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770ec6024f
Co-authored-by: simon-mo <xmo@berkeley.edu> Co-authored-by: Chang Su <chang.s.su@oracle.com> Co-authored-by: Simon Mo <simon.mo@hey.com> Co-authored-by: Roger Wang <136131678+ywang96@users.noreply.github.com> Co-authored-by: Roger Wang <ywang@roblox.com>
72 lines
2.4 KiB
Python
72 lines
2.4 KiB
Python
from functools import lru_cache
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import torch
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from PIL import Image
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from transformers.image_processing_base import BatchFeature
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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.processor import get_image_processor
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from vllm.utils import is_list_of
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from .base import MultiModalData, 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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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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mm_processor_kwargs = ({} if model_config.mm_processor_kwargs is None
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else model_config.mm_processor_kwargs)
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# We don't explicitly check kwarg overrides to the HF class
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# since the automodel just takes kwargs, so we can't inspect it
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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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**mm_processor_kwargs)
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def _default_input_mapper(
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self,
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ctx: InputContext,
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data: MultiModalData[object],
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) -> MultiModalInputs:
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model_config = ctx.model_config
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# Processed by input processor
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if isinstance(data, BatchFeature):
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return MultiModalInputs(data.data)
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# PIL image
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if isinstance(data, Image.Image) or is_list_of(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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# Image embedding
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elif isinstance(data, torch.Tensor) or is_list_of(data, torch.Tensor):
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return MultiModalInputs({"image_embeds": data})
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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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