mirror of
https://github.com/wassname/vllm.git
synced 2026-06-27 18:27:02 +08:00
Cyrus Leung
137 lines
4.7 KiB
Python
137 lines
4.7 KiB
Python
from functools import lru_cache
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from typing import List, Optional, Tuple, TypeVar
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import torch
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from PIL import Image
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from transformers import PreTrainedTokenizerBase
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from vllm.config import ModelConfig
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from vllm.inputs.registry import InputContext
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from vllm.logger import init_logger
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from vllm.transformers_utils.image_processor import get_image_processor
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from vllm.transformers_utils.tokenizer import get_tokenizer
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from .base import MultiModalInputs, MultiModalPlugin
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logger = init_logger(__name__)
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cached_get_image_processor = lru_cache(get_image_processor)
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cached_get_tokenizer = lru_cache(get_tokenizer)
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# Utilities for image input processors
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_T = TypeVar("_T", str, int)
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def repeat_and_pad_token(
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token: _T,
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*,
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repeat_count: int = 1,
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pad_token_left: Optional[_T] = None,
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pad_token_right: Optional[_T] = None,
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) -> List[_T]:
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replacement = [token] * repeat_count
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if pad_token_left is not None:
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replacement = [pad_token_left] + replacement
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if pad_token_right is not None:
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replacement = replacement + [pad_token_right]
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return replacement
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def repeat_and_pad_image_tokens(
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tokenizer: PreTrainedTokenizerBase,
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prompt: Optional[str],
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prompt_token_ids: List[int],
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*,
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image_token_id: int,
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repeat_count: int = 1,
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pad_token_left: Optional[int] = None,
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pad_token_right: Optional[int] = None,
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) -> Tuple[Optional[str], List[int]]:
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if prompt is None:
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new_prompt = None
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else:
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image_token_str = tokenizer.decode(image_token_id)
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pad_token_str_left = (None if pad_token_left is None else
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tokenizer.decode(pad_token_left))
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pad_token_str_right = (None if pad_token_right is None else
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tokenizer.decode(pad_token_right))
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replacement_str = "".join(
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repeat_and_pad_token(
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image_token_str,
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repeat_count=repeat_count,
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pad_token_left=pad_token_str_left,
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pad_token_right=pad_token_str_right,
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))
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image_token_count = prompt.count(image_token_str)
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# This is an arbitrary number to distinguish between the two cases
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if image_token_count > 16:
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logger.warning(
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"Please follow the prompt format that is "
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"documented on HuggingFace which does not involve "
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"repeating %s tokens.", image_token_str)
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elif image_token_count > 1:
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logger.warning("Multiple image input is not supported yet, "
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"so any extra image tokens will be treated "
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"as plain text.")
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# The image tokens are removed to be consistent with HuggingFace
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new_prompt = prompt.replace(image_token_str, replacement_str, 1)
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new_token_ids: List[int] = []
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for i, token in enumerate(prompt_token_ids):
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if token == image_token_id:
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replacement_ids = repeat_and_pad_token(
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image_token_id,
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repeat_count=repeat_count,
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pad_token_left=pad_token_left,
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pad_token_right=pad_token_right,
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)
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new_token_ids.extend(replacement_ids)
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# No need to further scan the list since we only replace once
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new_token_ids.extend(prompt_token_ids[i + 1:])
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break
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else:
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new_token_ids.append(token)
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return new_prompt, new_token_ids
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class ImagePlugin(MultiModalPlugin):
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"""Plugin for image data."""
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def get_data_key(self) -> str:
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return "image"
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def _get_hf_image_processor(self, model_config: ModelConfig):
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return cached_get_image_processor(
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model_config.model,
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trust_remote_code=model_config.trust_remote_code)
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def _default_input_mapper(self, ctx: InputContext,
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data: object) -> MultiModalInputs:
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model_config = ctx.model_config
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if isinstance(data, Image.Image):
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image_processor = self._get_hf_image_processor(model_config)
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if image_processor is None:
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raise RuntimeError("No HuggingFace processor is available "
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"to process the image object")
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try:
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batch_data = image_processor \
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.preprocess(data, return_tensors="pt") \
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.data
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except Exception:
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logger.error("Failed to process image (%s)", data)
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raise
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return MultiModalInputs(batch_data)
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elif isinstance(data, torch.Tensor):
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raise NotImplementedError("Embeddings input is not supported yet")
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raise TypeError(f"Invalid image type: {type(data)}")
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def _default_max_multimodal_tokens(self, ctx: InputContext) -> int:
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return 3000
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