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.
87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
from functools import lru_cache
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from typing import Any, Dict, List, Optional, Union
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import numpy as np
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from vllm.config import ModelConfig
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from vllm.inputs.registry import InputContext
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from vllm.logger import init_logger
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from vllm.transformers_utils.processor import get_video_processor
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from vllm.transformers_utils.tokenizer import get_tokenizer
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from vllm.utils import is_list_of
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from .base import MultiModalData, MultiModalInputs
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from .image import ImagePlugin
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logger = init_logger(__name__)
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cached_get_video_processor = lru_cache(get_video_processor)
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cached_get_tokenizer = lru_cache(get_tokenizer)
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VideoInput = Union[
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"np.ndarray", # single video input
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List["np.ndarray"],
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# TODO: support more types
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# List[Image.Image], List[List[Image.Image]],
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# "torch.Tensor",
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# List["torch.Tensor"],
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# List[List["np.ndarrray"]],
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# List[List["torch.Tensor"]],
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]
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class VideoPlugin(ImagePlugin):
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"""Plugin for video data."""
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def get_data_key(self) -> str:
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return "video"
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def _get_hf_video_processor(
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self,
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model_config: ModelConfig,
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mm_processor_kwargs: Optional[Dict[str, Any]] = None,
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):
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if mm_processor_kwargs is None:
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mm_processor_kwargs = {}
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return cached_get_video_processor(
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model_config.model,
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trust_remote_code=model_config.trust_remote_code,
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**mm_processor_kwargs)
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def _default_input_mapper(
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self,
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ctx: InputContext,
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data: MultiModalData[object],
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**mm_processor_kwargs,
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) -> MultiModalInputs:
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model_config = ctx.model_config
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# single video input as np.ndarray
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if isinstance(data, np.ndarray):
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video_processor = self._get_hf_video_processor(
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model_config,
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mm_processor_kwargs,
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)
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if video_processor is None:
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raise RuntimeError("No HuggingFace processor is available "
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"to process the image object")
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try:
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# NOTE: Similar to image; it may be a good idea to filter and
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# pass mm_processor_kwargs here too, but for now we don't to
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# avoid extra complexity if the initializer and preprocess
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# signatures of the processor don't align
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batch_data = video_processor(data, return_tensors="pt").data
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except Exception:
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logger.error("Failed to process image (%s)", data)
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raise
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return MultiModalInputs(batch_data)
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elif is_list_of(data, np.ndarray):
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raise NotImplementedError(
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"Multi video for a prompt is not supported yet")
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raise TypeError(f"Invalid video type: {type(data)}")
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def _default_max_multimodal_tokens(self, ctx: InputContext) -> int:
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return 4096
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