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

tests/test_model.py

@@ -0,0 +1,34 @@
+from __future__ import annotations
+
+import unittest
+
+import torch
+
+from V2_dpskw.model import DynamicMemoryConfig, DynamicMemoryLM
+from V2_dpskw.tasks import sample_associative_batch
+
+
+class DynamicMemoryModelTest(unittest.TestCase):
+    def setUp(self) -> None:
+        self.device = torch.device("cpu")
+        self.config = DynamicMemoryConfig(vocab_size=32, max_seq_len=16, d_model=32, n_layers=1, n_heads=4, memory_slots=2)
+        self.model = DynamicMemoryLM(self.config).to(self.device)
+
+    def test_shapes_and_loss(self) -> None:
+        batch = sample_associative_batch(batch_size=3, vocab_size=self.config.vocab_size, device=self.device)
+        memory = self.model(batch.learn_chunks[0]).memory
+        output = self.model(batch.query_input, memory=memory, update_memory=False, labels=batch.query_labels)
+        self.assertEqual(tuple(output.logits.shape), (3, 2, self.config.vocab_size))
+        self.assertEqual(tuple(output.memory.shape), (3, self.config.memory_slots, self.config.d_model))
+        self.assertIsNotNone(output.loss)
+        output.loss.backward()
+
+    def test_memory_changes_after_learning_chunk(self) -> None:
+        batch = sample_associative_batch(batch_size=2, vocab_size=self.config.vocab_size, device=self.device)
+        initial = self.model.memory.initial_state(2, device=self.device, dtype=torch.float32)
+        updated = self.model(batch.learn_chunks[0], memory=initial, update_memory=True).memory
+        self.assertFalse(torch.allclose(initial, updated))
+
+
+if __name__ == "__main__":
+    unittest.main()