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6ac5e06f7c
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn> Signed-off-by: isotr0py <2037008807@qq.com>
88 lines
2.7 KiB
Python
88 lines
2.7 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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import torch
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import torch.nn as nn
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from huggingface_hub import snapshot_download
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from transformers import AutoConfig, AutoModel, CLIPImageProcessor
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from vllm.distributed import cleanup_dist_env_and_memory
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from vllm.model_executor.models.radio import RadioModel
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from vllm.transformers_utils.configs.radio import RadioConfig
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from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
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from ....conftest import ImageTestAssets
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# we use snapshot_download to prevent conflicts between
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# dynamic_module and trust_remote_code for hf_runner
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DOWNLOAD_PATTERN = ["*.json", "*.py", "*.safetensors", "*.txt", "*.model"]
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@torch.inference_mode()
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def run_radio_test(
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image_assets: ImageTestAssets,
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model_id: str,
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*,
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dtype: str,
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):
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model = snapshot_download(model_id, allow_patterns=DOWNLOAD_PATTERN)
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torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[dtype]
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img_processor = CLIPImageProcessor.from_pretrained(model)
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images = [asset.pil_image for asset in image_assets]
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# Input resolution must be a multiple of `self.min_resolution_step`.
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# Using `self.get_nearest_supported_resolution`, for assets 432x642 the
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# nearest supported resolution is 432x640.
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pixel_values = [
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img_processor(image, return_tensors="pt").pixel_values.to(torch_dtype)[
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:, :, :, :640
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]
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for image in images
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]
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config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
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hf_model = AutoModel.from_pretrained(
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model_id,
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config=config,
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dtype=torch_dtype,
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trust_remote_code=True,
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).to("cuda")
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hf_model.eval()
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hf_outputs_per_image = [
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hf_model(pixel_value.to("cuda")).features for pixel_value in pixel_values
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]
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radio_config = RadioConfig(
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model_name=config.args["model"], reg_tokens=config.args["register_multiple"]
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)
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vllm_model = RadioModel(radio_config)
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vllm_model.load_weights(hf_model.state_dict())
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vllm_model = vllm_model.to("cuda", torch_dtype)
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vllm_outputs_per_image = [
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vllm_model(pixel_values=pixel_value.to("cuda")) for pixel_value in pixel_values
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]
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del vllm_model, hf_model
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cleanup_dist_env_and_memory()
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cos_similar = nn.CosineSimilarity(dim=-1)
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for vllm_output, hf_output in zip(vllm_outputs_per_image, hf_outputs_per_image):
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assert cos_similar(vllm_output, hf_output).mean() > 0.99
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@pytest.mark.parametrize(
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"model_id",
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[
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"nvidia/C-RADIOv2-H",
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],
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)
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@pytest.mark.parametrize("dtype", ["half"])
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def test_radio(dist_init, image_assets, model_id, dtype: str) -> None:
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run_radio_test(
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image_assets,
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model_id,
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dtype=dtype,
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)
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