chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Mapping
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from typing import Any, overload
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from typing_extensions import assert_never
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from vllm.config import VllmConfig
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from vllm.inputs import build_enc_dec_input
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from vllm.logger import init_logger
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from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
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from vllm.renderers import BaseRenderer, renderer_from_config
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from vllm.renderers.inputs import (
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DecoderDictPrompt,
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DecoderOnlyDictPrompt,
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EncoderDecoderDictPrompt,
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EncoderDictPrompt,
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SingletonDictPrompt,
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)
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from vllm.renderers.inputs.preprocess import parse_dec_only_prompt, parse_enc_dec_prompt
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from vllm.tokenizers import TokenizerLike
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from .engine import (
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DecoderEngineInput,
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DecoderOnlyEngineInput,
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EmbedsInput,
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EncoderDecoderInput,
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EncoderInput,
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EngineInput,
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MultiModalInput,
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SingletonInput,
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TokensInput,
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tokens_input,
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)
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from .llm import (
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EmbedsPrompt,
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MultiModalDataDict,
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MultiModalUUIDDict,
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PromptType,
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TextPrompt,
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TokensPrompt,
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)
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logger = init_logger(__name__)
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class InputPreprocessor:
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def __init__(
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self,
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vllm_config: VllmConfig,
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renderer: BaseRenderer | None = None,
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mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
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) -> None:
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super().__init__()
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self.model_config = vllm_config.model_config
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self.renderer = renderer or renderer_from_config(vllm_config)
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self.mm_registry = mm_registry
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@property
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def tokenizer(self) -> TokenizerLike | None:
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return self.renderer.tokenizer
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def get_tokenizer(self) -> TokenizerLike:
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return self.renderer.get_tokenizer()
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def _tokenize_prompt(
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self,
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prompt: str,
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tokenization_kwargs: dict[str, Any] | None = None,
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) -> list[int]:
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"""
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Apply the model's tokenizer to a text prompt, returning the
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corresponding token IDs.
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"""
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renderer = self.renderer
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tok_params = renderer.default_cmpl_tok_params.with_kwargs(
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**(tokenization_kwargs or {})
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)
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tok_prompt = renderer._tokenize_singleton_prompt(
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TextPrompt(prompt=prompt),
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tok_params,
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)
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return tok_prompt["prompt_token_ids"]
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def _process_multimodal(
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self,
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prompt: str | list[int],
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mm_data: MultiModalDataDict,
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mm_processor_kwargs: Mapping[str, object] | None = None,
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tokenization_kwargs: dict[str, Any] | None = None,
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*,
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mm_uuids: MultiModalUUIDDict | None = None,
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) -> MultiModalInput:
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"""
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Apply the model's multi-modal processor to a multi-modal prompt,
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returning the corresponding token IDs and metadata.
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"""
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return self.renderer._process_multimodal(
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prompt,
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mm_data,
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mm_uuids=mm_uuids,
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mm_processor_kwargs=mm_processor_kwargs,
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tokenization_kwargs=tokenization_kwargs,
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)
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def _process_embeds(
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self,
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parsed_content: EmbedsPrompt,
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) -> EmbedsInput:
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return self.renderer._process_embeds(parsed_content)
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def _truncate_inputs(
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self, inputs: list[int], tokenization_kwargs: dict[str, Any] | None = None
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) -> list[int]:
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renderer = self.renderer
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tok_params = renderer.default_cmpl_tok_params.with_kwargs(
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**(tokenization_kwargs or {})
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)
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tok_prompt = renderer._tokenize_singleton_prompt(
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TokensPrompt(prompt_token_ids=inputs),
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tok_params,
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)
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return tok_prompt["prompt_token_ids"]
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def _process_tokens(
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self,
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parsed_content: TokensPrompt,
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tokenization_kwargs: dict[str, Any] | None = None,
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) -> TokensInput | MultiModalInput:
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prompt_token_ids = self._truncate_inputs(
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parsed_content["prompt_token_ids"], tokenization_kwargs
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)
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inputs: TokensInput | MultiModalInput
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if multi_modal_data := parsed_content.get("multi_modal_data"):
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inputs = self._process_multimodal(
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prompt_token_ids,
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multi_modal_data,
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parsed_content.get("mm_processor_kwargs"),
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tokenization_kwargs=tokenization_kwargs,
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mm_uuids=parsed_content.get("multi_modal_uuids"),
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)
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else:
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inputs = tokens_input(prompt_token_ids)
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if prompt_text := parsed_content.get("prompt"):
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inputs["prompt"] = prompt_text
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if cache_salt := parsed_content.get("cache_salt"):
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inputs["cache_salt"] = cache_salt
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return inputs
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def _process_text(
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self,
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parsed_content: TextPrompt,
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tokenization_kwargs: dict[str, Any] | None = None,
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) -> TokensInput | MultiModalInput:
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prompt_text = parsed_content["prompt"]
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inputs: TokensInput | MultiModalInput
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if multi_modal_data := parsed_content.get("multi_modal_data"):
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inputs = self._process_multimodal(
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prompt_text,
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multi_modal_data,
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parsed_content.get("mm_processor_kwargs") or {},
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tokenization_kwargs=tokenization_kwargs,
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)
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else:
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prompt_token_ids = self._tokenize_prompt(
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prompt_text,
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tokenization_kwargs=tokenization_kwargs,
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)
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inputs = tokens_input(prompt_token_ids)
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inputs["prompt"] = prompt_text
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if cache_salt := parsed_content.get("cache_salt"):
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inputs["cache_salt"] = cache_salt
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return inputs
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@overload
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def _prompt_to_llm_inputs(
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self,
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prompt: EncoderDictPrompt,
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tokenization_kwargs: dict[str, Any] | None = None,
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) -> EncoderInput: ...
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@overload
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def _prompt_to_llm_inputs( # type: ignore[misc]
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self,
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prompt: DecoderDictPrompt,
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tokenization_kwargs: dict[str, Any] | None = None,
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) -> DecoderEngineInput: ...
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@overload
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def _prompt_to_llm_inputs( # type: ignore[misc]
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self,
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prompt: DecoderOnlyDictPrompt,
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tokenization_kwargs: dict[str, Any] | None = None,
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) -> DecoderOnlyEngineInput: ...
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def _prompt_to_llm_inputs(
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self,
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prompt: SingletonDictPrompt,
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tokenization_kwargs: dict[str, Any] | None = None,
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) -> SingletonInput:
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if "prompt_embeds" in prompt:
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return self._process_embeds(prompt) # type: ignore[arg-type]
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if "prompt_token_ids" in prompt:
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return self._process_tokens(prompt) # type: ignore[arg-type]
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if "prompt" in prompt:
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return self._process_text(
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prompt, # type: ignore[arg-type]
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tokenization_kwargs=tokenization_kwargs,
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)
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assert_never(prompt) # type: ignore[arg-type]
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def _process_encoder_decoder_prompt(
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self,
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prompt: EncoderDecoderDictPrompt,
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tokenization_kwargs: dict[str, Any] | None = None,
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) -> EncoderDecoderInput:
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encoder_prompt = prompt["encoder_prompt"]
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decoder_prompt = prompt["decoder_prompt"]
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skip_decoder_start_token = False
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if self.renderer.mm_processor is not None:
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from vllm.multimodal.processing import EncDecMultiModalProcessor
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if isinstance(self.renderer.mm_processor, EncDecMultiModalProcessor):
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skip_decoder_start_token = (
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self.renderer.mm_processor.skip_decoder_start_token
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)
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return build_enc_dec_input(
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encoder_input=self._prompt_to_llm_inputs(
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encoder_prompt,
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tokenization_kwargs=tokenization_kwargs,
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),
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decoder_input=(
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None
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if decoder_prompt is None
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else self._prompt_to_llm_inputs(
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decoder_prompt,
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tokenization_kwargs=tokenization_kwargs,
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)
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),
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decoder_start_token_id=self.renderer.get_dec_start_token_id(),
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skip_decoder_start_token=skip_decoder_start_token,
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)
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def _process_decoder_only_prompt(
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self,
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prompt: DecoderOnlyDictPrompt,
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tokenization_kwargs: dict[str, Any] | None = None,
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) -> DecoderOnlyEngineInput:
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return self._prompt_to_llm_inputs(
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prompt,
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tokenization_kwargs=tokenization_kwargs,
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)
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def preprocess(
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self,
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prompt: PromptType,
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tokenization_kwargs: dict[str, Any] | None = None,
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) -> EngineInput:
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"""Preprocess the input prompt."""
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if self.model_config.is_encoder_decoder:
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# Encoder-decoder model requires special mapping of
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# input prompts to encoder & decoder.
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return self._process_encoder_decoder_prompt(
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parse_enc_dec_prompt(prompt),
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tokenization_kwargs,
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)
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return self._process_decoder_only_prompt(
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parse_dec_only_prompt(prompt),
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tokenization_kwargs=tokenization_kwargs,
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)
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