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

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

vllm/model_executor/models/gemma3n.py

@@ -685,7 +685,7 @@ class Gemma3nSelfDecoder(nn.Module):
             per_layer_inputs = per_layer_projection
         return per_layer_inputs
 
-    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) * self.embed_scale
 
     def altup_embed(self, hidden_states_0: torch.Tensor) -> torch.Tensor:
@@ -712,7 +712,7 @@ class Gemma3nSelfDecoder(nn.Module):
         if inputs_embeds is not None:
             hidden_states_0 = inputs_embeds
         else:
-            hidden_states_0 = self.get_input_embeddings(input_ids)
+            hidden_states_0 = self.embed_input_ids(input_ids)
 
         adjusted_per_layer_inputs = self.get_per_layer_inputs(
             hidden_states_0, per_layer_inputs
@@ -881,8 +881,8 @@ class Gemma3nTextModel(nn.Module, SupportsQuant):
     def get_per_layer_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.self_decoder.get_per_layer_input_embeddings(input_ids)
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.self_decoder.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.self_decoder.embed_input_ids(input_ids)
 
     def fast_prefill_forward(
         self,
@@ -1125,8 +1125,8 @@ class Gemma3nForCausalLM(nn.Module):
             config.vocab_size, soft_cap=config.final_logit_softcapping
         )
 
-    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/gemma3n_mm.py

@@ -645,7 +645,7 @@ class Gemma3nForConditionalGeneration(
     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 mm_input_by_modality is None:
             return []
@@ -664,7 +664,7 @@ class Gemma3nForConditionalGeneration(
                 multimodal_embeddings.extend(audio_embeddings)
         return multimodal_embeddings
 
-    def get_input_embeddings(
+    def embed_input_ids(
         self,
         input_ids: torch.Tensor,
         multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
@@ -689,9 +689,9 @@ class Gemma3nForConditionalGeneration(
 
         # 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,
@@ -709,10 +709,10 @@ class Gemma3nForConditionalGeneration(
         if intermediate_tensors is not None:
             inputs_embeds = None
 
-        # NOTE (NickLucche) During profiling, `get_input_embeddings` is not
+        # NOTE (NickLucche) During profiling, `embed_input_ids` is not
         # called, hence we don't have input_ids to compute PLEs. We simply
         # select a chunk of pre-allocated PLEs. During normal execution,
-        # `get_input_embeddings` is called before forward, hence this slice
+        # `embed_input_ids` is called before forward, hence this slice
         # will contain PLEs computed from the actual input_ids.
         per_layer_inputs = self.per_layer_embeddings[: inputs_embeds.shape[0]]
 

vllm/model_executor/models/glm4.py

@@ -275,8 +275,8 @@ class Glm4ForCausalLM(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/glm4_1v.py

@@ -1594,9 +1594,7 @@ class Glm4vForConditionalGeneration(
     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

vllm/model_executor/models/glm4_moe.py

@@ -455,7 +455,7 @@ class Glm4MoeModel(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(
@@ -469,7 +469,7 @@ class Glm4MoeModel(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
@@ -704,8 +704,8 @@ class Glm4MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA, Glm4MixtureOfExper
 
         self.extract_moe_parameters(example_moe)
 
-    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/glm4_moe_mtp.py

@@ -149,7 +149,7 @@ class Glm4MoeMultiTokenPredictor(nn.Module):
         )
         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:
         return self.embed_tokens(input_ids)
 
     def forward(
@@ -211,8 +211,8 @@ class Glm4MoeMTP(nn.Module, SupportsPP, Glm4MixtureOfExperts):
                 self.moe_layers.append(layer.mlp.experts)
         self.extract_moe_parameters(example_moe)
 
-    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/glm4v.py

@@ -756,9 +756,9 @@ class GLM4VForCausalLM(
     def get_language_model(self) -> torch.nn.Module:
         return self.transformer
 
-    get_input_embeddings = SupportsMultiModal.get_input_embeddings
+    embed_input_ids = SupportsMultiModal.embed_input_ids
 
-    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/gpt2.py

@@ -213,7 +213,7 @@ class GPT2Model(nn.Module):
             ["hidden_states"], config.n_embd
         )
 
-    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(
@@ -225,7 +225,7 @@ class GPT2Model(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)
             position_embeds = self.wpe(position_ids)
             hidden_states = inputs_embeds + position_embeds
         else:
@@ -293,8 +293,8 @@ class GPT2LMHeadModel(nn.Module, SupportsPP):
             self.transformer.make_empty_intermediate_tensors
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.transformer.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.transformer.embed_input_ids(input_ids)
 
     def forward(
         self,
@@ -365,8 +365,8 @@ class GPT2ForSequenceClassification(nn.Module, SupportsCrossEncoding):
             }
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.transformer.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.transformer.embed_input_ids(input_ids)
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
         loader = AutoWeightsLoader(self)

vllm/model_executor/models/gpt_bigcode.py

@@ -230,7 +230,7 @@ class GPTBigCodeModel(nn.Module):
             ["hidden_states"], config.n_embd
         )
 
-    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(
@@ -242,7 +242,7 @@ class GPTBigCodeModel(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)
             hidden_states = inputs_embeds + self.wpe(position_ids)
         else:
             hidden_states = intermediate_tensors["hidden_states"]
@@ -306,8 +306,8 @@ class GPTBigCodeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             self.transformer.make_empty_intermediate_tensors
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.transformer.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.transformer.embed_input_ids(input_ids)
 
     def forward(
         self,

vllm/model_executor/models/gpt_j.py

@@ -215,7 +215,7 @@ class GPTJModel(nn.Module):
             ["hidden_states"], config.n_embd
         )
 
-    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(
@@ -229,7 +229,7 @@ class GPTJModel(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:
             hidden_states = intermediate_tensors["hidden_states"]
         for layer in islice(self.h, self.start_layer, self.end_layer):
@@ -319,8 +319,8 @@ class GPTJForCausalLM(nn.Module, SupportsPP):
             self.transformer.make_empty_intermediate_tensors
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.transformer.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.transformer.embed_input_ids(input_ids)
 
     def forward(
         self,

vllm/model_executor/models/gpt_neox.py

@@ -229,7 +229,7 @@ class GPTNeoXModel(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_in(input_ids)
 
     def forward(
@@ -243,7 +243,7 @@ class GPTNeoXModel(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:
             hidden_states = intermediate_tensors["hidden_states"]
         for layer in islice(self.layers, self.start_layer, self.end_layer):
@@ -317,8 +317,8 @@ class GPTNeoXForCausalLM(nn.Module, SupportsPP):
             self.gpt_neox.make_empty_intermediate_tensors
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.gpt_neox.get_input_embeddings(input_ids)
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.gpt_neox.embed_input_ids(input_ids)
 
     def forward(
         self,

vllm/model_executor/models/gpt_oss.py

@@ -269,7 +269,7 @@ class GptOssModel(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.embedding(input_ids)
 
     def forward(
@@ -283,7 +283,7 @@ class GptOssModel(nn.Module):
             if inputs_embeds is not None:
                 x = inputs_embeds
             else:
-                x = self.get_input_embeddings(input_ids)
+                x = self.embed_input_ids(input_ids)
 
             residual = None
         else:
@@ -703,8 +703,8 @@ class GptOssForCausalLM(nn.Module, SupportsPP, SupportsEagle3, SupportsLoRA):
         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/granite.py

@@ -318,7 +318,7 @@ class GraniteModel(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(
@@ -332,7 +332,7 @@ class GraniteModel(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)
 
             hidden_states *= self.config.embedding_multiplier
         else:
@@ -473,8 +473,8 @@ class GraniteForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         else:
             self.lm_head = PPMissingLayer()
 
-    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/granite_speech.py

@@ -767,7 +767,7 @@ class GraniteSpeechForConditionalGeneration(
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
+    def embed_multimodal(
         self,
         **kwargs: object,
     ) -> MultiModalEmbeddings:
@@ -779,7 +779,7 @@ class GraniteSpeechForConditionalGeneration(
         audio_features = self._process_audio_input(audio_input)
         return audio_features
 
-    def get_input_embeddings(
+    def embed_input_ids(
         self,
         input_ids: torch.Tensor,
         multimodal_embeddings: MultiModalEmbeddings | None = None,
@@ -790,9 +790,9 @@ class GraniteSpeechForConditionalGeneration(
     ) -> 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/granitemoe.py

@@ -315,7 +315,7 @@ class GraniteMoeModel(nn.Module):
 
         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(
@@ -329,7 +329,7 @@ class GraniteMoeModel(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)
             hidden_states *= self.embedding_multiplier
         else:
             assert intermediate_tensors is not None
@@ -531,8 +531,8 @@ class GraniteMoeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             scale=1 / self.config.logits_scaling,
         )
 
-    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/granitemoehybrid.py

@@ -366,7 +366,7 @@ class GraniteMoeHybridModel(nn.Module):
 
         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(
@@ -380,7 +380,7 @@ class GraniteMoeHybridModel(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)
                 hidden_states = hidden_states * self.embedding_multiplier
             residual = None
         else:
@@ -680,8 +680,8 @@ class GraniteMoeHybridForCausalLM(
             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/granitemoeshared.py

@@ -182,7 +182,7 @@ class GraniteMoeSharedModel(nn.Module):
 
         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(
@@ -196,7 +196,7 @@ class GraniteMoeSharedModel(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)
             hidden_states *= self.embedding_multiplier
         else:
             assert intermediate_tensors is not None
@@ -295,8 +295,8 @@ class GraniteMoeSharedForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             scale=1 / self.config.logits_scaling,
         )
 
-    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/grok1.py

@@ -334,7 +334,7 @@ class Grok1Model(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:
         hidden_states = self.embed_tokens(input_ids)
         hidden_states = hidden_states * self.embedding_multiplier_scale
         return hidden_states
@@ -350,7 +350,7 @@ class Grok1Model(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
@@ -522,8 +522,8 @@ class Grok1ForCausalLM(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/hunyuan_v1.py

@@ -643,7 +643,7 @@ class HunYuanModel(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(
@@ -657,7 +657,7 @@ class HunYuanModel(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
@@ -987,8 +987,8 @@ class HunyuanV1ModelBase(nn.Module, SupportsLoRA, SupportsPP):
         )
         return loader.load_weights(weights)
 
-    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)
 
 
 class HunYuanMoEV1Base(HunyuanV1ModelBase, MixtureOfExperts):

vllm/model_executor/models/hyperclovax_vision.py

@@ -732,7 +732,7 @@ class HCXVisionForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
+    def embed_multimodal(
         self,
         **kwargs: object,
     ) -> MultiModalEmbeddings: