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https://git.datalinker.icu/vllm-project/vllm.git
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138 lines
3.5 KiB
Python
138 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 transformers import SiglipModel
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from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner
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from ...utils import check_embeddings_close
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HF_TEXT_PROMPTS = [
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"a photo of a stop sign",
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"a photo of a cherry blossom",
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]
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HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts(
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{
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"stop_sign": "",
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"cherry_blossom": "",
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}
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)
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MODELS = ["google/siglip-base-patch16-224", "google/siglip2-base-patch16-224"]
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def _run_test(
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hf_runner: type[HfRunner],
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vllm_runner: type[VllmRunner],
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input_texts: list[str],
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input_images: PromptImageInput,
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model: str,
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*,
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dtype: str,
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) -> None:
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with vllm_runner(
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model, runner="pooling", dtype=dtype, enforce_eager=True, max_model_len=64
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) as vllm_model:
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vllm_outputs = vllm_model.embed(input_texts, images=input_images)
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with hf_runner(model, dtype=dtype, auto_cls=SiglipModel) as hf_model:
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all_inputs = hf_model.get_inputs(input_texts, images=input_images)
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all_outputs = []
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for inputs in all_inputs:
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inputs = hf_model.wrap_device(inputs)
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if "pixel_values" in inputs:
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pooled_output = hf_model.model.get_image_features(
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pixel_values=inputs.pixel_values,
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).squeeze(0)
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else:
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pooled_output = hf_model.model.get_text_features(
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input_ids=inputs.input_ids,
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).squeeze(0)
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all_outputs.append(pooled_output.tolist())
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hf_outputs = all_outputs
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check_embeddings_close(
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embeddings_0_lst=hf_outputs,
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embeddings_1_lst=vllm_outputs,
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name_0="hf",
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name_1="vllm",
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)
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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def test_models_text(
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hf_runner,
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vllm_runner,
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image_assets,
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model: str,
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dtype: str,
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) -> None:
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input_texts_images = [(text, None) for text in HF_TEXT_PROMPTS]
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input_texts = [text for text, _ in input_texts_images]
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input_images = [image for _, image in input_texts_images]
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_run_test(
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hf_runner,
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vllm_runner,
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input_texts,
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input_images, # type: ignore
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model,
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dtype=dtype,
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)
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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def test_models_image(
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hf_runner,
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vllm_runner,
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image_assets,
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model: str,
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dtype: str,
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) -> None:
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input_texts_images = [
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(text, asset.pil_image) for text, asset in zip(HF_IMAGE_PROMPTS, image_assets)
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]
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input_texts = [text for text, _ in input_texts_images]
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input_images = [image for _, image in input_texts_images]
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_run_test(
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hf_runner,
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vllm_runner,
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input_texts,
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input_images,
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model,
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dtype=dtype,
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)
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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def test_models_text_image_no_crash(
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vllm_runner,
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image_assets,
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model: str,
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dtype: str,
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) -> None:
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texts = [HF_TEXT_PROMPTS[0]]
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images = [image_assets[0].pil_image]
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with vllm_runner(
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model,
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runner="pooling",
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dtype=dtype,
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enforce_eager=True,
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max_model_len=64,
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) as vllm_model:
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with pytest.raises(ValueError, match="not both"):
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vllm_model.embed(texts, images=images)
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vllm_model.embed(texts)
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vllm_model.embed([""], images=images)
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