Rename clashing method names for vLLM model protocol (#27583)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

vllm/model_executor/models/nemotron.py

@@ -351,7 +351,7 @@ class NemotronModel(nn.Module):
             ["hidden_states", "residual"], config.hidden_size
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
 
     def forward(
@@ -365,7 +365,7 @@ class NemotronModel(nn.Module):
             if inputs_embeds is not None:
                 hidden_states = inputs_embeds
             else:
-                hidden_states = self.get_input_embeddings(input_ids)
+                hidden_states = self.embed_input_ids(input_ids)
             residual = None
         else:
             assert intermediate_tensors is not None
@@ -491,8 +491,8 @@ class NemotronForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             self.model.make_empty_intermediate_tensors
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
 
     def forward(
         self,

vllm/model_executor/models/nemotron_h.py

@@ -548,7 +548,7 @@ class NemotronHModel(nn.Module):
 
         self.norm_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
 
     def forward(
@@ -562,7 +562,7 @@ class NemotronHModel(nn.Module):
             if inputs_embeds is not None:
                 hidden_states = inputs_embeds
             else:
-                hidden_states = self.get_input_embeddings(input_ids)
+                hidden_states = self.embed_input_ids(input_ids)
             residual = None
         else:
             assert intermediate_tensors is not None
@@ -823,8 +823,8 @@ class NemotronHForCausalLM(
                 moe.n_redundant_experts = self.num_redundant_experts
                 moe.experts.update_expert_map()
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
 
     def forward(
         self,

vllm/model_executor/models/nemotron_nas.py

@@ -291,7 +291,7 @@ class DeciModel(nn.Module):
             ["hidden_states", "residual"], config.hidden_size
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
 
     def forward(
@@ -305,7 +305,7 @@ class DeciModel(nn.Module):
             if inputs_embeds is not None:
                 hidden_states = inputs_embeds
             else:
-                hidden_states = self.get_input_embeddings(input_ids)
+                hidden_states = self.embed_input_ids(input_ids)
             residual = None
         else:
             assert intermediate_tensors is not None
@@ -461,8 +461,8 @@ class DeciLMForCausalLM(nn.Module, SupportsLoRA, SupportsPP, HasNoOps):
     def _init_model(self, vllm_config: VllmConfig, prefix: str = ""):
         return DeciModel(vllm_config=vllm_config, prefix=prefix)
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
 
     def forward(
         self,

vllm/model_executor/models/nemotron_vl.py

@@ -561,7 +561,7 @@ class LlamaNemotronVLChatModel(nn.Module, SupportsMultiModal, SupportsPP, Suppor
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(self, **kwargs: object) -> MultiModalEmbeddings:
+    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
         modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
         if not modalities:
             return []
@@ -580,7 +580,7 @@ class LlamaNemotronVLChatModel(nn.Module, SupportsMultiModal, SupportsPP, Suppor
 
         return multimodal_embeddings
 
-    def get_input_embeddings(
+    def embed_input_ids(
         self,
         input_ids: torch.Tensor,
         multimodal_embeddings: MultiModalEmbeddings | None = None,
@@ -593,9 +593,9 @@ class LlamaNemotronVLChatModel(nn.Module, SupportsMultiModal, SupportsPP, Suppor
 
         # This is to satisfy the type checker for each overload
         if multimodal_embeddings is None or is_multimodal is None:
-            return super().get_input_embeddings(input_ids)
+            return super().embed_input_ids(input_ids)
 
-        return super().get_input_embeddings(
+        return super().embed_input_ids(
             input_ids,
             multimodal_embeddings=multimodal_embeddings,
             is_multimodal=is_multimodal,

vllm/model_executor/models/olmo.py

@@ -268,7 +268,7 @@ class OlmoModel(nn.Module):
             ["hidden_states"], config.hidden_size
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
 
     def forward(
@@ -285,7 +285,7 @@ class OlmoModel(nn.Module):
             if inputs_embeds is not None:
                 hidden_states = inputs_embeds
             else:
-                hidden_states = self.get_input_embeddings(input_ids)
+                hidden_states = self.embed_input_ids(input_ids)
         else:
             assert intermediate_tensors is not None
             hidden_states = intermediate_tensors["hidden_states"]
@@ -379,8 +379,8 @@ class OlmoForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
             self.model.make_empty_intermediate_tensors
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
 
     def forward(
         self,

vllm/model_executor/models/olmo2.py

@@ -304,7 +304,7 @@ class Olmo2Model(nn.Module):
             ["hidden_states"], self.config.hidden_size
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
 
     def forward(
@@ -419,8 +419,8 @@ class Olmo2ForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
             self.model.make_empty_intermediate_tensors
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
 
     def forward(
         self,

vllm/model_executor/models/olmoe.py

@@ -296,7 +296,7 @@ class OlmoeModel(nn.Module):
             ["hidden_states", "residual"], config.hidden_size
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
 
     def forward(
@@ -310,7 +310,7 @@ class OlmoeModel(nn.Module):
             if inputs_embeds is not None:
                 hidden_states = inputs_embeds
             else:
-                hidden_states = self.get_input_embeddings(input_ids)
+                hidden_states = self.embed_input_ids(input_ids)
             residual = None
         else:
             assert intermediate_tensors is not None
@@ -471,8 +471,8 @@ class OlmoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
             self.model.make_empty_intermediate_tensors
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
 
     def forward(
         self,

vllm/model_executor/models/openpangu.py

@@ -753,7 +753,7 @@ class OpenPanguModel(nn.Module):
             ["hidden_states", "residual"], config.hidden_size
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
 
     def forward(
@@ -767,7 +767,7 @@ class OpenPanguModel(nn.Module):
             if inputs_embeds is not None:
                 hidden_states = inputs_embeds
             else:
-                hidden_states = self.get_input_embeddings(input_ids)
+                hidden_states = self.embed_input_ids(input_ids)
             residual = None
         else:
             assert intermediate_tensors is not None
@@ -969,8 +969,8 @@ class OpenPanguModelBase(nn.Module, SupportsPP, SupportsLoRA):
             self.model.make_empty_intermediate_tensors
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
 
     def forward(
         self,

vllm/model_executor/models/openpangu_mtp.py

@@ -100,8 +100,8 @@ class OpenPanguMTP(nn.Module, SupportsPP):
             vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
 
     def forward(
         self,

vllm/model_executor/models/opt.py

@@ -262,7 +262,7 @@ class OPTDecoder(nn.Module):
             prefix=f"{prefix}.layers",
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
 
     def forward(
@@ -274,7 +274,7 @@ class OPTDecoder(nn.Module):
     ) -> torch.Tensor | IntermediateTensors:
         if get_pp_group().is_first_rank:
             if inputs_embeds is None:
-                inputs_embeds = self.get_input_embeddings(input_ids)
+                inputs_embeds = self.embed_input_ids(input_ids)
             pos_embeds = self.embed_positions(positions)
             if self.project_in is not None:
                 inputs_embeds, _ = self.project_in(inputs_embeds)
@@ -311,8 +311,8 @@ class OPTModel(nn.Module):
             ["hidden_states"], config.hidden_size
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.decoder.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.decoder.embed_input_ids(input_ids)
 
     def forward(
         self,
@@ -394,8 +394,8 @@ class OPTForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
             self.model.make_empty_intermediate_tensors
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
 
     def forward(
         self,

vllm/model_executor/models/orion.py

@@ -255,7 +255,7 @@ class OrionModel(nn.Module):
             config.hidden_size,
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
 
     def forward(
@@ -269,7 +269,7 @@ class OrionModel(nn.Module):
             if inputs_embeds is not None:
                 hidden_states = inputs_embeds
             else:
-                hidden_states = self.get_input_embeddings(input_ids)
+                hidden_states = self.embed_input_ids(input_ids)
         else:
             assert intermediate_tensors is not None
             hidden_states = intermediate_tensors["hidden_states"]
@@ -345,8 +345,8 @@ class OrionForCausalLM(nn.Module, SupportsPP):
             self.model.make_empty_intermediate_tensors
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
 
     def forward(
         self,

vllm/model_executor/models/ouro.py

@@ -361,7 +361,7 @@ class OuroModel(nn.Module):
 
         self.total_ut_steps = getattr(self.config, "total_ut_steps", 4)
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
 
     def forward(
@@ -374,7 +374,7 @@ class OuroModel(nn.Module):
         if inputs_embeds is not None:
             hidden_states = inputs_embeds
         else:
-            hidden_states = self.get_input_embeddings(input_ids)
+            hidden_states = self.embed_input_ids(input_ids)
 
         for current_ut in range(self.total_ut_steps):
             residual = None
@@ -486,8 +486,8 @@ class OuroForCausalLM(nn.Module, SupportsLoRA):
             self.model.make_empty_intermediate_tensors
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
 
     def forward(
         self,

vllm/model_executor/models/ovis.py

@@ -514,7 +514,7 @@ class Ovis(nn.Module, SupportsMultiModal, SupportsPP):
 
         return tuple(vision_embeddings)
 
-    def get_multimodal_embeddings(self, **kwargs: object) -> MultiModalEmbeddings:
+    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
             return []

vllm/model_executor/models/ovis2_5.py

@@ -617,7 +617,7 @@ class Ovis2_5(nn.Module, SupportsMultiModal, SupportsPP):
 
         return modalities
 
-    def get_multimodal_embeddings(self, **kwargs: object) -> MultiModalEmbeddings:
+    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
         modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
         if not modalities:
             return []

vllm/model_executor/models/paddleocr_vl.py

@@ -1328,10 +1328,10 @@ class PaddleOCRVLForConditionalGeneration(nn.Module, SupportsMultiModal, Support
             inputs_embeds = None
 
         elif inputs_embeds is None:
-            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            vision_embeddings = self.embed_multimodal(**kwargs)
             is_multimodal = kwargs.pop("is_multimodal", None)
             handle_oov_mm_token = kwargs.pop("handle_oov_mm_token", False)
-            inputs_embeds = self.get_input_embeddings(
+            inputs_embeds = self.embed_input_ids(
                 input_ids,
                 vision_embeddings,
                 is_multimodal=is_multimodal,
@@ -1391,7 +1391,7 @@ class PaddleOCRVLForConditionalGeneration(nn.Module, SupportsMultiModal, Support
         image_embeds = self.mlp_AR(vision_outputs, image_grid_thw)
         return image_embeds
 
-    def get_multimodal_embeddings(self, **kwargs) -> MultiModalEmbeddings:
+    def embed_multimodal(self, **kwargs) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
             return ()

vllm/model_executor/models/paligemma.py

@@ -375,7 +375,7 @@ class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsP
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(self, **kwargs: object) -> MultiModalEmbeddings:
+    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
             return []

vllm/model_executor/models/persimmon.py

@@ -270,7 +270,7 @@ class PersimmonModel(nn.Module):
             ["hidden_states"], config.hidden_size
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
 
     def forward(
@@ -284,7 +284,7 @@ class PersimmonModel(nn.Module):
             if inputs_embeds is not None:
                 hidden_states = inputs_embeds
             else:
-                hidden_states = self.get_input_embeddings(input_ids)
+                hidden_states = self.embed_input_ids(input_ids)
         else:
             assert intermediate_tensors is not None
             hidden_states = intermediate_tensors["hidden_states"]
@@ -347,8 +347,8 @@ class PersimmonForCausalLM(nn.Module, SupportsPP):
             self.model.make_empty_intermediate_tensors
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
 
     def forward(
         self,

vllm/model_executor/models/phi.py

@@ -240,7 +240,7 @@ class PhiModel(nn.Module):
             ["hidden_states"], config.hidden_size
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
 
     def forward(
@@ -254,7 +254,7 @@ class PhiModel(nn.Module):
             if inputs_embeds is not None:
                 hidden_states = inputs_embeds
             else:
-                hidden_states = self.get_input_embeddings(input_ids)
+                hidden_states = self.embed_input_ids(input_ids)
         else:
             assert intermediate_tensors is not None
             hidden_states = intermediate_tensors["hidden_states"]
@@ -346,8 +346,8 @@ class PhiForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             self.model.make_empty_intermediate_tensors
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
 
     def forward(
         self,

vllm/model_executor/models/phi3v.py

@@ -664,14 +664,14 @@ class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsQuant)
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(self, **kwargs: object) -> MultiModalEmbeddings:
+    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
             return []
         vision_embeddings = self._process_image_input(image_input)
         return vision_embeddings
 
-    def get_input_embeddings(
+    def embed_input_ids(
         self,
         input_ids: torch.Tensor,
         multimodal_embeddings: MultiModalEmbeddings | None = None,
@@ -679,7 +679,7 @@ class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsQuant)
         is_multimodal: torch.Tensor | None = None,
         handle_oov_mm_token: bool = False,
     ) -> torch.Tensor:
-        inputs_embeds = self._get_text_embeddings(
+        inputs_embeds = self._embed_text_input_ids(
             input_ids,
             self.embed_tokens,
             is_multimodal=is_multimodal,
@@ -691,7 +691,7 @@ class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsQuant)
 
         if is_multimodal is None:
             raise ValueError(
-                "`get_input_embeddings` now requires `is_multimodal` arg, "
+                "`embed_input_ids` now requires `is_multimodal` arg, "
                 "please update your model runner according to "
                 "https://github.com/vllm-project/vllm/pull/16229."
             )

vllm/model_executor/models/phi4_multimodal.py

@@ -1371,7 +1371,7 @@ class Phi4MultimodalForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
             )
         return image_embeds
 
-    def get_multimodal_embeddings(self, **kwargs: object) -> MultiModalEmbeddings:
+    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
         modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
         if not modalities:
             return []