Natural Memory NM2.1: 记忆路由器分叉、数据集缺陷修复与全轴评测证据
- 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
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WpyQwq
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from __future__ import annotations
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import unittest
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import torch
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from V2_dpskw.benchmark_dirty_real_corpus_4b import (
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_failure_stage,
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_is_refusal_strict,
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)
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from V2_dpskw.memory_os_v2 import MemoryRouterV2, PagedMemoryBankV2
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from V2_dpskw.qwen_integration import (
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_looks_like_grounded_memory_query,
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format_memory_evidence,
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)
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class FailureDrivenV2Test(unittest.TestCase):
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def setUp(self) -> None:
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torch.manual_seed(19)
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self.router = MemoryRouterV2(16, router_dim=8, num_heads=2, max_hops=3)
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def test_grounding_guard_is_scoped_to_personal_and_project_facts(self) -> None:
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self.assertTrue(_looks_like_grounded_memory_query("当前项目的模型叫什么"))
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self.assertTrue(_looks_like_grounded_memory_query("README 是否记录了重启方式"))
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self.assertFalse(_looks_like_grounded_memory_query("北京今天会下雨吗"))
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def test_unknown_wording_is_a_hard_refusal_signal(self) -> None:
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self.assertTrue(_is_refusal_strict("仓库中没有列出任何具体的部署区域。"))
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self.assertTrue(_is_refusal_strict("这个字段没有出现在保存的记录里，我无法确认。"))
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def test_structured_evidence_keeps_raw_fragment_first(self) -> None:
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evidence = format_memory_evidence(
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"用户给当前模型定名：Natural Memory v1。",
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entity="user",
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attribute="模型名称",
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value="Natural Memory v1",
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)
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self.assertLess(evidence.index("事实"), evidence.index("已确认值"))
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self.assertIn("已确认值：Natural Memory v1", evidence)
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def test_failure_stage_classification_is_observable(self) -> None:
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self.assertEqual(
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_failure_stage(
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{
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"correct": False,
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"answerable": True,
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"retrieval_target_found": False,
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"response": "不知道",
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}
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),
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"routing_miss",
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)
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self.assertEqual(
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_failure_stage(
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{
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"correct": False,
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"answerable": True,
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"retrieval_target_found": True,
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"expected_anchors": ["H:\\Memory", "Natural Memory"],
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"response": "项目在 H:\\Memory。",
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}
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),
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"evidence_fusion",
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)
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self.assertEqual(
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_failure_stage(
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{
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"correct": False,
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"answerable": True,
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"retrieval_target_found": True,
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"expected_anchors": ["H:\\Memory"],
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"response": "没有记录。",
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}
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),
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"generation_control",
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)
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self.assertEqual(
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_failure_stage(
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{
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"correct": False,
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"answerable": False,
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"retrieval_target_found": False,
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"response": "我无法确认。",
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}
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),
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"unknown_refusal_control",
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)
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def test_attribute_alias_routes_without_exact_source_wording(self) -> None:
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bank = PagedMemoryBankV2(
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16,
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router=self.router,
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page_capacity=2,
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top_k_pages=2,
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top_k_records=2,
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max_hops=2,
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)
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record, _ = bank.write(
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text="记忆主体优先放在进程内存，显存只保存热点记录。",
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key=torch.randn(16),
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entity="user",
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attribute="记忆存储优先级",
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value="DRAM",
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confidence=0.98,
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)
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selected, _ = bank.query(
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query_key=torch.randn(16),
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query_text="容量不够时才降级，平时优先使用哪层内存？",
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top_k_pages=2,
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top_k_records=2,
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)
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self.assertIn(record.record_id, {item.record_id for item in selected})
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def test_cjk_phrase_alias_reaches_operational_record(self) -> None:
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bank = PagedMemoryBankV2(
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16,
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router=self.router,
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page_capacity=2,
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top_k_pages=2,
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top_k_records=2,
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)
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record, _ = bank.write(
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text="当前 token 不会对一百万个 slot 做全量注意力。",
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key=torch.randn(16),
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semantic_key=torch.randn(16),
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entity="README_NATURAL_MEMORY_V2.md",
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attribute="operational:1",
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value="Top-K；全量注意力",
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confidence=0.98,
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)
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selected, _ = bank.query(
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query_key=torch.randn(16),
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query_text="百万 slot 会不会进行全量注意力？",
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top_k_pages=2,
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top_k_records=2,
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)
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self.assertIn(record.record_id, {item.record_id for item in selected})
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def test_ambiguous_symbol_fans_out_but_stays_bounded(self) -> None:
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bank = PagedMemoryBankV2(
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16,
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router=self.router,
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page_capacity=1,
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top_k_pages=8,
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top_k_records=1,
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max_pages=64,
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)
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expected = set()
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for index in range(6):
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record, _ = bank.write(
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text=f"第 {index} 个文件定义了 run 函数。",
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key=torch.randn(16),
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entity=f"module_{index}.py",
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attribute="symbol:run",
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value="run",
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confidence=0.9,
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)
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expected.add(record.record_id)
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selected, _ = bank.query(
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query_key=torch.randn(16),
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query_text="run 函数分别在哪些文件中定义？",
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top_k_pages=8,
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top_k_records=1,
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)
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selected_ids = {item.record_id for item in selected}
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self.assertEqual(selected_ids, expected)
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self.assertLessEqual(len(selected), 8)
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if __name__ == "__main__":
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unittest.main()
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