Perf: use F.linear for MLP (#4513)

It brings <1% speedup.

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

- **Refactor**
- Simplified linear transformation implementation in the neural network
layer
	- Improved code readability and efficiency in matrix operations
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

deepmd/pt/model/network/mlp.py

@@ -8,6 +8,7 @@
 import numpy as np
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 
 from deepmd.pt.utils import (
     env,
@@ -202,18 +203,14 @@ def forward(
         ori_prec = xx.dtype
         if not env.DP_DTYPE_PROMOTION_STRICT:
             xx = xx.to(self.prec)
-        yy = (
-            torch.matmul(xx, self.matrix) + self.bias
-            if self.bias is not None
-            else torch.matmul(xx, self.matrix)
-        )
-        yy = self.activate(yy).clone()
+        yy = F.linear(xx, self.matrix.t(), self.bias)
+        yy = self.activate(yy)
         yy = yy * self.idt if self.idt is not None else yy
         if self.resnet:
             if xx.shape[-1] == yy.shape[-1]:
-                yy += xx
+                yy = yy + xx
             elif 2 * xx.shape[-1] == yy.shape[-1]:
-                yy += torch.concat([xx, xx], dim=-1)
+                yy = yy + torch.concat([xx, xx], dim=-1)
             else:
                 yy = yy
         if not env.DP_DTYPE_PROMOTION_STRICT: