[Models] Replace all nn.Conv2d with vLLM's Conv2dLayer (#28842)

Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>

vllm/model_executor/layers/conv.py

@@ -3,6 +3,7 @@
 """Conv Layer Class."""
 
 import math
+from typing import Literal
 
 import torch
 import torch.nn as nn
@@ -23,11 +24,11 @@ class ConvLayerBase(CustomOp):
         out_channels: int,
         kernel_size: int | tuple[int, ...],
         stride: int | tuple[int, ...] = 1,
-        padding: int | tuple[int, ...] = 0,
+        padding: int | tuple[int, ...] | Literal["same", "valid"] = 0,
         dilation: int | tuple[int, ...] = 1,
         groups: int = 1,
         bias: bool = True,
-        padding_mode: str = "zeros",
+        padding_mode: Literal["zeros", "reflect", "replicate", "circular"] = "zeros",
         *,
         params_dtype: torch.dtype | None = None,
     ) -> None:
@@ -36,6 +37,22 @@ class ConvLayerBase(CustomOp):
         if params_dtype is None:
             params_dtype = torch.get_default_dtype()
 
+        valid_padding_strings = {"same", "valid"}
+        if isinstance(padding, str) and padding not in valid_padding_strings:
+            raise ValueError(
+                f"Invalid padding string '{padding}'. "
+                f"Expected one of {valid_padding_strings}."
+            )
+
+        if padding == "same":
+            padding = (
+                kernel_size // 2
+                if isinstance(kernel_size, int)
+                else tuple(k // 2 for k in kernel_size)
+            )
+        elif padding == "valid":
+            padding = 0
+
         kernel_size = (
             (kernel_size,) * self.num_dim
             if isinstance(kernel_size, int)
@@ -45,6 +62,9 @@ class ConvLayerBase(CustomOp):
         padding = (padding,) * self.num_dim if isinstance(padding, int) else padding
         dilation = (dilation,) * self.num_dim if isinstance(dilation, int) else dilation
 
+        if padding == "same" and any(s != 1 for s in stride):
+            raise ValueError("padding='same' is not supported for strided convolutions")
+
         self.in_channels = in_channels
         self.out_channels = out_channels
         self.kernel_size = kernel_size