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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"""Train the internal high-recall automatic memory write policy.
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The frozen Qwen backbone and the existing native memory controller provide
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the representation. Only a small policy head is trained. Positive examples
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cover durable personal facts, preferences, plans, project constraints and
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corrections; negative examples cover questions, requests, hypotheticals and
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casual conversation. The runtime still stores the exact user token sequence,
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so this head decides *whether* to remember rather than compressing the fact.
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"""
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from __future__ import annotations
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import argparse
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import json
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import random
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from pathlib import Path
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import torch
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import torch.nn.functional as F
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from .qwen_integration import load_memory_config, load_qwen_dynamic, load_tokenizer
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POSITIVE = (
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"我叫{value}。",
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"我的常住城市是{value}。",
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"我最喜欢的水果是{value}。",
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"我正在开发{value}项目。",
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"以后请把代码默认写成{value}。",
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"我计划在{value}完成这个任务。",
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"我的工作地点是{value}。",
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"我的常用时区是{value}。",
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"请记住这条信息：{value}。",
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"更正一下，刚才的内容应该是{value}。",
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"这是我的长期偏好：{value}。",
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"这个项目的重要约束是{value}。",
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)
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NEGATIVE = (
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"帮我写一段关于{value}的代码。",
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"请解释{value}是什么意思。",
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"{value}是什么？",
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"你觉得{value}怎么样？",
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"如果以后遇到{value}，应该怎么办？",
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"今天天气不错，随便聊聊{value}。",
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"请把{value}翻译成英文。",
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"计算一下{value}。",
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"给我介绍一下{value}。",
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"哈哈，{value}真有意思。",
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"我想知道之前有没有提到{value}。",
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"假设我选择{value}，会发生什么？",
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"我叫什么？",
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"我的名字是什么？",
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"你还记得我叫什么吗？",
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"我正在开发什么项目？",
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"我的项目叫什么？",
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"请问我的项目叫什么？",
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"我的工作地点是什么？",
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"我的工作地点代号是什么？",
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"工作地点代号是多少？",
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"你记得我的工作地点吗？",
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"请告诉我之前有没有说过{value}。",
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"我之前有没有告诉过你{value}？",
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"能不能帮我完成{value}？",
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"如何处理{value}？",
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"请给我一个{value}的方案。",
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)
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VALUES = (
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"小明",
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"上海",
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"红富士苹果",
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"个人记忆系统",
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"Python",
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"下周五",
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"R7",
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"Asia/Shanghai",
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"不要删除用户数据",
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"使用简洁中文",
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"每天晚上八点",
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"蓝鲸-47",
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)
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def make_examples(seed: int, count: int) -> list[tuple[str, float]]:
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rng = random.Random(seed)
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examples: list[tuple[str, float]] = []
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for _ in range(count):
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examples.append((rng.choice(POSITIVE).format(value=rng.choice(VALUES)), 1.0))
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examples.append((rng.choice(NEGATIVE).format(value=rng.choice(VALUES)), 0.0))
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rng.shuffle(examples)
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return examples
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def encode_batch(tokenizer, texts: list[str], device: torch.device):
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rows = []
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masks = []
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for text in texts:
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encoded = tokenizer.apply_chat_template(
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[{"role": "user", "content": text}],
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tokenize=True,
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add_generation_prompt=True,
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return_tensors="pt",
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return_dict=True,
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enable_thinking=False,
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)
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rows.append(encoded["input_ids"][0])
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masks.append(encoded.get("attention_mask", torch.ones_like(encoded["input_ids"]))[0])
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max_length = max(row.numel() for row in rows)
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input_ids = torch.zeros(len(rows), max_length, dtype=torch.long, device=device)
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attention_mask = torch.zeros(len(rows), max_length, dtype=torch.long, device=device)
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for index, (row, mask) in enumerate(zip(rows, masks)):
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input_ids[index, : row.numel()] = row.to(device)
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attention_mask[index, : mask.numel()] = mask.to(device)
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return input_ids, attention_mask
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@torch.inference_mode()
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def collect_representations(model, tokenizer, examples, *, batch_size: int, device):
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vectors = []
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labels = []
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for start in range(0, len(examples), batch_size):
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batch = examples[start : start + batch_size]
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input_ids, attention_mask = encode_batch(
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tokenizer,
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[item[0] for item in batch],
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device,
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)
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model.reset_memory(batch_size=len(batch), device=device)
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model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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read_memory=False,
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update_memory=True,
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return_memory=True,
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use_cache=False,
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)
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representation = getattr(model.memory, "last_write_representation", None)
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if representation is None:
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raise RuntimeError("native controller did not expose a write representation")
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vectors.append(representation.detach().float().cpu())
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labels.extend(item[1] for item in batch)
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return torch.cat(vectors), torch.tensor(labels, dtype=torch.float32)
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def evaluate(policy, vectors, labels, threshold: float) -> dict[str, float]:
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with torch.inference_mode():
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probabilities = torch.sigmoid(policy(vectors)).cpu()
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labels = labels.cpu()
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predictions = probabilities >= threshold
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positive = labels >= 0.5
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negative = ~positive
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true_positive = (predictions & positive).sum().item()
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false_negative = ((~predictions) & positive).sum().item()
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false_positive = (predictions & negative).sum().item()
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true_negative = ((~predictions) & negative).sum().item()
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return {
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"threshold": threshold,
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"accuracy": float((predictions == positive).float().mean()),
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"positive_recall": true_positive / max(1, true_positive + false_negative),
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"negative_specificity": true_negative / max(1, true_negative + false_positive),
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"false_positive_rate": false_positive / max(1, false_positive + true_negative),
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}
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def main() -> None:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--model-path", default=".")
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parser.add_argument(
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"--base-adapter",
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default="V2_dpskw/qwen_memory_adapter_natural_controller_v3",
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)
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parser.add_argument(
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"--output-adapter",
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default="V2_dpskw/qwen_memory_adapter_natural_auto_v3",
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)
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parser.add_argument("--steps", type=int, default=2400)
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parser.add_argument("--example-count", type=int, default=1280)
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parser.add_argument("--batch-size", type=int, default=32)
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parser.add_argument("--lr", type=float, default=2e-4)
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parser.add_argument("--threshold", type=float, default=0.35)
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parser.add_argument(
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"--text-memory-threshold",
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type=float,
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default=0.30,
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help="retrieval threshold used by the automatic-memory adapter",
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)
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parser.add_argument("--seed", type=int, default=20260904)
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parser.add_argument("--no-4bit", action="store_true")
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args = parser.parse_args()
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random.seed(args.seed)
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torch.manual_seed(args.seed)
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tokenizer = load_tokenizer(args.model_path)
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config = load_memory_config(args.base_adapter)
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config.natural_language_memory = True
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config.automatic_memory = True
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config.auto_memory_threshold = args.threshold
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config.text_memory_threshold = args.text_memory_threshold
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config.persistent_memory = False
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model = load_qwen_dynamic(
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args.model_path,
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memory_config=config,
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load_in_4bit=not args.no_4bit,
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)
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model.load_memory_adapter(args.base_adapter, strict=True)
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if model.memory_policy is None:
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raise RuntimeError("automatic memory policy was not created")
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model.eval()
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model.memory_policy.train()
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device = model._find_layer_device()
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examples = make_examples(args.seed, args.example_count)
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split = int(len(examples) * 0.8)
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train_examples = examples[:split]
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eval_examples = examples[split:]
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train_vectors, train_labels = collect_representations(
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model,
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tokenizer,
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train_examples,
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batch_size=args.batch_size,
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device=device,
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)
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eval_vectors, eval_labels = collect_representations(
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model,
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tokenizer,
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eval_examples,
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batch_size=args.batch_size,
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device=device,
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)
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train_vectors = train_vectors.to(device)
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train_labels = train_labels.to(device)
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eval_vectors = eval_vectors.to(device)
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eval_labels = eval_labels.to(device)
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optimizer = torch.optim.AdamW(model.memory_policy.parameters(), lr=args.lr, weight_decay=0.01)
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positive_weight = torch.tensor([1.5], device=device)
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rng = random.Random(args.seed + 1)
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for step in range(1, args.steps + 1):
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indices = torch.tensor(
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[rng.randrange(train_vectors.shape[0]) for _ in range(args.batch_size)],
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dtype=torch.long,
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device=device,
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)
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logits = model.memory_policy(train_vectors[indices])
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weights = torch.where(train_labels[indices] >= 0.5, positive_weight, torch.ones_like(logits))
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loss = F.binary_cross_entropy_with_logits(logits, train_labels[indices], weight=weights)
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optimizer.zero_grad(set_to_none=True)
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loss.backward()
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torch.nn.utils.clip_grad_norm_(model.memory_policy.parameters(), 1.0)
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optimizer.step()
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if step == 1 or step % 100 == 0 or step == args.steps:
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stats = evaluate(model.memory_policy, train_vectors, train_labels, args.threshold)
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print(
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f"step={step} loss={float(loss.detach()):.5f} "
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f"train_accuracy={stats['accuracy']:.3f} "
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f"positive_recall={stats['positive_recall']:.3f} "
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f"false_positive_rate={stats['false_positive_rate']:.3f}"
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)
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model.memory_policy.eval()
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model._memory_policy_ready = True
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output_dir = Path(args.output_adapter)
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model.save_memory_adapter(output_dir)
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report = {
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"steps": args.steps,
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"example_count_per_class": args.example_count,
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"train_examples": len(train_examples),
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"eval_examples": len(eval_examples),
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"source_adapter": str(args.base_adapter),
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"policy": "high_recall_automatic_memory_importance",
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"threshold": args.threshold,
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"train": evaluate(model.memory_policy, train_vectors, train_labels, args.threshold),
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"eval": evaluate(model.memory_policy, eval_vectors, eval_labels, args.threshold),
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}
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(output_dir / "auto_policy_training.json").write_text(
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json.dumps(report, ensure_ascii=False, indent=2),
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encoding="utf-8",
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)
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print(json.dumps(report, ensure_ascii=False, indent=2))
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if __name__ == "__main__":
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main()
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