530 lines
17 KiB
Python
530 lines
17 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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from abc import ABC, abstractmethod
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from collections.abc import Sequence
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from functools import cached_property
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from typing import TYPE_CHECKING, Any, Generic, overload
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from typing_extensions import TypeVar
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from vllm.inputs import EmbedsPrompt, TextPrompt, TokensPrompt
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from vllm.logger import init_logger
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from vllm.tokenizers import TokenizerLike
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from vllm.utils.async_utils import AsyncMicrobatchTokenizer
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from vllm.utils.torch_utils import set_default_torch_num_threads
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from vllm.v1.metrics.stats import MultiModalCacheStats
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from .embed_utils import safe_load_prompt_embeds
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from .inputs import (
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DictPrompt,
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EncoderDecoderDictPrompt,
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EncoderDecoderTokPrompt,
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TokPrompt,
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)
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from .inputs.preprocess import extract_target_prompt
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from .params import ChatParams, TokenizeParams
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if TYPE_CHECKING:
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from vllm.config import VllmConfig
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from vllm.entrypoints.chat_utils import (
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ChatCompletionMessageParam,
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ConversationMessage,
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)
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from vllm.multimodal.cache import BaseMultiModalProcessorCache
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from vllm.multimodal.processing import BaseMultiModalProcessor
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logger = init_logger(__name__)
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_T = TypeVar("_T", bound=TokenizerLike, default=TokenizerLike)
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class BaseRenderer(ABC, Generic[_T]):
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@classmethod
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@abstractmethod
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def from_config(
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cls,
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config: "VllmConfig",
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tokenizer_kwargs: dict[str, Any],
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) -> "BaseRenderer":
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raise NotImplementedError
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def __init__(self, config: "VllmConfig", tokenizer: _T | None) -> None:
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super().__init__()
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self.config = config
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self.model_config = config.model_config
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self.tokenizer = tokenizer
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# Lazy initialization since offline LLM doesn't use async
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self._async_tokenizer: AsyncMicrobatchTokenizer | None = None
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self.mm_processor: BaseMultiModalProcessor | None = None
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self._mm_cache_stats: MultiModalCacheStats | None = None
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if config.model_config.is_multimodal_model:
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from vllm.multimodal import MULTIMODAL_REGISTRY as mm_registry
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mm_processor_cache = mm_registry.processor_cache_from_config(config)
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with set_default_torch_num_threads():
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self.mm_processor = mm_registry.create_processor(
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config.model_config,
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config.observability_config,
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tokenizer=tokenizer,
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cache=mm_processor_cache,
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)
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if mm_processor_cache:
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self._mm_cache_stats = MultiModalCacheStats()
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def get_tokenizer(self) -> _T:
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tokenizer = self.tokenizer
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if tokenizer is None:
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raise ValueError("Tokenizer not available when `skip_tokenizer_init=True`")
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return tokenizer
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def get_async_tokenizer(self) -> AsyncMicrobatchTokenizer:
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if self._async_tokenizer is None:
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self._async_tokenizer = AsyncMicrobatchTokenizer(self.get_tokenizer())
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return self._async_tokenizer
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def get_mm_processor(self) -> "BaseMultiModalProcessor":
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if self.mm_processor is None:
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raise ValueError("Multi-modal processor not available for text-only models")
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return self.mm_processor
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@property
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def mm_processor_cache(self) -> "BaseMultiModalProcessorCache | None":
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if self.mm_processor is None:
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return None
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return self.mm_processor.cache
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def stat_mm_cache(self) -> MultiModalCacheStats | None:
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mm_cache_stats = self._mm_cache_stats
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if mm_cache_stats is None:
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return None
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self._mm_cache_stats = MultiModalCacheStats()
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return mm_cache_stats
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def update_mm_cache_stats(self) -> None:
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mm_processor_cache = self.mm_processor_cache
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mm_cache_stats = self._mm_cache_stats
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if mm_processor_cache and mm_cache_stats:
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delta = mm_processor_cache.make_stats(delta=True)
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mm_cache_stats.record(delta.total, delta.hits)
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def clear_mm_cache(self) -> None:
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mm_processor_cache = self.mm_processor_cache
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if mm_processor_cache is not None:
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mm_processor_cache.clear_cache()
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if self._mm_cache_stats is not None:
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self._mm_cache_stats.reset = True
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def shutdown(self) -> None:
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mm_processor_cache = self.mm_processor_cache
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if mm_processor_cache is not None:
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mm_processor_cache.close()
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def get_bos_token_id(self) -> int | None:
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if self.tokenizer is None:
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logger.warning_once(
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"Using None for BOS token id because tokenizer is not initialized"
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)
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return None
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return self.tokenizer.bos_token_id
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def get_eos_token_id(self) -> int | None:
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if self.tokenizer is None:
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logger.warning_once(
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"Using None for EOS token id because tokenizer is not initialized"
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)
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return None
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return self.tokenizer.eos_token_id
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def get_dec_start_token_id(self) -> int:
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"""
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Obtain the decoder start token id employed by an encoder/decoder model,
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raising an error if it is not available.
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"""
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dec_start_token_id = getattr(
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self.model_config.hf_config, "decoder_start_token_id", None
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)
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if dec_start_token_id is None:
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logger.warning_once(
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"Falling back on <BOS> for decoder start token id "
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"because decoder start token id is not available."
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)
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dec_start_token_id = self.get_bos_token_id()
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if dec_start_token_id is None:
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raise RuntimeError("Cannot find decoder start token id or <BOS>")
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return dec_start_token_id
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@cached_property
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def default_cmpl_tok_params(self) -> TokenizeParams:
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mm_processor = self.mm_processor
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if mm_processor is not None:
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return mm_processor.info.default_tok_params
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model_config = self.model_config
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encoder_config = model_config.encoder_config or {}
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return TokenizeParams(
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max_total_tokens=model_config.max_model_len,
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do_lower_case=encoder_config.get("do_lower_case", False),
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add_special_tokens=True,
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)
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@cached_property
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def default_chat_tok_params(self) -> TokenizeParams:
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mm_processor = self.mm_processor
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if mm_processor is not None:
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return mm_processor.info.default_tok_params
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model_config = self.model_config
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encoder_config = model_config.encoder_config or {}
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return TokenizeParams(
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max_total_tokens=model_config.max_model_len,
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do_lower_case=encoder_config.get("do_lower_case", False),
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add_special_tokens=False,
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)
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# Step 1: Convert raw inputs to prompts
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def render_prompt(
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self,
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prompt: DictPrompt | bytes,
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) -> DictPrompt:
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if isinstance(prompt, bytes):
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embeds = safe_load_prompt_embeds(self.model_config, prompt)
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prompt = EmbedsPrompt(prompt_embeds=embeds)
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return prompt
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def render_prompts(
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self,
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prompts: Sequence[DictPrompt | bytes],
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) -> list[DictPrompt]:
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if len(prompts) == 0:
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raise ValueError("You must pass at least one prompt")
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return [self.render_prompt(prompt) for prompt in prompts]
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async def render_prompts_async(
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self,
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prompts: Sequence[DictPrompt | bytes],
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) -> list[DictPrompt]:
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return self.render_prompts(prompts)
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@abstractmethod
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def render_messages(
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self,
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messages: list["ChatCompletionMessageParam"],
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params: ChatParams,
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) -> tuple[list["ConversationMessage"], DictPrompt]:
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raise NotImplementedError
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async def render_messages_async(
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self,
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messages: list["ChatCompletionMessageParam"],
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params: ChatParams,
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) -> tuple[list["ConversationMessage"], DictPrompt]:
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return self.render_messages(messages, params)
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# Step 2: Tokenize prompts if necessary
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def _tokenize_prompt(
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self,
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prompt: TextPrompt,
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params: TokenizeParams,
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) -> TokensPrompt:
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tokenizer = self.get_tokenizer()
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prompt_token_ids = tokenizer.encode(
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prompt["prompt"],
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**params.get_encode_kwargs(),
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)
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return TokensPrompt(prompt_token_ids=prompt_token_ids, **prompt)
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async def _tokenize_prompt_async(
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self,
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prompt: TextPrompt,
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params: TokenizeParams,
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) -> TokensPrompt:
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tokenizer = self.get_async_tokenizer()
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prompt_token_ids = await tokenizer.encode(
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prompt["prompt"],
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**params.get_encode_kwargs(),
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)
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return TokensPrompt(prompt_token_ids=prompt_token_ids, **prompt)
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def _detokenize_prompt(self, prompt: TokensPrompt) -> TokensPrompt:
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tokenizer = self.get_tokenizer()
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prompt["prompt"] = tokenizer.decode(prompt["prompt_token_ids"])
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return prompt
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async def _detokenize_prompt_async(self, prompt: TokensPrompt) -> TokensPrompt:
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tokenizer = self.get_async_tokenizer()
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prompt["prompt"] = await tokenizer.decode(prompt["prompt_token_ids"])
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return prompt
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def _tokenize_enc_dec_prompt(
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self,
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prompt: EncoderDecoderDictPrompt,
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params: TokenizeParams,
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) -> EncoderDecoderTokPrompt:
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enc_prompt, dec_prompt = (
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self.tokenize_prompt(prompt["encoder_prompt"], params),
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(
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None
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if prompt["decoder_prompt"] is None
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else self.tokenize_prompt(prompt["decoder_prompt"], params)
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),
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)
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return EncoderDecoderTokPrompt(
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encoder_prompt=enc_prompt,
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decoder_prompt=dec_prompt,
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)
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async def _tokenize_enc_dec_prompt_async(
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self,
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prompt: EncoderDecoderDictPrompt,
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params: TokenizeParams,
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) -> EncoderDecoderTokPrompt:
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enc_prompt, dec_prompt = await asyncio.gather(
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self.tokenize_prompt_async(prompt["encoder_prompt"], params),
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(
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asyncio.sleep(0)
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if prompt["decoder_prompt"] is None
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else self.tokenize_prompt_async(prompt["decoder_prompt"], params)
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),
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)
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return EncoderDecoderTokPrompt(
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encoder_prompt=enc_prompt,
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decoder_prompt=dec_prompt,
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)
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@overload
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def tokenize_prompt(
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self,
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prompt: TextPrompt | TokensPrompt,
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params: TokenizeParams,
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) -> TokensPrompt: ...
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@overload
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def tokenize_prompt( # type: ignore[misc]
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self,
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prompt: EmbedsPrompt,
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params: TokenizeParams,
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) -> EmbedsPrompt: ...
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@overload
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def tokenize_prompt( # type: ignore[misc]
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self,
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prompt: EncoderDecoderDictPrompt,
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params: TokenizeParams,
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) -> EncoderDecoderTokPrompt: ...
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def tokenize_prompt(
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self,
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prompt: DictPrompt,
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params: TokenizeParams,
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) -> TokPrompt:
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if "encoder_prompt" in prompt:
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return self._tokenize_enc_dec_prompt(prompt, params) # type: ignore[arg-type]
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if "prompt_token_ids" not in prompt and "prompt_embeds" not in prompt:
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prompt = params.apply_pre_tokenization(self.tokenizer, prompt)
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prompt = self._tokenize_prompt(prompt, params)
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if params.needs_detokenization and "prompt" not in prompt:
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if "prompt_token_ids" not in prompt:
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raise RuntimeError("Cannot run detokenization on embeddings")
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prompt = self._detokenize_prompt(prompt) # type: ignore[arg-type]
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return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type]
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def tokenize_prompts(
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self,
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prompts: Sequence[DictPrompt],
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params: TokenizeParams,
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) -> list[TokPrompt]:
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return [self.tokenize_prompt(prompt, params) for prompt in prompts]
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@overload
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async def tokenize_prompt_async(
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self,
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prompt: TextPrompt | TokensPrompt,
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params: TokenizeParams,
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) -> TokensPrompt: ...
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@overload
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async def tokenize_prompt_async( # type: ignore[misc]
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self,
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prompt: EmbedsPrompt,
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params: TokenizeParams,
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) -> EmbedsPrompt: ...
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@overload
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async def tokenize_prompt_async( # type: ignore[misc]
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self,
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prompt: EncoderDecoderDictPrompt,
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params: TokenizeParams,
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) -> EncoderDecoderTokPrompt: ...
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async def tokenize_prompt_async(
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self,
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prompt: DictPrompt,
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params: TokenizeParams,
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) -> TokPrompt:
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if "encoder_prompt" in prompt:
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return await self._tokenize_enc_dec_prompt_async(prompt, params) # type: ignore[arg-type]
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if "prompt_token_ids" not in prompt and "prompt_embeds" not in prompt:
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prompt = params.apply_pre_tokenization(self.tokenizer, prompt)
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prompt = await self._tokenize_prompt_async(prompt, params)
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if params.needs_detokenization and "prompt" not in prompt:
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if "prompt_token_ids" not in prompt:
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raise RuntimeError("Cannot run detokenization on embeddings")
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prompt = await self._detokenize_prompt_async(prompt) # type: ignore[arg-type]
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return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type]
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async def tokenize_prompts_async(
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self,
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prompts: Sequence[DictPrompt],
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params: TokenizeParams,
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) -> list[TokPrompt]:
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return await asyncio.gather(
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*(self.tokenize_prompt_async(prompt, params) for prompt in prompts)
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)
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# Step 3: Add extra keys to the prompts
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def _apply_prompt_extras(
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self,
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prompts: Sequence[DictPrompt | TokPrompt],
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prompt_extras: dict[str, Any] | None,
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):
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if not prompt_extras:
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return
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for prompt in prompts:
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target_prompt = extract_target_prompt(self.model_config, prompt)
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target_prompt.update(prompt_extras) # type: ignore[arg-type]
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# Top-level methods
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def render_cmpl(
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self,
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prompts: Sequence[DictPrompt | bytes],
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tok_params: TokenizeParams | None = None,
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*,
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prompt_extras: dict[str, Any] | None = None,
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):
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if tok_params is None:
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tok_params = self.default_cmpl_tok_params
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dict_prompts = self.render_prompts(prompts)
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tok_prompts = self.tokenize_prompts(dict_prompts, tok_params)
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self._apply_prompt_extras(tok_prompts, prompt_extras)
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# TODO: Apply multi-modal processor
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return tok_prompts
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async def render_cmpl_async(
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self,
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prompts: Sequence[DictPrompt | bytes],
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tok_params: TokenizeParams | None = None,
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*,
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prompt_extras: dict[str, Any] | None = None,
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):
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if tok_params is None:
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tok_params = self.default_cmpl_tok_params
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dict_prompts = await self.render_prompts_async(prompts)
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tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params)
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self._apply_prompt_extras(tok_prompts, prompt_extras)
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# TODO: Apply multi-modal processor
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return tok_prompts
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def render_chat(
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self,
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conversations: Sequence[list["ChatCompletionMessageParam"]],
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chat_params: ChatParams,
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tok_params: TokenizeParams | None = None,
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*,
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prompt_extras: dict[str, Any] | None = None,
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):
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if tok_params is None:
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tok_params = self.default_chat_tok_params
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rendered = [
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self.render_messages(conversation, chat_params)
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for conversation in conversations
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]
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out_conversations = list[list["ConversationMessage"]]()
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dict_prompts = list[DictPrompt]()
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for conv, prompt in rendered:
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out_conversations.append(conv)
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dict_prompts.append(prompt)
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tok_prompts = self.tokenize_prompts(dict_prompts, tok_params)
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self._apply_prompt_extras(tok_prompts, prompt_extras)
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# TODO: Apply multi-modal processor
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return out_conversations, tok_prompts
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async def render_chat_async(
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self,
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conversations: Sequence[list["ChatCompletionMessageParam"]],
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chat_params: ChatParams,
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tok_params: TokenizeParams | None = None,
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*,
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prompt_extras: dict[str, Any] | None = None,
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):
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if tok_params is None:
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tok_params = self.default_chat_tok_params
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rendered = [
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self.render_messages_async(conversation, chat_params)
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for conversation in conversations
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]
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out_conversations = list[list["ConversationMessage"]]()
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dict_prompts = list[DictPrompt]()
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for conv, prompt in await asyncio.gather(*rendered):
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out_conversations.append(conv)
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dict_prompts.append(prompt)
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tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params)
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self._apply_prompt_extras(tok_prompts, prompt_extras)
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# TODO: Apply multi-modal processor
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return out_conversations, tok_prompts
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