99 lines
3.3 KiB
Python
99 lines
3.3 KiB
Python
import numpy as np
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import pytest
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from transformers import CLIPImageProcessor
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from vllm.config import ModelConfig, VisionLanguageConfig
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.image import ImagePixelData
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@pytest.mark.parametrize("dtype", ["half", "bfloat16", "float"])
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def test_clip_image_processor(hf_images, dtype):
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MODEL_NAME = "llava-hf/llava-1.5-7b-hf"
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IMAGE_HEIGHT = IMAGE_WIDTH = 33
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hf_processor = CLIPImageProcessor.from_pretrained(MODEL_NAME)
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assert isinstance(hf_processor, CLIPImageProcessor)
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model_config = ModelConfig(
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model=MODEL_NAME,
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tokenizer=MODEL_NAME,
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tokenizer_mode="auto",
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trust_remote_code=False,
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seed=0,
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dtype=dtype,
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revision=None,
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)
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vlm_config = VisionLanguageConfig(
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image_input_type=VisionLanguageConfig.ImageInputType.PIXEL_VALUES,
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image_token_id=32000,
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image_input_shape=(1, 3, IMAGE_HEIGHT, IMAGE_WIDTH),
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image_feature_size=576,
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image_processor=MODEL_NAME,
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image_processor_revision=None,
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)
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for image in hf_images:
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hf_result = hf_processor.preprocess(
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image,
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return_tensors="np",
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)
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vllm_result = MULTIMODAL_REGISTRY.process_input(
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ImagePixelData(image),
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model_config=model_config,
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vlm_config=vlm_config,
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)
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assert hf_result.keys() == vllm_result.keys()
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for key, hf_arr in hf_result.items():
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vllm_arr: np.ndarray = vllm_result[key].numpy()
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assert hf_arr.shape == vllm_arr.shape, f"Failed for key={key}"
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assert np.allclose(hf_arr, vllm_arr), f"Failed for key={key}"
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@pytest.mark.parametrize("dtype", ["float"])
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def test_image_pixel_types(hf_images, vllm_image_tensors, dtype):
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MODEL_NAME = "llava-hf/llava-1.5-7b-hf"
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IMAGE_HEIGHT = IMAGE_WIDTH = 33
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model_config = ModelConfig(
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model=MODEL_NAME,
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tokenizer=MODEL_NAME,
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tokenizer_mode="auto",
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trust_remote_code=False,
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seed=0,
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dtype=dtype,
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revision=None,
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)
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vlm_config = VisionLanguageConfig(
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image_input_type=VisionLanguageConfig.ImageInputType.PIXEL_VALUES,
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image_token_id=32000,
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image_input_shape=(1, 3, IMAGE_HEIGHT, IMAGE_WIDTH),
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image_feature_size=576,
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image_processor=MODEL_NAME,
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image_processor_revision=None,
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)
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for image, tensor in zip(hf_images, vllm_image_tensors):
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image_result = MULTIMODAL_REGISTRY.process_input(
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ImagePixelData(image),
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model_config=model_config,
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vlm_config=vlm_config,
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)
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tensor_result = MULTIMODAL_REGISTRY.process_input(
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ImagePixelData(tensor),
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model_config=model_config,
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vlm_config=vlm_config,
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)
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assert image_result.keys() == tensor_result.keys()
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for key, image_arr in image_result.items():
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tensor_arr: np.ndarray = tensor_result[key].numpy()
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assert image_arr.shape == tensor_arr.shape, f"Failed for key={key}"
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# The examples in PR#3042 have slightly different preprocessing from
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# HuggingFace's LlavaProcessor, causing the test to fail.
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# assert np.allclose(image_arr, tensor_arr), f"Failed for key={key}"
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