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vllm/entrypoints/renderer.py
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395
vllm/entrypoints/renderer.py
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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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import asyncio
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import io
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from abc import ABC, abstractmethod
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from dataclasses import dataclass
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from typing import Annotated, Optional, Union
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import pybase64
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import torch
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from pydantic import Field
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from vllm.config import ModelConfig
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from vllm.inputs.data import EmbedsPrompt as EngineEmbedsPrompt
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from vllm.inputs.data import TokensPrompt as EngineTokensPrompt
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from vllm.inputs.parse import parse_and_batch_prompt
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from vllm.transformers_utils.tokenizer import AnyTokenizer
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from vllm.utils import AsyncMicrobatchTokenizer
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@dataclass(frozen=True)
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class RenderConfig:
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"""Configuration to control how prompts are prepared."""
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max_length: Optional[int] = None
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"""Maximum allowable total input token length. If provided,
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token inputs longer than this raise ``ValueError``."""
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truncate_prompt_tokens: Optional[int] = None
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"""Number of tokens to keep. ``None`` means no truncation.
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``0`` yields an empty list (and skips embeds).
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``-1`` maps to ``model_config.max_model_len``."""
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add_special_tokens: Optional[bool] = True
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"""Whether to add model-specific special tokens during tokenization."""
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cache_salt: Optional[str] = None
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"""String to disambiguate prefix cache entries."""
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needs_detokenization: Optional[bool] = False
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"""If True, detokenize IDs back to text for inclusion in outputs."""
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class BaseRenderer(ABC):
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"""
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Base class for unified input processing and rendering.
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The Renderer serves as a unified input processor that consolidates
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tokenization, chat template formatting, and multimodal input handling
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into a single component.
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It converts high-level API requests (OpenAI-style JSON) into token IDs and
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multimodal features ready for engine consumption.
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Key responsibilities:
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- Convert text prompts to token sequences with proper special tokens
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- Apply chat templates and format conversations
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- Handle multimodal inputs (images, audio, etc.) when applicable
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- Manage prompt truncation and length validation
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- Provide clean separation between API layer and engine core
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"""
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def __init__(
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self,
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model_config: ModelConfig,
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tokenizer: Optional[AnyTokenizer] = None,
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):
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super().__init__()
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self.model_config = model_config
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self.tokenizer = tokenizer
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@abstractmethod
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async def render_prompt(
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self,
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*,
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prompt_or_prompts: Union[str, list[str], list[int], list[list[int]]],
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config: "RenderConfig",
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) -> list[EngineTokensPrompt]:
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"""
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Convert text or token inputs into engine-ready TokensPrompt objects.
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This method accepts text or token inputs and produces a
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list of [`TokensPrompt`][vllm.inputs.data.TokensPrompt] objects
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for the engine.
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Args:
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prompt_or_prompts: One of:
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- ``str``: Single text prompt.
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- ``list[str]``: Batch of text prompts.
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- ``list[int]``: Single pre-tokenized sequence.
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- ``list[list[int]]``: Batch of pre-tokenized sequences.
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config: Render configuration controlling how prompts are prepared
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(e.g., tokenization and length handling).
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Returns:
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list[EngineTokensPrompt]: Engine-ready token prompts.
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Raises:
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ValueError: If input formats are invalid or length limits exceeded.
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"""
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raise NotImplementedError
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@abstractmethod
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async def render_prompt_and_embeds(
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self,
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*,
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prompt_or_prompts: Optional[Union[str, list[str], list[int],
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list[list[int]]]] = None,
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prompt_embeds: Optional[Union[bytes, list[bytes]]] = None,
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config: "RenderConfig",
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) -> list[Union[EngineTokensPrompt, EngineEmbedsPrompt]]:
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"""
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Convert text/token and/or base64-encoded embeddings inputs into
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engine-ready prompt objects using a unified RenderConfig.
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At least one of ``prompt_or_prompts`` or ``prompt_embeds`` must be
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provided and non-empty. If both are omitted or empty (e.g., empty
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string and empty list), a ``ValueError`` is raised.
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Args:
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prompt_or_prompts: Text or token inputs to include.
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prompt_embeds: Base64-encoded bytes (or list thereof) containing a
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torch-saved tensor to be used as prompt embeddings.
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config: Render configuration controlling how prompts are prepared
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(e.g., tokenization and length handling).
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Returns:
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list[Union[EngineTokensPrompt, EngineEmbedsPrompt]]:
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Engine-ready prompt objects.
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Raises:
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ValueError: If both ``prompt_or_prompts`` and ``prompt_embeds``
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are omitted or empty (decoder prompt cannot be empty), or if
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length limits are exceeded.
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"""
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raise NotImplementedError
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@classmethod
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def load_prompt_embeds(
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cls,
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prompt_embeds: Union[bytes, list[bytes]],
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truncate_prompt_tokens: Optional[Annotated[int, Field(ge=0)]] = None,
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cache_salt: Optional[str] = None,
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) -> list[EngineEmbedsPrompt]:
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"""Load and validate base64-encoded embeddings into prompt objects."""
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def _load_and_validate_embed(embed: bytes) -> EngineEmbedsPrompt:
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tensor = torch.load(
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io.BytesIO(pybase64.b64decode(embed, validate=True)),
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weights_only=True,
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map_location=torch.device("cpu"),
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)
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assert isinstance(tensor, torch.Tensor) and tensor.dtype in (
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torch.float32,
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torch.bfloat16,
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torch.float16,
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)
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tensor = tensor.to_dense()
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if tensor.dim() > 2:
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tensor = tensor.squeeze(0)
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assert tensor.dim() == 2
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if truncate_prompt_tokens is not None:
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tensor = tensor[-truncate_prompt_tokens:]
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embeds_prompt = EngineEmbedsPrompt(prompt_embeds=tensor)
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if cache_salt is not None:
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embeds_prompt["cache_salt"] = cache_salt
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return embeds_prompt
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if isinstance(prompt_embeds, list):
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return [_load_and_validate_embed(embed) for embed in prompt_embeds]
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return [_load_and_validate_embed(prompt_embeds)]
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class CompletionRenderer(BaseRenderer):
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def __init__(
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self,
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model_config: ModelConfig,
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tokenizer: Optional[AnyTokenizer] = None,
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async_tokenizer_pool: Optional[dict[AnyTokenizer,
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AsyncMicrobatchTokenizer]] = None,
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):
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super().__init__(model_config, tokenizer)
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self.async_tokenizer_pool = async_tokenizer_pool
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self.async_tokenizer: Optional[AsyncMicrobatchTokenizer] = None
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async def render_prompt(
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self,
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*,
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prompt_or_prompts: Union[str, list[str], list[int], list[list[int]]],
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config: "RenderConfig",
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) -> list[EngineTokensPrompt]:
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"""Implementation of prompt rendering for completion-style requests.
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Uses async tokenizer pooling for improved performance. See base class
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for detailed parameter documentation.
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"""
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truncate_prompt_tokens = self._validate_and_normalize_truncate_tokens(
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config.truncate_prompt_tokens, config.max_length)
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if truncate_prompt_tokens == 0:
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return []
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# Parse and batch the input prompts
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batch_inputs = parse_and_batch_prompt(prompt_or_prompts)
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tasks = []
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for prompt_input in batch_inputs:
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if prompt_input["is_tokens"] is True:
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# Token input
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# Note: detokenization is needed when echo is enabled,
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# where the input token IDs are decoded back to text.
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task = self._maybe_detokenize(prompt_input["content"],
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config.max_length,
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truncate_prompt_tokens,
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config.cache_salt,
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config.needs_detokenization)
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else:
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# Text input
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task = self._tokenize(prompt_input["content"],
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config.max_length,
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truncate_prompt_tokens,
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config.add_special_tokens,
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config.cache_salt)
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tasks.append(task)
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# Wait for all text tokenization to finish
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if tasks:
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tokenized_text_prompts = await asyncio.gather(*tasks)
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return tokenized_text_prompts
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return []
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async def render_prompt_and_embeds(
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self,
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*,
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prompt_or_prompts: Optional[Union[str, list[str], list[int],
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list[list[int]]]] = None,
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prompt_embeds: Optional[Union[bytes, list[bytes]]] = None,
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config: "RenderConfig",
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) -> list[Union[EngineTokensPrompt, EngineEmbedsPrompt]]:
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"""
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Render text/token prompts and/or precomputed embedding prompts. At
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least one of `prompt_or_prompts` or `prompt_embeds` must be provided.
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"""
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truncate_prompt_tokens = self._validate_and_normalize_truncate_tokens(
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config.truncate_prompt_tokens, config.max_length)
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if truncate_prompt_tokens == 0:
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return []
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rendered: list[Union[EngineTokensPrompt, EngineEmbedsPrompt]] = []
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if prompt_embeds is not None:
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rendered.extend(
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self.load_prompt_embeds(prompt_embeds, truncate_prompt_tokens,
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config.cache_salt))
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if prompt_or_prompts is None or prompt_or_prompts == "":
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return rendered
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token_prompts = await self.render_prompt(
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prompt_or_prompts=prompt_or_prompts,
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config=config,
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)
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rendered.extend(token_prompts)
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return rendered
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def _validate_and_normalize_truncate_tokens(
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self,
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truncate_prompt_tokens: Optional[int],
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max_length: Optional[int],
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) -> Optional[int]:
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"""Validate and normalize truncate_prompt_tokens parameter."""
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if truncate_prompt_tokens is None:
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return None
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if truncate_prompt_tokens == 0:
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return 0
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if truncate_prompt_tokens < 0:
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truncate_prompt_tokens = self.model_config.max_model_len
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if max_length is not None and truncate_prompt_tokens > max_length: # type: ignore[operator]
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raise ValueError(
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f"truncate_prompt_tokens ({truncate_prompt_tokens}) "
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f"cannot be greater than max_length ({max_length}). "
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f"Please select a smaller truncation size.")
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return truncate_prompt_tokens
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def _maybe_apply_truncation(
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self, token_ids: list[int],
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truncate_prompt_tokens: Optional[int]) -> list[int]:
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"""Apply truncation to token sequence."""
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if truncate_prompt_tokens is None:
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return token_ids
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if truncate_prompt_tokens >= len(token_ids):
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return token_ids
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return token_ids[-truncate_prompt_tokens:]
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async def _tokenize(
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self,
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text: str,
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max_length: Optional[int],
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truncate_prompt_tokens: Optional[int],
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add_special_tokens: Optional[bool],
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cache_salt: Optional[str],
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) -> EngineTokensPrompt:
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"""Tokenize text input asynchronously."""
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async_tokenizer = self._get_async_tokenizer()
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# Handle encoder-specific preprocessing
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if (self.model_config.encoder_config is not None
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and self.model_config.encoder_config.get(
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"do_lower_case", False)):
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text = text.lower()
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# Tokenize texts
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if truncate_prompt_tokens is None:
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encoded = await async_tokenizer(
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text, add_special_tokens=add_special_tokens)
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else:
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encoded = await async_tokenizer(
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text,
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add_special_tokens=add_special_tokens,
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truncation=True,
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max_length=truncate_prompt_tokens)
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return self._create_tokens_prompt(encoded.input_ids, max_length,
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cache_salt, text)
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async def _maybe_detokenize(
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self,
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token_ids: list[int],
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max_length: Optional[int],
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truncate_prompt_tokens: Optional[int],
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cache_salt: Optional[str],
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needs_detokenization: Optional[bool] = False,
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) -> EngineTokensPrompt:
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"""Optionally detokenize token IDs and build a tokens prompt."""
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token_ids = self._maybe_apply_truncation(token_ids,
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truncate_prompt_tokens)
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prompt = None
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if needs_detokenization is True:
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async_tokenizer = self._get_async_tokenizer()
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prompt = await async_tokenizer.decode(token_ids)
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return self._create_tokens_prompt(token_ids=token_ids,
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max_length=max_length,
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cache_salt=cache_salt,
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prompt=prompt)
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def _get_async_tokenizer(self) -> AsyncMicrobatchTokenizer:
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"""Get or create async tokenizer using shared pool."""
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async_tokenizer = self.async_tokenizer
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if async_tokenizer is not None:
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return async_tokenizer
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tokenizer = self.tokenizer
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if self.tokenizer is None:
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raise ValueError(
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"No tokenizer available for text input processing")
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if self.async_tokenizer_pool is None:
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async_tokenizer = AsyncMicrobatchTokenizer(tokenizer)
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else:
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async_tokenizer = self.async_tokenizer_pool.get(tokenizer)
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if async_tokenizer is None:
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async_tokenizer = AsyncMicrobatchTokenizer(tokenizer)
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self.async_tokenizer_pool[tokenizer] = async_tokenizer
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self.async_tokenizer = async_tokenizer
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return async_tokenizer
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def _create_tokens_prompt(
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self,
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token_ids: list[int],
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max_length: Optional[int] = None,
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cache_salt: Optional[str] = None,
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prompt: Optional[str] = None,
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) -> EngineTokensPrompt:
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"""Create validated EngineTokensPrompt."""
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if max_length is not None and len(token_ids) > max_length:
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raise ValueError(
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f"This model's maximum context length is {max_length} tokens. "
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f"However, your request has {len(token_ids)} input tokens. "
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"Please reduce the length of the input messages.")
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tokens_prompt = EngineTokensPrompt(prompt_token_ids=token_ids)
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if cache_salt is not None:
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tokens_prompt["cache_salt"] = cache_salt
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if prompt is not None:
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tokens_prompt["prompt"] = prompt
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return tokens_prompt
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