[Chore] Deprecate SupportsMultiModal.merge_by_field_config (#30170)

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

vllm/model_executor/models/aria.py

@@ -499,8 +499,6 @@ class AriaForConditionalGeneration(nn.Module, SupportsMultiModal):
     model to perform tasks that involve both image and text inputs.
     """
 
-    merge_by_field_config = True
-
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_prefix={
             # mapping for new names in checkpoint saved after transformers v4.52

vllm/model_executor/models/aya_vision.py

@@ -318,8 +318,6 @@ def _get_layer_index(feature_layer_index: int, num_hidden_layers: int) -> int:
     dummy_inputs=AyaVisionDummyInputsBuilder,
 )
 class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_prefix={
             # mapping for new names in checkpoint saved after transformers v4.52

vllm/model_executor/models/blip2.py

@@ -523,8 +523,6 @@ class Blip2MultiModalProcessor(BaseMultiModalProcessor[Blip2ProcessingInfo]):
 class Blip2ForConditionalGeneration(
     nn.Module, SupportsMultiModal, SupportsPP, SupportsQuant
 ):
-    merge_by_field_config = True
-
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> str | None:
         if modality.startswith("image"):

vllm/model_executor/models/chameleon.py

@@ -918,8 +918,6 @@ class ChameleonModel(nn.Module):
 class ChameleonForConditionalGeneration(
     nn.Module, SupportsMultiModal, SupportsPP, SupportsQuant
 ):
-    merge_by_field_config = True
-
     packed_modules_mapping = {
         "qkv_proj": ["q_proj", "k_proj", "v_proj"],
         "gate_up_proj": ["gate_proj", "up_proj"],

vllm/model_executor/models/clip.py

@@ -784,7 +784,6 @@ class CLIPEmbeddingModel(nn.Module, SupportsMultiModal, SupportsQuant):
     is_pooling_model = True
 
     packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
-    merge_by_field_config = True
 
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> str | None:

vllm/model_executor/models/cohere2_vision.py

@@ -331,8 +331,6 @@ class Cohere2VisionMultiModalProcessor(
     dummy_inputs=Cohere2VisionDummyInputsBuilder,
 )
 class Cohere2VisionForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_prefix={
             "model.vision_tower.": "vision_tower.",

vllm/model_executor/models/deepseek_ocr.py

@@ -344,8 +344,6 @@ class DeepseekOCRMultiModalProcessor(
     dummy_inputs=DeepseekOCRDummyInputsBuilder,
 )
 class DeepseekOCRForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_prefix={
             # map prefix for language backbone

vllm/model_executor/models/deepseek_vl2.py

@@ -344,8 +344,6 @@ class DeepseekVL2MultiModalProcessor(
     dummy_inputs=DeepseekVL2DummyInputsBuilder,
 )
 class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_prefix={
             "language.": "language_model.",

vllm/model_executor/models/dots_ocr.py

@@ -690,8 +690,6 @@ class DotsVisionTransformer(nn.Module):
     dummy_inputs=DotsOCRDummyInputsBuilder,
 )
 class DotsOCRForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA):
-    merge_by_field_config = True
-
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_substr={
             ".attn.qkv_proj.": ".attn.qkv.",

vllm/model_executor/models/ernie45_vl.py

@@ -1254,8 +1254,6 @@ class Ernie4_5_VLDummyInputsBuilder(BaseDummyInputsBuilder[Ernie4_5_VLProcessing
 class Ernie4_5_VLMoeForConditionalGeneration(
     nn.Module, SupportsMultiModal, SupportsLoRA, SupportsPP, SupportsMRoPE
 ):
-    merge_by_field_config = True
-
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",

vllm/model_executor/models/fuyu.py

@@ -260,8 +260,6 @@ class FuyuMultiModalProcessor(BaseMultiModalProcessor[FuyuProcessingInfo]):
     dummy_inputs=FuyuDummyInputsBuilder,
 )
 class FuyuForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_prefix={
             "model.vision_embed_tokens.": "vision_embed_tokens.",

vllm/model_executor/models/gemma3_mm.py

@@ -483,8 +483,6 @@ class Gemma3MultiModalProjector(nn.Module):
 class Gemma3ForConditionalGeneration(
     nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA
 ):
-    merge_by_field_config = True
-
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",

vllm/model_executor/models/gemma3n_mm.py

@@ -463,7 +463,6 @@ class Gemma3nMultimodalEmbedder(nn.Module):
 class Gemma3nForConditionalGeneration(
     nn.Module, SupportsMultiModal, SupportsTranscription
 ):
-    merge_by_field_config = True
     supported_languages = ISO639_1_SUPPORTED_LANGS
 
     packed_modules_mapping = {

vllm/model_executor/models/glm4_1v.py

@@ -1424,8 +1424,6 @@ class Glm4vMultiModalProcessor(BaseMultiModalProcessor[Glm4vProcessingInfo]):
 class Glm4vForConditionalGeneration(
     nn.Module, SupportsMultiModal, SupportsLoRA, SupportsPP, SupportsMRoPE
 ):
-    merge_by_field_config = True
-
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",

vllm/model_executor/models/glm4v.py

@@ -561,8 +561,6 @@ class GLM4VMultiModalProcessor(BaseMultiModalProcessor[GLM4VProcessingInfo]):
 class GLM4VForCausalLM(
     ChatGLMBaseModel, SupportsMultiModal, SupportsLoRA, SupportsPP, SupportsMRoPE
 ):
-    merge_by_field_config = True
-
     packed_modules_mapping = {
         "query_key_value": ["query_key_value"],
         "dense_h_to_4h": ["dense_h_to_4h"],

vllm/model_executor/models/granite_speech.py

@@ -564,7 +564,6 @@ class GraniteSpeechForConditionalGeneration(
     SupportsLoRA,
     SupportsTranscription,
 ):
-    merge_by_field_config = True
     supported_languages = ISO639_1_SUPPORTED_LANGS
 
     packed_modules_mapping = {

vllm/model_executor/models/hunyuan_vision.py

@@ -786,7 +786,6 @@ class HunYuanVLForConditionalGeneration(
     SupportsQuant,
     SupportsXDRoPE,
 ):
-    merge_by_field_config = True
     multimodal_cpu_fields = {"image_grid_thw"}
 
     # To ensure correct weight loading and mapping.

vllm/model_executor/models/hyperclovax_vision.py

@@ -592,8 +592,6 @@ class HCXVisionCAbstractor(nn.Module):
     dummy_inputs=HCXVisionDummyInputsBuilder,
 )
 class HCXVisionForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     packed_modules_mapping = {
         "qkv_proj": ["q_proj", "k_proj", "v_proj"],
         "gate_up_proj": ["gate_proj", "up_proj"],

vllm/model_executor/models/idefics3.py

@@ -576,8 +576,6 @@ class Idefics3Model(nn.Module):
     dummy_inputs=Idefics3DummyInputsBuilder,
 )
 class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsLoRA):
-    merge_by_field_config = True
-
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",

vllm/model_executor/models/interfaces.py

@@ -78,9 +78,9 @@ class SupportsMultiModal(Protocol):
     `multimodal_config.mm_encoder_tp_mode="data"`.
     """
 
-    merge_by_field_config: ClassVar[bool] = True
+    merge_by_field_config: ClassVar[bool | None] = None
     """
-    A flag that indicates which implementation of
+    [DEPRECATED] A flag that indicates which implementation of
     `vllm.multimodal.utils.group_mm_kwargs_by_modality` to use.
     """
 
@@ -260,7 +260,26 @@ def supports_multimodal(model: object) -> TypeIs[SupportsMultiModal]: ...
 def supports_multimodal(
     model: type[object] | object,
 ) -> TypeIs[type[SupportsMultiModal]] | TypeIs[SupportsMultiModal]:
-    return getattr(model, "supports_multimodal", False)
+    res = getattr(model, "supports_multimodal", False)
+
+    if res:
+        # We can remove this starting from v0.14
+        merge_by_field_config = getattr(model, "merge_by_field_config", None)
+        if merge_by_field_config is False:
+            raise ValueError(
+                "`merge_by_field_config=False` is no longer effective, "
+                "please update your model to consider the new batching logic "
+                "in `group_mm_kwargs_by_modality` (refer to "
+                "https://github.com/vllm-project/vllm/issues/26149), "
+                "and then remove the override from your model."
+            )
+        if merge_by_field_config is True:
+            logger.warning_once(
+                "`merge_by_field_config=True` is redundant, "
+                "please remove the override from your model."
+            )
+
+    return res
 
 
 def supports_multimodal_raw_input_only(model: type[object] | object) -> bool:

vllm/model_executor/models/interns1.py

@@ -509,8 +509,6 @@ class InternS1MultiModalProcessor(BaseMultiModalProcessor[InternS1ProcessingInfo
 class InternS1ForConditionalGeneration(
     nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA
 ):
-    merge_by_field_config = True
-
     # To ensure correct weight loading and mapping.
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_prefix={

vllm/model_executor/models/internvl.py

@@ -1074,8 +1074,6 @@ class InternVLMultiModalProcessor(
     dummy_inputs=InternVLDummyInputsBuilder,
 )
 class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA):
-    merge_by_field_config = True
-
     supports_encoder_tp_data = True
 
     @classmethod

vllm/model_executor/models/keye.py

@@ -1292,8 +1292,6 @@ class KeyeMultiModalProcessor(BaseMultiModalProcessor[KeyeProcessingInfo]):
 
 
 class BaseKeyeModule(nn.Module):
-    merge_by_field_config = True
-
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",

vllm/model_executor/models/kimi_vl.py

@@ -298,8 +298,6 @@ class KimiVLMultiModalProcessor(BaseMultiModalProcessor[KimiVLProcessingInfo]):
     dummy_inputs=KimiVLDummyInputsBuilder,
 )
 class KimiVLForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     supports_encoder_tp_data = True
 
     @classmethod

vllm/model_executor/models/llava.py

@@ -506,8 +506,6 @@ def init_vision_tower_for_llava(
     dummy_inputs=LlavaDummyInputsBuilder,
 )
 class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     packed_modules_mapping = {
         "qkv_proj": ["q_proj", "k_proj", "v_proj"],
         "gate_up_proj": ["gate_proj", "up_proj"],

vllm/model_executor/models/llava_next.py

@@ -223,8 +223,6 @@ class LlavaNextMultiModalProcessor(
     dummy_inputs=LlavaDummyInputsBuilder,
 )
 class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_prefix={
             # mapping for new names in checkpoint saved after transformers v4.52

vllm/model_executor/models/llava_next_video.py

@@ -299,8 +299,6 @@ class LlavaNextMultiModalProjector(nn.Module):
     dummy_inputs=LlavaNextVideoDummyInputsBuilder,
 )
 class LlavaNextVideoForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_prefix={
             # mapping for new names in checkpoint saved after transformers v4.52

vllm/model_executor/models/llava_onevision.py

@@ -479,8 +479,6 @@ class LlavaOnevisionMultiModalProjector(nn.Module):
     dummy_inputs=LlavaOnevisionDummyInputsBuilder,
 )
 class LlavaOnevisionForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_prefix={
             # mapping for new names in checkpoint saved after transformers v4.52

vllm/model_executor/models/midashenglm.py

@@ -683,8 +683,6 @@ class MiDashengLMMultiModalProcessor(
     dummy_inputs=MiDashengLMDummyInputsBuilder,
 )
 class MiDashengLMModel(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",

vllm/model_executor/models/minicpmv.py

@@ -1003,8 +1003,6 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
     instantiated.
     """
 
-    merge_by_field_config = True
-
     supports_encoder_tp_data = True
 
     @classmethod

vllm/model_executor/models/minimax_vl_01.py

@@ -179,8 +179,6 @@ class MiniMaxVL01MultiModalProcessor(
     dummy_inputs=MiniMaxVL01DummyInputsBuilder,
 )
 class MiniMaxVL01ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     packed_modules_mapping = {
         "qkv_proj": ["q_proj", "k_proj", "v_proj"],
         "gate_up_proj": ["gate_proj", "up_proj"],

vllm/model_executor/models/mistral3.py

@@ -423,8 +423,6 @@ def init_vision_tower_for_llava(
 class Mistral3ForConditionalGeneration(
     nn.Module, SupportsLoRA, SupportsMultiModal, SupportsPP
 ):
-    merge_by_field_config = True
-
     packed_modules_mapping = {
         "qkv_proj": ["q_proj", "k_proj", "v_proj"],
         "gate_up_proj": ["gate_proj", "up_proj"],

vllm/model_executor/models/mllama4.py

@@ -741,8 +741,6 @@ class Llama4ForConditionalGeneration(
     SupportsEagle3,
     SupportsLoRA,
 ):
-    merge_by_field_config = True
-
     packed_modules_mapping = {
         "qkv_proj": ["q_proj", "k_proj", "v_proj"],
         "gate_up_proj": ["gate_proj", "up_proj"],

vllm/model_executor/models/molmo.py

@@ -1354,8 +1354,6 @@ class MolmoMultiModalProcessor(BaseMultiModalProcessor[MolmoProcessingInfo]):
 class MolmoForCausalLM(
     nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA, SupportsQuant
 ):
-    merge_by_field_config = True
-
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_substr={
             # vision backbone mapping

vllm/model_executor/models/nano_nemotron_vl.py

@@ -1116,8 +1116,6 @@ class NanoNemotronVLDummyInputsBuilder(
 class NemotronH_Nano_VL_V2(
     nn.Module, HasInnerState, IsHybrid, SupportsMultiModal, SupportsMultiModalPruning
 ):
-    merge_by_field_config = True
-
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> str | None:
         if modality.startswith("image"):

vllm/model_executor/models/nemotron_vl.py

@@ -358,8 +358,6 @@ class NemotronVLProcessingInfo(BaseInternVLProcessingInfo):
     dummy_inputs=BaseInternVLDummyInputsBuilder[NemotronVLProcessingInfo],
 )
 class LlamaNemotronVLChatModel(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA):
-    merge_by_field_config = True
-
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> str | None:
         if modality.startswith("image"):

vllm/model_executor/models/opencua.py

@@ -201,7 +201,6 @@ class OpenCUADummyInputsBuilder(Qwen2VLDummyInputsBuilder):
     dummy_inputs=OpenCUADummyInputsBuilder,
 )
 class OpenCUAForConditionalGeneration(Qwen2_5_VLForConditionalGeneration):
-    merge_by_field_config = True
     multimodal_cpu_fields = {"image_grid_thw"}
 
     packed_modules_mapping = {

vllm/model_executor/models/ovis.py

@@ -414,8 +414,6 @@ class OvisMultiModalProcessor(BaseMultiModalProcessor[OvisProcessingInfo]):
     dummy_inputs=OvisDummyInputsBuilder,
 )
 class Ovis(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> str | None:
         if modality.startswith("image"):

vllm/model_executor/models/ovis2_5.py

@@ -456,8 +456,6 @@ class Ovis2_5MultiModalProcessor(BaseMultiModalProcessor[Ovis2_5ProcessingInfo])
     dummy_inputs=Ovis2_5DummyInputsBuilder,
 )
 class Ovis2_5(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config

vllm/model_executor/models/paddleocr_vl.py

@@ -1103,8 +1103,6 @@ class SiglipVisionModel(nn.Module):
     dummy_inputs=PaddleOCRVLDummyInputsBuilder,
 )
 class PaddleOCRVLForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsMRoPE):
-    merge_by_field_config = True
-
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_prefix={
             "model.": "language_model.model.",

vllm/model_executor/models/paligemma.py

@@ -251,8 +251,6 @@ class PaliGemmaMultiModalProcessor(BaseMultiModalProcessor[PaliGemmaProcessingIn
     dummy_inputs=PaliGemmaDummyInputsBuilder,
 )
 class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",

vllm/model_executor/models/phi3v.py

@@ -562,8 +562,6 @@ class Phi3VMultiModalProcessor(BaseMultiModalProcessor[Phi3VProcessingInfo]):
     dummy_inputs=Phi3VDummyInputsBuilder,
 )
 class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsQuant):
-    merge_by_field_config = True
-
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_prefix={
             "model.vision_embed_tokens.wte": "embed_tokens",

vllm/model_executor/models/phi4mm.py

@@ -984,8 +984,6 @@ class Phi4MMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
     Implements the Phi-4-multimodal-instruct model in vLLM.
     """
 
-    merge_by_field_config = True
-
     packed_modules_mapping = {
         "qkv_proj": [
             "qkv_proj",

vllm/model_executor/models/pixtral.py

@@ -365,8 +365,6 @@ class PixtralMultiModalProcessor(BaseMultiModalProcessor[PixtralProcessingInfo])
     dummy_inputs=PixtralDummyInputsBuilder,
 )
 class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> str | None:
         if modality.startswith("image"):

vllm/model_executor/models/qwen2_5_omni_thinker.py

@@ -773,8 +773,6 @@ class Qwen2_5OmniThinkerForConditionalGeneration(
     SupportsMRoPE,
     Qwen2_5OmniConditionalGenerationMixin,
 ):
-    merge_by_field_config = True
-
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_prefix={
             "thinker.lm_head.": "language_model.lm_head.",

vllm/model_executor/models/qwen2_5_vl.py

@@ -1039,7 +1039,6 @@ class Qwen2_5_VLForConditionalGeneration(
     SupportsMultiModalPruning,
     SupportsMRoPE,
 ):
-    merge_by_field_config = True
     multimodal_cpu_fields = {"image_grid_thw", "video_grid_thw"}
 
     packed_modules_mapping = {

vllm/model_executor/models/qwen2_audio.py

@@ -313,8 +313,6 @@ class Qwen2AudioMultiModalProcessor(BaseMultiModalProcessor[Qwen2AudioProcessing
     dummy_inputs=Qwen2AudioDummyInputsBuilder,
 )
 class Qwen2AudioForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> str | None:
         if modality.startswith("audio"):

vllm/model_executor/models/qwen2_vl.py

@@ -1131,7 +1131,6 @@ class Qwen2VLMultiModalProcessor(BaseMultiModalProcessor[Qwen2VLProcessingInfo])
 class Qwen2VLForConditionalGeneration(
     nn.Module, SupportsMultiModal, SupportsLoRA, SupportsPP, SupportsMRoPE
 ):
-    merge_by_field_config = True
     multimodal_cpu_fields = {"image_grid_thw", "video_grid_thw"}
 
     # To ensure correct weight loading and mapping.

vllm/model_executor/models/qwen3_omni_moe_thinker.py

@@ -1131,8 +1131,6 @@ class Qwen3OmniMoeThinkerForConditionalGeneration(
     SupportsMRoPE,
     Qwen3OmniMoeConditionalGenerationMixin,
 ):
-    merge_by_field_config = True
-
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_prefix={
             "thinker.lm_head.": "language_model.lm_head.",

vllm/model_executor/models/qwen3_vl.py

@@ -1190,7 +1190,6 @@ class Qwen3VLForConditionalGeneration(
     SupportsMRoPE,
     SupportsEagle3,
 ):
-    merge_by_field_config = True
     multimodal_cpu_fields = {"image_grid_thw", "video_grid_thw"}
 
     packed_modules_mapping = {

vllm/model_executor/models/qwen_vl.py

@@ -703,8 +703,6 @@ class QwenVLMultiModalProcessor(BaseMultiModalProcessor[QwenVLProcessingInfo]):
 class QwenVLForConditionalGeneration(
     QWenBaseModel, SupportsPP, SupportsLoRA, SupportsMultiModal
 ):
-    merge_by_field_config = True
-
     packed_modules_mapping = {
         "c_attn": ["c_attn"],
         "gate_up_proj": [

vllm/model_executor/models/siglip.py

@@ -989,7 +989,6 @@ class SiglipEmbeddingModel(nn.Module, SupportsMultiModal, SupportsQuant):
     is_pooling_model = True
 
     packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
-    merge_by_field_config = True
 
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> str | None:

vllm/model_executor/models/skyworkr1v.py

@@ -647,8 +647,6 @@ class SkyworkR1VMultiModalProcessor(BaseMultiModalProcessor[SkyworkR1VProcessing
     dummy_inputs=SkyworkR1VDummyInputsBuilder,
 )
 class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> str | None:
         if modality.startswith("image"):

vllm/model_executor/models/step3_vl.py

@@ -916,8 +916,6 @@ class Step3VisionTransformer(nn.Module):
     dummy_inputs=Step3VLDummyInputsBuilder,
 )
 class Step3VLForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_prefix={
             "model.": "language_model.model.",

vllm/model_executor/models/tarsier.py

@@ -400,8 +400,6 @@ def init_vision_tower_for_tarsier(
     dummy_inputs=TarsierDummyInputsBuilder,
 )
 class TarsierForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
-    merge_by_field_config = True
-
     packed_modules_mapping = {
         "qkv_proj": ["q_proj", "k_proj", "v_proj"],
         "gate_up_proj": ["gate_proj", "up_proj"],

vllm/model_executor/models/terratorch.py

@@ -227,7 +227,6 @@ class TerratorchMultiModalProcessor(BaseMultiModalProcessor):
     dummy_inputs=TerratorchInputBuilder,
 )
 class Terratorch(nn.Module, IsAttentionFree, SupportsMultiModal):
-    merge_by_field_config = True
     supports_multimodal_raw_input_only = True
     is_pooling_model = True
 

vllm/model_executor/models/transformers/multimodal.py

@@ -264,7 +264,7 @@ class MultiModalProcessor(BaseMultiModalProcessor[MultiModalProcessingInfo]):
 
 class MultiModalMixin(SupportsMultiModal, SupportsMRoPE):
     supports_multimodal_raw_input_only = True
-    merge_by_field_config = True
+
     # Backwards compatibility for prev released models. State dicts back then
     # had different formats and cannot be loaded with `AutoModel` mapping as is
     hf_to_vllm_mapper = WeightsMapper(

vllm/model_executor/models/ultravox.py

@@ -498,8 +498,6 @@ class ModifiedWhisperEncoder(WhisperEncoder):
     dummy_inputs=UltravoxDummyInputsBuilder,
 )
 class UltravoxModel(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA):
-    merge_by_field_config = True
-
     packed_modules_mapping = {
         "qkv_proj": ["q_proj", "k_proj", "v_proj"],
         "gate_up_proj": ["gate_proj", "up_proj"],

vllm/model_executor/models/voxtral.py

@@ -330,8 +330,6 @@ class VoxtralMultiModalProcessor(BaseMultiModalProcessor[VoxtralProcessingInfo])
 class VoxtralForConditionalGeneration(
     nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA, SupportsTranscription
 ):
-    merge_by_field_config = True
-
     supported_languages = ISO639_1_SUPPORTED_LANGS
 
     packed_modules_mapping = {

vllm/model_executor/models/whisper.py

@@ -775,7 +775,6 @@ class WhisperMultiModalProcessor(EncDecMultiModalProcessor[WhisperProcessingInfo
 class WhisperForConditionalGeneration(
     nn.Module, SupportsTranscription, SupportsMultiModal
 ):
-    merge_by_field_config = True
     packed_modules_mapping = {
         "self_attn.qkv_proj": [
             "self_attn.q_proj",

vllm/multimodal/utils.py

@@ -426,7 +426,6 @@ def group_mm_kwargs_by_modality(
     Yields:
         A tuple `(modality, num_items, grouped_kwargs)`.
     """
-    # TODO: After v0.13, remove merge_by_field_config attribute from model impls
     if merge_by_field_config is not None:
         logger.warning_once(
             "The `merge_by_field_config` argument of `group_mm_kwargs_by_modality` "