sgl-project--sglang/python/sglang/srt/models/glm4_moe.py

# Copyright 2025-2026 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

"""Inference-only GLM-4.5, GLM-4.6 and GLM-4.7 model compatible with HuggingFace weights"""

import logging
import re
from typing import Any, Dict, Iterable, List, Optional, Tuple, Union

import torch
import torch.nn.functional as F
from torch import nn
from transformers import PretrainedConfig

from sglang.srt.batch_overlap.single_batch_overlap import SboFlags
from sglang.srt.batch_overlap.two_batch_overlap import model_forward_maybe_tbo
from sglang.srt.distributed import (
    get_pp_group,
    get_pp_indices,
    parallel_state,
    tensor_model_parallel_all_reduce,
)
from sglang.srt.distributed.device_communicators.pynccl_allocator import (
    use_symmetric_memory,
)
from sglang.srt.environ import envs
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.activation import SiluAndMul
from sglang.srt.layers.communicator import (
    LayerCommunicator,
    LayerScatterModes,
    enable_moe_dense_fully_dp,
)
from sglang.srt.layers.dp_attention import (
    is_allocation_symmetric,
    is_dp_attention_enabled,
)
from sglang.srt.layers.layernorm import RMSNorm
from sglang.srt.layers.linear import (
    MergedColumnParallelLinear,
    QKVParallelLinear,
    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,
    should_use_flashinfer_cutlass_moe_fp4_allgather,
)
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.kt_ep_wrapper import KTEPWrapperMethod
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.quantization.fp8_kernel import is_fp8_fnuz
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.layers.rotary_embedding import get_rope
from sglang.srt.layers.utils import PPMissingLayer
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_executor.runner import get_is_capture_mode
from sglang.srt.model_loader.weight_utils import default_weight_loader
from sglang.srt.models.deepseek_nextn import DeepseekV3ForCausalLMNextN
from sglang.srt.models.deepseek_v2 import DeepseekV2ForCausalLM
from sglang.srt.models.utils import WeightsMapper, apply_qk_norm
from sglang.srt.runtime_context import (
    get_forward,
    get_parallel,
    get_server_args,
    get_stream,
)
from sglang.srt.utils import (
    add_prefix,
    cpu_has_amx_support,
    get_bool_env_var,
    get_device_sm,
    is_cpu,
    is_cuda,
    is_hip,
    is_non_idle_and_non_empty,
    is_npu,
    log_info_on_rank0,
    make_layers,
)
from sglang.srt.utils.hf_transformers_utils import get_rope_config

_is_hip = is_hip()
_is_cuda = is_cuda()
_is_fp8_fnuz = is_fp8_fnuz()
_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
_is_cpu_amx_available = cpu_has_amx_support()
_is_cpu = is_cpu()
_is_npu = is_npu()
_device_sm = get_device_sm()

logger = logging.getLogger(__name__)

if _is_npu:
    from sgl_kernel_npu.norm.split_qkv_rmsnorm_rope import split_qkv_rmsnorm_rope

    from sglang.srt.hardware_backend.npu.utils import (
        process_shared_expert,
        wait_share_stream,
    )


class Glm4MoeMLP(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
        quant_config: Optional[QuantizationConfig] = None,
        reduce_results: bool = True,
        prefix: str = "",
        tp_rank: Optional[int] = None,
        tp_size: Optional[int] = None,
    ) -> None:
        super().__init__()
        self.tp_size = tp_size

        self.gate_up_proj = MergedColumnParallelLinear(
            hidden_size,
            [intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=add_prefix("gate_up_proj", prefix),
            tp_rank=tp_rank,
            tp_size=tp_size,
        )
        self.down_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=False,
            quant_config=quant_config,
            reduce_results=reduce_results,
            prefix=add_prefix("down_proj", prefix),
            tp_rank=tp_rank,
            tp_size=tp_size,
        )
        if hidden_act != "silu":
            raise ValueError(
                f"Unsupported activation: {hidden_act}. Only silu is supported for now."
            )
        self.act_fn = SiluAndMul()

    def forward(
        self,
        x,
        forward_batch=None,
    ):
        if (self.tp_size == 1) and x.shape[0] == 0:
            return x

        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


class Glm4MoeAttention(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        layer_id: int = 0,
        start_layer: int = 0,
        rope_theta: float = 1000000,
        partial_rotary_factor: float = 0.5,
        rope_scaling: Optional[Dict[str, Any]] = None,
        max_position_embeddings: int = 8192,
        head_dim: Optional[int] = None,
        rms_norm_eps: float = 1e-05,
        attention_bias: bool = True,
        quant_config: Optional[QuantizationConfig] = None,
        use_qk_norm: bool = False,
        prefix: str = "",
        alt_stream: Optional[torch.cuda.Stream] = None,
    ) -> None:
        super().__init__()
        self.hidden_size = hidden_size
        self.start_layer = start_layer

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

        self.total_num_heads = num_heads
        assert self.total_num_heads % attn_tp_size == 0
        self.num_heads = self.total_num_heads // attn_tp_size
        self.total_num_kv_heads = num_kv_heads
        if self.total_num_kv_heads >= attn_tp_size:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel GPUs.
            assert self.total_num_kv_heads % attn_tp_size == 0
        else:
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel GPUs.
            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 = head_dim or 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.use_qk_norm = use_qk_norm
        self.max_position_embeddings = max_position_embeddings
        self.tp_rank = get_parallel().tp_rank

        self.qkv_proj = QKVParallelLinear(
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=attention_bias,
            quant_config=quant_config,
            tp_rank=attn_tp_rank,
            tp_size=attn_tp_size,
            prefix=add_prefix("qkv_proj", prefix),
        )

        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            hidden_size,
            bias=False,
            quant_config=quant_config,
            tp_rank=attn_tp_rank,
            tp_size=attn_tp_size,
            reduce_results=False,
            prefix=add_prefix("o_proj", prefix),
        )

        self.rotary_emb = get_rope(
            self.head_dim,
            rotary_dim=self.head_dim,
            max_position=max_position_embeddings,
            partial_rotary_factor=partial_rotary_factor,
            base=rope_theta,
            rope_scaling=rope_scaling,
        )
        self.attn = RadixAttention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            layer_id=layer_id,
            prefix=add_prefix("attn", prefix),
        )

        if self.use_qk_norm:
            self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
            self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
        self.alt_stream = alt_stream

    def op_prepare(self, state):
        state.attn_intermediate_state = self.forward_prepare(
            positions=state.positions,
            hidden_states=state.pop("hidden_states_after_comm_pre_attn"),
            forward_batch=state.forward_batch,
        )

    def op_core(self, state):
        state.hidden_states_after_attn = self.forward_core(
            state.pop("attn_intermediate_state")
        )

    def forward_prepare(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
    ):
        # hidden_states can be a (fp8_tensor, scale) tuple from fused RMSNorm+Quant
        hs = hidden_states[0] if isinstance(hidden_states, tuple) else hidden_states
        if hs.shape[0] == 0:
            return hidden_states, forward_batch, None
        qkv, _ = self.qkv_proj(hidden_states)

        if (
            not _is_npu
            or forward_batch.forward_mode.is_extend_or_draft_extend_or_mixed()
        ):
            q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
            if self.use_qk_norm:
                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)
        else:
            if self.attn.layer_id == self.start_layer:
                self.rotary_emb.get_cos_sin_with_position(positions)
            if self.use_qk_norm:
                eps = self.q_norm.variance_epsilon
                q_weight = self.q_norm.weight
                k_weight = self.k_norm.weight
                q_bias = getattr(self.q_norm, "bias", None)
                k_bias = getattr(self.k_norm, "bias", None)
            else:
                eps = None
                q_weight = None
                k_weight = None
                q_bias = None
                k_bias = None
            q, k, v = split_qkv_rmsnorm_rope(
                qkv,
                self.rotary_emb.position_sin,
                self.rotary_emb.position_cos,
                self.q_size,
                self.kv_size,
                self.head_dim,
                eps=eps,
                q_weight=q_weight,
                k_weight=k_weight,
                q_bias=q_bias,
                k_bias=k_bias,
            )

        inner_state = q, k, v, forward_batch
        return None, forward_batch, inner_state

    def forward_core(self, intermediate_state):
        hidden_states, forward_batch, inner_state = intermediate_state
        if inner_state is None:
            return hidden_states
        attn_output = self.attn(*inner_state)
        output, _ = self.o_proj(attn_output)
        return output

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
    ) -> torch.Tensor:
        s = self.forward_prepare(
            positions=positions,
            hidden_states=hidden_states,
            forward_batch=forward_batch,
        )
        return self.forward_core(s)


class Glm4MoeGate(nn.Module):
    def __init__(
        self,
        config,
        prefix: str = "",
    ):
        super().__init__()
        self.weight = nn.Parameter(
            torch.empty((config.n_routed_experts, config.hidden_size))
        )
        self.e_score_correction_bias = nn.Parameter(
            torch.empty((config.n_routed_experts), dtype=torch.float32)
        )
        # GLM requires FP32 gate projection; cache to avoid per-forward cast.
        # FIXME: if gate weight is updated at runtime (e.g. expert rebalancing), _weight_fp32 must be invalidated.
        self.register_buffer("_weight_fp32", None, persistent=False)

    def forward(self, hidden_states):
        if self._weight_fp32 is None:
            self._weight_fp32 = self.weight.data.to(torch.float32)
        logits = F.linear(hidden_states.to(torch.float32), self._weight_fp32, None)
        return logits


class Glm4MoeSparseMoeBlock(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        layer_id: int,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        alt_stream: Optional[torch.cuda.Stream] = None,
    ):
        nn.Module.__init__(self)
        self.top_k = config.num_experts_per_tok
        self.tp_size = get_parallel().tp_size
        self.moe_ep_size = get_parallel().moe_ep_size
        self.routed_scaling_factor = config.routed_scaling_factor
        self.n_shared_experts = config.n_shared_experts
        self.num_fused_shared_experts = (
            0
            if get_server_args().disable_shared_experts_fusion
            else config.n_shared_experts
        )

        self.config = config
        self.layer_id = layer_id
        self.alt_stream = alt_stream

        if self.tp_size > config.n_routed_experts:
            raise ValueError(
                f"Tensor parallel size {self.tp_size} is greater than "
                f"the number of experts {config.n_routed_experts}."
            )

        if config.hidden_act != "silu":
            raise ValueError(
                f"Unsupported activation: {config.hidden_act}. "
                "Only silu is supported for now."
            )

        self.gate = Glm4MoeGate(config=config, prefix=add_prefix("gate", prefix))

        self.experts = get_moe_impl_class(quant_config)(
            num_experts=config.n_routed_experts + self.num_fused_shared_experts,
            num_fused_shared_experts=self.num_fused_shared_experts,
            top_k=self.top_k + self.num_fused_shared_experts,
            layer_id=self.layer_id,
            hidden_size=config.hidden_size,
            intermediate_size=config.moe_intermediate_size,
            quant_config=quant_config,
            routed_scaling_factor=self.routed_scaling_factor,
            routing_method_type=RoutingMethodType.DeepSeekV3,
            prefix=add_prefix("experts", prefix),
        )

        self.topk = TopK(
            top_k=self.top_k + self.num_fused_shared_experts,
            layer_id=self.layer_id,
            renormalize=config.norm_topk_prob,
            use_grouped_topk=True,
            num_expert_group=config.n_group,
            topk_group=config.topk_group,
            correction_bias=self.gate.e_score_correction_bias,
            routed_scaling_factor=self.routed_scaling_factor,
            num_fused_shared_experts=self.num_fused_shared_experts,
            apply_routed_scaling_factor_on_output=getattr(
                self.experts, "should_fuse_routed_scaling_factor_in_topk", False
            ),
            fused_shared_experts_scaling_factor=1,
        )

        self.shared_experts_is_int8 = False
        self.shared_experts_is_fp8 = False
        self.shared_experts_weight_block_size = None
        if config.n_shared_experts is not None and self.num_fused_shared_experts == 0:
            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
            # disable tp for shared experts when enable deepep moe, or with fp4 allgather
            self.shared_experts = Glm4MoeMLP(
                hidden_size=config.hidden_size,
                intermediate_size=intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
                reduce_results=False,
                prefix=add_prefix("shared_experts", prefix),
                **(
                    dict(tp_rank=0, tp_size=1)
                    if get_moe_a2a_backend().is_deepep()
                    or get_moe_a2a_backend().is_mooncake()
                    or get_moe_a2a_backend().is_nixl()
                    or get_moe_a2a_backend().is_mori()
                    or get_moe_a2a_backend().is_ascend_fuseep()
                    or get_moe_a2a_backend().is_flashinfer()
                    or should_use_flashinfer_cutlass_moe_fp4_allgather()
                    else {}
                ),
            )
            is_packed_weight = hasattr(
                self.shared_experts.gate_up_proj.quant_method, "quant_config"
            ) and self.shared_experts.gate_up_proj.quant_method.quant_config.get_name() in {
                "awq",
                "awq_marlin",
                "moe_wna16",
            }
            self.shared_experts_is_int8 = (
                not is_packed_weight
                and self.shared_experts.gate_up_proj.weight.dtype == torch.int8
            )
            self.shared_experts_is_fp8 = (
                not is_packed_weight
                and self.shared_experts.gate_up_proj.weight.dtype == torch.float8_e4m3fn
            )
            if self.shared_experts_is_fp8:
                if (
                    _use_aiter
                    and config.quantization_config.get("quant_method")
                    == "compressed-tensors"
                ):
                    # For compressed-tensors ptpc model, don't need to check the weight_block_size
                    pass
                else:
                    assert (
                        self.shared_experts.gate_up_proj.quant_method.quant_config.weight_block_size
                        == self.shared_experts.down_proj.quant_method.quant_config.weight_block_size
                    )
                    self.shared_experts_weight_block_size = (
                        self.shared_experts.gate_up_proj.quant_method.quant_config.weight_block_size
                    )

        self.top_k = config.num_experts_per_tok

        if (
            get_moe_a2a_backend().is_deepep()
            or get_moe_a2a_backend().is_mooncake()
            or get_moe_a2a_backend().is_nixl()
            or get_moe_a2a_backend().is_mori()
            or get_moe_a2a_backend().is_ascend_fuseep()
        ):
            # TODO: we will support tp < ep in the future
            self.ep_size = get_parallel().moe_ep_size
            self.num_experts = (
                config.n_routed_experts + get_server_args().ep_num_redundant_experts
            )
            self.renormalize = config.norm_topk_prob
            self.topk_group = config.topk_group
            self.num_expert_group = config.n_group
            self.correction_bias = (
                self.gate.e_score_correction_bias.data
                if self.gate.e_score_correction_bias is not None
                else None
            )

        self._enable_a2a_moe = (
            get_moe_a2a_backend().is_deepep()
            or get_moe_a2a_backend().is_mooncake()
            or get_moe_a2a_backend().is_nixl()
            or get_moe_a2a_backend().is_mori()
            or get_moe_a2a_backend().is_ascend_fuseep()
            or get_moe_a2a_backend().is_flashinfer()
        )
        self._fuse_shared_experts_inside_sbo = SboFlags.fuse_shared_experts_inside_sbo()

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

    def forward(
        self,
        hidden_states: torch.Tensor,
        forward_batch: Optional[ForwardBatch] = None,
    ) -> torch.Tensor:
        if not self._enable_a2a_moe:
            if (
                self.alt_stream is not None
                and self.num_fused_shared_experts == 0
                and hidden_states.shape[0] > 0
                and get_is_capture_mode()
            ):
                return self.forward_normal_dual_stream(hidden_states)
            else:
                return self.forward_normal(hidden_states)
        else:
            return self.forward_deepep(hidden_states, forward_batch)

    def forward_normal_dual_stream(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        current_stream = torch.cuda.current_stream()
        self.alt_stream.wait_stream(current_stream)
        shared_output = self._forward_shared_experts(hidden_states)

        with torch.cuda.stream(self.alt_stream):
            # router_logits: (num_tokens, n_experts)
            router_logits = self.gate(hidden_states)
            topk_output = self.topk(hidden_states, router_logits)
            final_hidden_states = self.experts(hidden_states, topk_output)
            if not _is_cuda or isinstance(self.experts.quant_method, KTEPWrapperMethod):
                final_hidden_states *= self.routed_scaling_factor

        current_stream.wait_stream(self.alt_stream)
        final_hidden_states += shared_output
        if self.tp_size > 1 and not should_skip_post_experts_all_reduce(
            is_tp_path=True,
        ):
            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
        return final_hidden_states

    def forward_normal(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        if hidden_states.shape[0] > 0:
            shared_output = self._forward_shared_experts(hidden_states)
            # router_logits: (num_tokens, n_experts)
            router_logits = self.gate(hidden_states)
            topk_output = self.topk(hidden_states, router_logits)
        else:
            shared_output = None
            topk_output = self.topk.empty_topk_output(hidden_states.device)

        final_hidden_states = self.experts(hidden_states, topk_output)
        if not _is_cuda and not _use_aiter:
            final_hidden_states *= self.routed_scaling_factor
        if shared_output is not None:
            with use_symmetric_memory(
                parallel_state.get_tp_group(), disabled=not is_allocation_symmetric()
            ):
                final_hidden_states_out = torch.empty_like(final_hidden_states)
            torch.add(final_hidden_states, shared_output, out=final_hidden_states_out)
            final_hidden_states = final_hidden_states_out
        if self.tp_size > 1 and not should_skip_post_experts_all_reduce(
            is_tp_path=True,
        ):
            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
        return final_hidden_states

    def forward_deepep(
        self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
    ) -> torch.Tensor:
        shared_output = None
        enable_npu_dual_stream = (
            _is_npu
            and (
                forward_batch.forward_mode.is_extend()
                or forward_batch.forward_mode.is_target_verify()
            )
            and envs.SGLANG_NPU_USE_MULTI_STREAM.get()
        )

        if hidden_states.shape[0] > 0:
            # router_logits: (num_tokens, n_experts)
            router_logits = self.gate(hidden_states)
            if enable_npu_dual_stream:
                shared_output = process_shared_expert(
                    hidden_states, self._forward_shared_experts
                )
            else:
                shared_output = self._forward_shared_experts(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)

        final_hidden_states = self.experts(
            hidden_states=hidden_states,
            topk_output=topk_output,
        )
        if enable_npu_dual_stream:
            wait_share_stream()

        if shared_output is not None:
            x = shared_output
            if self.experts.should_fuse_routed_scaling_factor_in_topk:
                x.add_(final_hidden_states)
            else:
                x.add_(final_hidden_states, alpha=self.routed_scaling_factor)
            final_hidden_states = x
        else:
            if not self.experts.should_fuse_routed_scaling_factor_in_topk:
                final_hidden_states *= self.routed_scaling_factor

        return final_hidden_states

    def _forward_shared_experts(self, hidden_states: torch.Tensor):
        if (hidden_states.shape[0] > 0) and (self.num_fused_shared_experts == 0):
            return self.shared_experts(hidden_states)
        else:
            return None

    def op_gate(self, state):
        if is_non_idle_and_non_empty(
            state.forward_batch.forward_mode, state.hidden_states_mlp_input
        ):
            # router_logits: (num_tokens, n_experts)
            state.router_logits = self.gate(state.hidden_states_mlp_input)
        else:
            state.router_logits = None

    def op_select_experts(self, state):
        router_logits = state.pop("router_logits")
        hidden_states = state.hidden_states_mlp_input

        if router_logits is not None:
            with get_global_expert_distribution_recorder().with_current_layer(
                self.layer_id
            ):
                state.topk_output = self.topk(
                    hidden_states=hidden_states,
                    router_logits=router_logits,
                    num_token_non_padded=state.forward_batch.num_token_non_padded,
                    expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
                        layer_id=self.layer_id,
                    ),
                )
        else:
            state.topk_output = self.topk.empty_topk_output(hidden_states.device)

    def op_dispatch_a(self, state):
        if self.ep_size > 1:
            self.experts.dispatcher.dispatch_a(
                hidden_states=state.hidden_states_mlp_input,
                topk_output=state.pop("topk_output"),
                tbo_subbatch_index=state.get("tbo_subbatch_index"),
            )

    def op_dispatch_b(self, state):
        if self.ep_size > 1:
            with get_global_expert_distribution_recorder().with_current_layer(
                self.layer_id
            ):
                state.dispatch_output = self.experts.dispatcher.dispatch_b(
                    tbo_subbatch_index=state.get("tbo_subbatch_index"),
                )

    def op_experts(self, state):
        state.combine_input = self.experts.run_moe_core(
            dispatch_output=state.dispatch_output,
        )

    def op_combine_a(self, state):
        if self.ep_size > 1:
            self.experts.dispatcher.combine_a(
                combine_input=state.pop("combine_input"),
                tbo_subbatch_index=state.get("tbo_subbatch_index"),
            )
            state.pop("dispatch_output")

    def op_combine_b(self, state):
        if self.ep_size > 1:
            state.hidden_states_after_combine = self.experts.dispatcher.combine_b(
                tbo_subbatch_index=state.get("tbo_subbatch_index"),
            )

    def op_output(self, state):
        final_hidden_states = state.pop("hidden_states_after_combine")

        if (shared_output := state.pop("shared_output")) is not None:
            x = shared_output
            x.add_(final_hidden_states, alpha=self.routed_scaling_factor)
            final_hidden_states = x
        else:
            final_hidden_states *= self.routed_scaling_factor

        state.hidden_states_mlp_output = final_hidden_states


class Glm4MoeDecoderLayer(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        layer_id: int,
        start_layer: int = 0,
        quant_config: Optional[QuantizationConfig] = None,
        is_nextn: bool = False,
        prefix: str = "",
        alt_stream: Optional[torch.cuda.Stream] = None,
    ) -> None:
        nn.Module.__init__(self)
        self.hidden_size = config.hidden_size
        self.config = config
        rope_theta, rope_scaling = get_rope_config(config)
        partial_rotary_factor = (rope_scaling or {}).get("partial_rotary_factor")
        if partial_rotary_factor is None:
            partial_rotary_factor = getattr(config, "partial_rotary_factor", 0.5)
        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
        head_dim = getattr(
            config, "head_dim", config.hidden_size // config.num_attention_heads
        )
        rms_norm_eps = config.rms_norm_eps
        attention_bias = config.attention_bias
        self.layer_id = layer_id

        use_qk_norm = config.use_qk_norm if hasattr(config, "use_qk_norm") else False

        self.self_attn = Glm4MoeAttention(
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            num_kv_heads=config.num_key_value_heads,
            layer_id=layer_id,
            start_layer=start_layer,
            rope_theta=rope_theta,
            rope_scaling=rope_scaling,
            partial_rotary_factor=partial_rotary_factor,
            max_position_embeddings=max_position_embeddings,
            head_dim=head_dim,
            rms_norm_eps=rms_norm_eps,
            attention_bias=attention_bias,
            quant_config=quant_config,
            prefix=add_prefix("self_attn", prefix),
            use_qk_norm=use_qk_norm,
            alt_stream=alt_stream,
        )

        self.is_layer_sparse = self._is_layer_sparse(layer_id, is_nextn=is_nextn)
        is_previous_layer_sparse = self._is_layer_sparse(layer_id - 1, is_nextn=False)
        is_next_layer_sparse = self._is_layer_sparse(layer_id + 1, is_nextn=False)

        self.layer_scatter_modes = LayerScatterModes.init_new(
            layer_id=layer_id,
            num_layers=1 if is_nextn else config.num_hidden_layers,
            is_layer_sparse=self.is_layer_sparse,
            is_previous_layer_sparse=is_previous_layer_sparse,
            is_next_layer_sparse=is_next_layer_sparse,
        )

        if self.is_layer_sparse:
            self.mlp = Glm4MoeSparseMoeBlock(
                config=config,
                quant_config=quant_config,
                prefix=add_prefix("mlp", prefix),
                layer_id=self.layer_id,
                alt_stream=alt_stream,
            )
        else:
            if enable_moe_dense_fully_dp():
                mlp_tp_rank, mlp_tp_size = 0, 1
            else:
                mlp_tp_rank, mlp_tp_size = None, None
            self.mlp = Glm4MoeMLP(
                hidden_size=config.hidden_size,
                intermediate_size=config.intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
                prefix=add_prefix("mlp", prefix),
                tp_rank=mlp_tp_rank,
                tp_size=mlp_tp_size,
            )

        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )

        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=(
                is_nextn or (self.layer_id == self.config.num_hidden_layers - 1)
            ),
        )

        # Detect if QKV uses aiter FP8 per-token quant so we can fuse
        # RMSNorm + FP8 quant into a single kernel in prepare_attn
        self.attn_quant_format = ""
        self._detect_attn_quant_format()

    def _detect_fp8_per_token_quant(self, linear_layer, label: str) -> str:
        """Check if a linear layer uses aiter FP8 per-token quantization."""
        from sglang.srt.utils import get_bool_env_var, is_hip

        if not (get_bool_env_var("SGLANG_USE_AITER") and is_hip()):
            return ""
        if not hasattr(linear_layer, "quant_method"):
            return ""
        scheme = getattr(linear_layer, "scheme", None) or getattr(
            linear_layer.quant_method, "scheme", None
        )
        if scheme is not None:
            from compressed_tensors.quantization import QuantizationStrategy

            from sglang.srt.layers.quantization.compressed_tensors.schemes.compressed_tensors_w8a8_fp8 import (
                CompressedTensorsW8A8Fp8,
            )

            if (
                isinstance(scheme, CompressedTensorsW8A8Fp8)
                and scheme.strategy == QuantizationStrategy.CHANNEL
            ):
                logger.info(
                    "layer_%d Fused RMSNorm+Quant %s: ENABLED (fp8_per_token)",
                    self.layer_id,
                    label,
                )
                return "fp8_per_token"
        logger.info(
            "layer_%d Fused RMSNorm+Quant %s: skipped",
            self.layer_id,
            label,
        )
        return ""

    def _detect_attn_quant_format(self):
        self.attn_quant_format = self._detect_fp8_per_token_quant(
            self.self_attn.qkv_proj, "attn"
        )

    def _is_layer_sparse(self, layer_id: int, is_nextn: bool) -> bool:
        return is_nextn or (
            self.config.n_routed_experts is not None
            and layer_id >= self.config.first_k_dense_replace
        )

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

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

        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
            )
        )

        # For DP with padding, reduce scatter can be used instead of all-reduce.
        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)

        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

    def op_comm_prepare_attn(
        self,
        state,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
        residual: Optional[torch.Tensor],
        tbo_subbatch_index: Optional[int] = None,
    ):
        state.hidden_states_after_comm_pre_attn, state.residual_after_input_ln = (
            self.layer_communicator.prepare_attn(
                hidden_states,
                residual,
                forward_batch,
                quant_format=self.attn_quant_format,
            )
        )
        state.update(
            dict(
                forward_batch=forward_batch,
                positions=positions,
                tbo_subbatch_index=tbo_subbatch_index,
            )
        )

    def op_comm_prepare_mlp(self, state):
        state.hidden_states_mlp_input, state.residual_after_comm_pre_mlp = (
            self.layer_communicator.prepare_mlp(
                state.pop("hidden_states_after_attn"),
                state.pop("residual_after_input_ln"),
                state.forward_batch,
            )
        )

    def op_comm_postprocess_layer(self, state):
        hidden_states, residual = self.layer_communicator.postprocess_layer(
            state.pop("hidden_states_mlp_output"),
            state.pop("residual_after_comm_pre_mlp"),
            state.forward_batch,
        )

        output = dict(
            positions=state.positions,
            hidden_states=hidden_states,
            residual=residual,
            forward_batch=state.forward_batch,
            tbo_subbatch_index=state.tbo_subbatch_index,
        )

        state.clear(
            expect_keys={
                "positions",
                "forward_batch",
                "tbo_subbatch_index",
            }
        )
        return output


class Glm4MoeModel(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.pp_group = get_pp_group()
        self.config = config
        self.vocab_size = config.vocab_size
        self.first_k_dense_replace = config.first_k_dense_replace
        self.embed_dim = config.hidden_size
        if self.pp_group.is_first_rank:
            self.embed_tokens = VocabParallelEmbedding(
                config.vocab_size,
                config.hidden_size,
                use_attn_tp_group=is_dp_attention_enabled(),
            )
        else:
            self.embed_tokens = PPMissingLayer()

        self.alt_stream = get_stream("alt") if _is_cuda else None
        pp_start_layer, _ = get_pp_indices(
            config.num_hidden_layers,
            self.pp_group.rank_in_group,
            self.pp_group.world_size,
        )
        self.layers, self.start_layer, self.end_layer = make_layers(
            config.num_hidden_layers,
            lambda idx, prefix: Glm4MoeDecoderLayer(
                layer_id=idx,
                start_layer=pp_start_layer,
                config=config,
                quant_config=quant_config,
                prefix=prefix,
                alt_stream=self.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:
            self.norm = RMSNorm(self.embed_dim, eps=config.rms_norm_eps)
        else:
            self.norm = PPMissingLayer(return_tuple=True)

        self.layers_to_capture = []

    def get_input_embeddings(self) -> torch.Tensor:
        return self.embed_tokens

    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:
            if input_embeds is None:
                hidden_states = self.embed_tokens(input_ids)
            else:
                hidden_states = input_embeds
            residual = None
        else:
            assert pp_proxy_tensors is not None
            hidden_states = pp_proxy_tensors["hidden_states"]
            residual = pp_proxy_tensors["residual"]

        normal_start_layer = self.start_layer
        normal_end_layer = self.end_layer
        if forward_batch.can_run_tbo:
            if (
                self.first_k_dense_replace > normal_start_layer
                and self.first_k_dense_replace < normal_end_layer
            ):
                normal_end_layer = self.first_k_dense_replace
            elif self.first_k_dense_replace < normal_start_layer:
                normal_end_layer = normal_start_layer = 0

        aux_hidden_states = []
        for i in range(normal_start_layer, normal_end_layer):
            with get_global_expert_distribution_recorder().with_current_layer(i):
                if i in self.layers_to_capture:
                    aux_hidden_states.append(hidden_states + residual)
                layer = self.layers[i]
                hidden_states, residual = layer(
                    positions,
                    hidden_states,
                    forward_batch,
                    residual,
                )

        if normal_end_layer != self.end_layer:
            hidden_states, residual = model_forward_maybe_tbo(
                layers=self.layers[normal_end_layer : self.end_layer],
                enable_tbo=True,
                positions=positions,
                forward_batch=forward_batch,
                hidden_states=hidden_states,
                residual=residual,
                input_data_scatter_mode=self.layers[
                    normal_end_layer - 1
                ].layer_scatter_modes.layer_output_mode,
            )

        if not self.pp_group.is_last_rank:
            return PPProxyTensors(
                {
                    "hidden_states": hidden_states,
                    "residual": residual,
                }
            )
        else:
            if not forward_batch.forward_mode.is_idle():
                if residual is None:
                    hidden_states = self.norm(hidden_states)
                else:
                    hidden_states, _ = self.norm(hidden_states, residual)
        if len(aux_hidden_states) == 0:
            return hidden_states
        return hidden_states, aux_hidden_states


class Glm4MoeForCausalLM(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        nn.Module.__init__(self)
        self.pp_group = get_pp_group()
        self.config = config
        self.tp_size = get_parallel().tp_size
        self.quant_config = quant_config
        self.num_fused_shared_experts = 0
        self.determine_num_fused_shared_experts()
        self.model = Glm4MoeModel(
            config, quant_config, prefix=add_prefix("model", prefix)
        )
        self.lm_head = ParallelLMHead(
            config.vocab_size,
            config.hidden_size,
            quant_config=quant_config,
            prefix=add_prefix("lm_head", prefix),
            use_attn_tp_group=get_server_args().enable_dp_lm_head,
        )
        self.logits_processor = LogitsProcessor(config)

        # For EAGLE3 support
        self.capture_aux_hidden_states = False

    def determine_num_fused_shared_experts(self):
        if get_server_args().disable_shared_experts_fusion:
            return

        disable_reason = None
        if (not _is_cuda or torch.cuda.get_device_capability("cuda") < (8, 0)) and (
            not _is_hip or torch.cuda.get_device_capability("cuda") < (9, 4)
        ):
            disable_reason = (
                "Only GLM-4.5 on NV-platform with capability >= 80 "
                "or AMD-platform with capability >= gfx942(MI30x) can use shared experts fusion optimization."
            )
        elif get_parallel().moe_ep_size > 1 and (
            not _is_hip or torch.cuda.get_device_capability("cuda") < (9, 4)
        ):
            disable_reason = "Only GLM-4.5 on AMD-platform with capability >= gfx942(MI30x) can use shared experts fusion optimization under expert parallelism."
        elif disable_reason is None and (
            get_moe_a2a_backend().is_deepep() or get_moe_a2a_backend().is_mori()
        ):
            disable_reason = "GLM-4.5 cannot use shared experts fusion optimization under deepep expert parallelism."
        elif self.quant_config and self.quant_config.get_name() == "w4afp8":
            disable_reason = "GLM-4.5 W4AFP8 model uses different quant method for routed experts and shared experts."

        if disable_reason is not None:
            from sglang.srt.arg_groups.overrides import declare_load_time_override

            declare_load_time_override(
                "Glm4MoeForCausalLM.determine_num_fused_shared_experts",
                {"disable_shared_experts_fusion": True},
            )
            self.num_fused_shared_experts = 0
            log_info_on_rank0(
                logger,
                f"{disable_reason} Shared experts fusion optimization is disabled.",
            )
            return

        self.num_fused_shared_experts = self.config.n_shared_experts

    def get_input_embeddings(self) -> nn.Embedding:
        return self.model.embed_tokens

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

        if self.pp_group.is_last_rank:
            return self.logits_processor(
                input_ids, hidden_states, self.lm_head, forward_batch, aux_hidden_states
            )
        else:
            return hidden_states

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

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

    def load_weights(
        self,
        weights: Iterable[Tuple[str, torch.Tensor]],
        is_nextn=False,
        params_dict=None,
    ):
        if is_nextn:
            if hasattr(self.config, "num_nextn_predict_layers"):
                num_nextn_layers = self.config.num_nextn_predict_layers
                assert num_nextn_layers == 1, "Only 1 nextn layer is supported"
                # compatible with old design
                nextn_layer_id = (
                    0
                    if self.config.num_hidden_layers == 1
                    else self.config.num_hidden_layers
                )
            else:
                raise ValueError("num_nextn_predict_layers is not in the config")

        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),
        ]

        if self.num_fused_shared_experts > 0:
            assert self.num_fused_shared_experts == 1

            def iter_weights_with_fused_shared_experts(
                weights: Iterable[Tuple[str, torch.Tensor]],
            ) -> Iterable[Tuple[str, torch.Tensor]]:

                pattern = re.compile(
                    r"^model\.layers\.(\d+)\.mlp\.shared_experts\.(.+)$"
                )
                for name, weight in weights:
                    match = pattern.match(name)
                    if match:
                        layer_id = int(match.group(1))
                        suffix = match.group(2)
                        name = f"model.layers.{layer_id}.mlp.experts.{self.config.n_routed_experts}.{suffix}"
                    yield name, weight

            weights = iter_weights_with_fused_shared_experts(weights)

        # 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.n_routed_experts + self.num_fused_shared_experts,
        )

        if is_nextn:
            nextn_layer_prefix = f"model.layers.{nextn_layer_id}"
            nextn_spec_weight_names = [
                "shared_head.norm",
                "eh_proj",
                "enorm",
                "hnorm",
            ]
        else:
            nextn_layer_prefix = None
            nextn_spec_weight_names = []

        if params_dict is None:
            params_dict = dict(self.named_parameters())

        weight_names = []
        for name, loaded_weight in weights:
            weight_names.append(name)

            if not is_nextn:
                if hasattr(self.config, "num_nextn_predict_layers"):
                    num_nextn_layers = self.config.num_nextn_predict_layers
                    if num_nextn_layers > 0 and name.startswith("model.layers"):
                        name_list = name.split(".")
                        if (
                            len(name_list) >= 3
                            and int(name_list[2]) >= self.config.num_hidden_layers
                        ):
                            continue
            else:
                if nextn_layer_prefix and not name.startswith(nextn_layer_prefix):
                    continue

                if nextn_layer_prefix is not None:  # mtp
                    # Use shared head and embed weights from target model
                    if "shared_head.head" in name or "embed_tokens" in name:
                        continue

                    is_decoder = True
                    # For nextn specific weights
                    for weight_name in nextn_spec_weight_names:
                        if weight_name in name:
                            name = name.replace(nextn_layer_prefix, "model")
                            is_decoder = False
                            break
                    # For decoder layer weights
                    if is_decoder:
                        name = name.replace(nextn_layer_prefix, "model.decoder")

            if "rotary_emb.inv_freq" in name:
                continue
            for param_name, weight_name, shard_id in stacked_params_mapping:
                # Skip non-stacked layers and experts (experts handled below).
                if weight_name not in name:
                    continue
                # We have mlp.experts[0].gate_proj in the checkpoint.
                # Since we handle the experts below in expert_params_mapping,
                # we need to skip here BEFORE we update the name, otherwise
                # name will be updated to mlp.experts[0].gate_up_proj, which
                # will then be updated below in expert_params_mapping
                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
                if "mlp.experts" in name:
                    continue
                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    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:
                # Track if this is an expert weight to enable early skipping
                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

                    # Mark as expert weight regardless of whether we can process it
                    is_expert_weight = True

                    name = name.replace(weight_name, param_name)
                    if name not in params_dict:
                        # Expert weight not on this rank, will be skipped below
                        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:
                    if is_expert_weight:
                        # This is an expert weight but not mapped to this rank, skip all remaining processing
                        continue

                    # Skip loading extra bias for GPTQ models.
                    if name.endswith(".bias") and name not in params_dict:
                        continue

                    if name not in params_dict:
                        continue

                    if name in params_dict.keys():
                        param = params_dict[name]
                        weight_loader = getattr(
                            param, "weight_loader", default_weight_loader
                        )
                        weight_loader(param, loaded_weight)
                    else:
                        logger.warning(f"Parameter {name} not found in params_dict")

    def get_embed_and_head(self):
        return self.model.embed_tokens.weight, self.lm_head.weight

    def set_embed_and_head(self, embed, head):
        del self.model.embed_tokens.weight
        del self.lm_head.weight
        self.model.embed_tokens.weight = embed
        self.lm_head.weight = head
        torch.cuda.empty_cache()
        torch.cuda.synchronize()

    @classmethod
    def get_model_config_for_expert_location(cls, config):
        return ModelConfigForExpertLocation(
            num_layers=config.num_hidden_layers,
            num_logical_experts=config.n_routed_experts,
            num_groups=config.n_group,
        )

    def set_eagle3_layers_to_capture(self, layer_ids: Optional[List[int]] = None):
        if not self.pp_group.is_last_rank:
            return

        if layer_ids is None:
            self.capture_aux_hidden_states = True
            num_layers = self.config.num_hidden_layers
            self.model.layers_to_capture = [2, num_layers // 2, num_layers - 3]
        else:
            self.capture_aux_hidden_states = True
            # we plus 1 here because in sglang, for the ith layer, it takes the output
            # of the (i-1)th layer as aux hidden state
            self.model.layers_to_capture = [val + 1 for val in layer_ids]


class GlmMoeDsaForCausalLM(DeepseekV2ForCausalLM):
    def determine_num_fused_shared_experts(self):
        super().determine_num_fused_shared_experts("GlmMoeDsaForCausalLM")


class GlmMoeDsaForCausalLMNextN(DeepseekV3ForCausalLMNextN):
    # GLM-5.2's MTP layer index differs from DeepSeek's (61), so the inherited
    # substr mapping would wrongly rewrite GLM's real layer-61 weights.
    # exclude_layers remapping for the MTP layer is handled explicitly in
    # _resolve_nextn_quant_config below instead.
    hf_to_sglang_mapper = WeightsMapper()

    _NEXTN_SPEC_WEIGHT_NAMES = ("shared_head.norm", "eh_proj", "enorm", "hnorm")

    @classmethod
    def _map_mtp_ckpt_name(cls, name: str, layer_prefix: str) -> str:
        # Keep this mapping in sync with DeepseekV2WeightLoaderMixin's
        # NextN rule: MTP-specific weights live under model.*, while the
        # decoder block weights live under model.decoder.*.
        if any(part in name for part in cls._NEXTN_SPEC_WEIGHT_NAMES):
            return name.replace(layer_prefix, "model", 1)
        return name.replace(layer_prefix, "model.decoder", 1)

    def _resolve_nextn_quant_config(self, config, quant_config):
        if quant_config is None or quant_config.get_name() != "quark":
            return quant_config

        layer_prefix = f"model.layers.{config.num_hidden_layers}"

        # Quark's per-module scheme selection (e.g. MTP self_attn in PTPC-FP8
        # while MTP MoE is MXFP4) is keyed by "layer_quant_config" patterns
        # using the checkpoint's "model.layers.<N>.*" naming. SGLang queries
        # schemes by the runtime "model.*"/"model.decoder.*" prefix, so those
        # keys need the same remap as exclude_layers below, or they silently
        # fall back to the wrong (layer-type/global) scheme.
        layer_quant_config = quant_config.quant_config.get("layer_quant_config")
        if layer_quant_config:
            quant_config.quant_config["layer_quant_config"] = {
                (
                    self._map_mtp_ckpt_name(pattern, layer_prefix)
                    if pattern.startswith(layer_prefix + ".")
                    else pattern
                ): pattern_config
                for pattern, pattern_config in layer_quant_config.items()
            }

        mtp_excluded = [
            name
            for name in quant_config.exclude_layers
            if name.startswith(layer_prefix + ".")
        ]
        if not mtp_excluded:
            return quant_config

        names = set(quant_config.exclude_layers)
        for name in mtp_excluded:
            names.add(self._map_mtp_ckpt_name(name, layer_prefix))

        # Fused routed experts are queried by the coarse module prefix
        # "model.decoder.mlp.experts". Expanded per-expert leaf excludes do not
        # match that prefix, so add the coarse prefix when any routed expert in
        # the MTP layer is excluded. This keeps only that fused MoE module bf16
        # while allowing the remaining draft modules to use their quant config.
        if any(".mlp.experts." in name for name in mtp_excluded):
            names.add("model.decoder.mlp.experts")

        import copy

        quant_config = copy.copy(quant_config)
        quant_config.exclude_layers = list(names)
        return quant_config


EntryClass = [Glm4MoeForCausalLM, GlmMoeDsaForCausalLM, GlmMoeDsaForCausalLMNextN]