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
110 lines
3.9 KiB
Python
110 lines
3.9 KiB
Python
"""Package learned memory into an adapter checkpoint and verify restart/reset."""
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from __future__ import annotations
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import argparse
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import gc
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import json
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from pathlib import Path
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import torch
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from .evaluate_native_memory import _controller_step, _encode_prompt
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from .qwen_integration import (
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DEFAULT_MEMORY_RESET_TOKEN,
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load_memory_config,
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load_qwen_dynamic,
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load_tokenizer,
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resolve_memory_reset_token,
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)
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from .train_native_memory import load_records
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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("--adapter", default="V2_dpskw/qwen_memory_adapter_native_v3")
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parser.add_argument("--data", default="V2_dpskw/data/native_memory/eval.jsonl")
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parser.add_argument(
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"--output-adapter",
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default="V2_dpskw/qwen_memory_adapter_native_v3_persistent",
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)
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parser.add_argument("--max-length", type=int, default=192)
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parser.add_argument("--max-new-tokens", type=int, default=8)
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parser.add_argument("--no-4bit", action="store_true")
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args = parser.parse_args()
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records = load_records(args.data)
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record = next(record for record in records if record.get("answerable"))
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tokenizer = load_tokenizer(args.model_path)
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config = load_memory_config(args.adapter)
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config.persistent_memory = True
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config.reset_token_id = resolve_memory_reset_token(tokenizer, DEFAULT_MEMORY_RESET_TOKEN)
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model = load_qwen_dynamic(args.model_path, memory_config=config, load_in_4bit=not args.no_4bit)
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model.load_memory_adapter(args.adapter)
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model.reset_memory()
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for chunk in record["memory_chunks"]:
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_controller_step(model, tokenizer, chunk, args.max_length)
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output_adapter = Path(args.output_adapter)
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model.save_persistent_memory_checkpoint(output_adapter)
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saved_norm = float(model.runtime.state.detach().float().norm())
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del model
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gc.collect()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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restart_config = load_memory_config(output_adapter)
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restarted = load_qwen_dynamic(
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args.model_path,
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memory_config=restart_config,
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load_in_4bit=not args.no_4bit,
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)
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restarted.load_memory_adapter(output_adapter)
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answer = str(record["answer"])
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prompt = _encode_prompt(tokenizer, record["query"][:-1])
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device = restarted._find_layer_device()
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generated = restarted.generate(
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input_ids=prompt.to(device),
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attention_mask=torch.ones_like(prompt, device=device),
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max_new_tokens=args.max_new_tokens,
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do_sample=False,
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use_cache=False,
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update_memory=False,
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)
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response = tokenizer.decode(
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generated[:, prompt.shape[1] :][0].detach().cpu().tolist(),
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skip_special_tokens=True,
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).strip()
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reset_prompt = _encode_prompt(tokenizer, [{"role": "user", "content": DEFAULT_MEMORY_RESET_TOKEN}])
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restarted.generate(
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input_ids=reset_prompt.to(device),
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attention_mask=torch.ones_like(reset_prompt, device=device),
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max_new_tokens=1,
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do_sample=False,
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use_cache=False,
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update_memory=False,
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)
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reset_norm = float(restarted.runtime.state.detach().float().norm())
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report = {
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"source_adapter": str(args.adapter),
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"output_adapter": str(output_adapter),
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"record_id": record.get("id"),
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"answer": answer,
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"generated_after_restart": response,
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"restart_contains_answer": answer in response,
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"saved_memory_norm": saved_norm,
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"reset_token": DEFAULT_MEMORY_RESET_TOKEN,
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"reset_token_id": restart_config.reset_token_id,
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"memory_norm_after_reset_token": reset_norm,
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"reset_zeroed_memory": reset_norm < 1e-5,
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}
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(output_adapter / "native_checkpoint_verification.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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