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
114 lines
3.6 KiB
Python
114 lines
3.6 KiB
Python
import base64
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from io import BytesIO
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from typing import Any, Dict, List, Optional, Tuple
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from PIL import Image
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from xllm_export import MMData
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def _bytes_to_data_url(data: bytes) -> str:
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encoded = base64.b64encode(data).decode("ascii")
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return f"data:image;base64,{encoded}"
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def _pil_to_data_url(image: Image.Image) -> str:
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buf = BytesIO()
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fmt = image.format or "PNG"
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image.save(buf, format=fmt)
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return _bytes_to_data_url(buf.getvalue())
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def normalize_vllm_style_inputs(
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prompts: Any,
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) -> Tuple[List[str], Optional[List[MMData]], Optional[List[List[str]]]]:
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if isinstance(prompts, dict):
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requests = [prompts]
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return _parse_vllm_style_requests(requests)
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if isinstance(prompts, list) and prompts and all(isinstance(x, dict) for x in prompts):
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return _parse_vllm_style_requests(prompts)
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raise TypeError(
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"VLM-style inputs must be dict/List[dict] with key 'prompt', e.g. "
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"{'prompt': '...', 'multi_modal_data': {'image': image}}"
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)
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def _parse_vllm_style_requests(
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requests: List[Dict[str, Any]],
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) -> Tuple[List[str], Optional[List[MMData]], Optional[List[List[str]]]]:
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prompts: List[str] = []
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mm_datas: List[MMData] = []
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image_urls: List[List[str]] = []
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use_mm_data: Optional[bool] = None
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for req in requests:
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if "prompt" not in req:
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raise ValueError("Each request dict must contain key 'prompt'")
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prompt = req["prompt"]
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if not isinstance(prompt, str):
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raise TypeError("request['prompt'] must be a string")
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prompts.append(prompt)
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if "multi_modal_data" not in req:
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if use_mm_data is True:
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raise TypeError("Cannot mix MMData and empty multi_modal_data in one batch")
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use_mm_data = False
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image_urls.append([])
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continue
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payload = req["multi_modal_data"]
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if isinstance(payload, MMData):
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if use_mm_data is False:
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raise TypeError("Cannot mix MMData and image inputs in one batch")
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use_mm_data = True
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mm_datas.append(payload)
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else:
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if use_mm_data is True:
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raise TypeError("Cannot mix MMData and image inputs in one batch")
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use_mm_data = False
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image_urls.append(_to_image_urls(payload))
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if use_mm_data:
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return prompts, mm_datas, None
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return prompts, None, image_urls
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def _to_image_urls(payload: Any) -> List[str]:
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if not isinstance(payload, dict):
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raise TypeError("multi_modal_data must be dict or MMData")
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if "image" in payload:
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images = payload["image"]
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return _normalize_images(images)
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if "video" in payload:
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raise NotImplementedError("video multi_modal_data is not supported yet")
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if "audio" in payload:
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raise NotImplementedError("audio multi_modal_data is not supported yet")
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raise ValueError(
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"Unsupported multi_modal_data format. Expected {'image': ...} or MMData."
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)
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def _normalize_images(images: Any) -> List[str]:
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if isinstance(images, (list, tuple)):
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if len(images) == 0:
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raise ValueError("multi_modal_data['image'] cannot be empty")
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return [_to_image_url(img) for img in images]
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return [_to_image_url(images)]
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def _to_image_url(image: Any) -> str:
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if isinstance(image, str):
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return image
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if isinstance(image, Image.Image):
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return _pil_to_data_url(image.convert("RGB"))
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if isinstance(image, (bytes, bytearray)):
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return _bytes_to_data_url(bytes(image))
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raise TypeError(
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"image must be image path/url string, PIL.Image, bytes, "
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"or a list of these"
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)
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