[BE] Rename should_torch_compile_mm_vit to should_torch_compile_mm_encoder

docs/design/torch_compile_multimodal.md

@@ -26,7 +26,7 @@ This feature is off by default, but can be enabled by setting `compile_mm_encode
 
 To compile a multimodal component such as an encoder, we follow the same mechanism as the LLM text backbone, with a few additional scaffoldings:
 
-1. The `@support_torch_compile` decorator should include `enable_if=should_torch_compile_mm_vit`. This will gate the compilation behind our
+1. The `@support_torch_compile` decorator should include `enable_if=should_torch_compile_mm_encoder`. This will gate the compilation behind our
 `compile_mm_encoder` configuration
 
 2. `with set_model_tag("<component_name>", is_encoder=True)` context manager should be used around the nn.Module's instantiation. Since torch.compile

vllm/compilation/decorators.py

@@ -47,6 +47,11 @@
 _T = TypeVar("_T", bound=nn.Module)
 
 
+def should_torch_compile_mm_encoder(vllm_config: VllmConfig) -> bool:
+    """Callable to be passed to `@support_torch_compile`'s `enable_if` argument."""
+    return vllm_config.compilation_config.compile_mm_encoder
+
+
 def ignore_torch_compile(cls: type[_T]) -> type[_T]:
     """
     A decorator to ignore support_torch_compile decorator

vllm/model_executor/models/lfm2_siglip2.py

@@ -10,7 +10,10 @@
 from torch.nn import functional as F
 from transformers import Siglip2VisionConfig
 
-from vllm.compilation.decorators import support_torch_compile
+from vllm.compilation.decorators import (
+    should_torch_compile_mm_encoder,
+    support_torch_compile,
+)
 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.attention import MMEncoderAttention
@@ -25,7 +28,6 @@
 from .vision import (
     is_vit_use_data_parallel,
     resolve_visual_encoder_outputs,
-    should_torch_compile_mm_vit,
 )
 
 
@@ -269,7 +271,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
 
 @support_torch_compile(
     dynamic_arg_dims={"hidden_states": [0, 1], "cu_seqlens": 0},
-    enable_if=should_torch_compile_mm_vit,
+    enable_if=should_torch_compile_mm_encoder,
 )
 class Siglip2EncoderLayer(nn.Module):
     def __init__(

vllm/model_executor/models/mllama4.py

@@ -31,7 +31,10 @@
     get_best_fit,
 )
 
-from vllm.compilation.decorators import support_torch_compile
+from vllm.compilation.decorators import (
+    should_torch_compile_mm_encoder,
+    support_torch_compile,
+)
 from vllm.config import VllmConfig, set_current_vllm_config
 from vllm.config.multimodal import BaseDummyOptions
 from vllm.distributed import get_tensor_model_parallel_world_size
@@ -49,7 +52,6 @@
 from vllm.model_executor.model_loader.utils import initialize_model
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.module_mapping import MultiModelKeys
-from vllm.model_executor.models.vision import should_torch_compile_mm_vit
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import (
     MultiModalDataDict,
@@ -454,7 +456,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
 
 
 @support_torch_compile(
-    dynamic_arg_dims={"images_flattened": 0}, enable_if=should_torch_compile_mm_vit
+    dynamic_arg_dims={"images_flattened": 0}, enable_if=should_torch_compile_mm_encoder
 )
 class Llama4VisionModel(nn.Module):
     def __init__(

vllm/model_executor/models/qwen2_5_vl.py

@@ -42,7 +42,10 @@
     Qwen2_5_VLVisionConfig,
 )
 
-from vllm.compilation.decorators import support_torch_compile
+from vllm.compilation.decorators import (
+    should_torch_compile_mm_encoder,
+    support_torch_compile,
+)
 from vllm.config import VllmConfig
 from vllm.distributed import parallel_state
 from vllm.distributed import utils as dist_utils
@@ -65,7 +68,6 @@
 )
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.module_mapping import MultiModelKeys
-from vllm.model_executor.models.vision import should_torch_compile_mm_vit
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.evs import (
     compute_mrope_for_media,
@@ -424,7 +426,7 @@ def forward(
         "rotary_pos_emb_cos": 0,
         "rotary_pos_emb_sin": 0,
     },
-    enable_if=should_torch_compile_mm_vit,
+    enable_if=should_torch_compile_mm_encoder,
 )
 class Qwen2_5_VisionBlock(nn.Module):
     def __init__(
@@ -483,7 +485,7 @@ def forward(
     dynamic_arg_dims={
         "x": 0,
     },
-    enable_if=should_torch_compile_mm_vit,
+    enable_if=should_torch_compile_mm_encoder,
 )
 class Qwen2_5_VisionPatchEmbed(nn.Module):
     def __init__(
@@ -518,7 +520,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
     dynamic_arg_dims={
         "x": 0,
     },
-    enable_if=should_torch_compile_mm_vit,
+    enable_if=should_torch_compile_mm_encoder,
 )
 class Qwen2_5_VisionPatchMerger(nn.Module):
     def __init__(

vllm/model_executor/models/vision.py

@@ -143,11 +143,6 @@ def is_vit_use_data_parallel():
     return mm_encoder_tp_mode == "data"
 
 
-def should_torch_compile_mm_vit(vllm_config: VllmConfig) -> bool:
-    """Callable to be passed to `@support_torch_compile`'s `enable_if` argument."""
-    return vllm_config.compilation_config.compile_mm_encoder
-
-
 VisionFeatureSelectStrategyStr = Literal["class", "default", "full"]
 
 VisionFeatureSelectStrategy: TypeAlias = (