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91 lines
3.5 KiB
Python
91 lines
3.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import pytest
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from ....conftest import ImageTestAssets
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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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@pytest.mark.parametrize(
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("mm_processor_kwargs", "expected_toks_per_img", "expected_pixels_shape"),
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[
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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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)
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@pytest.mark.parametrize("num_imgs", [1, 2])
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@pytest.mark.parametrize("kwargs_on_init", [True, False])
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def test_processor_override(
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image_assets: ImageTestAssets,
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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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kwargs_on_init: bool,
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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_id,
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mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
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limit_mm_per_prompt={"image": num_imgs},
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)
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processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
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tokenizer = processor.info.get_tokenizer()
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hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
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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, hf_processor_mm_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(**hf_processor_mm_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"].get_data()["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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@pytest.mark.parametrize("model_id", ["Qwen/Qwen2-VL-2B-Instruct"])
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@pytest.mark.parametrize("max_pixels", [1280 * 28 * 28, 1283 * 28 * 28])
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def test_get_image_size_with_most_features(
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image_assets: ImageTestAssets,
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model_id: str,
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max_pixels: int,
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):
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ctx = build_model_context(
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model_id,
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mm_processor_kwargs={"max_pixels": max_pixels},
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limit_mm_per_prompt={"image": 1},
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)
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processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
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hf_processor_mm_kwargs: dict[str, object] = {}
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hf_processor = processor.info.get_hf_processor(**hf_processor_mm_kwargs)
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merge_size = processor.info.get_hf_config().vision_config.spatial_merge_size
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max_image_size = processor.info.get_image_size_with_most_features()
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max_tokens = processor.info.get_num_image_tokens(
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image_width=max_image_size.width,
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image_height=max_image_size.height,
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image_processor=hf_processor.image_processor,
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)
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prompt = "<|vision_start|><|image_pad|><|vision_end|>"
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for asset in image_assets:
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mm_data = {"image": [asset.pil_image]}
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processed_inputs = processor.apply(prompt, mm_data, hf_processor_mm_kwargs)
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grid_thw = processed_inputs["mm_kwargs"].get_data()["image_grid_thw"].tolist()
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t, h, w = grid_thw[0]
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tokens = (t * h * w) // (merge_size**2)
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assert tokens < max_tokens
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