41 lines
1.7 KiB
Python
41 lines
1.7 KiB
Python
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"""Check direct Qwen prompt-to-layer-50 repeatability across multiple prompts."""
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import argparse
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import torch
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from h3_blackwell_runtime.qwen3vl_text import Qwen3VLPromptConditioner
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PROMPTS = [
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"A brass-and-paper dragon flies above a rain-washed old city at blue hour.",
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"A tiny clockwork fox curls up beside a glowing campfire in fresh snow.",
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"An old library aquarium floats through a thunderstorm, shelves full of blue fish.",
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"A painterly red balloon drifts over a quiet desert train station at sunrise.",
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"A ceramic astronaut waters moss on the moon while Earth rises behind them.",
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"A silver moth made of folded maps circles a lighthouse in green fog.",
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"A friendly robot chef flips pancakes in a kitchen made of clouds.",
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"A crystal whale swims through a canyon of stars and ringing glass bells.",
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"A paper boat sails down a neon alley after summer rain.",
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"A lantern-lit turtle carries a miniature village across a black lake.",
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]
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parser = argparse.ArgumentParser()
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parser.add_argument("--qwen", default="/text-encoders/qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors")
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parser.add_argument("--tokenizer", default="/opt/h3-blackwell-runtime/src/h3_blackwell_runtime/qwen25_tokenizer")
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args = parser.parse_args()
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conditioner = Qwen3VLPromptConditioner(args.qwen, args.tokenizer)
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for index, prompt in enumerate(PROMPTS):
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with torch.inference_mode():
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qwen_a = conditioner(prompt)
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qwen_b = conditioner(prompt)
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qwen_delta = (qwen_a.float() - qwen_b.float()).abs()
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print(
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f"prompt={index:02d} "
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f"tokens={qwen_a.shape[1]} "
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f"qwen_max={qwen_delta.max().item():.6g} qwen_mean={qwen_delta.mean().item():.6g}"
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)
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