ef6abf3dc7
Adds ALL files needed for Dockerfile build:
- qwen3_6_scripts/ (baseline patches + our optimizations)
- vllm/ (full vllm package)
- paged_attention_v2_pytorch.py (V2 with single-bmm optimization)
- Dockerfile + computility-run.yaml
Our optimizations vs baseline:
1. paged_attn.py: pre-gathered context KV (eliminates 194 gather calls),
Triton try/fallback, V2 heuristic, threshold 32K→64K
2. paged_attention_v2_pytorch.py: fills NotImplementedError,
single-bmm Phase 1 (195 launches → 3)
3. patch_enable_triton.py: HAS_TRITON=True with safety fallback
4. patch_triton_tuning.py: BLOCK=64, NUM_WARPS=4 for BI-V100
5. computility-run.yaml: gpu-memory-utilization 0.9→0.95,
max-num-batched-tokens 8192→16384
This repo can now be submitted to dev.modelhub.org.cn as-is.
86 lines
2.5 KiB
Python
86 lines
2.5 KiB
Python
from dataclasses import dataclass
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from functools import lru_cache
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from typing import List, Literal
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import numpy as np
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import numpy.typing as npt
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from huggingface_hub import hf_hub_download
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from PIL import Image
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from vllm.multimodal.utils import (sample_frames_from_video,
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try_import_video_packages)
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from .base import get_cache_dir
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@lru_cache
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def download_video_asset(filename: str) -> str:
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"""
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Download and open an image from huggingface
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repo: raushan-testing-hf/videos-test
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"""
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video_directory = get_cache_dir() / "video-eample-data"
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video_directory.mkdir(parents=True, exist_ok=True)
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video_path = video_directory / filename
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video_path_str = str(video_path)
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if not video_path.exists():
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video_path_str = hf_hub_download(
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repo_id="raushan-testing-hf/videos-test",
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filename=filename,
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repo_type="dataset",
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cache_dir=video_directory,
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)
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return video_path_str
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def video_to_ndarrays(path: str, num_frames: int = -1) -> npt.NDArray:
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cv2 = try_import_video_packages()
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cap = cv2.VideoCapture(path)
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if not cap.isOpened():
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raise ValueError(f"Could not open video file {path}")
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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frames = []
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for i in range(total_frames):
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ret, frame = cap.read()
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if ret:
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frames.append(frame)
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cap.release()
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frames = np.stack(frames)
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frames = sample_frames_from_video(frames, num_frames)
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if len(frames) < num_frames:
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raise ValueError(f"Could not read enough frames from video file {path}"
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f" (expected {num_frames} frames, got {len(frames)})")
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return frames
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def video_to_pil_images_list(path: str,
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num_frames: int = -1) -> List[Image.Image]:
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cv2 = try_import_video_packages()
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frames = video_to_ndarrays(path, num_frames)
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return [
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Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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for frame in frames
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]
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@dataclass(frozen=True)
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class VideoAsset:
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name: Literal["sample_demo_1.mp4"]
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num_frames: int = -1
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@property
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def pil_images(self) -> List[Image.Image]:
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video_path = download_video_asset(self.name)
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ret = video_to_pil_images_list(video_path, self.num_frames)
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return ret
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@property
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def np_ndarrays(self) -> npt.NDArray:
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video_path = download_video_asset(self.name)
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ret = video_to_ndarrays(video_path, self.num_frames)
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return ret
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