WpyQwq
643e22ecb9
- 引入 MemoryRouterXL 与 v5/v6 流式多线程训练/编码管线 - 修复 prepare_memory_router_dataset 候选池重建缺陷（mega 家族 3568x 加速，输出逐字节相同） - 修复 v5 被破坏的拒答与多跳标签（train 未知样本 319 -> 16319，multi_hop 平均正例 1.00 -> 2.00） - 同存储预算下 V2-128 v6 逐轴 22/22 通过：Top-1 41.12% -> 94.62%，未知拒答 0.00% -> 100.00% - 记录三条被实测推翻的显然优化（logits_to_keep=1 反而慢 55%、XL 容量未带来收益） - 记忆手术跨架构可移植性 14/14，读写关闭时与原生模型逐位相同
55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
"""Unified entry point for Natural Memory v2 local production workflows.
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Examples:
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python -m V2_dpskw.natural_memory_app chat --model-path ...
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python -m V2_dpskw.natural_memory_app serve --port 8765
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python -m V2_dpskw.natural_memory_app build-dataset
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python -m V2_dpskw.natural_memory_app train-policy --steps 240
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python -m V2_dpskw.natural_memory_app stress --rounds 40
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python -m V2_dpskw.natural_memory_app make-mega-validation
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python -m V2_dpskw.natural_memory_app benchmark-kv --limit 32
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"""
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from __future__ import annotations
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import argparse
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import sys
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def main() -> None:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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"command",
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choices=(
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"chat",
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"serve",
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"build-dataset",
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"train-policy",
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"stress",
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"make-mega-validation",
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"benchmark-kv",
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),
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help="workflow to run; remaining arguments are passed to that workflow",
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)
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args, remaining = parser.parse_known_args()
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sys.argv = [sys.argv[0], *remaining]
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if args.command == "chat":
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from .stream_chat_qwen_memory import main as run
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elif args.command == "serve":
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from .natural_memory_service import main as run
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elif args.command == "build-dataset":
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from .build_production_memory_dataset import main as run
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elif args.command == "train-policy":
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from .train_production_memory_policy import main as run
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elif args.command == "stress":
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from .stress_test_natural_memory import main as run
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elif args.command == "make-mega-validation":
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from .make_mega_memory_validation import main as run
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else:
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from .benchmark_memory_vs_full_kv import main as run
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run()
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if __name__ == "__main__":
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main()
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