[Models] Replace all nn.Conv2d with vLLM's Conv2dLayer

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 @@ def __init__(
         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 @@ def __init__(
         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 @@ def __init__(
         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

vllm/model_executor/models/aimv2.py

@@ -12,6 +12,7 @@
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.distributed.utils import divide
 from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.conv import Conv2dLayer
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -58,7 +59,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 class AIMv2PatchEmbed(nn.Module):
     def __init__(self, config: AIMv2Config):
         super().__init__()
-        self.proj = nn.Conv2d(
+        self.proj = Conv2dLayer(
             config.num_channels,
             config.hidden_size,
             kernel_size=(config.patch_size, config.patch_size),

vllm/model_executor/models/blip.py

@@ -12,6 +12,7 @@
 from vllm.attention.layer import MultiHeadAttention
 from vllm.distributed import divide, get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.conv import Conv2dLayer
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
     QKVParallelLinear,
@@ -47,7 +48,7 @@ def __init__(self, config: BlipVisionConfig | Blip2VisionConfig):
 
         self.class_embedding = nn.Parameter(torch.randn(1, 1, self.embed_dim))
 
-        self.patch_embedding = nn.Conv2d(
+        self.patch_embedding = Conv2dLayer(
             in_channels=3,
             out_channels=self.embed_dim,
             kernel_size=self.patch_size,

vllm/model_executor/models/chameleon.py

@@ -22,6 +22,7 @@
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.conv import Conv2dLayer
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -549,7 +550,7 @@ def forward(self, hidden_state: torch.Tensor):
 class ChameleonVQVAEEncoderConvDownsample(nn.Module):
     def __init__(self, in_channels: int):
         super().__init__()
-        self.conv = nn.Conv2d(
+        self.conv = Conv2dLayer(
             in_channels, in_channels, kernel_size=3, stride=2, padding=0
         )
 
@@ -577,23 +578,23 @@ def __init__(
         self.norm1 = torch.nn.GroupNorm(
             num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
         )
-        self.conv1 = torch.nn.Conv2d(
+        self.conv1 = Conv2dLayer(
             in_channels, out_channels, kernel_size=3, stride=1, padding=1
         )
         self.norm2 = torch.nn.GroupNorm(
             num_groups=32, num_channels=out_channels, eps=1e-6, affine=True
         )
         self.dropout = torch.nn.Dropout(config.dropout)
-        self.conv2 = torch.nn.Conv2d(
+        self.conv2 = Conv2dLayer(
             out_channels, out_channels, kernel_size=3, stride=1, padding=1
         )
         if self.in_channels != self.out_channels:
             if self.use_conv_shortcut:
-                self.conv_shortcut = torch.nn.Conv2d(
+                self.conv_shortcut = Conv2dLayer(
                     in_channels, out_channels, kernel_size=3, stride=1, padding=1
                 )
             else:
-                self.nin_shortcut = torch.nn.Conv2d(
+                self.nin_shortcut = Conv2dLayer(
                     in_channels, out_channels, kernel_size=1, stride=1, padding=0
                 )
 
@@ -626,16 +627,16 @@ def __init__(self, in_channels: int):
         self.norm = torch.nn.GroupNorm(
             num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
         )
-        self.q = torch.nn.Conv2d(
+        self.q = Conv2dLayer(
             in_channels, in_channels, kernel_size=1, stride=1, padding=0
         )
-        self.k = torch.nn.Conv2d(
+        self.k = Conv2dLayer(
             in_channels, in_channels, kernel_size=1, stride=1, padding=0
         )
-        self.v = torch.nn.Conv2d(
+        self.v = Conv2dLayer(
             in_channels, in_channels, kernel_size=1, stride=1, padding=0
         )
-        self.proj_out = torch.nn.Conv2d(
+        self.proj_out = Conv2dLayer(
             in_channels, in_channels, kernel_size=1, stride=1, padding=0
         )
 
@@ -681,7 +682,7 @@ def __init__(self, config: ChameleonVQVAEConfig):
         latent_channels = config.latent_channels
         channel_multiplier = config.channel_multiplier
 
-        self.conv_in = torch.nn.Conv2d(
+        self.conv_in = Conv2dLayer(
             in_channels, base_channels, kernel_size=3, stride=1, padding=1
         )
 
@@ -738,7 +739,7 @@ def __init__(self, config: ChameleonVQVAEConfig):
         self.norm_out = torch.nn.GroupNorm(
             num_groups=32, num_channels=block_in, eps=1e-6, affine=True
         )
-        self.conv_out = torch.nn.Conv2d(
+        self.conv_out = Conv2dLayer(
             block_in,
             2 * latent_channels if double_latent else latent_channels,
             kernel_size=3,
@@ -779,10 +780,8 @@ def __init__(self, config: ChameleonVQVAEConfig):
         super().__init__()
         self.encoder = ChameleonVQVAEEncoder(config)
         self.quantize = ChameleonVQVAEVectorQuantizer(config)
-        self.quant_conv = torch.nn.Conv2d(config.latent_channels, config.embed_dim, 1)
-        self.post_quant_conv = torch.nn.Conv2d(
-            config.embed_dim, config.latent_channels, 1
-        )
+        self.quant_conv = Conv2dLayer(config.latent_channels, config.embed_dim, 1)
+        self.post_quant_conv = Conv2dLayer(config.embed_dim, config.latent_channels, 1)
         self.eval()  # Chameleon's VQ model is frozen
 
     def encode(

vllm/model_executor/models/deepencoder.py

@@ -19,6 +19,7 @@
 from transformers import CLIPVisionConfig
 
 from vllm.attention.layer import MultiHeadAttention
+from vllm.model_executor.layers.conv import Conv2dLayer
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
@@ -133,14 +134,14 @@ def __init__(
             self.blocks.append(block)
 
         self.neck = nn.Sequential(
-            nn.Conv2d(
+            Conv2dLayer(
                 embed_dim,
                 out_chans,
                 kernel_size=1,
                 bias=False,
             ),
             LayerNorm2d(out_chans),
-            nn.Conv2d(
+            Conv2dLayer(
                 out_chans,
                 out_chans,
                 kernel_size=3,
@@ -150,8 +151,10 @@ def __init__(
             LayerNorm2d(out_chans),
         )
 
-        self.net_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False)
-        self.net_3 = nn.Conv2d(
+        self.net_2 = Conv2dLayer(
+            256, 512, kernel_size=3, stride=2, padding=1, bias=False
+        )
+        self.net_3 = Conv2dLayer(
             512, 1024, kernel_size=3, stride=2, padding=1, bias=False
         )
 
@@ -500,7 +503,7 @@ def __init__(
         """
         super().__init__()
 
-        self.proj = nn.Conv2d(
+        self.proj = Conv2dLayer(
             in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
         )
 

vllm/model_executor/models/dots_ocr.py

@@ -22,6 +22,7 @@
     get_tensor_model_parallel_world_size,
 )
 from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.conv import Conv2dLayer
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -471,7 +472,7 @@ def __init__(self, config):
         self.temporal_patch_size = config.temporal_patch_size
         self.embed_dim = config.embed_dim
         self.config = config
-        self.proj = nn.Conv2d(
+        self.proj = Conv2dLayer(
             config.num_channels,
             config.embed_dim,
             kernel_size=(config.patch_size, config.patch_size),

vllm/model_executor/models/glm4_1v.py

@@ -56,7 +56,7 @@
 from vllm.distributed import get_tensor_model_parallel_world_size, parallel_state
 from vllm.distributed import utils as dist_utils
 from vllm.logger import init_logger
-from vllm.model_executor.layers.conv import Conv3dLayer
+from vllm.model_executor.layers.conv import Conv2dLayer, Conv3dLayer
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -734,7 +734,7 @@ def __init__(
         self.post_conv_layernorm = RMSNorm(
             vision_config.hidden_size, eps=vision_config.rms_norm_eps
         )
-        self.downsample = nn.Conv2d(
+        self.downsample = Conv2dLayer(
             in_channels=vision_config.hidden_size,
             out_channels=vision_config.out_hidden_size,
             kernel_size=vision_config.spatial_merge_size,

vllm/model_executor/models/glm4v.py

@@ -24,6 +24,7 @@
 from vllm.config.multimodal import BaseDummyOptions
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import SiluAndMul, get_act_fn
+from vllm.model_executor.layers.conv import Conv2dLayer
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
     MergedColumnParallelLinear,
@@ -78,7 +79,7 @@ class GLMVImagePixelInputs(TensorSchema):
 class EVA2CLIPPatchEmbedding(nn.Module):
     def __init__(self, config):
         super().__init__()
-        self.proj = nn.Conv2d(
+        self.proj = Conv2dLayer(
             config.in_channels,
             config.hidden_size,
             kernel_size=config.patch_size,
@@ -333,7 +334,7 @@ def __init__(
             quant_config=quant_config,
             prefix=f"{prefix}.linear_proj",
         )
-        self.conv = nn.Conv2d(
+        self.conv = Conv2dLayer(
             in_channels=vision_config.hidden_size,
             out_channels=config.hidden_size,
             kernel_size=2,

vllm/model_executor/models/idefics2_vision_model.py

@@ -30,6 +30,7 @@
 from vllm.attention.layer import MultiHeadAttention
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.conv import Conv2dLayer
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
     QKVParallelLinear,
@@ -60,7 +61,7 @@ def __init__(self, config: Idefics2VisionConfig):
         self.embed_dim = config.hidden_size
         self.image_size = config.image_size
         self.patch_size = config.patch_size
-        self.patch_embedding = nn.Conv2d(
+        self.patch_embedding = Conv2dLayer(
             in_channels=config.num_channels,
             out_channels=self.embed_dim,
             kernel_size=self.patch_size,

vllm/model_executor/models/intern_vit.py

@@ -24,6 +24,7 @@
     tensor_model_parallel_all_gather,
 )
 from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.conv import Conv2dLayer
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -51,7 +52,7 @@ def __init__(self, config: PretrainedConfig):
 
         self.class_embedding = nn.Parameter(torch.randn(1, 1, self.embed_dim))
 
-        self.patch_embedding = nn.Conv2d(
+        self.patch_embedding = Conv2dLayer(
             in_channels=3,
             out_channels=self.embed_dim,
             kernel_size=self.patch_size,

vllm/model_executor/models/interns1_vit.py

@@ -16,6 +16,7 @@
 
 from vllm.attention.layer import MultiHeadAttention
 from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.conv import Conv2dLayer
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import ColumnParallelLinear, RowParallelLinear
 from vllm.model_executor.layers.quantization import QuantizationConfig
@@ -43,7 +44,7 @@ def __init__(self, config):
         self.num_patches = num_patches
         self.patch_shape = patch_shape
 
-        self.projection = nn.Conv2d(
+        self.projection = Conv2dLayer(
             num_channels, hidden_size, kernel_size=patch_size, stride=patch_size
         )
 

vllm/model_executor/models/keye.py

@@ -24,6 +24,7 @@
 from vllm.config.multimodal import BaseDummyOptions
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.logger import init_logger
+from vllm.model_executor.layers.conv import Conv2dLayer
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
     QKVParallelLinear,
@@ -204,7 +205,7 @@ def __init__(self, config: PretrainedConfig):
         self.image_size = config.image_size
         self.patch_size = config.patch_size
 
-        self.patch_embedding = nn.Conv2d(
+        self.patch_embedding = Conv2dLayer(
             in_channels=config.num_channels,
             out_channels=self.embed_dim,
             kernel_size=self.patch_size,

vllm/model_executor/models/midashenglm.py

@@ -39,6 +39,7 @@
 from vllm.config.multimodal import BaseDummyOptions
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.conv import Conv2dLayer
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
     QKVParallelLinear,
@@ -120,7 +121,7 @@ def __init__(
         self.num_patches = self.grid_size[0] * self.grid_size[1]
         self.flatten = flatten
 
-        self.proj = nn.Conv2d(
+        self.proj = Conv2dLayer(
             in_chans,
             embed_dim,
             kernel_size=self.patch_size,

vllm/model_executor/models/moonvit.py

@@ -53,6 +53,7 @@
 from transformers.modeling_utils import PreTrainedModel
 from transformers.utils import is_flash_attn_2_available
 
+from vllm.model_executor.layers.conv import Conv2dLayer
 from vllm.model_executor.layers.linear import ReplicatedLinear
 from vllm.model_executor.models.utils import maybe_prefix
 from vllm.transformers_utils.configs.moonvit import MoonViTConfig
@@ -244,7 +245,7 @@ def __init__(
         )
         self.patch_size = patch_size
 
-        self.proj = nn.Conv2d(
+        self.proj = Conv2dLayer(
             in_dim, out_dim, kernel_size=patch_size, stride=patch_size
         )