[Model] Pass param prefix to LLMHead (#24862)

Signed-off-by: whx-sjtu <2952154980@qq.com>

vllm/model_executor/models/arctic.py

@@ -427,6 +427,7 @@ class ArcticForCausalLM(nn.Module, SupportsPP, SupportsQuant):
             self.vocab_size,
             config.hidden_size,
             quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         if self.config.tie_word_embeddings:
             self.lm_head.weight = self.model.embed_tokens.weight

vllm/model_executor/models/aria.py

@@ -539,6 +539,7 @@ class AriaForConditionalGeneration(nn.Module, SupportsMultiModal):
             config.text_config.hidden_size,
             org_num_embeddings=self.language_model.org_vocab_size,
             quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         logit_scale = getattr(config, "logit_scale", 1.0)
         self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,

vllm/model_executor/models/baichuan.py

@@ -51,7 +51,8 @@ from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsLoRA, SupportsPP, SupportsQuant
 from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
-                    make_empty_intermediate_tensors_factory, make_layers)
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
 
 
 def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
@@ -394,7 +395,8 @@ class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA, SupportsPP,
                                    position_embedding=position_embedding)
         self.lm_head = ParallelLMHead(config.vocab_size,
                                       config.hidden_size,
-                                      quant_config=quant_config)
+                                      quant_config=quant_config,
+                                      prefix=maybe_prefix(prefix, "lm_head"))
         self.lm_head.weight.weight_loader = self.lm_head_weight_loader
         if self.config.tie_word_embeddings:
             self.lm_head.weight = self.model.embed_tokens.weight

vllm/model_executor/models/bamba.py

@@ -514,6 +514,7 @@ class BambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
             # We need bigger padding if using lora for kernel
             # compatibility
             if not lora_config else lora_config.lora_vocab_padding_size,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         # Used to track and store by the Mamba cache between steps.
         self.mamba_cache: Optional[MambaCacheManager] = None

vllm/model_executor/models/bloom.py

@@ -330,7 +330,9 @@ class BloomForCausalLM(nn.Module, SupportsPP, SupportsQuant):
             self.lm_head = self.transformer.word_embeddings
         else:
             self.lm_head = ParallelLMHead(self.config.vocab_size,
-                                          self.config.hidden_size)
+                                          self.config.hidden_size,
+                                          prefix=maybe_prefix(
+                                              prefix, "lm_head"))
 
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.make_empty_intermediate_tensors = (

vllm/model_executor/models/chameleon.py

@@ -960,6 +960,7 @@ class ChameleonForConditionalGeneration(nn.Module, SupportsMultiModal,
         self.lm_head = ParallelLMHead(
             self.unpadded_vocab_size,
             config.hidden_size,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         if config.tie_word_embeddings:
             self.lm_head.weight = self.model.embed_tokens.weight

vllm/model_executor/models/dbrx.py

@@ -438,6 +438,7 @@ class DbrxForCausalLM(nn.Module, SupportsPP):
             org_num_embeddings=config.vocab_size,
             padding_size=DEFAULT_VOCAB_PADDING_SIZE,
             quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                 config.vocab_size)

vllm/model_executor/models/deepseek.py

@@ -453,9 +453,12 @@ class DeepseekForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         self.quant_config = quant_config
         self.model = DeepseekModel(vllm_config=vllm_config,
                                    prefix=maybe_prefix(prefix, "model"))
-        self.lm_head = ParallelLMHead(config.vocab_size,
-                                      config.hidden_size,
-                                      quant_config=quant_config)
+        self.lm_head = ParallelLMHead(
+            config.vocab_size,
+            config.hidden_size,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "lm_head"),
+        )
         if self.config.tie_word_embeddings:
             self.lm_head.weight = self.model.embed_tokens.weight
         self.logits_processor = LogitsProcessor(config.vocab_size)

vllm/model_executor/models/deepseek_eagle.py

@@ -199,7 +199,8 @@ class EagleDeepseekV3ForCausalLM(DeepseekV3ForCausalLM):
 
         self.lm_head = ParallelLMHead(self.config.vocab_size,
                                       self.config.hidden_size,
-                                      quant_config=quant_config)
+                                      quant_config=quant_config,
+                                      prefix=maybe_prefix(prefix, "lm_head"))
 
         logit_scale = getattr(self.config, "logit_scale", 1.0)
         self.logits_processor = LogitsProcessor(self.config.vocab_size,

vllm/model_executor/models/deepseek_v2.py

@@ -823,9 +823,12 @@ class DeepseekV2ForCausalLM(nn.Module, SupportsPP, MixtureOfExperts,
         self.model = DeepseekV2Model(vllm_config=vllm_config,
                                      prefix=maybe_prefix(prefix, "model"))
         if get_pp_group().is_last_rank:
-            self.lm_head = ParallelLMHead(config.vocab_size,
-                                          config.hidden_size,
-                                          quant_config=quant_config)
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
         else:
             self.lm_head = PPMissingLayer()
         self.logits_processor = LogitsProcessor(config.vocab_size)

vllm/model_executor/models/dots1.py

@@ -504,7 +504,9 @@ class Dots1ForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
         if get_pp_group().is_last_rank:
             self.lm_head = ParallelLMHead(config.vocab_size,
                                           config.hidden_size,
-                                          quant_config=quant_config)
+                                          quant_config=quant_config,
+                                          prefix=maybe_prefix(
+                                              prefix, "lm_head"))
         else:
             self.lm_head = PPMissingLayer()
         self.logits_processor = LogitsProcessor(config.vocab_size)

vllm/model_executor/models/ernie45_moe.py

@@ -562,7 +562,9 @@ class Ernie4_5_MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
         if get_pp_group().is_last_rank:
             self.lm_head = ParallelLMHead(config.vocab_size,
                                           config.hidden_size,
-                                          quant_config=quant_config)
+                                          quant_config=quant_config,
+                                          prefix=maybe_prefix(
+                                              prefix, "lm_head"))
         else:
             self.lm_head = PPMissingLayer()
 

vllm/model_executor/models/ernie45_vl_moe.py

@@ -557,7 +557,9 @@ class Ernie4_5_VLMoeForCausalLM(nn.Module, SupportsPP):
         if get_pp_group().is_last_rank:
             self.lm_head = ParallelLMHead(config.vocab_size,
                                           config.hidden_size,
-                                          quant_config=quant_config)
+                                          quant_config=quant_config,
+                                          prefix=maybe_prefix(
+                                              prefix, "lm_head"))
         else:
             self.lm_head = PPMissingLayer()
 

vllm/model_executor/models/ernie_mtp.py

@@ -158,7 +158,8 @@ class ErnieMTP(nn.Module, SupportsPP):
                                               prefix=maybe_prefix(
                                                   prefix, "model"))
         self.lm_head = ParallelLMHead(self.config.vocab_size,
-                                      self.config.hidden_size)
+                                      self.config.hidden_size,
+                                      prefix=maybe_prefix(prefix, "lm_head"))
         self.sampler = get_sampler()
 
         if self.config.tie_word_embeddings:

vllm/model_executor/models/exaone.py

@@ -502,6 +502,7 @@ class ExaoneForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                 # compatibility
                 if not lora_config else lora_config.lora_vocab_padding_size,
                 quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
             )
             if config.tie_word_embeddings:
                 self.lm_head.weight = self.transformer.wte.weight

vllm/model_executor/models/exaone4.py

@@ -485,6 +485,7 @@ class Exaone4ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                 # compatibility
                 if not lora_config else lora_config.lora_vocab_padding_size,
                 quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
             )
             if config.tie_word_embeddings:
                 self.lm_head.weight = self.model.embed_tokens.weight

vllm/model_executor/models/falcon.py

@@ -473,6 +473,7 @@ class FalconForCausalLM(nn.Module, SupportsPP):
                 config.vocab_size,
                 config.hidden_size,
                 quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
             )
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.make_empty_intermediate_tensors = (

vllm/model_executor/models/falcon_h1.py

@@ -607,6 +607,7 @@ class FalconH1ForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
                     # compatibility
                     if not lora_config else
                     lora_config.lora_vocab_padding_size),
+                prefix=maybe_prefix(prefix, "lm_head"),
             )
             self.lm_head_multiplier = config.lm_head_multiplier
             if self.tie_word_embeddings:

vllm/model_executor/models/glm4_moe.py

@@ -608,7 +608,9 @@ class Glm4MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
         if get_pp_group().is_last_rank:
             self.lm_head = ParallelLMHead(config.vocab_size,
                                           config.hidden_size,
-                                          quant_config=quant_config)
+                                          quant_config=quant_config,
+                                          prefix=maybe_prefix(
+                                              prefix, "lm_head"))
         else:
             self.lm_head = PPMissingLayer()
         self.logits_processor = LogitsProcessor(config.vocab_size)

vllm/model_executor/models/gpt_bigcode.py

@@ -302,7 +302,8 @@ class GPTBigCodeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             self.lm_head = ParallelLMHead(
                 self.transformer.vocab_size,
                 self.transformer.embed_dim,
-                org_num_embeddings=self.config.vocab_size)
+                org_num_embeddings=self.config.vocab_size,
+                prefix=maybe_prefix(prefix, "lm_head"))
         self.unpadded_vocab_size = config.vocab_size
         if lora_config:
             self.unpadded_vocab_size += lora_config.lora_extra_vocab_size

vllm/model_executor/models/gpt_j.py

@@ -306,6 +306,7 @@ class GPTJForCausalLM(nn.Module, SupportsPP):
             config.n_embd,
             bias=True,
             quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.make_empty_intermediate_tensors = (

vllm/model_executor/models/gpt_oss.py

@@ -655,6 +655,7 @@ class GptOssForCausalLM(nn.Module, SupportsPP):
         self.lm_head = ParallelLMHead(
             self.config.vocab_size,
             self.config.hidden_size,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         self.logits_processor = LogitsProcessor(self.config.vocab_size)
         self.make_empty_intermediate_tensors = (

vllm/model_executor/models/granite.py

@@ -434,6 +434,7 @@ class GraniteForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                 # compatibility
                 if not lora_config else lora_config.lora_vocab_padding_size,
                 quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
             )
             if config.tie_word_embeddings:
                 self.lm_head.weight = self.model.embed_tokens.weight

vllm/model_executor/models/granitemoe.py

@@ -487,6 +487,7 @@ class GraniteMoeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             # compatibility
             if not lora_config else lora_config.lora_vocab_padding_size,
             quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         if config.tie_word_embeddings:
             self.lm_head.weight = self.model.embed_tokens.weight

vllm/model_executor/models/hunyuan_v1.py

@@ -58,7 +58,7 @@ from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsLoRA, SupportsPP
 from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
-                    make_layers)
+                    make_layers, maybe_prefix)
 
 
 def _is_moe(config: PretrainedConfig) -> bool:
@@ -871,6 +871,7 @@ class HunYuanV1Base(nn.Module, SupportsLoRA, SupportsPP):
                 org_num_embeddings=config.vocab_size,
                 padding_size=DEFAULT_VOCAB_PADDING_SIZE,
                 quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
             )
             if config.tie_word_embeddings:
                 self.lm_head.weight = self.model.embed_tokens.weight

vllm/model_executor/models/idefics3.py

@@ -606,6 +606,7 @@ class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal,
             config.text_config.vocab_size,
             config.text_config.hidden_size,
             quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         if self.config.text_config.tie_word_embeddings:
             self.lm_head.weight = self.model.text_model.wte.weight

vllm/model_executor/models/jais.py

@@ -302,7 +302,9 @@ class JAISLMHeadModel(nn.Module, SupportsPP):
             self.lm_head = self.transformer.wte
         else:
             self.lm_head = ParallelLMHead(self.config.vocab_size,
-                                          self.config.hidden_size)
+                                          self.config.hidden_size,
+                                          prefix=maybe_prefix(
+                                              prefix, "lm_head"))
         if hasattr(config, "width_scale"):
             self.output_logits_scale = config.width_scale
         else:

vllm/model_executor/models/jamba.py

@@ -502,6 +502,7 @@ class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
             # We need bigger padding if using lora for kernel
             # compatibility
             if not lora_config else lora_config.lora_vocab_padding_size,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         # Used to track and store by the Mamba cache between steps.
         self.mamba_cache: Optional[MambaCacheManager] = None

vllm/model_executor/models/kimi_vl.py

@@ -328,6 +328,7 @@ class KimiVLForConditionalGeneration(nn.Module, SupportsMultiModal,
                 config.text_config.hidden_size,
                 org_num_embeddings=self.config.text_config.vocab_size,
                 padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+                prefix=maybe_prefix(prefix, "lm_head"),
             )
         else:
             self.lm_head = PPMissingLayer()

vllm/model_executor/models/llama_eagle3.py

@@ -220,7 +220,7 @@ class Eagle3LlamaForCausalLM(LlamaForCausalLM):
             self.config.hidden_size,
             org_num_embeddings=self.config.draft_vocab_size,
             padding_size=(DEFAULT_VOCAB_PADDING_SIZE),
-            prefix="")
+            prefix=maybe_prefix(prefix, "lm_head"))
         self.logits_processor = LogitsProcessor(self.config.draft_vocab_size,
                                                 scale=logit_scale)
         self.draft_id_to_target_id = nn.Parameter(

vllm/model_executor/models/mamba.py

@@ -223,6 +223,7 @@ class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree, SupportsPP):
                 # We need bigger padding if using lora for kernel
                 # compatibility
                 if not lora_config else lora_config.lora_vocab_padding_size,
+                prefix=maybe_prefix(prefix, "lm_head"),
             )
 
         # Used to track and store by the Mamba cache between steps.

vllm/model_executor/models/mamba2.py

@@ -278,6 +278,7 @@ class Mamba2ForCausalLM(nn.Module, HasInnerState, IsAttentionFree):
             # We need bigger padding if using lora for kernel
             # compatibility
             if not lora_config else lora_config.lora_vocab_padding_size,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         if config.tie_word_embeddings:
             self.lm_head = self.lm_head.tie_weights(self.backbone.embeddings)

vllm/model_executor/models/medusa.py

@@ -15,6 +15,8 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 
+from .utils import maybe_prefix
+
 
 class ResidualBlock(nn.Module):
 
@@ -71,6 +73,7 @@ class Medusa(nn.Module):
                 config.hidden_size,
                 org_num_embeddings=self.truncated_vocab_size,
                 padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+                prefix=maybe_prefix(prefix, "lm_head"),
             )
             self.lm_heads = [
                 self.lm_head for _ in range(self.config.num_heads)

vllm/model_executor/models/mimo_mtp.py

@@ -158,7 +158,8 @@ class MiMoMTP(nn.Module):
                                              prefix=maybe_prefix(
                                                  prefix, "model"))
         self.lm_head = ParallelLMHead(self.config.vocab_size,
-                                      self.config.hidden_size)
+                                      self.config.hidden_size,
+                                      prefix=maybe_prefix(prefix, "lm_head"))
 
     def forward(
         self,

vllm/model_executor/models/minicpm.py

@@ -547,6 +547,7 @@ class MiniCPMForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             # compatibility
             if not lora_config else lora_config.lora_vocab_padding_size,
             quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         if config.tie_word_embeddings:
             self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)

vllm/model_executor/models/minicpm_eagle.py

@@ -338,6 +338,7 @@ class EagleMiniCPMForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             # compatibility
             if not lora_config else lora_config.lora_vocab_padding_size,
             quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         if config.tie_word_embeddings:
             self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)

vllm/model_executor/models/minimax_text_01.py

@@ -702,6 +702,7 @@ class MiniMaxText01ForCausalLM(nn.Module, HasInnerState, IsHybrid):
                 self.config.hidden_size,
                 org_num_embeddings=self.config.vocab_size,
                 padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+                prefix=maybe_prefix(prefix, "lm_head"),
             )
 
             self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,

vllm/model_executor/models/mixtral.py

@@ -507,6 +507,7 @@ class MixtralForCausalLM(nn.Module, SupportsLoRA, SupportsPP,
             # compatibility
             if not lora_config else lora_config.lora_vocab_padding_size,
             quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         if self.config.tie_word_embeddings:
             self.lm_head.weight = self.model.embed_tokens.weight

vllm/model_executor/models/molmo.py

@@ -1403,6 +1403,7 @@ class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA,
                 config.embedding_size or config.vocab_size,
                 config.hidden_size,
                 quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
             )
 
         self.logits_processor = LogitsProcessor(config.embedding_size

vllm/model_executor/models/nemotron.py

@@ -466,6 +466,7 @@ class NemotronForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                 # compatibility
                 if not lora_config else lora_config.lora_vocab_padding_size,
                 quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
             )
             if config.tie_word_embeddings:
                 self.lm_head.weight = self.model.embed_tokens.weight

vllm/model_executor/models/nemotron_h.py

@@ -565,6 +565,7 @@ class NemotronHForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
             # We need bigger padding if using lora for kernel
             # compatibility
             if not lora_config else lora_config.lora_vocab_padding_size,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         # Used to track and store by the Mamba cache between steps.
         self.mamba_cache: Optional[MambaCacheManager] = None

vllm/model_executor/models/olmo.py

@@ -364,6 +364,7 @@ class OlmoForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
                 config.hidden_size,
                 org_num_embeddings=config.vocab_size,
                 quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
             )
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.make_empty_intermediate_tensors = (

vllm/model_executor/models/olmoe.py

@@ -450,7 +450,8 @@ class OlmoeForCausalLM(nn.Module, SupportsPP):
                                 prefix=maybe_prefix(prefix, "model"))
         self.lm_head = ParallelLMHead(config.vocab_size,
                                       config.hidden_size,
-                                      quant_config=quant_config)
+                                      quant_config=quant_config,
+                                      prefix=maybe_prefix(prefix, "lm_head"))
         self.logits_processor = LogitsProcessor(config.vocab_size)
 
         self.make_empty_intermediate_tensors = (

vllm/model_executor/models/opt.py

@@ -375,7 +375,9 @@ class OPTForCausalLM(nn.Module, SupportsPP):
             self.lm_head = self.model.decoder.embed_tokens
         else:
             self.lm_head = ParallelLMHead(config.vocab_size,
-                                          config.word_embed_proj_dim)
+                                          config.word_embed_proj_dim,
+                                          prefix=maybe_prefix(
+                                              prefix, "lm_head"))
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.make_empty_intermediate_tensors = (
             self.model.make_empty_intermediate_tensors)

vllm/model_executor/models/orion.py

@@ -314,7 +314,8 @@ class OrionForCausalLM(nn.Module, SupportsPP):
                                 prefix=maybe_prefix(prefix, "model"))
         self.lm_head = ParallelLMHead(config.vocab_size,
                                       config.hidden_size,
-                                      quant_config=quant_config)
+                                      quant_config=quant_config,
+                                      prefix=maybe_prefix(prefix, "lm_head"))
         if self.config.tie_word_embeddings:
             self.lm_head.weight = self.model.embed_tokens.weight
         self.logits_processor = LogitsProcessor(config.vocab_size)

vllm/model_executor/models/persimmon.py

@@ -307,7 +307,8 @@ class PersimmonForCausalLM(nn.Module, SupportsPP):
                                     prefix=maybe_prefix(prefix, "model"))
         self.lm_head = ParallelLMHead(config.vocab_size,
                                       config.hidden_size,
-                                      bias=False)
+                                      bias=False,
+                                      prefix=maybe_prefix(prefix, "lm_head"))
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.make_empty_intermediate_tensors = (
             self.model.make_empty_intermediate_tensors)

vllm/model_executor/models/phi.py

@@ -322,7 +322,8 @@ class PhiForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         self.lm_head = ParallelLMHead(config.vocab_size,
                                       config.hidden_size,
                                       bias=True,
-                                      quant_config=quant_config)
+                                      quant_config=quant_config,
+                                      prefix=maybe_prefix(prefix, "lm_head"))
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.make_empty_intermediate_tensors = (
             self.model.make_empty_intermediate_tensors)

vllm/model_executor/models/phi4flash.py

@@ -630,6 +630,7 @@ class Phi4FlashForCausalLM(nn.Module, HasInnerState, IsHybrid, SupportsV0Only):
                 # compatibility
                 if not lora_config else lora_config.lora_vocab_padding_size),
             quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         self.embedding_bias = None
         # Used to track and store by the Mamba cache between steps.

vllm/model_executor/models/phi4mm.py

@@ -989,6 +989,7 @@ class Phi4MMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
             org_num_embeddings=config.vocab_size,
             padding_size=DEFAULT_VOCAB_PADDING_SIZE,
             quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         if config.tie_word_embeddings:
             self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)

vllm/model_executor/models/phimoe.py

@@ -645,6 +645,7 @@ class PhiMoEForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                 if not lora_config else lora_config.lora_vocab_padding_size),
             quant_config=None,
             bias=True,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                 config.vocab_size)

vllm/model_executor/models/qwen.py

@@ -271,7 +271,8 @@ class QWenBaseModel(nn.Module):
                                                 prefix, "transformer"))
         self.lm_head = ParallelLMHead(config.vocab_size,
                                       config.hidden_size,
-                                      quant_config=quant_config)
+                                      quant_config=quant_config,
+                                      prefix=maybe_prefix(prefix, "lm_head"))
         if self.config.tie_word_embeddings:
             self.lm_head.weight = self.transformer.wte.weight
         self.logits_processor = LogitsProcessor(config.vocab_size)

vllm/model_executor/models/qwen2_moe.py

@@ -519,7 +519,8 @@ class Qwen2MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
                                    prefix=maybe_prefix(prefix, "model"))
         self.lm_head = ParallelLMHead(config.vocab_size,
                                       config.hidden_size,
-                                      quant_config=quant_config)
+                                      quant_config=quant_config,
+                                      prefix=maybe_prefix(prefix, "lm_head"))
         if self.config.tie_word_embeddings:
             self.lm_head.weight = self.model.embed_tokens.weight
         self.logits_processor = LogitsProcessor(config.vocab_size)

vllm/model_executor/models/qwen3_moe.py

@@ -605,7 +605,8 @@ class Qwen3MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA,
                                    prefix=maybe_prefix(prefix, "model"))
         self.lm_head = ParallelLMHead(config.vocab_size,
                                       config.hidden_size,
-                                      quant_config=quant_config)
+                                      quant_config=quant_config,
+                                      prefix=maybe_prefix(prefix, "lm_head"))
         if self.config.tie_word_embeddings:
             self.lm_head.weight = self.model.embed_tokens.weight
         self.logits_processor = LogitsProcessor(config.vocab_size)

vllm/model_executor/models/qwen3_next.py

@@ -1089,7 +1089,7 @@ class Qwen3NextForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
             # We need bigger padding if using lora for kernel
             # compatibility
             if not lora_config else lora_config.lora_vocab_padding_size,
-        )
+            prefix=maybe_prefix(prefix, "lm_head"))
         self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                 config.vocab_size)
         self.make_empty_intermediate_tensors = (

vllm/model_executor/models/qwen3_next_mtp.py

@@ -238,7 +238,8 @@ class Qwen3NextMTP(nn.Module, SupportsPP):
         self.lm_head = ParallelLMHead(self.unpadded_vocab_size,
                                       config.hidden_size,
                                       org_num_embeddings=config.vocab_size,
-                                      padding_size=DEFAULT_VOCAB_PADDING_SIZE)
+                                      padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+                                      prefix=maybe_prefix(prefix, "lm_head"))
         self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                 config.vocab_size)
         self.make_empty_intermediate_tensors = (

vllm/model_executor/models/solar.py

@@ -469,6 +469,7 @@ class SolarForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                 # compatibility
                 if not lora_config else lora_config.lora_vocab_padding_size,
                 quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
             )
             if config.tie_word_embeddings:
                 self.lm_head.weight = self.model.embed_tokens.weight

vllm/model_executor/models/step3_text.py

@@ -35,7 +35,8 @@ from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsPP
 from .utils import (PPMissingLayer, is_pp_missing_parameter,
-                    make_empty_intermediate_tensors_factory, make_layers)
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
 
 logger = init_logger(__name__)
 
@@ -386,6 +387,7 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
                 org_num_embeddings=config.vocab_size,
                 padding_size=DEFAULT_VOCAB_PADDING_SIZE
                 if not lora_config else lora_config.lora_vocab_padding_size,
+                prefix=maybe_prefix(prefix, "lm_head"),
             )
             self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                     config.vocab_size)

vllm/model_executor/models/zamba2.py

@@ -941,6 +941,7 @@ class Zamba2ForCausalLM(nn.Module, HasInnerState, IsHybrid):
             # We need bigger padding if using lora for kernel
             # compatibility
             if not lora_config else lora_config.lora_vocab_padding_size,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         # Tie weights with input embeddings if using same dimensions
         self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)