141 lines
4.1 KiB
Python
141 lines
4.1 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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import transformers
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from transformers import AutoModelForVision2Seq
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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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llama3_template = '<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n \n' # noqa: E501
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HF_TEXT_PROMPTS = [
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# T -> X
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llama3_template.format(
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"The label of the object is stop sign\nSummary above sentence in one word: " # noqa: E501
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),
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# T -> X
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llama3_template.format(
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"cherry blossom\nSummary above sentence in one word: "),
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]
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HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
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# I -> X
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"stop_sign":
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llama3_template.format("<image>\nSummary above image in one word: "),
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# I -> X
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"cherry_blossom":
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llama3_template.format("<image>\nSummary above image in one word: "),
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})
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MODELS = ["royokong/e5-v"]
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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,
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task="embed",
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dtype=dtype,
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max_model_len=4096,
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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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with hf_runner(model, dtype=dtype,
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auto_cls=AutoModelForVision2Seq) as hf_model:
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# Patch the issue where image_token_id
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# exceeds the maximum allowed vocab size
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hf_model.model.resize_token_embeddings(
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hf_model.model.language_model.vocab_size + 1)
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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://huggingface.co/royokong/e5-v
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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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pooled_output = F.normalize(outputs.hidden_states[-1][0, -1, :],
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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.skipif(transformers.__version__ >= "4.46",
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reason="Model broken with changes in transformers 4.46")
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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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