perf: SMoE 消除 GPU 同步 + CTRModel 去冗余 reshape

1. SMoE: 移除 if not mask.any()（64次GPU→CPU同步/forward）
   - k=2时每个expert都分到token，检查从不跳过
   - 改用 token_idx.numel()==0 判断（元数据操作，不同步）
2. SMoE: out_flat reshape 提到循环外（省7次重复）
3. CTRModel: encoder_output.reshape().squeeze() → .squeeze()

代码/code/infer.py

@@ -343,24 +343,20 @@ class SMoE(nn.Module):
         x_flat = x.reshape(-1, D)                # [B*S, D]
         idx_flat = topk_idx.reshape(-1, self.k)  # [B*S, k]
         score_flat = topk_score.reshape(-1, self.k)
+        out_flat = out.reshape(-1, D)            # 提前 reshape，避免循环内重复
 
         for i in range(self.num_experts):
             # 找到被路由到 expert i 的 token
             mask = (idx_flat == i)  # [B*S, k]
+            # 注：k=2 时几乎所有 expert 都分到 token，移除 .any() 检查避免 GPU 同步
 
-            if not mask.any():
+            token_idx, k_idx = mask.nonzero(as_tuple=True)
+            if token_idx.numel() == 0:
                 continue
 
-            # 哪些 token 命中了 expert i
-            token_idx, k_idx = mask.nonzero(as_tuple=True)
-
             selected_x = x_flat[token_idx]  # [N, D]
-
             expert_out = self.experts[i](selected_x)  # [N, D]
-
             weight = score_flat[token_idx, k_idx].unsqueeze(-1)
-
-            out_flat = out.reshape(-1, D)
             out_flat[token_idx] += expert_out * weight
 
         importance = probs.sum(dim=(0,1))  # [E]
@@ -443,8 +439,7 @@ class CTRModel(nn.Module):
             x=seq_input,
             extension={"mask": seq_mask.unsqueeze(0).unsqueeze(0)},
         )
-        encoder_output_dim = encoder_output.shape[-1]
+        encoder_output = encoder_output.squeeze(0)
-        encoder_output = encoder_output.reshape(1, -1, encoder_output_dim).squeeze(0)
         pred = self.linear(encoder_output)
         pred_logits = torch.clamp(pred, min=-15.0, max=15.0)
         return pred_logits, moe_loss