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
76 lines
2.1 KiB
Python
Executable File
76 lines
2.1 KiB
Python
Executable File
# python generate_vlm.py --model /path/to/Qwen2.5-VL-7B-Instruct/ --disable_prefix_cache --disable_chunked_prefill --max_seqs_per_batch 4 --devices='npu:0' --enable_shm
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from xllm import ArgumentParser, SamplingParams
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from xllm import LLM
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# from xllm import VLM
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import base64
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import os
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def encode_image_from_file(file_path: str) -> str:
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if not os.path.exists(file_path):
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raise FileNotFoundError(f"not found image: {file_path}")
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with open(file_path, "rb") as image_file:
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result = base64.b64encode(image_file.read()).decode("utf-8")
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return result
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parser = ArgumentParser()
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args = parser.parse_args()
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# vlm = VLM(**vars(args))
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vlm = LLM(**vars(args))
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image_1 = "./images/3.jpg"
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image_2 = "./images/4.jpg"
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# image_base64_1 = encode_image_from_file(image_1)
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# image_base64_2 = encode_image_from_file(image_2)
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# image_1 = f"data:image/jpeg;base64,{image_base64_1}"
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# image_2 = f"data:image/jpeg;base64,{image_base64_2}"
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requests = [
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{
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"prompt": (
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"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
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"<|im_start|>user\n"
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"<|vision_start|><|image_pad|><|vision_end|>"
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"请描述这张图片。<|im_end|>\n"
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"<|im_start|>assistant\n"
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),
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"multi_modal_data": {
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"image": image_1,
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},
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},
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{
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"prompt": (
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"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
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"<|im_start|>user\n"
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"<|vision_start|><|image_pad|><|vision_end|>"
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"<|vision_start|><|image_pad|><|vision_end|>"
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"请对比这两张图片的主要区别。<|im_end|>\n"
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"<|im_start|>assistant\n"
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),
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"multi_modal_data": {
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"image": [image_1, image_2],
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},
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},
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]
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sampling_params = SamplingParams(
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temperature=0.8,
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top_p=0.95,
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max_tokens=50,
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)
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outputs = vlm.generate(
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requests,
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sampling_params=sampling_params
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)
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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vlm.finish()
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