feat: infer.py 接入 CONFIG 实验开关 + 新增 bench.py 测量闭环

- infer.py: 模块级 CONFIG（fp16/keep_fp32_modules/expert_merge/
  merge_threshold/signid_mode/sync_timing），默认值=当前最优行为；
  load_model 按 CONFIG 控制半精度/FP32敏感层/expert合并；
  RepEncoder 支持 clamp/modulo 两种 sign-id 处理；
  新增 _force_fp32_io 钩子让敏感层在FP16模型里以FP32 IO 计算。
- bench.py: 设置 CONFIG → 跑推理 → cuda.synchronize 真实计时 →
  _cal_score 打印 AUC/PCOC/延迟/总分，支持配置/batch扫描。不进提交包。
- EXPERIMENTS.md: 实验记录表。

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

代码/code/bench.py

@@ -0,0 +1,110 @@
+"""本地测量闭环：设置 infer.CONFIG，跑推理，同步计时，打印 AUC/PCOC/延迟/总分。
+
+不进提交包。在 AI Studio notebook（带 dataset/ 与 ckpt.pt）里运行：
+
+    %cd /home/aistudio/code
+    !python bench.py                       # 默认配置基准
+
+或在 notebook cell 里逐配置扫描：
+
+    import bench
+    bench.run_once({"fp16": False, "expert_merge": False})   # FP32 参考跑
+    bench.run_once({"signid_mode": "modulo"})                # 取模 vs clamp
+"""
+import time
+from pathlib import Path
+
+import torch
+from torch.utils.data import DataLoader
+
+import infer  # 同目录
+
+
+def run_once(config_override=None, batch_size=50, max_batches=None, max_feasign_per_slot=None):
+    """跑一次本地推理并打分。
+
+    Args:
+        config_override: 覆盖 infer.CONFIG 的字典（如 {"fp16": False}）
+        batch_size: DataLoader 的 batch 大小（本地参考；评测端可能自有设定）
+        max_batches: 只跑前 N 个 batch（快速冒烟用），None=全量
+        max_feasign_per_slot: 传给 CTRTestSeqDataset 的截断字典，None=不截断；
+                              默认沿用 baseline 的 {1: 2}
+    Returns:
+        infer._cal_score 的结果 dict
+    """
+    if config_override is None:
+        config_override = {}
+    if max_feasign_per_slot is None:
+        max_feasign_per_slot = {1: 2}
+
+    infer.CONFIG.update(config_override)
+    infer.CONFIG["sync_timing"] = True
+
+    cur = Path(__file__).parent
+    ref = cur / "dataset"
+    history = ref / "history"
+    test_csv = ref / "test.csv"
+    label_file = ref / "label_data.txt"
+
+    # ----- 加载数据 -----
+    files = (sorted(history.glob("*.csv")) if history.exists() else []) + [test_csv]
+    item_dict, user_seq = infer.load_sample_files(files)
+    test_logids = infer.load_logids_from_file(test_csv)
+    ds = infer.CTRTestSeqDataset(
+        test_logids_ordered=list(test_logids),
+        item_dict=item_dict,
+        user_seq=user_seq,
+        max_feasign_per_slot=max_feasign_per_slot,
+        max_ctx_len=None,
+    )
+    loader = DataLoader(
+        ds, batch_size=batch_size, shuffle=False, num_workers=0,
+        collate_fn=infer.make_collate_fn(ds.max_slot_id),
+    )
+    batches = []
+    for b in loader:
+        batches.append(infer.move_batch_to_device(b, torch.device("cpu")))
+        if max_batches is not None and len(batches) >= max_batches:
+            break
+
+    # ----- 加载模型 -----
+    model, dev = infer.load_model(ckpt_path=None)
+
+    # ----- 推理 + 同步计时 -----
+    logid2p = {}
+    t_sum = 0.0
+    cuda = (dev.type == "cuda")
+    with torch.inference_mode():
+        for b in batches:
+            b = infer.move_batch_to_device(b, dev)
+            pm = b["pred_mask"].bool()
+            if cuda:
+                torch.cuda.synchronize()
+            t0 = time.time()
+            logits, _ = model(b)
+            probs = torch.sigmoid(logits.squeeze(-1))
+            if cuda:
+                torch.cuda.synchronize()
+            t_sum += time.time() - t0
+            for lid, p in zip(b["logid"][pm].cpu().tolist(), probs[pm].cpu().tolist()):
+                logid2p[lid] = p
+
+    # ----- 按 test.csv 顺序写 predict.txt 并打分 -----
+    order = [int(l.split(",")[0]) for l in open(test_csv) if l.strip()]
+    pred_path = cur / "predict.txt"
+    with open(pred_path, "w") as f:
+        for lid in order:
+            f.write(f"{logid2p[lid]}\n")
+
+    res = infer._cal_score(pred_path, label_file, default_latency=t_sum)
+    print(
+        f"[BENCH] cfg={config_override} bs={batch_size}"
+        f"{'' if max_batches is None else f' (first {max_batches} batches)'}"
+        f" -> AUC={res['auc']:.5f} PCOC={res['pcoc']:.4f}"
+        f" lat={res['latency']:.2f}s score={res['score_all']:.2f}"
+    )
+    return res
+
+
+if __name__ == "__main__":
+    run_once({})  # 默认配置基准