002f9879b2
Replaces cherry-picked upstream_ref with complete source trees. xllm/ — Iluvatar official C++ inference engine (15MB, 1470 files) Complete: kernels → layers → models → runtime → scheduler → api Excluded: .git, binary images, third_party submodule checkouts ds_vllm/ — Iluvatar official vllm fork (8MB, 703 files) Included: csrc/ (ALL CUDA kernels), fused_moe/, qwen3_5 model, _custom_ops Excluded: tests, benchmarks, docs, examples (not needed for reference) Critical call chains now fully traceable: MoE: moe_topk_softmax_kernels.cuh → ixformer.h → fused_moe.cpp → layer GDN: qwen3_gated_delta_net_base.cpp → qwen3_5_gated_delta_net.cpp Attention: ixformer.h → xllm_paged_attention → attention.cpp
71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
from typing import Any, Dict, List, cast
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from functools import lru_cache
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from io import BytesIO
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from PIL import Image
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import torch
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@lru_cache(maxsize=1)
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def __cache_image_processor(
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processor_name: str,
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*args: Any,
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trust_remote_code: bool = False,
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**kwargs: Any,
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):
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"""Load an image processor for the given model name via HuggingFace."""
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# don't put this import at the top level
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# it will call torch.cuda.device_count()
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from transformers import AutoImageProcessor
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from transformers.image_processing_utils import BaseImageProcessor
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try:
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processor = AutoImageProcessor.from_pretrained(
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processor_name,
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*args,
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trust_remote_code=trust_remote_code,
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**kwargs)
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except ValueError as e:
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if not trust_remote_code:
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err_msg = (
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"Failed to load the image processor. If the image processor is "
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"a custom processor not yet available in the HuggingFace "
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"transformers library, consider setting "
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"`trust_remote_code=True` in LLM or using the "
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"`--trust-remote-code` flag in the CLI.")
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raise RuntimeError(err_msg) from e
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else:
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raise e
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return cast(BaseImageProcessor, processor)
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def try_cat_feature(item):
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if isinstance(item, torch.Tensor):
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return item
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assert isinstance(item, list), f"expected list, got {type(item)}"
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lst = []
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for i in item:
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res = try_cat_feature(i)
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if isinstance(res, list):
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lst.extend(res)
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elif isinstance(res, torch.Tensor):
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lst.append(res)
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else:
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raise TypeError(f"expected list or torch.Tensor, got {type(res)}")
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if len(lst) == 1:
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return lst[0]
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if any(t.shape[1:] != lst[0].shape[1:] for t in lst):
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return lst
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return torch.cat(lst)
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def preprocess(lst: List[str], model: str) -> Dict[str, Any]:
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images = [Image.open(BytesIO(item)) for item in lst]
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image_processor = __cache_image_processor(model, trust_remote_code=True)
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data = image_processor.preprocess(images, return_tensors="pt").data
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return { key: try_cat_feature(val)
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for key, val in data.items()
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} |