sgl-project--sglang/python/sglang/srt/models/sdar_moe.py

# coding=utf-8
"""
SGLang SDARMoeModelLM (block diffusion / dLLM-style forward) with MoE MLP.
"""

import logging
from typing import Iterable, Optional, Tuple, Union

import torch
from torch import nn
from transformers import PretrainedConfig

from sglang.srt.distributed import (
    get_pp_group,
    tensor_model_parallel_all_reduce,
)
from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation
from sglang.srt.eplb.expert_location_dispatch import ExpertLocationDispatchInfo
from sglang.srt.layers.communicator import LayerCommunicator, LayerScatterModes
from sglang.srt.layers.dp_attention import (
    is_dp_attention_enabled,
)
from sglang.srt.layers.layernorm import RMSNorm
from sglang.srt.layers.linear import (
    QKVParallelLinear,
    ReplicatedLinear,
    RowParallelLinear,
)
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.moe import (
    get_moe_a2a_backend,
    should_skip_post_experts_all_reduce,
)
from sglang.srt.layers.moe.ep_moe.layer import get_moe_impl_class
from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
from sglang.srt.layers.moe.topk import TopK
from sglang.srt.layers.moe.utils import (
    RoutingMethodType,
    filter_moe_weight_param_global_expert,
)
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.radix_attention import AttentionType, RadixAttention
from sglang.srt.layers.rotary_embedding import get_rope
from sglang.srt.layers.utils import PPMissingLayer, get_layer_id
from sglang.srt.layers.vocab_parallel_embedding import (
    ParallelLMHead,
    VocabParallelEmbedding,
)
from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
from sglang.srt.model_loader.weight_utils import (
    default_weight_loader,
    maybe_remap_kv_scale_name,
)
from sglang.srt.models.utils import (
    apply_qk_norm,
    create_fused_set_kv_buffer_arg,
    enable_fused_set_kv_buffer,
)
from sglang.srt.runtime_context import (
    get_forward,
    get_parallel,
    get_server_args,
    get_stream,
)
from sglang.srt.utils import LazyValue, add_prefix, is_cuda, make_layers

logger = logging.getLogger(__name__)
_is_cuda = is_cuda()


class SDARMoeSparseMoeBlock(nn.Module):
    """
    Qwen3MoE-style sparse MoE block:
      - gate: ReplicatedLinear(hidden, num_experts)
      - topk routing: TopK
      - experts: get_moe_impl_class(quant_config)(...)
    """

    def __init__(
        self,
        layer_id: int,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.layer_id = layer_id
        self.tp_size = get_parallel().tp_size

        if self.tp_size > config.num_experts:
            raise ValueError(
                f"Tensor parallel size {self.tp_size} > num_experts {config.num_experts}."
            )

        self.topk = TopK(
            top_k=config.num_experts_per_tok,
            renormalize=config.norm_topk_prob,
            use_grouped_topk=False,
            layer_id=layer_id,
        )

        self.experts = get_moe_impl_class(quant_config)(
            num_experts=config.num_experts + get_server_args().ep_num_redundant_experts,
            top_k=config.num_experts_per_tok,
            layer_id=layer_id,
            hidden_size=config.hidden_size,
            intermediate_size=config.moe_intermediate_size,
            quant_config=quant_config,
            prefix=add_prefix("experts", prefix),
            routing_method_type=RoutingMethodType.Renormalize,
        )

        self.gate = ReplicatedLinear(
            config.hidden_size,
            config.num_experts,
            bias=False,
            quant_config=None,
            prefix=add_prefix("gate", prefix),
        )

        # Deepep / FuseEP support
        if get_moe_a2a_backend().is_deepep():
            self.ep_size = get_parallel().moe_ep_size
            self.num_experts = (
                config.num_experts + get_server_args().ep_num_redundant_experts
            )
            self.top_k = config.num_experts_per_tok

    def forward(
        self,
        hidden_states: torch.Tensor,
        forward_batch: Optional[ForwardBatch] = None,
    ) -> torch.Tensor:
        if (
            not get_moe_a2a_backend().is_deepep()
            and not get_moe_a2a_backend().is_ascend_fuseep()
        ):
            return self.forward_normal(hidden_states)
        else:
            assert forward_batch is not None, "deepep/fuseep MoE needs forward_batch"
            return self.forward_deepep(hidden_states, forward_batch)

    def forward_normal(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        num_tokens, hidden_dim = hidden_states.shape
        hidden_states = hidden_states.view(-1, hidden_dim)

        router_logits, _ = self.gate(hidden_states)  # (T, E)
        topk_output = self.topk(hidden_states, router_logits)
        out = self.experts(hidden_states, topk_output)  # (T, H)

        if self.tp_size > 1 and not should_skip_post_experts_all_reduce(
            is_tp_path=True,
        ):
            out = tensor_model_parallel_all_reduce(out)

        return out.view(num_tokens, hidden_dim)

    def forward_deepep(self, hidden_states: torch.Tensor, forward_batch: ForwardBatch):
        if hidden_states.shape[0] > 0:
            router_logits, _ = self.gate(hidden_states)
            topk_output = self.topk(
                hidden_states,
                router_logits,
                num_token_non_padded=forward_batch.num_token_non_padded,
                expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
                    layer_id=self.layer_id
                ),
            )
        else:
            topk_output = self.topk.empty_topk_output(hidden_states.device)

        out = self.experts(hidden_states=hidden_states, topk_output=topk_output)
        return out

    def get_moe_weights(self):
        return [
            p.data
            for name, p in self.experts.named_parameters()
            if name not in ["correction_bias"]
            and filter_moe_weight_param_global_expert(
                name, p, self.experts.num_local_experts
            )
        ]


class SDARMoeAttention(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        layer_id: int,
        quant_config: Optional[QuantizationConfig] = None,
        reduce_results: bool = True,
        prefix: str = "",
        alt_stream: Optional[torch.cuda.Stream] = None,
    ):
        super().__init__()
        self.layer_id = layer_id
        self.hidden_size = config.hidden_size
        self.total_num_heads = config.num_attention_heads

        attn_tp_rank = get_parallel().attn_tp_rank
        attn_tp_size = get_parallel().attn_tp_size

        assert self.total_num_heads % attn_tp_size == 0
        self.num_heads = self.total_num_heads // attn_tp_size

        self.total_num_kv_heads = config.num_key_value_heads
        if self.total_num_kv_heads >= attn_tp_size:
            assert self.total_num_kv_heads % attn_tp_size == 0
        else:
            assert attn_tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // attn_tp_size)

        self.head_dim = getattr(
            config, "head_dim", self.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.scale = self.head_dim**-0.5

        self.qkv_proj = QKVParallelLinear(
            self.hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=getattr(config, "attention_bias", False),
            quant_config=quant_config,
            prefix=add_prefix("qkv_proj", prefix),
        )
        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            self.hidden_size,
            bias=getattr(config, "attention_bias", False),
            quant_config=quant_config,
            reduce_results=reduce_results,
            tp_rank=attn_tp_rank,
            tp_size=attn_tp_size,
            prefix=add_prefix("o_proj", prefix),
        )

        self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
        self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)

        rope_theta = getattr(config, "rope_theta", 10000.0)
        rope_scaling = getattr(config, "rope_scaling", None)
        max_pos = getattr(config, "max_position_embeddings", 32768)
        self.rotary_dim = self.head_dim
        self.rotary_emb = get_rope(
            self.head_dim,
            rotary_dim=self.rotary_dim,
            max_position=max_pos,
            base=rope_theta,
            rope_scaling=rope_scaling,
        )

        self.attn = RadixAttention(
            self.num_heads,
            self.head_dim,
            self.scale,
            num_kv_heads=self.num_kv_heads,
            layer_id=layer_id,
            attn_type=AttentionType.ENCODER_ONLY,
            prefix=add_prefix("attn", prefix),
        )
        self.alt_stream = alt_stream

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
    ) -> torch.Tensor:
        if get_server_args().rl_on_policy_target is not None:
            hidden_states = hidden_states.bfloat16()

        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        q, k = apply_qk_norm(
            q=q,
            k=k,
            q_norm=self.q_norm,
            k_norm=self.k_norm,
            head_dim=self.head_dim,
            alt_stream=self.alt_stream,
        )
        q, k = self.rotary_emb(
            positions,
            q,
            k,
            fused_set_kv_buffer_arg=(
                create_fused_set_kv_buffer_arg(
                    value=v,
                    layer=self.attn,
                    forward_batch=forward_batch,
                )
                if enable_fused_set_kv_buffer(forward_batch)
                else None
            ),
        )

        if get_server_args().rl_on_policy_target is not None:
            q = q.to(torch.bfloat16)
            k = k.to(torch.bfloat16)

        context = self.attn(
            q,
            k,
            v,
            forward_batch,
            save_kv_cache=not enable_fused_set_kv_buffer(forward_batch),
        )
        out, _ = self.o_proj(context)
        return out


class SDARMoeBlock(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        layer_id: int,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        alt_stream: Optional[torch.cuda.Stream] = None,
    ):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.layer_id = layer_id

        norm_kwargs = (
            dict(
                weight_dtype=torch.float32,
                cast_x_before_out_mul=True,
                override_orig_dtype=torch.float32,
                fp32_residual=True,
            )
            if get_server_args().rl_on_policy_target is not None
            else {}
        )
        self.input_layernorm = RMSNorm(
            self.hidden_size, eps=config.rms_norm_eps, **norm_kwargs
        )
        self.post_attention_layernorm = RMSNorm(
            self.hidden_size, eps=config.rms_norm_eps, **norm_kwargs
        )

        self.self_attn = SDARMoeAttention(
            config=config,
            layer_id=layer_id,
            quant_config=quant_config,
            reduce_results=False,
            prefix=add_prefix("self_attn", prefix),
            alt_stream=alt_stream,
        )

        self.mlp = SDARMoeSparseMoeBlock(
            layer_id=layer_id,
            config=config,
            quant_config=quant_config,
            prefix=add_prefix("mlp", prefix),
        )

        self.layer_scatter_modes = LayerScatterModes.init_new(
            layer_id=layer_id,
            num_layers=config.num_hidden_layers,
            is_layer_sparse=True,
            is_previous_layer_sparse=True,
            is_next_layer_sparse=True,
        )

        self.layer_communicator = LayerCommunicator(
            layer_scatter_modes=self.layer_scatter_modes,
            input_layernorm=self.input_layernorm,
            post_attention_layernorm=self.post_attention_layernorm,
            allow_reduce_scatter=True,
            is_last_layer=(layer_id == config.num_hidden_layers - 1),
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
        residual: Optional[torch.Tensor],
    ) -> Tuple[torch.Tensor, torch.Tensor]:

        hidden_states, residual = self.layer_communicator.prepare_attn(
            hidden_states, residual, forward_batch
        )

        if hidden_states.shape[0] != 0:
            hidden_states = self.self_attn(
                positions=positions,
                hidden_states=hidden_states,
                forward_batch=forward_batch,
            )

        hidden_states, residual = self.layer_communicator.prepare_mlp(
            hidden_states, residual, forward_batch
        )

        fuse_mlp_allreduce = (
            self.layer_communicator.should_fuse_mlp_allreduce_with_next_layer(
                forward_batch
            )
        )
        mlp_reduce_scatter = self.layer_communicator.should_use_reduce_scatter(
            forward_batch
        )

        with get_forward().scoped(
            fuse_mlp_allreduce=fuse_mlp_allreduce,
            mlp_reduce_scatter=mlp_reduce_scatter,
        ):
            hidden_states = self.mlp(
                hidden_states,
                forward_batch=forward_batch,
            )

        if fuse_mlp_allreduce:
            hidden_states._sglang_needs_allreduce_fusion = True
        else:
            hidden_states, residual = self.layer_communicator.postprocess_layer(
                hidden_states, residual, forward_batch
            )

        return hidden_states, residual


class SDARMoeModel(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        alt_stream: Optional[torch.cuda.Stream] = None,
    ):
        super().__init__()
        self.config = config
        self.vocab_size = config.vocab_size
        self.embed_dim = config.hidden_size
        self.pp_group = get_pp_group()

        if self.pp_group.is_first_rank:
            self.embed_tokens = VocabParallelEmbedding(
                self.vocab_size,
                self.embed_dim,
                quant_config=quant_config,
                use_attn_tp_group=is_dp_attention_enabled(),
                prefix=add_prefix("embed_tokens", prefix),
            )
        else:
            self.embed_tokens = PPMissingLayer()

        self.layers, self.start_layer, self.end_layer = make_layers(
            config.num_hidden_layers,
            lambda idx, prefix: SDARMoeBlock(
                config=config,
                layer_id=idx,
                quant_config=quant_config,
                prefix=prefix,
                alt_stream=alt_stream,
            ),
            pp_rank=self.pp_group.rank_in_group,
            pp_size=self.pp_group.world_size,
            prefix=add_prefix("layers", prefix),
        )

        if self.pp_group.is_last_rank:
            norm_kwargs = (
                dict(
                    weight_dtype=torch.float32,
                    cast_x_before_out_mul=True,
                    override_orig_dtype=torch.float32,
                    fp32_residual=True,
                )
                if get_server_args().rl_on_policy_target is not None
                else {}
            )
            self.norm = RMSNorm(self.embed_dim, eps=config.rms_norm_eps, **norm_kwargs)
        else:
            self.norm = PPMissingLayer(return_tuple=True)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        input_embeds: torch.Tensor = None,
        pp_proxy_tensors: Optional[PPProxyTensors] = None,
    ) -> Union[torch.Tensor, PPProxyTensors]:
        if self.pp_group.is_first_rank:
            hidden_states = (
                self.embed_tokens(input_ids) if input_embeds is None else input_embeds
            )
            residual = None
        else:
            assert pp_proxy_tensors is not None
            hidden_states = pp_proxy_tensors["hidden_states"]
            residual = pp_proxy_tensors.get("residual", None)

        for i in range(self.start_layer, self.end_layer):
            layer = self.layers[i]
            with get_global_expert_distribution_recorder().with_current_layer(i):
                hidden_states, residual = layer(
                    positions, hidden_states, forward_batch, residual
                )

        if not self.pp_group.is_last_rank:
            return PPProxyTensors(
                {"hidden_states": hidden_states, "residual": residual}
            )

        if not forward_batch.forward_mode.is_idle():
            hidden_states, residual = self.norm(hidden_states, residual)
        return hidden_states


class SDARMoeForCausalLM(nn.Module):
    fall_back_to_pt_during_load = False

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.pp_group = get_pp_group()
        assert self.pp_group.world_size == 1, (
            f"SDARMoeForCausalLM does not support pipeline parallel (pp_size={self.pp_group.world_size}). "
            "Please set pp_size=1."
        )

        self.pp_group = get_pp_group()
        self.config = config
        self.quant_config = quant_config
        alt_stream = get_stream("alt") if _is_cuda else None

        self.model = SDARMoeModel(
            config,
            quant_config=quant_config,
            prefix=add_prefix("model", ""),
            alt_stream=alt_stream,
        )

        if self.pp_group.is_last_rank:
            tp_size = get_parallel().tp_size
            if (
                self.pp_group.world_size == 1
                and getattr(config, "tie_word_embeddings", False)
                and tp_size == 1
            ):
                self.lm_head = self.model.embed_tokens
            else:
                self.lm_head = ParallelLMHead(
                    config.vocab_size,
                    config.hidden_size,
                    quant_config=quant_config,
                    use_attn_tp_group=get_server_args().enable_dp_lm_head,
                    prefix=add_prefix("lm_head", prefix),
                )
        else:
            self.lm_head = PPMissingLayer()

        self.logits_processor = LogitsProcessor(config, return_full_logits=True)

    @property
    def start_layer(self):
        return self.model.start_layer

    @property
    def end_layer(self):
        return self.model.end_layer

    @torch.no_grad()
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        input_embeds: Optional[torch.Tensor] = None,
        pp_proxy_tensors: Optional[PPProxyTensors] = None,
    ) -> torch.Tensor:
        hidden_states = self.model(
            input_ids=input_ids,
            positions=positions,
            forward_batch=forward_batch,
            input_embeds=input_embeds,
            pp_proxy_tensors=pp_proxy_tensors,
        )
        if self.pp_group.is_last_rank:
            return self.logits_processor(
                input_ids, hidden_states, self.lm_head, forward_batch
            )
        return hidden_states

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        stacked_params_mapping = [
            ("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),
        ]

        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,
        )

        if not hasattr(self, "_cached_params_dict"):
            self._cached_params_dict = dict(self.named_parameters())
        params_dict = self._cached_params_dict

        for name, loaded_weight in weights:
            if not name.startswith("model.") and (
                name.startswith("layers.")
                or name.startswith("embed_tokens.")
                or name.startswith("norm.")
            ):
                name = add_prefix(name, "model")

            if name == "model.embed_tokens.weight":
                if self.pp_group.is_last_rank and getattr(
                    self.config, "tie_word_embeddings", False
                ):
                    if "lm_head.weight" in params_dict:
                        param = params_dict["lm_head.weight"]
                        weight_loader = getattr(
                            param, "weight_loader", default_weight_loader
                        )
                        weight_loader(param, loaded_weight)

            layer_id = get_layer_id(name)
            if (
                layer_id is not None
                and hasattr(self.model, "start_layer")
                and (
                    layer_id < self.model.start_layer
                    or layer_id >= self.model.end_layer
                )
            ):
                continue

            if "rotary_emb.inv_freq" in name or "projector" in name:
                continue
            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
                continue

            if "scale" in name:
                name = maybe_remap_kv_scale_name(name, params_dict)
                if name is None:
                    continue

            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                if "mlp.experts" in name:
                    continue

                name2 = name.replace(weight_name, param_name)
                if name2.endswith(".bias") and name2 not in params_dict:
                    continue
                if name2 not in params_dict:
                    continue

                param = params_dict[name2]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                is_expert_weight = False
                for mapping in expert_params_mapping:
                    param_name, weight_name, expert_id, shard_id = mapping
                    if weight_name not in name:
                        continue
                    is_expert_weight = True

                    name2 = name.replace(weight_name, param_name)
                    if name2 not in params_dict:
                        continue

                    param = params_dict[name2]
                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
                    weight_loader(
                        param,
                        loaded_weight,
                        name2,
                        shard_id=shard_id,
                        expert_id=expert_id,
                    )
                    break
                else:
                    if is_expert_weight:
                        continue

                    # 3) regular params
                    if name.endswith(".bias") and name not in params_dict:
                        continue
                    if name not in params_dict:
                        continue

                    param = params_dict[name]
                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
                    weight_loader(param, loaded_weight)

        if not hasattr(self, "routed_experts_weights_of_layer"):
            self.routed_experts_weights_of_layer = LazyValue(
                lambda: {
                    lid: self.model.layers[lid].mlp.get_moe_weights()
                    for lid in range(self.start_layer, self.end_layer)
                    if isinstance(self.model.layers[lid].mlp, SDARMoeSparseMoeBlock)
                }
            )

    @classmethod
    def get_model_config_for_expert_location(cls, config):
        return ModelConfigForExpertLocation(
            num_layers=config.num_hidden_layers,
            num_logical_experts=config.num_experts,
            num_groups=None,
        )


EntryClass = SDARMoeForCausalLM