代码/code/infer.py

@@ -30,6 +30,7 @@ CONFIG = {
     "merge_threshold": 0.90,  # 合并的余弦相似度阈值
     "signid_mode": "clamp",   # "clamp" 或 "modulo"：处理超界 sign id 的方式
     "sync_timing": False,     # bench 里设 True，做 torch.cuda.synchronize 真实计时
+    "filter_test_users": True,  # 只处理含测试样本的用户（跳过会被丢弃的用户，省算力）
 }
 
 
@@ -165,11 +166,22 @@ class CTRTestSeqDataset(Dataset):
         self.max_ctx_len = max_ctx_len
         self.pred_logids = set(test_logids_ordered) if test_logids_ordered else set()
 
+        # 只处理“含测试样本的用户”：其余用户的前向输出会被丢弃，跳过以省算力。
+        # 不同用户被因果 mask 完全隔离，过滤不改变任何测试样本的预测（AUC/PCOC 不变）。
+        keep_users = None
+        if CONFIG.get("filter_test_users", True) and self.pred_logids:
+            keep_users = {rec['userid'] for logid, rec in item_dict.items()
+                          if logid in self.pred_logids}
+
         self.user_items = defaultdict(list)
+        max_sign = 0
         for logid, rec in item_dict.items():
             userid = rec['userid']
+            if keep_users is not None and userid not in keep_users:
+                continue
+            signs_list = rec['signs'].tolist()
             feasign = defaultdict(list)
-            for slot, sign in zip(rec['slots'].tolist(), rec['signs'].tolist()):
+            for slot, sign in zip(rec['slots'].tolist(), signs_list):
                 feasign[slot].append(sign)
             if max_feasign_per_slot is not None:
                 feasign = {slot: signs[:max_feasign_per_slot[slot]]
@@ -178,16 +190,16 @@ class CTRTestSeqDataset(Dataset):
             feasign = dict(feasign)
             label = rec['clk']
             self.user_items[userid].append((logid, feasign, label))
+            if signs_list:
+                m = max(signs_list)
+                if m > max_sign:
+                    max_sign = m
 
         self.user_ids = sorted(self.user_items.keys())
         self.num_users = len(self.user_ids)
-        self.total_samples = len(item_dict)
-
-        all_signs = set()
-        for rec in item_dict.values():
-            all_signs.update(rec['signs'].tolist())
+        self.total_samples = sum(len(v) for v in self.user_items.values())
         self.max_slot_id = 28
-        self.max_sign_id = max(all_signs) if all_signs else 0
+        self.max_sign_id = max_sign
 
     def __len__(self):
         return self.num_users