55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
import pytest
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.utils import cached_get_tokenizer
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from ....conftest import _ImageAssets
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from ...utils import build_model_context
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@pytest.mark.parametrize("model_id", ["Qwen/Qwen2-VL-2B-Instruct"])
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# yapf: disable
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@pytest.mark.parametrize(
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("mm_processor_kwargs", "expected_toks_per_img", "expected_pixels_shape"), [
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({}, 1426, (5704, 1176)),
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({"min_pixels": 64**2, "max_pixels": 512**2}, 330, (1320, 1176)),
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])
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# yapf: enable
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@pytest.mark.parametrize("num_imgs", [1, 2])
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def test_processor_override(
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image_assets: _ImageAssets,
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model_id: str,
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mm_processor_kwargs: dict[str, object],
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expected_toks_per_img: int,
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expected_pixels_shape: tuple[int, int],
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num_imgs: int,
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):
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"""Ensure Qwen2VLMultiModalProcessor handles min/max pixels properly."""
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ctx = build_model_context(
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model_name=model_id,
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tokenizer_name=model_id,
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mm_processor_kwargs=None,
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limit_mm_per_prompt={"image": num_imgs},
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)
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tokenizer = cached_get_tokenizer(ctx.model_config.tokenizer)
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processor = MULTIMODAL_REGISTRY.create_processor(
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ctx.model_config,
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tokenizer=tokenizer,
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)
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# Build the image str / prompt based on the number of images we pass
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prompt = "<|vision_start|><|image_pad|><|vision_end|>" * num_imgs
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mm_data = {"image": [image_assets[0].pil_image] * num_imgs}
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processed_inputs = processor.apply(prompt, mm_data, mm_processor_kwargs)
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# Ensure we have the right number of placeholders per num_crops size
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hf_processor = processor.info.get_hf_processor(**mm_processor_kwargs)
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image_token_id = tokenizer.convert_tokens_to_ids(hf_processor.image_token)
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img_tok_count = processed_inputs["prompt_token_ids"].count(image_token_id)
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pixel_shape = processed_inputs["mm_kwargs"]["pixel_values"].shape
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assert img_tok_count == expected_toks_per_img * num_imgs
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assert pixel_shape[0] == expected_pixels_shape[0] * num_imgs
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assert pixel_shape[1] == expected_pixels_shape[1]
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