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
65 lines
2.3 KiB
Python
65 lines
2.3 KiB
Python
"""Evaluate a Qwen dynamic-memory adapter on streaming JSONL records."""
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from __future__ import annotations
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import argparse
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import torch
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from .qwen_integration import load_qwen_dynamic, load_tokenizer
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from .train_qwen_memory import encode_messages, load_records, pad_batch
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def main() -> None:
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parser = argparse.ArgumentParser()
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parser.add_argument("--model-path", default=".")
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parser.add_argument("--data", default="V2_dpskw/data/demo_stream.jsonl")
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parser.add_argument("--adapter", default="V2_dpskw/qwen_memory_adapter")
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parser.add_argument("--max-length", type=int, default=512)
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parser.add_argument("--no-4bit", action="store_true")
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args = parser.parse_args()
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tokenizer = load_tokenizer(args.model_path)
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model = load_qwen_dynamic(args.model_path, load_in_4bit=not args.no_4bit)
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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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pad_id = int(tokenizer.pad_token_id)
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records = load_records(args.data)
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correct = 0
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for record in records:
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memory_input, memory_mask, _ = pad_batch(
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[encode_messages(tokenizer, record["memory"], args.max_length)], pad_id
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)
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query_input, query_mask, query_labels = pad_batch(
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[encode_messages(tokenizer, record["query"], args.max_length)], pad_id
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)
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with torch.no_grad():
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memory_output = 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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)
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output = model(
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input_ids=query_input.to(device),
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attention_mask=query_mask.to(device),
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memory_state=memory_output.memory,
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read_memory=True,
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update_memory=False,
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)
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labels = query_labels.to(device)
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shifted_labels = labels[..., 1:]
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predictions = output.logits[..., :-1, :].argmax(dim=-1)
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target_positions = shifted_labels != -100
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sequence_ok = bool((predictions[target_positions] == shifted_labels[target_positions]).all())
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correct += int(sequence_ok)
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print(f"sequence_ok={sequence_ok}")
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print(f"exact_sequence_accuracy={correct / len(records):.3f}")
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if __name__ == "__main__":
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main()
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