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update video infer examples #2840

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Merged
merged 5 commits into from
Jan 13, 2025
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update video infer examples #2840

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ef34592
update
Jintao-Huang Jan 2, 2025
fa74b2a
Merge branch 'main' into infer_video_examples
Jintao-Huang Jan 13, 2025
6dafaed
update
Jintao-Huang Jan 13, 2025
dd10a67
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Jintao-Huang Jan 13, 2025
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Jintao-Huang Jan 13, 2025
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23 changes: 18 additions & 5 deletions examples/infer/demo_mllm.py
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Expand Up @@ -104,20 +104,33 @@ def get_data(mm_type: Literal['text', 'image', 'video', 'audio']):
if infer_backend == 'pt':
model = 'Qwen/Qwen2-Audio-7B-Instruct'
mm_type = 'audio'
dataset = 'speech_asr/speech_asr_aishell1_trainsets:validation#1000'
engine = PtEngine(model, max_batch_size=64)
elif infer_backend == 'vllm':
# test env: vllm==0.6.5, transformers==4.48.*
from swift.llm import VllmEngine
model = 'Qwen/Qwen2-VL-2B-Instruct'
mm_type = 'video'
dataset = 'AI-ModelScope/LaTeX_OCR:small#1000'
# If you encounter insufficient GPU memory, please reduce `max_model_len` and set `max_num_seqs=5`.
engine = VllmEngine(model, max_model_len=32768, limit_mm_per_prompt={'image': 5, 'video': 2})
mm_type = 'video' # or 'image'
elif infer_backend == 'lmdeploy':
# test env: lmdeploy==0.6.4
from swift.llm import LmdeployEngine
model = 'OpenGVLab/InternVL2_5-1B'
mm_type = 'video'
dataset = 'AI-ModelScope/LaTeX_OCR:small#1000'
engine = LmdeployEngine(model, vision_batch_size=8)
mm_type = 'video' # or 'image'

# infer dataset
if mm_type == 'audio':
dataset = 'speech_asr/speech_asr_aishell1_trainsets:validation#1000'
elif mm_type == 'image':
# The meaning of environment variables can be found at:
# https://swift.readthedocs.io/zh-cn/latest/Instruction/%E5%91%BD%E4%BB%A4%E8%A1%8C%E5%8F%82%E6%95%B0.html#id17
os.environ['MAX_PIXELS'] = '1003520'
dataset = 'AI-ModelScope/LaTeX_OCR:small#1000'
elif mm_type == 'video':
os.environ['VIDEO_MAX_PIXELS'] = '50176'
os.environ['FPS_MAX_FRAMES'] = '12'
dataset = 'swift/VideoChatGPT:Generic#100'

# Here, `load_dataset` is used for convenience; `infer_batch` does not require creating a dataset.
dataset = load_dataset([dataset], seed=42)[0]
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