122 lines
3.5 KiB
Python
122 lines
3.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
from typing import Any
|
|
|
|
from vllm.config import ModelConfig
|
|
from vllm.entrypoints.chat_utils import (
|
|
ChatCompletionMessageParam,
|
|
ConversationMessage,
|
|
parse_chat_messages,
|
|
parse_chat_messages_async,
|
|
)
|
|
from vllm.inputs import TextPrompt, TokensPrompt
|
|
from vllm.logger import init_logger
|
|
from vllm.tokenizers import cached_get_tokenizer
|
|
from vllm.tokenizers.grok2 import Grok2Tokenizer
|
|
|
|
from .protocol import RendererLike
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
|
|
class Grok2Renderer(RendererLike):
|
|
@classmethod
|
|
def from_config(
|
|
cls,
|
|
config: ModelConfig,
|
|
tokenizer_kwargs: dict[str, Any],
|
|
) -> "RendererLike":
|
|
return cls(config, tokenizer_kwargs)
|
|
|
|
def __init__(
|
|
self,
|
|
config: ModelConfig,
|
|
tokenizer_kwargs: dict[str, Any],
|
|
) -> None:
|
|
super().__init__()
|
|
|
|
self.config = config
|
|
|
|
if config.skip_tokenizer_init:
|
|
tokenizer = None
|
|
else:
|
|
tokenizer = cached_get_tokenizer(
|
|
tokenizer_cls=Grok2Tokenizer,
|
|
**tokenizer_kwargs,
|
|
)
|
|
|
|
self._tokenizer = tokenizer
|
|
|
|
@property
|
|
def tokenizer(self) -> Grok2Tokenizer | None:
|
|
return self._tokenizer
|
|
|
|
def get_tokenizer(self) -> Grok2Tokenizer:
|
|
tokenizer = self.tokenizer
|
|
if tokenizer is None:
|
|
raise ValueError("Tokenizer not available when `skip_tokenizer_init=True`")
|
|
|
|
return tokenizer
|
|
|
|
def render_messages(
|
|
self,
|
|
messages: list[ChatCompletionMessageParam],
|
|
**kwargs,
|
|
) -> tuple[list[ConversationMessage], TextPrompt | TokensPrompt]:
|
|
tokenizer = self.get_tokenizer()
|
|
conversation, mm_data, mm_uuids = parse_chat_messages(
|
|
messages,
|
|
self.config,
|
|
content_format="string",
|
|
)
|
|
|
|
kwargs["return_dict"] = False
|
|
prompt_raw = tokenizer.apply_chat_template(
|
|
conversation=conversation,
|
|
messages=messages,
|
|
**kwargs,
|
|
)
|
|
|
|
prompt = (
|
|
TextPrompt(prompt=prompt_raw)
|
|
if isinstance(prompt_raw, str)
|
|
else TokensPrompt(prompt_token_ids=prompt_raw)
|
|
)
|
|
if mm_data is not None:
|
|
prompt["multi_modal_data"] = mm_data
|
|
if mm_uuids is not None:
|
|
prompt["multi_modal_uuids"] = mm_uuids
|
|
|
|
return conversation, prompt # type: ignore[return-value]
|
|
|
|
async def render_messages_async(
|
|
self,
|
|
messages: list[ChatCompletionMessageParam],
|
|
**kwargs,
|
|
) -> tuple[list[ConversationMessage], TextPrompt | TokensPrompt]:
|
|
tokenizer = self.get_tokenizer()
|
|
conversation, mm_data, mm_uuids = await parse_chat_messages_async(
|
|
messages,
|
|
self.config,
|
|
content_format="string",
|
|
)
|
|
|
|
kwargs["return_dict"] = False
|
|
prompt_raw = tokenizer.apply_chat_template(
|
|
conversation=conversation,
|
|
messages=messages,
|
|
**kwargs,
|
|
)
|
|
|
|
prompt = (
|
|
TextPrompt(prompt=prompt_raw)
|
|
if isinstance(prompt_raw, str)
|
|
else TokensPrompt(prompt_token_ids=prompt_raw)
|
|
)
|
|
if mm_data is not None:
|
|
prompt["multi_modal_data"] = mm_data
|
|
if mm_uuids is not None:
|
|
prompt["multi_modal_uuids"] = mm_uuids
|
|
|
|
return conversation, prompt # type: ignore[return-value]
|