[Chore] Replace swish with silu (#32459)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

vllm/model_executor/models/phi4mm_utils.py

@@ -36,10 +36,13 @@ def get_activation(name: str = "relu") -> torch.nn.Module:
     if name == "gelu":
         return nn.GELU()
     if name == "swish":
-        return Swish()
+        return nn.SiLU()
     if name == "sigmoid":
-        return torch.nn.Sigmoid()
-    return nn.Identity()
+        return nn.Sigmoid()
+    if name == "identity":
+        return nn.Identity()
+
+    raise NotImplementedError(name)
 
 
 def adaptive_enc_mask(
@@ -93,44 +96,14 @@ def adaptive_enc_mask(
     return mask_left & mask_right
 
 
-class Swish(nn.Module):
-    """Implement Swish activation module.
-    From https://arxiv.org/pdf/2005.03191.pdf
-
-    """
-
-    def __init__(self) -> None:
-        super().__init__()
-        self.act_fn = nn.Sigmoid()
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Apply Swish function
-
-        Args:
-            x: torch.Tensor
-                Input.
-        """
-        return x * self.act_fn(x)
-
-
 class GLU(nn.Module):
     """Implement Gated Linear Unit (GLU) module"""
 
     def __init__(self, dim: int = -1, act_name: str = "sigmoid") -> None:
         super().__init__()
+
         self.dim = dim
-        self.act_name = act_name.lower()
-
-        if self.act_name == "relu":
-            self.act_fn = nn.ReLU(inplace=True)
-        elif self.act_name == "gelu":
-            self.act_fn = nn.GELU()
-        elif self.act_name == "swish":
-            self.act_fn = Swish()
-        elif self.act_name == "sigmoid":
-            self.act_fn = nn.Sigmoid()
-        else:
-            self.act_fn = nn.Identity()
+        self.act_fn = get_activation(act_name)
 
     def forward(self, x: Tensor) -> Tensor:
         """GLU forward
@@ -204,16 +177,7 @@ class GLUPointWiseConv(nn.Module):
                 padding=(kernel_size - 1) // 2,
             )
 
-        if glu_type == "sigmoid":
-            self.glu_act = nn.Sigmoid()
-        elif glu_type == "relu":
-            self.glu_act = nn.ReLU()
-        elif glu_type == "gelu":
-            self.glu_act = nn.GELU()
-        elif glu_type == "swish":
-            self.glu_act = Swish()
-        else:
-            raise ValueError(f"Unsupported activation type {self.glu_act}")
+        self.glu_act = get_activation(glu_type)
 
         if bias_in_glu:
             self.b1 = nn.Parameter(torch.zeros(1, output_dim, 1))