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
52 lines
1.4 KiB
Python
52 lines
1.4 KiB
Python
# python examples/sample.py --model='/path/models/Qwen3-8B' --devices='npu:0'
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# python examples/sample.py --model='/path/models/Qwen3-8B' --devices='npu:0,npu:1'
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from xllm import ArgumentParser, LLM, RequestParams
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# Create an LLM.
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parser = ArgumentParser()
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llm = LLM(**vars(parser.parse_args()))
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# selector must be a stable single special token in the target tokenizer.
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selector = "masked"
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prompts = [
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f"candidate_a={selector}, candidate_b={selector}",
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f"user_feature={selector}",
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]
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# RequestParams can still carry generic sampling knobs.
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# sample() will enforce:
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# - max_tokens=1
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# - n=1
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# - best_of=1
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# - logprobs=True
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request_params_list = []
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for _ in prompts:
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request_params = RequestParams()
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request_params.temperature = 0.0
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request_params.top_p = 1.0
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request_params_list.append(request_params)
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outputs = llm.sample(
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prompts,
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selector=selector,
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request_params=request_params_list,
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logprobs=5,
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wait_schedule_done=True,
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)
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# One RequestOutput per input prompt.
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# output.outputs is expanded by selector hits in that prompt.
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for i, output in enumerate(outputs):
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print(f"[prompt-{i}] {output.prompt!r}")
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for sample_output in output.outputs:
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print(
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f" sample_id={sample_output.index}, "
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f"token={sample_output.text!r}, "
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f"token_ids={sample_output.token_ids}, "
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f"finish_reason={sample_output.finish_reason}"
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)
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llm.finish()
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