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https://git.datalinker.icu/vllm-project/vllm.git
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77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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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 ....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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def run_intern_vit_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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img_processor = CLIPImageProcessor.from_pretrained(model)
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images = [asset.pil_image for asset in image_assets]
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pixel_values = [
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img_processor(images, return_tensors='pt').pixel_values.to(dtype)
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for images in images
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]
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config = AutoConfig.from_pretrained(model, trust_remote_code=True)
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if not getattr(config, "norm_type", None):
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config.norm_type = "rms_norm"
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hf_model = AutoModel.from_pretrained(model,
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torch_dtype=dtype,
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trust_remote_code=True).to("cuda")
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hf_outputs_per_image = [
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hf_model(pixel_value.to("cuda")).last_hidden_state
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for pixel_value in pixel_values
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]
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from vllm.model_executor.models.intern_vit import InternVisionModel
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vllm_model = InternVisionModel(config)
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vllm_model.load_weights(hf_model.state_dict().items())
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del hf_model
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cleanup_dist_env_and_memory()
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vllm_model = vllm_model.to("cuda", dtype)
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vllm_outputs_per_image = [
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vllm_model(pixel_values=pixel_value.to("cuda"))
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for pixel_value in pixel_values
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]
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del vllm_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,
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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("model_id", [
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"OpenGVLab/InternViT-300M-448px",
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"OpenGVLab/InternViT-6B-448px-V1-5",
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])
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@pytest.mark.parametrize("dtype", [torch.half])
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@torch.inference_mode()
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def test_models(image_assets, model_id, dtype: str) -> None:
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run_intern_vit_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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