[Model][MM] Extract conv layer as CustomOp (#28455)

Signed-off-by: shen-shanshan <467638484@qq.com>
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>

vllm/model_executor/models/qwen2_vl.py

@@ -25,7 +25,6 @@
 # limitations under the License.
 """Inference-only Qwen2-VL model compatible with HuggingFace weights."""
 
-import math
 from collections.abc import Callable, Iterable, Mapping, Sequence
 from functools import partial
 from typing import Annotated, Any, Literal, TypeAlias
@@ -54,9 +53,9 @@ from vllm.distributed import parallel_state
 from vllm.distributed import utils as dist_utils
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import QuickGELU
+from vllm.model_executor.layers.conv import Conv3dLayer
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
-    ReplicatedLinear,
     RowParallelLinear,
 )
 from vllm.model_executor.layers.quantization import QuantizationConfig
@@ -107,7 +106,6 @@ from .utils import (
     maybe_prefix,
 )
 from .vision import (
-    conv3d_to_linear_weight,
     get_vit_attn_backend,
     run_dp_sharded_mrope_vision_model,
 )
@@ -566,15 +564,18 @@ class Qwen2VisionPatchEmbed(nn.Module):
         self.embed_dim = embed_dim
 
         kernel_size = (temporal_patch_size, patch_size, patch_size)
-        self.proj = ReplicatedLinear(
-            in_channels * math.prod(kernel_size),
+        self.proj = Conv3dLayer(
+            in_channels,
             embed_dim,
+            kernel_size=kernel_size,
+            stride=kernel_size,
             bias=False,
-            return_bias=False,
         )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.proj(x)
+        L, C = x.shape
+        x = x.view(L, -1, self.temporal_patch_size, self.patch_size, self.patch_size)
+        x = self.proj(x).view(L, self.embed_dim)
         return x
 
 
@@ -844,9 +845,6 @@ class Qwen2VisionTransformer(nn.Module):
         loaded_params: set[str] = set()
 
         for name, loaded_weight in weights:
-            if name.endswith("patch_embed.proj.weight"):
-                loaded_weight = conv3d_to_linear_weight(loaded_weight)
-
             for param_name, weight_name, shard_id in stacked_params_mapping:
                 if weight_name not in name:
                     continue