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

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

vllm/model_executor/models/phi4mm.py

@@ -1180,7 +1180,7 @@ class Phi4MMForCausalLM(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 []

vllm/model_executor/models/phimoe.py

@@ -482,7 +482,7 @@ class PhiMoEModel(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(
@@ -496,7 +496,7 @@ class PhiMoEModel(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
@@ -648,8 +648,8 @@ class PhiMoEForCausalLM(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/pixtral.py

@@ -461,7 +461,7 @@ class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP)
     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/plamo2.py

@@ -762,7 +762,7 @@ class Plamo2Model(torch.nn.Module):
         self.layers = Plamo2Decoder(vllm_config=vllm_config, prefix=f"{prefix}.layers")
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
-    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(
@@ -776,7 +776,7 @@ class Plamo2Model(torch.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
@@ -839,8 +839,8 @@ class Plamo2ForCausalLM(torch.nn.Module, HasInnerState, SupportsPP, IsHybrid):
             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/qwen.py

@@ -221,7 +221,7 @@ class QWenModel(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.wte(input_ids)
 
     def forward(
@@ -235,7 +235,7 @@ class QWenModel(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

vllm/model_executor/models/qwen2.py

@@ -355,7 +355,7 @@ class Qwen2Model(nn.Module):
 
         self.aux_hidden_state_layers = tuple[int, ...]()
 
-    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(
@@ -369,7 +369,7 @@ class Qwen2Model(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
@@ -504,8 +504,8 @@ class Qwen2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
             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 set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
         self.model.aux_hidden_state_layers = layers

vllm/model_executor/models/qwen2_5_omni_thinker.py

@@ -1132,7 +1132,7 @@ class Qwen2_5OmniThinkerForConditionalGeneration(
 
         return llm_positions, mrope_position_delta
 
-    def get_multimodal_embeddings(self, **kwargs: object) -> MultiModalEmbeddings:
+    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
         mm_input_by_modality = self._parse_and_validate_multimodal_inputs(**kwargs)
         if not mm_input_by_modality:
             return []
@@ -1158,7 +1158,7 @@ class Qwen2_5OmniThinkerForConditionalGeneration(
 
     # TODO (ywang96): support overlapping modality embeddings so that
     # `use_audio_in_video` will work on V1.
-    def get_input_embeddings(
+    def embed_input_ids(
         self,
         input_ids: torch.Tensor,
         multimodal_embeddings: MultiModalEmbeddings | None = None,
@@ -1168,16 +1168,16 @@ class Qwen2_5OmniThinkerForConditionalGeneration(
     ) -> torch.Tensor:
         # 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,
             handle_oov_mm_token=handle_oov_mm_token,
         )
 
-    def get_multimodal_embeddings_v0(self, **kwargs: object) -> NestedTensors | None:
+    def embed_multimodal_v0(self, **kwargs: object) -> NestedTensors | None:
         audio_input = self._parse_and_validate_audio_input(**kwargs)
         image_input = self._parse_and_validate_image_input(**kwargs)
         video_input = self._parse_and_validate_video_input(**kwargs)

vllm/model_executor/models/qwen2_5_vl.py

@@ -1534,7 +1534,7 @@ class Qwen2_5_VLForConditionalGeneration(
     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:
         mm_input_by_modality = self._parse_and_validate_multimodal_inputs(**kwargs)
         if not mm_input_by_modality:
             return []

vllm/model_executor/models/qwen2_audio.py

@@ -439,7 +439,7 @@ class Qwen2AudioForConditionalGeneration(nn.Module, SupportsMultiModal, Supports
     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:
         audio_input = self._parse_and_validate_audio_input(**kwargs)
         if audio_input is None:
             return []

vllm/model_executor/models/qwen2_moe.py

@@ -389,7 +389,7 @@ class Qwen2MoeModel(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(
@@ -403,7 +403,7 @@ class Qwen2MoeModel(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
@@ -566,8 +566,8 @@ class Qwen2MoeForCausalLM(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/qwen2_rm.py

@@ -73,8 +73,8 @@ class Qwen2RewardBaseModel(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/qwen2_vl.py

@@ -1507,7 +1507,7 @@ class Qwen2VLForConditionalGeneration(
     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 []

vllm/model_executor/models/qwen3.py

@@ -306,8 +306,8 @@ class Qwen3ForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
         num_layers = len(self.model.layers)
         return (2, num_layers // 2, num_layers - 3)
 
-    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/qwen3_moe.py

@@ -427,7 +427,7 @@ class Qwen3MoeModel(nn.Module):
         # Track layers for auxiliary hidden state outputs (EAGLE3)
         self.aux_hidden_state_layers: tuple[int, ...] = ()
 
-    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(
@@ -441,7 +441,7 @@ class Qwen3MoeModel(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
@@ -714,8 +714,8 @@ class Qwen3MoeForCausalLM(
         num_layers = len(self.model.layers)
         return (2, num_layers // 2, num_layers - 3)
 
-    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/qwen3_next.py

@@ -998,7 +998,7 @@ class Qwen3NextModel(nn.Module):
         else:
             self.norm = PPMissingLayer()
 
-    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(
@@ -1012,7 +1012,7 @@ class Qwen3NextModel(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
@@ -1217,8 +1217,8 @@ class Qwen3NextForCausalLM(
         # Set MoE hyperparameters
         self.set_moe_parameters()
 
-    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/qwen3_next_mtp.py

@@ -93,7 +93,7 @@ class Qwen3NextMultiTokenPredictor(nn.Module):
             config.hidden_size, eps=config.rms_norm_eps
         )
 
-    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(
@@ -107,7 +107,7 @@ class Qwen3NextMultiTokenPredictor(nn.Module):
     ) -> torch.Tensor:
         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)
             assert hidden_states.shape[-1] == inputs_embeds.shape[-1]
             inputs_embeds = self.pre_fc_norm_embedding(inputs_embeds)
             hidden_states = self.pre_fc_norm_hidden(hidden_states)
@@ -257,8 +257,8 @@ class Qwen3NextMTP(nn.Module, SupportsPP, QwenNextMixtureOfExperts):
         )
         self.set_moe_parameters()
 
-    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/qwen3_omni_moe_thinker.py

@@ -613,7 +613,7 @@ class Qwen3MoeLLMModel(Qwen3MoeModel):
             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
@@ -1252,9 +1252,7 @@ class Qwen3OmniMoeThinkerForConditionalGeneration(
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-        self, **kwargs: object
-    ) -> MultiModalEmbeddings | None:
+    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings | None:
         mm_input_by_modality = self._parse_and_validate_multimodal_inputs(**kwargs)
         if not mm_input_by_modality:
             return []
@@ -1278,7 +1276,7 @@ class Qwen3OmniMoeThinkerForConditionalGeneration(
                 multimodal_embeddings += tuple(audio_embeddings)
         return multimodal_embeddings
 
-    def get_input_embeddings(
+    def embed_input_ids(
         self,
         input_ids: torch.Tensor,
         multimodal_embeddings: MultiModalEmbeddings | None = None,
@@ -1286,9 +1284,9 @@ class Qwen3OmniMoeThinkerForConditionalGeneration(
         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.language_model.get_input_embeddings,
+            self.language_model.embed_input_ids,
             is_multimodal=is_multimodal,
             handle_oov_mm_token=handle_oov_mm_token,
         )

vllm/model_executor/models/qwen3_vl.py

@@ -1100,7 +1100,7 @@ class Qwen3LLMModel(Qwen3Model):
             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
@@ -1493,9 +1493,7 @@ class Qwen3VLForConditionalGeneration(
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-        self, **kwargs: object
-    ) -> MultiModalEmbeddings | None:
+    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings | None:
         mm_input_by_modality = self._parse_and_validate_multimodal_inputs(**kwargs)
         if not mm_input_by_modality:
             return None
@@ -1557,7 +1555,7 @@ class Qwen3VLForConditionalGeneration(
 
         return deepstack_input_embeds, multimodal_embeddings
 
-    def get_input_embeddings(
+    def embed_input_ids(
         self,
         input_ids: torch.Tensor,
         multimodal_embeddings: MultiModalEmbeddings | None = None,
@@ -1565,9 +1563,9 @@ class Qwen3VLForConditionalGeneration(
         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.language_model.get_input_embeddings,
+            self.language_model.embed_input_ids,
             is_multimodal=is_multimodal,
             handle_oov_mm_token=handle_oov_mm_token,
         )
@@ -1577,7 +1575,7 @@ class Qwen3VLForConditionalGeneration(
 
         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/qwen3_vl_moe.py

@@ -97,7 +97,7 @@ class Qwen3MoeLLMModel(Qwen3MoeModel):
             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

vllm/model_executor/models/qwen_vl.py

@@ -777,7 +777,7 @@ class QwenVLForConditionalGeneration(
     def get_language_model(self) -> torch.nn.Module:
         return self.transformer
 
-    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 []