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install_vllm_support.sh

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+#!/bin/bash
+# KORMo MoE vLLM Support Installation Script
+# This script automatically installs the necessary files for vLLM support
+
+set -e
+
+echo "=========================================="
+echo "KORMo MoE vLLM Support Installer"
+echo "=========================================="
+echo ""
+
+# 1. vLLM 설치 확인
+echo "Step 1: Checking vLLM installation..."
+if ! python3 -c "import vllm" 2>/dev/null; then
+    echo "❌ vLLM is not installed."
+    echo "Installing vLLM..."
+    pip install vllm
+    echo "✅ vLLM installed successfully"
+else
+    echo "✅ vLLM is already installed"
+fi
+echo ""
+
+# 2. vLLM 설치 경로 찾기
+echo "Step 2: Finding vLLM installation path..."
+VLLM_PATH=$(python3 -c "import vllm; import os; print(os.path.dirname(vllm.__file__))")
+echo "vLLM path: $VLLM_PATH"
+echo ""
+
+# 3. kormo_moe_vllm.py 다운로드
+echo "Step 3: Downloading KORMo MoE vLLM implementation..."
+if [ -f "kormo_moe_vllm.py" ]; then
+    echo "✅ kormo_moe_vllm.py found locally"
+else
+    echo "Downloading from HuggingFace (dev7halo/KORMo-10B-sft-moe)..."
+    wget https://huggingface.co/dev7halo/KORMo-10B-sft-moe/resolve/main/kormo_moe_vllm.py -O kormo_moe_vllm.py 2>/dev/null || \
+    curl -L https://huggingface.co/dev7halo/KORMo-10B-sft-moe/resolve/main/kormo_moe_vllm.py -o kormo_moe_vllm.py 2>/dev/null || {
+        echo "❌ Failed to download. Please ensure kormo_moe_vllm.py is in the current directory."
+        echo "You can manually download it from:"
+        echo "https://huggingface.co/dev7halo/KORMo-10B-sft-moe/blob/main/kormo_moe_vllm.py"
+        exit 1
+    }
+    echo "✅ Downloaded successfully"
+fi
+echo ""
+
+# 4. 파일 복사
+echo "Step 4: Installing KORMo MoE model file..."
+TARGET_PATH="$VLLM_PATH/model_executor/models/kormo_moe.py"
+cp kormo_moe_vllm.py "$TARGET_PATH"
+echo "✅ Copied to $TARGET_PATH"
+echo ""
+
+# 5. 레지스트리 수정
+echo "Step 5: Registering KORMo MoE in vLLM..."
+REGISTRY_PATH="$VLLM_PATH/model_executor/models/registry.py"
+
+# 이미 등록되어 있는지 확인
+if grep -q "KORMoMoeForCausalLM" "$REGISTRY_PATH"; then
+    echo "✅ KORMo MoE is already registered"
+else
+    echo "Adding KORMo MoE to registry..."
+
+    # 백업 생성
+    cp "$REGISTRY_PATH" "$REGISTRY_PATH.backup"
+    echo "Created backup: $REGISTRY_PATH.backup"
+
+    # JambaForCausalLM 다음에 KORMo MoE 추가
+    if grep -q "JambaForCausalLM" "$REGISTRY_PATH"; then
+        sed -i '/\"JambaForCausalLM\"/a\    \"KORMoMoeForCausalLM\": (\"kormo_moe\", \"KORMoMoeForCausalLM\"),' "$REGISTRY_PATH"
+        echo "✅ KORMo MoE registered successfully"
+    else
+        echo "⚠️  Could not find JambaForCausalLM in registry."
+        echo "Please manually add the following line to $REGISTRY_PATH in _TEXT_GENERATION_MODELS:"
+        echo '    "KORMoMoeForCausalLM": ("kormo_moe", "KORMoMoeForCausalLM"),'
+    fi
+fi
+echo ""
+
+# 6. 설치 확인
+echo "Step 6: Verifying installation..."
+python3 << EOF
+try:
+    from vllm.model_executor.models.registry import ModelRegistry
+    if "KORMoMoeForCausalLM" in ModelRegistry.get_supported_archs():
+        print("✅ Installation successful! KORMo MoE is now supported in vLLM")
+    else:
+        print("❌ Registration verification failed")
+        exit(1)
+except Exception as e:
+    print(f"❌ Error during verification: {e}")
+    exit(1)
+EOF
+
+echo ""
+echo "=========================================="
+echo "Installation Complete!"
+echo "=========================================="
+echo ""
+echo "You can now use KORMo MoE with vLLM:"
+echo ""
+echo "Example usage:"
+echo ""
+echo "from vllm import LLM, SamplingParams"
+echo ""
+echo "# Load the model"
+echo "llm = LLM(model='dev7halo/KORMo-10B-sft-moe', dtype='float16')"
+echo ""
+echo "# Generate text"
+echo "prompts = ['안녕하세요']"
+echo "outputs = llm.generate(prompts, SamplingParams(temperature=0.8, max_tokens=100))"
+echo ""

kormo_moe_vllm.py

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+"""
+vLLM-compatible implementation of KORMo MoE
+
+This file should be placed in: /usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/kormo_moe.py
+
+Usage:
+    from vllm import LLM
+
+    llm = LLM(
+        model="/path/to/kormo_moe_model",
+        trust_remote_code=False,  # Not needed with this implementation
+        dtype="float16",
+    )
+"""
+
+from collections.abc import Iterable
+from typing import Any, Optional, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+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
+
+try:
+    from transformers import PretrainedConfig
+except ImportError:
+    # Fallback for environments without transformers
+    PretrainedConfig = object
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (
+    AutoWeightsLoader,
+    extract_layer_index,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
+
+logger = init_logger(__name__)
+
+
+class KORMoMoeConfig(PretrainedConfig):
+    """Configuration class for KORMo MoE"""
+
+    model_type = "kormo_moe"
+
+    def __init__(
+        self,
+        vocab_size=112576,
+        hidden_size=6144,
+        intermediate_size=21504,
+        num_hidden_layers=48,
+        num_attention_heads=40,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=131072,
+        initializer_range=0.02,
+        rms_norm_eps=1e-05,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=0,
+        eos_token_id=1,
+        tie_word_embeddings=False,
+        rope_theta=500000.0,
+        attention_dropout=0.0,
+        rope_scaling=None,
+        head_dim=128,
+        # MoE specific
+        num_experts=2,
+        num_experts_per_tok=2,
+        moe_intermediate_size=None,
+        shared_expert_intermediate_size=None,
+        norm_topk_prob=True,
+        decoder_sparse_step=1,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads or num_attention_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_dropout = attention_dropout
+        self.head_dim = head_dim or (self.hidden_size // self.num_attention_heads)
+
+        # MoE specific
+        self.num_experts = num_experts
+        self.num_experts_per_tok = num_experts_per_tok
+        self.moe_intermediate_size = (
+            moe_intermediate_size if moe_intermediate_size is not None else intermediate_size
+        )
+        self.shared_expert_intermediate_size = shared_expert_intermediate_size
+        self.norm_topk_prob = norm_topk_prob
+        self.decoder_sparse_step = decoder_sparse_step
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+class KORMoMoEMLP(nn.Module):
+    """MLP for KORMo, used for shared expert"""
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        reduce_results: bool = True,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size,
+            [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            reduce_results=reduce_results,
+        )
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. Only silu is supported.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class KORMoSparseMoeBlock(nn.Module):
+    """KORMo Sparse MoE Block optimized for vLLM"""
+
+    def __init__(
+        self,
+        config: KORMoMoeConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+
+        if self.tp_size > config.num_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {config.num_experts}."
+            )
+
+        # Use vLLM's FusedMoE for optimized expert routing
+        self.experts = FusedMoE(
+            num_experts=config.num_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            reduce_results=False,
+            renormalize=config.norm_topk_prob,
+            quant_config=quant_config,
+            prefix=f"{prefix}.experts",
+        )
+
+        # Router/gate
+        self.gate = ReplicatedLinear(
+            config.hidden_size,
+            config.num_experts,
+            bias=False,
+            quant_config=None,
+        )
+
+        # Shared expert (optional)
+        if config.shared_expert_intermediate_size and config.shared_expert_intermediate_size > 0:
+            self.shared_expert = KORMoMoEMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=config.shared_expert_intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                reduce_results=self.experts.must_reduce_shared_expert_outputs(),
+            )
+            self.shared_expert_gate = nn.Linear(config.hidden_size, 1, bias=False)
+        else:
+            self.shared_expert = None
+            self.shared_expert_gate = None
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # NOTE: hidden_states can have either 1D or 2D shape.
+        orig_shape = hidden_states.shape
+        hidden_dim = hidden_states.shape[-1]
+        hidden_states = hidden_states.view(-1, hidden_dim)
+
+        # Shared expert처리
+        shared_output = None
+        if self.shared_expert is not None:
+            shared_output = self.shared_expert(hidden_states)
+            if self.shared_expert_gate is not None:
+                shared_output = F.sigmoid(
+                    self.shared_expert_gate(hidden_states)
+                ) * shared_output
+
+        # Router logits: (num_tokens, n_experts)
+        router_logits, _ = self.gate(hidden_states)
+
+        # FusedMoE에서 expert routing 수행
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states,
+            router_logits=router_logits,
+        )
+
+        # Shared expert 결과 추가
+        if shared_output is not None:
+            final_hidden_states = final_hidden_states + shared_output
+
+        # Tensor parallel reduction
+        if self.tp_size > 1:
+            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
+                final_hidden_states
+            )
+
+        return final_hidden_states.view(orig_shape)
+
+
+class KORMoMoeAttention(nn.Module):
+    """KORMo MoE Attention mechanism"""
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 500000,
+        rope_scaling: Optional[dict[str, Any]] = None,
+        max_position_embeddings: int = 131072,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+
+        self.head_dim = hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+        )
+
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+        )
+
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class KORMoMoeDecoderLayer(nn.Module):
+    """KORMo MoE Decoder Layer"""
+
+    def __init__(
+        self,
+        config: KORMoMoeConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        # Attention
+        self.self_attn = KORMoMoeAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=config.rope_theta,
+            rope_scaling=config.rope_scaling,
+            max_position_embeddings=config.max_position_embeddings,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+
+        # MoE MLP
+        self.mlp = KORMoSparseMoeBlock(
+            config=config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+
+        # LayerNorms (using KORMo naming convention)
+        self.pre_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.pre_mlp_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.pre_attention_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.pre_attention_layernorm(hidden_states, residual)
+
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+
+        # MoE MLP
+        hidden_states, residual = self.pre_mlp_layernorm(hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+
+        return hidden_states, residual
+
+
+@support_torch_compile
+class KORMoMoeModel(nn.Module):
+    """KORMo MoE Model"""
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.vocab_size = config.vocab_size
+        self.config = config
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: KORMoMoeDecoderLayer(
+                config=config,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                prefix=prefix,
+            ),
+            prefix=f"{prefix}.layers",
+        )
+
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states", "residual"], config.hidden_size
+        )
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(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]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        for layer in self.layers[self.start_layer : self.end_layer]:
+            hidden_states, residual = layer(positions, hidden_states, residual)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual,
+            })
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        """Return expert parameter mapping for weight loading"""
+        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 = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        expert_params_mapping = self.get_expert_mapping()
+
+        for name, loaded_weight in weights:
+            # Handle stacked parameters
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                if "mlp.experts" in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                if (name.endswith(".bias") or name.endswith("_bias")) and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                if name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Handle expert parameters
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    if (name.endswith(".bias") or name.endswith("_bias")) and name not in params_dict:
+                        continue
+
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(
+                        param,
+                        loaded_weight,
+                        name,
+                        shard_id=shard_id,
+                        expert_id=expert_id,
+                    )
+                    break
+                else:
+                    # Handle regular parameters
+                    if (name.endswith(".bias") or name.endswith("_bias")) and name not in params_dict:
+                        continue
+                    if is_pp_missing_parameter(name, self):
+                        continue
+
+                    # Fix gate weight naming: gate.linear.weight -> gate.weight
+                    if ".gate.linear.weight" in name:
+                        name = name.replace(".gate.linear.weight", ".gate.weight")
+
+                    if name not in params_dict:
+                        logger.warning(f"Parameter {name} not found in model")
+                        continue
+
+                    param = params_dict[name]
+                    weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                    weight_loader(param, loaded_weight)
+
+            loaded_params.add(name)
+
+        return loaded_params
+
+
+class KORMoMoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
+    """KORMo MoE for Causal Language Modeling"""
+
+    fall_back_to_pt_during_load = False
+    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 = KORMoMoeModel(vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model"))
+        self.lm_head = ParallelLMHead(
+            config.vocab_size,
+            config.hidden_size,
+            quant_config=quant_config,
+        )
+
+        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)
+        return loader.load_weights(weights)
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()