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.
89 lines
3.0 KiB
Python
89 lines
3.0 KiB
Python
from functools import lru_cache
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from typing import Any, Dict, Optional
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import torch
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from PIL import Image
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from transformers.image_processing_base import BatchFeature
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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_image_processor
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from vllm.utils import is_list_of
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from .base import MultiModalData, MultiModalInputs, MultiModalPlugin
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logger = init_logger(__name__)
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cached_get_image_processor = lru_cache(get_image_processor)
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class ImagePlugin(MultiModalPlugin):
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"""Plugin for image data."""
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def get_data_key(self) -> str:
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return "image"
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def _get_hf_image_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_image_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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# Processed by input processor
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if isinstance(data, BatchFeature):
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return MultiModalInputs(data.data)
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# PIL image
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if isinstance(data, Image.Image) or is_list_of(data, Image.Image):
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image_processor = self._get_hf_image_processor(
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model_config,
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mm_processor_kwargs,
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)
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if image_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: It may make sense to forward the mm_processor_kwargs
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# here too. For now, to keep it simple, we only allow it be
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# used for the initialization call though, just in case the
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# signatures of the preprocessor initializer don't match
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# preprocess()
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batch_data = image_processor \
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.preprocess(data, return_tensors="pt") \
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.data
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except Exception:
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logger.error(
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"Failed to process image (%s) with the default mapper. "
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"This is most likely an edge-case with this model's image "
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"processor in transformers (type: %s), and not vLLM.",
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data,
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type(image_processor).__name__)
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raise
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return MultiModalInputs(batch_data)
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# Image embedding
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elif isinstance(data, torch.Tensor) or is_list_of(data, torch.Tensor):
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return MultiModalInputs({"image_embeds": data})
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raise TypeError(f"Invalid image type: {type(data)}")
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def _default_max_multimodal_tokens(self, ctx: InputContext) -> int:
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return 3000
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