103 lines
3.1 KiB
Python
103 lines
3.1 KiB
Python
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import re
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from typing import List, Optional, Type
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import pytest
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from vllm.multimodal.utils import rescale_image_size
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from ..conftest import IMAGE_ASSETS, VllmRunner, _ImageAssets
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pytestmark = pytest.mark.vlm
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HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
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"stop_sign":
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"USER: <image>\nWhat's the content of the image?\nASSISTANT:",
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"cherry_blossom":
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"USER: <image>\nWhat is the season?\nASSISTANT:",
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})
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models = ["facebook/chameleon-7b"]
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#TODO (ywang96): Add correctness test when chameleon is
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# available on transformers.
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def run_test(
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vllm_runner: Type[VllmRunner],
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image_assets: _ImageAssets,
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model: str,
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*,
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size_factors: List[float],
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dtype: str,
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max_tokens: int,
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tensor_parallel_size: int,
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distributed_executor_backend: Optional[str] = None,
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):
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"""Test if the model can generate text given
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a batch of images and prompts.
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"""
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images = [asset.pil_image for asset in image_assets]
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inputs_per_image = [(
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[prompt for _ in size_factors],
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[rescale_image_size(image, factor) for factor in size_factors],
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) for image, prompt in zip(images, HF_IMAGE_PROMPTS)]
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with vllm_runner(model,
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max_model_len=4096,
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dtype=dtype,
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tensor_parallel_size=tensor_parallel_size,
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distributed_executor_backend=distributed_executor_backend,
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enforce_eager=True) as vllm_model:
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for prompts, images in inputs_per_image:
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vllm_outputs = vllm_model.generate_greedy(prompts,
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max_tokens,
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images=images)
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for i in range(len(vllm_outputs)):
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# format prompt back to original
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replacements = {
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"<racm3:break>": "",
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"<eoss>": "",
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"<reserved08706>": ""
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}
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pattern = '|'.join(replacements.keys())
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vllm_result = re.sub(
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pattern,
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lambda match: replacements[match.group(0)], #noqa B023
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vllm_outputs[i][1])
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vllm_result = vllm_result.replace("<image>", "", 1023)
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assert vllm_result[:len(prompts[i])] == prompts[i]
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# assert at least 10 new characters are generated
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# (to take stop token into account)
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assert len(vllm_outputs[i][1]) - len(prompts[i]) > 10
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@pytest.mark.parametrize("model", models)
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@pytest.mark.parametrize(
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"size_factors",
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[
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# Single-scale
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[1.0],
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# Single-scale, batched
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[1.0, 1.0, 1.0],
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# Multi-scale
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[0.25, 0.5, 1.0],
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],
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)
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@pytest.mark.parametrize("dtype", ["bfloat16"])
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@pytest.mark.parametrize("max_tokens", [128])
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def test_models(vllm_runner, image_assets, model, size_factors, dtype: str,
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max_tokens: int) -> None:
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run_test(
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vllm_runner,
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image_assets,
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model,
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size_factors=size_factors,
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dtype=dtype,
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max_tokens=max_tokens,
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tensor_parallel_size=1,
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)
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