[Model][VLM] Add LLaVA-Onevision model support (#8486)
Co-authored-by: litianjian <litianjian@bytedance.com> Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com> Co-authored-by: Roger Wang <ywang@roblox.com> Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
litianjian
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from typing import List, Optional, Tuple, Type, overload
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import pytest
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import transformers
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from transformers import (AutoConfig, AutoModelForVision2Seq, AutoTokenizer,
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BatchEncoding)
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from vllm.multimodal.utils import (rescale_image_size, rescale_video_size,
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resize_video, sample_frames_from_video)
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from vllm.sequence import SampleLogprobs
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from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE
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from ....conftest import (VIDEO_ASSETS, HfRunner, PromptImageInput, VllmRunner,
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_VideoAssets)
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from ...utils import check_logprobs_close
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# Video test
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HF_VIDEO_PROMPTS = VIDEO_ASSETS.prompts({
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"sample_demo_1":
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"<|im_start|>user <video>\nwhy is this video funny? \
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<|im_end|><|im_start|>assistant\n"
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})
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models = ["llava-hf/llava-onevision-qwen2-7b-ov-hf"]
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def vllm_to_hf_output(vllm_output: Tuple[List[int], str,
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Optional[SampleLogprobs]],
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model: str):
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"""Sanitize vllm output to be comparable with hf output."""
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output_ids, output_str, out_logprobs = vllm_output
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config = AutoConfig.from_pretrained(model)
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video_token_id = config.video_token_index
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tokenizer = AutoTokenizer.from_pretrained(model)
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eos_token_id = tokenizer.eos_token_id
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hf_output_ids = [
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token_id for idx, token_id in enumerate(output_ids)
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if token_id != video_token_id or output_ids[idx - 1] != video_token_id
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]
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hf_output_str = output_str
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if hf_output_ids[-1] == eos_token_id:
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hf_output_str = hf_output_str + tokenizer.decode(eos_token_id)
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return hf_output_ids, hf_output_str, out_logprobs
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@overload
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def run_video_test(
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hf_runner: Type[HfRunner],
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vllm_runner: Type[VllmRunner],
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video_assets: _VideoAssets,
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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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num_logprobs: int,
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num_frames: 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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...
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@overload
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def run_video_test(
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hf_runner: Type[HfRunner],
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vllm_runner: Type[VllmRunner],
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video_assets: _VideoAssets,
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model: str,
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*,
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sizes: List[Tuple[int, int]],
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dtype: str,
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max_tokens: int,
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num_logprobs: int,
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num_frames: 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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...
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def run_video_test(
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hf_runner: Type[HfRunner],
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vllm_runner: Type[VllmRunner],
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video_assets: _VideoAssets,
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model: str,
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*,
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size_factors: Optional[List[float]] = None,
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sizes: Optional[List[Tuple[int, int]]] = None,
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dtype: str,
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max_tokens: int,
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num_logprobs: int,
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num_frames: 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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torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[dtype]
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videos = [
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sample_frames_from_video(asset.np_ndarrays, num_frames)
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for asset in video_assets
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]
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if size_factors is not None:
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inputs_per_video = [(
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[prompt for _ in size_factors],
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[rescale_video_size(video, factor) for factor in size_factors],
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) for video, prompt in zip(videos, HF_VIDEO_PROMPTS)]
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elif sizes is not None:
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inputs_per_video = [(
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[prompt for _ in sizes],
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[resize_video(video, size) for size in sizes],
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) for video, prompt in zip(videos, HF_VIDEO_PROMPTS)]
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else:
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raise ValueError("You must provide either `size_factors` or `sizes`")
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# max_model_len should be greater than image_feature_size
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with vllm_runner(model,
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dtype=dtype,
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max_model_len=4096,
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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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vllm_outputs_per_video = [
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vllm_model.generate_greedy_logprobs(prompts,
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max_tokens,
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num_logprobs=num_logprobs,
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videos=videos)
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for prompts, videos in inputs_per_video
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]
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def process(hf_inputs: BatchEncoding):
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hf_inputs["pixel_values_videos"] = hf_inputs["pixel_values_videos"] \
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.to(torch_dtype) # type: ignore
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return hf_inputs
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with hf_runner(model,
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dtype=dtype,
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postprocess_inputs=process,
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auto_cls=AutoModelForVision2Seq) as hf_model:
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hf_outputs_per_video = [
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hf_model.generate_greedy_logprobs_limit(prompts,
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max_tokens,
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num_logprobs=num_logprobs,
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videos=videos)
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for prompts, videos in inputs_per_video
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]
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for hf_outputs, vllm_outputs in zip(hf_outputs_per_video,
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vllm_outputs_per_video):
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# TODO: Check whether using original CLIPVisionModel can improve
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# consistency against HF
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check_logprobs_close(
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outputs_0_lst=hf_outputs,
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outputs_1_lst=[
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vllm_to_hf_output(vllm_output, model)
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for vllm_output in vllm_outputs
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],
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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__ < "4.45",
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reason="Waiting for next transformers release")
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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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# No video
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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", ["half"])
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@pytest.mark.parametrize("max_tokens", [128])
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@pytest.mark.parametrize("num_logprobs", [5])
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@pytest.mark.parametrize("num_frames", [16])
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def test_models(hf_runner, vllm_runner, video_assets, model, size_factors,
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dtype, max_tokens, num_logprobs, num_frames) -> None:
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"""Inference result should be the same between hf and vllm.
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All the image fixtures for the test is under tests/videos.
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For huggingface runner, we provide the np.ndarray as input.
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For vllm runner, we provide MultiModalDataDict objects
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and corresponding MultiModalConfig as input.
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Note, the text input is also adjusted to abide by vllm contract.
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The text output is sanitized to be able to compare with hf.
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"""
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run_video_test(
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hf_runner,
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vllm_runner,
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video_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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num_logprobs=num_logprobs,
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num_frames=num_frames,
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tensor_parallel_size=1,
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)
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@pytest.mark.skipif(transformers.__version__ < "4.45",
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reason="Waiting for next transformers release")
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@pytest.mark.parametrize("model", models)
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@pytest.mark.parametrize(
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"sizes",
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[[(1669, 2560), (2560, 1669), (183, 488), (488, 183)]],
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)
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@pytest.mark.parametrize("dtype", ["half"])
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@pytest.mark.parametrize("max_tokens", [128])
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@pytest.mark.parametrize("num_logprobs", [5])
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@pytest.mark.parametrize("num_frames", [16])
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def test_models_fixed_sizes(hf_runner, vllm_runner, video_assets, model, sizes,
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dtype, max_tokens, num_logprobs,
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num_frames) -> None:
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run_video_test(
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hf_runner,
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vllm_runner,
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video_assets,
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model,
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sizes=sizes,
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dtype=dtype,
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max_tokens=max_tokens,
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num_logprobs=num_logprobs,
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num_frames=num_frames,
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tensor_parallel_size=1,
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)
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# Image test
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_LIMIT_IMAGE_PER_PROMPT = 4
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def run_image_test(
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hf_runner: Type[HfRunner],
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vllm_runner: Type[VllmRunner],
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inputs: List[Tuple[List[str], PromptImageInput]],
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model: str,
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dtype: str,
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max_tokens: int,
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num_logprobs: 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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torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[dtype]
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# max_model_len should be greater than image_feature_size
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with vllm_runner(model,
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dtype=dtype,
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max_model_len=32768,
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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,
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limit_mm_per_prompt={"image": _LIMIT_IMAGE_PER_PROMPT
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}) as vllm_model:
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vllm_outputs_per_image = [
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vllm_model.generate_greedy_logprobs(prompts,
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max_tokens,
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num_logprobs=num_logprobs,
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images=images)
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for prompts, images in inputs
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]
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def process(hf_inputs: BatchEncoding):
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hf_inputs["pixel_values"] = hf_inputs["pixel_values"] \
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.to(torch_dtype) # type: ignore
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return hf_inputs
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with hf_runner(model,
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dtype=dtype,
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postprocess_inputs=process,
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auto_cls=AutoModelForVision2Seq) as hf_model:
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hf_outputs_per_image = [
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hf_model.generate_greedy_logprobs_limit(prompts,
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max_tokens,
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num_logprobs=num_logprobs,
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images=images)
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for prompts, images in inputs
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]
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for hf_outputs, vllm_outputs in zip(hf_outputs_per_image,
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vllm_outputs_per_image):
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# TODO: Check whether using original CLIPVisionModel can improve
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# consistency against HF
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check_logprobs_close(
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outputs_0_lst=hf_outputs,
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outputs_1_lst=[
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vllm_to_hf_output(vllm_output, model)
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for vllm_output in vllm_outputs
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],
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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__ < "4.45",
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reason="Waiting for next transformers release")
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@pytest.mark.parametrize("model", models)
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@pytest.mark.parametrize("dtype", ["half"])
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@pytest.mark.parametrize("max_tokens", [128])
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@pytest.mark.parametrize("num_logprobs", [5])
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def test_models_multiple_image_inputs(hf_runner, vllm_runner, image_assets,
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model, dtype, max_tokens,
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num_logprobs) -> None:
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stop_sign = image_assets[0].pil_image
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cherry_blossom = image_assets[1].pil_image
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inputs = [(
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[
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"<|im_start|>user <image><image>\nDescribe 2 images. \
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<|im_end|><|im_start|>assistant\n",
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"<|im_start|>user <image><image>\nDescribe 2 images. \
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<|im_end|><|im_start|>assistant\n",
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"<|im_start|>user <image><image><image><image>\nDescribe 4 images. \
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<|im_end|><|im_start|>assistant\n",
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"<|im_start|>user <image>\nWhat is the season? \
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<|im_end|><|im_start|>assistant\n",
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],
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[
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[stop_sign, cherry_blossom],
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# Images with different sizes and aspect-ratios
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[
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rescale_image_size(stop_sign, 0.1),
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stop_sign,
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],
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[
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stop_sign,
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rescale_image_size(stop_sign, 0.25),
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cherry_blossom.resize((183, 488)),
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cherry_blossom.resize((488, 183))
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],
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cherry_blossom,
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])]
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run_image_test(
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hf_runner,
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vllm_runner,
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inputs,
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model,
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dtype=dtype,
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max_tokens=max_tokens,
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num_logprobs=num_logprobs,
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tensor_parallel_size=1,
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)
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