127 lines
3.8 KiB
Python
127 lines
3.8 KiB
Python
from typing import List, Type
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import pytest
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import torch.nn.functional as F
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from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner
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from ....utils import large_gpu_test
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from ..utils import check_embeddings_close
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HF_TEXT_PROMPTS = [
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# T -> X
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"Find me an everyday image that matches the given caption: The label of the object is stop sign", # noqa: E501
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# T -> X
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"Retrieve an image of this caption: cherry blossom",
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]
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HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
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# T + I -> X
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"stop_sign":
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"<|image_1|> Select the portion of the image that isolates the object of the given label: The label of the object is stop sign", # noqa: E501
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# I -> X
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"cherry_blossom":
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"<|image_1|> Represent the given image for classification", # noqa: E501
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})
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MODELS = ["TIGER-Lab/VLM2Vec-Full"]
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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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# NOTE: take care of the order. run vLLM first, and then run HF.
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# vLLM needs a fresh new process without cuda initialization.
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# if we run HF first, the cuda initialization will be done and it
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# will hurt multiprocessing backend with fork method (the default method).
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with vllm_runner(model, task="embed", dtype=dtype,
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enforce_eager=True) as vllm_model:
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vllm_outputs = vllm_model.encode(input_texts, images=input_images)
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# use eager mode for hf runner, since phi3_v didn't work with flash_attn
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hf_model_kwargs = {"_attn_implementation": "eager"}
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with hf_runner(model, dtype=dtype,
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model_kwargs=hf_model_kwargs) 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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# Based on: https://github.com/TIGER-AI-Lab/VLM2Vec/blob/db3b951bccabba220c1f53ab46a734e50dd2fc08/src/model.py
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outputs = hf_model.model(
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**hf_model.wrap_device(inputs,
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device=hf_model.model.device.type),
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return_dict=True,
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output_hidden_states=True,
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)
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last_hidden_state = outputs.hidden_states[-1][0]
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reps = last_hidden_state[inputs.attention_mask[0].sum() - 1]
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pooled_output = F.normalize(reps, p=2, dim=-1)
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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.core_model
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["half"])
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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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@large_gpu_test(min_gb=48)
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@pytest.mark.core_model
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["half"])
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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)
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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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