sgl-project--sglang/python/sglang/srt/models/glm4_moe_lite.py

# Copyright 2026-2027 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-4.7-Flash model compatible with HuggingFace weights."""

import logging
import re
from typing import 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,
    parallel_state,
    tensor_model_parallel_all_reduce,
)
from sglang.srt.distributed.device_communicators.pynccl_allocator import (
    use_symmetric_memory,
)
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,
    get_attn_tp_context,
)
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, 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, TopKOutputFormat
from sglang.srt.layers.moe.utils import filter_moe_weight_param_global_expert
from sglang.srt.layers.quantization.base_config import QuantizationConfig
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_common.deepseek_weight_loader import (
    DeepseekV2WeightLoaderMixin,
)
from sglang.srt.models.deepseek_common.utils import _is_cuda, _use_aiter
from sglang.srt.models.deepseek_v2 import DeepseekV2AttentionMLA
from sglang.srt.runtime_context import (
    get_forward,
    get_parallel,
    get_server_args,
    get_stream,
)
from sglang.srt.utils import (
    BumpAllocator,
    LazyValue,
    add_prefix,
    is_non_idle_and_non_empty,
    log_info_on_rank0,
    make_layers,
)
from sglang.srt.utils.hf_transformers_utils import get_rope_config

logger = logging.getLogger(__name__)


class Glm4MoeLiteMLP(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 Glm4MoeLiteGate(nn.Module):
    def __init__(
        self,
        config,
        prefix: str = "",
        is_nextn: bool = False,
    ):
        super().__init__()
        self.is_nextn = is_nextn
        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 Glm4MoeLiteSparseMoeBlock(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        layer_id: int,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        alt_stream: Optional[torch.cuda.Stream] = None,
        is_nextn: bool = False,
    ):
        super().__init__()
        self.tp_size = get_parallel().tp_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
        self.is_nextn = is_nextn

        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 = Glm4MoeLiteGate(
            config=config, prefix=add_prefix("gate", prefix), is_nextn=is_nextn
        )

        self.experts = get_moe_impl_class(quant_config)(
            num_experts=config.n_routed_experts
            + self.num_fused_shared_experts
            + get_server_args().ep_num_redundant_experts,
            num_fused_shared_experts=self.num_fused_shared_experts,
            top_k=config.num_experts_per_tok + self.num_fused_shared_experts,
            hidden_size=config.hidden_size,
            intermediate_size=config.moe_intermediate_size,
            layer_id=self.layer_id,
            quant_config=quant_config,
            routed_scaling_factor=self.routed_scaling_factor,
            prefix=add_prefix("experts", prefix),
        )

        self.topk = TopK(
            top_k=config.num_experts_per_tok + 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,
            num_fused_shared_experts=self.num_fused_shared_experts,
            topk_group=config.topk_group,
            correction_bias=self.gate.e_score_correction_bias,
            quant_config=quant_config,
            routed_scaling_factor=self.routed_scaling_factor,
            apply_routed_scaling_factor_on_output=self.experts.should_fuse_routed_scaling_factor_in_topk,
            # Some Fp4 MoE backends require the output format to be bypassed but the MTP layers are unquantized
            # and requires the output format to be standard. We use quant_config to determine the output format.
            output_format=TopKOutputFormat.STANDARD if quant_config is None else None,
        )

        self.shared_experts_is_int8 = False
        self.shared_experts_is_fp8 = False
        self.shared_experts_weight_block_size = None
        self._shared_expert_tp1 = False
        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 = Glm4MoeLiteMLP(
                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 should_use_flashinfer_cutlass_moe_fp4_allgather()
                    else {}
                ),
            )
            is_packed_weight = hasattr(
                self.shared_experts.gate_up_proj.quant_method, "quant_config"
            )
            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
            )

        self.top_k = config.num_experts_per_tok

        if get_moe_a2a_backend().is_deepep() or get_moe_a2a_backend().is_mooncake():
            # 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()
        )
        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
        if hidden_states.shape[0] > 0:
            # router_logits: (num_tokens, n_experts)
            router_logits = self.gate(hidden_states)
            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 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_shared_experts(self, state):
        hidden_states_mlp_input = state.pop("hidden_states_mlp_input")
        if (self.num_fused_shared_experts == 0) and is_non_idle_and_non_empty(
            state.forward_batch.forward_mode, hidden_states_mlp_input
        ):
            state.shared_output = self.shared_experts(hidden_states_mlp_input)
        else:
            state.shared_output = 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 get_moe_a2a_backend().is_mori():
            num_tokens = state.pop("num_tokens")
            final_hidden_states = final_hidden_states[:num_tokens]

        if (shared_output := state.pop("shared_output")) is not None:
            x = shared_output
            if _use_aiter:
                x.add_(final_hidden_states)
            else:
                x.add_(final_hidden_states, alpha=self.routed_scaling_factor)
            final_hidden_states = x
        elif _use_aiter:
            # fused in aiter_biased_grouped_topk so we can skip here
            pass
        else:
            final_hidden_states *= self.routed_scaling_factor

        state.hidden_states_mlp_output = final_hidden_states


class Glm4MoeLiteDecoderLayer(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        layer_id: int,
        quant_config: Optional[QuantizationConfig] = None,
        is_nextn: bool = False,
        prefix: str = "",
        alt_stream: Optional[torch.cuda.Stream] = None,
    ) -> None:

        super().__init__()
        # Required for MTP: Glm4MoeLiteModelNextN bypasses Glm4MoeLiteForCausalLM.__init__
        config.moe_layer_freq = 1
        self.hidden_size = config.hidden_size
        self.config = config
        rope_theta, rope_scaling = get_rope_config(config)
        max_position_embeddings = getattr(config, "max_position_embeddings", 202752)
        self.layer_id = layer_id
        self.is_nextn = is_nextn

        self.self_attn = DeepseekV2AttentionMLA(
            config=config,
            hidden_size=config.hidden_size,
            num_heads=config.num_attention_heads,
            qk_nope_head_dim=config.qk_nope_head_dim,
            qk_rope_head_dim=config.qk_rope_head_dim,
            v_head_dim=config.v_head_dim,
            q_lora_rank=config.q_lora_rank,
            kv_lora_rank=config.kv_lora_rank,
            rope_theta=rope_theta,
            rope_scaling=rope_scaling,
            max_position_embeddings=max_position_embeddings,
            quant_config=quant_config,
            reduce_results=False,
            layer_id=layer_id,
            prefix=add_prefix("self_attn", prefix),
        )

        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 = Glm4MoeLiteSparseMoeBlock(
                config=config,
                quant_config=quant_config,
                prefix=add_prefix("mlp", prefix),
                layer_id=self.layer_id,
                alt_stream=alt_stream,
                is_nextn=is_nextn,
            )
        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 = Glm4MoeLiteMLP(
                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._gfx95_quant_format = self._detect_gfx95_quant_format()

        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)
            ),
            qkv_latent_func=self.self_attn.prepare_qkv_latent,
        )

    def _detect_gfx95_quant_format(self) -> str:
        from sglang.srt.models.deepseek_common.utils import _is_gfx95_supported

        if not _is_gfx95_supported:
            return ""
        weight = getattr(
            getattr(self.self_attn, "fused_qkv_a_proj_with_mqa", None), "weight", None
        )
        if weight is None:
            return ""
        if weight.dtype == torch.uint8:
            return "mxfp4"
        if weight.dtype == getattr(torch, "float8_e4m3fn", None):
            return "fp8"
        return ""

    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
            and layer_id % self.config.moe_layer_freq == 0
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
        residual: Optional[torch.Tensor],
        zero_allocator: BumpAllocator,
    ) -> torch.Tensor:
        hidden_states, residual = self.layer_communicator.prepare_attn(
            hidden_states,
            residual,
            forward_batch,
            getattr(self, "_gfx95_quant_format", ""),
        )

        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
            forward_batch=forward_batch,
            zero_allocator=zero_allocator,
            layer_scatter_modes=self.layer_scatter_modes,
        )
        if isinstance(hidden_states, tuple):
            hidden_states = hidden_states[0]
        get_attn_tp_context().clear_attn_inputs()

        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],
        zero_allocator: BumpAllocator,
        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)
        )
        if get_moe_a2a_backend().is_mori():
            state.num_tokens = hidden_states.shape[0]
        state.update(
            dict(
                forward_batch=forward_batch,
                positions=positions,
                zero_allocator=zero_allocator,
                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,
            zero_allocator=state.zero_allocator,
            tbo_subbatch_index=state.tbo_subbatch_index,
        )

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


class Glm4MoeLiteModel(nn.Module):
    fall_back_to_pt_during_load = False

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.padding_id = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.first_k_dense_replace = config.first_k_dense_replace
        self.pp_group = get_pp_group()

        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
        self.layers, self.start_layer, self.end_layer = make_layers(
            config.num_hidden_layers,
            lambda idx, prefix: Glm4MoeLiteDecoderLayer(
                config=config,
                layer_id=idx,
                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(config.hidden_size, 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]:
        total_num_layers = self.end_layer - self.start_layer
        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"]
        device = hidden_states.device
        zero_allocator = BumpAllocator(
            buffer_size=total_num_layers * 2 * (2 if forward_batch.can_run_tbo else 1),
            dtype=torch.float32,
            device=device,
        )

        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,
                    zero_allocator,
                )

        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,
                zero_allocator=zero_allocator,
            )

        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 Glm4MoeLiteForCausalLM(nn.Module, DeepseekV2WeightLoaderMixin):
    # for quark model load
    packed_modules_mapping = {}

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        config.moe_layer_freq = 1
        self.config = config
        self.tp_size = get_parallel().tp_size
        self.quant_config = quant_config
        self.pp_group = get_pp_group()
        self.determine_num_fused_shared_experts("Glm4MoeLiteForCausalLM")
        self.model = Glm4MoeLiteModel(
            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)

        self._routed_experts_weights_of_layer = LazyValue(
            lambda: {
                layer_id: layer.mlp.get_moe_weights()
                for layer_id, layer in enumerate(self.model.layers)
                if isinstance(layer.mlp, Glm4MoeLiteSparseMoeBlock)
            }
        )
        self.capture_aux_hidden_states = False

    @property
    def routed_experts_weights_of_layer(self):
        return self._routed_experts_weights_of_layer.value

    def determine_num_fused_shared_experts(
        self, architecture: str = "Glm4MoeLiteForCausalLM"
    ):
        self.num_fused_shared_experts = 0
        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)
            or self.config.architectures[0] != architecture
            or self.config.n_shared_experts != 1
        ):
            disable_reason = "Only GLM-4.5 or GLM-4.6 on NV-platform with capability >= 80 can use shared experts fusion optimization."
        elif get_parallel().moe_ep_size > 1:
            disable_reason = "GLM-4.5 or GLM-4.6 cannot use shared experts fusion optimization under expert parallelism."

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

            declare_load_time_override(
                "Glm4MoeLiteForCausalLM.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:
        with get_attn_tp_context().maybe_input_scattered(forward_batch):
            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 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
            # TODO (Qiaolin-Yu): check if other draft models need similar layer id
            # adjustment
            if layer_ids and layer_ids[0] == 1:
                self.model.layers_to_capture = [val + 1 for val in layer_ids]
            else:
                self.model.layers_to_capture = list(layer_ids)

    def set_dflash_layers_to_capture(self, layer_ids: List[int]):
        if not self.pp_group.is_last_rank:
            return

        if layer_ids is None:
            raise ValueError(
                "DFLASH requires explicit layer_ids for aux hidden capture."
            )

        self.capture_aux_hidden_states = True
        self.model.layers_to_capture = [val + 1 for val in layer_ids]

    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,
        )

        # Fuse q_a_proj and kv_a_proj_with_mqa along output dimension when q_lora_rank is not None
        fuse_qkv_a_proj = hasattr(self.config, "q_lora_rank") and (
            self.config.q_lora_rank is not None
        )
        cached_a_proj = {} if fuse_qkv_a_proj else None

        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

                    # GLM NOTE: for MLA
                    if fuse_qkv_a_proj and (
                        "q_a_proj" in name or "kv_a_proj_with_mqa" in name
                    ):
                        cached_a_proj[name] = loaded_weight
                        q_a_proj_name = (
                            name
                            if "q_a_proj" in name
                            else name.replace("kv_a_proj_with_mqa", "q_a_proj")
                        )
                        kv_a_proj_name = (
                            name
                            if "kv_a_proj_with_mqa" in name
                            else name.replace("q_a_proj", "kv_a_proj_with_mqa")
                        )

                        # When both q_a_proj and kv_a_proj_with_mqa has been cached, load the fused weight to parameter
                        if (
                            q_a_proj_name in cached_a_proj
                            and kv_a_proj_name in cached_a_proj
                        ):
                            q_a_proj_weight = cached_a_proj[q_a_proj_name]
                            kv_a_proj_weight = cached_a_proj[kv_a_proj_name]
                            fused_weight = torch.cat(
                                [q_a_proj_weight, kv_a_proj_weight], dim=0
                            )
                            param_name = (
                                name.replace("q_a_proj", "fused_qkv_a_proj_with_mqa")
                                if "q_a_proj" in name
                                else name.replace(
                                    "kv_a_proj_with_mqa", "fused_qkv_a_proj_with_mqa"
                                )
                            )
                            if param_name not in params_dict:
                                continue
                            param = params_dict[param_name]

                            weight_loader = getattr(
                                param, "weight_loader", default_weight_loader
                            )
                            weight_loader(param, fused_weight)
                            cached_a_proj.pop(q_a_proj_name)
                            cached_a_proj.pop(kv_a_proj_name)
                    else:
                        if (
                            "k_scale" in name or "v_scale" in name
                        ) and name not in params_dict:
                            # modelopt attn kv scale is named differently
                            if any(scale in name for scale in ["k_scale", "v_scale"]):
                                name = name.replace("_proj", "attn_mqa")
                            else:
                                logger.warning(
                                    f"Unknown scale found in checkpoint: {name}"
                                )

                    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")

        # DeepseekV2AttentionMLA.forward_* expects post_load_weights() to populate
        # per-layer packed weights like `w_kc`/`w_vc` (used during CUDA graph capture).
        # GLM-4.7-Flash configs not set `config.mla`, but this model always uses
        # DeepseekV2AttentionMLA, so we must run the post-load processing.
        # Use weight_names=None to ensure we always process all layers. Some checkpoints /
        # naming schemes may not include "kv_b_proj" in `weight_names`, but `w_kc`/`w_vc`
        # are still required by DeepseekV2AttentionMLA at runtime.
        self.post_load_weights(is_nextn=is_nextn, weight_names=None)


EntryClass = [Glm4MoeLiteForCausalLM]