sync v0.15.1

vllm/model_executor/models/minicpmv.py

@@ -1147,7 +1147,7 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -1740,4 +1740,4 @@ class MiniCPMV(MiniCPMVBaseModel, SupportsMultiModal, SupportsLoRA):
         # so update values before init is called
         cls.packed_modules_mapping.update(instance_cls.packed_modules_mapping)
         cls.embedding_modules.update(instance_cls.embedding_modules)
-        return instance_cls(vllm_config=vllm_config, prefix=prefix)
+        return instance_cls(vllm_config=vllm_config, prefix=prefix)
\ No newline at end of file

vllm/model_executor/models/minimax_m2.py

@@ -362,7 +362,7 @@ class MiniMaxM2Model(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None,
         inputs_embeds: torch.Tensor | None = None,
@@ -521,7 +521,7 @@ class MiniMaxM2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -555,4 +555,4 @@ def get_spec_layer_idx_from_weight_name(
         for i in range(config.num_mtp_modules):
             if weight_name.startswith(f"model.layers.{layer_idx + i}."):
                 return layer_idx + i
-    return None
+    return None
\ No newline at end of file

vllm/model_executor/models/minimax_text_01.py

@@ -712,7 +712,7 @@ class MiniMaxText01ForCausalLM(nn.Module, HasInnerState, IsHybrid):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -1011,4 +1011,4 @@ class MiniMaxText01ForCausalLM(nn.Module, HasInnerState, IsHybrid):
 
     @classmethod
     def get_mamba_state_copy_func(cls) -> tuple[MambaStateCopyFunc]:
-        return MambaStateCopyFuncCalculator.linear_attention_state_copy_func()
+        return MambaStateCopyFuncCalculator.linear_attention_state_copy_func()
\ No newline at end of file

vllm/model_executor/models/minimax_vl_01.py

@@ -359,7 +359,7 @@ class MiniMaxVL01ForConditionalGeneration(nn.Module, SupportsMultiModal, Support
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -382,4 +382,4 @@ class MiniMaxVL01ForConditionalGeneration(nn.Module, SupportsMultiModal, Support
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         loader = AutoWeightsLoader(self)
-        return loader.load_weights(weights)
+        return loader.load_weights(weights)
\ No newline at end of file

vllm/model_executor/models/mistral3.py

@@ -539,7 +539,7 @@ class Mistral3ForConditionalGeneration(
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -609,4 +609,4 @@ class Mistral3ForConditionalGeneration(
             language_model="language_model",
             connector="multi_modal_projector",
             tower_model="vision_tower",
-        )
+        )
\ No newline at end of file

vllm/model_executor/models/mixtral.py

@@ -338,7 +338,7 @@ class MixtralModel(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None,
         inputs_embeds: torch.Tensor | None = None,
@@ -574,7 +574,7 @@ class MixtralForCausalLM(nn.Module, SupportsLoRA, SupportsPP, MixtureOfExperts):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -596,4 +596,4 @@ class MixtralForCausalLM(nn.Module, SupportsLoRA, SupportsPP, MixtureOfExperts):
         return loader.load_weights(weights)
 
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
-        return self.model.get_expert_mapping()
+        return self.model.get_expert_mapping()
\ No newline at end of file

vllm/model_executor/models/mllama4.py

@@ -584,7 +584,6 @@ class Mllama4ProcessingInfo(BaseProcessingInfo):
         image_processor = self.get_hf_processor().image_processor
         return image_processor.max_patches
 
-
     def get_image_size_with_most_features(self) -> ImageSize:
         vision_config = self.get_hf_config().vision_config
         image_size = vision_config.image_size
@@ -732,6 +731,7 @@ class Mllama4DummyInputsBuilder(BaseDummyInputsBuilder[Mllama4ProcessingInfo]):
             )
         }
 
+
 @MULTIMODAL_REGISTRY.register_processor(
     Mllama4MultiModalProcessor,
     info=Mllama4ProcessingInfo,
@@ -901,7 +901,7 @@ class Llama4ForConditionalGeneration(
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -1161,4 +1161,4 @@ class Llama4ForConditionalGeneration(
             language_model="language_model",
             connector="multi_modal_projector.",
             tower_model="vision_model.",
-        )
+        )
\ No newline at end of file

vllm/model_executor/models/modernbert.py

@@ -54,11 +54,12 @@ class ModernBertEmbeddings(nn.Module):
         input_ids: torch.Tensor,
         inputs_embeds: torch.Tensor | None = None,
     ) -> torch.Tensor:
-        if inputs_embeds is None:
+        if inputs_embeds is not None:
+            return self.norm(inputs_embeds)
+        else:
             inputs_embeds = self.tok_embeddings(input_ids)
-
-        embeddings = self.norm(inputs_embeds)
-        return embeddings
+            embeddings = self.norm(inputs_embeds)
+            return embeddings
 
 
 class ModernBertAttention(nn.Module):
@@ -454,4 +455,4 @@ class ModernBertForTokenClassification(nn.Module):
         )
         hidden_states = self.head(hidden_states)
         hidden_states = hidden_states.to(self.head_dtype)
-        return self.classifier(hidden_states)
+        return self.classifier(hidden_states)
\ No newline at end of file

vllm/model_executor/models/molmo.py

@@ -871,7 +871,7 @@ class MolmoModel(nn.Module, SupportsQuant):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -1591,4 +1591,4 @@ def _get_weights_with_merged_embedding(
         [embedding_weights["embedding"], embedding_weights["new_embedding"]],
         dim=0,
     )
-    yield ("model.embed_tokens.weight", embedding_weights)
+    yield ("model.embed_tokens.weight", embedding_weights)
\ No newline at end of file

vllm/model_executor/models/molmo2.py

@@ -1217,7 +1217,7 @@ class Molmo2TextModel(nn.Module, SupportsQuant):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -2805,4 +2805,4 @@ def _get_weights_with_merged_embedding(
         [embedding_weights["embedding"], embedding_weights["new_embedding"]],
         dim=0,
     )
-    yield ("model.embed_tokens.weight", embedding_weights)
+    yield ("model.embed_tokens.weight", embedding_weights)
\ No newline at end of file

vllm/model_executor/models/mpt.py

@@ -253,7 +253,7 @@ class MPTModel(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         position_ids: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None,
         inputs_embeds: torch.Tensor | None = None,
@@ -313,7 +313,7 @@ class MPTForCausalLM(nn.Module, SupportsPP):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -332,4 +332,4 @@ class MPTForCausalLM(nn.Module, SupportsPP):
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         loader = AutoWeightsLoader(self)
-        return loader.load_weights(weights)
+        return loader.load_weights(weights)
\ No newline at end of file

vllm/model_executor/models/nano_nemotron_vl.py

@@ -1917,7 +1917,7 @@ class NemotronH_Nano_VL_V2(
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,

vllm/model_executor/models/nemotron.py

@@ -477,7 +477,7 @@ class NemotronForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -496,4 +496,4 @@ class NemotronForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         loader = AutoWeightsLoader(self)
-        return loader.load_weights(weights)
+        return loader.load_weights(weights)
\ No newline at end of file

vllm/model_executor/models/nemotron_h.py

@@ -601,7 +601,7 @@ class NemotronHModel(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -887,7 +887,7 @@ class NemotronHForCausalLM(
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -908,4 +908,4 @@ class NemotronHForCausalLM(
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         loader = AutoWeightsLoader(self, skip_prefixes=["mtp"])
-        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
\ No newline at end of file

vllm/model_executor/models/nemotron_nas.py

@@ -449,7 +449,7 @@ class DeciLMForCausalLM(nn.Module, SupportsLoRA, SupportsPP, HasNoOps):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -471,4 +471,4 @@ class DeciLMForCausalLM(nn.Module, SupportsLoRA, SupportsPP, HasNoOps):
             self,
             skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
         )
-        return loader.load_weights(weights)
+        return loader.load_weights(weights)
\ No newline at end of file

vllm/model_executor/models/nemotron_parse.py

@@ -289,7 +289,7 @@ class MBartDecoderNoPos(nn.Module):
 
     def forward(
         self,
-        decoder_input_ids: torch.Tensor | None,
+        decoder_input_ids: torch.Tensor,
         *,
         encoder_hidden_states: torch.Tensor | None,
         inputs_embeds: torch.Tensor | None = None,
@@ -897,7 +897,7 @@ class NemotronParseForConditionalGeneration(nn.Module, SupportsMultiModal):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         encoder_outputs: list[torch.Tensor] | None = None,
         **kwargs,
@@ -957,4 +957,4 @@ class NemotronParseForConditionalGeneration(nn.Module, SupportsMultiModal):
         # Load encoder weights
         self.encoder.load_weights(encoder_weights)
         # Load decoder weights
-        self.decoder.load_weights(decoder_weights)
+        self.decoder.load_weights(decoder_weights)
\ No newline at end of file

vllm/model_executor/models/nemotron_vl.py

@@ -597,7 +597,7 @@ class LlamaNemotronVLChatModel(nn.Module, SupportsMultiModal, SupportsPP, Suppor
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -642,4 +642,4 @@ class LlamaNemotronVLChatModel(nn.Module, SupportsMultiModal, SupportsPP, Suppor
             language_model="language_model",
             connector="mlp1",
             tower_model="vision_model",
-        )
+        )
\ No newline at end of file

vllm/model_executor/models/olmo.py

@@ -271,7 +271,7 @@ class OlmoModel(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None,
         inputs_embeds: torch.Tensor | None = None,
@@ -382,7 +382,7 @@ class OlmoForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -409,4 +409,4 @@ class OlmoForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
                 ["lm_head.weight"] if self.config.tie_word_embeddings else None
             ),
         )
-        return loader.load_weights(weights)
+        return loader.load_weights(weights)
\ No newline at end of file

vllm/model_executor/models/olmo2.py

@@ -309,7 +309,7 @@ class Olmo2Model(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None,
         inputs_embeds: torch.Tensor | None = None,
@@ -424,7 +424,7 @@ class Olmo2ForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -451,4 +451,4 @@ class Olmo2ForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
                 ["lm_head.weight"] if self.config.tie_word_embeddings else None
             ),
         )
-        return loader.load_weights(weights)
+        return loader.load_weights(weights)
\ No newline at end of file

vllm/model_executor/models/olmoe.py

@@ -300,7 +300,7 @@ class OlmoeModel(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None,
         inputs_embeds: torch.Tensor | None = None,
@@ -476,7 +476,7 @@ class OlmoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -495,4 +495,4 @@ class OlmoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
         return loader.load_weights(weights)
 
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
-        return self.model.get_expert_mapping()
+        return self.model.get_expert_mapping()
\ No newline at end of file