[Model] Supplement to PR 24862: Pass param prefix to LLMHead (#25805)

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

vllm/model_executor/models/deepseek_mtp.py

@@ -28,13 +28,15 @@ class SharedHead(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
+        prefix: str,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.head = ParallelLMHead(config.vocab_size,
                                    config.hidden_size,
-                                   quant_config=quant_config)
+                                   quant_config=quant_config,
+                                   prefix=maybe_prefix(prefix, "head"))
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         return self.norm(hidden_states)
@@ -64,7 +66,9 @@ class DeepSeekMultiTokenPredictorLayer(nn.Module):
                 device="cuda")
         else:
             topk_indices_buffer = None
-        self.shared_head = SharedHead(config=config, quant_config=quant_config)
+        self.shared_head = SharedHead(config=config,
+                                      prefix=prefix,
+                                      quant_config=quant_config)
         self.mtp_block = DeepseekV2DecoderLayer(vllm_config, prefix,
                                                 topk_indices_buffer)
 

vllm/model_executor/models/glm4_moe_mtp.py

@@ -50,13 +50,15 @@ class SharedHead(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
+        prefix: str,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.head = ParallelLMHead(config.vocab_size,
                                    config.hidden_size,
-                                   quant_config=quant_config)
+                                   quant_config=quant_config,
+                                   prefix=maybe_prefix(prefix, "head"))
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         return self.norm(hidden_states)
@@ -77,7 +79,9 @@ class Glm4MoeMultiTokenPredictorLayer(nn.Module):
         self.eh_proj = nn.Linear(config.hidden_size * 2,
                                  config.hidden_size,
                                  bias=False)
-        self.shared_head = SharedHead(config=config, quant_config=quant_config)
+        self.shared_head = SharedHead(config=config,
+                                      prefix=prefix,
+                                      quant_config=quant_config)
         self.mtp_block = Glm4MoeDecoderLayer(config=config,
                                              cache_config=cache_config,
                                              quant_config=quant_config,

vllm/model_executor/models/gpt_neox.py

@@ -296,6 +296,7 @@ class GPTNeoXForCausalLM(nn.Module, SupportsPP):
             config.vocab_size,
             config.hidden_size,
             quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "embed_out"),
         )
         if self.config.tie_word_embeddings:
             self.embed_out.weight = self.gpt_neox.embed_in.weight

vllm/model_executor/models/longcat_flash_mtp.py

@@ -140,6 +140,7 @@ class LongCatFlashMTP(nn.Module, SupportsPP):
             self.config.vocab_size,
             self.config.hidden_size,
             quant_config=self.quant_config,
+            prefix=maybe_prefix(prefix, "lm_head"),
         )
         self.logits_processor = LogitsProcessor(self.config.vocab_size)
 

vllm/model_executor/models/medusa.py

@@ -82,7 +82,8 @@ class Medusa(nn.Module):
                     config.hidden_size,
                     org_num_embeddings=self.truncated_vocab_size,
                     padding_size=DEFAULT_VOCAB_PADDING_SIZE,
-                ) for _ in range(self.config.num_heads)
+                    prefix=maybe_prefix(prefix, f"lm_heads.{i}"),
+                ) for i in range(self.config.num_heads)
             ])
 
         logit_scale = getattr(config, "logit_scale", 1.0)

vllm/model_executor/models/mlp_speculator.py

@@ -13,6 +13,8 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
+from .utils import maybe_prefix
+
 SQRT2 = 2**0.5
 
 
@@ -97,8 +99,13 @@ class MLPSpeculator(nn.Module):
             self.proj = nn.ModuleList([proj_first] + [proj_tied] *
                                       (self.max_speculative_tokens - 1))
 
-            head = ParallelLMHead(self.vocab_size, self.inner_dim, bias=False)
-            self.head = nn.ModuleList([head] * self.max_speculative_tokens)
+            self.head = nn.ModuleList([
+                ParallelLMHead(self.vocab_size,
+                               self.inner_dim,
+                               bias=False,
+                               prefix=maybe_prefix(prefix, f"head.{i}"))
+                for i in range(self.max_speculative_tokens)
+            ])
 
             ln = MLPSpeculatorLayerNorm(self.inner_dim,
                                         elementwise_scale_and_shift=True)
@@ -120,8 +127,11 @@ class MLPSpeculator(nn.Module):
             ])
 
             self.head = nn.ModuleList([
-                ParallelLMHead(self.vocab_size, self.inner_dim, bias=False)
-                for _ in range(self.max_speculative_tokens)
+                ParallelLMHead(self.vocab_size,
+                               self.inner_dim,
+                               bias=False,
+                               prefix=maybe_prefix(prefix, f"head.{i}"))
+                for i in range(self.max_speculative_tokens)
             ])
             self.ln = nn.ModuleList([
                 MLPSpeculatorLayerNorm(self.inner_dim,

vllm/model_executor/models/qwen3_vl_moe.py

@@ -296,7 +296,8 @@ class Qwen3MoeLLMForCausalLM(Qwen3MoeForCausalLM):
                                       prefix=maybe_prefix(prefix, "model"))
         self.lm_head = ParallelLMHead(self.config.vocab_size,
                                       self.config.hidden_size,
-                                      quant_config=self.quant_config)
+                                      quant_config=self.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(self.config.vocab_size)

vllm/model_executor/models/whisper.py

@@ -45,7 +45,7 @@ from vllm.utils.tensor_schema import TensorSchema, TensorShape
 from .interfaces import (MultiModalEmbeddings, SupportsMultiModal,
                          SupportsTranscription)
 from .utils import (AutoWeightsLoader, WeightsMapper, cast_overflow_tensors,
-                    make_layers)
+                    make_layers, maybe_prefix)
 
 logger = init_logger(__name__)
 
@@ -885,7 +885,8 @@ class WhisperForConditionalGeneration(nn.Module, SupportsTranscription,
         self.unpadded_vocab_size = config.vocab_size
         self.proj_out = ParallelLMHead(config.vocab_size,
                                        config.d_model,
-                                       quant_config=quant_config)
+                                       quant_config=quant_config,
+                                       prefix=maybe_prefix(prefix, "proj_out"))
         self.proj_out = self.proj_out.tie_weights(
             self.model.decoder.embed_tokens)
         logit_scale = getattr(config, "logit_scale", 1.0)