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

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

vllm/model_executor/models/roberta.py

@@ -220,8 +220,8 @@ class RobertaForSequenceClassification(nn.Module, SupportsCrossEncoding):
         loader = AutoWeightsLoader(self)
         return loader.load_weights(weights, mapper=self.jina_to_vllm_mapper)
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.roberta.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.roberta.embed_input_ids(input_ids)
 
     def forward(
         self,

vllm/model_executor/models/seed_oss.py

@@ -334,7 +334,7 @@ class SeedOssModel(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(
@@ -348,7 +348,7 @@ class SeedOssModel(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
@@ -467,8 +467,8 @@ class SeedOssForCausalLM(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/siglip.py

@@ -595,7 +595,7 @@ class SiglipTextTransformer(nn.Module):
         self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
         self.head = nn.Linear(embed_dim, config.projection_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.embeddings.token_embedding(input_ids)
 
     def forward(
@@ -1117,7 +1117,7 @@ class SiglipEmbeddingModel(nn.Module, SupportsMultiModal, SupportsQuant):
     def get_language_model(self) -> torch.nn.Module:
         return self.text_model
 
-    def get_input_embeddings(
+    def embed_input_ids(
         self,
         input_ids: torch.Tensor,
         multimodal_embeddings: MultiModalEmbeddings | None = None,
@@ -1130,16 +1130,16 @@ class SiglipEmbeddingModel(nn.Module, SupportsMultiModal, SupportsQuant):
         )
 
         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(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/skyworkr1v.py

@@ -872,14 +872,14 @@ class SkyworkR1VChatModel(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 []
 
         return self._process_image_input(image_input)
 
-    def get_input_embeddings(
+    def embed_input_ids(
         self,
         input_ids: torch.Tensor,
         multimodal_embeddings: MultiModalEmbeddings | None = None,
@@ -892,9 +892,9 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
 
         # 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/solar.py

@@ -310,7 +310,7 @@ class SolarModel(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(
@@ -324,7 +324,7 @@ class SolarModel(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
@@ -478,8 +478,8 @@ class SolarForCausalLM(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/stablelm.py

@@ -246,7 +246,7 @@ class StableLMEpochModel(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(
@@ -260,7 +260,7 @@ class StableLMEpochModel(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"]
@@ -332,8 +332,8 @@ class StablelmForCausalLM(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/starcoder2.py

@@ -249,7 +249,7 @@ class Starcoder2Model(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(
@@ -263,7 +263,7 @@ class Starcoder2Model(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"]
@@ -333,8 +333,8 @@ class Starcoder2ForCausalLM(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/step3_text.py

@@ -354,7 +354,7 @@ class Step3TextModel(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(
@@ -368,7 +368,7 @@ class Step3TextModel(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
@@ -419,8 +419,8 @@ class Step3TextForCausalLM(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/step3_vl.py

@@ -1075,14 +1075,14 @@ class Step3VLForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP)
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    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 []
         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,
@@ -1093,9 +1093,9 @@ class Step3VLForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP)
     ) -> 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,
@@ -1113,8 +1113,8 @@ class Step3VLForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP)
         if intermediate_tensors is not None:
             inputs_embeds = None
         elif inputs_embeds is None:
-            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
-            inputs_embeds = self.get_input_embeddings(
+            vision_embeddings = self.embed_multimodal(**kwargs)
+            inputs_embeds = self.embed_input_ids(
                 input_ids,
                 vision_embeddings,
                 is_multimodal=input_ids == self.config.image_token_id,

vllm/model_executor/models/tarsier.py

@@ -576,7 +576,7 @@ class TarsierForConditionalGeneration(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 []
@@ -593,8 +593,8 @@ class TarsierForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP)
         if intermediate_tensors is not None:
             inputs_embeds = None
         elif inputs_embeds is None:
-            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
-            inputs_embeds = self.get_input_embeddings(
+            vision_embeddings = self.embed_multimodal(**kwargs)
+            inputs_embeds = self.embed_input_ids(
                 input_ids,
                 vision_embeddings,
                 is_multimodal=input_ids == self.config.image_token_index,

vllm/model_executor/models/teleflm.py

@@ -57,7 +57,7 @@ class TeleFLMModel(LlamaModel):
         if self.use_mup:
             self.input_mult = self.config.input_mult
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         embedding = self.embed_tokens(input_ids)
         if self.use_mup:
             embedding = embedding * self.input_mult

vllm/model_executor/models/terratorch.py

@@ -251,7 +251,7 @@ class Terratorch(nn.Module, IsAttentionFree, SupportsMultiModal):
 
         self.pooler = DispatchPooler({"plugin": DummyPooler()})
 
-    def get_input_embeddings(
+    def embed_input_ids(
         self,
         input_ids: torch.Tensor,
         multimodal_embeddings: MultiModalEmbeddings | None = None,

vllm/model_executor/models/transformers/base.py

@@ -385,7 +385,7 @@ class Base(nn.Module, VllmModel, SupportsQuant, SupportsLoRA, SupportsPP):
 
         _init_parameters(module, dtype)
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         inputs_embeds = self.model.get_input_embeddings()(input_ids)
         if self.embed_scale is not None:
             inputs_embeds *= self.embed_scale
@@ -416,7 +416,7 @@ class Base(nn.Module, VllmModel, SupportsQuant, SupportsLoRA, SupportsPP):
             and input_ids is not None
             and inputs_embeds is None
         ):
-            inputs_embeds = self.get_input_embeddings(input_ids)
+            inputs_embeds = self.embed_input_ids(input_ids)
             input_ids = None
 
         if self.model_config.uses_mrope:

vllm/model_executor/models/transformers/multimodal.py

@@ -330,7 +330,7 @@ class MultiModalMixin(SupportsMultiModal, SupportsMRoPE):
 
         return LanguageModel(self)
 
-    def get_multimodal_embeddings(self, **kwargs):
+    def embed_multimodal(self, **kwargs):
         pixel_values: torch.Tensor | None = kwargs.pop("pixel_values", None)
         image_embeds: torch.Tensor | None = kwargs.pop("image_embeds", None)
         # Model might use `image_patches` instead of `pixel_values`

vllm/model_executor/models/ultravox.py

@@ -579,14 +579,14 @@ class UltravoxModel(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA):
     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 []
         audio_embeddings = self._process_audio_input(audio_input)
         return audio_embeddings
 
-    def get_input_embeddings(
+    def embed_input_ids(
         self,
         input_ids: torch.Tensor,
         multimodal_embeddings: MultiModalEmbeddings | None = None,
@@ -597,9 +597,9 @@ class UltravoxModel(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA):
     ) -> 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,

vllm/model_executor/models/utils.py

@@ -474,7 +474,7 @@ def _merge_multimodal_embeddings(
 
 @deprecated(
     "`merge_multimodal_embeddings` has been replaced with "
-    "`SupportsMultiModal.get_input_embeddings` and will be "
+    "`SupportsMultiModal.embed_input_ids` and will be "
     "removed in v0.12."
 )
 def merge_multimodal_embeddings(

vllm/model_executor/models/voxtral.py

@@ -399,7 +399,7 @@ class VoxtralForConditionalGeneration(
 
         return hidden_states
 
-    def get_multimodal_embeddings(
+    def embed_multimodal(
         self, **kwargs
     ) -> list[torch.Tensor] | torch.Tensor | tuple[torch.Tensor, ...] | None:
         audio_inputs = self._parse_and_validate_audio_arrays(**kwargs)

vllm/model_executor/models/whisper.py

@@ -570,7 +570,7 @@ class WhisperDecoder(nn.Module):
         positions: torch.Tensor,
         encoder_hidden_states: torch.Tensor | None,
     ):
-        inputs_embeds = self.get_input_embeddings(input_ids)
+        inputs_embeds = self.embed_input_ids(input_ids)
         positions = self.embed_positions(positions)
         hidden_states = inputs_embeds + positions
 
@@ -583,7 +583,7 @@ class WhisperDecoder(nn.Module):
         hidden_states = self.layer_norm(hidden_states)
         return hidden_states
 
-    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)
 
 
@@ -907,12 +907,12 @@ class WhisperForConditionalGeneration(
     def get_language_model(self) -> torch.nn.Module:
         return self.model.decoder
 
-    def get_multimodal_embeddings(self, **kwargs: object) -> MultiModalEmbeddings:
+    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
         # Required as part of SupportsMultiModal interface.
         audio_input = self._parse_and_validate_audio_input(**kwargs)
         return [self.model.get_encoder_outputs(audio_input["input_features"])]
 
-    def get_input_embeddings(
+    def embed_input_ids(
         self,
         input_ids: torch.Tensor,
         multimodal_embeddings: MultiModalEmbeddings | None = None,
@@ -922,7 +922,7 @@ class WhisperForConditionalGeneration(
     ) -> torch.Tensor:
         # This method just returns the decoder sequence embeddings since
         # Whisper does not have encoder text tokens.
-        return self.model.decoder.get_input_embeddings(input_ids)
+        return self.model.decoder.embed_input_ids(input_ids)
 
     def _parse_and_validate_audio_input(self, **kwargs: object) -> WhisperAudioInputs:
         input_features = kwargs.pop("input_features", None)

vllm/model_executor/models/zamba2.py

@@ -756,7 +756,7 @@ class Zamba2Model(nn.Module):
         # Final layer normalization
         self.final_layernorm = 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:
         """Convert input token IDs to embeddings.
 
         Args:
@@ -786,7 +786,7 @@ class Zamba2Model(nn.Module):
         """
         # Handle pipeline parallelism for first rank
         if inputs_embeds is None:
-            inputs_embeds = self.get_input_embeddings(input_ids)
+            inputs_embeds = self.embed_input_ids(input_ids)
         hidden_states = inputs_embeds
 
         # Process through layers
@@ -930,14 +930,14 @@ class Zamba2ForCausalLM(nn.Module, HasInnerState, IsHybrid, SupportsMambaPrefixC
         # Initialize logits processing and sampling
         self.logits_processor = LogitsProcessor(config.vocab_size)
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         """Convert input token IDs to embeddings.
         Args:
             input_ids: Tensor of input token IDs
         Returns:
             Embedded representation of the input tokens
         """
-        return self.model.get_input_embeddings(input_ids)
+        return self.model.embed_input_ids(input_ids)
 
     def forward(
         self,

vllm/multimodal/processing.py

@@ -208,7 +208,7 @@ class PromptUpdateDetails(Generic[_S]):
     `None` (default) means to assign embeddings to all positions of `full`.
 
     The embeddings are obtained by calling
-    [`SupportsMultiModal.get_multimodal_embeddings`][vllm.model_executor.models.interfaces.SupportsMultiModal.get_multimodal_embeddings].
+    [`SupportsMultiModal.embed_multimodal`][vllm.model_executor.models.interfaces.SupportsMultiModal.embed_multimodal].
     """
 
     @staticmethod