[Quantization] Enable BNB support for more MoE models (#21100)

Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>

docs/models/supported_models.md

@@ -316,7 +316,7 @@ Specified using `--task generate`.
 | `AquilaForCausalLM` | Aquila, Aquila2 | `BAAI/Aquila-7B`, `BAAI/AquilaChat-7B`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `ArcticForCausalLM` | Arctic | `Snowflake/snowflake-arctic-base`, `Snowflake/snowflake-arctic-instruct`, etc. | | ✅︎ | ✅︎ |
 | `BaiChuanForCausalLM` | Baichuan2, Baichuan | `baichuan-inc/Baichuan2-13B-Chat`, `baichuan-inc/Baichuan-7B`, etc. | ✅︎ | ✅︎ | ✅︎ |
-| `BailingMoeForCausalLM` | Ling | `inclusionAI/Ling-lite-1.5`, `inclusionAI/Ling-plus`, etc. | | ✅︎ | ✅︎ |
+| `BailingMoeForCausalLM` | Ling | `inclusionAI/Ling-lite-1.5`, `inclusionAI/Ling-plus`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `BambaForCausalLM` | Bamba | `ibm-ai-platform/Bamba-9B-fp8`, `ibm-ai-platform/Bamba-9B` | ✅︎ | ✅︎ | ✅︎ |
 | `BloomForCausalLM` | BLOOM, BLOOMZ, BLOOMChat | `bigscience/bloom`, `bigscience/bloomz`, etc. | | ✅︎ | |
 | `BartForConditionalGeneration` | BART | `facebook/bart-base`, `facebook/bart-large-cnn`, etc. | | | |
@@ -328,8 +328,8 @@ Specified using `--task generate`.
 | `DeepseekV2ForCausalLM` | DeepSeek-V2 | `deepseek-ai/DeepSeek-V2`, `deepseek-ai/DeepSeek-V2-Chat`, etc. | | ✅︎ | ✅︎ |
 | `DeepseekV3ForCausalLM` | DeepSeek-V3 | `deepseek-ai/DeepSeek-V3-Base`, `deepseek-ai/DeepSeek-V3`, etc. | | ✅︎ | ✅︎ |
 | `Dots1ForCausalLM` | dots.llm1 | `rednote-hilab/dots.llm1.base`, `rednote-hilab/dots.llm1.inst`, etc. | | ✅︎ | ✅︎ |
-| `Ernie4_5_ForCausalLM` | Ernie4.5 | `baidu/ERNIE-4.5-0.3B-PT`, etc. | | ✅︎ | ✅︎ |
+| `Ernie4_5_ForCausalLM` | Ernie4.5 | `baidu/ERNIE-4.5-0.3B-PT`, etc. | ✅︎ | ✅︎ | ✅︎ |
-| `Ernie4_5_MoeForCausalLM` | Ernie4.5MoE | `baidu/ERNIE-4.5-21B-A3B-PT`, `baidu/ERNIE-4.5-300B-A47B-PT`, etc. | | ✅︎ | ✅︎ |
+| `Ernie4_5_MoeForCausalLM` | Ernie4.5MoE | `baidu/ERNIE-4.5-21B-A3B-PT`, `baidu/ERNIE-4.5-300B-A47B-PT`, etc. |✅︎| ✅︎ | ✅︎ |
 | `ExaoneForCausalLM` | EXAONE-3 | `LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `Fairseq2LlamaForCausalLM` | Llama (fairseq2 format) | `mgleize/fairseq2-dummy-Llama-3.2-1B`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `FalconForCausalLM` | Falcon | `tiiuae/falcon-7b`, `tiiuae/falcon-40b`, `tiiuae/falcon-rw-7b`, etc. | | ✅︎ | ✅︎ |
@@ -351,7 +351,7 @@ Specified using `--task generate`.
 | `GraniteMoeSharedForCausalLM` | Granite MoE Shared | `ibm-research/moe-7b-1b-active-shared-experts` (test model) | ✅︎ | ✅︎ | ✅︎ |
 | `GritLM` | GritLM | `parasail-ai/GritLM-7B-vllm`. | ✅︎ | ✅︎ | |
 | `Grok1ModelForCausalLM` | Grok1 | `hpcai-tech/grok-1`. | ✅︎ | ✅︎ | ✅︎ |
-| `HunYuanMoEV1ForCausalLM` | Hunyuan-80B-A13B | `tencent/Hunyuan-A13B-Instruct`, `tencent/Hunyuan-A13B-Pretrain`, `tencent/Hunyuan-A13B-Instruct-FP8`, etc. | | | ✅︎ |
+| `HunYuanMoEV1ForCausalLM` | Hunyuan-80B-A13B | `tencent/Hunyuan-A13B-Instruct`, `tencent/Hunyuan-A13B-Pretrain`, `tencent/Hunyuan-A13B-Instruct-FP8`, etc. | ✅︎ | | ✅︎ |
 | `InternLMForCausalLM` | InternLM | `internlm/internlm-7b`, `internlm/internlm-chat-7b`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `InternLM2ForCausalLM` | InternLM2 | `internlm/internlm2-7b`, `internlm/internlm2-chat-7b`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `InternLM3ForCausalLM` | InternLM3 | `internlm/internlm3-8b-instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |

vllm/model_executor/models/bailing_moe.py

@@ -53,7 +53,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import SupportsPP
+from .interfaces import SupportsLoRA, SupportsPP
 from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
@@ -374,6 +374,14 @@ class BailingMoeModel(nn.Module):
         hidden_states, _ = self.norm(hidden_states, residual)
         return hidden_states
 
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.num_experts,
+        )
+
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:
         stacked_params_mapping = [
@@ -381,14 +389,10 @@ class BailingMoeModel(nn.Module):
             ("gate_up_proj", "gate_proj", 0),
             ("gate_up_proj", "up_proj", 1),
         ]
-        expert_params_mapping = FusedMoE.make_expert_params_mapping(
-            ckpt_gate_proj_name="gate_proj",
-            ckpt_down_proj_name="down_proj",
-            ckpt_up_proj_name="up_proj",
-            num_experts=self.config.num_experts)
 
         params_dict = dict(self.named_parameters(remove_duplicate=False))
         loaded_params: set[str] = set()
+        expert_params_mapping = self.get_expert_mapping()
         for name, loaded_weight in weights:
             if self.config.norm_head and "lm_head.weight" in name:
                 loaded_weight = F.normalize(loaded_weight,
@@ -449,7 +453,7 @@ class BailingMoeModel(nn.Module):
         return loaded_params
 
 
-class BailingMoeForCausalLM(nn.Module, SupportsPP):
+class BailingMoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
 
     packed_modules_mapping = {
         "query_key_value": ["query_key_value"],
@@ -518,3 +522,6 @@ class BailingMoeForCausalLM(nn.Module, SupportsPP):
                            if self.config.tie_word_embeddings else None),
         )
         return loader.load_weights(weights)
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()

vllm/model_executor/models/ernie45_moe.py

@@ -51,8 +51,8 @@ from vllm.model_executor.model_loader.weight_utils import (
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import SupportsPP
+from .interfaces import SupportsLoRA, SupportsPP
-from .utils import (PPMissingLayer, extract_layer_index,
+from .utils import (AutoWeightsLoader, PPMissingLayer, extract_layer_index,
                     is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
@@ -427,66 +427,15 @@ class Ernie4_5_MoeModel(nn.Module):
 
         return hidden_states
 
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
 
-class Ernie4_5_MoeForCausalLM(nn.Module, SupportsPP):
+        # Params for weights, fp8 weight scales, fp8 activation scales
-    packed_modules_mapping = {
+        # (param_name, weight_name, expert_id, shard_id)
-        "qkv_proj": [
+        return FusedMoE.make_expert_params_mapping(
-            "q_proj",
+            ckpt_gate_proj_name="gate_proj",
-            "k_proj",
+            ckpt_down_proj_name="down_proj",
-            "v_proj",
+            ckpt_up_proj_name="up_proj",
-        ],
+            num_experts=self.config.moe_num_experts)
-        "gate_up_proj": [
-            "gate_proj",
-            "up_proj",
-        ],
-    }
-
-    fall_back_to_pt_during_load = False
-
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        super().__init__()
-        config = vllm_config.model_config.hf_config
-        quant_config = vllm_config.quant_config
-        self.config = config
-        self.quant_config = quant_config
-        self.model = Ernie4_5_MoeModel(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)
-        else:
-            self.lm_head = PPMissingLayer()
-
-        if self.config.tie_word_embeddings:
-            self.lm_head.weight = self.model.embed_tokens.weight
-        self.logits_processor = LogitsProcessor(config.vocab_size)
-        self.make_empty_intermediate_tensors = (
-            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 forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-    ) -> Union[torch.Tensor, IntermediateTensors]:
-        hidden_states = self.model(input_ids, positions, intermediate_tensors,
-                                   inputs_embeds)
-        return hidden_states
-
-    def compute_logits(
-        self,
-        hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
-    ) -> Optional[torch.Tensor]:
-        logits = self.logits_processor(self.lm_head, hidden_states,
-                                       sampling_metadata)
-        return logits
 
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:
@@ -499,16 +448,9 @@ class Ernie4_5_MoeForCausalLM(nn.Module, SupportsPP):
             ("gate_up_proj", "up_proj", 1),
         ]
 
-        # Params for weights, fp8 weight scales, fp8 activation scales
-        # (param_name, weight_name, expert_id, shard_id)
-        expert_params_mapping = FusedMoE.make_expert_params_mapping(
-            ckpt_gate_proj_name="gate_proj",
-            ckpt_down_proj_name="down_proj",
-            ckpt_up_proj_name="up_proj",
-            num_experts=self.config.moe_num_experts)
-
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
+        expert_params_mapping = self.get_expert_mapping()
         for name, loaded_weight in weights:
             if self.config.tie_word_embeddings and name.endswith(
                     "lm_head.weight"):
@@ -581,3 +523,76 @@ class Ernie4_5_MoeForCausalLM(nn.Module, SupportsPP):
                     weight_loader(param, loaded_weight)
             loaded_params.add(name)
         return loaded_params
+
+
+class Ernie4_5_MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    fall_back_to_pt_during_load = False
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+        self.model = Ernie4_5_MoeModel(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)
+        else:
+            self.lm_head = PPMissingLayer()
+
+        if self.config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            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 forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."]
+                           if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()

vllm/model_executor/models/grok1.py

@@ -360,6 +360,16 @@ class Grok1Model(nn.Module):
         hidden_states, _ = self.norm(hidden_states, residual)
         return hidden_states
 
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        # Map Grok1's unique expert parameter names to standard names
+        # Grok1 uses "num_experts" in its config
+        num_experts = getattr(self.config, "num_experts", 8)
+        return FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="linear",  # Grok1 specific
+            ckpt_down_proj_name="linear_1",  # Grok1 specific
+            ckpt_up_proj_name="linear_v",  # Grok1 specific
+            num_experts=num_experts)
+
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:
         stacked_params_mapping = [
@@ -369,18 +379,9 @@ class Grok1Model(nn.Module):
             ("qkv_proj", "v_proj", "v"),
         ]
 
-        # Map Grok1's unique expert parameter names to standard names
-        # Grok1 uses "num_experts" in its config
-        num_experts = getattr(self.config, "num_experts", 8)
-        expert_params_mapping = FusedMoE.make_expert_params_mapping(
-            ckpt_gate_proj_name="linear",  # Grok1 specific
-            ckpt_down_proj_name="linear_1",  # Grok1 specific
-            ckpt_up_proj_name="linear_v",  # Grok1 specific
-            num_experts=num_experts)
-
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
-
+        expert_params_mapping = self.get_expert_mapping()
         for name, loaded_weight in weights:
             if (self.quant_config is not None and
                 (scale_name := self.quant_config.get_cache_scale(name))):
@@ -544,3 +545,6 @@ class Grok1ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             skip_prefixes=skip_prefixes,
         )
         return loader.load_weights(weights)
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()

vllm/model_executor/models/hunyuan_v1_moe.py

@@ -56,7 +56,9 @@ from vllm.model_executor.model_loader.weight_utils import (
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
-from .utils import PPMissingLayer, is_pp_missing_parameter, make_layers
+from .interfaces import SupportsLoRA
+from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
+                    make_layers)
 
 
 def _get_cla_factor(config: PretrainedConfig) -> int:
@@ -617,86 +619,6 @@ class HunYuanModel(nn.Module):
         hidden_states, _ = self.norm(hidden_states, residual)
         return hidden_states
 
-
-class HunYuanMoEV1ForCausalLM(nn.Module):
-    packed_modules_mapping = {
-        "qkv_proj": [
-            "q_proj",
-            "k_proj",
-            "v_proj",
-        ],
-        "gate_up_proj": [
-            "gate_proj",
-            "up_proj",
-        ],
-    }
-
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        super().__init__()
-
-        config = vllm_config.model_config.hf_config
-        quant_config = vllm_config.quant_config
-        lora_config = vllm_config.lora_config
-        self.config = config
-        self.quant_config = quant_config
-        self.lora_config = lora_config
-
-        self.model = HunYuanModel(vllm_config=vllm_config, prefix="model")
-        if get_pp_group().is_last_rank:
-            self.unpadded_vocab_size = config.vocab_size
-            if lora_config:
-                self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
-            self.lm_head = ParallelLMHead(
-                self.unpadded_vocab_size,
-                config.hidden_size,
-                org_num_embeddings=config.vocab_size,
-                padding_size=DEFAULT_VOCAB_PADDING_SIZE,
-                quant_config=quant_config,
-            )
-            if config.tie_word_embeddings:
-                self.lm_head.weight = self.model.embed_tokens.weight
-
-            logit_scale = getattr(config, "logit_scale", 1.0)
-            self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
-                                                    config.vocab_size,
-                                                    logit_scale)
-        else:
-            self.lm_head = PPMissingLayer()
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-    ) -> Union[torch.Tensor, IntermediateTensors]:
-        model_output = self.model(input_ids, positions, intermediate_tensors,
-                                  inputs_embeds)
-        return model_output
-
-    def compute_logits(
-        self,
-        hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
-    ) -> Optional[torch.Tensor]:
-        logits = self.logits_processor(self.lm_head, hidden_states,
-                                       sampling_metadata)
-        return logits
-
-    def make_empty_intermediate_tensors(
-            self, batch_size: int, dtype: torch.dtype,
-            device: torch.device) -> IntermediateTensors:
-        return IntermediateTensors({
-            "hidden_states":
-            torch.zeros((batch_size, self.config.hidden_size),
-                        dtype=dtype,
-                        device=device),
-            "residual":
-            torch.zeros((batch_size, self.config.hidden_size),
-                        dtype=dtype,
-                        device=device),
-        })
-
     def _split_qkv_weight(self, qkv: torch.Tensor):
         num_attention_heads = self.config.num_attention_heads
         num_kv_heads = getattr(self.config, "num_key_value_heads",
@@ -719,6 +641,17 @@ class HunYuanMoEV1ForCausalLM(nn.Module):
         v = v.reshape(-1, hidden_size)
         return torch.concat((q, k, v))
 
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        return FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.num_experts,
+        )
+
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
         cla_factor = _get_cla_factor(self.config)
         stacked_params_mapping = [
@@ -745,16 +678,9 @@ class HunYuanMoEV1ForCausalLM(nn.Module):
             ),
         ]
 
-        # Params for weights, fp8 weight scales, fp8 activation scales
-        # (param_name, weight_name, expert_id, shard_id)
-        expert_params_mapping = FusedMoE.make_expert_params_mapping(
-            ckpt_gate_proj_name="gate_proj",
-            ckpt_down_proj_name="down_proj",
-            ckpt_up_proj_name="up_proj",
-            num_experts=self.config.num_experts,
-        )
-
         params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        expert_params_mapping = self.get_expert_mapping()
         for name, loaded_weight in weights:
             if "rotary_emb.inv_freq" in name:
                 continue
@@ -806,7 +732,7 @@ class HunYuanMoEV1ForCausalLM(nn.Module):
                 param = params_dict[name]
                 weight_loader = param.weight_loader
                 weight_loader(param, loaded_weight, shard_id)
-
+                loaded_params.add(name)
                 is_found = True
                 break
             if is_found:
@@ -885,3 +811,93 @@ class HunYuanMoEV1ForCausalLM(nn.Module):
                     weight_loader = getattr(param, "weight_loader",
                                             default_weight_loader)
                     weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class HunYuanMoEV1ForCausalLM(nn.Module, SupportsLoRA):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+
+        self.model = HunYuanModel(vllm_config=vllm_config, prefix="model")
+        if get_pp_group().is_last_rank:
+            self.unpadded_vocab_size = config.vocab_size
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+                quant_config=quant_config,
+            )
+            if config.tie_word_embeddings:
+                self.lm_head.weight = self.model.embed_tokens.weight
+
+            logit_scale = getattr(config, "logit_scale", 1.0)
+            self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                    config.vocab_size,
+                                                    logit_scale)
+        else:
+            self.lm_head = PPMissingLayer()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        model_output = self.model(input_ids, positions, intermediate_tensors,
+                                  inputs_embeds)
+        return model_output
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def make_empty_intermediate_tensors(
+            self, batch_size: int, dtype: torch.dtype,
+            device: torch.device) -> IntermediateTensors:
+        return IntermediateTensors({
+            "hidden_states":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+            "residual":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+        })
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."]
+                           if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()