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
This commit is contained in:
WpyQwq
@@ -0,0 +1,203 @@
|
||||
"""Low-pressure end-to-end test for the embedded Natural Memory v2 path.
|
||||
|
||||
This benchmark intentionally uses only the model package's third safetensors
|
||||
memory shard. It does not create SQLite files or exercise disk paging. The
|
||||
long-context cases lower the temporary KV budget so the test measures the
|
||||
model-owned archive/read path without asking a 12 GiB GPU to hold a huge KV.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import gc
|
||||
import json
|
||||
import random
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import torch
|
||||
|
||||
from .qwen_integration import load_qwen_dynamic, load_tokenizer
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument(
|
||||
"--model-path",
|
||||
default=r"H:\Memory\V2_dpskw\qwen3_5_4b_natural_memory_v2",
|
||||
)
|
||||
parser.add_argument("--lengths", default="4096,8192,16384,32768")
|
||||
parser.add_argument("--kv-budget", type=int, default=2048)
|
||||
parser.add_argument("--chunk-tokens", type=int, default=1024)
|
||||
parser.add_argument("--max-new-tokens", type=int, default=8)
|
||||
parser.add_argument("--no-4bit", action="store_true")
|
||||
parser.add_argument(
|
||||
"--output",
|
||||
default=r"H:\Memory\V2_dpskw\embedded_memory_v2_long_benchmark.json",
|
||||
)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def build_prompt(tokenizer: Any, target_tokens: int, seed: int) -> tuple[str, str, int]:
|
||||
rng = random.Random(seed + target_tokens)
|
||||
answer = f"EMBEDDED-LONG-{target_tokens}-{rng.randrange(100000, 999999)}"
|
||||
needle = f"长期记忆锚点：唯一编号是 {answer}。"
|
||||
filler = (
|
||||
"这是长文本记忆压力测试中的普通背景段落，包含项目说明、日期、日志和无关备注。"
|
||||
"这些内容不是问题答案，读取时应保留原文但忽略干扰。"
|
||||
)
|
||||
chunks: list[str] = []
|
||||
while len(tokenizer(" ".join(chunks + [filler, needle]), add_special_tokens=False)["input_ids"]) < target_tokens:
|
||||
chunks.append(filler)
|
||||
# Keep the needle safely inside the archived prefix even in the smallest
|
||||
# case, so a pass must come from memory rather than the retained window.
|
||||
pivot = max(1, len(chunks) // 3)
|
||||
material = " ".join(chunks[:pivot] + [needle] + chunks[pivot:])
|
||||
prompt = (
|
||||
"请阅读下面的长材料，回答末尾问题，只输出编号，不要解释。\n"
|
||||
"---开始材料---\n"
|
||||
f"{material}\n"
|
||||
"---结束材料---\n"
|
||||
"问题：长期记忆锚点的唯一编号是什么？"
|
||||
)
|
||||
prompt_tokens = len(tokenizer(prompt, add_special_tokens=False)["input_ids"])
|
||||
return prompt, answer, prompt_tokens
|
||||
|
||||
|
||||
def chat_inputs(tokenizer: Any, text: str, device: torch.device) -> dict[str, torch.Tensor]:
|
||||
encoded = tokenizer.apply_chat_template(
|
||||
[{"role": "user", "content": text}],
|
||||
tokenize=True,
|
||||
add_generation_prompt=True,
|
||||
return_tensors="pt",
|
||||
return_dict=True,
|
||||
enable_thinking=False,
|
||||
)
|
||||
return {
|
||||
key: value.to(device)
|
||||
for key, value in encoded.items()
|
||||
if isinstance(value, torch.Tensor)
|
||||
}
|
||||
|
||||
|
||||
def run_case(model: Any, tokenizer: Any, target_tokens: int, args: argparse.Namespace) -> dict[str, Any]:
|
||||
device = model._find_layer_device()
|
||||
model.reset_memory(batch_size=1, device=device)
|
||||
assert model.memory_os_v2 is not None
|
||||
model.memory_os_v2.kv_budget.max_tokens = int(args.kv_budget)
|
||||
prompt, answer, prompt_tokens = build_prompt(tokenizer, target_tokens, 20260904)
|
||||
encoded = chat_inputs(tokenizer, prompt, device)
|
||||
query_text = "长期记忆锚点的唯一编号"
|
||||
query = tokenizer(query_text, add_special_tokens=False, return_tensors="pt")
|
||||
query_ids = query["input_ids"].to(device)
|
||||
query_mask = query.get("attention_mask")
|
||||
if query_mask is None:
|
||||
query_mask = torch.ones_like(query_ids)
|
||||
query_mask = query_mask.to(device)
|
||||
started = time.perf_counter()
|
||||
with torch.inference_mode():
|
||||
output = model.generate(
|
||||
**encoded,
|
||||
max_new_tokens=args.max_new_tokens,
|
||||
do_sample=False,
|
||||
use_cache=True,
|
||||
update_memory=False,
|
||||
pad_token_id=tokenizer.pad_token_id,
|
||||
memory_query_input_ids=query_ids,
|
||||
memory_query_attention_mask=query_mask,
|
||||
memory_query_text=query_text,
|
||||
)
|
||||
if device.type == "cuda":
|
||||
torch.cuda.synchronize(device)
|
||||
elapsed = time.perf_counter() - started
|
||||
response_ids = output[0, encoded["input_ids"].shape[1] :]
|
||||
response = tokenizer.decode(response_ids.detach().cpu().tolist(), skip_special_tokens=True).strip()
|
||||
stats = model.memory_v2_stats()
|
||||
records = model.memory_os_v2.bank.records
|
||||
archived_text_hit = any(
|
||||
answer in tokenizer.decode(record.token_ids.tolist(), skip_special_tokens=True)
|
||||
for record in records.values()
|
||||
if record.memory_type == "context_chunk" and isinstance(record.token_ids, torch.Tensor)
|
||||
)
|
||||
query_key = model._encode_model_key(query_ids, query_mask)[0]
|
||||
retrieved, decision = model.read_hierarchical_memory(
|
||||
query_key,
|
||||
query_text=query_text,
|
||||
query_token_ids=query_ids[0],
|
||||
top_k_pages=model.memory_config.memory_top_k_pages,
|
||||
top_k_records=model.memory_config.memory_top_k_records,
|
||||
max_hops=model.memory_config.memory_max_hops,
|
||||
)
|
||||
retrieved_hit = any(
|
||||
answer in tokenizer.decode(record.token_ids.tolist(), skip_special_tokens=True)
|
||||
for record in retrieved
|
||||
if isinstance(record.token_ids, torch.Tensor)
|
||||
)
|
||||
return {
|
||||
"target_tokens": target_tokens,
|
||||
"prompt_tokens": prompt_tokens,
|
||||
"kv_budget_tokens": args.kv_budget,
|
||||
"chunk_tokens": args.chunk_tokens,
|
||||
"response": response,
|
||||
"expected": answer,
|
||||
"generation_hit": answer in response,
|
||||
"archived_text_hit": archived_text_hit,
|
||||
"retrieved_text_hit": retrieved_hit,
|
||||
"retrieved_records": len(retrieved),
|
||||
"router_stop_reason": decision.stop_reason,
|
||||
"router_hop_count": decision.hop_count,
|
||||
"seconds": elapsed,
|
||||
"records": stats.get("records", 0),
|
||||
"pages": stats.get("pages", 0),
|
||||
"gpu_cache_records": stats.get("gpu_cache_records", 0),
|
||||
"gpu_cache_tokens": stats.get("gpu_cache_tokens", 0),
|
||||
"gpu_cache_device": stats.get("gpu_cache_device", "none"),
|
||||
"passed": bool(archived_text_hit and retrieved_hit),
|
||||
}
|
||||
|
||||
|
||||
def main() -> None:
|
||||
args = parse_args()
|
||||
lengths = [int(item.strip()) for item in args.lengths.split(",") if item.strip()]
|
||||
tokenizer = load_tokenizer(args.model_path)
|
||||
model = load_qwen_dynamic(args.model_path, load_in_4bit=not args.no_4bit)
|
||||
model.eval()
|
||||
model.memory_config.context_chunk_tokens = int(args.chunk_tokens)
|
||||
report: dict[str, Any] = {
|
||||
"benchmark": "Natural Memory v2 embedded third-shard long-memory test",
|
||||
"storage_mode": model.memory_config.memory_storage_mode,
|
||||
"tier_store_enabled": bool(model.memory_os_v2 and model.memory_os_v2.bank.tier_store is not None),
|
||||
"model_path": str(Path(args.model_path).resolve()),
|
||||
"lengths": lengths,
|
||||
"quantization": "4bit_nf4" if not args.no_4bit else "none",
|
||||
"kv_budget_tokens": args.kv_budget,
|
||||
"chunk_tokens": args.chunk_tokens,
|
||||
"rows": [],
|
||||
}
|
||||
try:
|
||||
if report["storage_mode"] != "embedded" or report["tier_store_enabled"]:
|
||||
raise RuntimeError("embedded benchmark requires memory_storage_mode=embedded and no tier store")
|
||||
for target_tokens in lengths:
|
||||
row = run_case(model, tokenizer, target_tokens, args)
|
||||
report["rows"].append(row)
|
||||
print(json.dumps(row, ensure_ascii=False))
|
||||
finally:
|
||||
model.reset_memory(batch_size=1, device=model._find_layer_device())
|
||||
model.close_memory_storage()
|
||||
del model
|
||||
gc.collect()
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
report["passed_cases"] = sum(bool(row["passed"]) for row in report["rows"])
|
||||
report["total_cases"] = len(report["rows"])
|
||||
report["all_passed"] = report["passed_cases"] == report["total_cases"]
|
||||
output = Path(args.output)
|
||||
output.parent.mkdir(parents=True, exist_ok=True)
|
||||
output.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
|
||||
print(json.dumps(report, ensure_ascii=False, indent=2))
|
||||
print(f"saved={output}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user