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
108 lines
3.4 KiB
Python
108 lines
3.4 KiB
Python
"""Verify answering after a model restart using only a saved user memory state."""
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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 .benchmark_qwen import _generation_prompt
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from .qwen_integration import load_memory_config, load_qwen_dynamic, load_tokenizer
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from .train_qwen_memory import encode_messages, pad_batch
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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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"--adapter",
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default="V2_dpskw/qwen_memory_adapter_pointer",
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)
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parser.add_argument(
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"--data",
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default="V2_dpskw/data/benchmark_eval.jsonl",
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)
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parser.add_argument("--record-index", type=int, default=0)
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parser.add_argument(
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"--memory-state",
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default="V2_dpskw/data/user_memory_demo.pt",
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)
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parser.add_argument("--max-length", type=int, default=128)
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parser.add_argument("--max-new-tokens", type=int, default=4)
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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 = [
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json.loads(line)
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for line in Path(args.data).read_text(encoding="utf-8").splitlines()
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if line.strip()
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]
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record = records[args.record_index]
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tokenizer = load_tokenizer(args.model_path)
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memory_config = load_memory_config(args.adapter)
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model = load_qwen_dynamic(
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args.model_path,
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memory_config=memory_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.adapter)
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model.eval()
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device = model._find_layer_device()
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memory = encode_messages(tokenizer, record["memory"], args.max_length)
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memory_input, memory_mask, _ = pad_batch([memory], int(tokenizer.pad_token_id))
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state_path = Path(args.memory_state)
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with torch.inference_mode():
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model(
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input_ids=memory_input.to(device),
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attention_mask=memory_mask.to(device),
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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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model.save_runtime_memory(state_path)
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print(f"saved_memory_state={state_path}")
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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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restarted = load_qwen_dynamic(
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args.model_path,
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memory_config=memory_config,
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load_in_4bit=not args.no_4bit,
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)
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restarted.load_memory_adapter(args.adapter)
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restarted.eval()
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device = restarted._find_layer_device()
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restarted.load_runtime_memory(state_path, device=device)
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prompt = _generation_prompt(tokenizer, record["query"][:-1], device)
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with torch.inference_mode():
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output = restarted.generate(
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**prompt,
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max_new_tokens=args.max_new_tokens,
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do_sample=False,
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update_memory=False,
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use_cache=True,
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pad_token_id=tokenizer.pad_token_id,
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)
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generated = tokenizer.decode(
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output[0, prompt["input_ids"].shape[1] :],
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skip_special_tokens=True,
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).replace(" ", "").replace("\r", "").replace("\n", "").strip()
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expected = str(record["answer"])
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print(f"query_contains_history=false")
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print(f"expected={expected}")
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print(f"restarted_generated={generated}")
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print(f"correct={generated.startswith(expected)}")
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if __name__ == "__main__":
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main()
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