kormo_moe_vllm.py

"""
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()