OpenAI spec.
|
|
if "tool_calls" in parsed_msg and parsed_msg["tool_calls"] is not None:
|
|
result_msg["tool_calls"] = list(parsed_msg["tool_calls"])
|
|
elif role == "tool":
|
|
parsed_msg = _ToolParser(message)
|
|
if "tool_call_id" in parsed_msg:
|
|
result_msg["tool_call_id"] = parsed_msg["tool_call_id"]
|
|
|
|
if "name" in message and isinstance(message["name"], str):
|
|
result_msg["name"] = message["name"]
|
|
|
|
return result
|
|
|
|
|
|
def _postprocess_messages(messages: list[ConversationMessage]) -> None:
|
|
# per the Transformers docs & maintainers, tool call arguments in
|
|
# assistant-role messages with tool_calls need to be dicts not JSON str -
|
|
# this is how tool-use chat templates will expect them moving forwards
|
|
# so, for messages that have tool_calls, parse the string (which we get
|
|
# from openAI format) to dict
|
|
for message in messages:
|
|
if (
|
|
message["role"] == "assistant"
|
|
and "tool_calls" in message
|
|
and isinstance(message["tool_calls"], list)
|
|
):
|
|
for item in message["tool_calls"]:
|
|
# if arguments is None or empty string, set to {}
|
|
if content := item["function"].get("arguments"):
|
|
item["function"]["arguments"] = json.loads(content)
|
|
else:
|
|
item["function"]["arguments"] = {}
|
|
|
|
|
|
def parse_chat_messages(
|
|
messages: list[ChatCompletionMessageParam],
|
|
model_config: ModelConfig,
|
|
tokenizer: AnyTokenizer,
|
|
content_format: _ChatTemplateContentFormat,
|
|
) -> tuple[
|
|
list[ConversationMessage],
|
|
MultiModalDataDict | None,
|
|
MultiModalUUIDDict | None,
|
|
]:
|
|
conversation: list[ConversationMessage] = []
|
|
mm_tracker = MultiModalItemTracker(model_config, tokenizer)
|
|
|
|
for msg in messages:
|
|
sub_messages = _parse_chat_message_content(
|
|
msg,
|
|
mm_tracker,
|
|
content_format,
|
|
interleave_strings=(
|
|
content_format == "string"
|
|
and model_config.multimodal_config is not None
|
|
and model_config.multimodal_config.interleave_mm_strings
|
|
),
|
|
)
|
|
|
|
conversation.extend(sub_messages)
|
|
|
|
_postprocess_messages(conversation)
|
|
|
|
return conversation, mm_tracker.all_mm_data(), mm_tracker.all_mm_uuids()
|
|
|
|
|
|
def parse_chat_messages_futures(
|
|
messages: list[ChatCompletionMessageParam],
|
|
model_config: ModelConfig,
|
|
tokenizer: AnyTokenizer,
|
|
content_format: _ChatTemplateContentFormat,
|
|
) -> tuple[
|
|
list[ConversationMessage],
|
|
Awaitable[MultiModalDataDict | None],
|
|
MultiModalUUIDDict | None,
|
|
]:
|
|
conversation: list[ConversationMessage] = []
|
|
mm_tracker = AsyncMultiModalItemTracker(model_config, tokenizer)
|
|
|
|
for msg in messages:
|
|
sub_messages = _parse_chat_message_content(
|
|
msg,
|
|
mm_tracker,
|
|
content_format,
|
|
interleave_strings=(
|
|
content_format == "string"
|
|
and model_config.multimodal_config is not None
|
|
and model_config.multimodal_config.interleave_mm_strings
|
|
),
|
|
)
|
|
|
|
conversation.extend(sub_messages)
|
|
|
|
_postprocess_messages(conversation)
|
|
|
|
return conversation, mm_tracker.all_mm_data(), mm_tracker.all_mm_uuids()
|
|
|
|
|
|
# adapted from https://github.com/huggingface/transformers/blob/v4.56.2/src/transformers/utils/chat_template_utils.py#L398-L412
|
|
# only preserve the parse function used to resolve chat template kwargs
|
|
class AssistantTracker(jinja2.ext.Extension):
|
|
tags = {"generation"}
|
|
|
|
def parse(self, parser: jinja2.parser.Parser) -> jinja2.nodes.CallBlock:
|
|
lineno = next(parser.stream).lineno
|
|
body = parser.parse_statements(["name:endgeneration"], drop_needle=True)
|
|
call = self.call_method("_generation_support")
|
|
call_block = jinja2.nodes.CallBlock(call, [], [], body)
|
|
return call_block.set_lineno(lineno)
|
|
|
|
|
|
def _resolve_chat_template_kwargs(
|
|
chat_template: str,
|
|
):
|
|
env = jinja2.sandbox.ImmutableSandboxedEnvironment(
|
|
trim_blocks=True,
|
|
lstrip_blocks=True,
|
|
extensions=[AssistantTracker, jinja2.ext.loopcontrols],
|
|
)
|
|
parsed_content = env.parse(chat_template)
|
|
template_vars = jinja2.meta.find_undeclared_variables(parsed_content)
|
|
return template_vars
|
|
|
|
|
|
_cached_resolve_chat_template_kwargs = lru_cache(_resolve_chat_template_kwargs)
|
|
|
|
|
|
@lru_cache
|
|
def _get_hf_base_chat_template_params() -> frozenset[str]:
|
|
# Get standard parameters from HuggingFace's base tokenizer class.
|
|
# This dynamically extracts parameters from PreTrainedTokenizer's
|
|
# apply_chat_template method, ensuring compatibility with tokenizers
|
|
# that use **kwargs to receive standard parameters.
|
|
|
|
# Read signature from HF's base class - the single source of truth
|
|
base_sig = inspect.signature(PreTrainedTokenizer.apply_chat_template)
|
|
# Exclude VAR_KEYWORD (**kwargs) and VAR_POSITIONAL (*args) placeholders
|
|
return frozenset(
|
|
p.name
|
|
for p in base_sig.parameters.values()
|
|
if p.kind
|
|
not in (inspect.Parameter.VAR_KEYWORD, inspect.Parameter.VAR_POSITIONAL)
|
|
)
|
|
|
|
|
|
def resolve_chat_template_kwargs(
|
|
tokenizer: PreTrainedTokenizer | PreTrainedTokenizerFast,
|
|
chat_template: str,
|
|
chat_template_kwargs: dict[str, Any],
|
|
raise_on_unexpected: bool = True,
|
|
) -> dict[str, Any]:
|
|
# We exclude chat_template from kwargs here, because
|
|
# chat template has been already resolved at this stage
|
|
unexpected_vars = {"chat_template", "tokenize"}
|
|
if raise_on_unexpected and (
|
|
unexpected_in_kwargs := unexpected_vars & chat_template_kwargs.keys()
|
|
):
|
|
raise ValueError(
|
|
"Found unexpected chat template kwargs from request: "
|
|
f"{unexpected_in_kwargs}"
|
|
)
|
|
|
|
fn_kw = {
|
|
k
|
|
for k in chat_template_kwargs
|
|
if supports_kw(tokenizer.apply_chat_template, k, allow_var_kwargs=False)
|
|
}
|
|
template_vars = _cached_resolve_chat_template_kwargs(chat_template)
|
|
|
|
# Allow standard HF parameters even if tokenizer uses **kwargs to receive them
|
|
hf_base_params = _get_hf_base_chat_template_params()
|
|
|
|
accept_vars = (fn_kw | template_vars | hf_base_params) - unexpected_vars
|
|
return {k: v for k, v in chat_template_kwargs.items() if k in accept_vars}
|
|
|
|
|
|
def apply_hf_chat_template(
|
|
tokenizer: PreTrainedTokenizer | PreTrainedTokenizerFast,
|
|
conversation: list[ConversationMessage],
|
|
chat_template: str | None,
|
|
tools: list[dict[str, Any]] | None,
|
|
*,
|
|
model_config: ModelConfig,
|
|
**kwargs: Any,
|
|
) -> str:
|
|
hf_chat_template = resolve_hf_chat_template(
|
|
tokenizer,
|
|
chat_template=chat_template,
|
|
tools=tools,
|
|
model_config=model_config,
|
|
)
|
|
|
|
if hf_chat_template is None:
|
|
raise ValueError(
|
|
"As of transformers v4.44, default chat template is no longer "
|
|
"allowed, so you must provide a chat template if the tokenizer "
|
|
"does not define one."
|
|
)
|
|
|
|
resolved_kwargs = resolve_chat_template_kwargs(
|
|
tokenizer=tokenizer,
|
|
chat_template=hf_chat_template,
|
|
chat_template_kwargs=kwargs,
|
|
)
|
|
|
|
try:
|
|
return tokenizer.apply_chat_template(
|
|
conversation=conversation, # type: ignore[arg-type]
|
|
tools=tools, # type: ignore[arg-type]
|
|
chat_template=hf_chat_template,
|
|
tokenize=False,
|
|
**resolved_kwargs,
|
|
)
|
|
|
|
# External library exceptions can sometimes occur despite the framework's
|
|
# internal exception management capabilities.
|
|
except Exception as e:
|
|
# Log and report any library-related exceptions for further
|
|
# investigation.
|
|
logger.exception(
|
|
"An error occurred in `transformers` while applying chat template"
|
|
)
|
|
raise ValueError(str(e)) from e
|
|
|
|
|
|
def apply_mistral_chat_template(
|
|
tokenizer: MistralTokenizer,
|
|
messages: list[ChatCompletionMessageParam],
|
|
chat_template: str | None,
|
|
tools: list[dict[str, Any]] | None,
|
|
**kwargs: Any,
|
|
) -> list[int]:
|
|
from mistral_common.exceptions import MistralCommonException
|
|
|
|
# The return value of resolve_mistral_chat_template is always None,
|
|
# and we won't use it.
|
|
resolve_mistral_chat_template(
|
|
chat_template=chat_template,
|
|
**kwargs,
|
|
)
|
|
|
|
try:
|
|
return tokenizer.apply_chat_template(
|
|
messages=messages,
|
|
tools=tools,
|
|
**kwargs,
|
|
)
|
|
# mistral-common uses assert statements to stop processing of input
|
|
# if input does not comply with the expected format.
|
|
# We convert those assertion errors to ValueErrors so they can be
|
|
# properly caught in the preprocessing_input step
|
|
except (AssertionError, MistralCommonException) as e:
|
|
raise ValueError(str(e)) from e
|
|
|
|
# External library exceptions can sometimes occur despite the framework's
|
|
# internal exception management capabilities.
|
|
except Exception as e:
|
|
# Log and report any library-related exceptions for further
|
|
# investigation.
|
|
logger.exception(
|
|
"An error occurred in `mistral_common` while applying chat template"
|
|
)
|
|
raise ValueError(str(e)) from e
|
|
|
|
|
|
def get_history_tool_calls_cnt(conversation: list[ConversationMessage]):
|
|
idx = 0
|
|
for msg in conversation:
|
|
if msg["role"] == "assistant":
|
|
tool_calls = msg.get("tool_calls")
|
|
idx += len(list(tool_calls)) if tool_calls is not None else 0 # noqa
|
|
return idx
|
|
|
|
|
|
def make_tool_call_id(id_type: str = "random", func_name=None, idx=None):
|
|
if id_type == "kimi_k2":
|
|
return f"functions.{func_name}:{idx}"
|
|
else:
|
|
# by default return random
|
|
return f"chatcmpl-tool-{random_uuid()}"
|