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[Model][VLM] Add multi-video support for LLaVA-Onevision (#8905)

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Co-authored-by: litianjian <litianjian@bytedance.com>
Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
litianjian
2024-10-29 02:04:10 +08:00
committed by GitHub
parent 8b0e4f2ad7
commit 5f8d8075f9
5 changed files with 123 additions and 162 deletions
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173
tests/models/decoder_only/vision_language/test_llava_onevision.py
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@ -1,4 +1,4 @@
from typing import List, Optional, Tuple, Type, overload
from typing import List, Optional, Tuple, Type
import pytest
from transformers import (AutoConfig, AutoModelForVision2Seq, AutoTokenizer,
@ -9,9 +9,8 @@ from vllm.multimodal.utils import (rescale_image_size, rescale_video_size,
from vllm.sequence import SampleLogprobs
from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE
from ....conftest import (VIDEO_ASSETS, HfRunner, PromptImageInput, VllmRunner,
_VideoAssets)
from ....utils import large_gpu_test
from ....conftest import (VIDEO_ASSETS, HfRunner, PromptImageInput,
PromptVideoInput, VllmRunner)
from ...utils import check_logprobs_close
# Video test
@ -20,7 +19,7 @@ HF_VIDEO_PROMPTS = VIDEO_ASSETS.prompts({
"<|im_start|>user\n<video>\nwhy is this video funny?<|im_end|>\n<|im_start|>assistant\n" # noqa: E501
})
models = ["llava-hf/llava-onevision-qwen2-7b-ov-hf"]
models = ["llava-hf/llava-onevision-qwen2-0.5b-ov-hf"]
def vllm_to_hf_output(vllm_output: Tuple[List[int], str,
@ -47,50 +46,16 @@ def vllm_to_hf_output(vllm_output: Tuple[List[int], str,
return hf_output_ids, hf_output_str, out_logprobs
@overload
def run_video_test(
hf_runner: Type[HfRunner],
vllm_runner: Type[VllmRunner],
video_assets: _VideoAssets,
model: str,
*,
size_factors: List[float],
dtype: str,
max_tokens: int,
num_logprobs: int,
num_frames: int,
tensor_parallel_size: int,
distributed_executor_backend: Optional[str] = None,
):
...
@overload
def run_video_test(
hf_runner: Type[HfRunner],
vllm_runner: Type[VllmRunner],
video_assets: _VideoAssets,
model: str,
*,
sizes: List[Tuple[int, int]],
dtype: str,
max_tokens: int,
num_logprobs: int,
num_frames: int,
tensor_parallel_size: int,
distributed_executor_backend: Optional[str] = None,
):
...
# Video test
_LIMIT_VIDEO_PER_PROMPT = 4
def run_video_test(
hf_runner: Type[HfRunner],
vllm_runner: Type[VllmRunner],
video_assets: _VideoAssets,
inputs: List[Tuple[List[str], PromptVideoInput]],
model: str,
*,
size_factors: Optional[List[float]] = None,
sizes: Optional[List[Tuple[int, int]]] = None,
dtype: str,
max_tokens: int,
num_logprobs: int,
@ -99,38 +64,20 @@ def run_video_test(
distributed_executor_backend: Optional[str] = None,
):
torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[dtype]
videos = [
sample_frames_from_video(asset.np_ndarrays, num_frames)
for asset in video_assets
]
if size_factors is not None:
inputs_per_video = [(
[prompt for _ in size_factors],
[rescale_video_size(video, factor) for factor in size_factors],
) for video, prompt in zip(videos, HF_VIDEO_PROMPTS)]
elif sizes is not None:
inputs_per_video = [(
[prompt for _ in sizes],
[resize_video(video, size) for size in sizes],
) for video, prompt in zip(videos, HF_VIDEO_PROMPTS)]
else:
raise ValueError("You must provide either `size_factors` or `sizes`")
# max_model_len should be greater than image_feature_size
with vllm_runner(model,
dtype=dtype,
max_model_len=4096,
max_model_len=16384,
tensor_parallel_size=tensor_parallel_size,
distributed_executor_backend=distributed_executor_backend,
enforce_eager=True) as vllm_model:
vllm_outputs_per_video = [
enforce_eager=True,
limit_mm_per_prompt={"video": _LIMIT_VIDEO_PER_PROMPT
}) as vllm_model:
vllm_outputs_per_input = [
vllm_model.generate_greedy_logprobs(prompts,
max_tokens,
num_logprobs=num_logprobs,
videos=videos)
for prompts, videos in inputs_per_video
for prompts, videos in inputs
]
def process(hf_inputs: BatchEncoding):
@ -142,16 +89,16 @@ def run_video_test(
dtype=dtype,
postprocess_inputs=process,
auto_cls=AutoModelForVision2Seq) as hf_model:
hf_outputs_per_video = [
hf_outputs_per_input = [
hf_model.generate_greedy_logprobs_limit(prompts,
max_tokens,
num_logprobs=num_logprobs,
videos=videos)
for prompts, videos in inputs_per_video
for prompts, videos in inputs
]
for hf_outputs, vllm_outputs in zip(hf_outputs_per_video,
vllm_outputs_per_video):
for hf_outputs, vllm_outputs in zip(hf_outputs_per_input,
vllm_outputs_per_input):
# TODO: Check whether using original CLIPVisionModel can improve
# consistency against HF
check_logprobs_close(
@ -165,74 +112,51 @@ def run_video_test(
)
@large_gpu_test(min_gb=48)
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize(
"size_factors",
[
# No video
[],
# Single-scale
[1.0],
# Single-scale, batched
[1.0, 1.0, 1.0],
# Multi-scale
[0.25, 0.5, 1.0],
],
)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [5])
@pytest.mark.parametrize("num_frames", [16])
def test_models(hf_runner, vllm_runner, video_assets, model, size_factors,
dtype, max_tokens, num_logprobs, num_frames) -> None:
"""Inference result should be the same between hf and vllm.
All the image fixtures for the test is under tests/videos.
For huggingface runner, we provide the np.ndarray as input.
For vllm runner, we provide MultiModalDataDict objects
and corresponding MultiModalConfig as input.
Note, the text input is also adjusted to abide by vllm contract.
The text output is sanitized to be able to compare with hf.
"""
def test_models_multiple_video_inputs(hf_runner, vllm_runner, video_assets,
model, dtype, max_tokens, num_logprobs,
num_frames) -> None:
video = sample_frames_from_video(video_assets[0].np_ndarrays, num_frames)
inputs = [(
[
"<|im_start|>user <video><video>\nDescribe 2 videos. \
<|im_end|><|im_start|>assistant\n",
"<|im_start|>user <video><video>\nDescribe 2 videos. \
<|im_end|><|im_start|>assistant\n",
"<|im_start|>user <video><video><video><video>\nDescribe 4 videos. \
<|im_end|><|im_start|>assistant\n",
"<|im_start|>user <video>\nwhy is this video funny? \
<|im_end|><|im_start|>assistant\n",
],
[
[video, video],
# Images with different sizes and aspect-ratios
[
rescale_video_size(video, 0.1),
video,
],
[
video,
rescale_video_size(video, 0.25),
resize_video(video, (183, 488)),
resize_video(video, (488, 183))
],
video,
])]
run_video_test(
hf_runner,
vllm_runner,
video_assets,
inputs,
model,
size_factors=size_factors,
dtype=dtype,
max_tokens=max_tokens,
num_logprobs=num_logprobs,
num_frames=num_frames,
tensor_parallel_size=1,
)
@large_gpu_test(min_gb=48)
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize(
"sizes",
[[(1669, 2560), (2560, 1669), (183, 488), (488, 183)]],
)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [5])
@pytest.mark.parametrize("num_frames", [16])
def test_models_fixed_sizes(hf_runner, vllm_runner, video_assets, model, sizes,
dtype, max_tokens, num_logprobs,
num_frames) -> None:
run_video_test(
hf_runner,
vllm_runner,
video_assets,
model,
sizes=sizes,
dtype=dtype,
max_tokens=max_tokens,
num_logprobs=num_logprobs,
num_frames=num_frames,
tensor_parallel_size=1,
)
@ -303,7 +227,6 @@ def run_image_test(
)
@large_gpu_test(min_gb=48)
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", [128])